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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00349-6 10.1016/j.jpsychires.2021.05.073 Article What have we learned in the past year? A study on pharmacy purchases of psychiatric drugs from wholesalers in the days prior to the first and second COVID-19 lockdowns in Germany Jacob Louis abc Bohlken Jens d Kostev Karel e∗ a Research and Development Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, Dr. Antoni Pujadas, 42, Sant Boi de Llobregat, Barcelona, 08830, Spain b Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain c Faculty of Medicine, University of Versailles Saint-Quentin-en-Yvelines, Montigny-le-Bretonneux, 78180, France d Institute for Social Medicine, Occupational Medicine, and Public Health (ISAP) of the Medical Faculty at the University of Leipzig, Leipzig, Germany e Epidemiology, IQVIA, Frankfurt, Germany ∗ Corresponding author. Epidemiology, IQVIA, Unterschweinstiege 2-14, 60549, Frankfurt am Main, Germany. 2 6 2021 8 2021 2 6 2021 140 346349 8 4 2021 21 5 2021 29 5 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. No study has yet investigated how the second coronavirus disease (COVID-19) lockdown has impacted the consumption of psychiatric medications in Germany. Therefore, the goal of this study was to analyze weekly pharmacy purchases of psychiatric drugs from wholesalers in this country in 2019 and 2020 using data from the IMS RPM® (Regional Pharmaceutical Market) Weekly Database. The outcome was the number of pharmacy purchases of psychiatric drugs per week from wholesalers between Calendar Week 2 and Calendar Week 52 in 2019 and 2020. Calendar Weeks 12 and 51 in 2020 corresponded to the days prior to the first and second German COVID-19 lockdowns, respectively. Descriptively, compared with 2019, the number of weekly pharmacy purchases of psychiatric drugs increased by 32% between Calendar Weeks 2–11 and Calendar Week 12 in 2020, while there was a 9% increase between Calendar Weeks 13–50 and Calendar Week 51 that same year. Overall, the relative increase in the weekly pharmacy purchases of psychiatric drugs from wholesalers was less pronounced before the second COVID-19 lockdown in Germany than before the first. Further studies are warranted to identify factors (e.g., decreases in panic buying) that may have contributed to this decreasing trend. Keywords Pharmacy purchases Psychiatric drugs Lockdown Coronavirus disease 2019 COVID-19 Germany ==== Body pmcIntroduction The coronavirus disease (COVID-19) emerged in China at the end of 2019 and was declared a global pandemic in March 2020 (Atzrodt et al., 2020). As of May 15, 2021, the number of confirmed cases has exceeded 161 million, while there have been around 3.4 million related deaths (World Health Organization, 2021). In this context, many measures have been implemented to mitigate the spread of the disease (e.g., increased availability of personal protective equipment, cancellation of small gatherings, and national lockdowns), and these measures have been found to be relatively effective (Haug et al., 2020). Concurrently, COVID-19 has received massive media coverage, with the media playing a significant role in sharing critical messages pertaining to the pandemic with the general public (Basch et al., 2020). Nonetheless, these public health measures and this media coverage have also had a wide range of deleterious effects on mental health, such as an increase in anxiety (Gao et al., 2020; Prati and Mancini, 2021) and depressive symptoms (Bendau et al., 2020; Fiorillo et al., 2020). In parallel, there has been a rise in the demand for psychiatric drugs since the beginning of the COVID-19 era (Ammassari et al., 2021; Howard et al., 2020; Kostev and Lauterbach, 2020; Rabeea et al., 2021; Stall et al., 2021; Vaduganathan et al., 2020). In Germany, pharmacy purchases of psychotropic and neurological drugs from wholesalers increased in the days prior to the first lockdown in March 2020 (Kostev and Lauterbach, 2020). A few months later, on December 16, 2020, Germany entered a second lockdown to prevent an increase in the number of new cases that might otherwise have occurred during the holiday season (Reuters, 2021). To date, little is known about how this second lockdown impacted the consumption of psychiatric drugs in this country (Moradian et al., 2021). Therefore, the aim of this study was to analyze weekly pharmacy purchases of psychiatric drugs from wholesalers in Germany in 2019 and 2020. It was hypothesized that the general population had adapted at least somewhat to the COVID-19 crisis (PeConga et al., 2020) and that the second lockdown had had a reduced impact on the purchase of psychiatric drugs compared with the first lockdown. Materials and methods Database This study used data from the IMS RPM® (Regional Pharmaceutical Market) Weekly Database, which contains data on the weekly purchases by public pharmacies from wholesalers in Germany. Each calendar week runs from Saturday to Friday. In Germany, public pharmacies are supplied with drugs by wholesalers after manufacturers and importers sell these drugs on the market (Kostev and Lauterbach, 2020). Values reported in this database are packing units that correspond to the number of packs purchased weekly. Finally, this database only contains aggregated data, and no pharmacy identification number is available. Drugs of interest The drugs of interest included antidepressants (Anatomical Classification of Pharmaceutical Products of the European Pharmaceutical Market Research Association (EphMRA): N06A), antipsychotics (N05A), hypnotics and sedatives (N05B), tranquilizers (N05C), antidementia drugs (N07D), and anticonvulsants (N03). As anticonvulsants are not only prescribed for epilepsy but also a wide range of psychiatric disorders (Nadkarni and Devinsky, 2005), these drugs were also included in the present analyses. Study outcome The outcome of this study was the number of pharmacy purchases of psychiatric drugs per week from wholesalers in Calendar Week 12 descriptively compared with that in Calendar Weeks 2–11 (average), and the same figure for Calendar Week 51 descriptively compared with that for Calendar Weeks 13–50 (average) in 2019 and 2020. Calendar Week 1 was not included in the analyses because it was not a whole calendar week (i.e., some days of the week fell in the previous calendar year). Calendar Weeks 12 and 51 in 2020 corresponded to the last days prior to the first and second COVID-19 lockdowns in Germany, respectively (Kostev and Lauterbach, 2020; Reuters, 2021). To analyze the impact of the COVID-19 pandemic on pharmacy purchases of psychiatric drugs, descriptive changes between Weeks 2–11 and Week 12, and between Weeks 13–50 and Week 51, were estimated in 2019 and 2020. An adjusted difference between 2019 and 2020 was further calculated for both Week 12 and Week 51. Statistical analysis This was a descriptive study of aggregated data, and no statistical test was therefore performed. Descriptive changes between Weeks 2–11 and Week 12, and between Weeks 13–50 and Week 51 in 2019 and 2020 were estimated in percentages. An adjusted difference in these changes between 2019 and 2020 was further calculated. Results The number of packing units of psychiatric drugs purchased weekly between Calendar Weeks 2 and 52 in 2019 and 2020 is displayed in Fig. 1 . Table 1 shows the descriptive changes in the number of packing units of psychiatric drugs purchased per week between Calendar Weeks 2–11 and Calendar Week 12, and between Calendar Weeks 13–50 and Calendar Week 51 in 2019 and 2020. Descriptively, compared with 2019, there was a 32% increase in the number of packing units of psychiatric drugs purchased between Calendar Weeks 2–11 and Calendar Week 12 in 2020, while the number of purchases increased by 9% between Calendar Weeks 13–50 and Calendar Week 51 that same year.Fig. 1 Number of packing units of psychiatric drugs purchased per week in 2019 and 2020 As Calendar Week 1 does not correspond to a full week, this week was not included in the analyses. Fig. 1 Table 1 Descriptive changes in the number of packing units of psychiatric drugs purchased per week between Calendar Weeks 2–11 and Calendar Week 12, and between Calendar Weeks 13–50 and Calendar Week 51 in 2019 and 2020. Table 1Drugs Weeks 2–11, 2020 (average) Week 12, 2020 Descriptive change between Weeks 2–11, 2020 and Week 12, 2020 (%) Descriptive change between Weeks 2–11, 2019 and Week 12, 2019 (%) Adjusted difference between 2019 and 2020 (%) Weeks 13–50, 2020 (average) Week 51, 2020 Descriptive change between Weeks 13–50, 2020 and Week 51, 2020 (%) Descriptive change between Weeks 13–50, 2019 and Week 51, 2019 (%) Adjusted difference between 2019 and 2020 (%) Psychiatric drugs (all classes) 1,720,783 2,193,883 27% −5% 32% 1,539,288 2,276,822 48% 39% 9% Antidepressants 537,211 671,647 25% −4% 29% 466,661 692,581 48% 39% 9% Antipsychotics 288,595 379,673 32% −5% 37% 273,663 406,741 49% 43% 6% Hypnotics and sedatives 418,705 487,663 16% −11% 27% 362,340 553,456 53% 35% 18% Tranquilizers 139,394 186,594 34% −2% 36% 127,731 179,294 40% 42% −2% Antidementia drugs 30,907 36,059 17% −5% 22% 28,033 40,997 46% 35% 11% Anticonvulsants 305,972 432,247 41% 0% 41% 280,860 403,753 44% 39% 5% As Calendar Week 1 does not correspond to a full week, this week was not included in the analyses. Discussion This study conducted in Germany showed that there was an increase in pharmacy purchases of psychiatric drugs from wholesalers in the days prior to both the first and second COVID-19 lockdowns. Given the aggregated nature of the data, no statistical test was performed. Interestingly, the relative increase in the number of purchases was less pronounced before the second COVID-19 lockdown than before the first. To the best of our knowledge, this is the first study to compare the impact of two different lockdowns in the same country on the consumption of psychiatric drugs. One important finding of this study is that the official announcement of the two COVID-19 lockdowns was descriptively associated with an increase in weekly purchases of psychiatric drugs. This result is in line with recent literature also reporting an increase in the consumption of psychiatric medications since the beginning of the COVID-19 pandemic (Ammassari et al., 2021; Howard et al., 2020; Kostev and Lauterbach, 2020; Rabeea et al., 2021; Stall et al., 2021; Vaduganathan et al., 2020). For example, a cross-sectional study revealed that the out-of-pocket purchases of anxiolytics significantly increased by 3.8% during the early phase of the COVID-19 crisis in Italy (Ammassari et al., 2021). Another study found that the number of prescriptions for antidepressants increased by 4 million and the related costs by £139 million in England in 2020 compared with 2019 (Rabeea et al., 2021). Finally, in a sample of 58,332 pharmacies from the United States, it was observed that sertraline was more frequently dispensed in February and March 2020 than in the same months in 2019 (Vaduganathan et al., 2020). COVID-19 lockdowns have been found to have deleterious effects on the mental health of the general population (Fiorillo et al., 2020; Prati and Mancini, 2021). This could explain the increase in the number of pharmacy purchases of psychiatric drugs per week in the days prior to the first and second COVID-19 lockdowns in Germany. These deleterious effects may be mediated by several factors such as loneliness, financial burden, and fear of COVID-19. Since the beginning of the COVID-19 pandemic, there has been a substantial reduction in the number of social contacts, which has favored the occurrence of loneliness (Groarke et al., 2020) and psychiatric conditions (Robb et al., 2020). This increased incidence of psychiatric disorders is of particular concern, given that the utilization of medical services has also declined (Michalowsky et al., 2021). In terms of financial burden, the COVID-19 crisis has resulted in a major economic crisis, and a substantial proportion of people have lost their jobs (Bui et al., 2020). This economic burden may have led to adverse mental health outcomes (Ruengorn et al., 2021). A third potential mediating factor is the irrational fear of COVID-19 (i.e., coronaphobia). This fear may interfere with daily living activities and predispose people to poorer overall mental well-being (Arora et al., 2020). It has also been shown that some of the COVID-19 information conveyed by the media may have been inaccurate (Tasnim et al., 2020), and this media coverage may have played a key role in the development of coronaphobia. Interestingly, this study further revealed that the number of weekly pharmacy purchases of psychiatric drugs from wholesalers was less impacted by the second COVID-19 lockdown than the first. Our hypothesis is that there was a decrease in panic buying, a consumer behavior characterized by buying an unnecessarily large amount of essential goods in the context of natural disasters and health or economic crises (Yuen et al., 2020). As China is one of the largest producers and manufacturers of drugs in the world (Miller and Cohrssen, 2020), people may have feared that imports of these drugs into their countries could have been delayed or even prevented after the beginning of the first lockdown, leading to a sharp increase in pharmacy purchases in the days following the official announcement of this lockdown. In Germany, measures were undertaken following the first lockdown to identify, address, and prevent drug shortages (Vogler and Fischer, 2020). In this context, the fear of drug shortages among the German public at the beginning of the second COVID-19 lockdown might have been lower than at the beginning of the first. Nevertheless, another study conducted in Germany suggested that there had been an increase in depressive symptoms between the first and the second COVID-19 lockdowns (Moradian et al., 2021), underlining the need for further research to clarify the discrepancies in the findings of these two studies. Although the present findings have advanced the field, this study is subject to several limitations that need to be acknowledged at this point. First, this study included pharmaceutical data only, while clinical data would have allowed for more detailed analyses. Second, given that these data were of aggregate nature, no statistical test was performed to compare the number of pharmacy purchases between 2019 and 2020. Third, pharmacy purchases were used as a proxy to indicate the purchasing behavior of patients with regard to psychiatric drugs, although pharmacy purchases may also be predicted by other factors such as the sociodemographic characteristics of pharmacies and their size or the geographical areas in which they are located. Fourth, most psychiatric medications are prescribed drugs, and it was not possible to study the influence of patient and primary care physician behavior on pharmacy purchases. Fifth, as a substantial proportion of patients may have bought medicines on the Internet (Fittler et al., 2018), the present study may have underestimated the effects of the COVID-19 lockdowns on the purchase of psychiatric drugs in Germany. In conclusion, there was an increase in the number of pharmacy purchases of psychiatric drugs per week from wholesalers in the days prior to the first and second lockdowns in Germany. However, this increase in the purchase of psychiatric medications was less pronounced before the second COVID-19 lockdown than before the first. Further research is needed to better understand the factors (e.g., decreases in panic buying) that played a significant role in the lessening impact of the lockdowns on the consumption of psychiatric drugs among the German population. Funding This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions Louis Jacob contributed to the design of the study, managed the literature searches, wrote the first draft of the manuscript, and corrected the manuscript. Jens Bohlken contributed to the design of the study and corrected the manuscript. Karel Kostev contributed to the design of the study, performed the statistical analyses, and corrected the manuscript. All authors contributed to and have approved the final manuscript. Declaration of competing interest The authors declare that they have no competing financial interests. Acknowledgments We would like to thank Claudia Herrmann for the management of the data used in this study. ==== Refs References Ammassari A. Di Filippo A. Trotta M.P. Traversa G. Pierantozzi A. Trotta F. Magrini N. Comparison of demand for drugs used for COVID-19 treatment and other drugs during the early phase of the COVID-19 pandemic in Italy JAMA Netw. Open 4 2021 e2037060 10.1001/jamanetworkopen.2020.37060 Arora A. Jha A.K. Alat P. Das S.S. Understanding coronaphobia Asian J. Psychiatr. 54 2020 102384 10.1016/j.ajp.2020.102384 33271693 Atzrodt C.L. Maknojia I. McCarthy R.D.P. Oldfield T.M. Po J. Ta K.T.L. Stepp H.E. Clements T.P. A Guide to COVID-19: a global pandemic caused by the novel coronavirus SARS-CoV-2 FEBS J. 287 2020 3633 3650 10.1111/febs.15375 32446285 Basch C.H. Kecojevic A. Wagner V.H. Coverage of the COVID-19 pandemic in the online versions of highly circulated U.S. Daily newspapers J. Community Health 45 2020 1089 1097 10.1007/s10900-020-00913-w 32902813 Bendau A. Petzold M.B. Pyrkosch L. Mascarell Maricic L. Betzler F. Rogoll J. Große J. Ströhle A. Plag J. Associations between COVID-19 related media consumption and symptoms of anxiety, depression and COVID-19 related fear in the general population in Germany Eur. Arch. Psychiatr. Clin. Neurosci. 2020 1 9 10.1007/s00406-020-01171-6 Bui T.T.M. Button P. Picciotti E.G. Early evidence on the impact of coronavirus disease 2019 (COVID-19) and the recession on older workers Public Pol. Aging Rep. 30 2020 154 159 10.1093/ppar/praa029 Fiorillo A. Sampogna G. Giallonardo V. Del Vecchio V. Luciano M. Albert U. Carmassi C. Carrà G. Cirulli F. Dell'Osso B. Nanni M.G. Pompili M. Sani G. Tortorella A. Volpe U. Effects of the lockdown on the mental health of the general population during the COVID-19 pandemic in Italy: results from the COMET collaborative network Eur. Psychiatr. 63 2020 e87 10.1192/j.eurpsy.2020.89 Fittler A. Vida R.G. Káplár M. Botz L. Consumers turning to the Internet pharmacy market: cross-sectional study on the frequency and attitudes of Hungarian patients purchasing medications online J. Med. Internet Res. 20 2018 10.2196/11115 Gao J. Zheng P. Jia Y. Chen H. Mao Y. Chen S. Wang Y. Fu H. Dai J. Mental health problems and social media exposure during COVID-19 outbreak PLoS One 15 2020 10.1371/journal.pone.0231924 Groarke J.M. Berry E. Graham-Wisener L. McKenna-Plumley P.E. McGlinchey E. Armour C. Loneliness in the UK during the COVID-19 pandemic: cross-sectional results from the COVID-19 psychological wellbeing study PLoS One 15 2020 10.1371/journal.pone.0239698 Haug N. Geyrhofer L. Londei A. Dervic E. Desvars-Larrive A. Loreto V. Pinior B. Thurner S. Klimek P. Ranking the effectiveness of worldwide COVID-19 government interventions Nat. Human Behav. 4 2020 1303 1312 10.1038/s41562-020-01009-0 33199859 Howard R. Burns A. Schneider L. Antipsychotic prescribing to people with dementia during COVID-19 Lancet Neurol. 19 2020 892 10.1016/S1474-4422(20)30370-7 Kostev K. Lauterbach S. Panic buying or good adherence? Increased pharmacy purchases of drugs from wholesalers in the last week prior to Covid-19 lockdown J. Psychiatr. Res. 130 2020 19 21 10.1016/j.jpsychires.2020.07.005 32768709 Michalowsky B. Hoffmann W. Bohlken J. Kostev K. Effect of the COVID-19 lockdown on disease recognition and utilisation of healthcare services in the older population in Germany: a cross-sectional study Age Ageing 50 2021 317 325 10.1093/ageing/afaa260 33205150 Miller H.I. Cohrssen J.J. China's coronavirus-induced paralysis threatens U.S Drug Supply Chain. Mo Med. 117 2020 86 88 32308220 Moradian S. Bäuerle A. Schweda A. Musche V. Kohler H. Fink M. Weismüller B. Benecke A.-V. Dörrie N. Skoda E.-M. Teufel M. Differences and similarities between the impact of the first and the second COVID-19-lockdown on mental health and safety behaviour in Germany J. Public Health 2021 10.1093/pubmed/fdab037 (Oxf) Nadkarni S. Devinsky O. Psychotropic effects of antiepileptic drugs Epilepsy Current 5 2005 176 10.1111/j.1535-7511.2005.00056.x PeConga E.K. Gauthier G.M. Holloway A. Walker R.S.W. Rosencrans P.L. Zoellner L.A. Bedard-Gilligan M. Resilience is spreading: mental health within the COVID-19 pandemic Psychol. Trauma 12 2020 S47 S48 10.1037/tra0000874 32496106 Prati G. Mancini A.D. The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments Psychol. Med. 51 2021 201 211 10.1017/S0033291721000015 33436130 Rabeea S.A. Merchant H.A. Khan M.U. Kow C.S. Hasan S.S. Surging Trends in Prescriptions and Costs of Antidepressants in England amid COVID-19 2021 10.1007/s40199-021-00390-z Daru Reuters Germany heading towards extension of hard lockdown [WWW Document] URL https://www.reuters.com/article/us-health-coronavirus-germany-idUSKBN299112 2021 accessed 3.26.21 Robb C.E. de Jager C.A. Ahmadi-Abhari S. Giannakopoulou P. Udeh-Momoh C. McKeand J. Price G. Car J. Majeed A. Ward H. Middleton L. Associations of social isolation with anxiety and depression during the early COVID-19 pandemic: a survey of older adults in london, UK Front. Psychiatr. 11 2020 10.3389/fpsyt.2020.591120 Ruengorn C. Awiphan R. Wongpakaran N. Wongpakaran T. Nochaiwong S. Health Outcomes and Mental Health Care Evaluation Survey Research Group (HOME-Survey) Association of job loss, income loss, and financial burden with adverse mental health outcomes during coronavirus disease 2019 pandemic in Thailand: A nationwide cross-sectional study 2021 10.1002/da.23155 Depress Anxiety Stall N.M. Zipursky J.S. Rangrej J. Jones A. Costa A.P. Hillmer M.P. Brown K. Assessment of psychotropic drug prescribing among nursing home residents in ontario, Canada, during the COVID-19 pandemic JAMA Intern. Med. 2021 10.1001/jamainternmed.2021.0224 Tasnim S. Hossain M.M. Mazumder H. Impact of rumors and misinformation on COVID-19 in social media J. Prev. Med. Public Health 53 2020 171 174 10.3961/jpmph.20.094 32498140 Vaduganathan M. van Meijgaard J. Mehra M.R. Joseph J. O'Donnell C.J. Warraich H.J. Prescription fill patterns for commonly used drugs during the COVID-19 pandemic in the United States J. Am. Med. Assoc. 323 2020 2524 2526 10.1001/jama.2020.9184 Vogler S. Fischer S. How to address medicines shortages: findings from a cross-sectional study of 24 countries Health Pol. 124 2020 1287 1296 10.1016/j.healthpol.2020.09.001 World Health Organization WHO coronavirus (COVID-19) dashboard [WWW document] URL https://covid19.who.int/ 2021 Yuen K.F. Wang X. Ma F. Li K.X. The psychological causes of panic buying following a health crisis Int. J. Environ. Res. Publ. Health 17 2020 10.3390/ijerph17103513

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==== Front J Mol Graph Model J Mol Graph Model Journal of Molecular Graphics & Modelling 1093-3263 1873-4243 Elsevier Inc. S1093-3263(22)00275-3 10.1016/j.jmgm.2022.108396 108396 Article Network analysis of the autophagy biochemical network in relation to various autophagy-targeted proteins found among SARS-CoV-2 variants of concern Cueno Marni E. ab∗ Taketsuna Keiichi b Saito Mitsuki b Inoue Sara b Imai Kenichi a a Department of Microbiology, Nihon University School of Dentistry, Tokyo, 101-8310, Japan b Immersion Biology Class, Department of Science, Tokyo Gakugei University International Secondary School, Tokyo, 178-0063, Japan ∗ Corresponding author. Department of Microbiology and Immunology, Nihon University School of Dentistry, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo, 101-8310, Japan. 14 12 2022 14 12 2022 10839628 7 2022 28 10 2022 9 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Autophagy is an important cellular process that triggers a coordinated action involving multiple individual proteins and protein complexes while SARS-CoV-2 (SARS2) was found to both hinder autophagy to evade host defense and utilize autophagy for viral replication. Interestingly, the possible significant stages of the autophagy biochemical network in relation to the corresponding autophagy-targeted SARS2 proteins from the different variants of concern (VOC) were never established. In this study, we performed the following: autophagy biochemical network design and centrality analyses; generated autophagy-targeted SARS2 protein models; and superimposed protein models for structural comparison. We identified 2 significant biochemical pathways (one starts from the ULK complex and the other starts from the PI3P complex) within the autophagy biochemical network. Similarly, we determined that among the autophagy-targeted SARS2 proteins (Nsp15, M, ORF7a, ORF3a, and E) are structurally conserved throughout the different SARS2 VOCs suggesting that the function of each protein is preserved during SARS2 evolution. Interestingly, among the autophagy-targeted SARS2 proteins, the M protein coincides with the 2 significant biochemical pathways we identified within the autophagy biochemical network. In this regard, we propose that the SARS2 M protein is the main determinant that would influence autophagy outcome in regards to SARS2 infection. Graphical abstract Image 1 Keywords Centrality measurements Membrane protein Network analysis SARS-CoV-2 (SARS2) Variants of concern ==== Body pmc1 Introduction Autophagy is an important cellular process responsible for degrading dysfunctional organelles, damaged cytosolic proteins, and intracellular pathogens [1,2]. Moreover, autophagy induction triggers a coordinated action involving multiple individual proteins and protein complexes [3] and is dependent on cellular energy status, nutrient abundance, amino acid (mammalian target of rapamycin), and growth factors [4,5]. Additionally, during a viral infection, the autophagy process is induced and provides an anti-virus response against the on-going infection [6,7]. In this regard, some viruses (i.e. herpes simplex virus, human cytomegalovirus, HIV-1, coronaviruses) inhibit the autophagic response by either exiting the autophagic process without being lysed or by blocking autophagic degaradation at the final stage, thereby, resulting to immune evasion [5,8,9]. Coronaviruses (CoV) are RNA viruses that are classified as: family Coronaviridae, order Nidovirales, and subfamily Othocoronavirinae [10]. Additionally, there are currently seven known human-infecting CoVs with only SARS-CoV-2 (SARS2) causing a pandemic which lead to coronavirus disease 2019 (COVID-19) [11]. Moreover, SARS2 was found to reduce autophagy induction as characterized by both SQSTM1 accumulation and LC3B-II increase which in-turn has been correlated to multiple SARS2 proteins, namely: non-structural protein 15 (Nsp15), membrane protein (M), open reading frame 7a (ORF7a), open reading frame 3a (ORF3a), and envelope protein (E) [12]. This highlights the effectiveness of SARS2 in inhibiting autophagy since there are multiple SARS2 proteins that can independently or in combination hamper the autophagy process. However, to our knowledge, it was never established which stage of the autophagy biochemical network and corresponding SARS2 proteins (Nsp15, M, ORF7a, ORF3a, E) play a significant role. Furthermore, after the detection of the original SARS2 strain, variants of concern (VOC) appeared which possessed higher virulence and transmissibility rates compared to the original strain [13]. This may likewise suggest that the autophagy process varies among the SARS2 variants. However, this was likewise not established. A common approach towards elucidating the interconnection of the various components involved in a given cellular process or pathway utilizes network analytics [[14], [15], [16]]. In this regard, this study attempted to use network analytics in order to identify significant sections within the autophagy pathway which in-turn may elucidate whether possible VOC-specific structural changes among the corresponding SARS2 proteins (Nsp15, M, ORF7a, ORF3a, E) coincide with the significant autophagy sections. A better understanding of the interconnections within the autophagy pathway and the potential correlation with the autophagy-inhibiting SARS2 proteins may help identify possible antivirulence drug targets within the autophagy pathway. 2 Materials and methods 2.1 Network design and analyses of the autophagy pathway Network design of the autophagy pathway was based on the animal autophagy pathway registered in the KEGG Pathway Database (http://www.genome.jp/kegg/pathway.html) and utilizes the Cytoscape software in order to design and connect interacting biochemical components into a single comprehensible conceptual framework [17]. Briefly, nodes represented the biochemical components involved in the animal autophagy while the directed edges (represented as an arrow) represented the direction of transition among the biochemical components. Network analyses was conducted through centrality measurements which likewise used the Cytoscape software [17]. In this study, we performed the following centrality measurements: (1) betweenness centrality to show which biochemical component is crucial in maintaining functionality and coherence within the autophagy network; (2) stress centrality to establish how important a biochemical component is in the autophagy network; (3) closeness centrality to highlight the biochemical component that is functionally relevant to other biochemical components in the autophagy network; (4) radiality centrality to elucidate the possibility of the biochemical component to be either relevant or irrelevant within the autophagy network; (5) eccentricity centrality to emphasize the easiness of a biochemical component to be reached by other components within the autophagy network; and (6) edge betweenness centrality to determine the degree of connection between two biochemical components within the autophagy network [18]. Briefly, in all centrality measurements made, the threshold for each centrality was first determined and, subsequently, the degree of centrality based on whether the nodal or edge values were higher than the threshold was identified for all 6 centrality measurements. Thus, node and edge centrality measurements with values greater than the threshold were considered significant. Moreover, results from all centrality measurements were combine into one network design (unified network) in order to show the common significant biochemical components. 2.2 Protein model generation and model quality assessment of autophagy-related SARS2proteins A minimum of ten amino acid sequences (n = 10) per autophagy-targeted protein (Nsp15, M, ORF7a, ORF3a, E) per SARS2 VOC were collected from the National Center for Biological Information (NCBI) website and used to generate protein models through the Phyre2 web server [19]. The following representative amino acid sequences were utilized for protein modeling with Genebank accession number and SARS2 variant classification indicated: NSP15 (YP_009724391, original; QST05185, alpha; QXN65578, beta; UCK74385, gamma; QYC29745, delta; UPX34687, omicron BA.1; UIZ70266, omicron BA.2; UNZ13405, omicron BA.3; UPL64192, omicron BA.4; UPX84051, omicron BA.5), M (YP_009724391, original; QQP64796, alpha; QUH61239, beta; QVE57575, gamma; QYV85798, delta; UPX83240, omicron BA.1; UNR40665, omicron BA.2; URF60233, omicron BA.3.1; UPL64197, omicron BA.4; UOZ63340, omicron BA.5), ORF7a (YP_009724391, original; UFA17860, alpha; UPI39091, beta; QVK69151, gamma; QZF95006, delta; UPX83242, omicron BA.1; UPP15924, omicron BA.2; URF60235, omicron BA.3; UPU86055, omicron BA.4; UPX24774, omicron BA.5), ORF3a (YP_009724391, original; QYV40058, alpha; QTA94395, beta; QQX12070, gamma; QTW58947, delta; UHB39408, omicron BA.1; UJT21513, omicron BA.2; UNH00891, omicron BA.3; UPP22435, omicron BA.4; UOZ50987, omicron BA.5), and E (YP_009724391, original; QTN70998, alpha; QWA53293, beta; QUG11508, gamma; QVU83934, delta; UHO08789, omicron BA.1; ULG28796, omicron BA.2; URF60232, omicron BA.3; UPU03031, omicron BA.4; UOZ63339, omicron BA.5). Protein models were visualized using the Jmol applet [20]. For protein model quality assessment, available crystal structures of autophagy-targeted SARS2 proteins were superimposed with generated protein models. Protein crystal structures used were the following: Nsp15 (PDB ID: 6VWW), M (PDB ID: 8CTK), ORF7a (PDB ID: 6W37), ORF3a (PDB ID: 6XDC), and E (PDB ID: 5X29). Model:crystal superimposition was done using TM-align [21] and Root Mean Square Deviation (RMSD) values were used to establish either structural similarity or differences. For this study, we considered RMSD <1.00 to insinuate structural similarity between the generated protein model and the corresponding protein crystal, whereas, RMSD >1.00 would imply structural difference between the generated protein model and the corresponding protein crystal. Similarly, coarse grain-molecular dynamics (CG-MD) simulation using the MDWeb server [22] was performed utilizing the radius of gyration (Rgyr) of the generated SARS2 protein models in order to establish model stability. CG-MD simulation conditions were set at: 1000 ps simulation time with Δt at 0.01 ps and output frequency collected at 10 ps. All protein models that were observed to have minimal Rgyr are considered stable and structurally reliable for further analyses. 2.3 Structural comparison and model pattern recognition Structural comparison of the 5 autophagy-targeted SARS2 proteins was made among the different SARS2 variants and subvariants using TM-align [21]. Similarly, we considered RMSD <1.00 to insinuate structural similarity among the SARS2 variants/subvariants, whereas, RMSD >1.00 would imply structural difference among the SARS2 variants/subvariants. Protein models that shared the same structural pattern (RMSD = 0) were identified and grouped together. Subsequently, identified structural patterns were likewise compared between other structural patterns. 3 Results 3.1 Autophagy network design and centrality measurements coincide with known drug modulators and putative SARS2 activity Before any analysis can be made on the autophagy biochemical network, it is imperative that the network design is accurate. In order to establish an accurate representation of the autophagy biosynthetic pathway, both the network design and centrality measurements of the autophagy biochemical network were likewise correlated with previously proven coronavirus-specific drug modulators. As seen in Fig. 1 , the designed autophagy network was based on the mTORC1 complex as the entry point of autophagy [23]. Moreover, the following biochemical components/complexes were considered significant based on various centrality measurements: (1) betweenness (Fig. 2 A) and stress (Fig. 2B) centralities: BP1 (branch point 1) found downstream the mTORC1 complex, ULK complex, PI3K complex, C9orf72-SMCR8 complex, ATG12-ATG5-ATG16 conjugate, and biochemical components involved in autolysosome formation; (2) closeness centrality (Fig. 2C): mTORC1 complex, ULK complex, PI3K complex plus biochemical components influencing Becklin1, C9orf72-SMCR8 complex, and the ATG12-ATG5-ATG16 conjugate; (3) eccentricity (Fig. 2D) centrality: mTORC1 complex, PI3K complex and biochemical components influencing Becklin1, biochemical components influenced by the C9orf72-SMCR8 complex, STX17-SNAP29-VAMP8 complex, and biochemical components influencing the degradation of the inner vesicle; (4) radiality (Fig. 2E) centrality: all biochemical components and complexes prior to lysosome fusion to form the autolysosome; and (5) edge betweenness (Fig. 2F) centrality: BP1 transitioning to the ULK complex, ULK complex transitioning to the C9orf72-SMCR8 complex, ULK complex transitioning to the ATG12-ATG5-ATG16 conjugate, ULK complex transitioning to the PI3K complex, ATG12-ATG5-ATG16 conjugate transitioning to autolysosome formation, and STX17-SNAP29-VAMP8 complex transitioning to autolysosome formation.Fig. 1 Network design of the overall autophagy biosynthetic network. Solid arrows represent transition between the different biochemical components. Solid lines represent association among biochemical components. Branch points (BP) represent biochemical transitions or associations that are related to more than 2 biochemical components. Fig. 1 Fig. 2 Centrality analyses of the overall autophagy biosynthetic network. Significant biochemical components based on (A) betweenness, (B) stress, (C) closeness, (D) eccentricity, (E) radiality, and (F) edge betweenness centralities are indicated. Significant nodes are colored green. Significant edges are marked by solid red arrow lines. Threshold for each centrality measurement is shown on the upper left. Fig. 2 Considering the unified network (Fig. 3 ), two biochemical networks within the autophagy network were putatively found to be holistically significant when the whole autophagy biochemical network was considered: (1) BP1 found after the mTORC1 complex transitioning to the ULK complex which subsequently transitions to both the C9orf72-SMCR8 and PI3K complexes, and (2) phosphatidylinositol 3-phosphate (PI3P) transitioning to WIP1 which similarly transitions to the ATG12-ATG5-ATG16 conjugate and ATG3. Previous works have shown that both nitazoxanide and repamycin/sirolimus reduced SARS2 infection by inhibiting mTORC1 thereby activating autophagy, whereas, both chloroquine and hydroxychloroquine block endocytosis-mediated cell entry (except in lung cells) of SARS2 thereby impeding autophagy function [[24], [25], [26], [27]]. This is consistent with our results (Fig. 3) showing that the activity of previous autophagy modulators used to treat SARS2 infection coincide with the significant nodes and edges highlighted in the unified network analysis of the autophagy biochemical network. Earlier publications have emphasized the dual nature (beneficial and detrimental) of autophagy, whereby, autophagy can serve as a host defense against viral infection through xenophagy (beneficial) and, likewise, autophagy can be manipulated by the viral pathogen to produce replication organelles (detrimental) [[28], [29], [30]]. In the case of SARS2, it was reported that SARS2 disrupts the ULK complex formation thereby functionally impairing autophagy (particularly the degradative capacity possibly found in xenophagy) [31] and, similarly, SARS2 was also shown to utilize the autophagy biochemical network to promote viral replication via PI3P [30]. These points are likewise consistent with our results (Fig. 3). Taken together, we postulate the following: (1) one of the putative significant biochemical network (via the ULK complex) within the autophagy network that is responsible for the beneficial nature of autophagy is possibly the same biochemical network stimulated by certain autophagy modulators (nitazoxanide and repamycin/sirolimus) and targeted by SARS2 to impair autophagy action involved in host defense; and (2) the other potential significant biochemical network (via PI3P) within the autophagy network that is manipulated by certain viruses for viral replication is the same biochemical network used by SARS2 for organelle formation.Fig. 3 Unified network highlighting common nodes and edges established from centrality measurements. Significant nodes are colored green. Significant edges are marked by solid red arrow lines. Branch points (BP) represent biochemical transitions or associations that are related to more than 2 biochemical components. Fig. 3 3.2 SARS2 M protein is putatively the main protein determinant that would influence autophagy outcome during a SARS2 infection Prior to further downstream protein structural analyses, it was previously suggested that protein structures regardless of being obtained theoretically (i.e. computer-based) or experimentally (i.e. crystallized) should undergo a quality assessment [32]. In this regard, to elucidate whether the generated protein models are ideal for further downstream protein structural analyses, protein structural superimpositions of both generated protein models and known crystal structures of autophagy-targeted proteins (Nsp15, M, ORF7a, ORF3a, and E) were performed. For this study, we considered superimpositions with RMSD <1.00 to be ideal for further downstream protein structural analyses. We first generated the Nsp15, M, ORF7a, ORF3a, and E protein models (Suppl. Fig. 1) and, afterwards, found that all model:crystal superimpositions of autophagy-targeted proteins have RMSD <1.00 (Suppl. Fig. 2) and, based on CG-MD simulation (Suppl. Fig. 3), protein models showed minimal Rgyr. These results would mean that the generated protein models are potentially suitable for further structural analyses. SARS2 genome regularly undergoes mutations [33] which may suggest that viral proteins (in this case autophagy-targeted proteins) may have differed within the different SARS2 variants and subvariants. To determine whether any structural variations occurred among the autophagy-targeted proteins found among the original SARS2 variant and the different VOC (both variants and subvariants), protein superimposition and RMSD score comparison were performed. Interestingly, we observed that Nsp15 (Suppl. Fig. 4), M (Suppl. 5), ORF7a (Suppl. Fig. 6), ORF3a (Suppl. Fig. 7) and E (Suppl. Fig. 8) proteins have no structural difference (RMSD = 0) among the original SARS2 variant and the different VOCs (both variants and subvariants). This may insinuate that throughout SARS2 evolution, these particular autophagy-targeted proteins were conserved which in-turn could mean that the role of these proteins in autophagy manipulation remained constant. Interestingly, among the autophagy-targeted SARS2 proteins, only the M protein coincides with the 2 significant biochemical pathways we previously identified (Fig. 3) [7]. SARS2 M protein is a glycosylated structural protein that plays a significant role in virion assembly and, likewise, inhibits TBK1-related innate antiviral immune response [34]. In this regard, we postulate that the SARS2 M protein is the main protein determinant influencing autophagy outcome during a SARS2 infection. Admittedly, additional experimental work is needed to further prove this suspicion. 4 Discussion Autophagy is involved in multiple physiological processes (i.e. cell survival, cell metabolism, and host defense) while also being associated with several diseases (i.e. cancer and metabolic diseases) [35,36]. Currently, there are three autophagy types, namely: microautophagy wherein lysosonal membrane protrusions are utilized for cargo capture; chaperone-mediated autophagy wherein membrane structure are not utilized to secure cargo; and macroautophagy wherein cargo is sequestered away from lysosome [37]. In addition, SARS2 has many viral proteins that are capable of manipulating autophagy (particularly macroautophagy) at different stages [7]. Throughout this study, we attempted to identify significant biochemical pathways within the autophagy biochemical network and, likewise, correlate these significant biochemical pathways to the different autophagy-targeted SARS2 proteins. Network data is a source of information found within complicated patterns such as biochemical pathway networks with network analytics being utilized to provide a holistic analyses which in-turn allows for the integration of complementary data thereby giving additional new insights [18,38]. One common approach in performing network analytics is centrality analysis which involves ranking and identifying network components into significant elements based on multiple centrality measurements [18,39]. Subsequently, in order to avoid major limitations of centrality analysis such as: centrality measures are unable to capture the information flow of the overall network, rankings of each centrality measure differ across all measures, and centrality measures only capture the localized information and do not take into consideration the overall global network [40,41], all centrality measurements are considered. In this regard and based on our network analytics results, we believe that the autophagy network design and analyses were accurate since the results were consistent with the dual nature of autophagy and known SARS2 drug modulators [[24], [25], [26], [27], [28], [29], [30]]. This would mean that the designed autophagy network can potentially be utilized for further downstream analyses. Subsequently, considering the multiple autophagy-targeted SARS2 proteins that affect different stages of autophagy, our results suggest that all autophagy-targeted SARS2 proteins (Nsp15, M, ORF7a, ORF3a, and E) may have no altered function since all 5 protein structures were consistent in all SARS2 VOC. This would putatively mean that the mechanism of autophagy manipulation associated to Nsp15, M, ORF7a, ORF3a, and E are conserved among SARS2 VOC which in-turn would emphasize that viral manipulation of the autophagy biochemical network is independent from structural variations associated with autophagy-targeted SARS2 proteins. Similarly, considering our autophagy biochemical network and analyses, the different stages of autophagy targeted by the Nsp15, ORF7a, ORF3a, and E proteins [7] were putatively not found to be significant, whereas, the 2 significant autophagy biochemical pathways we identified are putatively correlated with the M protein. This would insinuate that among the autophagy-targeted SARS2 proteins, the M protein potentially plays a vital role in influencing SARS2 pathogenicity and immunosuppression [34]. In general, viruses evolved multiple strategies to counteract selective autophagy like xenophagy, mitophagy, aggrephagy, lipophagy, ferritinophagy, ER-phagy [42]. More specifically, viruses have developed counteraction strategies that may include resisting, escaping, subverting, and hijacking autophagy to enhance viral replication [43]. Considering the 2 significant autophagy biochemical pathways we identified are putatively correlated with the M protein, we hypothesize that a possible counteraction strategy associated with SARS2 pathogenicity and immunosuppression [34] involves the SARS2 M protein subverting the ULK complex formation to functionally impair autophagy response [31] and, likewise, the SARS2 M protein hijacking the PI3P complex to promote viral replication [30]. Our hypothesis is consistent with previous autophagy modulators used to treat COVID-19 [[24], [25], [26], [27],44]. In summary, we determined 2 significant biochemical pathways (one starts from the ULK complex and the other starts from the PI3P complex) within the autophagy biochemical network. Additionally, we established that all autophagy-targeted SARS2 proteins (Nsp15, M, ORF7a, ORF3a, and E proteins) are structurally conserved, thus, SARS2-linked autophagy manipulation is not associated with structural variation among the autophagy-targeted SARS2 proteins. Moreover, we found that the M protein is correlated to the 2 significant biochemical pathways we identified within the autophagy biochemical network which potentially emphasizes the vital role of the SARS2 M protein in autophagy manipulation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability Data will be made available on request. Acknowledgements This work was supported by JSPS 10.13039/501100001691 KAKENHI Grant Number 22K09932; Uemura Fund, 10.13039/100014420 Dental Research Center, Nihon University School of Dentistry . Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jmgm.2022.108396. ==== Refs References 1 Levine B. Kroemer G. Biological functions of autophagy genes: a disease perspective Cell 176 2019 11 42 30633901 2 Mizushima N. Levine B. Cuervo A.M. Klionsky D.J. Autophagy fights disease through cellular self-digestion Nature 451 2008 1069 1075 18305538 3 Bello-Perez M. Sola I. Novoa B. Klionsky D.J. Falco A. Canonical and noncanonical autophagy as potential targets for COVID-19 Cells 9 2020 4 Behrends C. Sowa M.E. Gygi S.P. Harper J.W. Network organization of the human autophagy system Nature 466 2010 68 76 20562859 5 Chiramel A.I. Brady N.R. Bartenschlager R. Divergent roles of autophagy in virus infection Cells 2 2013 83 104 24709646 6 Cottam E.M. 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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(22)00778-5 10.1016/j.molstruc.2022.133153 133153 Article Novel indolo [3,2-c]isoquinoline-5-one-6-yl [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazole analogues: Design, synthesis, anticancer activity, docking with SARS-CoV-2 Omicron protease and MESP/TD-DFT approaches☆ Verma Vaijinath A. a⁎ Saundane Anand R. b Shamrao Raju c Meti Rajkumar S. d Shinde Venkat M. e a Department of Chemistry, Sri Prabhu Arts, Science and J. M. Bohra Commerce Degree College, Shorapur-585 224, Yadgir, Karnataka, India b Department of P.G. Studies and Research in Chemistry, Gulbarga University, Kalaburagi, 585106, Karnataka, India c Department of Chemistry, Government First Grade College, Shahapur-585 223, Yadgir, Karnataka, India d Department of Biochemistry, Mangalore University, P.G. Centre Chikka Aluvara, 571234, Karnataka, India e Department of Botany, Gulbarga University, Kalaburagi, Karnataka, 585 106, India ⁎ Corresponding author. 25 4 2022 15 9 2022 25 4 2022 1264 133153133153 10 3 2022 31 3 2022 16 4 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Indoloisoquinoline derivatives are associated with varieties of biological and pharmacological properties. Therefore, we herein reported the synthesis of novel series of indolo [3,2-c]isoquinoline incorporated with [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazole moieties. Spectroscopic methods were used to determine the chemical structures of these molecules. Whereas, the B3LYP functional with the def2-SVP basis set were used to improve TD-DFT geometries and solvent effects. Investigations, which are directly connected to the optical spectra (absorption and emission) of molecules. These findings reveals that the compound 3d-f with a strong electron acceptor NO2 exhibited UV−visible spectra peaks to near infrared (NIR) range in solvents. Compound 3e exhibited a lowest ∆E of 2.28 eV in MeCN. Further, among the newly synthesized compounds 3d and 3g exhibits highest activity against four cell lines with strongest potent cytotoxicity, as contrasted to the control drug (Doxorubicin). Docking experiments revealed that compounds in contrast to 3a and 3d had strong interactions with Asn322, Met323, Ala387,Ala386, Gln506 and Gly326 with a greater binding affinity which are important amino acid residues that play a key role in SARS-CoV-2 Omicron main protease (Mpro) through hydrophobic, hydrogen bonding, Pi-sigma, Pi-sulfur and van der Waals interactions. Graphical Abstract Image, graphical abstract Keywords Indolo [3,2-c]isoquinoline Molecular docking TDDFT MESP: anticancer SARS-CoV-2 omicron protease ==== Body pmc1 Introduction The indoloisoquinoline core structure contains an indole fused to isoquinolinone systems presenting broad spectrum of pharmacological activities. [1], [1b], [1c] Isoquinolin-1(2H)-one motifs are a kind of heterocycle found in numerous natural products, pharmaceuticals drugs. [2a], [2b], [2c], [2d] They display number of physiological and therapeutic properties. [3a], [3b], [3c], [3d] Interestingly, tetracyclic skeleton of indoloisoquinoline alkaloid scaffolds and synthetic compounds have attracted significant concern to their intriguing pharmacological effects such as anti-tumor, fungicidal, analgesic, anti-inflammatory, anthelmintic and antibacterial activities. [4a], [4b], [4c], [4d], [4e] The precursor molecule 11H-indolo [3,2-c]isoquinoline has yet not been confirmed in nature. [5] Considering the significance, in this investigation, we were especially enthusiastic in 11H-indolo [3,2-c]isoquinoline, a distant analogous of cryptosanguinolentine and cryptolepine. Recently, we have reported 6H-indolo [3,2-c]isoquinolin-5(11H)-ones. [6], [7], [8] The core emphasis of this study is to synthesize and broadcast there in vitro anticancer activity. In computational chemistry, DFT (density functional theory) is indeed a quantum mechanical (QM) approach to identify the atomic structure (electron structure) of molecules. So, as consequence of DFT, it has become currently one of most effective approach to investigate the electronic structures and characteristics of the compounds. Simultaneously, TD-DFT (time-dependent density functional theory) is very often employed strategy in modern chemistry for simulating excited-state, geometries, frontier orbital energies, oscillator and rotatory strengths of compounds. [9], [10], [11], [11a], [11b], [12], [13a], [13a], [13b], [14]. Finally, newly synthesized compounds were compelled to theoretical calculations of TD-DFT. The goal of our was investigations to identify the characteristics of electronic transitions and their ramifications with electronic concerns resulting from electron donating and electron withdrawing substituents, for example CH3, H, NH2, Cl and NO2. Further, to investigate the electronic structure using UV spectroscopy and quantum chemistry approaches, which influences their reactivity and anti-cancer activity. Based on the spectroscopic (IR, 1H NMR, 13C NMR and mass) data, the information of elemental analysis structures of newly synthesized indolo [3,2-c]isoquinoline compounds were confirmed. 2 Results and discussion 2.1 Synthesis The initial compounds were synthesised using the methods outlined. [6,8] Our focus was to incorporate [1,2,4]triazolo [3,4-b] [1,3,4] thiadiazole ring structures with indolo [3,2-c]isoquinoline moiety. In present investigations, synthetic strategies of indolo [3,2-c]isoquinoline derivatives 3a-i were illustrated in Schemes 1 . Condensation of compound 2a with substituted aromatic acids by phosphorous oxychloride under reflux condition for 3–5 h to afford the appropriate 6-{6-(4-Aminophenyl)−1,7a-dihydro- [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazol-3-yl}−8‑chloro-6H-indolo [3,2-c]isoquinolin-5(11H)-one 3a. NH, NH2 and C = O bands of absorption of 3a (at 3278, 3209, and 1679 cm−1, respectively) which were shown in the infrared spectrum (IR) spectra. One proton of indole NH attributed a downfield singlet at δ 12.3 and amino group of toluidine was assigned a singlet at δ 6.1 merging for two protons and δ 8.6 NH of triazole ring which were observed in the proton NMR spectra of 3a. Eleven aromatic protons were consolidated in the multiplets at 6.8–8.2 δ. 13C NMR spectrum of 3a showed δ 174.2, 160.9 of carbonyl function. Compound 3a showed isotopic molecular ion peaks at m/z 485 (M +) and 487 (M ++2) in its mass spectrum. Compounds 3b-i possess their structures confirmed by spectral data. Thin layer chromatography (TLC) with silica gel-G coated aluminum plates (Merck) and chloroform–methanol (6:2) as the solvent system was implemented to monitor the completion of the reaction. The melting points have been determined in an open capillary with a melting point equipment and are uncorrected.Scheme 1 Synthetic protocol of the indolo [3,2-c]isoquinoline derivatives. Scheme 1 2.2 Plausible mechanism Protonation is the initial step of acid appeared by dehydration and attack at the same time of nitrogen lone pair to the electron deficient resonance-stabilized cation (acylium ion) to establish an intermediate. In the second stage, the intermediate is subjected to an adjacent group involvement with nucleophilic sulfur, this results in the formation of C-S bond by removing the water molecule (Scheme 2). Ultimately, the title compounds are obtained as a result of deprotonation (3a-i).Scheme 2 Plausible mechanism of title compounds. Scheme 2 3 Computational details 3.1 Molecular electrostatic potential (MESP) analysis Our initial endeavours were concentrated on molecular electrostatic potential (MESP), it was implemented to establish the electrostatic attraction among molecules. Similarly, the MESP assessment accounted for hydrogen bonding, dipole moment, electro-negativity, partial charges and chemical reactivity site of a molecule [15]. The density of electrons would be an essential component in determining the reactivity of electrophilic and nucleophilic regions, including hydrogen bonding interactions [16,17]. So, as to forecast the reactivity of electrophilic and nucleophilic areas attack with relation to compounds under investigation, we used the B3LYP level of the optimised geometry to MESP [18]. The color sequence red to yellow (negative regions) denotes low electrostatic potential and are associated with electrophilic reactivity. Whereas, blue to green (positive regions) denotes nucleophilic reactivity sites with strong electrostatic potential and the MESP represented ESP gradually increasing in intensity in the order of red-yellow-green-blue (regions in between 0.03 to 0.94 a.u.). The high density of electrons on the surface of molecules is indicated by the red color which correlate to electrophilic attacks. While the blue color with high electrostatic potential is associated with nucleophilic reactivity sites and is accountable for atomic nuclei rejecting proton. The highest negative areas of the MESP maps are related to oxygen and chlorine atoms and were identified and as most favourable regions for electrophilic attack. Similarly, it is noteworthy that hydrogen atoms of indole NH are nucleophilic attack. MESP plots are depicted in Fig. 1 .Fig. 1 Molecular electrostatic potential surfaces calculated for prepared compounds. Fig 1 3.2 Time-Dependent density functional theory (TDDFT) To determine the time dependent density functional theory (TDDFT) computational parameters, the Orca software program version 4.2.1. [19] were used [13a,20]. The estimation of ground state, dipole moment, absorption wavelength, excitation energy, oscillator strength (ƒ), applied functional B3LYP/ basic set def2-SVP, Gas phase: GP, solvatochromic solvents: n-hexane: Hex. Methanol MeOH: Acetonitrile (methyl cyanide): MeCN were employed [21]. Similarly, the UV–vis spectra of newly synthetised compounds were also recorded. 3.3 UV- absorption and emission wavelength analysis The quantum computation on electronic absorption spectra were done using TD-DFT/(B3LYP/def2-SVP) level to geometry optimized of the compounds and comprehended parameters for instance absorption wavelengths (λ max), oscillator strengths (ƒ), energy gap ∆E (eV), ground state dipole moment (Debye, ∆µ) and electronic transitions were determined in the gaseous phase to examine the solvatochromic effect of solvents (Hex, MeOH, and MeCN) for our compounds. The absorption and emission spectra, molecular extinction coefficient of all compounds ranges from 10,000 to 90,000 M−1 cm−1 were noticed. The spectra exhibited a consistent distribution with a most intense band at higher energies range between 340 and 758 nm, this shows the nature of π-π* transitions. It could be clear to observe that the maximum absorption (λabs) and emission (λem) wavelengths are influenced by the substituents. It denotes that the absorption transition has a distinctive intramolecular charge transfer (ICT) and as a consequence, the excited state is not the same as in the emission transition. In gas phase: the newly synthesized compounds have strong and wide absorption bands in order of absorption wavelengths which are clearly distinguishable: 3e ˃ 3f ˃ 3d ˃ 3 h ˃ 3i ˃ 3c ˃ 3a ˃ 3b ˃ 3g. The maximum wavelength of absorption was by compound 3e (λabs = 494 nm) and 3 g the (λabs = 350 nm) minimum. When compared to other compounds, the absorption wavelength of 3e is red shift. The initial excited state was optimised using CPCM (conductor-like polarizable continuum model)-TDDFT, the status of excited peaks has been anticipated accurately by accredation to the transition from S1 to S0. It's worth noting that the excited bands solvatochromism are considerably in the order: 3d ˃ 3f ˃ 3e ˃ 3c ˃ 3i ˃ 3a ˃ 3h ˃ 3b ˃ 3g. Compound 3d exhibited maximum (λabs max = 574 nm) in hexane and 3f (λabs max = 633 and 611 nm) in MeOH and MeCN, respectively. As a consequence, the absorption wavelength increases as delocalization increases. All the compounds (3a–i) emission maxima exhibited increasingly redshift and significantly increases reliance on the substituents included. Compound 3e noticed the highest red shift (λem max 564 nm) in the gas phase and λem max 758 nm in the MeCN solvent. Because of the EDG-EWG [electron donating group (EDG) and strong electron-withdrawing group (EWG)], rings of indolo [3,2-c]isoquinoline with p-CH3 substituted and 1,3,4-thiadiazolyl phenyl with p –NO2 groups existence and excited state, ICT should be effective because of the conjugated molecule's π-π* electron transition. Alternatively, EWG-EDG, EDG-EWG and EWG-EWG substituents all showed gradual red shifts, maximum emission wavelength of 728 nm (MeCN) for 3d, 756 nm (MeOH) for 3e, and 754 nm (MeOH), 749 nm (MeCN) for 3f respectively. Some of the organic compounds with consistent donor–π-acceptor (D–π-A) like benzothiazole, benzo [e]indole and quinoline had significant emission maximum wavelengths in the ultraviolet-visible (UV–vis) to near infrared (NIR) range. The π linkage among the units that donate and accept electrons has improved ICT [22], [23], [24]. In contrast, λabs max to λem max red shifted by 59 nm (3 h) in GP and 159 nm (3e) in MeCN, both compounds connected with EDG-EWG (CH3, Cl and NO2). Due to coulombic interaction between the dipolar solute and solvent molecules, [25,26] the excited state of 3e seems to be dramatically stabilized. The electron delocalization from the electron donor methyl moiety to the electron acceptor nitro group results in a red-shift (high stokes shift) λem max in 3e. Due to the π -π* characteristic of the transition, the greater stability of the excited state in relation to the ground state results in a red-shift appearance in wavelengths of absorption and emission. Accordingly, Hammett's substituent constants are correlated [27]. The strong electron acceptor –NO2 group at the p-position on phenyl ring has shown considerable electron delocalization within excited state, in contrast to the weak electron acceptor Cl group. These aspects might have an impact in a decrease of the energy band gap between donor and acceptor substitutions [28]. The substantial stokes shifts of title compounds 3e and 3f showed that the molecules alter structure when excited. In contrast to dipole moment for 3a-3c (2.16–6.17 debye) and 3 g-3i (4.27–7.76 debye), there is a significant shift in 3d −3f (7.22–9.52 debye). The dipole moment of the strong electron donor-acceptor-acceptor (NO2) and the weak electron acceptor or donor groups (Cl, CH3, H and NH2) differs. Oscillator strength (ƒ) is the chance of electromagnetic radiation absorption or emission in transition between energy levels of atoms or molecules. The computed oscillator strength values ranged from 0.002 to 0.168. The structure and behavior of electrically excited states are revealed by quantum yield. The range of quantum yield(Φ) was shown to be between 1.206 to 1.372% and these findings demonstrated by compounds 3e and 3g. The absorption spectrum was predicted by Avogadro software as represented in Table 1 and Fig. 2 .Table 1 TDDFT(B3LYP/def2-SVP) UV spectra of absorption (λabs max, nm) and emission (λem max, nm) for compounds 3a-i in gas phase and various solvents, Stockes shift cm−1 (SS), ground state dipole moment (Debye) (∆μ), oscillator strength (ƒ), aemission quantum yield(Φ), energy gap ΔE (eV), and transition electronic and Assignment. Table 1Com Solvents λabs λem S ƒ ∆µ Φa (%) ∆E (eV) Transition (%) Assignment 3a GP 359 441 82 0.101 4.17 1.228 3.87 H-1 L(62) π π* Hex 371 481 110 0.093 4.85 1.296 3.87 H-1 L(68) π π* MeOH 376 484 108 0.084 5.95 1.287 3.87 H-1 L (75) π π* MeCN 372 469 97 0.084 6.17 1.261 3.87 H-1 L(75) π π* 3b GP 351 428 77 0.134 2.16 1.219 4.13 H L2 (88) π π* Hex 367 471 104 0.119 2.16 1.283 4.12 H L + 2 (89) π π* MeOH 372 473 101 0.112 3.32 1.272 4.12 H L + 2(90) π π* MeCN 373 483 110 0.111 3.33 1.295 4.12 H L + 2 (90) π π* 3c GP 363 448 85 0.084 2.93 1.234 4.1 H-1 L + 1 (65) π π* Hex 375 476 101 0.078 3.49 1.269 4.34 H-2 L (71) π π* MeOH 383 493 110 0.074 4.38 1.287 3.85 H-1 L (76) π π* MeCN 380 500 120 0.073 4.39 1.316 4.16 H-1 L + 1 (76) π π* 3d GP 473 538 65 0.039 7.22 1.137 3.58 H L + 1 (80) π π* Hex 574 646 72 0.032 8.15 1.125 3.94 H L + 2 (80) π π* MeOH 613 727 114 0.032 9.51 1.186 3.57 H L + 1 (78) π π* MeCN 596 728 132 0.032 9.52 1.221 3.57 H L + 1(78) π π* 3e GP 494 564 70 0.050 6.86 1.142 3.61 H L + 1 (70) π π* Hex 554 668 114 0.041 7.65 1.206 3.93 H L + 2(69) π π* MeOH 617 756 139 0.037 8.78 1.225 3.57 H L + 1 (66) π π* MeCN 599 758 159 0.037 8.79 1.265 3.90 H L + 2(66) π π* 3f GP 488 554 66 0.009 6.91 1.135 3.53 H-2 L (84) π π* Hex 571 664 93 0.036 7.71 1.163 3.95 H L + 1(71) π π* MeOH 633 754 121 0.034 8.87 1.191 3.95 H L + 1(69) π π* MeCN 611 749 138 0.034 8.88 1.226 3.58 H L + 1(69) π π* 3 g GP 351 430 79 0.132 5.36 1.225 4.21 H L + 2 (66) π π* Hex 349 479 130 0.168 6.28 1.372 4.53 H L + 2 (75) π π* MeOH 349 467 118 0.161 7.75 1.338 3.71 H L + 1 (79) π π* MeCN 340 459 119 0.161 7.76 1.350 4.06 H L + 2(79) π π* 3 h GP 388 447 59 0.008 4.44 1.152 3.86 H L + 2 (94) π π* Hex 368 478 110 0.016 5.13 1.299 3.80 H-1 L (89) π π* MeOH 433 531 98 0.011 6.19 1.226 3.97 H L + 2 (93) π π* MeCN 434 552 118 0.011 6.2 1.272 3.97 H L + 2 (93) π π* 3i GP 385 450 65 0.006 4.27 1.169 3.86 H L + 2 (95) π π* Hex 372 484 112 0.015 4.95 1.301 3.75 H-1 L(90) π π* MeOH 445 531 86 0.030 6.01 1.193 3.75 H L + 1 (41) π π* MeCN 415 541 126 0.002 6.02 1.304 3.74 H-1 L (57) π π* Fig. 2 TD-DFT(B3LYP/ def2-SVP) computed UV–visible optical absorption and emission spectra of the title compounds in gas phase (A) and different solvents (B, C and D). Fig 2 3.4 Frontier molecular orbital energies (FMOs) The FMOs (highest occupied molecular orbital HOMO and the lowest unoccupied molecular orbital LUMO) play a significant role in influencing molecular features such as UV–visible absorption, optical and electronic characteristics [29]. The energies of HOMO and LUMO diagrams shows the electronic structural and excitation properties quantitatively. Compound 3e exhibit a lowest HOMO-LUMO energy gap of 2.28 eV in MeCN solvent and 2.53 eV, 2.44 eV and 2.3 eV, respectively for gas phase, hexane, MeOH, respectively. Consequently, resulting in a decrease in the HOMO-LUMO energy gap on altering the polarity of the solvents from n-hexane to acetonitrile. Further, HOMO-1,−2 to LUMO + 1, +2, transitions for electron donating groups (CH3, NH2 and H), largest ∆E gap ranges between 3.87–4.34 eV compounds of 3b and 3c, while electron accepting groups (Cl and NO2), transitions ∆E gap ranges between 3.53–4.53 eV. Interestingly, HOMO-1,−2 to LUMO + 1, +2, transition energy gap are increased when electron-donating replaced by EWGs. However, when p-CH3, p-NO2 substitutions are introduced at indolo [3,2-c]isoquinoline and 1,3,4-thiadiazolyl phenyl rings, the energy gap decrease in all solvents, while incorporation of p-NH2, p-Cl and p-H groups in other compounds ranges from 2.56 to 2.53 eV in gas phase Table 1 and graphical presentation in Fig. 3 (see figs. S1-S3 and table S1 supplementary data) show the frontier orbital energies from HOMO-1,−2 to LUMO + 1, +2, as well as the HOMO-LUMO energy gaps.Fig. 3 Molecular orbitals (HOMO–LUMO) and energy gap of compounds 3a-i were calculated by TDDFT (B3LYP/ def2-SVP) method in MeCN and on transition, the red and green surfaces represent density. Fig 3 The various global reactivity variables are derived from the following equations [30], for example chemical potential (energy which received or discharged) μ = -(I + A)/2; Global hardness (the resistance to deformation is described to as hardness) η = (I-A)/2; Electronegativity (attractiveness of electrons) χ =(I + A)/2; Global softness (indicate the ease with which a molecule may polarised) S = 1/2η; Global electrophilicity index (ability to accept electrons) ω = μ2/2η. The qualities of global hardness (η) and softness (S) are used to evaluate their significance in terms of charge transfer reduction, stability and reactivity of molecules. The hardness value of compound 3d was high compared to 3c (6.80 eV and 2.75 eV, respectively) and values of S (6.80 eV and 5.51 eV, respectively) which were low. On the other hand, the electrophilicity index is a measure of a chemical species' ability to take any number of electrons owing to a maximal flow of electrons from a donor environment. Compound 3b exhibited the highest electrophilicity index (0.277 eV), while compound 3e showed the lowest (0. 0.120 eV), in current investigation. Furthermore, electronegativity, which is defined as the negative of the chemical potential in DFT, is a measure of the inclination to fascinate electrons in a chemical bond [31]. Consequently, 3b has the highest 1.75 eV, while 3e has the lowest 1.26 eV as shown in Table 2 .Table 2 The computed frontier molecular orbital parameters for compounds 3a-i. Table 2Comp MW RB HBA HBD MR TPSA Å LRV logP logS 3a 485.95 2 3 3 153.26 129.1 1 3.66 −6.44 3b 463.51 2 4 2 135.93 135.13 0 3.51 −6.14 3c 451.5 2 3 3 148.25 129.1 0 3.17 −5.85 3d 515.93 3 5 2 157.68 148.9 1 3.40 −6.85 3e 495.51 3 5 2 157.63 148.9 0 3.28 −6.56 3f 481.49 3 5 2 152.67 148.9 0 2.96 −6.26 3 g 505.38 2 3 2 153.87 103.08 2 4.72 −7.38 3h 484.96 2 3 2 153.82 103.08 1 4.56 −7.09 3i 470.93 2 3 2 148.86 103.08 1 4.25 −6.79 MW: Molecular Weight, RB: Rotatable bonds, HBA: H-bond acceptors, HBD: H-bond donors, MR: Molar refractivity, LRV: Lipinski rule violations. 4 Biological evaluation Aqueous solubility (log S) and Lipophilicity (log P) are determined before considering the molecule's anticancer activity. SwissADME is used to evaluate log P and log S. [32] Log P is directly connected to drug transport and contact to receptors, whereas log S is strongly linked to bioavailability. Compound 3f shows Log P 2.96, indicating that the chemical can diffuse through cell membranes, as a result of which it can be used in drug delivery applications. The compound 3c value log S (−5.85) confirms the permeability of molecule through cell membranes. The PASS online program is used to estimate various antineoplastic activities of the title compounds. Cancers of the brain, lung, cervical, ovarian, gastric, pancreatic, and bladder are examples. The PASS software's estimated findings have an average accuracy of roughly 85%. Only antineoplastic activities were listed in this investigation [33] as presented in Pa > 70% in Fig. 4 (Table S2. see supplementary information).Fig. 4 Same antineoplastic (anticancer) activities predications of compounds computed by PAAS with Pa > 70%. Fig 4 4.1 In vitro anticancer study The cytotoxic consequences of synthesized compounds 3a-i has been evaluated employing 3-(4,5- dimethylthiazol-2-yl)−2,5-diphenyl tetrazolium bromide (MTT) [34] test against MCF-7, A549, HeLa, and Panc-1 the four panel of human cancer cell lines. The doxorubicin was employed as a standard. The IC50 values were accustomed to represent the concentrations that inhibit cancer cell growth by 50%. The findings showed that compounds 3d (IC50 value of MCF-7:0.43 ± 0.41, A-549:0.42± 1.12, HeLa: 0.55 ± 1.81and Panc-1: 1.15 ± 1.34 µM, respectively) and 3 g (IC50 value MCF-7:0.32 ± 0.23, A-549=0.43± 1.34, HeLa:0.51 ± 0.81, Panc-1:1.10 ± 1.34 µM, respectively) demonstrated against four cell lines with strongest potent cytotoxicity as contrasted to the control drug (Doxorubicin). Compound 3a displayed significantly cytotoxic consequence in context of MCF-7 and Panc-1 with IC50 0.46± 1.12 and 1.17 ± 0.28 µM, correspondingly and equivalent to the standard. Furthermore, 3e, 3f, 3h and 3i demonstrated strong cytotoxic action against MCF-7, Panc-1and HeLa with IC50, 0.78. ± 0.91, 0.8 ± 1.31, 1.17 ± 1.50 and 0.60± 0.23 µM, respectively. The findings are summarised in Table 3 . Interestingly, as shown by the afore mentioned study of the structure activity relationship, inclusion of Cl and NO2 groups (electron-withdrawing) are significant for activity. Moreover, compounds having electron-withdrawing (chloro and nitro) abilities can attract electrons from other atoms and the inductive action will induce a dipole moment within the compound. It might enhance solubility in water and allow for drug interface with biomolecule. The electron deficiency or rich of the indolo [3,2-c]isoquinoline compounds likewise, might impact the anticancer activity to some extent.Table 3 The FMOs parameters were computed for the synthesized compounds ascertained by B3LYP/def2-SVP level. Table 3Comp EHOMO ELUMO ΔE (eV) μ η S χ ω 3a −4.87 −1.52 3.35 −1.67 2.81 5.63 1.67 0.249 3b −4.85 −1.35 3.50 −1.75 2.76 5.53 1.75 0.277 3c −4.82 −1.38 3.44 −1.72 2.75 5.51 1.72 0.269 3d −5.40 −2.79 2.61 −1.31 3.40 6.80 1.31 0.126 3e −5.29 −2.76 2.53 −1.26 3.33 6.67 1.26 0.120 3f −5.32 −2.77 2.56 −1.28 3.35 6.70 1.28 0.122 3 g −5.29 −2.36 2.92 −1.46 3.23 6.47 1.46 0.165 3h −5.09 −1.56 3.53 −1.77 2.94 5.87 1.77 0.266 3i −5.11 −1.59 3.53 −1.76 2.95 5.90 1.76 0.263 4.2 ADME and drug-likeness analyses A molecule's ADME (absorption, distribution, metabolism and excretion) are important procedures to examine during the phases of drug development. [33] All of the synthesized compounds 3a-3i were initially evaluated using ADME-Profiling and the results are highlighted in Table 4 . The drug-likeness features of the chosen compounds were analysed using the Lipinski rule of five (L5). A drug-like compounds must have a molecular weight of less than 500 gmol−1, HBD (hydrogen bond donor) number: ≤ 5, HBA (hydrogen bond acceptors): ≤ 10 and logP (octanol-water partition coefficient): ≤5, L5 as per the rule.Table 4 IC50 values of the compounds 3a-i for the anticancer activity. Table 4 IC50 µMa Comp R R1 MCF-7 A-549 HeLa Panc-1 3a Cl NH2 0.46± 1.12 6.5 ± 0.45 6.45 ± 2.73 1.17 ± 0.21 3b CH3 NH2 4.7 ± 1.14 4.1 ± 2.70 5.0 ± 1.90 7.6 ± 0.80 3c H NH2 9.1 ± 1.01 7.3 ± 2.11 6.2 ± 2.47 7.7 ± 1.22 3d Cl NO2 0.43 ± 0.41 0.42± 1.12 0.55± 1.81 1.15 ± 1.14 3e CH3 NO2 8.6 ± 1.21 0.78. ± 0.91 6.1 ± 1.22 9.2 ± 1.13 3f H NO2 0.8 ± 1.31 8.2 ± 2.13 5.1 ± 0.9 6.4 ± 1.05 3 g Cl Cl 0.32 ± 0.23 0.43± 1.34 0.51 ± 0.81 1.10 ± 1.34 3h CH3 Cl 4.1 ± 1.45 3.6 ± 1.40 2.5 ± 0.67 1.17 ± 1.50 3i H Cl 7.3 ± 1.21 4.5 ± 0.59 0.60± 0.23 6.1 ± 1.78 Doxorubicin – – 0.46 ± 0.21 0.49± 0.15 0.56 ± 1.70 1.17± 0.36 a IC50 values are indicated as mean ± SD of three independent tests. 4.2.1 Molecular docking study The COVID-19 panic pandemic is a significant threat to mankind and it has been spreading rapidly over the last two years. Newly identified SARS-CoV-2 Omicron virus variant B.1.1.529 was first reported by WHO on 24 November 2021 [35]. Hence, in present investigation, we looked into a molecular docking study to identify feasible binding affinity among newly synthesized compounds (3a-i) with SARS-CoV-2 Omicron PDB ID:7T9L (Cryo-EM structure of SARS-CoV-2 Omicron spike protein complex with human ACE2, EMD-25,761) obtained from https://www.rcsb.org/structure/7T9L. Docking study was performed with online server (https://mcule.com/apps/1-click-docking) and for visualisations Discovery Studio visualizer-2021 was employed [36]. The investigation of docking, appears that all the indolo [2,3-c]isoquinoline analogues interact with the SARS-CoV-2 Omicron protease. As a result, the evaluated compounds may be successful in accomplishing binding interactions into the omicron PDB binding pocket and endeavours of structure-binding showed that hydrophobic and H-bonds are interplayers of key bindings. The results of the molecular docking computations revealed that the best docking scores were −8.4 and −8.2 kcal/mol shown for synthesised molecules 3a and 3d, respectively. Energetically, the NH and carbonyl groups in compound 3a revealed four hydrogen bonds with Asn322, Met323, Ala386 and Ala387 at bond distance 2.95, 2.33, 2.98 and 2.42 Å, respectively. The p-NH2 attached to 1,3,4-thiadiazolyl phenyl has a Pi-sigma hydrophobic interaction with Thr354 (3.86 Å) and Phe356(bond distance 5.82 Å) forms a Pi-sulfur with 1,3,4-thiadiazole moiety of the compound 3a with binding sites of SARS-CoV-2 Omicron main protease (Mpro). The NO2 group of compound 3d exhibited two hydrogen interactions with Gln506, Gly326 and NH of 1,2,4-triazole ring system to Asn322 (2.26–2.61 Å bond distance). The examined aromatic group of ligands has some van der Waals interactions with Gly502, Met383, Thr324, Asp405, Phe356, Gly354 and His505, whereas, Pi-sigma to Val503. These observations lead us to believe that indolo [3,2-c]isoquinoline compounds under investigation may have inhibitory properties. However, biological experiments are required to confirm the computational projections. The consequences of molecular docking postures are shown in Fig. 5 (Table S3, Supplementary materials).Fig. 5 Best hits of SARS-CoV-2 Omicron protease in 2D interactions 3a(a), 3d(c) and hydrophobicity surface at active binding sites 3a (b)and 3d(d) compounds. Fig 5 5 Conclusion In this investigation, numerous indolo [3,2-c]isoquinolinyl- [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazole analogous were synthesized. The time-dependent density functional (TD-DFT) in conjunction with B3LYP (Becke, 3-parameter, Lee–Yang–Parr) function and def2-SVP basis set were accustomed to the predict spectrum of absorption and emission of newly synthesized compounds. The outcomes indicate that compound 3f exhibits maximum absorption spectra at 633 and 611 nm in MeOH and MeCN, respectively. In MeCN, compound 3e exhibited maximum emission spectra at 758 nm and 2.28 eV is the lowest ∆E of HOMO-LUMO. Further, compounds 3d and 3g highlighted the most effective activity against human cancer cell lines. The molecular docking findings revealed that compounds 3a and 3d demonstrated appropriate affinity for the amino acids at the active site of SARS-CoV-2 Omicron main protease. Ultimately, the findings indicated that both molecules might be investigated further in the search for a novel antiviral drug to SARS-CoV-2 Omicron. Nevertheless, further wet laboratory validation is required. Consequently, DFT is often used to investigate electronic properties and HOMO-LUMO energy gap, which could be the key reasons for its indicated innate biological properties. The global chemical reactivity features suggest that this molecule has a proclivity for chemical reactions. The MESP is in inverse relation to the electrical density and an important distinction among electrophilic and nucleophilic attack sites, contacts, including hydrogen bonding interactions [37]. The MESP surface of the title compounds are suited for the pharmacological action which contains electrophilic sites located around the indolo [3,2-c]isoquinoline and [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazole ring systems. That has also been recognized to residue sites besides its biological values, molecular docking strategy was accustomed to screen. It is indeed worth noting that the inclusion of chloro and oxygen atoms provide the maximum strengths on the description compounds which possess the greatest possible electron density and would preferentially converse with microorganisms and amplify the anticancer potential. 6 Experimental procedure 8-Substituted-5-oxo-5H-indolo[3,2-c]isoquinoline-6(11H)-carbohydrazides 1a-1c. [6] 6-(4,5-Dihydro-4-amino-5-thioxo-1H-1,2,4-triazol-3-yl)−8- substituted −6H-indolo [3,2-c]isoquinolin-5(11H)-ones 2a-2c. [8] 8-Substituted−6-{6-(4-substituted phenyl)−1,7a-dihydro- [1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-ones 3a-3i In phosphorus oxychloride (10 mL), an equimolar mixture of compounds 2a-c (0.01 mol) and substituted aromatic acids (0.01 mol) were added and refluxed for 3–5 h. The reaction mixtures were allowed to cool to room temperature before being slowly poured onto crushed ice while stirring. The solutions were left to remain overnight, after which the solids were filtered, treated with a dilute sodium hydroxide solution and thoroughly rinsed with cold water. The resultant compound was dried and recrystallized in ethanol. 6-{6-(4-Aminophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−8‑chloro-6H-indolo[3,2-c]isoquinolin-5(11H)-one 3a Yellow crystals, yield: 65%, m.p. 291–292 °C; FTIR (KBr cm−1): 3278, 3209 (NH,NH2), 1679 (C = O), 715 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 12.3(s, 1H, indole-NH), 8.6 (s, 1H, NH), 6.8–8.2 (m, 11H, Ar-H), 6.1(s, 2H, NH2) 13C NMR (DMSO‑d6, δ, ppm); 177.5, 161.8 (C = O), 154.5, 148.5, 145.0, 137.1, 134.3, 132.7, 131.1, 130.9, 130.5,129.1, 128.6, 128.2, 127.6, 127.1,123.9,123.6, 120.2,119.3,115.4,114.3,113.9, 101.1; MS(m/z) 485 (M +) and 487 (M ++2); Anal. Calcd. for C24H14N7OSCl: C, 59.32; H, 2.92; N, 20.18; Found: C, 59.29; H, 2.90; N, 20.15%. 6-{6-(4-Aminophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−8-methyl-6H-indolo[3,2-c]isoquinolin-5(11H)-one 3b Light yellow solid, yield: 69%, m.p.247–248 °C; FTIR (KBr cm−1): 3231, 3209 (NH,NH2), 1652 (C = O), 712 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 12.2(s, 1H, indole-NH), 8.4 (s, 1H, NH), 7.1–8.1 (m, 11H, Ar-H), 5.6(s, 2H, NH2), 2.7(s, 3H, CH3); 13C NMR (DMSO‑d6, δ, ppm); 181.1, 162.3 (C = O), 149.2, 148.7, 148.4, 136.6, 134.1, 133.1, 132.4, 131.2, 130.3,130.0, 128.7, 128.1, 127.3, 126.4,121.7, 121.2, 120.6, 120.3,115.2,114.9,111.8, 104.2, 25.3 (CH3); Anal. Calcd. for C25H17N7OS: C, 64.50; H, 3.70; N, 21.06; Found: C, 64.52; H, 3.68; N, 21.03%. 6-{6-(4-Aminophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-one 3c Greenish, solids yield: 79%, m.p. 298–299 °C; FTIR (KBr cm-1): 3241, 3210 (NH,NH2), 1684, (C = O), 698 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 11.7(s, 1H, indole-NH), 8.2 (s, 1H, NH), 6.9–8.1 (m, 12H, Ar-H), 5.1(s, 2H, NH2): 13C NMR (DMSO‑d6, δ, ppm); 175.6, 161.2 (C = O), 150.3, 150.1, 148.1, 137.6, 136.1, 133.2, 131.4, 131.2, 130.2,130.1, 128.9, 128.0, 127.7, 126.7,121.5, 121.4, 121.2, 119.7,116.2,115.9,110.5, 103.1; Anal. Calcd. for C24H15N7OS: C, 63.84; H, 3.36; N, 21.72; Found: C, 63.80; H, 3.33; N, 21.70%. 8-Chloro-6-{1,7a-dihydro-6-(4-nitrophenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-one 3d Yellow crystals, yield: 71%, m.p. 311–312 °C; FTIR (KBr cm−1): 3267, (NH), 1720, (C = O), 1527(NO2), 692 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 11.9(s, 1H, indole-NH), 8.7 (s, 1H, NH), 7.0–8.1 (m, 7H, Ar-H), 13C NMR (DMSO‑d6, δ, ppm); 175.2 (C = N), 162.1 (C = O), 149.4, 148.5, 148.3, 139.6, 137.1, 133.6, 132.7, 131.6, 130.4,1 28.9, 128.4, 128.4,128.0, 127.6, 126.8, 126.6, 121.9, 121.9, 121.6,119.3, 112.5, 104.2; Anal. Calcd. for C24H12N7O3SCl C, 55.87; H,2.35; N, 19.00; Found: C, 55.84; H, 2.32; N, 18.97%. 6-{1,7a-Dihydro-6-(4-nitrophenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−8-methyl-6H-indolo[3,2-c]isoquinolin-5(11H)-one 3e Orange crystals, yield: 62%, m.p. 284–285 °C; FTIR (KBr cm−1): 3252 (NH), 1692(C = O), 1539(NO2), 685 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 12.0(s, 1H, indole-NH), 8.2 (s, 1H, NH), 7.0–8.1 (m, 11H, Ar-H), 2.4(s, 3H, CH3);13C NMR (DMSO‑d6, δ, ppm); 171.5 (C = N), 160.3 (C = O), 150.4, 149.2, 146.3, 140.9, 136.9, 132.8, 131.3, 130.9, 129.4, 128.6, 128.3, 128.1,127.9, 127.5, 126.6, 126.4, 122.4, 121.5, 121.2, 120.1, 116.2, 106.1, 25,4 (CH3); Anal. Calcd. for C25H15N7O3S: C, 60.60; H, 3.06; N, 19.79; Found: C, 60.58; H, 3.03; N, 19.76%. 6-{1,7a-Dihydro-6-(4-nitrophenyl)-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-one 3f Brown crystal, yield: 63%, m.p. 274–275 °C; FTIR (KBr cm−1): 3232(NH), 1687(C = O), 1562(NO2), 713 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 12.1(s, 1H, indole-NH), 8.2 (s, 1H, NH), 7.0–8.1 (m, 12H, Ar-H);13C NMR (DMSO‑d6, δ, ppm); 181.2 (C = N), 162.1(C = O), 151.4, 150.2, 148.3, 139.6, 137.3, 133.5, 132.5, 131.2, 130.3, 129.6, 128.7, 128.5,128.2, 127.8, 125.8, 124.9, 123.2, 122.1, 120.2,119.9, 118.2, 104.2; Anal. Calcd. for C24H15N7O3S: C, 59.87; H, 3.14; N, 20.36; Found: C, 59.85; H, 3.10; N, 20.33%. 8-Chloro-6-{6-(4-chlorophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-one 3 g Yellow solid, yield: 83%, m.p. 287–288 °C; FTIR (KBr cm−1): 3232(NH), 1687(C = O), 697 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 11.4(s, 1H, indole-NH), 8.3 (s, 1H, NH), 7.0–8.1 (m, 11H, Ar-H);13C NMR (DMSO‑d6, δ, ppm); 175.3(C = N), 160.7, 148.9, 148.4, 137.1,134.3, 133.6, 132.7,131.8, 131.4,130.6,129.8, 129.5, 129.0, 128.9, 128.5, 128.0,127.6, 126.8, 121.9, 121.6,119.3, 112.5,104.2; Anal. Calcd. for C24H12N6OSCl2: C, 57.04; H, 2.40; N, 16.63; Found C, 57.01; H, 2.38; N, 16.59%. 6-{6-(4-Chlorophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−8-methyl-6H-indolo[3,2-c]isoquinolin-5(11H)-one 3 h Green solid, yield: 69%, m.p. 290–291 °C; FTIR (KBr cm−1): 3252 (NH), 1687(C = O), 711 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 11.5(s, 1H, indole-NH), 8.6 (s, 1H, NH), 6.8–8.0 (m, 11H, Ar-H), 2.9(s, 3H, CH3);13C NMR (DMSO‑d6, δ, ppm); 176.5(C = N), 163.6(C = O), 148.9, 148.3, 136.8, 136.2, 132.7, 132.7,131.5, 131.1,130.2,128.9, 128.5, 128.1, 127.9, 127.7, 126.9,126.3, 124.4, 120.9, 120.6,118.3, 114.5,107.3, 23.5(CH3); Anal. Calcd. for C25H15N6OSCl: C, 61.92; H, 3.13; N, 17.33; Found: C, 61.90; H, 3.11; N, 17.29%. 6-{6-(4-Chlorophenyl)−1,7a-dihydro-[1,2,4]triazolo[3,4-b][1,3,4]thiadiazol-3-yl}−6H-indolo[3,2-c]isoquinolin-5(11H)-one 3i Colourless crystal, yield: 72%, m.p. 301–302 °C; FTIR (KBr cm−1): 3241(NH), 1694, (C = O), 715 (C-S-C); 1H NMR (DMSO‑d6, δ, ppm): 12.2(s, 1H, indole-NH), 8.4 (s, 1H, NH), 6.8–7.9 (m, 12H, Ar-H), 13C NMR (DMSO‑d6, δ, ppm); 178.1(C = N), 162.6 (C = O), 150.1, 149.2, 137.2, 134.5, 133.6, 133.4,132.2, 131.3,130.5,129.8, 128.7, 127.8, 127.5, 125.7, 124.7,124.6, 122.1, 119.8, 118.5,117.5, 116.2,106.2; Anal. Calcd. for C24H13N6OSCl: C, 61.21; H, 3.79; N, 17.85; Found: C, 61.19; H, 3.76; N, 17.81%. 7 Biological procedure 7.1 Anticancer activity Four different human cancer cell lines such as MCF-7 (breast), A549 (lung), HeLa (cervical) and Panc-1 (pancreas) were used to investigate the anticancer activities of synthesized compounds. The synthesized compounds were diluted in Dimethyl Sulfoxide (DMSO) to different concentrations (10, 5, 2.5, and 1.25 g ML-1) and evaluated using 3-(4, 5-Dimethyl-2-yl-2, 5-diphenyl tetrazolium bromide (MTT assay). Cells were treated with various concentrations of the described compounds and their anticancer activity was tested. The control was maintained, untreated cells (negative control) and Doxorubicin (positive control). The independent t-test in the SPSS 12 software was used to examine the statistical significance of the sample and negative control. Non-linear regression analysis was used to calculate the compounds concentrations necessary to kill half of the cell population (IC50). The average IC50 of three separate studies was used to calculate cytotoxic activity. Declaration of Competing Interest The authors confirm that there is no conflict of interest in the content of this article. Appendix Supplementary materials Image, application 1 Acknowledgements The authors are thankful to the Principal, Sri Prabhu Arts, Science and J. M. Bohra Commerce Degree College, Shorapur-585 224, Yadgir, Karnataka, India for provide laboratory facilities. Authors are grateful to the Directors, IIT Madras, Chennai, India provide spectral data, National Collection of Industrial Microorganisms (NCIM), National Chemical Laboratory (NCL) and National centre for Cell Science (NCCS), Pune, India to providing test materials. ☆ 6-(6-phenyl- [1,2,4]triazolo [3,4-b] [1,3,4]thiadiazol-3-yl)−6H-indolo [3,2-c]isoquinolin-5(11H)-one. Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.molstruc.2022.133153. ==== Refs References 1 [1a] Saundane A.R. Verma V.A. Vijaykumar K. Synthesis of some new indolo [2,3-c]isoquinolinyl pyrazoles, -1,3,4-oxadiazoles and their biological activities Med. Chem. 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Unrevealing sequence and structural features of novel coronavirus using in silico approaches: the main protease as molecular target EXCLI J 19 2020 400 409 10.17179/excli2020-1189 32210741 31 Parr R.G. Yang W. Density-functional Theory of Atoms and Molecules 1989 Oxford University Press New York 32 Daina A. Michelin O. Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules Sci. Rep. 7 42717 2017 10.1038/srep42717 1?13 33 Jorgensen W.L. Duffy E.M. Prediction of drug solubility from Monte Carlo simulations Bioorg. Med. Chem. Lett. 10 11 2000 1155 1158 10.1016/S0960-894X(00)00172-4 10866370 34 Mosmann T. Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays J. Immunol. Methods 1-2 1983 55 63 10.1016/0022-1759(83)90303-4 https://www.who.int/news/item/26-11-2021-classification-of-omicron-(b.1.1.529)-sars-cov-2-variant-of-concern 35 R Kiss Sandor M. Szalai F.A. http://Mcule.com: a public web service for drug discovery J. Cheminform 4 17 2012 10.1186/1758-2946-4-S1-P17 36 Politzer P. Murray J.S. The fundamental nature and role of the electrostatic potential in atoms and molecules Theor. Chem. Acc. 108 3 2002 134 142

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==== Front J Aerosol Sci J Aerosol Sci Journal of Aerosol Science 0021-8502 0021-8502 Elsevier Ltd. S0021-8502(21)00640-6 10.1016/j.jaerosci.2021.105914 105914 Article Impact of washing cycles on the performances of face masks Charvet Augustin a∗ Bardin-Monnier Nathalie a Thomas Dominique a Dufaud Olivier a Pfrimmer Marielle a Barrault Mathieu b Bourrous Soleiman b Mocho Victor b Ouf François-Xavier b Poirier Stéphane b Jeanmichel Laurence c Segovia César c Ferry Daniel d Grauby Olivier d a Université de Lorraine, CNRS, LRGP, F-54000, Nancy, France b Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSN-RES, SCA, Gif-Sur-Yvette, 91192, France c CETELOR, Université de Lorraine, F-88000, Épinal, France d Aix-Marseille Univ, CNRS, CINaM, F-13009, Marseille, France ∗ Corresponding author. 20 11 2021 2 2022 20 11 2021 160 105914105914 3 7 2021 9 11 2021 10 11 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The tension on the supply of surgical and FFP2 masks during the first wave of the COVID-19 pandemic leads to study the potential reuse of these masks. As washing is easily adaptable at home, this treatment solution was retained. In this work, thirty-six references of surgical masks and four FFP2 masks were tested without being worn or washed and after several washing cycles. The results highlighted a great heterogeneity of performances depending on the mask trademarks, both for surgical masks and FFP2. The quality of the meltblown and spunbond layers and the presence/absence of electrostatic charges at the fiber surface are put forward to explain the variability of results, both on differential pressures and filtration efficiencies. The differential pressure and the particle filtration efficiency of the washed masks were maintained up to 10 washing cycles and met the standard requirements. However, an immersion in water with a detergent induces an efficiency decrease for submicronic particles. This lower performance, constant after the first washing cycle, can be explained by the loss of electrostatic charges during the washing cycle. The modifications of surface properties after washing also lead to a loss of the hydrophobic behavior of type IIR surgical masks, which can therefore no more be considered as resistant to blood projections. Keywords Surgical masks FFP2 Reuse Washing Electrostatic charge ==== Body pmc1 Introduction During the COVID-19 pandemic, both scientific community and the World Health Organization confirmed that aerosol-based transmission is a major contributor to disease spread. Transmission of SARS-CoV-2 can occur through direct, indirect or close contact with infected people through infected secretions such as saliva and respiratory secretions or respiratory droplets expelled by infected contaminated person. The size of particles emitted by an individual are influenced by the individual themselves (mucus properties) (Lee et al., 2019) and their activities (normal breathing, talking, sneezing, coughing) (Gralton et al., 2010). Moreover, after their emission, droplets are subject to evaporation and settling, therefore their diameter evolves according to the environmental conditions (temperature, relative humidity) (Xie et al., 2007; Ji et al., 2017). Consequently, the concentration and the particle size distribution that have to be considered when designing barrier mask are very broad. Even if particle size distributions are highly heterogeneous (individual and environment-dependent), their diameter can be assumed as mainly smaller than 5 μm (Asadi et al., 2019; Johnson et al., 2011; Morawska et al., 2009; Papineni & Rosenthal, 1997). Furthermore, Lindsley et al. (2010) collected cough-generated particles produced by individuals with influenza-like symptoms and concluded that viral ribonucleic acid was present in the droplets, whatever their size. Lednicky et al. (2020) showed that viable SARS-CoV-2 was detected in aerosols within the room of a COVID-19 patient. Van Doremalen et al. (2020) and Fears et al. (2020) observed that this virus remained viable and infectious in aerosol after 3 and 16 h, respectively. As a consequence, contamination through droplet transmission can occur when a person is in close contact with an infected person who has respiratory symptoms (e.g. coughing or sneezing) (Bourouiba et al., 2014) or who is talking, singing or playing music (He et al., 2021) but also through airborne particles of lower diameters remaining in suspension for prolonged periods and exposing individuals at a greater distance from the source. Expiratory particles size emitted during breathing and speech are sufficiently large to carry viable virus, i.e. within 60–140 nm according to several authors (Kim et al., 2020; Matsuyama et al., 2020; Park et al., 2020; Ren et al., 2020). But they are small enough to be inhaled, penetrate deeper into the respiratory tract and, consequently, have more serious health implications. As they persist in air for long time periods, indirect transmission of virus by aerosols might be a plausible hypothesis (Asadi et al., 2020). Therefore, wearing a mask appears essential to limit the pandemic spread as shown by Liang et al. (2020) in their systematic review and meta-analysis. Yan et al. (2019) modelled the evolution of the basic reproduction number R0 and of the incidence rate, as a function of the mask efficiency and the ratio of the population wearing a mask, respectively. They concluded that even if masks have a moderate efficiency (around 50%), a negligible transmission occurs if a majority of the population is protected. Historically, medical face masks are intended for the limitation of the transmission of infective agents during surgical procedures. These masks are used by surgical staff but also by patients and general public for the reduction of contamination during epidemic or pandemic situations. Before the pandemic, type II surgical masks were recommended in care services, surgical masks of type IIR for the medical staff during a care with a risk of projection (operating rooms) and filtering facepiece (FFP) respirators during particular care of a patient placed under special precautions. During the pandemic, filtering facepiece respirators are urged for invasive medical gesture or maneuvers in the respiratory sphere of a patient carrying SARS-CoV-2; type IIR surgical masks are requested for all other types of care. In comparison with FFP, surgical masks are more comfortable, are cheaper but fit loosely to the face and are not associated to a protection factor and leakage test. As they present lower collection efficiency, surgical masks do not offer a protection comparable to filtering facepiece respirators and are considered as a protection for others instead of for oneself. The tension on the supply of these single-use devices during the first wave of the pandemic of COVID-19 leads to a health use policy that was not without risks for patients and staff. A possible strategy to prevent a mask shortage would be the treatment (to eliminate viral and microbiological risks) and the reuse of these devices. Moreover, during the pandemic, approximatively 3.4 billion masks are discharged daily (Benson et al., 2021). This extensive use of face masks, containing polypropylene or other synthetic polymers, induces serious consequences on the environment as these plastic materials may remain in marine or land environments and increase the microplastic and nanoparticle pollution (Akber et al., 2020; Selvaranian et al., 2021; Sullivan et al., 2021). Therefore, studying the feasibility of the reuse of medical face masks and filtering facepiece respirators, appears interesting both for supply and environmental reasons. Mask reuse obviously implies a thorough decontamination phase between two uses for a hygiene issue. From the beginning of the pandemic, the decontamination and the reuse of N95 filtering facemask respirators have been the subject of an increasing number of studies (Schumm et al., 2021). As they preserve N95 mask integrity in terms of penetration, air flow resistance and physical appearance, the most promising methods seem to be UV irradiation (Bergman et al., 2010; Liao et al., 2020; Ou et al., 2020; Viscusi et al., 2007), ethylene oxide exposure (Bergman et al., 2010; Viscusi et al., 2009) and vaporized hydrogen peroxide decontaminations (Bergman et al., 2010; Cai & Floyd, 2020; Fisher et al., 2020; Richter et al., 2016; Viscusi et al., 2009). Regarding heat treatment, Liao et al. (2020) showed that heating (dry or in the presence of humidity) at temperatures up to 100 °C can preserve the efficiency of the mask after 10 cycles, whereas Fisher et al. (2020) highlighted that 70 °C dry heating allows maintaining performances only up to 2 cycles. Ou et al. (2020) considered thermal treatment as the most applicable decontamination method for the general public because of its simplicity of implementation at home and no significant degradation of the collection efficiency after 10 thermal dry treatment cycles (30 min at 77 °C). Microwave oven use (Bergman et al., 2010; Viscusi et al., 2011) and bleach treatment (Bergman et al., 2010; Viscusi et al., 2007) do not induce significant changes in penetration and air flow resistance but can lead to media or head straps melting and tarnishing of nosebands, respectively. Treatment with an autoclave, immersion in a 70%(v) isopropyl alcohol (Ou et al., 2020; Viscusi et al., 2007) or ethanol solution (Liao et al., 2020) conduct to a drastic degradation of filtration performances, which are no more consistent with normative criteria. However, the conservation of performances not only depends on the kind of treatment but also of the N95 model (Rodriguez-Martinez et al., 2020). In the same way, Suen et al. (2020) concluded that, among different treatments on surgical masks, non-fluid-based methods such as UV irradiation maintain filtration efficiency after three cycles; while immersion into water or alcohol induces the loss of electrostatic charges of the mask. To recover electret effect of masks and increase the efficiency degraded by sterilization treatments, Hossain et al. (2020) proposed a simple recharging method based on an electrical field. Similarly, Wang et al. (2020) showed that after hot water decontamination, the drying with a hair drier allows recovering 90% of the electret effect of masks. As washing can easily be realized at home, this treatment solution was retained and the influence of washing cycles on the performances of surgical masks and filtering facepiece respirators was studied. It should be noticed that disinfection performances (i.e. elimination of viral and microbiological risks) were not addressed in the paper. However, a previous study shows that applying such washing procedures leads to a total number of colonies forming units five times lower than the normative limit (30 cfu/g) described in the EN 14683+AC standard (Alcaraz et al., 2022). 2 Materials & methods 2.1 Samples Thirty-six references of surgical masks (5 masks of type I, 13 type II and 18 type IIR) and four FFP2 masks were tested without washing (considered as new) and/or after some washing cycles (Appendix 1). For references appearing several times in the table, different batches were tested over different periods of time to evaluate the repeatability. In a pandemic context, with a highly contagious disease, all the tested masks have never been worn. Moreover, Carsi and Alonso (2022) recently showed that, in a submicron range, there is no significant evolution of the filtration efficiency of surgical and FFF2 masks after 8 h of continuous use, the maximum time of use recommended by health authorities. 2.2 Washing procedure Depending on the origin of the washed masks, various protocols were applied:- W1: a cycle in an industrial washer machine, which here corresponds to 12 min of washing at 60 °C with 5 mL/kg of disinfectant and 1 mL/kg of detergent; 1 min of draining, 3 min of rinse at 30 °C and 3 min of spin at 550 rpm. The masks were then placed in the dryer with 3 cycles (3 min drying/3 min cooling) gradually increasing the temperature up to 45 °C and decreasing it to 20 °C. - W2: a washing cycle corresponds to the one used for the gown washing in the teaching hospital of Nancy; i.e. 15 min of washing at 60 °C with a detergent, 15 min of washing at 60 °C with a bleaching agent, 2 min of intermediate spin, 3 min of rinse and 3 min of fast spin. - W3: Masks were washed in an individual washer machine at 40 °C and with a liquid detergent. After a rinse and an intermediate spin at 500 rpm, the masks were dried in open air. - W4: The washing was realized at 60 °C with a detergent during 30 min. After 4 cycles of rinse (3 min, 3 min, 2 min, 2 min) and a 5-min spin at 800 rpm, the masks were placed in a dryer during 40 min at 80 °C. - W5: Masks were washed in an individual washer machine at 30 °C with or without a liquid detergent. 2.3 Performance requirements After treatments, medical face masks must remain in compliance with the EN 14863 standard in terms of filtration efficiency and differential pressure (Table 1 ). The differential pressure mentioned in the standard corresponds to a pressure drop per mask surface area: the higher this value, the higher the breathing effort. Concerning equivalence with other international standards, note that a mask meeting the requirements of the American standard ASTM F2100-19 level 1 guarantees compliance with the Type I of the European Standard EN 14683:2019, whereas levels 2 and 3 of ASTM F2100-19 guarantee compliance with the Type IIR of EN 14683:2019. Furthermore, a mask consistent with the Chinese standards YY/T 0969–2013 or YY 0469–2011 meets requirements of the European Standard Type I.Table 1 Performance requirements for medical face masks (according to EN 14683+AC). Table 1 Type I * Type II Type IIR Bacterial filtration efficiency (BFE) ≥95% ≥98% ≥98% Differential pressure <40 Pa/cm2 <40 Pa/cm2 <60 Pa/cm2 Splash resistance pressure Not required Not required ≥16 kPa Microbial cleanliness ≤30 colonies forming units (cfu) per gram Regarding filtering facepiece respirators, they still should preserve the requirements listed in Table 2 after washing cycles. It should be mentioned that the filtration efficiency is determined on the material constituting the mask, as for surgical masks, and that leakage tests are also carried out on the filtering facepiece respirators worn on the face.Table 2 Performance requirements for filtering facepiece respirators. Table 2 N95 (USA) FFP2 (Europe) KN95 (China) Norm NIOSH–42C-FR84 EN 149-2009 GB2626-2006 Total collection efficiency ≥95% ≥94% ≥95% Test aerosol NaCl NaCl, Paraffin oil NaCl Pressure drop (Pa) ≤343 Pa (at 85 l/min) ≤70 Pa (at 30 l/min) ≤240 Pa (at 95 l/min) ≤350 Pa (at 85 l/min) 2.4 Differential pressure and particle filtration efficiency As mentioned in the NF EN 14683 standard “Surgical masks - Requirements and test method“, test specimens are cut from complete masks. These samples are taken far enough away from the bonding areas. All the layers composing a medical face mask sample are placed in a filter holder with a filtration surface of 28.3 cm2. This surface is smaller than the one recommended for BFE in the standard (>49 cm2) but sufficient to be representative of the nonwoven media. The value of the initial pressure drop is recorded for various filtration velocities. From this graph, which is linear in a laminar regime, the pressure drop is determined for a filtration velocity equal to 27.2 cm/s, which is the one specified in the EN 14683+AC standard. To be compared to the normative requirements, this pressured drop is then divided by the standard surface area (4.9 cm2) to obtain the differential pressure. According to the EN 14863 protocol, a suspension of Staphylococcus aureus should be nebulized and the particles generated (with a mean size of 3 ± 0.3 μm) should be collected on a six-stage cascade impactor. The bacterial filtration efficiency is determined by counting the number of colony forming units on all the plates, after an incubation at 37 °C during 20–52 h. The experimental procedure was adapted and a Particle Filtration Efficiency (PFE) was determined instead of a Bacterial Filtration Efficiency (BFE). For the determination of surgical mask efficiency, a micron-sized DEHS (di-ethyl-hexyl-sebacate) or a submicron salt (KCl) aerosol was produced by an AGK 2000 Palas® generator and diluted with compressed air; while, a NaCl aerosol was generated for the measurement of FFP2 efficiency. As suggested in the EN 149–2009 standard, test aerosols were not neutralized. Thus, the generated DEHS aerosol is globally neutral and the salt aerosol is globally negatively charged. Not controlling the charge level of the aerosol is one of the drawbacks of this standard as this parameter can influence the determined filtration efficiency. Zoller et al. (2021) also suggested that this standard should precise a narrower particle size distribution and clearly define the metrics applied in the calculation of efficiency (number, mass or intensity). Despite these drawbacks, the same protocol and the same aerosols are used during the measurement campaign which will enable to conclude on the influence of the mask trademarks and of the washing on the filtration performances. For both kinds of masks, filtration velocity is adjusted at 9.6 cm/s, (corresponding to the velocity used in the EN 14683+AC standard) and the particle size distribution is measured upstream and downstream of the filter with various detectors (size spectrometer or photometer), depending of the nature of the aerosol (cf. Table 3 ). The DEHS mean number aerodynamic equivalent diameter (APS measurement) was close to 0.85 μm. For the KCl and NaCl aerosols, the number particle size distributions present mean mobility equivalent diameters (SMPS measurement) close to 75 nm and a Geometric Standard Deviation of about 2.2. The mean mass diameter is close to 600 nm as recommended in the EN 149–2009 standard.Table 3 Detectors and test aerosols used according to mask type. Table 3 Test aerosol DEHS KCl NaCl Aerodynamic Particle Sizer Surgical masks FFP2 Scanning Mobility Particle Sizer Surgical masks FFP2 Flame photometer FFP2 The spectral efficiency has been calculated from comparison of particle size distributions measured upstream and downstream of the sample. This spectral efficiency was calculated as follows for a given particle size, dP:EN(dp)=1−CN,down(dP)CN,up(dP) where CN,down and CN,up were the particle number concentration downstream and upstream of the filter, respectively. In addition to fractional efficiency, overall filtration efficiency, based on NaCl mass concentration measurements, was determined specifically for FFP2 mask according to flame photometer in agreement with EN149+A1 standard. Upstream concentrations were measured after and before downstream concentrations. The mean of these concentrations allows limiting the influence of potential variations of the generated particle size distribution. For surgical masks, efficiency measurements were repeated 3 times on the same mask sample which corresponds to a repeatability analysis. Measurements were conducted on at least three samples of a same medical face mask (reproducibility test). Upstream aerosol concentration was low enough to prevent significant filter loading effect and ensure the determination of the initial efficiency. Moreover, for each sample, the pressure drop has been measured before and after the aerosol generation in order to verify that no loading effect occurs. To obtain more robust efficiency measurements, the distribution tails (dp < 25 nm and dp > 530 nm) have not been considered because of too low concentrations (<200 particles/cm3). Surgical masks and filtering facepiece respirators performances were determined on the LRGP and IRSN test benches, respectively. More details on experimental test benches are available in a previous paper (Bourrous et al., 2021) which demonstrated that despite different test aerosols, measurement methods, protocols and test bench configurations, permeability and collection efficiency for 3 μm particle diameter were in good agreement. According the EN 14683+AC protocol, each sample of surgical mask shall be conditioned at (21 ± 5) °C and (85 ± 5) % relative humidity for a minimum of 4 h to ensure equilibrium prior to testing. Preliminary tests with and without this conditioning procedure lead to similar results, in terms of permeability and collection efficiency. To simplify our test procedure, this conditioning step was consequently not applied, for both surgical and FFP2 masks. 2.4.1 Projection resistance The experimental procedure and equipment needed to determine the resistance against penetration by synthetic blood are described by ISO 22609:2004 standard. In the health-care context, the experimental set-up described in the standard was slightly adapted to the apparatuses and materials available. The pneumatic valve was replaced by an electrovalve SMC VX21 and a needle (gauge 18) was used as a canula. It should be stressed that the properties of the valve assembly were consistent with those of the standard: i.e., 13 mm long canula (instead of 12.7 mm), an inner diameter of 0.8 mm (instead of 0.84 mm) and the possibility to adjust the injection duration by 0.1 s step. The set-up was placed in a glove box which can be opened rapidly in order to check the blood stains on the mask placed on a holding fixture (Fig. 1 ). A targeting plate with a 0.5 cm hole is located 1 cm in front of the mask and cups are used to collect the blood in excess. Before testing the washed masks, calibration and validation tests were performed on new masks.Fig. 1 Experimental setup for projection resistance tests (adapted from ISO 22609:2004). (1: Electrovalve; 2: Needle; 3: Mask holding fixture; 4: Glove box; 5: Valve controller; 6: Synthetic blood tank with pressure gauge). Fig. 1 The preparation and composition of the synthetic blood is detailed in Annex B of the ISO 22609:2004 standard. In addition to distilled water, a thickening agent and red colorant are the products used to adjust the viscosity, surface tension and color of the synthetic blood. However, due to the lack of some reagents (urgency of the situation combined to shortage due to the lockdown), an alternative blood composition was developed (Table 4 ).Table 4 Comparison of the synthetic blood compositions. Table 4Reagents Standard Alternative mixture Water 500 mL 500 mL Thickening agent 12.5 g Ammonium salt of acid-acrylic 12.5 g Sodium salt of acid-acrylic +150 mL glycerine Colorant 5 g Direct Red 81 3 g Direct Red 28 Surfactant – 0.3 g Tween As different reagents were used, it was essential to check and adjust the synthetic blood viscosity and surface tension. The surface tension was determined using the stalagmometric method (Tate's method). By weighing a single droplet (of mass m) dropping from a canula of known radius (r), the surface tension γ can be determined after several replicates:γ=m⋅g2⋅π⋅r After addition of a small amount of surfactant, the surface tension of the synthetic blood is 40 ± 2 mN/m, which is consistent with the expected (42 mN/m). Correlation of the literature was used to determine the dynamic viscosity of glycerol/water mixtures and reach 4 mPa s, which corresponds to the viscosity of blood at 37 °C (Cheng, 2008). Preliminary tests were carried out at various pressures and injection times. In order to obtain the same volume generated in 0.57 s (here 0.6 s) at 21.3 kPa (standard values), the pressure has to be slightly increased up to 29 kPa, which can be due to different pressure drops in the valve assembly. Under these conditions, the blood volume injected during a test agrees with the volume imposed by the ISO standard. 3 Results & discussion 3.1 Differential pressure and particle filtration efficiency of new masks Performances of each surgical mask trademark are represented on Fig. 2 . Horizontal dashed lines correspond to standard requirements. A cross means that the considered masks do not reached the recommendations (>x % for efficiency and < x Pa/cm2 for differential pressure). Error bars correspond to standard deviations determined from a reproducibility analysis on 4 to 8 samples for differential pressure and on 2 to 4 samples for collection efficiency for 3 μm particle diameter. These measurements conducted on masks of a same batch and/or on two samples cut in a same mask provide an indication on the heterogeneities of this non-woven material.Fig. 2 Differential pressure (left) and collection efficiency (right) of the various surgical mask trademark before washing. Fig. 2 Some surgical masks are not sufficiently permeable and not in compliance with EN 14683+AC:2019 standard. It should be pointed out that the protocol does not fulfil the normative requirements. Nevertheless, without corresponding exactly to the conditions of the standard, the tests carried out allow a precise intercomparison of the different masks. Despite similar structure (spunbond/meltblown/spunbond), the mask trademarks of a same type present strong heterogeneities in terms of differential pressure. Despite a particle filtration efficiency very high (>99%), even for type I masks, this parameter seems to be trademark-dependent. Only the surgical mask presenting the lower differential pressure (Ref. 16) does not meet the requirements of the European Standard EN 14683:2019 in terms of collection efficiency. For this trademark, the collection efficiency measurements realized on 3 samples are highly heterogeneous (98.5%, 95.1% and 95.2%). Tests carried out with a DEHS aerosol could therefore be considered as an alternative to bacterial filtration efficiency measurements for which the uncertainties are numerous (Pourchez et al., 2021). A Mann-Whitney U statistical test is carried out in order to conclude with performances of the different kinds of masks. This non-parametric test is used to determine if all the values from two groups are independent of each other. Even if they give very similar results, for distributions sufficiently far from Gaussian, the Mann–Whitney U test is considerably more efficient than the Student one. It consists in assigning numeric ranks (by ascending order) to all the observations (permeability or efficiency in our case) of the two groups and then determining the sum of the ranks, S, of each group. A statistic, called U, is calculated for each group:U1=S1−n1⋅(n1+1)2 U2=S2−n2⋅(n2+1)2 where, n1 and n2 represent the sample size of the groups 1 and 2, respectively. S1 and S2 are the sum of the ranks of each group. The mean, M(U), and the variance, V(U), are determined:M(U)=12⋅n1⋅n2 V(U)=112⋅(n1+n2+1)⋅n1⋅n2 The experimental standardized value, Z, is calculated considering a continuity correction for small groups:Z=|min(U1;U2)−M(U)|−0.5V(U) With a confidence interval of (1-α) for a two-sided test, the values of differential pressure and particle filtration efficiency for the population 1 and the population 2 are considered to be the same, if the absolute value of the experimental standardized value, Z, is comprised between 0 and the t-value of the Student distribution corresponding to (1-α/2) and (n1+n2-2) degrees of freedom. For the test conducted on the filtration efficiency, the sizes of the population are 13, 44 and 72, respectively for surgical masks of type I, II and IIR; while for the test on the differential pressure, the populations contain 24, 89 and 127 values, respectively for types I, II and IIR. The differences between the populations of Type I, II and IIR masks have been statistically tested in pairs and, among all the references tested, there is no significant variation of differential pressure and collection efficiency for 3 μm particle diameter between the different kinds of masks for a confidence interval of 95%. The observations (differential pressure and efficiency) can be considered as similar until a limit significance level (Table 5 ). Each percentage indicates the maximal risk of being wrong supposing that the performances of masks of various types are similar.Table 5 Confidence interval obtained with Mann-Whitney U statistical test. Table 5 Type I vs Type II Type I vs Type IIR Type II vs Type IIR Differential pressure (degree of freedom) 67.7% (111) 73.2% (149) 27.0% (214) Particle Filtration Efficiency (degree of freedom) 83.2% (55) 80.5% (83) 6.4% (114) Even if the performances do not seem to be influenced by the kind of surgical masks, results of Table 5 tend to highlight that the similarity is higher between types II and IIR, both for the differential pressure and for the efficiency for 3 μm particle diameter. Filtering facepiece respirators (FFP2) should collect more than 94% of a NaCl aerosol with a mean mass diameter close to 600 nm. For the four FFP2 tested, the total particle efficiency determined with a photometer was higher than 99.5% (Fig. 3 , left). Calculating this total collection efficiency from the SMPS upstream and downstream concentration conducted to lower values. Indeed, particles with a mobility-equivalent diameter higher than 550 nm were not counted by the SMPS while they were collected with a high efficiency due to interception and inertial mechanisms.Fig. 3 Total collection efficiency of the filtering facepiece respirators (left) and the surgical masks (right) before washing. Fig. 3 This SMPS total collection efficiency was also determined for the surgical masks (Fig. 3, right). As expected, efficiencies of filtering facepiece respirators are higher due to their structure (higher solid volume fraction and/or number of layers), but most of the surgical masks present performances similar to those of FFP1 and some of them could be considered as efficient as FFP2, in regards to the EN 149–2009 standard. It should be reminded that only the material constituting masks are tested and that leakage are not considered. Three references (04, 10 and 16) have a total collection efficiency close to 60% and the determination of spectral efficiency will allow giving some explanations on these lower performances. Fig. 4 represents the collection efficiency of the different kinds of masks according to the particle diameter. As particle concentrations upstream and downstream of a mask sample were measured with a SMPS and an APS, the diameter on the abscissa axis is a mobility-equivalent diameter on the range 20–500 nm and an aerodynamic diameter for particles higher than 1 μm. It should be noted that despite various measurement principles and equivalent diameters, the instrumental responses are in reasonably good agreement. As surgical masks are constituted of non-woven material, their spectral efficiency present a classical U-shape due to the interaction of the main collection mechanisms (diffusion, interception, inertial impaction and electrostatic effect) and a most penetrating particle size (MPPS) between 0.2 and 0.5 μm. For filtering facepiece respirators, this MPPS is shifted to lower diameters due to electrostatic effects. These results also highlight a great heterogeneity of performances depending on the mask trademarks, both for surgical masks (whatever the type) and FFP2. As the collection efficiency and the width of the MPPS are directly dependent onn the fibrous structure (solid volume fraction, fiber size distribution and thickness), the quality of the meltblown and spunbond layers can probably contribute to these heterogeneities. If the collection efficiency for surgical masks is, in most cases, higher than 70–80% for the whole range of particle diameters, some references (04, 10 and 16) present efficiency lower than 40% for the most penetrating particle size. This marked evolution is probably due to the absence of electrostatic charges at the fiber surface of these surgical masks.Fig. 4 Spectral efficiency of type I, II, IIR surgical masks and FFP2 masks before washing. Fig. 4 3.2 Differential pressure and particle filtration efficiency of washed masks The performances of washed surgical mask trademarks are represented on Fig. 5, Fig. 6 . As previously, the horizontal dashed lines correspond to standard requirements. The error bars correspond to standard deviations determined from a reproducibility analysis on 4 to 8 samples for differential pressure and on 6 to 12 samples for collection efficiency for 3 μm particle diameter.Fig. 5 Differential pressure according to the number of washings for surgical masks. Fig. 5 Fig. 6 Collection efficiency according to the number of washings for surgical masks. Fig. 6 Whatever the reference and the washing procedure, the first cycle induces a slight decrease of differential pressure. Therefore, if masks meet the requirements of the standard before washing, they also remain in compliance with it after a washing cycle. As there is a relationship between the pressure drop and the collection efficiency (Bourrous et al., 2021), this modification of the non-woven structure leads to a decrease of collection efficiency for 3 μm particle diameter. However, performances remain constant hereafter a cycle and up to 10 cycles, i.e. the maximal cycle number tested for 7 references of surgical masks. For each reference, the Mann-Whitney U test is carried out to determine if washing is statistically responsible for a performance decrease (with a significance level of 5%). This statistical test shows that, for both the differential pressure and the efficiency for 3 μm particle diameter, the results obtained on new and washed masks cannot be considered significantly different, except for some trademarks. A significant modification of differential pressure is noted for the references 10 and 13–1 after 5 washing cycles but it does not lead to a significant decrease of collection efficiency. A significant decrease of efficiency can also be observed after the washing of the references 16 and 23. Nevertheless, trademarks 10 and 16 have been previously identified as less efficient and highly heterogeneous; more samples should be tested to definitively conclude on the influence of washing on these surgical masks. Concerning the reference 23, the inner and outer layers of the mask have the particularity of being composed of cellulose fibers. This characteristic could maybe explain the PFE decrease which is not significant for the majority of the masks composed of polypropylene fibers. If the collection efficiency for 3 μm particle diameter remains greater than 95 or 98% whatever the type of surgical mask and the number of washings, the performance of the masks is impacted by the washing for lower particle sizes (Fig. 7 A–D). As for filtering facepiece respirators, a total collection efficiency is measured with a photometer. These masks are no more in compliance with the requirements of the EN 149–2009 standard (Table 2) after washing.Fig. 7 Spectral efficiency of washed surgical masks (A–B), of washed and discharged surgical masks (C), of FFP2 masks washed with or without detergent (D). Fig. 7 As the presence of an intermediate meltblown layer of charged polypropylene fibers contributes to the collection efficiency by electrostatic effects, this decrease in efficiency, constant hereafter one cycle, can be explained by the loss of electrostatic charges during the washing cycle as confirmed by the results obtained on a mask discharged by immersion in isopropanol (Fig. 7C). A Kelvin probe was used to measure the global charge of polypropylene fibers for one of the filtering facepiece respirators (reference A). The registered mean surface potential, close to −500 V for the non-washed FFP2, decreases until a value close to −20 V after a washing cycle and confirms the charge neutralization and the removal of the electret effect on the washed masks. Moreover, the experiments conducted on the reference 16, with an efficiency lower than 40% for the most penetrating particle size before washing (Fig. 4), confirm the absence of electrostatic charges at the fiber surface; the collection efficiency before and after washing being similar on the whole particle size range. As a washing without detergent maintained the performances of the mask sample at the same level as the new FFP2 (Fig. 7D), the loss of electrostatic effects could likely be attributed to the presence of cationic surfactants in fabric softeners. These compounds, notably esterquats, possess excellent antistatic properties and are used to prevent the accumulation of static charges and make the textile surface more conductive (Mishra & Tyagi, 2007; Murphy, 2015). This positive charge of cationic surfactants (Agarwal et al., 2012) reinforce results obtained with the Kelvin probe. Visualizations of one of the filtering facepiece respirators (reference A) with a JSM-7900F (Jeol) scanning electron microscope (SEM) as well as analysis by X-ray energy-dispersive spectrometry (EDX) suggest that surfactant residues (presenting significant contributions of Fe, Mg, Al and Si) and an organic film (mainly C, O and N) could be deposited at the fiber surface (without modification of their diameter or abrasion of their surface) after a washing cycle and contribute to their neutralization (Fig. 8 ). Such observations have also been highlighted by Parvinzadeh and Hajiraissi (2008) and Obendorf et al. (2009).Fig. 8 SEM images of filter fibers before washing (top row) and after washing with detergent (middle row). The bottom row shows EDX spectra of impurities deposited on the washed fibers, indicated by the arrows in the SEM images. Fig. 8 3.3 Projection resistance The projection resistance tests were performed on IIR masks under the conditions described by the standard, at a blood ejection rate of 550 cm/s corresponding to a blood pressure of 16 kPa. The tests were repeated once for each type of mask under the same conditions. To be fully compliant with ISO 22609, nearly 30 tests should be performed for each type of mask, which was obviously not possible in the pandemic and lockdown context. Visual observations show that, after one to two washings, the ‘anti-splash’ properties of the masks are preserved according to the ISO 22609 standard, i.e. no trace of blood was detected on the inner face of the mask, 10 s after the blood projection. However, the protective properties of the first layer are degraded after about 4 washings and the blood enters the mask. As the blood only accumulates in the lower part of the mask without succeeding in passing through the three layers, the projection resistance property is preserved according to the standard (Fig. 9 ). After more washing cycles, the accumulation of blood within the inner layers is such that it can pass through the internal barrier in case of pressure or buffering. Results of the test carried out according to ISO 22609 are then negative.Fig. 9 Examples of outer and inner faces of masks after a synthetic blood projection. Fig. 9 To explain this property loss, the contact angle between a drop of synthetic blood and a mask was measured on new and washed IIR masks (Reference n°30). It appears that washing cycles change the surface properties of the outer layer of masks and that the contact angle θ rapidly evolves from values greater than 90° (hydrophobic behavior) to angles lower than 90° (hydrophilic behavior) for a washed mask (Fig. 10 ). These modifications of surface properties, in agreement with previous conclusions on the fiber state of charge, lead to a loss of the projection resistance (“R” function) after few washing steps, whatever the type IIR mask brand used. The projection resistance cannot be claimed after a washing treatment and wearing a washed IIR masks in an operating room should therefore be proscribed for medical staff.Fig. 10 Comparison of contact angles of blood drops and mask surfaces for new (left) and 9-time washed masks (right) after a) 2 s; b) 30 s; c) 2 min 30 s and d) 5 min. Fig. 10 4 Conclusion Comparison of a large number of masks highlighted a great variability of PFE and differential pressure depending on the mask trademarks, both for surgical masks (whatever the type) and for FFP2. The quality of the meltblown and spunbond layers and the absence of electrostatic charges at the fiber surface can explain the lower fractional efficiency of some references. For medical face masks, even if the performances seem to be independent from the mask type, results tend to highlight that types II and IIR exhibit a similar behavior, both for the differential pressure and for the efficiency for 3 μm particle diameter. Washing, probably the most easily adaptable treatment for the general public, was the solution adopted for the mask decontamination. It should be noted that, whatever the mask reference and the washing procedure, the first cycle induces a slight decrease of the differential pressure and of the collection efficiency for 3 μm particle diameter. The performances of the washed surgical masks were maintained up to 10 washing cycles and met the requirements of the standards. Nevertheless, a statistical Mann-Whitney U test showed that, for both the PFE and the differential pressure, the results obtained on new and washed surgical masks cannot be considered significantly different for the majority of the trademarks. Moreover, if the PFE for 3 μm particle diameter remains greater than 95 or 98%, whatever the type of surgical mask and the number of washings, the performance of the masks is impacted by the washing for submicronic particles. As a consequence, the total collection efficiency of filtering facepiece respirators is no more in compliance with the standard requirements. The treatment leads to a loss of electrostatic charges during the washing cycle as confirmed by the results obtained on a mask discharged by immersion in isopropanol and measurements of fiber state of charge. The modifications of surface properties after a washing cycle also lead to a loss of the hydrophobic behavior of type IIR surgical masks which can therefore no more be considered as resistant to blood projections. Washing surgical masks can be a convenient solution in case of shortage of these single-use devices, but also to reduce the consumption of plastic materials. As long as the head straps and the nosebands will not break, the protection level and the differential pressure of these masks remain similar to the performances of new masks. Nevertheless, the projection resistance cannot be claimed after a washing treatment and wearing a washed IIR masks in an operating room should therefore be proscribed for medical staff. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Appendix 1 Surgical and FFP2 mask references Appendix 1Trademark Type Ref. Number of washing cycles Washing protocol 0 1 2 3 5 10 BYD CARE (YY/T 0969–2013) I 01 X Moen (602A-01)) I 02 X Sunrise Nursing I 03 X X X W2 LiangYa (DGTMYY) I 04 X X W2 FITEXIN I 05 X X X W4 MEDWELL II 06 X CA Diffusion 1931 II 07–1 X X X X X W2 CA Diffusion 1931 II 07–2 X X X W4 ALLMED II 08 X Henan YADU Industrial Co. II 09 X SAVOY International II 10 X X X W4 WK Well Klean II 11 X LyncMed (302089-CMA010) II 12 X LyncMed (302089-CMA006) II 13–1 X X X X X W2 LyncMed (302089-CMA006) II 13–2 X Naguma (NA-05) II 14 X Saudel (85002) II 15 X TD Professional 45455 II 16 X X W2 TSC II 17–1 X X W3 TSC II 17–2 X X W4 Global II 18 X X W4 Kolmi OP’R (M36101-30) IIR 19 X X X W4 Kolmi OPAIR (M31101-30)) IIR 20 X X X W4 Kolmi OpairONE (M34101-30) IIR 21 X X X W4 CA Diffusion 1960 IIR 22–1 X X X W4 CA Diffusion 1960 IIR 22–2 X X X X W2 CA Diffusion 1960 IIR 22–3 X X X X X X W1 Ansell (Sandel) IIR 23 X X W4 FCHA Fengchenhan IIR 24 X Segetex-eif (M193-25) IIR 25 X X X W4 France Cardio (France) IIR 26 X X W4 MIF Medical (WA-FM) IIR 27 X Yongli (YLEN104) IIR 28 X Xiantao Xingrong (XR001) IIR 29 X Jiangxi Hongda (Hygial) IIR 30 X LCH (Aerokyn PLM.01R) IIR 31–1 X X X X X W2 LCH (Aerokyn PLM.01R) IIR 31–2 X X X W2 Medicom (2015–30) IIR 32 X X X X X W2 Paul Boyé (MPB-CH1) IIR 33 X X X W2 Kimberly Clark (The Lite One) IIR 34 X Solida IIR 35 X X W3 Ultrafilter (Ultramask EASM 198R) IIR 36 X VALMY (VR202F) FFP2 A X X W5 KOLMI (OpAir Pro white) FFP2 B X X W1 KOLMI (OpAir Pro violet) FFP2 C X X W1 Paul Boyé (MPB2.1-B.27069-TU-00) FFP2 D X X W1 Acknowledgments This study was conducted within the framework of the LIMA joint research program between the Institut de Radioprotection et de Sûreté Nucléaire and the Laboratoire Réactions et Génie des Procédés of the Université de Lorraine/CNRS. Some results were obtained as part of the RESI-OPTIPI project financially supported by the French National Agency for Research (ANR) and the Grand-Est region. Authors are grateful for the Regional University Hospital Center of Nancy for the washing of some masks considered in this study. They also warmly thank Dr. Jean-Charles Matéo-Vélez from the French Aerospace Lab (ONERA) for the measurements of fiber charge with a Kelvin probe. ==== Refs References Agarwal G. Perwuelz A. Koehl L. Lee K.S. Interaction between the surface properties of the textiles and the deposition of cationic softener Journal of Surfactants and Detergents 15 2012 97 105 10.1007/s11743-011-1273-4 Akber A.S. Khalil A.B. Arslan M. Extensive use of face masks during COVID-19 pandemic: (micro-)plastic pollution and potential health concerns in the Arabian Peninsula Saudi Journal of Biological Sciences 27 2020 3181 3186 10.1016/j.sjbs.2020.09.054 33052188 Alcaraz J.-P. Le Coq L. Pourchez J. Thomas D. Chazelet S. 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Modeling the effectiveness of respiratory protective devices in reducing influenza outbreak Risk Analysis 39 3 2019 647 661 10.1111/risa.13181 30229968 Zoller J. Meyer J. Dittler A. A critical note on filtering-face-piece filtration efficiency determination applying EN 149 Journal of Aerosol Science 158 2021 105830 10.1016/j.jaerosci.2021.105830

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==== Front J Aerosol Sci J Aerosol Sci Journal of Aerosol Science 0021-8502 0021-8502 Elsevier Ltd. S0021-8502(21)00640-6 10.1016/j.jaerosci.2021.105914 105914 Article Impact of washing cycles on the performances of face masks Charvet Augustin a∗ Bardin-Monnier Nathalie a Thomas Dominique a Dufaud Olivier a Pfrimmer Marielle a Barrault Mathieu b Bourrous Soleiman b Mocho Victor b Ouf François-Xavier b Poirier Stéphane b Jeanmichel Laurence c Segovia César c Ferry Daniel d Grauby Olivier d a Université de Lorraine, CNRS, LRGP, F-54000, Nancy, France b Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSN-RES, SCA, Gif-Sur-Yvette, 91192, France c CETELOR, Université de Lorraine, F-88000, Épinal, France d Aix-Marseille Univ, CNRS, CINaM, F-13009, Marseille, France ∗ Corresponding author. 20 11 2021 2 2022 20 11 2021 160 105914105914 3 7 2021 9 11 2021 10 11 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The tension on the supply of surgical and FFP2 masks during the first wave of the COVID-19 pandemic leads to study the potential reuse of these masks. As washing is easily adaptable at home, this treatment solution was retained. In this work, thirty-six references of surgical masks and four FFP2 masks were tested without being worn or washed and after several washing cycles. The results highlighted a great heterogeneity of performances depending on the mask trademarks, both for surgical masks and FFP2. The quality of the meltblown and spunbond layers and the presence/absence of electrostatic charges at the fiber surface are put forward to explain the variability of results, both on differential pressures and filtration efficiencies. The differential pressure and the particle filtration efficiency of the washed masks were maintained up to 10 washing cycles and met the standard requirements. However, an immersion in water with a detergent induces an efficiency decrease for submicronic particles. This lower performance, constant after the first washing cycle, can be explained by the loss of electrostatic charges during the washing cycle. The modifications of surface properties after washing also lead to a loss of the hydrophobic behavior of type IIR surgical masks, which can therefore no more be considered as resistant to blood projections. Keywords Surgical masks FFP2 Reuse Washing Electrostatic charge ==== Body pmc1 Introduction During the COVID-19 pandemic, both scientific community and the World Health Organization confirmed that aerosol-based transmission is a major contributor to disease spread. Transmission of SARS-CoV-2 can occur through direct, indirect or close contact with infected people through infected secretions such as saliva and respiratory secretions or respiratory droplets expelled by infected contaminated person. The size of particles emitted by an individual are influenced by the individual themselves (mucus properties) (Lee et al., 2019) and their activities (normal breathing, talking, sneezing, coughing) (Gralton et al., 2010). Moreover, after their emission, droplets are subject to evaporation and settling, therefore their diameter evolves according to the environmental conditions (temperature, relative humidity) (Xie et al., 2007; Ji et al., 2017). Consequently, the concentration and the particle size distribution that have to be considered when designing barrier mask are very broad. Even if particle size distributions are highly heterogeneous (individual and environment-dependent), their diameter can be assumed as mainly smaller than 5 μm (Asadi et al., 2019; Johnson et al., 2011; Morawska et al., 2009; Papineni & Rosenthal, 1997). Furthermore, Lindsley et al. (2010) collected cough-generated particles produced by individuals with influenza-like symptoms and concluded that viral ribonucleic acid was present in the droplets, whatever their size. Lednicky et al. (2020) showed that viable SARS-CoV-2 was detected in aerosols within the room of a COVID-19 patient. Van Doremalen et al. (2020) and Fears et al. (2020) observed that this virus remained viable and infectious in aerosol after 3 and 16 h, respectively. As a consequence, contamination through droplet transmission can occur when a person is in close contact with an infected person who has respiratory symptoms (e.g. coughing or sneezing) (Bourouiba et al., 2014) or who is talking, singing or playing music (He et al., 2021) but also through airborne particles of lower diameters remaining in suspension for prolonged periods and exposing individuals at a greater distance from the source. Expiratory particles size emitted during breathing and speech are sufficiently large to carry viable virus, i.e. within 60–140 nm according to several authors (Kim et al., 2020; Matsuyama et al., 2020; Park et al., 2020; Ren et al., 2020). But they are small enough to be inhaled, penetrate deeper into the respiratory tract and, consequently, have more serious health implications. As they persist in air for long time periods, indirect transmission of virus by aerosols might be a plausible hypothesis (Asadi et al., 2020). Therefore, wearing a mask appears essential to limit the pandemic spread as shown by Liang et al. (2020) in their systematic review and meta-analysis. Yan et al. (2019) modelled the evolution of the basic reproduction number R0 and of the incidence rate, as a function of the mask efficiency and the ratio of the population wearing a mask, respectively. They concluded that even if masks have a moderate efficiency (around 50%), a negligible transmission occurs if a majority of the population is protected. Historically, medical face masks are intended for the limitation of the transmission of infective agents during surgical procedures. These masks are used by surgical staff but also by patients and general public for the reduction of contamination during epidemic or pandemic situations. Before the pandemic, type II surgical masks were recommended in care services, surgical masks of type IIR for the medical staff during a care with a risk of projection (operating rooms) and filtering facepiece (FFP) respirators during particular care of a patient placed under special precautions. During the pandemic, filtering facepiece respirators are urged for invasive medical gesture or maneuvers in the respiratory sphere of a patient carrying SARS-CoV-2; type IIR surgical masks are requested for all other types of care. In comparison with FFP, surgical masks are more comfortable, are cheaper but fit loosely to the face and are not associated to a protection factor and leakage test. As they present lower collection efficiency, surgical masks do not offer a protection comparable to filtering facepiece respirators and are considered as a protection for others instead of for oneself. The tension on the supply of these single-use devices during the first wave of the pandemic of COVID-19 leads to a health use policy that was not without risks for patients and staff. A possible strategy to prevent a mask shortage would be the treatment (to eliminate viral and microbiological risks) and the reuse of these devices. Moreover, during the pandemic, approximatively 3.4 billion masks are discharged daily (Benson et al., 2021). This extensive use of face masks, containing polypropylene or other synthetic polymers, induces serious consequences on the environment as these plastic materials may remain in marine or land environments and increase the microplastic and nanoparticle pollution (Akber et al., 2020; Selvaranian et al., 2021; Sullivan et al., 2021). Therefore, studying the feasibility of the reuse of medical face masks and filtering facepiece respirators, appears interesting both for supply and environmental reasons. Mask reuse obviously implies a thorough decontamination phase between two uses for a hygiene issue. From the beginning of the pandemic, the decontamination and the reuse of N95 filtering facemask respirators have been the subject of an increasing number of studies (Schumm et al., 2021). As they preserve N95 mask integrity in terms of penetration, air flow resistance and physical appearance, the most promising methods seem to be UV irradiation (Bergman et al., 2010; Liao et al., 2020; Ou et al., 2020; Viscusi et al., 2007), ethylene oxide exposure (Bergman et al., 2010; Viscusi et al., 2009) and vaporized hydrogen peroxide decontaminations (Bergman et al., 2010; Cai & Floyd, 2020; Fisher et al., 2020; Richter et al., 2016; Viscusi et al., 2009). Regarding heat treatment, Liao et al. (2020) showed that heating (dry or in the presence of humidity) at temperatures up to 100 °C can preserve the efficiency of the mask after 10 cycles, whereas Fisher et al. (2020) highlighted that 70 °C dry heating allows maintaining performances only up to 2 cycles. Ou et al. (2020) considered thermal treatment as the most applicable decontamination method for the general public because of its simplicity of implementation at home and no significant degradation of the collection efficiency after 10 thermal dry treatment cycles (30 min at 77 °C). Microwave oven use (Bergman et al., 2010; Viscusi et al., 2011) and bleach treatment (Bergman et al., 2010; Viscusi et al., 2007) do not induce significant changes in penetration and air flow resistance but can lead to media or head straps melting and tarnishing of nosebands, respectively. Treatment with an autoclave, immersion in a 70%(v) isopropyl alcohol (Ou et al., 2020; Viscusi et al., 2007) or ethanol solution (Liao et al., 2020) conduct to a drastic degradation of filtration performances, which are no more consistent with normative criteria. However, the conservation of performances not only depends on the kind of treatment but also of the N95 model (Rodriguez-Martinez et al., 2020). In the same way, Suen et al. (2020) concluded that, among different treatments on surgical masks, non-fluid-based methods such as UV irradiation maintain filtration efficiency after three cycles; while immersion into water or alcohol induces the loss of electrostatic charges of the mask. To recover electret effect of masks and increase the efficiency degraded by sterilization treatments, Hossain et al. (2020) proposed a simple recharging method based on an electrical field. Similarly, Wang et al. (2020) showed that after hot water decontamination, the drying with a hair drier allows recovering 90% of the electret effect of masks. As washing can easily be realized at home, this treatment solution was retained and the influence of washing cycles on the performances of surgical masks and filtering facepiece respirators was studied. It should be noticed that disinfection performances (i.e. elimination of viral and microbiological risks) were not addressed in the paper. However, a previous study shows that applying such washing procedures leads to a total number of colonies forming units five times lower than the normative limit (30 cfu/g) described in the EN 14683+AC standard (Alcaraz et al., 2022). 2 Materials & methods 2.1 Samples Thirty-six references of surgical masks (5 masks of type I, 13 type II and 18 type IIR) and four FFP2 masks were tested without washing (considered as new) and/or after some washing cycles (Appendix 1). For references appearing several times in the table, different batches were tested over different periods of time to evaluate the repeatability. In a pandemic context, with a highly contagious disease, all the tested masks have never been worn. Moreover, Carsi and Alonso (2022) recently showed that, in a submicron range, there is no significant evolution of the filtration efficiency of surgical and FFF2 masks after 8 h of continuous use, the maximum time of use recommended by health authorities. 2.2 Washing procedure Depending on the origin of the washed masks, various protocols were applied:- W1: a cycle in an industrial washer machine, which here corresponds to 12 min of washing at 60 °C with 5 mL/kg of disinfectant and 1 mL/kg of detergent; 1 min of draining, 3 min of rinse at 30 °C and 3 min of spin at 550 rpm. The masks were then placed in the dryer with 3 cycles (3 min drying/3 min cooling) gradually increasing the temperature up to 45 °C and decreasing it to 20 °C. - W2: a washing cycle corresponds to the one used for the gown washing in the teaching hospital of Nancy; i.e. 15 min of washing at 60 °C with a detergent, 15 min of washing at 60 °C with a bleaching agent, 2 min of intermediate spin, 3 min of rinse and 3 min of fast spin. - W3: Masks were washed in an individual washer machine at 40 °C and with a liquid detergent. After a rinse and an intermediate spin at 500 rpm, the masks were dried in open air. - W4: The washing was realized at 60 °C with a detergent during 30 min. After 4 cycles of rinse (3 min, 3 min, 2 min, 2 min) and a 5-min spin at 800 rpm, the masks were placed in a dryer during 40 min at 80 °C. - W5: Masks were washed in an individual washer machine at 30 °C with or without a liquid detergent. 2.3 Performance requirements After treatments, medical face masks must remain in compliance with the EN 14863 standard in terms of filtration efficiency and differential pressure (Table 1 ). The differential pressure mentioned in the standard corresponds to a pressure drop per mask surface area: the higher this value, the higher the breathing effort. Concerning equivalence with other international standards, note that a mask meeting the requirements of the American standard ASTM F2100-19 level 1 guarantees compliance with the Type I of the European Standard EN 14683:2019, whereas levels 2 and 3 of ASTM F2100-19 guarantee compliance with the Type IIR of EN 14683:2019. Furthermore, a mask consistent with the Chinese standards YY/T 0969–2013 or YY 0469–2011 meets requirements of the European Standard Type I.Table 1 Performance requirements for medical face masks (according to EN 14683+AC). Table 1 Type I * Type II Type IIR Bacterial filtration efficiency (BFE) ≥95% ≥98% ≥98% Differential pressure <40 Pa/cm2 <40 Pa/cm2 <60 Pa/cm2 Splash resistance pressure Not required Not required ≥16 kPa Microbial cleanliness ≤30 colonies forming units (cfu) per gram Regarding filtering facepiece respirators, they still should preserve the requirements listed in Table 2 after washing cycles. It should be mentioned that the filtration efficiency is determined on the material constituting the mask, as for surgical masks, and that leakage tests are also carried out on the filtering facepiece respirators worn on the face.Table 2 Performance requirements for filtering facepiece respirators. Table 2 N95 (USA) FFP2 (Europe) KN95 (China) Norm NIOSH–42C-FR84 EN 149-2009 GB2626-2006 Total collection efficiency ≥95% ≥94% ≥95% Test aerosol NaCl NaCl, Paraffin oil NaCl Pressure drop (Pa) ≤343 Pa (at 85 l/min) ≤70 Pa (at 30 l/min) ≤240 Pa (at 95 l/min) ≤350 Pa (at 85 l/min) 2.4 Differential pressure and particle filtration efficiency As mentioned in the NF EN 14683 standard “Surgical masks - Requirements and test method“, test specimens are cut from complete masks. These samples are taken far enough away from the bonding areas. All the layers composing a medical face mask sample are placed in a filter holder with a filtration surface of 28.3 cm2. This surface is smaller than the one recommended for BFE in the standard (>49 cm2) but sufficient to be representative of the nonwoven media. The value of the initial pressure drop is recorded for various filtration velocities. From this graph, which is linear in a laminar regime, the pressure drop is determined for a filtration velocity equal to 27.2 cm/s, which is the one specified in the EN 14683+AC standard. To be compared to the normative requirements, this pressured drop is then divided by the standard surface area (4.9 cm2) to obtain the differential pressure. According to the EN 14863 protocol, a suspension of Staphylococcus aureus should be nebulized and the particles generated (with a mean size of 3 ± 0.3 μm) should be collected on a six-stage cascade impactor. The bacterial filtration efficiency is determined by counting the number of colony forming units on all the plates, after an incubation at 37 °C during 20–52 h. The experimental procedure was adapted and a Particle Filtration Efficiency (PFE) was determined instead of a Bacterial Filtration Efficiency (BFE). For the determination of surgical mask efficiency, a micron-sized DEHS (di-ethyl-hexyl-sebacate) or a submicron salt (KCl) aerosol was produced by an AGK 2000 Palas® generator and diluted with compressed air; while, a NaCl aerosol was generated for the measurement of FFP2 efficiency. As suggested in the EN 149–2009 standard, test aerosols were not neutralized. Thus, the generated DEHS aerosol is globally neutral and the salt aerosol is globally negatively charged. Not controlling the charge level of the aerosol is one of the drawbacks of this standard as this parameter can influence the determined filtration efficiency. Zoller et al. (2021) also suggested that this standard should precise a narrower particle size distribution and clearly define the metrics applied in the calculation of efficiency (number, mass or intensity). Despite these drawbacks, the same protocol and the same aerosols are used during the measurement campaign which will enable to conclude on the influence of the mask trademarks and of the washing on the filtration performances. For both kinds of masks, filtration velocity is adjusted at 9.6 cm/s, (corresponding to the velocity used in the EN 14683+AC standard) and the particle size distribution is measured upstream and downstream of the filter with various detectors (size spectrometer or photometer), depending of the nature of the aerosol (cf. Table 3 ). The DEHS mean number aerodynamic equivalent diameter (APS measurement) was close to 0.85 μm. For the KCl and NaCl aerosols, the number particle size distributions present mean mobility equivalent diameters (SMPS measurement) close to 75 nm and a Geometric Standard Deviation of about 2.2. The mean mass diameter is close to 600 nm as recommended in the EN 149–2009 standard.Table 3 Detectors and test aerosols used according to mask type. Table 3 Test aerosol DEHS KCl NaCl Aerodynamic Particle Sizer Surgical masks FFP2 Scanning Mobility Particle Sizer Surgical masks FFP2 Flame photometer FFP2 The spectral efficiency has been calculated from comparison of particle size distributions measured upstream and downstream of the sample. This spectral efficiency was calculated as follows for a given particle size, dP:EN(dp)=1−CN,down(dP)CN,up(dP) where CN,down and CN,up were the particle number concentration downstream and upstream of the filter, respectively. In addition to fractional efficiency, overall filtration efficiency, based on NaCl mass concentration measurements, was determined specifically for FFP2 mask according to flame photometer in agreement with EN149+A1 standard. Upstream concentrations were measured after and before downstream concentrations. The mean of these concentrations allows limiting the influence of potential variations of the generated particle size distribution. For surgical masks, efficiency measurements were repeated 3 times on the same mask sample which corresponds to a repeatability analysis. Measurements were conducted on at least three samples of a same medical face mask (reproducibility test). Upstream aerosol concentration was low enough to prevent significant filter loading effect and ensure the determination of the initial efficiency. Moreover, for each sample, the pressure drop has been measured before and after the aerosol generation in order to verify that no loading effect occurs. To obtain more robust efficiency measurements, the distribution tails (dp < 25 nm and dp > 530 nm) have not been considered because of too low concentrations (<200 particles/cm3). Surgical masks and filtering facepiece respirators performances were determined on the LRGP and IRSN test benches, respectively. More details on experimental test benches are available in a previous paper (Bourrous et al., 2021) which demonstrated that despite different test aerosols, measurement methods, protocols and test bench configurations, permeability and collection efficiency for 3 μm particle diameter were in good agreement. According the EN 14683+AC protocol, each sample of surgical mask shall be conditioned at (21 ± 5) °C and (85 ± 5) % relative humidity for a minimum of 4 h to ensure equilibrium prior to testing. Preliminary tests with and without this conditioning procedure lead to similar results, in terms of permeability and collection efficiency. To simplify our test procedure, this conditioning step was consequently not applied, for both surgical and FFP2 masks. 2.4.1 Projection resistance The experimental procedure and equipment needed to determine the resistance against penetration by synthetic blood are described by ISO 22609:2004 standard. In the health-care context, the experimental set-up described in the standard was slightly adapted to the apparatuses and materials available. The pneumatic valve was replaced by an electrovalve SMC VX21 and a needle (gauge 18) was used as a canula. It should be stressed that the properties of the valve assembly were consistent with those of the standard: i.e., 13 mm long canula (instead of 12.7 mm), an inner diameter of 0.8 mm (instead of 0.84 mm) and the possibility to adjust the injection duration by 0.1 s step. The set-up was placed in a glove box which can be opened rapidly in order to check the blood stains on the mask placed on a holding fixture (Fig. 1 ). A targeting plate with a 0.5 cm hole is located 1 cm in front of the mask and cups are used to collect the blood in excess. Before testing the washed masks, calibration and validation tests were performed on new masks.Fig. 1 Experimental setup for projection resistance tests (adapted from ISO 22609:2004). (1: Electrovalve; 2: Needle; 3: Mask holding fixture; 4: Glove box; 5: Valve controller; 6: Synthetic blood tank with pressure gauge). Fig. 1 The preparation and composition of the synthetic blood is detailed in Annex B of the ISO 22609:2004 standard. In addition to distilled water, a thickening agent and red colorant are the products used to adjust the viscosity, surface tension and color of the synthetic blood. However, due to the lack of some reagents (urgency of the situation combined to shortage due to the lockdown), an alternative blood composition was developed (Table 4 ).Table 4 Comparison of the synthetic blood compositions. Table 4Reagents Standard Alternative mixture Water 500 mL 500 mL Thickening agent 12.5 g Ammonium salt of acid-acrylic 12.5 g Sodium salt of acid-acrylic +150 mL glycerine Colorant 5 g Direct Red 81 3 g Direct Red 28 Surfactant – 0.3 g Tween As different reagents were used, it was essential to check and adjust the synthetic blood viscosity and surface tension. The surface tension was determined using the stalagmometric method (Tate's method). By weighing a single droplet (of mass m) dropping from a canula of known radius (r), the surface tension γ can be determined after several replicates:γ=m⋅g2⋅π⋅r After addition of a small amount of surfactant, the surface tension of the synthetic blood is 40 ± 2 mN/m, which is consistent with the expected (42 mN/m). Correlation of the literature was used to determine the dynamic viscosity of glycerol/water mixtures and reach 4 mPa s, which corresponds to the viscosity of blood at 37 °C (Cheng, 2008). Preliminary tests were carried out at various pressures and injection times. In order to obtain the same volume generated in 0.57 s (here 0.6 s) at 21.3 kPa (standard values), the pressure has to be slightly increased up to 29 kPa, which can be due to different pressure drops in the valve assembly. Under these conditions, the blood volume injected during a test agrees with the volume imposed by the ISO standard. 3 Results & discussion 3.1 Differential pressure and particle filtration efficiency of new masks Performances of each surgical mask trademark are represented on Fig. 2 . Horizontal dashed lines correspond to standard requirements. A cross means that the considered masks do not reached the recommendations (>x % for efficiency and < x Pa/cm2 for differential pressure). Error bars correspond to standard deviations determined from a reproducibility analysis on 4 to 8 samples for differential pressure and on 2 to 4 samples for collection efficiency for 3 μm particle diameter. These measurements conducted on masks of a same batch and/or on two samples cut in a same mask provide an indication on the heterogeneities of this non-woven material.Fig. 2 Differential pressure (left) and collection efficiency (right) of the various surgical mask trademark before washing. Fig. 2 Some surgical masks are not sufficiently permeable and not in compliance with EN 14683+AC:2019 standard. It should be pointed out that the protocol does not fulfil the normative requirements. Nevertheless, without corresponding exactly to the conditions of the standard, the tests carried out allow a precise intercomparison of the different masks. Despite similar structure (spunbond/meltblown/spunbond), the mask trademarks of a same type present strong heterogeneities in terms of differential pressure. Despite a particle filtration efficiency very high (>99%), even for type I masks, this parameter seems to be trademark-dependent. Only the surgical mask presenting the lower differential pressure (Ref. 16) does not meet the requirements of the European Standard EN 14683:2019 in terms of collection efficiency. For this trademark, the collection efficiency measurements realized on 3 samples are highly heterogeneous (98.5%, 95.1% and 95.2%). Tests carried out with a DEHS aerosol could therefore be considered as an alternative to bacterial filtration efficiency measurements for which the uncertainties are numerous (Pourchez et al., 2021). A Mann-Whitney U statistical test is carried out in order to conclude with performances of the different kinds of masks. This non-parametric test is used to determine if all the values from two groups are independent of each other. Even if they give very similar results, for distributions sufficiently far from Gaussian, the Mann–Whitney U test is considerably more efficient than the Student one. It consists in assigning numeric ranks (by ascending order) to all the observations (permeability or efficiency in our case) of the two groups and then determining the sum of the ranks, S, of each group. A statistic, called U, is calculated for each group:U1=S1−n1⋅(n1+1)2 U2=S2−n2⋅(n2+1)2 where, n1 and n2 represent the sample size of the groups 1 and 2, respectively. S1 and S2 are the sum of the ranks of each group. The mean, M(U), and the variance, V(U), are determined:M(U)=12⋅n1⋅n2 V(U)=112⋅(n1+n2+1)⋅n1⋅n2 The experimental standardized value, Z, is calculated considering a continuity correction for small groups:Z=|min(U1;U2)−M(U)|−0.5V(U) With a confidence interval of (1-α) for a two-sided test, the values of differential pressure and particle filtration efficiency for the population 1 and the population 2 are considered to be the same, if the absolute value of the experimental standardized value, Z, is comprised between 0 and the t-value of the Student distribution corresponding to (1-α/2) and (n1+n2-2) degrees of freedom. For the test conducted on the filtration efficiency, the sizes of the population are 13, 44 and 72, respectively for surgical masks of type I, II and IIR; while for the test on the differential pressure, the populations contain 24, 89 and 127 values, respectively for types I, II and IIR. The differences between the populations of Type I, II and IIR masks have been statistically tested in pairs and, among all the references tested, there is no significant variation of differential pressure and collection efficiency for 3 μm particle diameter between the different kinds of masks for a confidence interval of 95%. The observations (differential pressure and efficiency) can be considered as similar until a limit significance level (Table 5 ). Each percentage indicates the maximal risk of being wrong supposing that the performances of masks of various types are similar.Table 5 Confidence interval obtained with Mann-Whitney U statistical test. Table 5 Type I vs Type II Type I vs Type IIR Type II vs Type IIR Differential pressure (degree of freedom) 67.7% (111) 73.2% (149) 27.0% (214) Particle Filtration Efficiency (degree of freedom) 83.2% (55) 80.5% (83) 6.4% (114) Even if the performances do not seem to be influenced by the kind of surgical masks, results of Table 5 tend to highlight that the similarity is higher between types II and IIR, both for the differential pressure and for the efficiency for 3 μm particle diameter. Filtering facepiece respirators (FFP2) should collect more than 94% of a NaCl aerosol with a mean mass diameter close to 600 nm. For the four FFP2 tested, the total particle efficiency determined with a photometer was higher than 99.5% (Fig. 3 , left). Calculating this total collection efficiency from the SMPS upstream and downstream concentration conducted to lower values. Indeed, particles with a mobility-equivalent diameter higher than 550 nm were not counted by the SMPS while they were collected with a high efficiency due to interception and inertial mechanisms.Fig. 3 Total collection efficiency of the filtering facepiece respirators (left) and the surgical masks (right) before washing. Fig. 3 This SMPS total collection efficiency was also determined for the surgical masks (Fig. 3, right). As expected, efficiencies of filtering facepiece respirators are higher due to their structure (higher solid volume fraction and/or number of layers), but most of the surgical masks present performances similar to those of FFP1 and some of them could be considered as efficient as FFP2, in regards to the EN 149–2009 standard. It should be reminded that only the material constituting masks are tested and that leakage are not considered. Three references (04, 10 and 16) have a total collection efficiency close to 60% and the determination of spectral efficiency will allow giving some explanations on these lower performances. Fig. 4 represents the collection efficiency of the different kinds of masks according to the particle diameter. As particle concentrations upstream and downstream of a mask sample were measured with a SMPS and an APS, the diameter on the abscissa axis is a mobility-equivalent diameter on the range 20–500 nm and an aerodynamic diameter for particles higher than 1 μm. It should be noted that despite various measurement principles and equivalent diameters, the instrumental responses are in reasonably good agreement. As surgical masks are constituted of non-woven material, their spectral efficiency present a classical U-shape due to the interaction of the main collection mechanisms (diffusion, interception, inertial impaction and electrostatic effect) and a most penetrating particle size (MPPS) between 0.2 and 0.5 μm. For filtering facepiece respirators, this MPPS is shifted to lower diameters due to electrostatic effects. These results also highlight a great heterogeneity of performances depending on the mask trademarks, both for surgical masks (whatever the type) and FFP2. As the collection efficiency and the width of the MPPS are directly dependent onn the fibrous structure (solid volume fraction, fiber size distribution and thickness), the quality of the meltblown and spunbond layers can probably contribute to these heterogeneities. If the collection efficiency for surgical masks is, in most cases, higher than 70–80% for the whole range of particle diameters, some references (04, 10 and 16) present efficiency lower than 40% for the most penetrating particle size. This marked evolution is probably due to the absence of electrostatic charges at the fiber surface of these surgical masks.Fig. 4 Spectral efficiency of type I, II, IIR surgical masks and FFP2 masks before washing. Fig. 4 3.2 Differential pressure and particle filtration efficiency of washed masks The performances of washed surgical mask trademarks are represented on Fig. 5, Fig. 6 . As previously, the horizontal dashed lines correspond to standard requirements. The error bars correspond to standard deviations determined from a reproducibility analysis on 4 to 8 samples for differential pressure and on 6 to 12 samples for collection efficiency for 3 μm particle diameter.Fig. 5 Differential pressure according to the number of washings for surgical masks. Fig. 5 Fig. 6 Collection efficiency according to the number of washings for surgical masks. Fig. 6 Whatever the reference and the washing procedure, the first cycle induces a slight decrease of differential pressure. Therefore, if masks meet the requirements of the standard before washing, they also remain in compliance with it after a washing cycle. As there is a relationship between the pressure drop and the collection efficiency (Bourrous et al., 2021), this modification of the non-woven structure leads to a decrease of collection efficiency for 3 μm particle diameter. However, performances remain constant hereafter a cycle and up to 10 cycles, i.e. the maximal cycle number tested for 7 references of surgical masks. For each reference, the Mann-Whitney U test is carried out to determine if washing is statistically responsible for a performance decrease (with a significance level of 5%). This statistical test shows that, for both the differential pressure and the efficiency for 3 μm particle diameter, the results obtained on new and washed masks cannot be considered significantly different, except for some trademarks. A significant modification of differential pressure is noted for the references 10 and 13–1 after 5 washing cycles but it does not lead to a significant decrease of collection efficiency. A significant decrease of efficiency can also be observed after the washing of the references 16 and 23. Nevertheless, trademarks 10 and 16 have been previously identified as less efficient and highly heterogeneous; more samples should be tested to definitively conclude on the influence of washing on these surgical masks. Concerning the reference 23, the inner and outer layers of the mask have the particularity of being composed of cellulose fibers. This characteristic could maybe explain the PFE decrease which is not significant for the majority of the masks composed of polypropylene fibers. If the collection efficiency for 3 μm particle diameter remains greater than 95 or 98% whatever the type of surgical mask and the number of washings, the performance of the masks is impacted by the washing for lower particle sizes (Fig. 7 A–D). As for filtering facepiece respirators, a total collection efficiency is measured with a photometer. These masks are no more in compliance with the requirements of the EN 149–2009 standard (Table 2) after washing.Fig. 7 Spectral efficiency of washed surgical masks (A–B), of washed and discharged surgical masks (C), of FFP2 masks washed with or without detergent (D). Fig. 7 As the presence of an intermediate meltblown layer of charged polypropylene fibers contributes to the collection efficiency by electrostatic effects, this decrease in efficiency, constant hereafter one cycle, can be explained by the loss of electrostatic charges during the washing cycle as confirmed by the results obtained on a mask discharged by immersion in isopropanol (Fig. 7C). A Kelvin probe was used to measure the global charge of polypropylene fibers for one of the filtering facepiece respirators (reference A). The registered mean surface potential, close to −500 V for the non-washed FFP2, decreases until a value close to −20 V after a washing cycle and confirms the charge neutralization and the removal of the electret effect on the washed masks. Moreover, the experiments conducted on the reference 16, with an efficiency lower than 40% for the most penetrating particle size before washing (Fig. 4), confirm the absence of electrostatic charges at the fiber surface; the collection efficiency before and after washing being similar on the whole particle size range. As a washing without detergent maintained the performances of the mask sample at the same level as the new FFP2 (Fig. 7D), the loss of electrostatic effects could likely be attributed to the presence of cationic surfactants in fabric softeners. These compounds, notably esterquats, possess excellent antistatic properties and are used to prevent the accumulation of static charges and make the textile surface more conductive (Mishra & Tyagi, 2007; Murphy, 2015). This positive charge of cationic surfactants (Agarwal et al., 2012) reinforce results obtained with the Kelvin probe. Visualizations of one of the filtering facepiece respirators (reference A) with a JSM-7900F (Jeol) scanning electron microscope (SEM) as well as analysis by X-ray energy-dispersive spectrometry (EDX) suggest that surfactant residues (presenting significant contributions of Fe, Mg, Al and Si) and an organic film (mainly C, O and N) could be deposited at the fiber surface (without modification of their diameter or abrasion of their surface) after a washing cycle and contribute to their neutralization (Fig. 8 ). Such observations have also been highlighted by Parvinzadeh and Hajiraissi (2008) and Obendorf et al. (2009).Fig. 8 SEM images of filter fibers before washing (top row) and after washing with detergent (middle row). The bottom row shows EDX spectra of impurities deposited on the washed fibers, indicated by the arrows in the SEM images. Fig. 8 3.3 Projection resistance The projection resistance tests were performed on IIR masks under the conditions described by the standard, at a blood ejection rate of 550 cm/s corresponding to a blood pressure of 16 kPa. The tests were repeated once for each type of mask under the same conditions. To be fully compliant with ISO 22609, nearly 30 tests should be performed for each type of mask, which was obviously not possible in the pandemic and lockdown context. Visual observations show that, after one to two washings, the ‘anti-splash’ properties of the masks are preserved according to the ISO 22609 standard, i.e. no trace of blood was detected on the inner face of the mask, 10 s after the blood projection. However, the protective properties of the first layer are degraded after about 4 washings and the blood enters the mask. As the blood only accumulates in the lower part of the mask without succeeding in passing through the three layers, the projection resistance property is preserved according to the standard (Fig. 9 ). After more washing cycles, the accumulation of blood within the inner layers is such that it can pass through the internal barrier in case of pressure or buffering. Results of the test carried out according to ISO 22609 are then negative.Fig. 9 Examples of outer and inner faces of masks after a synthetic blood projection. Fig. 9 To explain this property loss, the contact angle between a drop of synthetic blood and a mask was measured on new and washed IIR masks (Reference n°30). It appears that washing cycles change the surface properties of the outer layer of masks and that the contact angle θ rapidly evolves from values greater than 90° (hydrophobic behavior) to angles lower than 90° (hydrophilic behavior) for a washed mask (Fig. 10 ). These modifications of surface properties, in agreement with previous conclusions on the fiber state of charge, lead to a loss of the projection resistance (“R” function) after few washing steps, whatever the type IIR mask brand used. The projection resistance cannot be claimed after a washing treatment and wearing a washed IIR masks in an operating room should therefore be proscribed for medical staff.Fig. 10 Comparison of contact angles of blood drops and mask surfaces for new (left) and 9-time washed masks (right) after a) 2 s; b) 30 s; c) 2 min 30 s and d) 5 min. Fig. 10 4 Conclusion Comparison of a large number of masks highlighted a great variability of PFE and differential pressure depending on the mask trademarks, both for surgical masks (whatever the type) and for FFP2. The quality of the meltblown and spunbond layers and the absence of electrostatic charges at the fiber surface can explain the lower fractional efficiency of some references. For medical face masks, even if the performances seem to be independent from the mask type, results tend to highlight that types II and IIR exhibit a similar behavior, both for the differential pressure and for the efficiency for 3 μm particle diameter. Washing, probably the most easily adaptable treatment for the general public, was the solution adopted for the mask decontamination. It should be noted that, whatever the mask reference and the washing procedure, the first cycle induces a slight decrease of the differential pressure and of the collection efficiency for 3 μm particle diameter. The performances of the washed surgical masks were maintained up to 10 washing cycles and met the requirements of the standards. Nevertheless, a statistical Mann-Whitney U test showed that, for both the PFE and the differential pressure, the results obtained on new and washed surgical masks cannot be considered significantly different for the majority of the trademarks. Moreover, if the PFE for 3 μm particle diameter remains greater than 95 or 98%, whatever the type of surgical mask and the number of washings, the performance of the masks is impacted by the washing for submicronic particles. As a consequence, the total collection efficiency of filtering facepiece respirators is no more in compliance with the standard requirements. The treatment leads to a loss of electrostatic charges during the washing cycle as confirmed by the results obtained on a mask discharged by immersion in isopropanol and measurements of fiber state of charge. The modifications of surface properties after a washing cycle also lead to a loss of the hydrophobic behavior of type IIR surgical masks which can therefore no more be considered as resistant to blood projections. Washing surgical masks can be a convenient solution in case of shortage of these single-use devices, but also to reduce the consumption of plastic materials. As long as the head straps and the nosebands will not break, the protection level and the differential pressure of these masks remain similar to the performances of new masks. Nevertheless, the projection resistance cannot be claimed after a washing treatment and wearing a washed IIR masks in an operating room should therefore be proscribed for medical staff. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Appendix 1 Surgical and FFP2 mask references Appendix 1Trademark Type Ref. Number of washing cycles Washing protocol 0 1 2 3 5 10 BYD CARE (YY/T 0969–2013) I 01 X Moen (602A-01)) I 02 X Sunrise Nursing I 03 X X X W2 LiangYa (DGTMYY) I 04 X X W2 FITEXIN I 05 X X X W4 MEDWELL II 06 X CA Diffusion 1931 II 07–1 X X X X X W2 CA Diffusion 1931 II 07–2 X X X W4 ALLMED II 08 X Henan YADU Industrial Co. II 09 X SAVOY International II 10 X X X W4 WK Well Klean II 11 X LyncMed (302089-CMA010) II 12 X LyncMed (302089-CMA006) II 13–1 X X X X X W2 LyncMed (302089-CMA006) II 13–2 X Naguma (NA-05) II 14 X Saudel (85002) II 15 X TD Professional 45455 II 16 X X W2 TSC II 17–1 X X W3 TSC II 17–2 X X W4 Global II 18 X X W4 Kolmi OP’R (M36101-30) IIR 19 X X X W4 Kolmi OPAIR (M31101-30)) IIR 20 X X X W4 Kolmi OpairONE (M34101-30) IIR 21 X X X W4 CA Diffusion 1960 IIR 22–1 X X X W4 CA Diffusion 1960 IIR 22–2 X X X X W2 CA Diffusion 1960 IIR 22–3 X X X X X X W1 Ansell (Sandel) IIR 23 X X W4 FCHA Fengchenhan IIR 24 X Segetex-eif (M193-25) IIR 25 X X X W4 France Cardio (France) IIR 26 X X W4 MIF Medical (WA-FM) IIR 27 X Yongli (YLEN104) IIR 28 X Xiantao Xingrong (XR001) IIR 29 X Jiangxi Hongda (Hygial) IIR 30 X LCH (Aerokyn PLM.01R) IIR 31–1 X X X X X W2 LCH (Aerokyn PLM.01R) IIR 31–2 X X X W2 Medicom (2015–30) IIR 32 X X X X X W2 Paul Boyé (MPB-CH1) IIR 33 X X X W2 Kimberly Clark (The Lite One) IIR 34 X Solida IIR 35 X X W3 Ultrafilter (Ultramask EASM 198R) IIR 36 X VALMY (VR202F) FFP2 A X X W5 KOLMI (OpAir Pro white) FFP2 B X X W1 KOLMI (OpAir Pro violet) FFP2 C X X W1 Paul Boyé (MPB2.1-B.27069-TU-00) FFP2 D X X W1 Acknowledgments This study was conducted within the framework of the LIMA joint research program between the Institut de Radioprotection et de Sûreté Nucléaire and the Laboratoire Réactions et Génie des Procédés of the Université de Lorraine/CNRS. Some results were obtained as part of the RESI-OPTIPI project financially supported by the French National Agency for Research (ANR) and the Grand-Est region. Authors are grateful for the Regional University Hospital Center of Nancy for the washing of some masks considered in this study. They also warmly thank Dr. Jean-Charles Matéo-Vélez from the French Aerospace Lab (ONERA) for the measurements of fiber charge with a Kelvin probe. ==== Refs References Agarwal G. Perwuelz A. Koehl L. Lee K.S. Interaction between the surface properties of the textiles and the deposition of cationic softener Journal of Surfactants and Detergents 15 2012 97 105 10.1007/s11743-011-1273-4 Akber A.S. Khalil A.B. Arslan M. Extensive use of face masks during COVID-19 pandemic: (micro-)plastic pollution and potential health concerns in the Arabian Peninsula Saudi Journal of Biological Sciences 27 2020 3181 3186 10.1016/j.sjbs.2020.09.054 33052188 Alcaraz J.-P. Le Coq L. Pourchez J. Thomas D. Chazelet S. 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Impact of three biological decontamination methods on filtering facepiece respirator fit, odor, comfort, and donning ease Journal of Occupational and Environmental Hygiene 8 2011 426 436 10.1080/15459624.2011.585927 21732856 Viscusi D.J. King W.P. Schaffer R.E. Effect of decontamination on the filtration efficiency of two filtering facepiece respirator models Journal of the International Society for Respiratory Protection 24 2007 93 107 Wang D. Sun B.C. Wang Y.X. Zhou Y.Y. Chen Z.W. Fang Y. Yue W.-H. Liu S.M. Liu K.Y. Zeng X.F. Chu G.W. Chen J.F. Can masks Be reused after hot water decontamination during the COVID-19 pandemic? Engineering 6 2020 1115 1121 10.1016/j.eng.2020.05.016 32837748 Xie X. Li Y. Chwang A.T.Y. Ho P.L. Seto W.H. How far droplets can move in indoor environments – revisiting the Wells evaporation-falling curve Indoor Air 17 2007 211 225 https://doi: 10.1111/j.1600-0668.2007.00469.x 17542834 Yan J. Guha S. Hariharan P. Myers M. 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2022-12-15 23:23:21

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J Pediatr. 2022 Apr 18; 243:239-240

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J Pediatr

10.1016/j.jpeds.2021.12.026

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==== Front Industrial Marketing Management 0019-8501 0019-8501 The Authors. Published by Elsevier Inc. S0019-8501(22)00053-0 10.1016/j.indmarman.2022.03.008 Article The market-shaping potential of a crisis Pedersen Carsten Lund ab Ritter Thomas c⁎ a Department of Marketing, Copenhagen Business School, Solbjerg Plads 3, DK-2000 Frederiksberg, Denmark b Department of Business IT, IT University of Copenhagen, Rued Langgaards Vej 7, DK-2300 Copenhagen S, Denmark c Department of Strategy and Innovation, Copenhagen Business School, Kilevej 14A, DK-2000 Frederiksberg, Denmark ⁎ Corresponding author. 25 3 2022 5 2022 25 3 2022 103 146153 30 9 2020 15 2 2022 10 3 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. A crisis, like the COVID-19 pandemic or a cyber attack, not only creates the necessity for crisis management in business-to-business firms aimed at addressing the immediate challenges, but also offers opportunities to shape business markets by changing exchanges, collaborations, and institutions. In order to develop a conceptual framework to capture the market-shaping potential of a crisis, we integrate insights from risk management and strategic management, and discuss their implications for market shaping. As such, this paper builds a bridge between the reactive nature of crisis management during a crisis and proactive market shaping, and offers new insights into market shaping based on an underutilized source of inspiration, namely crisis management. Based on resilience (from risk management) and responsiveness (from strategic management), we propose four market-shaping opportunities. Beyond the theoretical novelty of contributing to our understanding of market shaping based on crisis management, our framework has managerial implications for market shaping and highlights a set of interesting research questions that can guide future studies. Keywords Market shaping Resilience Responsiveness Crisis Strategy ==== Body pmc1 Introduction In addition to being resilient and adaptive, firms should also utilize shocks such as COVID-19 to generate new business opportunities… This malleability, in turn, creates multiple opportunities for firms to shape their markets and hence drive the market's development in favorable directions. (Nenonen & Storbacka, 2020, p. 265) As this quote suggests, the COVID-19 pandemic is an external event that has required many firms to continually react in a timely manner by, for instance, being resilient or adaptive. Many of the impacts of the pandemic are sector specific (e.g., higher profits in online supermarkets and IT services; lower profits in business travel and industry fairs). As such, the pandemic entails both threats and opportunities for companies. Ritter and Pedersen (2020) differentiate among potential impacts that capture the COVID-19-infused outcome spectrum, which ranges from devastating effects to profitable growth. This view corresponds with the Chinese word for “crisis,” which entails one brush stroke for “danger” and another for “opportunity.”1 While external shocks and disruptions may have both positive and negative impacts, the literature on crisis management generally focuses on the negative impacts and the managerial issues that are associated with avoiding or mitigating those effects. In line with recent work (Pedersen, Ritter, & Di Benedetto, 2020, p. 315), we define a crisis as “a sequence of events that can have substantial negative consequences if not managed appropriately.” Given this definition, crisis management inherently involves reactions to a sequence of events and the mitigation of potential negative consequences based on the perceptions of those consequences. Therefore, whether a sequence of events is considered a crisis depends not only on the events per se but also on the perceptions of those events and their consequences. Therefore, the same events may be regarded differently by different actors, as actors vary in their perceptions of those events and their consequences. The events causing a crisis may also interrupt the stasis of a market system by forcing it “into movement” and, in so doing, create malleability in market-shaping behavior (Nenonen & Storbacka, 2020; for market shaping, cf. also, e.g., Baker & Nenonen, 2020; Kindström, Ottosson, & Carlborg, 2018; Kumar, Scheer, & Kotler, 2000; Maciel & Fischer, 2020; Peters, Nenonen, Polese, Frow, & Payne, 2020). The shape of a market is altered “by re-designing the content of exchange, and/or re-configuring the network of stakeholders involved, and/or re-forming the institutions that govern all stakeholders' behaviors in the market” (Nenonen, Storbacka, & Windahl, 2019, p. 618). Such alterations of market shape can be observed in relation to the COVID-19 pandemic. For instance, in-person maintenance services have been replaced with online customer tutorials (re-design of exchange), global suppliers have been replaced with local suppliers (re-configuration of the network), and social-distancing rules governing personal interactions have been introduced by authorities that formerly did not govern market behavior (re-forming institutions). As such, the pandemic shows that reactive crisis management and proactive market shaping coexist. We are interested in how an organization's crisis management may affect its market shaping—that is, whether the organization is proactive and engages in “purposive actions” (Nenonen et al., 2019, p. 618) aimed at changing the defining elements of a market. The business-marketing literature has surprisingly little to offer on crisis management (Pedersen et al., 2020). Moreover, crisis management has not yet been sufficiently discussed in terms of market shaping (for a notable exception, see Nenonen & Storbacka, 2020). Thus, we lack an understanding of the different crisis-management options, which might help explain market-shaping behavior. More specifically, a review of theories on resilience and responsiveness can inform the market-shaping literature by providing an understanding of how crisis management can create market-shaping potential. In this paper, we demonstrate how crisis-related research from the fields of risk management (i.e., resilience; see, e.g., Aven, 2011; Haimes, 2009) and strategic management (i.e., responsiveness; see, e.g., Bettis & Hitt, 1995; Teece, Pisano, Shuen, & David Teece, 1997) may inform market management (i.e., market shaping; see, e.g., Baker & Nenonen, 2020; Nenonen & Storbacka, 2020), where resilience and responsiveness are reactive behaviors in light of a crisis, and market shaping is a proactive behavior aimed at developing a market into its future form. In other words, resilience and responsiveness respond to a sequence of events that may have negative effects on the organization, whereas market shaping reflects the view of the market as an object for proactive, purposive actions. We contend that companies can leverage a crisis as a market-shaping opportunity and that the set of market-shaping choices at their disposal is predicated upon their crisis management. In other words, reactive behaviors during a crisis (i.e., the pursuit of resilience or responsiveness strategies) provide a basis for the pursuit of proactive market-shaping opportunities. We follow Jaakkola (2020) in integrating resilience and responsiveness (method theories) with market shaping (domain theory) by paying close attention to their commonalities. This paper makes two main contributions to the market-shaping literature. First, we introduce the paradox of “reactive market shaping,” which suggests that reactive crisis management is not meant to shape markets but may still do so. Second, we develop a conceptual framework that combines the dimensions of resilience and responsiveness, and classifies four managerial options in crisis management in order to determine how they can inform market-shaping opportunities. In this regard, we respond to Swedberg's (2012) call for theorizing that involves more creative discovery than justification through falsification or verification based on empirical testing. The article proceeds as follows. First, we conceptualize the dimensions of crisis management based on a combination of risk-management and strategic-management literature. We identify four options available to organizations when responding to a crisis. Second, we introduce the notion of market shaping and discuss the four crisis-management options in terms of their market-shaping potential. Finally, we discuss managerial implications and questions for future research. 2 Conceptualization of crises and crisis management 2.1 Crisis In line with established work (Pedersen et al., 2020, p. 315), we define a crisis as “a sequence of events that can have substantial negative consequences if not managed appropriately.” This definition encompasses three defining elements of a crisis. First, a crisis is triggered by a sequence of observable events. This allows for between-actor variations in the observation of events. Second, the events need to carry the potential for substantial negative consequences. In other words, there must be a potential threat. Third, there is an opportunity to manage the crisis in a way that mitigates (some of) the consequences. This means that the actor can benefit from taking action. Crises can be classified according to their nature (i.e., underlying causes and time horizons; see Fig. 1 ). For instance, a crisis can be categorized as man-made or inflicted by nature (e.g., Rosenthal & Kouzmin, 1993), and a crisis can be sudden (i.e., unexpected, happens overnight) or smoldering (i.e., structural and slowly developing) (e.g., James & Wooten, 2005). Consequently, a typology with four different types of crises emerges. As suggested by the matrix, a crisis can take different forms with varying implications for firms.Fig. 1 Generic crisis typology. Fig. 1 While most firms have been affected by the COVID-19 pandemic and the subsequent lockdowns and restrictions, they differ in terms of the pandemic's impact on their businesses. Some have benefitted from the pandemic (e.g., streaming, home delivery, online communication), while others have experienced grave consequences (e.g., airlines, restaurants, event venues). Crises may affect business models in six ways, ranging from antifragile to retired (Ritter and Pedersen, 2020). Our main focus in this paper is on crisis management and, therewith, on managerial issues triggered by a crisis' potential negative consequences. In contrast, Nenonen and Storbacka (2020) refer to the pandemic as a “shock” rather than a “crisis” and thus include potential positive outcomes. As such, they address a broader spectrum of situations than permitted by the term “crisis,” including situations in which companies discover that there are mainly positive outcomes and opportunities to exploit based on their current capabilities, rather than only focusing on negative outcomes of external events (e.g., crises). Moreover, firms may experience “a crisis” differently in terms of various sub-crises as well as the impact and duration of the crisis phases (Pedersen et al., 2020; Ritter & Pedersen, 2020). Another difference among crises is the extent to which human lives are concerned. In contrast to financial crises, the direct threat the pandemic poses to human beings is unique and requires a new approach to managing business operations (e.g., Cortez & Johnston, 2020). While an understanding of such differences is important in the analysis of a given crisis, for the remainder of this paper, we discuss crisis management in a general way without linking it to a particular type of crisis. As such, more detailed arguments and empirical tests would be welcomed extensions of the arguments advanced here. 2.2 Crisis management as method theory We contend that a crisis essentially represents a sub-category of market-shaping situations, as companies in trouble engage in reactive crisis management but their actions may simultaneously trigger proactive market-shaping behavior. Consequently, market shaping can be seen as a domain theory to which crisis management is added as a method theory. This study therefore follows the pattern of a “theory-synthesis” study, which “seeks to achieve conceptual integration across multiple theories or literature streams” with the aim of offering “a new or enhanced view of a concept or phenomenon by linking previously unconnected or incompatible pieces in a novel way” (Jaakkola, 2020, p. 21). Here, the phenomenon of interest is market-shaping opportunities during a crisis. As such, the domain theory to which we seek to contribute is market shaping. The method theories are those of resilience and responsiveness. In other words, we consider definitions and arguments from resilience and responsiveness to explain opportunities for market shaping. Moreover, this theory-synthesis paper “represents a form of theorizing that emphasizes narrative reasoning that seeks to unveil ‘big picture’ patterns and connections rather than specific causal mechanisms” (Jaakkola, 2020, p. 21). The commonalities among resilience, responsiveness, and market shaping are two-fold. First, they are all comprised of discrete capabilities, which implies that routines and learning underlie all three concepts. Second, they are all relevant in a crisis, as they help withstand (resilience), adapt to (responsiveness), and form (market shaping) changes in the environment. However, they differ in that resilience and responsiveness are discussed as reactions to events, whereas market shaping proactively introduces changes to the market and the environment. In the following, we explain the two method theories before we integrate them with the domain theory. 2.3 Resilience in the risk-management literature The literature on risk management focuses on assessing risk and mitigating its adverse effects (Covello & Mumpower, 1985). As noted by Covello and Mumpower (1985), epidemics have played an important role in the development of the academic field and the practice of risk management. The risk-management literature2 has long suggested ways to withstand disturbances and to facilitate continued operations in the face of environmental change, or to recover within an acceptable period of time with acceptable costs and risks (Haimes, 2009). The definition of resilience is extensively debated in the risk-management literature (e.g., Aven, 2011; Wied, Oehmen, & Welo, 2020). For instance, Wied et al. (2020) analyzed 251 definitions of resilience and found that research in this field is fragmented. Manyena (2006, p. 437) states that “resilience has been generally defined in two broad ways: as a desired outcome(s) or as a process leading to a desired outcome(s),” but also emphasizes that “the distinction may seem unnecessary.” Resilience is often applied as the antithesis of “vulnerability,” in which a system reacts adversely to disruptive events. Despite the diversity in definitions, resilience is generally acknowledged as referring to the capacity to withstand a disturbance or to recover after a disturbance has become manifest (Manyena, 2006; Sheffi & Rice, 2005). We adopt this view in our conceptualization in which we consider robustness (to withstand) and recovery (to rebound) as two dimensions of resilience. 2.4 Responsiveness in the strategic-management literature The strategy discipline's emphasis on responsiveness has much to offer to the field of crisis management, as this literature stream highlights how firm adaptation is positively related to firm performance in volatile environments. Consequently, it can provide a crisis-management option that highlights the strategic importance of adaptation in the face of a crisis. In terms of firms' reconfigurations and adaptations in response to change, the literature on responsiveness stresses that strategic-response capabilities (Andersen & Bettis, 2015; Andersen, Denrell, & Bettis, 2007; Bettis & Hitt, 1995), adaptive decision-making (Volberda, 1996), and dynamic capabilities (Teece et al., 1997) are important drivers of organizational performance in changing environmental settings. While each construct entails idiosyncratic operationalizations, they all rest on the conceptual commonality that firms must be adaptive to their environments in order to maintain above-average performance.3 This focus is fundamentally different from resilience, as the strategic-management literature focuses on changing the organization to better fit a changing environment as opposed to withstanding changes through robustness and efficiently re-establishing operations through recovery. The strategic-management literature generally addresses adaptation to changing environments using two concepts: ad hoc problem solving and dynamic capabilities. According to Winter (2003, p. 993), “ad hoc problem solving and the exercise of dynamic capabilities are two different ways to change.” Ad hoc problem solving occurs when an organization is disturbed and “pushed into ‘firefighting’ mode, a high-paced, contingent, opportunistic and perhaps creative search for satisfactory alternative behaviors” (Winter, 2003, p. 992, emphasis added). As such, it “is not routine; in particular, not highly patterned and not repetitious“(Winter, 2003, pp. 992–993). Therefore, ad hoc problem solving involves reacting to change with improvised, temporary, alternative behaviors. This concept has not attracted as much attention as its alternative strategic imperative: dynamic capabilities (for a recent review, see Schilke, Hu, & Helfat, 2018). The strategic-management field has coalesced around the presumption that the key to obtaining and maintaining a competitive advantage lies in companies' capabilities to dynamically respond to and evolve in changing environments through resource reconfigurations (Teece et al., 1997). According to Teece et al. (1997), dynamic capabilities entail the capacity to sense and seize opportunities in the environment, and to reconfigure the organization's resource base to develop innovative responses to evolving conditions. “A firm's ability to integrate, build, and reconfigure internal and external competences” (Teece et al., 1997, p. 516) and “the organizational and strategic routines by which firms achieve new resource configurations” (Eisenhardt & Martin, 2000, p. 1107) are generally assumed to have positive impacts on firm performance, especially for firms operating in “rapidly changing environments” (Teece et al., 1997, p. 516). Dynamic capabilities are widely debated (e.g., Eisenhardt & Martin, 2000; Winter, 2003) and, as a result, the conceptual boundaries are unclear. They are predominantly regarded as responsive to exogenous change. Teece, Raspin, and Cox (2020) suggests that they may also change the environment and can, therefore, be proactive. Yet, there is general consensus in the literature that dynamic capabilities are predicated upon resource reconfigurations that can be implemented and, therefore, are lasting solutions to a new or changed situation. We include resource reconfiguration as a fourth dimension of an organization's reaction to a crisis. The difference between ad hoc problem solving and resource reconfigurations as a result of dynamic capabilities lies in the structure of the response: ad hoc problem solving is spontaneous and improvised, while reconfiguration is based on routines. 2.5 The paradox of reactive and unintentional market shaping As suggested above, there is a paradoxical relationship between crisis management and market shaping. Crisis management is reactive in nature and does not explicitly seek to shape markets. In contrast, market shaping is proactive in nature and explicitly seeks to change markets. Therefore, the emergence of a crisis can introduce what we refer to as reactive and unintentional market shaping, whereby businesses unintentionally shape markets through their management of a crisis. Thus, crisis management is not intended to shape markets, but may end up doing so. Unintentional market shaping is at odds with the mainstream market-shaping literature, which presumes that market shaping is intentional and proactive. In the remainder of the paper, we provide a coherent explanation for this anomalous phenomenon, thereby adding insights to the theory and practice of market shaping. Moreover, in our discussion of managerial implications, we suggest that by understanding the market-shaping opportunities associated with crisis management, executives can intentionally engage in market shaping during a crisis. Doing so requires an awareness of the link between crisis management and market shaping, the paradox, and the deliberate use of a crisis and crisis management to form market-shaping strategies. In the following, we discuss how a deliberate approach to crisis-infused market shaping can be effectuated. 2.6 Four options for crisis management The brief review of resilience and responsiveness above suggests that organizations facing a crisis have four options. Table 1 provides a composite taxonomy of the different constructs from the various literature streams. More specifically, the table summarizes crisis management as a construct that entails four second-order dimensions (which we detail in the following) that differ in terms of their definitions, alternative terms, where they originate from, and key studies. For instance, an organization may seek to “weather the storm” by securing ongoing operations or it may choose to quickly recover after a breakdown (resilience). Alternatively, it can spontaneously or systematically adapt its behavior and resources to the crisis-induced changes (responsiveness). As a result, four options co-exist during a crisis (i.e., robustness and recovery for resilience, and ad hoc problem solving and reconfiguration for responsiveness).Table 1 A composite taxonomy of crisis management. Table 1Construct Crisis management First-order dimensions Resilience Responsiveness Second-order dimensions Robustness Recovery Ad hoc problem solving Reconfiguration Main theoretical origin Risk management Strategic management Key references Manyena (2006) Sheffi and Rice (2005) Winter (2003) Teece et al. (1997) Alternative terms and descriptions used Absorb change and disturbance, absorb and accommodate future events, persistence, tolerate, resist, sustain, withstand Bounce (backward or forward), rebound, return to equilibrium after displacement, resume, rebuild, repair Improvise, firefight, fix urgent problems Resourcefulness, resource fungibility, mutation, flexibility, adaption, innovation An organization can combine elements of resilience and responsiveness in a holistic crisis-management approach. Therefore, organizations have a portfolio of options at their disposal throughout a crisis, and an organization's set of decisions—as opposed to a single decision—comprises its crisis management. Thus, different approaches can deal with different parts of a business, address different phases in a crisis, and/or build different paths to increase the number of options available for later decisions. In sum, Table 1 depicts crisis management (the construct), which is comprised of resilience and responsiveness (first-order dimensions), which in turn reflect recovery and robustness (second-order dimensions for resilience) as well as ad hoc problem solving and reconfiguration (second-order dimensions for responsiveness). Correspondingly, Table 2 provides illustrative examples of crisis management, investment allocations, and key performance indicators for the second-order dimensions—that is, it illustrates how these dimensions can be managed in practice.Table 2 Illustrations of crisis management. Table 2Construct Crisis management First-order dimensions Resilience Responsiveness Second-order dimensions Robustness Recovery Ad hoc problem solving Reconfiguration Crisis management during COVID-19 crisis (illustrative) An organization securing production at normal levels despite restrictions because its production processes were compliant (e.g., through high levels of automation, single employee workstations, and remote monitoring) An organization returning to normal operations within 12 h of public announcements of restrictions being lifted An organization that gives autonomy to all units and sections to find local solutions of any kind to perform as well as possible An organization that changes supplier-performed onsite maintenance work into an online, video-based customer training program so that customers can perform such services themselves Potential investments focus (illustrative) Investments in slack (e.g., extra personnel, additional server capacity) and stock (e.g., having a reserve of materials for at least six months) Procedures and resources for recovery (e.g., emergency and rescue plans; training for production relocation; recovery equipment, such as emergency energy units) Spontaneity and fast judgement training; allowing ad hoc appropriation of resources for different purposes (e.g., using new equipment and showroom equipment as a spare part base); alternatively, no investments and reliance on luck Market learning (e.g., analyses, panels, relationships), R&D (e.g., engineers, labs, test sites), and flexibility (multipurpose resources that can be easily refitted to new processes) Key performance indicators (illustrative) Monitor size of slack resources before discontinuity hits Estimate and, in case of a crisis, monitor time, cost, and risk of recovery process Monitor number and effects of spontaneous fixes Monitor efficiency (e.g., time and costs) and effectiveness (e.g., quality of new solution) of reconfiguration Resultant market-shaping opportunities (illustrative) • Lobby for new supply chain standards (e.g., minimum acceptable robustness, such as 14 days stability; or maximum recovery times, such as reestablishing operations no more than 12 h after failure) • Develop resilience across the ecosystem (e.g., identify the weakest link in a supply chain and improve that link to meet an agreed minimum) • Price based on operational stability (resilience-based pricing) • Define an expected level of ad hoc problem solving, including degrees of freedom for variations from normal operations • Develop new solutions for crisis-affected segments and, thereby, make existing solutions obsolete In times of crisis, decisions must often be made very quickly. This “window of opportunity” is characterized by incomplete information (Ansoff, 1975, Ansoff, 1980). The choice of crisis-management options during a crisis results in an allocation of resources that locks the organization into a specific set of future opportunities and may preclude it from other opportunities. Therefore, we connect the four crisis-management options to forward-looking market shaping in order to understand the market-shaping potential of crisis management. 3 The market-shaping potential of crisis management 3.1 Market shaping Markets have traditionally been regarded as a given, and marketing has been concerned with the appropriate action given a certain market situation. Marketing research (or “market sensing”; Day, 1994) has been viewed as a means to understand the environment on a continuous basis, as “a market changes day by day through the very fact that goods are bought and sold” (Alderson & Cox, 1948, p. 151). In this setting, strategy involves finding a fit between an externally given but dynamic market and the firm's capabilities. Similarly, markets have been described as networks (Johanson & Mattsson, 1985) in which “there is no ‘invisible hand’ creating a situation of efficiency and health. Instead there are several ‘visible hands’ that try to create situations that are beneficial to themselves” (Håkansson, 1987, p. 89). Thus, markets are not fixed, predetermined, and stable institutions. Moreover, firms are not only “takers” of an environment but also “creators” of it, as they can drive markets instead of being driven by them (Jaworski, Kohli, & Sahay, 2000). In Kjellberg and Helgesson's (2007, p. 141) words, “we should study markets in the making, rather than markets ready-made.” Market shaping can be driven by an individual firm or by collective action (Jaworski, Kohli, & Sarin, 2020; Maciel & Fischer, 2020). Similarly, other important market actors, such as regulators and NGOs, are likely to engage in market shaping through regulation and lobbying activities. In fact, the market-shaping perspective can be extended into an ecosystem (Adner, 2017) and shareholder perspective in which all relevant actors have the potential to shape a market, especially during a crisis. Given this dynamic, interactive understanding of markets, market shaping is:A purposive process by a focal firm to (1) discover the value potential of linking intra- and inter-stakeholder resources in novel ways, (2) trigger changes in various market characteristics to enable the formations new resource linkages, and (3) mobilize relevant stakeholders to free up extant resources for new uses. (Nenonen et al., 2019, p. 619). While Nenonen and Storbacka (2020) propose a process perspective on market shaping during a crisis, we consider changes in the shape of markets triggered by different crisis-management options. We thus assess the market-shaping potential of crisis management. 3.2 Crisis management and market shaping While market shaping rests on the assumption of being able to shape one's environment, a crisis creates a certain set of novel circumstances that actors have to consider and react to. Etymologically, a crisis denotes the events that amount to a turning point or decisive moment due to the anticipated potential for negative effects (e.g., Pedersen et al., 2020). Therewith, the origin of crisis-management behaviors is inherently reactive—they emerge as the events unfold. Yet, although a crisis leads to reactions, those reactions can proactively influence markets in the future because the object of the action is different: a crisis to react to and a market that can be shaped. While a crisis provides the malleability needed to shape markets (Nenonen & Storbacka, 2020), the market-shaping opportunities available to marketers partly depend on the options chosen by the firm during the crisis (i.e., responsiveness, resilience, or both). In other words, resilience and responsiveness can stipulate market shaping in intricate ways during a crisis. Nenonen and Storbacka (2020) outline the opportunity for market shaping and the process of shaping markets in relation to a crisis (for a suggestion of a seven-step, market-driving approach, see also Jaworski et al., 2020). We highlight that crisis management itself can affect the opportunities available for market shaping. By explicitly considering market shaping in a crisis context, we emphasize a sub-category of market shaping that has been largely overlooked in the literature.4 The four second-order dimensions of crisis management offer four opportunities for market shaping. As a crisis may highlight pre-crisis fragility in markets (e.g., the strong focus on lean and cost minimization prior to the COVID-19 pandemic), new standards for robustness, recovery, ad hoc problem solving, and reconfiguration can be established in markets during and after a crisis through, for example, one market actor exhibiting behavior that stipulates expectations for future behavior (see illustrations in Table 2). These changes correspond to the notion that “market-shaping initiatives do not necessarily have their starting points in a technology or new product or process” (Nenonen et al., 2019, p. 619). Rather, more emphasis can be placed on, for instance, the risks associated with production processes, service-delivery process, and logistics, all of which will correspond to new expectations and new ways of working with a crisis. When triggered by a crisis, market shaping is more likely to appear on the “system level … at which norms and regulations set the boundaries and rules for an entire market” (Kindström et al., 2018, p. 38). Although some prior work has conflated the constructs of resilience and responsiveness, and treated them as similar phenomena (e.g., Sheffi & Rice, 2005; Teece et al., 2020), resilience and responsiveness are two distinct phenomena in a crisis setting (Ritter & Pedersen, 2020) with distinct implications for market shaping. While resilience aims to maintain an organization's role, position, and function in its ecosystem, responsiveness helps the organization adapt to the new environment. The ways in which firms respond during a crisis (i.e., resilience and/or responsiveness) predetermine the potential set of market-shaping choices available to them. A resilient firm may shape the market in a direction that is focused on holding onto and regaining the strengths and structures it had prior to the crisis. In contrast, a responsive firm may shape the market in a direction that is predisposed to spontaneous solutions and innovative ideas. We illustrate the market-shaping potential of crisis management by adopting the three elements of market shape from Nenonen et al. (2019) in connection with the four crisis-management options developed above (see Table 3 ). The examples support the idea that market shaping is possible in all suggested dimensions and in all four sub-dimensions of crisis management. For example, the risk-management literature highlights the importance of developing resilience (e.g., Aven, 2011; Haimes, 2009; Manyena, 2006; Sheffi & Rice, 2005; Wied et al., 2020), which can be used to redesign exchanges (e.g., offer resilient-capacity consulting and training), assess the robustness and recovery capabilities of (potential) exchange partners, and establish norms and standards for resilience as institutional, market-governing elements through regulation.Table 3 Changing market shapes triggered by crisis management. Table 3Elements of market shaping crisis-management options Re-design exchange Re-configure the network Re-form institutions Robustness Offering robustness (e.g., offering onsite storage to reduce supply disruptions for five days) Including only partners that offer a given robustness level Establishing new representations and norms for degrees of disturbance before failure is acceptable Recovery Offering recovery support after outage (e.g., re-installation and re-calibration services) Including only partners that offer recovery support Establishing new representations and norms for customers' downtime acceptance Ad hoc problem solving Offering improvisation as part of exchange (e.g., brainstorming session when crises emerge) Allowing temporary shifts to emergency suppliers Introducing levels and timeframes for suboptimal improvisation to handle problems Reconfiguration Redefining exchanges (e.g., training customers to service equipment instead of using supplier-provided service) Implementing local supply options and global reconfiguration of supply chains Introducing new representations and norms for flexibility, renewal, and innovation in a crisis Table 3 explicates the crisis-management options relative to the triggering capability sets proposed by Nenonen et al. (2019), and suggests that the crisis-management options (i.e., resilience, robustness, ad hoc problem solving, and reconfiguration) may materialize in different forms depending on the triggering capabilities of market shaping (re-design exchange, re-configuring the network, and re-forming institutions).5 In other words, each of the four crisis-management options materialize through the three triggering capabilities of market shaping, which illustrates how crisis management may inform and result in market shaping. Ultimately, this depicts the paradox of unintended market shaping, and demonstrates that although crisis management may not be intended to shape markets, it may still do so. As discussed above, the risk-management literature carries important implications for market-shaping opportunities, as the capacity to be resilient can be a source of heterogeneity among competing firms (Manyena, 2006), with the most capable organizations having an interest in shaping markets towards high resilience in order to utilize their advantages. Notably, the marketing discipline focuses on explaining why some firms outperform others in their markets. Part of the answer to this important question may be found in resilience during a crisis and in anticipated resilience before a potential crisis, which turns reactive resilience into proactive resilience-potential building. For instance, in the context of the COVID-19 pandemic, it is reasonable to assume that resilience will be a key element of value propositions in the future and that actors may unintentionally—or even intentionally—shape new standards in this regard. Within the market-shaping context, responsiveness refers to firms adapting to turbulence during a crisis. For instance, universities are adapting to COVID-19 by engaging in online teaching (responsiveness), but they may proactively seek to make online teaching an integral element of their value propositions in the future, thereby modifying exchanges. In general, a responsive firm may develop new capabilities and offerings that can be leveraged in a purposefully shaped market (e.g., Ritter & Pedersen, 2020). Provided that such innovations are successful, crisis-born innovations may persist as the “new normal” in the future. In addition to the proactive market shaping of suppliers, which is the main focus of the market-shaping literature, customers may form new expectations for exchanges, partners, and institutions (Harrison & Kjellberg, 2016) based on their experience of a crisis. Ulkuniemi, Araujo, and Tähtinen (2015) refer to this as “purchasing as market shaping.” Customers can establish market norms, especially when “the market exhibits a strong direct link between exchange and use” (Harrison & Kjellberg, 2016, p. 457), which becomes apparent with discontinued just-in-time delivery networks. Consequently, we expect market-shaping forces from both suppliers and customers in relation to robustness, recovery, ad hoc problem-solving, and resource reconfiguration. 4 Managerial implications We have argued that a crisis can be a catalyzing event, as it provides firms with opportunities to shape their markets through their crisis management. In other words, market-shaping initiatives may emerge from the crisis-management activities effectuated during a crisis. A recent McKinsey study predicted that supply chains and market exchanges will, at least partially, transition from “just-in-time” to “just-in-case” (Sneader & Singhal, 2020). Moreover, data from the COVID-19 pandemic demonstrates that B2B companies are, in fact, undergoing changes in terms of how they communicate with customers and suppliers, how they manage their work, and even the offerings they have in the market (Cortez & Johnston, 2020; Ritter & Pedersen, 2020). Thus, crisis reactions can result in market shaping. This has several managerial implications. First, executives need to recognize the market-shaping potential of crisis management—what is done in response to a crisis may have long-term impacts on the market's future functioning. The managerial challenge is to realize the future-oriented, market-shaping potential of the decisions made during times of operational turmoil. As decisions made early in a crisis determine the long-term strategic options, such “untimely” long-term thinking is necessary during the crisis (Pedersen et al., 2020) and can, in fact, be utilized in a proactive manner. With such an approach, executives can offset the initially reactive nature of crisis management by purposefully including a market-shaping perspective in their actions. Executives must consider more than just reactive implications in their crisis management (e.g., Manyena, 2006; Sheffi & Rice, 2005), as considering market-shaping opportunities is vital. For instance, Rapaccini, Saccani, Kowalkowski, Paiola, and Adrodegari (2020) observe that short-term actions during a crisis to deliver services that customers can accept (i.e., ad hoc problem solving) can result in a new norm of creating decentralized stocks of resources that can be orchestrated on the basis of customer needs in the new normal, such as increasing stocks on the customer's premises and promoting customers solving problems themselves (i.e., reconfiguration, re-design exchange, and re-form institutions in Fig. 2 ). In essence, managers must simultaneously concentrate on the crisis at hand and leverage the crisis to take advantage of novel opportunities (Pedersen, 2018).Fig. 2 Monitoring market shaping. Fig. 2 Second, beyond their cognitive openness to engaging in market shaping and their readiness to do so, executives must analyze their own crisis-management options. For instance, how, and how successfully, did they apply the four options in dealing with a crisis? How proficient can they become in implementing the four options? Based on this self-assessment, the business potential of different avenues can be evaluated (for an example of how to assess resilience, see Table 5 in Rapaccini et al., 2020; see Sharma, Rangarajan, & Paesbrugghe, 2020, for examples of how an adaptive sales force can create resilience). For instance, a company may excel at resilience and robustness, but may concurrently be below the industry average on ad hoc problem solving or reconfiguring its resource base. Third, the four options are not only available to one's own organization—competitors can also apply them. Therefore, firms need to monitor their competitors' efforts to react to a crisis, and they need to understand the market-shaping potential of those actions. This will provide them with an estimation of market-shaping initiatives, which they must respond to by either preempting them or following competitors' leads (Nenonen & Storbacka, 2020). For instance, a competitor may seek to provide more digital services in the wake of a crisis (e.g., Rapaccini et al., 2020; Sharma et al., 2020), at which point it is up to decision makers in one's own organization to either preempt or follow that competitor in shaping the new market exchanges. Fourth, as discussed above, customers may also initiate market shaping. Therefore, executives must monitor customers and their expectations for new exchanges and new standards that emerge from crisis-management initiatives. While the focus of customer expectations is predominantly aimed at suppliers, it can similarly revolve around general industry norms and standards. As with competitors, executives can choose to reject customers' initiatives in order to control the market-shaping agenda, or follow their lead and react to their market-shaping initiatives. We illustrate the relevant questions regarding the three actors of market shaping in Fig. 2, which can serve as a managerial guide for considering the market-shaping potential of a crisis. Here, the emphasis is on companies' own market-shaping potential, competitors' market-shaping potential, and customers' market-shaping potential. Taken together, decision makers can forecast: (i) the expected level and form of market shaping based on crisis management, and (ii) the expected position of the firm in the newly shaped market. Hence, the logic of Fig. 2 is in line with classic managerial discussions concerning market developments, strategy, and positioning, and demonstrates that the ongoing monitoring of market-shaping potential follows the rationale of both market learning and market orientation. 5 Opportunities for future research As with any research, this paper highlights several potential avenues for future research. In the present paper, we follow Jaakkola (2020) in integrating resilience and responsiveness (method theories) with market shaping (domain theory) in order to contribute to the market-shaping literature. However, this approach could also be reversed by using crisis management as a domain theory and market shaping as a method theory. Such elaborations could highlight the potential contributions of market shaping to crisis management. By doing so, the interdisciplinary cross-fertilization between risk management, strategic management, and marketing can be advanced. Moreover, the perspective presented in this paper is inherently conceptual and, thus, we need additional empirical studies to solidify the verisimilitude of the logic. While ample illustrative cases are available, several of which have been presented in this paper, more systematic empirical evidence is required to further advance the ideas expressed in this paper. Such evidence could allow for empirical comparisons of the market-shaping potential of different types of crises (as mentioned in Section 2.1 and illustrated in Fig. 1), the market-shaping success of firms pursuing a resilience or responsiveness approach during a crisis, and differences among industries. Empirical research could also clarify the boundary conditions of market shaping in times of crisis, especially given different kinds of crises. More specifically, more research is needed to establish exactly how market-shaping behavior may differ according to the type of crisis. For example, the time dimension is essential for understanding a crisis but may also be important for understanding differences in market shaping. Simultaneously considering the time dimensions of a crisis and market shaping may prove to be an interesting avenue for future research. In addition, as suggested by Nenonen and Storbacka (2020), executives must decide whether to be leaders or supporters in market shaping. We suggest extending this idea to include active followers (i.e., those who become aware of shaping activities by actively engaging with the questions in Fig. 2) and passive followers (i.e., those surprised by newly shaped markets). Such an extension would be particularly relevant in relation to a crisis in which the market is deemed more dynamic and malleable (Nenonen & Storbacka, 2020), such that a stronger distinction can be made between those who actively follow and those who passively follow the shaping of a market. Relevant issues for consideration include whether there are situations in which it would be positive to be a passive follower, as well as the role of government intervention in terms of choosing crisis-management options and deciding to be market-shaping leaders, supporters, or followers. We lack a general understanding of which strategies should be chosen to ensure and improve upon corporate success. We are in a similar situation with regard to active followers in an organization's wider ecosystem and network. In the literature, leaders are often assumed to be suppliers that shape markets. However, active following includes situations in which markets are shaped by actors other than competitors (e.g., customers, governments, or societies through norms and ethics). Are certain market shapers easier to follow? Can an organization actively choose to let other actors shape a market in a way that will be beneficial for the follower? Do different crisis-management options predispose firms to be either active or passive followers? While our focus in this paper is on market shaping undertaken by suppliers facing a crisis, we believe that similar ideas are relevant for non-market strategies, which include firms' interactions with governments and society. In fact, some of the suggested activities already target non-market actors because they take a wider network and ecosystem view. Future research may therefore disentangle market and non-market strategies and analyze their interconnectedness. Based on the considerations in this paper, firms may engage in proactive behavior before a crisis emerges in order to increase their preparedness (Pedersen et al., 2020). In other words, they can build capabilities for crisis handling prior to a crisis. In such a situation, the market-shaping potential of a crisis is preempted, as the market is shaped based on crisis management but that shaping occurs before the execution of such crisis management. While this perspective is valid, we have deliberately refrained from incorporating preparedness into our discussion in order to maintain parsimony. However, future studies should establish the role of preparedness in market shaping. In summary, this paper contributes to our understanding of market shaping by integrating it with insights from crisis management. We derive a conceptual model of four crisis-management options, and we argue that these reactive options carry market-shaping potential. As such, we develop a novel overview of crisis-management dimensions that suggests opportunities for market shaping. 1 https://www.jfklibrary.org/archives/other-resources/john-f-kennedy-speeches/indianapolis-in-19590412 2 While often described as a subdiscipline in finance, risk management is increasingly viewed as both a practical discipline and an academic field of study, as suggested by designated journals such as Risk Analysis and Risk Management. 3 We acknowledge that the strategic-management literature is divided on the conceptual boundaries and relations of these constructs. We therefore refrain from discussing a taxonomy of their mutual relations. 4 We are thankful to an anonymous reviewer for raising these points. 5 While aspects of this materialization may initially seem minor, they represent substantial changes in practice (e.g., changing norms), and they mirror the rationale of Nenonen, Storbacka, and Windahl (2019). ==== Refs References Adner R. 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Increasing resilience by creating an adaptive salesforce Industrial Marketing Management 88 2020 238 246 Sheffi Y. Rice J.B.J. A supply chain view of the resilient enterprise MIT Sloan Management Review2 47 1 2005 41 48 Sneader K. Singhal S. From thinking about the next normal to making it work: What to stop, start, and accelerate McKinsey Digital Article 15 2020 (May 202) Swedberg R. Theorizing in sociology and social science: Turning to the context of discovery Theory and Society 41 1 2012 1 40 Teece D.J. Pisano G. Shuen A. David Teece M.J. Dynamic capabilities and strategic management Strategic Management Journal 187 18 1997 509 533 Teece D.J. Raspin P.G. Cox D.R. Plotting strategy in a dynamic world MIT Sloan Management Review 08 September 2020 Ulkuniemi P. Araujo L. Tähtinen J. Purchasing as market-shaping: The case of component-based software engineering Industrial Marketing Management 44 2015 54 62 Volberda H.W. 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==== Front Futures Futures Futures 0016-3287 0016-3287 The Author(s). Published by Elsevier Ltd. S0016-3287(21)00132-4 10.1016/j.futures.2021.102822 102822 Article Engagements with uncertain futures – Analysing survivalist preparedness Parkkinen Marjukka Finland Futures Research Centre, University of Turku, Turku School of Economics, 20400, Turku, Finland 13 8 2021 10 2021 13 8 2021 133 102822102822 30 6 2020 21 3 2021 7 8 2021 © 2021 The Author(s) 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Much like any grand-scale social disturbance, COVID-19 has brought increasing attention to emergency preparedness on global, national, and individual levels. Although independent preparedness is considered to play an important role in disturbance and emergency-related survival, little attention has been paid to the ways in which individuals conduct their preparations or the social aspects of these preparatory activities. This article investigates the ways in which survivalists engage with uncertain futures by preparing. Survivalism is a cultural movement that anticipates and prepares for different kinds and scales of disasters and disturbances. The analysis seeks to clarify for what, how, and why Finnish survivalists prepare for. This study employs an experimental setup that combines two different types of media material; media representations about survivalists made by journalists, and online discussions written by survivalists themselves on a Finnish survivalist web forum page. The data was subjected to qualitative thematic text analysis. The factors that motivate preparedness vary from ordinary and personal inconveniences to general and global disaster scenarios, which co-exist despite their different scales. In practice, the survivalist approach continuously moves between speculative futures and the material present. The survivalist worldview balances between different, seemingly opposite poles: norm-criticality, self-sufficiency, privacy, and collectivism. Keywords Preparedness Survivalism Uncertainty ==== Body pmc1 Introduction Much like any grand-scale social disturbance, COVID-19 has brought increasing attention to emergency preparedness on global, national, and individual levels. The ensuing discussions have focused on, for example, surviving the crisis and, retrospectively, the inadequate level of preparedness. For citizens, the emergence of the pandemic in spring 2020 was a wake-up call to many in thinking about their own level of preparedness. Although independent preparedness is considered to play an important role in disturbance and emergency-related survival, little attention has been paid to the ways in which individuals conduct their preparations or the social aspects of these preparatory activities (Donahue, Eckel, & Wilson, 2014; Laurikainen, 2016). Studies have shown that individuals overestimate their preparedness, have poor understanding on possible risks, and consequently are not sufficiently prepared for catastrophes (Donahue et al., 2014). For these reasons, a more concise understanding of individual preparedness is of pivotal importance for surviving the hardships, and ultimately, viability. This article brings to focus the preparedness of a specific group of individuals: Finnish survivalists. Survivalism, which is also referred to as preparedness, is a loose cultural movement that anticipates and prepares for different kinds and scales of disasters and disturbances. As a group of people focused particularly on the unexpected hazards, survivalists or preppers provide an entry point to study the ways to engage with futures during a time when preparing for the forthcoming has become increasingly topical. Despite being a highly visible topic in popular culture – movies, fictional literature, TV series, and reality shows – survivalism is an understudied topic in academic context, particularly outside Northern America. Barker (2019) suggests that prepper culture has been subject to both “media ridicule and academic dismissal” (for media ridicule see also Campbell, Sinclair, & Browne, 2019). Survivalism has been described as a challenging research topic due to the informality and fluidity of the actions that preppers take (Mitchell, 2002). Furthermore, the concept has some unwanted ideological connotations related to it (see Lamy, 1996; 1997; Rantanen, 2015), which may have an impact on the popular perception of the culture. The objective of this article is to shed empirical understanding on the understudied topic of contemporary non-American survivalism, and to investigate how preppers cope in times of uncertainty. What is investigated are the ways in which survivalists and preppers engage with uncertain futures by preparing. Specifically, the analysis seeks to clarify i) for what, ii) how, and iii) why are survivalists preparing for. The first part of the analysis concerns the outspoken, explicit threats, for which Finnish survivalists prepare for. The second part addresses the actions through which these threats are prepared for. The third part focuses on the deeper, partly explicit yet partly implicit worldviews, values, and ideologies which guide survivalist preparedness. These three levels of analysis account for survivalist engagement with futures. This study employs an experimental setup that combines two different types of media material: 1) media representations about survivalist and preppers made by journalists, and 2) online discussions written by preppers themselves on a Finnish survivalist web forum page. The data has been gathered in 2010–2017, and is analysed with qualitative thematic text analysis. The remainder of the article proceeds in the following manner. Section 2 examines prior work on preparedness and survivalism. The research data and the methodological approach are elaborated in Section 3. The results of the three-level analysis are summarised in Section 4. In Section 5, the results are discussed as a survivalist engagement with uncertain futures. 2 Survivalism and preparedness Clarification on the use of the terms ‘survivalism’ and ‘preparedness’ in this article is required before the literature review on the concepts. Indeed, some of the people presented in the media texts that are analysed in this article, call themselves ‘preppers’ instead of ‘survivalists’. The concept of ‘preparedness’ is often favoured over ‘survivalism’ in Finnish public discourse, since survivalism is still considered to refer to Northern American contexts and practices that commenced after the Cold War (Karosto & Karppinen, 2011). In this article, ‘survivalism’ (alternatively, ‘prepping’) is the culture studied; ‘survivalist’ (alternatively, ‘prepper’) is used synonymously of the “members” of this culture, and ‘preparedness’ refers to their actions and approach to futures. 2.1 Preparedness Whereas preparedness is an essential part of survivalism, preparedness refers to a wider set of actions utilised in several other contexts than by survivalists only; by nation states, institutions, or other individuals. In the United Nations Office for Disaster Risk Reduction terminology, preparedness is defined as “[T]he knowledge and capacities developed by governments, response and recovery organizations, communities and individuals to effectively anticipate, respond to and recover from the impacts of likely, imminent or current disasters” (UNDDR, 2020). Independent preparedness or self-preparedness refer to the activities conducted by individuals or communities. Simply put, preparedness means readiness for an approaching event or conditions. Importantly, it includes the idea of being organised and obtaining the required resources to meet the circumstances (Donahue et al., 2014). For Anderson (2010), preparedness as a logic of legitimising anticipatory action has been formalised after 9/11, as a response to different potentially disastrous large-scale threats for liberal-democratic life. Although preparedness occurs in the here and now, there are different conceptualisations on its temporal ‘target’. For Lakoff (2007), preparedness is a form of security rationality, which offers security experts tools to encounter the uncertain, non-calculable threats with disastrous potential by intervening in the present. Preparedness is necessary in order to approach future events that are neither probabilistic nor necessarily calculable, but could prove to be disastrous (ibid.). Despite the emphasis on action, risk perception and mitigation are also in important role for preparedness (Donahue et al., 2014). Ben Anderson conceptualises preparedness a specific logic through which futures are acted on in the present moment. Action based on preparedness is targeted at mitigating the aftermath of an event. (Anderson, 2010). The opposite stance to preparedness for Lakoff (2007) is insurance, and for Anderson (2010) preemption and precaution. 2.2 Survivalism and prepping Philip Lamy describes survivalism as a “loosely structured yet pervasive belief system and set of practices focusing on disaster preparedness” (Lamy 1996, 14). Most survivalists believe that the social, economic, ecological and industrial world is in trouble and will collapse, and the anticipated man-made or natural disasters are answered with necessary practices, identified well before the cataclysm (Lamy, 1996; Wojcik, 1999). Even though their reasons for taking action may differ, what unites preppers is their anticipation of different possible futures and the preparation for encountering them (Mitchell, 2002). Indeed, the survivalist focus moves between the present and the forthcoming. Barker (2019) defines prepping to consist of three active, ongoing ‘imaginative-material’ practices, which concern value, a multiplicity of temporalities, and crisis. Survivalists turn different speculated scenarios into practice by exercising the skills and knowledge they consider necessary for survival. Next to definitive grand disasters such as end-of-the-world scenarios (Rahm, 2013) and nuclear disaster (yes, even after the Cold War) (Wojcik, 1999), non-apocalyptic and temporary threats are imagined (Mills, 2019). Prepping often manifests in the collection of items that are necessary for one’s defence, safety, relocation, or nutrition as well as in the improvement of social, mental and physical skills that are vital to one’s survival (Rahm, 2013; Wojcik, 1999). Escaping from society and self-sufficiency are oftentimes at the core of survivalist tactics (Lamy, 1996). In general, appropriate preparing helps survivalists to endure even the most serious disasters (Wojcik, 1999). In a western context, the origins of survivalism are often connected to the United States and the geopolitical tensions that prevailed there during the Cold War. The most recent influential event that has increased the interest towards preparedness — especially in the U.S.A. — occurred immediately after the 9/11 attacks in September 2011, after a quiet era. Nevertheless, the election of Donald Trump as the President of the United States had an impact as well: over 13 000 Americans were reported to have registered with immigrant authorities to seek residency in New Zealand immediately after Trump’s election (Osnos, 2017). The present-day survivalist movement and disaster-prepping phenomenon in the United States have been said to resemble a lifestyle movement (Kabel and Chmidling, 2014). Despite attracting Caucasian men “of conservative or independent political affiliations” (Wojcik, 1999), survivalism does not represent a cohesive, easily categorised group or subculture (Rantanen, 2015; Wojcik, 1999). Survivalism has been described as a contradictory project that both rebels against prevailing societal norms and reproduces and reinforces the status quo (Rantanen, 2015). It is both a response to and a consequence of modern times and rationalisation. Survivalists do not reject the social order, but desire something different – for example, creativity instead of control. (Mitchell, 2002.) Anonymity and secrecy are seen as critical to ensure one’s safety, which challenges any efforts to successfully estimate how widespread the culture is (Wojcik, 1999). Most of the academic studies have been conducted in the USA (Kabel & Chmidling, 2014; Lamy, 1996; Mills, 2019; Mitchell, 2002; Rantanen, 2015), with the few recent exceptions of prepper cultures e.g. in Europe (Campbell et al., 2019), UK (Barker, 2019); Sweden (Rahm, 2013); and Finland (thesis by Takkinen, 2020). Due to the prevalence of prepper culture in popular culture, there have been several studies on the media representations on the topic (Foster, 2014; Kelly, 2016). 3 Material and methods The privacy of many survivalists poses a challenge to analytical efforts. Some studies have successfully examined survivalists and preppers with ethnographic approach (Mitchell, 2002), and by gathering data with interviews (Barker, 2019; Takkinen, 2020). Despite the importance of online activity for survivalists, their virtual communication has not been widely investigated in academic literature. The few exceptions are Rahm (2013) study on Swedish Survivalist Forum and the netnographic study on European preppers by Campbell et al. (2019). In this study, the data was gathered from two different types open online sources. All items are listed in Table 1 . First, nine mainstream media texts, published between 2010–2017, were gathered from public media sources (items 1–9 in Table 1). These texts and audiovisual products were created by journalists, reporters, and a documentary maker for the public audience. The searches were first conducted within two large national media sources (Helsingin Sanomat, Finland’s largest daily newspaper, and YLE, the national public broadcasting company of Finland), followed by an additional data collection process using Google. All audiovisual items focusing primarily on Finnish preppers or survivalists were included in the sample. The low number of results shows that survivalism has remained a marginal topic in mainstream media.Table 1 Research data. Table 1Item nr. Source media Title of the text (translated by author) Type Content Reference 1 YLE – The Finnish Broadcasting Company What does a Finnish survivalist prepare for? Radio interview at transcribed as text by the author Interview of Pasi Karosto on survivalism and preparedness. Häkkinen, 2013 2 YLE – The Finnish Broadcasting Company Warrior of the collapse tribe Documentary television film transcribed as text by the author Miika Vanhapiha prepares for The End of the World as We Know It. Richt, 2016 3 YLE – The Finnish Broadcasting Company Survivalists prepare for everything Report Pasi Karosto discusses survivalism. Niiranen, 2010 4 YLE – The Finnish Broadcasting Company Finnish survivalist does not build a bunker Report Pasi Karosto discusses survivalism. Toivanen, 2013 5 Ylioppilaslehti (student magazine) May the end of the world come Report Pasi Karosto and moniker Metsäsusi discuss survivalism. References to survivalismi.com forum. Hallamaa, 2012 6 Aamulehti (daily newspaper) ”This is how I became a witch” – Miika Vanhapiha sees visions and gathers a collapse tribe in Forssa Report Miika Vanhapiha discusses collapse and witchcraft. Karhunkorpi, 2017 7 Maaseudun tulevaisuus (daily newspaper) Survivalist reserves food for a couple of months in home Report Pasi Karosto discusses home emergency supply. Koljonen, 2011 8 Helsingin Sanomat (daily newspaper) Markku Teräs has learned to hunt rabbits with a bow and to make lanterns out of pig bladder – survivalists prepare for the day when society collapses Report Markku Teräs and Vesa-Pekka Rantalainen discuss emergency preparedness Nieminen, 2017 9 VOIMA (free magazine) Survivalist wants to live! Report Several survivalismi.com forum users interviewed about survivalism. Vähähyyppä, 2010 10 Survivalismi.com web page Survivalismi.com Web page A list of seven categories, 52 sub-categories and 1022 discussion topics S.com 2017–2017e 11 Survivalismi.com web page How you prepped today Forum topic 484 posts S.com 2017–2017e 12 Survivalismi.com web page Personal SHTFa Forum topic 14 posts S.com 2017–2017e 13 Survivalismi.com web page Highlights of 2016 Forum topic 18 posts S.com 2017–2017e 14 Survivalismi.com web page Is survivalism out of fashion? Forum topic 50 posts S.com 2017–2017e 15 Survivalismi.com web page Survivalism and spouses and other close ones Forum topic 38 posts S.com 2017–2017e a “SHTF” is survivalist slang and stands for Shit Hits The Fan. It is used to refer to catastrophic events. Secondly, six online discussion text items were gathered from an online discussion forum for Finnish survivalists, survivalismi.com (items 10–15 in Table 1): the index of the website and five discussion forum topics. Each topic consists of messages submitted by online forum members who identify as survivalists or preppers or, alternatively, wish to discuss survivalism and preparedness within this online community. Due to the vast amount of material available on the website, filtering was made according to an initial analysis of what would best describe the survivalist engagement with futures. From the analyst, this required active use of the forum. This approach resembles digital ethnographic orientation, which aims to “develop an understanding from the inside” and observing a culture from a very close proximity (Hine 2015, 19). As a result, a purposive sample was gathered. Out of 1022 available topics, five were selected for further analysis. These five topics included 604 individual posts, which are pieces of text written by the members of the forum, identifiable only by their chosen nicknames. The length of each post varies from a few words to hundreds of words. The gathered material was openly available for everyone to see without registering to the website. Although media is the common denominator of the chosen material, these items can be categorised into two types according to each item’s origin, nature, and authorship. The mainstream media texts on survivalists and preppers have been made by reporters for the public, and the online forum texts are written by survivalists primarily for their own community. The online research setting in this article has some implications that are necessary to keep in mind. Firstly, studying the communication of a group of anonymous participants online sets challenges to the interpretation of the data, as the analyst may not understand the specific tones or meanings shared by the community. In order to increase the reliability of the study, items from mainstream media were collected for a more ‘official’ viewpoint on survivalism. Analysing web forum discussions only would not have provided a sufficient understanding on the topic. At the same time, only a few persons in Finland are willing to discuss their survivalist practices in public. Thus, mainstream media texts alone were not deemed to fully represent the diversity of Finnish survivalism. The online forum texts – written by the survivalists themselves, for the prepper community – were gathered to provide a plurality of viewpoints on the discreet prepper culture and to go beyond the ‘official’ description of the culture. Despite the differences between the two types of data, the topics they discuss are mostly the same ones. The main difference is in the plurality of viewpoints, as the online web forum offers an uncensored environment for multiple voices. Although interaction between the analyst and the preppers could have occured in the online environment, the analyst did not engage in the online web forum. Thus, there has been no change to ask elaborating questions from those studied. The results of this study cannot be read as a purely objective ‘truth’ of Finnish survivalism. One of the main advantages in studying survivalism online is that a digital platform offers an entrypoint to the social side of the culture. It would be challenging, if not impossible, to access the preppers as a community offline due to the discreetness of their movement. The study aligns partly with what Hine (2015) calls unobtrusive digital etnography: a non-reactive way of drawing on data that can be found. The approach suits to situations where acquiring authentic responses or observing does not work (ibid.). Accessing the particular social relations of survivalists would not been possible were it not the web forum. The analysis process utilised qualitative thematic text analysis, with all data analysed as one unit. In qualitative thematic text analysis, the construction of analysis categories can be formed either inductively from the data, or deductively, on the basis of a theory (Kuckartz, 2014). In this study, the analysis was data-based, making the approach inductive and iterative. One of the most important characteristics of qualitative content analysis is the establishment of analysis categories that are sufficiently precise (Silverman, 2001). The process of categorising and coding the material was multi-staged, which is typical of the thematic qualitative text analysis process (Kuckartz, 2014). In practice, after the first readings, the three levels of the analysis together with analysis questions were formulated. These three levels provide were considered to provide an account on survivalist engagement with futures by responding to what for (Section 4.1), how (Section 4.2), and why (Section 4.3) survivalists prepare. Next, the first coding process took place, and the data was assigned in the form of text passages to the three main categories in NVivo, a qualitative data analysis program. These contents were differentiated by creating sub-categories from the data, in accordance with any emerging differences. Next, the data was explored thoroughly, and text passages within each of the main categories were assigned to and analysed under sub-categories. 4 Survivalist preparedness in Finland 4.1 The threats Contrary to the stereotypical image of a survivalist who is armed from head to toe, actual survivalists consider their ordinary life as fundamentally important to their survival. Everyday, small-scale factors are matters of preparedness to survivalists. Overall, it is considered worthless to prepare for global issues if one is not prepared for the everyday coincidences. As one person in the forum notes,“It is more worthwhile to prepare for ordinary SHTF scenarios than any unlikely threats. Many like to prepare for every type of nuclear war, but then ignore their everyday matters completely.” The acronym SHTF (‘Shit Hits The Fan’) is survivalist slang that is used most commonly as a reference to large-scale catastrophic events. One of the discussion topics in the forum is called “Personal SHTF”. The posts in this topic imagine long-term disturbances to one’s accustomed way of living. One example of a personal-scale disaster is divorce, as one user explains:“My wife found another man and moved in with him. I was prepared for everything, even a zombie invasion, but this came completely out of the blue. No warnings, no signs whatsoever. An excellent example of how a situation can turn completely in a flash. In no way was I prepared for this.” Next to the sorrow caused by the loss of one’s companion and relationship, divorce also causes economic disturbances. Barker (2019) points out the prevalence of personal crises within UK preppers as an aftermath of precarity and increasingly uneven resources. Another forum member imagines a scenario where they do not have access to their current resources. Instead of an apocalypse, the imminent threats to the person are homelessness, unemployment, and a dependency on social welfare:”I prepare for situations where I could lose my apartment, as a person like me with no credit rating would immediately be in trouble. […] So, if you’re afraid of the world ending or something like that, and you fill your closet with gas masks, I, on the other hand, could very well end up on the street one day, like a hobo without booze.” The same forum member later explains how they practise surviving without an apartment. Yet, they try to do it in a way that does not make their prepping “a self-fulfilling prophecy”, in which they would end up homeless. Despite the frequent references to Finnish survivalism as an ordinary practice, a variety of political, economic, environmental, and social threats that go beyond one’s home and backyard are present in the data. These events are neither fixed nor known in advance, necessitating ongoing speculation. In general, everyone defines their own scenarios and required steps themselves. In the forum, topics such as the situation in North Korea or Finnish immigration policies are speculated. Overall, the unknown something that could occur is believed to originate from beyond Finland’s borders. According to a forum member, these threats are becoming too complex for an individual to encounter. While a few years ago people were interested in the ways in which one could survive with scarce economic resources or in a barter economy following an economic collapse, the reasons for preparing are currently becoming too varied and nearly impossible to respond to:”If you look at an updated list of threats and compare it to the earlier one, an ordinary individual survivalist can prepare for an economic collapse, but not necessarily for these current threats.” In survivalist slang, TEOTWAWKI stands for ‘The End Of The World As We Know It’, or the most severe threat imaginable. It emerges from global threats and escalates into uncontrollable measures. Interestingly, the emblematic threat in American survivalism – nuclear war – still serves as a reference point in Finnish discussions, despite the differences between these two cultures. One forum member explains that preparing for a nuclear explosion is a good idea, since, post-disaster, preppers can isolate themselves from the now-dangerous environment. Thus, assessing what is really needed in such a situation is a good exercise in general, and it includes another key advantage:”There is, however, one special factor when it comes to protecting oneself from a nuclear explosion. It is hurry. Specifically, the hurry to a shelter. The element of time, one’s reaction speed, is maybe the factor that divides the population into survivors and perishers.” Elevating the nuclear disaster to an iconic status illustrates the survivalist approach to threats well. It does not necessarily need to happen, but it serves as a reference point for any preparations related to encounters with large-scale hardships. In TEOTWAWKI scenarios, the threats themselves are not as important as the bleak futures that will follow them in the aftermath. In conclusion, the threats from which one wishes to survive come in different shapes and sizes, and they vary from the mundane and personal to the general and global. Interestingly, one can expect to survive only if one’s ordinary life is in order. Leaving the coffee maker on when you leave home can disturb the success of your daily life, but it does not prevent you from speculating the events of a worldwide societal collapse. 4.2 The actions In their guidebook on Finnish survivalism, Karosto and Karppinen suggest that survivalists aim to address threatening situations in advance by creating risk analyses and conducting the necessary preparations for these future scenarios. When an anticipated event takes place, their chances of survival – by following these pre-imagined procedures and using the acquired skills – have become more probable. (Karosto & Karppinen, 2011). Throughout the data, the Finnish approach is distinguished from the popular and stereotypical images of American survivalist culture. The ordinariness of Finnish preparedness is emphasised through regular references to the importance of keeping the everyday safe. Pasi Karosto, himself a practicing survivalist, describes his routine in the following way:“First, you think about how your day will go, from morning to evening. From the moment you wake up, think about where you will step after you get up from bed. Did the kids leave their toys on the floor, could you accidentally step on them and hurt yourself? […] When you leave home, check that you took your keys. Did you remember to switch off the coffee maker? And what about the yard, are the lights on? Is it sanded? Is it slippery?” (Häkkinen, 2013) Yet, there is a need to speculate further by acquiring knowledge on and evaluating possibilities for more severe hardships. One online forum member explains how survivalists tend to observe their surroundings constantly and stay up-to-date with the news:”I aim to keep up with domestic as well as foreign happenings every day. Particularly when there’s a disaster taking place in some part of the world, I follow the news a lot so that I can contemplate its possible effects here, in my home country.” (Vähähyyppä, 2010) After speculating and choosing their scenarios, the survivalist will prepare by acquiring the necessary material resources, skills, and knowhow for each scenario. Each item is imagined and selected for a specific purpose: a car that can navigate challenging terrain, a storage room with enough space, and the right shoes for specific weather conditions. Next to a large variety of skills from growing your own food, tying knots, and identifying plants, everyday actions, such as visits to the dentist, hemming a pair of pants, doing your laundry, or using a standing desk, are considered methods of preparation. An understanding of economic systems – value added tax, accounting, and the marginal tax rate – is of the essence, as better finances provide access to better resources. The idea of a future that is different from the present guides the practical preparedness in the present. Thus, consumption deviates from the mainstream: inadequate standard solutions are often modified to better meet one’s specific purposes. For example, one forum member explains how they have modified a refrigerator into a dehydrator, which works better than any solution available in stores. Next to this kind of ‘object potentiality’ of imagining it in different circumstances (see Barker, 2019), data features notions of ‘skill potentiality’. Traditional practices and, for example, farming tools are considered viable options, as modern tools and equipment can fail in a power shortage. Radical disruptions exert a fundamental influence on the skills that are required. For example, being able to light a fire using only a stone is considered important, since once production stops and resources run out, there is no use for a bunker if one cannot cope without any matches. (Nieminen, 2017). Survivalists hone their skills and test their materials in good time by what can considered as simulation of futures. Sleeping outside in cold weather, practising crisis relocation, or starting a fire in challenging conditions is easier in the present rather than during an actual emergency. The ability to cope with surprises is crucial for survival and can be rehearsed by practising the speculated scenarios in the material present, as one forum member explains:“I alter my routines. I’ve driven to neighbouring cities for groceries and searched for new petrol stations to fill up my tank. And, naturally, I’ve looked for new driving routes to these locations. This is because if scenario X actually happens, the ensuing hoarding could result in scarce resources or petrol running out at a specific location.” 4.3 The worldviews and ideologies Several ideologies co-exist in the data, which is most likely a result of the heterogeneous nature of survivalism. The four main ideological discourses emerging from the data concern relationships between survivalists and others, others being people and institutions. 4.3.1 Norm-criticality The survivalist worldview is critical and even dissident towards the official policies and state. Overall, the mainstream is abandoned as inadequate. For instance, survivalists consider official recommendations on e.g. home emergency supplies insufficient for survival. When asked if he considers the national emergency supply to be sufficient and up to date, the survivalist Karosto hesitates:”I am not exactly aware of its level, but we are always told that everything is ok. Nevertheless, I do not think that it is enough. Each individual should be personally responsible for themselves, as you cannot always trust the state or society to come and help on time if something does go wrong.” (Häkkinen, 2013) Rahm (2013) states that survivalists deem any business-as-usual approaches as insufficient, and concentrate instead on “speculative prophesying”, positioning survivalism as a norm-critical way of organising their everyday lives. Norm-criticality is indeed considered fundamental to survival. Despite the high level of trust that Finns place in the authorities in times of emergency (Laurikainen, 2016), distrust towards authorities in disasters is frequently mentioned in the data. One forum member feels that people suffer from a dangerous feeling of safety, specifying that they have not forgotten how things have descended into chaos before:”The more I read history, the more I look after my own supply of canned food.” Kabel & Chmidling (2014) suggest that some of the core ideologies within the prepper movement include a mistrust towards the federal government as well as a responsibility to provide for oneself and one’s family members in a disaster, as any governmental services will fail to do so. In prepper cultures, dependency is often seen as unjustified belief in a system (Campbell et al., 2019). Although Karosto denies that the idea of a state of anarchy guides his preparedness, he admits that there is truth to the saying that ‘we are nine meals from anarchy’ (Hallamaa, 2012). The prepper Miika Vanhapiha refers to the possibility of violence when discussing how a collapsed tribe may choose to defend itself:”If things escalate and we are faced with fundamental questions such as life or death, then people have the right to struggle for their survival.” (Richt, 2016) This is consistent with the previously drawn conclusion that the survivalist approach – convinced as it is of the forthcoming collapse – does not aim to reform the system (Lamy, 1996). With their own shadow infrastructure, they challenge capabilities of the networked infrastructure (Barker, 2019). Tierney (2013) suggests that resilience arises from and exists within social order. Thus, the imminent element for preventing future disasters is a better understanding of the social forces that produce them, and making communities and societies more resistant to disastrous events. Importantly, survivalists take the matter into their own hands. Indeed, Barker asks, whether preppers can challenge neoliberal processes. According to the results, survivalism does not aim to do so. Since the collapse cannot be prevented by those in power, it can only be survived by some. 4.3.2 Self-sufficiency One frequently addressed tactic for distancing oneself from the system is self-sufficiency. Picking and preserving berries and mushrooms as well as hunting are popular topics when people discuss daily preparedness. By imagining a future where basic metabolistic needs, such as food, shelter, and water are provided by a shadow economy instead of officials, preppers expose the infrastructural weakening and possible future failure of the state (Barker, 2019). One forum member states that their goal is to be as independent from the rest of society as possible. Blacksmith Markku Teräs, who prepares for crises – substantiates the point clearly by noting that self-sufficiency is pivotal for long-term preparedness and that good equipment will not guarantee one’s survival forever:”A bunker is a temporary comfort […] Sooner or later, it will run out of food if you cannot replace your supplies.” (Nieminen, 2017) The possibility of achieving self-sufficiency is considered to be better in the countryside. Urban lifestyles are seen as overly comfortable, numbing city dwellers into an illusion that everything is fine. In one article, an anonymous prepper paints a grim picture in which the lonely suburban survivor will reap their post-apocalyptic “reward”:”If you can manage to survive for around two years (and two cold winters) with your own supplies, you can finally crawl out of your ‘bunker’ into a bunch of riches. After society stops and every suburbanite has been wiped out by cold and hunger, you can expect a massive inheritance.” (Hallamaa, 2012) In an ideal case, the “post-industrial culture based on fast-paced consumption” is abandoned, as Vanhapiha suggests (Karhunkorpi, 2017). Yet, a complete escape beyond the prevailing capitalist economic system and culture of consumption is difficult. Preparedness, as illustrated earlier in this analysis, strongly revolves around supplies and equipment, which are more or less produced by the very system that self-sufficiency seeks to abandon. 4.3.3 Privacy The journalist Nieminen (2017) describes Finnish survivalism as akin to “[…]Finnish sorrow. Small-scale and ordinary”. Another attribute that suits the stereotypical image of Finnish sorrow is hiddenness, which also applies to survivalism as a privacy-oriented movement. According to Vähähyyppä (2010), one of the reasons for the discreet nature of Finnish survivalists is the perceptions of others: “[o]ne keeps quiet about their survivalist tendencies, since others easily consider them weird.” These perceptions partly stem from the representations of American survivalists. In one article, the associations between survivalism and far-right movements in the United States are estimated as the main reasons for wanting to remain anonymous (Hallamaa, 2012). However, secrecy also serves as a survival tactic:”An additional reason is that, in the event of a societal collapse, a survivalist who is prepared for it will not want a pack of beggars at their doorstep.” (Vähähyyppä, 2010) 4.3.4 Collectivism Yet, somewhat paradoxically to the concepts of self-sufficiency and privacy, collectivism plays an important role in Finnish survivalism. Social skills and goodwill are considered highly beneficial to one’s future endeavours. One forum member prepares by taking care of their neighbour’s cat. Conservatively, family is seen as a unit that provides its members with intergenerational support. Miika Vanhapiha takes this idea further: his communal unit of choice is a “collapse tribe”. He questions the idea of the mythical lone-wolf prepper:”I think that survivors are people who are social, who can cope with other people, get help and also offer help and who can network in different ways. So, in my opinion, the most important resource when it comes to self-sufficiency/collapse-oriented thinking is social capital.” (Richt, 2016) For many survivalist Finns, supporting one’s peers also increases one’s own chances of survival. Although there are notes towards suspicion towards others, the data is partly in contrast with the results of European preppers from Campbell et al. (2019), which emphasise preppers as against group ethics and not sharing advice. An example from an anonymous survivalist illustrates this difference:”I don’t lose anything if share what I know. If I can help someone survive on their own, that person will not be as big of a burden to rescue workers in a possible disaster situation.” (Vähähyyppä, 2010) Considering the norm-criticality and social ideologies of preppers, it can be suggested that a chosen community replaces society as the de facto unit of security and survival. However, survivalism is not an ideology of inclusivity, but rather a balance between selective collectivism and complete privacy. Barker (2019) refers to exclusionary prepper gaze, which divides people into the few survivors and the many who will not have the readiness to live through the upcoming disasters. 5 Discussion The results of the previous Section prove that survivalists assume multiple futures with different temporalities simultaneously: both the grand and mundane ones. Survivalists independently define the threats they prepare for. Gone are the days when preparations addressed a single threat. On the contrary, the end of the world scenarios co-exist with personal crises, such as homelessness or divorce. One reporter writes how a forum member “[…]prepares for a disaster that can be anything between a lay-off and a nuclear disaster” (Vähähyyppä, 2010). Norm-criticality sets survivalists free from the official futures. Instead, the future is seen as an empty canvas that is filled with carefully speculated components concerning the future and translated into the current moment as different types of action. For Barker (2019), preppers challenge the neoliberal privatisation of the imagination and production of secure futures. The survivalist engages with the multiple futures through speculation, by choosing and preparing without any limitations regarding the types of futures that may be considered. These encounters simultaneously encompass climate change and coffee makers, terrorist attacks and divorce. It is exactly the focus on the everyday that challenges the media representations of single-future focused, apocalypse-oriented survivalists. Instead, the focus expands in all directions, and everything can be connected to everything. When asked whether Finnish survivalism is free from any ideology, Karosto states that:”I would say that we have more of a blank canvas to work with. We can define the threats that we want to prepare for by ourselves.” Importantly, only what is necessary deserves focus. Thus, all survivalist engagements with the future are situated in between speculation and action, a phenomenon that is well-portrayed in the free and anonymous space provided by a web forum. Rahm (2013) characterises online survivalist discussions as a “play with alternative futures”, which is enabled and limited by the real-life capacities of the survivalists, making these discussions a “hypothetical justification for their current way of life”. The different temporalities of survivalist futures can thus be summarised by utilising Amara (1981) classification of different kinds of futures: possible (can be), probable (may be) or preferable (ought to be). For survivalists, a very wide range of challenging futures is possible. Yet, for those who are sufficiently prepared, the future after a hardship is still a possible one, unlike for those who have closed their eyes from risks. The alternative futures, imagined through threats, resonate with the concept of counterfactuals, which concerns the basic human capacity to detach oneself from the present and imagine reality – past or future – in a way that is different from now (Black, 2015). Although the threatening futures can be prepared to, they are unavoidable. The results support Anderson’s notion on preparedness as anticipatory action mitigating the aftermath of a disaster in order for life to carry on, instead of attempting to prevent the event in the first place (Anderson, 2010). Despite their critical attitude towards the mainstream, survivalists concentrate less on how to avoid or prevent disasters and more on surviving them, once they have occurred. According to Vanhapiha, the future indeed has an “expiration date”:”I find it hard to believe that these developments that are leading to a dramatic change or collapse could somehow be stopped or altered completely.” (Richt, 2016) Preparedness emerges in times of uncertainty, and discourses of the future as uncertain underpin the necessity of anticipatory action (Anderson, 2010). The threats in the future will most likely and unfortunately not cease to surprise us. Thus, survivalism provides an excellent entry point for analysing engagements with uncertainty. For survivalists, the future to be encountered is omnipresent, here and now, as this forum quote illustrates:”You see that basically anything can happen at any time. When something is too improbable, you can spend year after year saying that you just ‘don’t believe it’ or that ‘I’ll do it tomorrow’, and then it suddenly happens. And by then it may be too late.” Declaration of Competing Interest The authors report no declarations of interest. ==== Refs References Amara R. The futures field: Searching for definitions and boundaries The Futurist 15 1 1981 25 29 Anderson B. Preemption, precaution, preparedness: Anticipatory action and future geographies Progress in Human Geography 34 6 2010 777 798 Barker K. How to survive the end of the future: Preppers, pathology, and the everyday crisis of insecurity Transactions of the Institute of British Geographers 45 2019 483 496 32612296 Black J. Other pasts, different presents, alternative futures Retrieved 31 July 2018 from 2015 Indiana University Press Bloomington http://ebookcentral.proquest.com Campbell N. Sinclair G. Browne S. Preparing for a world without markets: Legitimising strategies of preppers Journal of Marketing Management 35 2019 798 817 Donahue A.K. Eckel C.C. Wilson R.K. Ready or not? How citizens and public officials perceive risk and preparedness The American Review of Public Administration 44 4S 2014 89S 111S Foster G.A. Hoarders, doomsday preppers, and the culture of apocalypse 2014 Palgrave Macmillan New York Häkkinen P. Mihin suomalainen survivalisti varautuu? [Radio broadcast] 2013 YLE Areena Retrieved 8 March 2017 http://areena.yle.fi/1-1802098 Hallamaa H. Tulkoon maailmanloppu 2012 Ylioppilaslehti Retrieved 31 May 2018 http://ylioppilaslehti.fi/2012/02/tulkoon-maailmanloppu/ Hine C. Ethnography for the internet: Embedded, embodied and everyday 2015 Bloomsbury London Kabel A. Chmidling C. Disaster prepper: Health, identity, and american survivalist culture Human Organization 73 3 2014 258 266 Karhunkorpi M. “Näin minusta tuli noita” – Miika Vanhapiha näkee näkyjä ja kokoaa Forssaan romahdusheimoa Aamulehti 2017 Retrieved 21 February 2017 http://www.aamulehti.fi/kotimaa/nain-minusta-tuli-noita-miika-vanhapiha-nakee-nakyja-ja-kokoaa-forssaan-romahdusheimoa-24262821/ Karosto P. Karppinen S. Suomalainen selviytymiskirja 2011 Jyväskylä Docendo Kelly C.R. The man-pocalpyse: Doomsday Preppers and the rituals of apocalyptic manhood Text and Performance Quarterly 36 2-3 2016 95 114 Koljonen K. Survivalisti varaa ruokaa kotiinsa pariksi kuukaudeksi 2011 Maaseudun tulevaisuus https://www.maaseuduntulevaisuus.fi/ymp%C3%A4rist%C3%B6/survivalisti-varaa-kotiinsa-ruokaa-pariksi-kuukaudeksi-1.7078, Retrieved 31 May 2018 Kuckartz U. Qualitative text analysis: A guide to methods, practice and using software 2014 Sage Publications Retrieved 25 April 2018 via ProQuest Ebook Central Lakoff A. Preparing for the next emergency Public Culture 19 2 2007 247 271 Lamy P. Secularizing the millenium: Survivalists, militias, and the New world order Robbins T. Palmer S.J. Millennium messiahs and mayhem – Contemporary apocalyptic movements 1997 Routledge New York 93 117 Lamy P. Millennium rage: Survivalists, white supermacists and the doomsday prophecy 1996 Plenum Press New York Laurikainen H. Kotitalouksien varautuminen Suomessa: Puhelinhaastattelututkimus normaaliolojen häiriötilanteisiin varautumisesta 2016 Suomen Pelastusalan Keskusjärjestö SPEK Retrieved 11 July 2018 http://www.spek.fi/loader.aspx?id=03718850-a8d7-4ced-90fc-48432a6683f3 Mills M.F. Preparing for the unknown… unknowns: ‘doomsday’ prepping and disaster risk anxiety in the United States Journal of Risk Research 22 10 2019 1267 1279 Mitchell R.G. Jr. Dancing at the armageddon: Survivalism and Chaos in modern times 2002 The University of Chicago Press Chicago Nieminen T. Markku Teräs on opetellut metsästämään rusakoita jousella ja tekemään sianrakkolyhtyjä – survivalistit valmistautuvat sitä päivää varten, jolloin yhteiskunta luhistuu 2017 Helsingin Sanomat Retrieved 2 July 2017 http://www.hs.fi/sunnuntai/art-2000005274385.html Niiranen P. Survivalistit varautuvat kaikkeen 2010 YLE Uutiset Retrieved 31 May 2018 https://yle.fi/uutiset/3-5514730 Osnos E. Doomsday prep for the super-rich 2017 The New Yorker Retrieved 4 February 2017 http://www.newyorker.com/magazine/2017/01/30/doomsday-prep-for-the-super-rich Rahm L. Who will survive? On bodies and boundaries after the apocalypse Gender Forum: An Internet Journal for Gender Studies 2013 Special Issue: Early Career Researchers I,45/2013 Rantanen L. Pahan päivän varalle – 2000-luvun survivalismi Yhdysvalloissa. Master’s thesis 2015 University of Helsinki Richt J. Perjantai-dokkari: Romahdusheimon soturi (Documentary television film) 2016 YLE Areena Retrieved 7 December 2016 http://areena.yle.fi/1-3754739 S.com (2017–2017e). survivalismi.com [Web page index and five forum topics: How you prepped today; Personal SHTF; Highlights of 2016; Is survivalism out of fashion?; Survivalism and spouses and other close ones]. http://www.survivalismi.com Retrieved 8 March 2017. Silverman D. Intepreting qualitative data: Methods for analysing talk, text and interaction 2001 Sage Publigations Ltd. London Takkinen K. Survivalismia Suomessa ‒ Näkökulmia viiden varautujan maailmankuvaan. Master’s thesis 2020 University of Helsinki Tierney K. The social roots of risk: Producing disasters, promoting resilience 2013 Stanford University Press Toivanen T. Suomalainen survivalisti ei rakenna bunkkeria 2013 YLE Uutiset Retrieved 8 March 2017 http://yle.fi/uutiset/3-6661570 UNDDR Preparedness 2020 Retrieved 20 March 2021 https://www.undrr.org/terminology/preparedness Vähähyyppä M. Survivalisti haluaa elää! 2010 Voima Retrieved 31 May 2018 http://voima.fi/blog/arkisto-voima/survivalisti-haluaa-elaa-2/ Wojcik D. The end of the world as we know it – Faith, fatalism, and apocalypse in America 1999 New York University Press New York

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Published by Mosby, Inc. S0022-3476(20)31554-7 10.1016/j.jpeds.2020.12.044 The Editors' Perspectives Kawasaki disease in African American children Daniels Stephen R. MD, PhD 21 1 2021 2 2021 21 1 2021 229 14 Copyright © 2020 Published by Mosby, Inc. 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcIt is well known that Kawasaki disease has racial and ethnic variations in prevalence, response to intravenous gamma globulin, and outcomes. However, relatively little is known about African American children. In this volume of The Journal, Padilla et al report on comparisons between African American and white children with respect to Kawasaki Disease outcomes. They found that African American children were more likely to be refractory to IVIG treatment, have more severe inflammation, and had a greater proportion of persistent coronary artery abnormalities at follow-up compared with white children. One important issue in understanding this comparison is whether there are health system disparities that could account for the observed differences between African American and white children. Padilla et al found no difference related to time to admission to the hospital or IVIG treatment compared to disease onset. There was also no significant difference in coronary artery abnormality at the time of admission. These finding suggest that the health care system disparities are not the likely explanation for the findings. Article page 54 ▸

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Published by Mosby, Inc. S0022-3476(20)31551-1 10.1016/j.jpeds.2020.12.041 The Editors' Perspectives Early clinical evidence regarding multisystem inflammatory syndrome in children (MIS-C) Long Sarah S. MD 21 1 2021 2 2021 21 1 2021 229 14 Copyright © 2020 Published by Mosby, Inc. 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcBy April 2020 physicians in the UK/France and the US recognized a unique hyperinflammatory syndrome characterized by fever, cardiovascular shock, and suspected SARS-CoV-2 infection and public health authorities published advisories concerning this entity. In the US, this has been called multisystem inflammatory syndrome in children (MIS-C). Two publications in this volume of The Journal of Pediatrics report data collected between April and July and results published following peer review online in early October 2020 that reflect the beginning of what would be a rapid transition from clinical experience to clinical evidence. Dove et al performed a survey of US children's medical centers to glean 48 institution's protocols related to diagnosis and management of MIS-C. Reflecting the pandemic spread itself, institutional case counts among participating centers ranged from >25 to none, with over-representation of eastern US institutions. Definitions of MIS-C across protocols generally were similar for presence, but variable for degrees of abnormalities, for fever, organ systems involved, and laboratory markers of inflammation. Management protocols include almost universal guidance for multispecialty consultations and the use of IVIG and corticosteroids, for tiered use of additional drugs (the specifics of which varied across protocols), and for universal cardiology subspecialty follow up. Pending prospective data and controlled drug trials, shared protocols provide a starting point for providers considering management options. It is also noteworthy that although protocols could not have been in place for more than a few weeks, almost one-half had already been revised. The report of Carlin et al from a large urban children's medical center in New York City was the result of a retrospective case–control study that attempted to find discriminating features between 44 children hospitalized for MIS-C who had treatment intervention and 181 children evaluated in acute-care outpatient visits who had common febrile illnesses. Major findings were the substantially greater odds in children with MIS-C of high fever (median 40°C), of long duration (median 5 days), and complaint of abdominal pain (OR 12.5, 95% CI 1.65–33.24) as well as several findings of the physical examination and abnormalities in laboratory test results (eg, decreased lymphocyte and platelet count and elevated C-reactive protein level). These discriminating features of children with MIS-C are useful for providers to have confidence in their re-assurances that most children evaluated have self-limited illnesses and to recognize manifestations that raise suspicion for progressive MIS-C in a few children. Articles pages 26 and 33 ▸

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Published by Mosby, Inc. S0022-3476(20)31553-5 10.1016/j.jpeds.2020.12.043 The Editors' Perspectives Analysis of temporal clusters: A new approach to unraveling the mystery of Kawasaki disease Newburger Jane W. MD, MPH 21 1 2021 2 2021 21 1 2021 229 14 Copyright © 2020 Published by Mosby, Inc. 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcKawasaki disease is an acute vasculitis of childhood characterized by high fever, rash, bilateral nonexudative conjunctival injection, erythema of the oral mucosa, unilateral cervical lymphadenopathy, and erythema and edema of the hands and feet in the acute phase, or periungual desquamation in the subacute phase. Patients who lack full features of Kawasaki disease may have incomplete Kawasaki disease. Whereas the presenting signs of Kawasaki disease are transient, long-term morbidity and mortality may ensue because of an associated necrotizing arteritis that causes coronary artery aneurysms. Indeed, Kawasaki disease has replaced rheumatic fever as the leading cause of acquired heart disease in children in low- and middle-income countries. There are no pathognomonic signs and no laboratory tests specific for diagnosis of Kawasaki disease. Despite almost 5 decades of research, its etiology is unknown. This has led many experts to posit that Kawasaki disease is an immune response that may be initiated by a variety of agents in genetically susceptible children. Investigators have focused on the association of temporal clusters of Kawasaki disease with environmental factors, including wind patterns, climate dynamics, and atmospheric counts of biological particles (Sci Rep 2011;1:152) (PLOS ONE 2018;13:e0191087) (Sci Rep 2018;8:16140). Whereas earlier studies have examined the environmental factors associated with clusters of Kawasaki disease cases, Burns et al analyze differences in the patterns of clinical and laboratory findings in patients within temporal clusters in the current volume of The Journal. Clusters can occur by chance alone, so the authors use sophisticated epidemiological and statistical techniques to compare the features of cases within and across clusters, as well as those occurring outside of clusters. Cases occurring within a cluster had demographic, clinical, and laboratory features that were more similar to each other than would be expected by chance. The authors suggest that the similarity of within-cluster patient characteristics could reflect different etiologies in disease clusters, for example, varying triggers for Kawasaki disease or different intensity of exposures. Future investigation of patients within clusters, for example through their antibody responses or unique environmental exposures, may reveal a range of etiologies for this still-mysterious disease. Article page 48 ▸

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10.1016/j.jpeds.2020.12.043

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Published by Mosby, Inc. S0022-3476(20)31553-5 10.1016/j.jpeds.2020.12.043 The Editors' Perspectives Analysis of temporal clusters: A new approach to unraveling the mystery of Kawasaki disease Newburger Jane W. MD, MPH 21 1 2021 2 2021 21 1 2021 229 14 Copyright © 2020 Published by Mosby, Inc. 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcKawasaki disease is an acute vasculitis of childhood characterized by high fever, rash, bilateral nonexudative conjunctival injection, erythema of the oral mucosa, unilateral cervical lymphadenopathy, and erythema and edema of the hands and feet in the acute phase, or periungual desquamation in the subacute phase. Patients who lack full features of Kawasaki disease may have incomplete Kawasaki disease. Whereas the presenting signs of Kawasaki disease are transient, long-term morbidity and mortality may ensue because of an associated necrotizing arteritis that causes coronary artery aneurysms. Indeed, Kawasaki disease has replaced rheumatic fever as the leading cause of acquired heart disease in children in low- and middle-income countries. There are no pathognomonic signs and no laboratory tests specific for diagnosis of Kawasaki disease. Despite almost 5 decades of research, its etiology is unknown. This has led many experts to posit that Kawasaki disease is an immune response that may be initiated by a variety of agents in genetically susceptible children. Investigators have focused on the association of temporal clusters of Kawasaki disease with environmental factors, including wind patterns, climate dynamics, and atmospheric counts of biological particles (Sci Rep 2011;1:152) (PLOS ONE 2018;13:e0191087) (Sci Rep 2018;8:16140). Whereas earlier studies have examined the environmental factors associated with clusters of Kawasaki disease cases, Burns et al analyze differences in the patterns of clinical and laboratory findings in patients within temporal clusters in the current volume of The Journal. Clusters can occur by chance alone, so the authors use sophisticated epidemiological and statistical techniques to compare the features of cases within and across clusters, as well as those occurring outside of clusters. Cases occurring within a cluster had demographic, clinical, and laboratory features that were more similar to each other than would be expected by chance. The authors suggest that the similarity of within-cluster patient characteristics could reflect different etiologies in disease clusters, for example, varying triggers for Kawasaki disease or different intensity of exposures. Future investigation of patients within clusters, for example through their antibody responses or unique environmental exposures, may reveal a range of etiologies for this still-mysterious disease. Article page 48 ▸

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10.1016/j.jpeds.2020.09.057

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00724-X 10.1016/j.jpsychires.2021.12.022 Article Impact of conspiracist ideation and psychotic-like experiences in patients with schizophrenia during the COVID-19 crisis Escolà-Gascón Álex ab∗ a School of Communication and International Relations, Blanquerna, Ramon Llull University, Barcelona, Spain b School of Psychology, Education and Sport Sciences, Blanquerna, Ramon Llull University, Barcelona, Spain ∗ School of Communication and International Relations, Blanquerna, Ramon Llull University, Barcelona, Spain. 24 12 2021 2 2022 24 12 2021 146 135148 18 7 2021 29 11 2021 10 12 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Conspiratorial belief is a type of argument that accepts implausible explanations in situations of great uncertainty or mystery. Claiming that the coronavirus is an artificial fabrication of laboratories is an example of conspiracist belief. The aim of this research was to analyze the impact of conspiracist ideation and psychotic-like experiences in patients with schizophrenia, patients with other mental disorders, and participants with no psychiatric history with a 132-day follow-up during the COVID-19 crisis. Analysis of variance (ANOVA) was applied and Bayesian inferences were carried out. The results conclude that conspiracist ideation and psychotic-like experiences increased significantly after 132 days of social-health restrictions in the general population. However, psychotic-like experiences did not increase in patients with schizophrenia. Conspiracist ideation has a quantitative degradation similar to the continuum model of psychosis; it is present both in patients with schizophrenia and in those participants with no clinical history. The psychopathological value of conspiracist ideation within the spectrum of psychosis is discussed. Keywords Conspiracist ideation Psychotic-like experiences Schizotypy Schizophrenia COVID-19 SARS-CoV-2 ==== Body pmcConspiracy theory beliefs consist of the acceptance of unnecessary and improbable assumptions in the face of other more plausible explanations (Aaronovitch, 2009). A current example of conspiracy theory is the claim that the coronavirus was intentionally created and released by pharmaceutical laboratories (e.g., Escolà-Gascón et al., 2020a ; Escolà-Gascón, 2021). There are many conspiracy theories, and some of them are related to behaviors and decisions that put people's health at risk (Brotherton et al., 2013 ; Jolley and Douglas, 2014), a leading example being the denial of the existence of the AIDS virus, which has led to many people not adopting the necessary prophylactic measures to prevent HIV transmission (Bogart et al., 2010 ; Ojikutu et al., 2020). The same is true for theories denying the efficacy of vaccines, which encouraged many people to decide not to adhere to vaccination programs against COVID-19 (Escolà-Gascón et al., 2021; Kata, 2010; Offit, 2011). In fact, conspiracy theories are related to pseudoscientific beliefs because they take scientifically unproven facts as truth (see Shermer, 2011; Fasce and Picó, 2018). Beliefs in conspiracy theories are related to a number of individual differences, which appear stable in healthy subjects and characterize conspiracist ideation (Swami et al., 2010 , 2011 , 2013 ; Swami and Furnham, 2012 ; Swami, 2012 ; Kay, 2021). From a psychological perspective, these individual differences are characterized by the presence of anxiety, stress, cognitive biases, and a tendency to experience new emotions (openness to experience) (Brotherton and French, 2014 ; Swami et al., 2016; Cichocka et al., 2015). At the psychiatric level, subclinical symptoms associated with paranoid and schizotypal personality traits predominate (Darwin et al., 2011 ; Barron et al., 2014 ; Dagnall et al., 2015 ; Dyrendal et al., 2021). Along these lines, there are also studies linking conspiracy theories to psychotic-like experiences (PLE) (Kelleher and Cannon, 2010 ; Livet et al., 2020). Although this concept is very broad, it is often considered an anomalous experience for two reasons (e.g., Brett et al., 2008): on the one hand, because they are disorders present in both the general and clinical population (see van Os et al., 2008 ; Moriyama et al., 2020), and on the other hand, because they are unusual or infrequent experiences when they occur in healthy subjects (Bourgin et al., 2020). Both schizotypy and PLEs are part of the psychotic phenotype (Preti et al., 2012). The psychotic phenotype is based on the idea that people with attenuated psychotic symptoms and PLEs are more at risk for severe psychotic pictures than individuals without this type of symptom (Murphy et al., 2018 ; Fonseca-Pedrero et al., 2020). Following this idea, it is very likely that conspiracist ideations are also related to the psychotic phenotype and continuum since they represent one more feature of schizotypy (see Barron et al., 2014; van der Tempel and Alcock, 2015). Although the scientific literature yields strong evidence linking conspiracist ideation to schizotypy and PLEs in subjects without a psychiatric history, no studies have been identified that analyzed the individual differences in conspiracist ideation between healthy subjects and patients diagnosed with schizophrenia (Kwapil et al., 2020). The COVID-19 pandemic generated changes in people's lifestyle and mental health (Khan et al., 2020; Mattioli et al., 2020). Some studies reported an increase in depressive symptoms of anxiety pictures in the general population during the first months of the pandemic (Choi et al., 2020; Shevlin et al., 2020). Similarly, there is evidence that PLEs varied in the adolescent population before, during and after the first COVID-19 crisis (e.g., Wu et al., 2021). In this direction, there is also research that found an increase in psychotic symptomatology in healthy subjects and patients with severe psychosis (see Brown et al., 2020; Escolà-Gascón et al., 2020b). Considering these increases and that conspiracist ideation should probably be related to the psychotic phenotype, an analysis of the presence of conspiracist ideation in subjects with schizophrenia, its variation over the months of the COVID-19 pandemic and its comparison with scores in healthy subjects is imperative. In the context of the COVID-19 crisis, the investigation of conspiracist ideation is crucial because it represents a variation of irrational thinking that has ceased to be marginal in society and has become a more predominant thought structure in the general population. This type of studies is essential since it helps to understand whether the impact of the popularization of conspiracy theories has developed similarly in healthy individuals and in patients suffering from schizophrenia or not. In addition, this could provide new insights into how conspiracy beliefs may be risk behaviors that predispose healthy individuals to suffer from possible psychotic episodes (see Escolà-Gascón and Wright, 2021). Therefore, in this research, the following hypotheses are proposed: (1) subjects with schizophrenia will present more intense conspiracist ideation than healthy subjects; and (2) between October 2020 and February 2021, conspiracist ideation will have increased in both subjects with schizophrenia and subjects with no psychiatric history. The main objective of this research was to understand the relationship between psychotic symptoms and conspiracist ideation. Likewise, we also wanted to analyze how conspiracist ideation and psychotic symptoms varied throughout the coronavirus pandemic. Understanding these dynamics will enable more effective preventions aimed at combatting conspiracist ideation and psychotic symptomatology. 1 Methods 1.1 Statement of ethical guarantees The author of this manuscript declares that this research was reviewed and favorably evaluated by the Committee of Ethical Guarantees of Ramon Llull University. Likewise, the author declares that all data collected from this study were anonymous and were blinded (including data related to the clinics and psychiatric centers that participated in this research). The procedures of this study adhere to the Spanish Government Data Protection Act 15/1999 and the Declaration of Helsinki of 1975, revised in 2013. 1.2 Participants A total of 121 participants residing in Spain participated, of whom 39 had been formally diagnosed with schizophrenia, 43 had a psychiatric history (not including psychotic spectrum disorders) and 39 had no clinical mental health history. Sociodemographic information for all participants is provided in Table 1 .Table 1 Data on sociodemographic variables for each group of subjects. Considering the sample size, only direct counts are given. Table 1Groups Age Means (SD) Sex Education level Community M W HS VT US CLM MAD BNC Patients with schizophrenia 35.38 (3.911) 35 4 13 21 5 9 18 12 Participants with a psychiatric history 36.37 (3.946) 19 24 17 15 11 13 16 14 Participants with no psychiatric history 35.05 (3.960) 18 21 11 12 16 9 9 21 Total 35.6 (3.339) 72 49 41 48 32 31 43 47 Note: SD = Standard deviation; M = Men; W = Women; HS = High school; VT = Vocational training; US = University studies; CLM = Castilla-La Mancha; MAD = Madrid; BCN = Barcelona. All the participants answered the questionnaires for this study completely voluntarily and anonymously. Likewise, the participants were also previously informed in writing of the development, stages and phases of this study. The application of the questionnaires was carried out completely online. Instead of signing a written consent form, participants had to click on an acceptance box that ensured their voluntary participation. Subsection 1.3.2 explains in more detail the procedures related to the test applications and inclusion criteria used in this project. 1.3 Procedures 1.3.1 Study design The design of this research was quasi-experimental (i.e., no random assignment of subjects to the three groups mentioned). Comparisons were made between groups of participants with schizophrenia, with psychiatric history and healthy subjects. Analyses were longitudinal (based on repeated samples) and cross-sectional (based on independent samples). Regarding the longitudinal analysis, due to the increase in psychotic symptomatology and pseudoscientific beliefs in the general population during the first social confinement related to coronavirus (see Escolà-Gascón et al., 2020b; Escolà-Gascón et al., 2021), it was decided to test whether these symptoms had remained stable between October (pretest) and February (posttest). The first week of October 2020 was chosen because a new wave of infections had started in Spain and new restrictive measures were implemented based on (1) the application of confinement by districts and/or municipalities; (2) the implementation of a 10 p.m. curfew; (3) the closure of establishments considered nonessential (e.g., gyms, cinemas, etc.); (4) the application of telematization at work and university; (5) the cancellation of popular parties and social events of more than six people; and (6) the closure of children's areas and public gardens. This new wave of infections was also experienced by some countries, such as the United Kingdom, France and Germany. Initially, the posttests were intended to be carried out between the last week of December 2020 and the first week of January 2021 because these were the dates when the government would phase out the restrictions for the Christmas holidays. However, the cases did not decrease sufficiently, and the measures were extended into February. At the beginning of the second week of February 2021, the application of the posttests began because some of the social measures mentioned above were withdrawn. Between the first application of the tests and the second, approximately 132 and 139 days had elapsed. Regarding the cross-sectional analyses, it was decided to compare groups of subjects with a diagnosis of schizophrenia, and with and without a psychiatric history. To assess the subjects who had received a diagnosis of schizophrenia and had a psychiatric history, six private mental health clinics collaborated. The participation of the clinics was voluntary, anonymous and with no profit motive. Each center assigned a responsible clinical psychologist or psychiatrist to manage the data collection. In contrast, participants with no clinical history were contacted from the original Escolà-Gascón (2020a) database, in which each participant had an e-mail address. This database consisted of 3224 cases. The collection and follow-up of the sample is explained in subsection 1.3.3. 1.3.2 Inclusion criteria and data collection All participants with a diagnosis of schizophrenia had to meet the following study inclusion criteria. (1) The patient had to possess a formal diagnosis of schizophrenia or the equivalent (e.g., psychotic spectrum disorders according to DSM-5) (see American Psychiatric Association, 2013) and the diagnosis had to be chronic (made at least one year prior to the date on which the patient agreed to participate in this study). (2) The patient had to be undergoing outpatient psychological treatment (with a minimum frequency of one visit per month, individually or in a group). (3) The patient had to be on a pharmacological treatment regimen supervised by a physician-psychiatrist. (4) The patient had to be in a stable phase of his or her illness (patients with acute psychotic symptoms were not included). (5) The patient had to be between 28 and 45 years of age. (6) The patient had to be in an adequate medical and psychological disposition to consciously answer the questionnaires of this study, meaning the following were not eligible: (6.1.) patients with cognitive deficits or impairments; (6.2.) patients hospitalized for medical reasons unrelated to this diagnosis; (6.3.) patients hospitalized because of their schizophrenia or on a day-hospital basis; or (6.4.) patients with other formally diagnosed chronic psychiatric disorders in addition to the diagnosis of schizophrenia. In this way, an attempt was made to reduce the variance associated with the comorbidity of psychotic disorders. Neither were (6.5.) patients with active suicidal ideation and/or previous suicide attempts accepted, or (6.6.) patients with declared handicaps or other medical illnesses that would disqualify them from participation in this study. A clinical psychologist and/or psychiatrist previously evaluated which patients from their respective centers could be included in this study. What the research consisted of was then explained to the patient, who was asked if he or she wished to participate on a completely anonymous and voluntary basis. Only the psychologists or psychiatrists responsible for each center were aware of the patients' data. Patient identification data were not recorded in the online application of the questionnaires. An alphanumeric code purposely developed by the heads of the collaborating clinical centers was used. The center coordinators were to give the code to each participant so that the combination of digits and letters could be recorded in the online application. The author and researcher of this study only used this code to correctly relate the data between the pretests and posttests. Likewise, the investigator at no time had contact with the patients. There were no incidents related to the treatment of patient data throughout the development of this study. The inclusion criteria for participants who had or had a psychiatric history were as follows: (1) not having or having had a diagnosis of schizophrenia; (2) possess or be formally diagnosed with any mental disorder not included in the psychotic spectrum disorders; the diagnosis did not necessarily have to be chronic; (3) be between 28 and 45 years of age; (4) be receiving or having received psychological and/or psychiatric treatment in the past; and (5) be in a medically and psychologically adequate disposition to consciously answer the questionnaires of this study. This criterion includes items 6.1., 6.2., 6.5. and 6.6. of the previous paragraph. In this case, the same conditions explained in the previous paragraph were applied for the anonymous collection of data. Thus, the researcher had no direct contact with these participants and was guided by an alphanumeric code for the appropriate relationship between the results of the pretests and posttests. Finally, the inclusion criteria for the group with no psychiatric history were as follows: (1) not having any psychiatric diagnosis; and (2) never having consulted with either a psychiatrist or a psychologist for clinical purposes. The exclusion criteria 6.1., 6.2., 6.5. and 6.6. of the previous paragraphs were also used. For this type of participant, the researcher had to use the e-mail from the Escolà-Gascón (2020a) database to properly follow up with the participants between pretests and posttests. The email was the only identifying data the researcher collected from this group of participants. 1.3.3 Obtaining the sample The collection of the sample was possible thanks to the collaboration of six private mental health clinics located in the communities of Madrid, Catalonia and Castilla-La Mancha. Considering that the health clinics were private and did not want outsiders to know that their patients were participating in research projects, the centers that collaborated with the study chose to remain anonymous. Each center assigned a professional who would be responsible for assessing the suitability of the patients who would participate according to the inclusion criteria specified in subsection 1.3.2 (Fig. 1 ).Fig. 1 Participants selection process and follow-up. Fig. 1 1.4 Materials 1.4.1 Community assessment of Psychic Experiences-42 This scale evaluates the psychotic phenotype with three dimensions: (1) Positive Dimension (PD), consisting of 20 items, (2) Negative Dimension (ND), consisting of 14 items, and (3) Depressive Dimension (DD), consisting of 8 items. The answers are coded using a Likert scale between 1 ("rarely") and 4 ("almost always"). In this research, only positive dimension and negative dimension were used. The Community Assessment of Psychic Experiences-42 (CAPE-42) presents enough evidence to endorse its validity and reliability (see Stefanis et al., 2002). The Spanish adaptation of the scale was used in this study (Fonseca-Pedrero et al., 2012). Cronbach's alpha coefficients of this sample were satisfactory for all dimensions (>0.8). 1.4.2 Multivariable multiaxial suggestibility Inventory-2 The Multivariable Multiaxial Suggestibility Inventory-2 (MMSI-2) is a psychometric inventory developed by Escolà-Gascón (2020a) consisting of 174 broad spectrum items whose subject matter focuses on anomalous phenomena as perceptual alterations (psychotic-like experiences). The MMSI-2 also includes other subclinical personality scales and other psychological indicators to detect unconscious lying, cognitive biases, inconsistencies and deliberate fraud. Nevertheless, in this study, only 6 of the 20–22 total scales of the test were used. The scales used are described as follows: (1) Visual-Auditory Anomalous Phenomena (Pva) (11 items); (2) Tactile Anomalous Phenomena (Pt) (7 items); (3) Olfactory Anomalous Phenomena (Po) (7 items); (4) Cenesthesic Anomalous Phenomena (Pc) (9 items); (5) Schizotypy (Ez) (11 items); and (6) Paranoia (Pa) (10 items). In the MMSI-2 items, participants had to indicate to what degree (from 1 to 5) they considered the content of each item to be true. The MMSI-2 offers guarantees of validity and reliability (omega coefficients >0.8) (see Escolà-Gascón, 2020a ; 2020b). Cronbach's alpha coefficients of this sample were also satisfactory for all scales (>0.8). 1.4.3 Generic conspiracist beliefs scale The Generic Conspiracist Beliefs Scale (GCBS) is a 15-item questionnaire that measures the degree to which a person believes and accepts conspiracy theories as true. This test includes five conspiracy beliefs related to government malfeasance, extraterrestrial cover-up, malevolent global and personal wellbeing, and control of information. Responses are coded on a Likert scale from 1 to 5. The participant must indicate how much he/she agrees with each item. The GCBS has a total score that is reliable and valid (Brotherton et al., 2013). Subsequent replications confirmed its psychometric goodness (see Drinkwater et al., 2020). In this study, we used a Spanish translation of our own elaboration. The reliability index based on the omega coefficient for the total test score was very satisfactory (>0.9). Cronbach's alpha coefficient for this sample was excellent (>0.9). 1.5 Statistical analysis The data were processed with JASP and JAMOVI software, which use the R programming language and are part of the same university project (see The Jamovi Project, 2020). A 2-factor analysis of variance (ANOVA) was applied: one factor was longitudinal (with pretest and posttest measures), and the other was completely randomized (had the categories "patients with schizophrenia", "participants with a psychiatric history" and "participants with no psychiatric history"). Statistical normality tests were previously analyzed with the Shapiro-Wilk fit coefficient, and the homogeneity of variances between the groups of the completely randomized factor was also examined. Effect size indices (based on the squared partial eta squared coefficients) and the Bayes factor in favor of the alternative hypothesis (BF 10) were added as a complement. The a priori probabilities were set at 50% for both the null hypothesis and the alternative hypothesis. In the case of mean comparisons, the BF 10 can be calculated from the following formula:(1) BF10=∫ΘH1P(D|θH1,H1)·π(θH1|H1)dθH1∫ΘH0P(D|θH0,H0)·π(θH0|H0)dθH0=P(D|H1)P(D|H0) where: P(D|H1) is the probability that the empirical data fit the distribution associated with the alternative hypothesis. In contrast, P(D|H0) is the probability that the data fit the expected distribution by chance. A BF10 greater than 10 provides evidence to discard the null hypothesis and retain the alternative. 2 Results 2.1 Descriptive statistics and normality tests Descriptive statistics were calculated for all variables. Descriptive statistics are presented for both the marginal measures and the measures for each group. This information can be found in Table 2, Table 3 .Table 2 Descriptive marginal statistics for each variable and group. Table 2DV Patients with schizophrenia Participants with a psychiatric history Participants with no psychiatric history Pretests (all 3 groups categories) Posttests (all 3 groups categories) M SD* M SD* M SD* M SD* M SD* CI 52.13 1.270 46.48 1.270 49.39 1.250 47.04 0.783 51.63 0.783 Ez 42.09 0.948 25.34 0.948 27.71 0.932 31.82 0.601 31.60 0.601 Pa 33.82 0.830 21.46 0.830 23.11 0.817 26.08 0.513 26.18 0.513 Pva 27.08 0.583 21.47 0.583 22.81 0.574 23.01 0.376 24.56 0.376 Pt 20.25 0.597 14.28 0.597 16.43 0.587 16.54 0.366 17.44 0.366 Pc 22.46 0.443 16.51 0.443 18.29 0.436 18.79 0.285 19.39 0.285 Po 18.32 0.611 16.15 0.611 17.59 0.601 17.31 0.378 17.40 0.378 PD 41.37 0.627 26.20 0.627 30.78 0.617 32.44 0.381 33.14 0.381 ND 36.63 0.693 26.82 0.693 28.17 0.682 30.48 0.436 30.60 0.436 Note: DV = Dependent variables; M = Means; SD = Standard deviation; CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. *SDs are based on marginal recounts (see also Table 4). Participants with no psychiatric history had different SDs because one group of this variable had four participants more than the others. Table 3 Descriptive statistics per variables and groups. Table 3DV Pretests Posttests Patients With a psychiatric history With no psychiatric history Patients With a psychiatric history With no Psychiatric history M (AA) SD M (BA) SD M (CA) SD M (AB) SD M (BB) SD M (CB) SD CI 50.90 9.017 46.47 8.857 43.74 8.902 53.36 8.567 52.33 8.185 49.21 8.125 Ez 42.15 6.831 27.88 6.142 25.82 6.920 42.28 6.621 27.79 6.108 25.13 7.083 Pa 33.85 6.327 23.16 5.389 21.54 5.633 34.00 6.061 23.26 5.174 21.59 5.255 Pva 27.13 4.444 21.26 3.155 20.74 3.126 27.10 5.165 24.42 4.233 22.26 4.339 Pt 20.15 4.777 15.54 3.494 13.97 3.475 20.39 5.260 17.37 3.310 14.62 3.529 Pc 23.44 3.478 17.40 2.735 15.59 2.721 21.54 4.242 19.23 2.671 17.49 2.684 Po 18.36 4.960 17.51 3.960 16.03 3.688 18.26 5.077 17.65 3.491 16.26 3.545 PD 41.31 5.872 30.14 2.503 26.08 3.475 41.56 5.871 31.58 3.164 26.46 3.153 ND 36.82 5.485 28.30 1.466 26.54 4.844 36.59 5.369 28.19 4.344 27.30 4.865 Note: DV = Dependent variables; M = Means; SD = Standard deviation; CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. Warning: AA, BA, CA, AB, BB and CB are mathematical notations that correspond to Table 5. Use these notations to understand the analyses of simple effects and simple interaction effects (see Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Table 13, Table 14). The probability that the observed data conformed to statistical normality was also calculated. These calculations are presented in Appendix A. All variables were classified according to patient groups and participants with and without psychiatric history sufficiently to the properties of the normal distribution. 2.2 Analysis of variance (ANOVA) 2x3 In two-factor ANOVAs, there are 4 types of effects to be analyzed: main effects, interaction effects, simple effects (also called simple main effects) and simple interaction effects between cells. Both main and interaction effects analyze differences based on marginal means. In contrast, the 2 types of simple effects are based on the comparison of the direct means of each of the variables distributed according to the groups to be tested. Table 4 should be consulted for a better understanding of these types of effects. Table 4 makes it easy to understand which contrasts were applied in this research. Comparisons between the means of the marginal cells are equivalent to main effects. In contrast, comparisons of the means between the "ij" cells (e.g., AA vs. BB) correspond to simple effects. Table 5 presents the main and interaction effects for the nine dependent variables.Table 4 Example of a contingency table with the location of each cell. Each cell contains the mean corresponding to each dependent variable. Table 4Groups Longitudinal tests Main effects Aj- Pretests Bj - Posttests Ai - Patients with Schizophrenia Means AA Means AB Means A+ Bi - Participants with a psychiatric history Means BA Means BB Means B+ Ci - Participants with no psychiatric history Means CA Means CB Means C+ Main effects +A +B Means +++ Note: The annotations in this table come from the proposals for Pardo and Ruiz (2015). Use the codes in each cell to understand the comparisons of the means in Table 6, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Table 13. Table 5 Analysis of variance, main effects of variables and Bayesian approach. Table 5DV IV F (p values) Post hoc p values with Bonferroni correction BF10 (% estimated error) P(H1|D) ηPartial2 CI Prepost 64.094 (<0.001*) – 20.860 (1.669%) 0.954 0.352 Groups 4.854 (0.009*) 1 vs. 2 = 0.377 1 vs. 3 = 0.007* 2 vs. 3 = 0.306 5.319 (2.465%) 0.842 0.060 Interaction 3.463 (0.003*) – 1.219 (2.957%) 0.549 0.005 Ez Prepost 0.183 (0.670) – 0.158 (2.944%) 0.136 0.002 Groups 90.366 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = 0.230 27.184 (3.047%) 0.965 0.605 Interaction 0.223 (0.801) – 0.090 (4.009%) 0.083 0.004 Pa Prepost 0.068 (0.794) – 0.146 (2.251%) 0.127 0.001 Groups 64.567 (<0.001*) 1 vs. 2 = <0.001 * 1 vs. 3 = <0.001* 2 vs. 3 = 0.475 24.824 (2.327%) 0.961 0.523 Interaction 0.006 (0.994) – 0.078 (2.972%) 0.072 <0.001 Pva Prepost 20.539 (<0.001*) – 9.944 (1.695%) 0.909 0.148 Groups 24.883 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = 0.310 840.308 (1.569%) 0.999 0.297 Interaction 7.377 (<0.001*) – 28.939 (2.205%) 0.967 0.111 Pt Prepost 12.232 (<0.001*) – 37.769 (1.450%) 0.974 0.094 Groups 25.199 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = 0.011 32.933 (4.169%) 0.971 0.299 Interaction 3.585 (0.031) – 1.605 (3.522%) 0.616 0.057 Pc Prepost 5.764 (0.018) – 1.428 (13.593%) 0.588 0.047 Groups 46.583 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = 0.015 28.454 (13.593%) 0.966 0.441 Interaction 23.827 (<0.001*) – 16.300 (13.655%) 0.942 0.288 Po Prepost 0.100 (0.752) – 0.146 (4.038%) 0.127 0.001 Groups 3.195 (0.045) 1 vs. 2 = ∼1 1 vs. 3 = 0.044 2 vs. 3 = 0282 1.581 (3.510%) 0.613 0.051 Interaction 0.121 (0.886) – 0.098 (6.336%) 0.089 0.002 PD Prepost 7.811 (0.006*) – 5.890 (2.417%) 0.855 0.062 Groups 150.952 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = <0.001* 47.188 (2.669%) 0.979 0.719 Interaction 2.360 (0.099) – 0.564 (6.221%) 0.361 0.038 ND Prepost 0.120 (0.730) – 0.147 (2.515%) 0.128 0.001 Groups 58.121 (<0.001*) 1 vs. 2 = <0.001* 1 vs. 3 = <0.001* 2 vs. 3 = 0.500 19.447 (3.182%) 0.951 0.496 Interaction 0.686 (0.506) – 0.133 (3.055%) 0.117 0.011 Note: *p < 0.01; DV = Dependent variables; IV = Independent variables; 1 = patients with schizophrenia; 2 = participants with psychiatric history; 3 = participants with no psychiatric history; F = Fisher's tests; BF10 = Bayes Factors in favor to alternative hypothesis; Eta partial square = explained variance of the VIs over VDs; CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. The results in Table 5 indicate that there was significant variation between the pretests and posttests of conspiracist ideation (CI) and anomalous tactile perceptions (Pt). The marginal means (see Table 2) indicated that beliefs in conspiracy theories and tactile perceptual disturbances had increased after this second period of social-health restrictions. The other variables showed no significant changes. Therefore, the hypothesis that beliefs in conspiracy theories increased during the coronavirus pandemic is maintained. Social health restrictions explained 35.2% of the increase in scores.Table 6 Simple main and interaction effects analysis for Conspiracist ideation (CI). Table 6Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −2.722 −8.400 2.957 1.905 −1.429 −0.130 CA vs. AA −7.154 −12.970 −1.338 1.950 −3.668* −0.333 CA vs. CB −5.462 −8.487 −2.436 1.010 −5.409* −0.492 CA vs. BB −8.582 −14.261 −2.903 1.905 −4.506* −0.410 CA vs. AB −9.615 −15.431 −3.800 1.950 −4.930* −0.448 BA vs. AA −4.432 −10.111 1.247 1.905 −2.327 −0.212 BA vs. CB −2.740 −8.419 2.939 1.905 −1.439 −0.131 BA vs. BB −5.860 −8.742 −2.979 0.962 −6.094* −0.554 BA vs. AB −6.894 −12.573 −1.215 1.905 −3.620* −0.329 AA vs. CB 1.692 −4.123 7.508 1.950 0.868 0.079 AA vs. BB −1.428 −7.107 4.251 1.905 −0.750 −0.068 AA vs. AB −2.462 −5.487 0.564 1.010 −2.438 −0.222 CB vs. BB −3.120 −8.799 2.558 1.905 −1.638 −0.149 CB vs. AB −4.154 −9.970 1.662 1.950 −2.130 −0.194 BB vs. AB −1.033 −6.712 4.645 1.905 −0.543 −0.049 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. Table 7 Simple main and interaction effects analysis for Schizotypy (Ez). Table 7Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −2.063 −6.417 2.290 1.462 −1.411 −0.128 CA vs. AA −16.333 −20.792 −11.875 1.497 −10.909* −0.992 CA vs. CB 0.692 −2.010 3.394 0.902 0.768 0.070 CA vs. BB −1.970 −6.324 2.383 1.462 −1.348 −0.123 CA vs. AB −16.462 −20.920 −12.003 1.497 −10.994* −0.999 BA vs. AA −14.270 −18.624 −9.917 1.462 −9.761* −0.887 BA vs. CB 2.756 −1.598 7.109 1.462 1.885 0.171 BA vs. BB 0.093 −2.480 2.666 0.859 0.108 0.010 BA vs. AB −14.398 −18.752 −10.045 1.462 −9.848* −0.895 AA vs. CB 17.026 12.567 21.484 1.497 11.371* 1.034 AA vs. BB 14.363 10.010 18.717 1.462 9.824* 0.893 AA vs. AB −0.128 −2.830 2.574 0.902 −0.142 −0.013 CB vs. BB −2.662 −7.016 1.691 1.462 −1.821 −0.166 CB vs. AB −17.154 −21.612 −12.695 1.497 −11.457* −1.042 BB vs. AB −14.491 −18.845 −10.138 1.462 −9.912* −0.901 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. The groups of patients and participants with and without psychiatric history showed significant differences for all variables except for anomalous olfactory perceptions (Po), in which no significant results were observed. The marginal means of each type of group (see Table 2) indicated that patients diagnosed with schizophrenia presented the highest scores compared to the rest of the groups. However, the Bayes factor for the conspiracist ideation variable was less than 10, and the variance explained was 6%. This means that there are reasons to be conservative and maintain the null hypothesis; patients with schizophrenia did not have higher CI scores than the other subject groups. Table 6 through 13 present the analyses of the simple effects and simple interaction effects.Table 8 Simple main and interaction effects analysis for Paranoia (Pa). Table 8Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −1.624 −5.345 2.096 1.248 −1.302 −0.118 CA vs. AA −12.308 −16.118 −8.497 1.278 −9.631* −0.876 CA vs. CB −0.051 −2.057 1.955 0.669 −0.077 −0.007 CA vs. BB −1.717 −5.438 2.003 1.248 −1.376 −0.125 CA vs. AB −12.462 −16.272 −8.651 1.278 −9.751* −0.886 BA vs. AA −10.683 −14.404 −6.963 1.248 −8.561* −0.778 BA vs. CB 1.573 −2.148 5.294 1.248 1.261 0.115 BA vs. BB −0.093 −2.003 1.817 0.638 −0.146 −0.013 BA vs. AB −10.837 −14.558 −7.117 1.248 −8.684* −0.789 AA vs. CB 12.256 8.446 16.067 1.278 9.590* 0.872 AA vs. BB 10.590 6.870 14.311 1.248 8.486* 0.771 AA vs. AB −0.154 −2.160 1.852 0.669 −0.230 −0.021 CB vs. BB −1.666 −5.387 2.055 1.248 −1.335 −0.121 CB vs. AB −12.410 −16.221 −8.600 1.278 −9.711* −0.883 BB vs. AB −10.744 −14.465 −7.024 1.248 −8.610* −0.783 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. Table 9 Simple main and interaction effects analysis for Visual-Auditory Anomalous Phenomena (Pva). Table 9Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −0.512 −3.233 2.208 0.914 −0.560 −0.051 CA vs. AA −6.385 −9.171 −3.599 0.936 −6.820* −0.620 CA vs. CB −1.513 −3.316 0.290 0.602 −2.514 −0.229 CA vs. BB −3.675 −6.395 −0.955 0.914 −4.020* −0.365 CA vs. AB −6.359 −9.145 −3.573 0.936 −6.792* −0.617 BA vs. AA −5.872 −8.593 −3.152 0.914 −6.424* −0.584 BA vs. CB −1.001 −3.721 1.720 0.914 −1.095 −0.100 BA vs. BB −3.163 −4.880 −1.446 0.573 −5.519* −0.502 BA vs. AB −5.847 −8.567 −3.126 0.914 −6.396* −0.581 AA vs. CB 4.872 2.086 7.658 0.936 5.204* 0.473 AA vs. BB 2.710 −0.011 5.430 0.914 2.964 0.269 AA vs. AB 0.026 −1.777 1.829 0.602 0.043 0.004 CB vs. BB −2.162 −4.883 0.558 0.914 −2.365 −0.215 CB vs. AB −4.846 −7.632 −2.060 0.936 −5.176* −0.471 BB vs. AB −2.684 −5.404 0.037 0.914 −2.936 −0.267 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. The simple effects of the Po variable were not included because the results in Table 5 for this variable were not significant. The results in the above tables are summarized according to subject groups:(1) For the group of subjects with no psychiatric history, the CI, Pva, Pt, Pc and ND scores significantly increased after the period of health restrictions. The effect sizes for these variables were medium (∼0.4) and small (∼0.1). (2) For the group of subjects with a psychiatric history, the scores of the CI, Pva, Pt, Pc and PD scales significantly increased after the confinement period. (3) For patients with schizophrenia, only the CI and Pc scale scores showed significant variations. Beliefs in conspiracy theories had increased. In contrast, cenesthetic hallucinations had decreased. Table 10 Simple main and interaction effects analysis for Tactile Anomalous Phenomena (Pt). Table 10Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −1.561 −4.215 1.094 0.890 −1.753 −0.159 CA vs. AA −6.179 −8.898 −3.461 0.911 −6.780* −0.616 CA vs. CB −0.641 −2.002 0.720 0.454 −1.411 −0.128 CA vs. BB −3.398 −6.052 −0.743 0.890 −3.818* −0.347 CA vs. AB −6.410 −9.129 −3.692 0.911 −7.033* −0.639 BA vs. AA −4.619 −7.274 −1.964 0.890 −5.190* −0.472 BA vs. CB 0.919 −1.735 3.574 0.890 1.033 0.094 BA vs. BB −1.837 −3.133 −0.541 0.433 −4.246* −0.386 BA vs. AB −4.850 −7.504 −2.195 0.890 −5.449* −0.495 AA vs. CB 5.538 2.820 8.257 0.911 6.076* 0.552 AA vs. BB 2.782 0.127 5.436 0.890 3.125 0.284 AA vs. AB −0.231 −1.592 1.130 0.454 −0.508* −0.046 CB vs. BB −2.757 −5.411 −0.102 0.890 −3.097 −0.282 CB vs. AB −5.769 −8.488 −3.051 0.911 −6.330* −0.575 BB vs. AB −3.013 −5.667 −0.358 0.890 −3.385 −0.308 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. Table 11 Simple main and interaction effects analysis for Cenesthesic Anomalous Phenomena (Pc). Table 11Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −1.806 −3.866 0.255 0.692 −2.609 −0.237 CA vs. AA −7.846 −9.956 −5.736 0.709 −11.069* −1.006 CA vs. CB −1.897 −3.242 −0.553 0.449 −4.228* −0.384 CA vs. BB −3.643 −5.703 −1.583 0.692 −5.263* −0.478 CA vs. AB −5.949 −8.059 −3.839 0.709 −8.392* −0.763 BA vs. AA −6.041 −8.101 −3.980 0.692 −8.727* −0.793 BA vs. CB −0.092 −2.152 1.968 0.692 −0.133 −0.012 BA vs. BB −1.837 −3.118 −0.557 0.427 −4.298* −0.391 BA vs. AB −4.143 −6.203 −2.083 0.692 −5.986* −0.544 AA vs. CB 5.949 3.839 8.059 0.709 8.392* 0.763 AA vs. BB 4.203 2.143 6.264 0.692 6.073* 0.552 AA vs. AB 1.897 0.553 3.242 0.449 4.228* 0.384 CB vs. BB −1.745 −3.806 0.315 0.692 −2.522 −0.229 CB vs. AB −4.051 −6.161 −1.941 0.709 −5.715* −0.520 BB vs. AB −2.306 −4.366 −0.246 0.692 −3.331 −0.303 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. Table 12 Simple main and interaction effects analysis for Positive Dimension (PD). Table 12Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −4.063 −6.827 −1.299 0.926 −4.387* −0.399 CA vs. AA −15.231 −18.061 −12.400 0.948 −16.058* −1.460 CA vs. CB −0.385 −1.694 0.925 0.437 −0.880 −0.080 CA vs. BB −5.504 −8.268 −2.740 0.926 −5.943* −0.540 CA vs. AB −15.487 −18.318 −12.657 0.948 −16.328* −1.484 BA vs. AA −11.168 −13.932 −8.404 0.926 −12.059* −1.096 BA vs. CB 3.678 0.914 6.442 0.926 3.971 0.361 BA vs. BB −1.442 −2.689 −0.195 0.416 −3.464 −0.315 BA vs. AB −11.425 −14.189 −8.661 0.926 −12.335* −1.121 AA vs. CB 14.846 12.016 17.677 0.948 15.653* 1.423 AA vs. BB 9.726 6.962 12.490 0.926 10.502* 0.955 AA vs. AB −0.256 −1.566 1.053 0.437 −0.587 −0.053 CB vs. BB −5.120 −7.884 −2.356 0.926 −5.528* −0.503 CB vs. AB −15.103 −17.933 −12.272 0.948 −15.923* −1.448 BB vs. AB −9.983 −12.747 −7.219 0.926 −10.779* −0.980 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. Table 13 Simple main and interaction effects analysis for Negative Dimension (ND). Table 13Means comparison Mean difference Confidence Interval (95%) Standard error t-test Cohen's d Lower Upper CA vs. BA −1.764 −4.923 1.396 1.061 −1.663 −0.151 CA vs. AA −10.282 −13.518 −7.046 1.086 −9.466* −0.861 CA vs. CB −0.718 −2.602 1.166 0.629 −1.142 −0.104 CA vs. BB −1.648 −4.807 1.512 1.061 −1.553 −0.141 CA vs. AB −10.051 −13.287 −6.816 1.086 −9.253* −0.841 BA vs. AA −8.518 −11.678 −5.359 1.061 −8.031* −0.730 BA vs. CB 1.046 −2.114 4.206 1.061 0.986 0.090 BA vs. BB 0.116 −1.678 1.910 0.599 0.194 0.018 BA vs. AB −8.287 −11.447 −5.128 1.061 −7.813* −0.710 AA vs. CB 9.564 6.328 12.800 1.086 8.805* 0.800 AA vs. BB 8.634 5.475 11.794 1.061 8.141* 0.740 AA vs. AB 0.231 −1.653 2.115 0.629 0.367 0.033 CB vs. BB −0.930 −4.089 2.230 1.061 −0.876 −0.080 CB vs. AB −9.333 −12.569 −6.098 1.086 −8.592* −0.781 BB vs. AB −8.404 −11.563 −5.244 1.061 −7.923* −0.720 Note: *p < 0.01. Bonferroni's correction was applied to all comparisons. Warning: Pretest and posttest comparisons (simple main effects) are highlighted in bold. One result to note is the following: Table 6 shows significant differences between the CI scores of patients with schizophrenia and those of participants with no psychiatric history. In this case, the healthy subjects scored 7.154 points lower than the patients with schizophrenia. The effect size of this contrast was medium (0.333). This result in the simple effects replaces the hypothesis decision taken from Table 5. Therefore, the subjects with schizophrenia did score higher on CI than the other groups of participants. In relation to the simple interaction effects of CI, it is crucial to note that the increase in conspiratorial beliefs in the healthy posttest subjects (M = 49.21) did not exceed the value of the mean of the subjects with schizophrenia pretests (M = 50.90). However, this difference was not significant. This observation is essential because the simple pretest and posttest effects on CI did show significant differences between patients and participants with no psychiatric history. The increase in CI in the healthy participants reached similar levels as in the patients with schizophrenia. This calls into question whether the patients' CI values are clinically significant scores or whether they are results within the subclinical (nonpsychopathological) spectrum. Fig. 2, Fig. 3 show the graphs of the CI means for each of the groups.Fig. 2 Mean plot of the conspiracist ideation variable pretests. Fig. 2 Fig. 3 Mean plot of the conspiracist ideation variable posttests. Fig. 3 2.3 Correlation analysis Taking into account the above significant differences, the scores of the dependent variables were correlated to test the degree of relationship between psychotic-like experiences, conspiracist ideation and psychotic phenotype. The pretest scales were correlated with the posttests. Table 14, Table 15, Table 16 provide Pearson's linear correlations.Table 14 Pearson correlation matrix between pretest scales and posttest scales for group patients. Table 14 Pretests CI Ez Pa Pva Pc Pt Po PD ND Posttests CI 0.759* 0.746* 0.302 0.557* 0.769* 0.774* 0.72* 0.447* 0.11 Ez 0.678* 0.882* 0.274 0.476* 0.685* 0.692* 0.631* 0.566* 0.022 Pa 0.303 0.198 0.763* 0.323 0.348 0.367 0.261 0.013 0.162 Pva 0.376* 0.318 0.277 0.657* 0.733* 0.708* 0.614* 0.426* 0.151 Pc 0.224 0.324 0.13 0.521* 0.587* 0.574* 0.627* 0.243 0.14 Pt 0.273 0.377* 0.128 0.672* 0.744* 0.777* 0.695* 0.373 0.236 Po 0.49* 0.491* 0.032 0.662* 0.891* 0.866* 0.787* 0.537* 0.235 PD 0.554* 0.56* 0.116 0.65* 0.836* 0.822* 0.772* 0.767* 0.236 ND 0.115 −0.18 0.072 0.076 0.178 0.133 0.037 −0.105 0.75* Note: *p < 0.01. C I= Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. Table 15 Correlation matrix between pretest scales and posttest scales for group participants with psychiatric history. Table 15 Pretests CI Ez Pa Pva Pc Pt Po PD ND Posttests CI 0.758* 0.478* 0.366* 0.394* 0.876* 0.731* 0.575* 0.58* 0.333 Ez 0.813* 0.591* 0.214 0.313 0.686* 0.715* 0.542* 0.648* 0.384* Pa 0.165 −0.152 0.758* 0.213 0.398* 0.192 −0.069 −0.021 0.156 Pva 0.373* 0.101 0.406* 0.7* 0.637* 0.505* 0.321 0.3 0.197 Pc 0.467* 0.171 0.34 0.332 0.746* 0.655* 0.598* 0.454* 0.297 Pt 0.468* 0.337 0.33 0.50*6 0.617* 0.74* 0.363* 0.333 0.199 Po 0.606* 0.636* 0.142 0.315 0.753* 0.722* 0.638* 0.627* 0.255 PD 0.533* 0.361* −0.034 0.211 0.526* 0.475* 0.468* 0.762* 0.203 ND 0.102 0.028 −0.168 0.106 0.138 0.062 0.138 0.186 0.399* Note: *p < 0.01. CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. Table 16 Correlation matrix between pretest scales and posttest scales for group participants with no psychiatric history. Table 16 Pretests CI Ez Pa Pva Pc Pt Po PD ND Posttests CI 0.68* 0.663* 0.122 0.095 0.504* 0.531* 0.407* 0.74* −0.239 Ez 0.4* 0.451* 0.17 −0.183 0.198 0.186 0.268 0.575* −0.284 Pa 0.152 0.343 0.647* 0.605* 0.411* 0.414* 0.279 0.068 0.127 Pva 0.225 0.437* 0.261 0.416* 0.609* 0.559* 0.312 0.266 0.055 Pc 0.279 0.332 −0.105 0.363 0.486* 0.394* 0.302 0.34 0.285 Pt 0.35 0.658* 0.003 0.332 0.575* 0.72* 0.662* 0.427* 0.079 Po 0.632* 0.694* 0.3 0.396* 0.682* 0.663* 0.7* 0.515* 0.061 PD 0.501* 0.602* 0.312 0.055 0.4* 0.417* 0.406* 0.893* −0.268 ND −0.057 −0.221 −0.046 0.212 0.221 −0.014 −0.018 −0.21 0.791* Note: *p < 0.01. CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; P t = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. Correlation matrices indicated that conspiracist ideation was significantly and positively correlated with schizotypy, psychotic-like experiences and positive symptoms of psychosis. These correlations were consistent for all three groups of subjects. Schizotypy was also positively correlated in all groups with some psychotic-like experiences and positive symptoms of psychosis but not with negative symptoms (ND). Overall, the most relevant correlations in Table 14, Table 15, Table 16 supported the hypotheses put forward in this research. 3 Discussion The aim of this research was to determine the impact of conspiracist ideation in groups of nonclinical, clinical and schizophrenia-diagnosed subjects. The impact of psychosis-like experiences and negative symptoms of psychosis was also analyzed. It is concluded that conspiracist ideation is more present in schizophrenic patients than in healthy participants. A reduction in Pc scores (Cenesthetic alternations) was observed in the posttests in the group of patients with schizophrenia. This result was not expected. The correlations in Table 14, Table 15 indicated that conspiracist ideation is related to schizotypy, psychotic phenotype, and some perceptual disturbances. The results raise the following questions: (1) Why on some scales did scores increase for participants with no clinical history and not for subjects with schizophrenia? (2) Why did psychotic-like experiences remain stable and cenesthetic hallucinations decrease in the posttests for the patient group? (3) Why was conspiracist ideation not correlated with negative symptoms of psychosis? and (4) Are there psychopathological risks associated with conspiracist ideation? 3.1 Interpretation of the results In relation to the first and second questions, it is important to keep in mind that the patients with schizophrenia who participated in this research were treated with antipsychotics. In some cases, the therapeutic doses could have varied and increased, generating a decrease in perceived hallucinations (see Sommer et al., 2012). Similarly, it is possible that pharmacological treatment may have promoted the stabilization of psychotic-like experiences during these 132 days. These reasons may explain why the perceptual disturbances in the patient group did not vary significantly. In addition, the patients with schizophrenia included in this study did not suffer any psychotic episodes during the follow-up of this investigation. This is also important because if they had, the scores on the Pva, Pc, Pt, Po and PD scales should have changed. Changes were observed in the scores relative to the rest of the groups, which are also in line with these arguments. The third question asks which dimensions of the psychotic phenotype (or schizotypy) conspiracist ideation are correlated. The results of this research show that conspiracist ideation is exclusively related to schizotypy on the dimension of positive symptoms and psychotic-like experiences. It is important to note that the MMSI-2 schizotypy scale (Ez) focuses its contents or items on positive symptoms but also includes magical thinking and irrational beliefs. Given this feature, it is very likely that conspiracist ideation is also correlated with magical ideation. Magical ideation is a schizotypal personality trait directly associated with paranormal and pseudoscientific beliefs (see Williams and Irwin, 1991; Karcher and Shean, 2012). This idea would be consistent with the results provided by Dyrendal et al. (2021), who propose magical and paranormal beliefs as a mediating variable in the relationship between schizotypy and conspiracist ideation. The lack of correlation between negative symptoms and conspiracist ideation is also consistent with other research, which noted that the negative dimension did not correlate directly with conspiracist ideation, although it did correlate indirectly (see Denovan et al., 2020). Some lines of research proposed that these types of beliefs were part of a "healthy schizotypy" because they did not generate any subjective discomfort and did not interfere with the emotional well-being of the patient (see McCreery and Claridge, 2002 ; Goulding, 2004 , 2005). However, this idea of "healthy shizotypy" is controversial and is not accepted by all mental health professionals because it challenges the predominant model of the psychotic phenotype (see Chabrol and Raynal, 2018 for a review). The fourth question is probably the most complex. If the psychotic phenotype assumes that attenuated symptoms of psychosis in the general population represent a risk to people's mental health (see Shapiro et al., 2019), to what extent conspiracist ideation would also constitute a psychopathological risk should also be discussed. Sticking to the results of this research, the means of conspiracist ideation (CI) obtained among the three groups present a quantitative degradation that is compatible with the continuum model of psychosis and psychotic phenotype. For example, the means in Fig. 2, Fig. 3 clearly show a decreasing trend in CI scale values as participants do not suffer from any psychiatric disorder. This supports the supposition that conspiracist ideation may be an includable psychopathological risk within the psychosis spectrum. However, simple interaction effects between patients (pretest conspiracist ideation) and participants with no history (posttest conspiracist ideation) showed no significant differences. This does not detract from the decreasing trend observed in Fig. 2, Fig. 3. Moreover, this result warns that the conspiracist ideation scores of the healthy participants reached posttest levels similar to the levels obtained by the participants with schizophrenia. Does this mean that CI levels after the 132 days of social-health restrictions increased in the healthy subjects to psychopathological or clinically-significant levels? This research provides the first evidence that GCBS scale scores greater than 49 points could have a significant clinical impact and be a risk score within attenuated psychotic symptoms. The reason for this interpretation is that it was the patients with schizophrenia who showed values close to 50, and the differences between the means 49.21 and 50.90 were not significant (see Table 6). However, further research is needed to replicate these results and to expand the sample size used in this investigation. If CI scores, psychosis-like experiences, and negative symptoms increased in participants with and without psychiatric histories during this 132-day period, it is necessary to question whether the restrictions and municipal confinements performed fostered attenuated psychotic states in the nonclinical population. Sociopolitical and medical decisions to prevent the spread of the coronavirus should not impair the quality of life of individuals and should not promote psychotic conditions in "healthy" subjects. The results indicate that psychotic symptoms and conspiracist ideation continued to worsen during this period of crisis. The urgency and necessity of vaccination and community immunization to remove these restrictions is emphasized. 3.2 Limitations The main limitation of this research focuses on the following points: (1) the methodology used was not experimental; (2) the sample size was not large; and (3) the measurement instruments used were adequate, but the results may vary if other questionnaires were used; in fact, no instruments were used to assess the degree of psychopathological severity of the patients. The methodology was not experimental because the direct effects of the social health restrictions on the study participants could not be controlled and the distribution of the subjects to the diagnostic groups was not random. Therefore, it is not possible to state that the cause of the increase in psychotic symptoms and conspiracist ideation is due to the social health restrictions. Considering the scores and the results, it is possible to infer a direct relationship that should be taken into account. The sample size affects the external validity and the generalizability of the results. In this case, conclusions about generalizability should be made cautiously and should be applied mainly to the Spanish- or Spanish-speaking population. In addition, other Western countries during these 132 days applied other more severe restrictions, generating a social and medical context that differs from the Spanish social-health care panorama. This should be taken into account if the procedures of this study were to be replicated in the future. Along these lines, it would be advisable to include larger samples in which social-health factors were controlled or recorded as covariates. In this study, it was not possible to expand the sample because no new mental health clinics were located that wished to collaborate with the research. Access and follow-up of patients is a complex procedure and is limited to the conditions of the collaborating clinics. Finally, it is crucial to explain that the tests used presented acceptable validity and reliability. The MMSI-2, CAPE-42 and GCBS scales were chosen because they were open access and their psychometric properties were excellent. However, in the case of the GCBS, a direct translation of the English version was used because the official Spanish adaptation was not available. This suggests that if other scales were used to measure conspiracist ideation, there may be a variance associated with the instrument that should be taken into account in future studies. This variability would be observed in the direct scores of the new application, which should be compared with the scores of the present report. Moreover, no structured protocols were used to measure the severity of psychotic symptoms (e.g., the PANSS scale; see Edgar et al., 2014). This should also be considered, since the observed differences could have a distinct variation if patients presented different levels of symptomatic severity. Nevertheless, in this study the majority of patients were in a stable episode of psychosis, which means that the observed differences should be consistent. 4 Conclusions This research, the results and discussion allow us to highlight the following conclusions:(1) Conspiracist ideation and psychotic-like experiences increased during the 132 days in which COVID-19 social health restrictions were applied. This increase was significant and especially worrisome for subjects with and without a psychiatric history. Surprisingly, patients with schizophrenia showed no significant variations between pretests and posttests. Specifically, patients with schizophrenia showed slightly elevated scores for conspiracist ideation and a significant reduction in cenesthetic hallucinations. These significant differences could be explained by the pharmacological treatment taken by the patients that involved the intake of antipsychotics. (2) Conspiracist ideation is highly correlated with schizotypy and psychotic-like experiences and correlates slightly with levels of paranoia. Thus, conspiracist ideation is an individually differential variable to be taken into account when assessing psychopathological risk related to psychosis. We found evidence supporting the possibility that conspiracist ideation could be integrated as a complementary attribute of the psychotic phenotype. However, conspiracist ideation was not correlated with negative symptoms of psychosis. A positive relationship was only obtained for positive symptoms of the psychotic phenotype. (3) Patients with schizophrenia tend to have higher scores on the conspiracist ideation scale than the other subject groups. This tendency is also observed in the scores for psychotic-like experiences and the other variables. Thus, the measurement of conspiracist ideation also has a quantitative degradation that can be extrapolated to the psychosis continuum (see Fig. 2, Fig. 3). However, this does not mean that it constitutes a severe psychopathological symptom, as it also occurs in milder subjects. Further studies are needed to confirm how to discriminate the threshold between clinical and subclinical scores. Ultimately, this research contributes to the scientific literature because it provides evidence of the relationship between schizophrenia and conspiracist ideation as an attribute to be taken into account within the spectrum of psychosis. Ethics approval and consent to participate The author of this manuscript declares that this research was reviewed and favorably evaluated by the Committee of Ethical Guarantees of Ramon Llull University. Likewise, the author declares that all data collected from this study were anonymous and were blinded (including data related to the clinics and psychiatric centers that participated in this research). The procedures of this study adhere to the Spanish Government Data Protection Act 15/1999 and the Declaration of Helsinki of 1975, revised in 2013. Funding The author confirms there has been no significant financial support for this work that could have influenced its outcome. Concerning preregistration This study was not preregistered. Declaration of competing interest The author confirms that there are no known conflicts of interest associated with this publication. Appendix Appendix A: Tests of Normality Table A1 Appendix A. Tests of normality using Shapiro-Wilk coefficient. Table A1DV Pretests Posttests Patients With a psychiatric history With no psychiatric history Patients With a psychiatric history With no Psychiatric history S p S p S p S p S p S p CI 0.969 0.350 0.964 0.199 0.968 0.335 0.957 0.144 0.959 0.123 0.975 0.539 Ez 0.971 0.404 0.972 0.365 0.962 0.213 0.967 0.302 0.970 0.323 0.970 0.377 Pa 0.960 0.180 0.972 0.364 0.978 0.632 0.968 0.337 0.980 0.640 0.990 0.971 Pva 0.958 0.150 0.967 0.255 0.973 0.462 0.970 0.381 0.957 0.104 0.960 0.176 Pt 0.981 0.730 0.973 0.410 0.984 0.851 0.986 0.899 0.963 0.180 0.961 0.193 Pc 0.966 0.278 0.974 0.440 0.965 0.255 0.982 0.769 0.961 0.154 0.961 0.196 Po 0.953 0.102 0.959 0.130 0.970 0.376 0.963 0.220 0.967 0.242 0.966 0.278 PD 0.985 0.861 0.973 0.387 0.966 0.286 0.967 0.292 0.969 0.284 0.984 0.829 ND 0.956 0.136 0.979 0.602 0.966 0.280 0.965 0.263 0.982 0.719 0.971 0.405 Note: DV = Dependent variables; S = Shapiro-Wilks coefficient; p = Probability that data fit the statistical normality; CI = Conspiracist ideation; Ez = Schizotypy; Pa = Paranoia; Pva = Anomalous Visual/Auditory Perceptions; Pt = Anomalous Tactile Perceptions; Po = Anomalous Olfactory Perceptions; Pc = Anomalous Synesthetic Perceptions; PD = Positive Dimension; ND = Negative Dimension. 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==== Front Vaccine Vaccine Vaccine 0264-410X 1873-2518 Published by Elsevier Ltd. S0264-410X(22)01226-9 10.1016/j.vaccine.2022.09.095 Article Impact of prenatal COVID-19 vaccination on delivery and neonatal outcomes: Results from a New York City cohort Ibroci Erona a Liu Xiaoqin b Lieb Whitney cde Jessel Rebecca c Gigase Frederieke A.J. a Chung Kyle a Graziani Mara a Lieber Molly c Ohrn Sophie c Lynch Jezelle e Castro Juliana a Marshall Christina c Tubassum Rushna e Mutawakil Farida c Kaplowitz Elianna T. e Ellington Sascha f Molenaar Nina a Sperling Rhoda S. cg Howell Elizabeth A. h Janevic Teresa de Dolan Siobhan M. c Stone Joanne c De Witte Lotje D. a Bergink Veerle a Rommel Anna-Sophie a⁎ a Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA b National Centre for Register-based Research, Aarhus University, Aarhus 8000, Denmark c Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA d Blavatnik Family Women’s Health Research Institute, Icahn School of Medicine at Mount Sinai, New York 10029, NY USA e Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA f Division of Reproductive Health, Centers for Disease Control and Prevention, Atlanta 30329, GA, USA g Department of Medicine, Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York 10029, NY, USA h Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 109104, PA, USA ⁎ Corresponding author. 14 12 2022 14 12 2022 18 6 2022 27 9 2022 29 9 2022 © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Research suggest prenatal vaccination against coronavirus disease-19 (COVID-19) is safe. However, previous studies utilized retrospectively collected data or examined late pregnancy vaccinations. We investigated the associations of COVID-19 vaccination throughout pregnancy with delivery and neonatal outcomes. We included 1,794 mother-neonate dyads enrolled in the Generation C Study with known prenatal COVID-19 vaccination status and complete covariate and outcome data. We used multivariable quantile regressions to estimate the effect of prenatal COVID-19 vaccination on birthweight, delivery gestational age, and blood loss at delivery; and Poisson generalized linear models for Caesarean delivery (CD) and Neonatal Intensive Care Unit (NICU) admission. Using the above methods, we estimated effects of trimester of vaccine initiation on these outcomes. In our sample, 13.7% (n = 250) received at least one prenatal dose of any COVID-19 vaccine. Vaccination was not associated with birthweight (β = 12.42 g [-90.5, 114.8]), gestational age (β = 0.2 days [-1.1, 1.5]), blood loss (β = -50.6 ml [-107.0, 5.8]), the risks of CD (RR = 0.8; [0.6, 1.1]) or NICU admission (RR = 0.9 [0.5, 1.7]). Trimester of vaccine initiation was also not associated with these outcomes. Our findings suggest that there is no associated risk between prenatal COVID-19 vaccination and adverse delivery and neonatal outcomes in a cohort sample from NYC. Keywords COVID-19 vaccine Pregnancy COVID-19 mRNA vaccine Delivery Neonatal ==== Body pmc1 Introduction As of April 2022, there have been ∼ 205,000 confirmed coronavirus disease-19 (COVID-19) cases in pregnant individuals in the U.S. [1], not including many likely asymptomatic and unconfirmed cases. Early observational studies showed that pregnant individuals with COVID-19 were more frequently admitted to intensive care units and required invasive ventilation compared to non-pregnant individuals of the same age [2]. Moreover, research has reported a myriad of adverse neonatal outcomes related to SARS-CoV-2 infection during pregnancy, including preterm birth and low birthweight [3], [4], [5], [6]. Due to the increased risk of severe COVID-19 illness and adverse pregnancy outcomes, the Centers for Disease Control and Prevention (CDC), the American College of Obstetrics and Gynecology (ACOG), and the Society for Maternal-Fetal Medicine (SMFM) encourage pregnant individuals to get vaccinated to protect themselves from COVID-19 [7], [8]. By mid-February 2022, around 69% of the pregnant US population had received at least one dose of the COVID-19 vaccine [9]. To protect against infection, vaccines must elicit an immune response, which is usually brief and may range from mild to severe with fever and increased cytokine levels [10], [11]. Evidence from animal [12], [13] and epidemiological [14], [15], [16] research suggests that prenatal exposure to inflammation may lead to adverse pregnancy outcomes. So far, research from national registries and hospital data suggests that prenatal COVID-19 vaccination is effective, protecting against COVID-19 illness and maternal/neonatal hospitalization [17], [18], [19], [20], and safe, i.e., not associated with adverse pregnancy outcomes (e.g., preterm birth, birthweight, Neonatal Intensive Care Unit (NICU) admissions, and postpartum hemorrhage) [21], [22], [23], [24], [25], [26], [27], [28], [29]. However, studies were restricted to studying the effect of COVID-19 vaccination in late pregnancy, registry-based retrospective cohorts, or compared outcomes to historical pregnancy data or non-pregnant persons [21], [22], [24], [26], [27], [28], [29], [30]. Additionally, studies either: 1) restricted their samples to those with no history of SARS-CoV-2 infection [25], [26], [27], [28], which has been associated with several adverse delivery outcomes such as preterm birth and low birthweight [3], [4], [5], [6]; or 2) classified history of SARS-CoV-2 infection using questionnaires or polymerase chain reaction (PCR) tests from medical charts, which could exclude asymptomatic SARS-CoV-2 infections which were never captured by a PCR test [21], [23], [24], [29], [30]. Here, we examine the associations of prenatal COVID-19 vaccination in all trimesters with birthweight, gestational age at delivery, quantitative blood loss at delivery, mode of delivery, and NICU admission in a large prospective pregnancy cohort in New York City (NYC). These outcomes were selected due to their general indication of maternal and neonatal health. Given the considerable proportion of individuals already vaccinated during pregnancy, the significant number of pregnant individuals who are not yet vaccinated, and the potential necessity of future booster shots, understanding the impact of prenatal COVID-19 vaccination on delivery and neonatal outcomes is crucial. 2 Materials and methods 2.1 Study design and population The Generation C Study (Generation C) is a prospective pregnancy cohort study within the Mount Sinai Health System (MSHS) in NYC, which delivers over 14,750 babies a year [31]. Generation C was designed to examine the impact of SARS-CoV-2 in pregnant individuals on pregnancy, fetal, and neonatal outcomes. A detailed cohort description can be found elsewhere [32]. Briefly, pregnant persons receiving obstetrical care within the MSHS were approached for study participation at a prenatal visit or on Labor and Delivery (L&D). Recruitment began in April 2020 and concluded in February 2022. As part of routine blood collections for prenatal lab testing, additional blood samples were obtained. Participants provided informed consent. 2.2 Measurements Information on COVID-19 vaccination status was ascertained from the patients’ electronic medical records (EMR). The EMR of the MSHS is linked the New York Citywide Immunization Registry (CIR), which consolidates all immunization information across NYC, including vaccination outside of the MSHS (e.g., immunization in pharmacies), into a centralized database. Reporting of all administered COVID-19 vaccine doses to the CIR is required within 24 h of administration. This information is accessible via the EMR of any NYC hospital system. Here, participants were considered ‘vaccinated’ if they received at least one dose of a viral-vector vaccine Janssen (Johnson & Johnson) (Ad26.COV2.S), or mRNA vaccines Moderna (mRNA-1273) and Pfizer-BioNTech (BNT162b2) at any point during pregnancy. None of the participants reported receiving a COVID-19 vaccine not authorized in the US. Timing (before, during, or after pregnancy) and trimester of each dose administration (if applicable) were calculated based on gestational age established using ultrasounds. Participants were categorized as ‘fully vaccinated’, defined as the completion of the two-dose series of the mRNA vaccines or one-dose of the viral-vector vaccine, or as ‘partially vaccinated’, defined as the completion of the first of two mRNA vaccine doses. Participants were considered ‘unvaccinated’ (i.e., not vaccinated during pregnancy) if they 1) were pregnant at a time when the COVID-19 vaccine was available but received the first dose of any COVID-19 vaccine after delivery or 2) delivered before the authorization of the vaccine in NYC (December 14, 2020). We excluded individuals who delivered after December 14, 2020, if their vaccination status was unknown (i.e., missing or incomplete EMR data on vaccination status), or if they were fully vaccinated before pregnancy (Fig. 1 ).Fig. 1 Participant flow chart. Information on SARS-CoV-2 infection history was obtained using several methods. First, an enzyme-linked immunosorbent assay (ELISA) was used to measure immunoglobulin G (IgG) titer levels to the SARS-CoV-2 spike protein in blood samples collected throughout pregnancy and at L&D. This ELISA has high sensitivity and specificity [33]. However, SARS-CoV-2 spike protein antibodies indicate prior exposure to either SARS-CoV-2 or the COVID-19 vaccine. Thus, for all participants with known vaccination status (as per the EMR) and with IgG positive samples collected after December 14, 2020 (when vaccination began in NYC), samples were further tested for the presence of SARS-CoV-2 nucleocapsid antibodies using the MILLIPLEX SARS-CoV-2 Antigen Panel 1 IgG from Millipore [34]. This assay allows distinction of antibodies produced in response to COVID-19 vaccination from antibodies produced in response to prior SARS-CoV-2 infection. Nucleocapsid antibodies are only found after prior infection, not after vaccination. Running the MILLIPLEX assay, thus, allowed us to establish prior infection history and confirm vaccination status based on the presence of nucleocapsid antibodies. We established which participants were 1) vaccinated but not infected, and which were 2) vaccinated and infected. Lastly, we used EMR data on SARS-CoV-2 polymerase chain reaction (PCR) tests to further identify SARS-CoV-2 infection history. In our analysis, we included SARS-CoV-2 infection history prior to delivery as covariate. No data were available on symptomatology or infection severity of SARS-CoV-2 infection. Additionally, participants were considered “unknown” for SARS-CoV-2 infection history if they did not have adequate amounts of plasma (n = 38) or if their samples had not yet been analyzed (n = 3). We examined the following delivery and neonatal outcomes: birthweight (grams), gestational age at delivery (days), quantitative blood loss at delivery (ml), mode of delivery (Caesarean vs vaginal delivery), and Neonatal Intensive Care Unit admission (NICU; yes, no). Due to low numbers, we excluded spontaneous abortions (pregnancy loss < 20 weeks gestation), intrauterine fetal demise, and induced abortions (i.e., elective abortions at any point during pregnancy) from the primary analysis; however, below we report the frequency of these outcomes by vaccination group. Outcome data were ascertained from the EMR. 2.3 Covariates Covariates were chosen based on published literature [21], [22], [28] and include SARS-CoV-2 infection history (yes, no, unknown), race and ethnicity (non-Hispanic Asian, non-Hispanic Black, Hispanic, unknown, non-Hispanic White, or none of the above), maternal age at delivery (years; 18–34, 35–50), parity (nulliparous, multiparous), pre-pregnancy hypertension (yes, no), pre-pregnancy diabetes (yes, no), and pre-pregnancy body mass index (BMI, kg/m2; underweight (<18.5), healthy weight (18.5–24.9), overweight (25–29.9), obesity (30 + )). We additionally controlled for study-specific variables: 1) enrollment center (prenatal appointment or L&D) to account for different recruitment strategies which may have influenced who was recruited, and 2) time between the first confirmed case of COVID-19 in NYC (March 1, 2020) and birth (months) to account for timing of the pandemic. Descriptive covariate data were ascertained through EMRs, with the addition of the above-mentioned ELISA and MILLIPLEX assays from blood samples to supplement SARS-CoV-2 infection history. 2.4 Statistical analysis Pearson’s chi-square, Fischer’s exact test, and Wilcoxon Rank-Sum tests were used to compare demographic variables, where appropriate. For the primary analysis, quantile regression analysis was used to obtain the beta values and 95% confidence intervals (CIs) to estimate the effect of the COVID-19 vaccine on the continuous variables birthweight (grams), gestational age at delivery (days), and quantitative blood loss at delivery (ml), as these outcomes are not normally distributed. Generalized linear mixed with Poisson distribution and log-link were used to obtain the risk ratios and 95% CIs to estimate the effect of the COVID-19 vaccine on the categorical variables delivery mode (Caesarean vs vaginal delivery) and NICU admission (yes, no). For the secondary analysis, we analyzed the effect of timing (trimester) of vaccine initiation (i.e., first dose) on our selected outcomes using the statistical methods outlined above. We compared individuals who initiated vaccination in the first, second or third trimester to unvaccinated individuals. Descriptive statistics for the selected outcomes per trimester are provided. We performed a sensitivity analysis restricting our analysis to participants who delivered after December 14, 2020 to investigate pregnancy outcomes of those who delayed vaccination until after delivery to those who chose to be vaccinated during pregnancy and control for timing of the pandemic and vaccine availability. All analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC, USA). 2.5 Ethics approval The institutional review board (IRB) at the Icahn School of Medicine at Mount Sinai reviewed and approved the study protocol (protocol IRB-20–00425, April 20, 2020). 3 Results Generation C enrolled 3,157 participants from April 2020 to February 2022. Here, we include 1,794 individuals with a live delivery encounter within the MSHS, who had data on vaccination status and timing, as well as covariate and outcome data. In our sample, 13.9% (n = 250) received at least one dose of any COVID-19 vaccine during pregnancy and 86.1% (n = 1,544) were unvaccinated during pregnancy (n = 353 received the first dose after pregnancy despite being eligible for prenatal COVID-19 vaccination and n = 1,191 delivered before December 14, 2020) (Fig. 1). Supplemental table 1 summarizes the demographics and characteristics of the included (n = 1,794) and excluded (n = 1,322) participants. A visual timeline of when COVID-19 vaccination occurred in our sample is presented as Supplemental Fig. 1. Sample characteristics of the vaccinated (n = 250) and unvaccinated (n = 1,544) participants are presented in Table 1 . Compared to unvaccinated participants, vaccinated participants were more likely to 1) not have a history of SARS-CoV-2 infection, 2) identify as non-Hispanic White, 3) be nulliparous, 4) have a healthy pre-pregnancy BMI, and be enrolled 5) at a prenatal appointment rather than at L&D, 6) earlier in pregnancy, and 7) later in the pandemic. There were no differences in pre-pregnancy hypertension and pre-pregnancy diabetes (Table 1). Outcome data by group are presented in Table 2 .Table 1 Sample characteristics of vaccinated and unvaccinated participants enrolled in the Generation C study at the Mount Sinai Health System, April 2020 – November 2021(N = 1,794). Characteristic Vaccinated n = 250 Unvaccinated n = 1,544 p-value* Previous SARS-CoV-2 infection, n (%) 0.01  Yes 36 (14) 336 (22)  No 211 (84) 1,171 (76)  Unknown 3 (1) 37 (2) Race and Ethnicity, n (%) <0.0001  Non-Hispanic Asian 33 (13) 160 (10)  Non-Hispanic Black 18 (7) 235 (15)  Hispanic 30 (12) 432 (28)  Unknown 10 (4) 68 (4)  Non-Hispanic White 135 (54) 633 (41)  None of the above 24 (10) 16 (1) Pre-pregnancy hypertension, n (%) 9 (4) 43 (3) 0.48 Pre-pregnancy diabetes, n (%) 7 (3) 21 (1) 0.09 Enrollment center, n (%) <0.0001  Prenatal appointment 213 (85) 879 (57)  Labor and Delivery 37 (15) 665 (43) Parity, n (%) 0.004  Nulliparous 153 (61) 792 (51)  Multiparous 97 (39) 752 (49) Age at delivery (years), n (%) <0.0001  18 to 34 95 (38) 922 (60)  35 to 50 155 (62) 622 (40) Pre-pregnancy BMI (kg/m2), n (%) 0.0008  Underweight (Below 18.5) 4 (2) 39 (3)  Healthy Weight (18.5 to 24.9) 133 (53) 648 (42)  Overweight (25 to 29.9) 75 (30) 460 (30)  Obesity (30 or higher) 38 (15) 397 (26) Gestational age at recruitment (weeks), median (IQR) 12 (9, 18) 36 (22, 39) <0.0001 Pandemic timing (months), median (IQR)** 15 (14, 17) 6 (4, 9) <0.0001 IQR = Interquartile range. *Pearson’s chi-square or Fischer’s exact test (where appropriate) test for categorical variables; Wilcoxon Two-Sample Test for continuous variables. **Months between first confirmed case of COVID-19 in New York City to delivery. Table 2 Summary statistics of delivery and neonatal outcomes of vaccinated and unvaccinated participants in the Generation C study at the Mount Sinai Health System, April 2020 – November 2021 (N = 1,794). Outcome Vaccinated n = 250 Unvaccinated n = 1,544 p-value* Birthweight (grams), median (IQR) 3,224.9 (2,935.0, 3,549.9) 3,259.9 (2,935.0, 3,572.5) 0.32  Small for gestational age,  n (%) 23 (9) 147 (9) 0.87 Delivery gestational age (weeks), median (IQR) 39.0 (38.0, 39.0) 39.0 (38.0, 39.0) 0.99  Preterm birth (<37 weeks), n (%) 16 (6) 139 (9) 0.17 Quantitative blood loss (ml), median (IQR) 327.0 (200.0, 582.0) 328.0 (200.0, 544.0) 0.58  >= 1,000 ml, n (%) 8 (3) 58 (4) 0.67 Caesarean delivery, n (%) 99 (40) 541 (35) 0.16 NICU admission, n (%) 25 (10) 152 (10) 0.94 NICU = Neonatal Intensive Care Unit; IQR = Interquartile range; Small for gestational age is defined as birthweight below 10th sex-specific percentile. *Pearson’s chi-square test for categorical variables; Wilcoxon Two-Sample Test for continuous variables. Overall, there were 55 non-live birth pregnancy outcomes out of 1,849 delivery encounters (3%). Of the 1,849 delivery encounters, 1,596 were unvaccinated and 253 were vaccinated. Among those unvaccinated (n = 1,596), 34 (2%) experienced spontaneous abortions, 3 (0.2%) experienced intrauterine fetal demise, and 15 (1%) had induced abortions. Of the vaccinated participants with a delivery encounter (n = 253), one experienced intrauterine fetal demise (0.4%) and two (0.8%) had an induced abortion. No spontaneous abortions occurred in the vaccinated group. For the sample characteristics of the participants with non-live birth outcomes, who were excluded from the analyses, see Supplemental table 2. Of the vaccinated participants included in our sample (n = 250), 87% (n = 217) received two doses of an mRNA vaccine in pregnancy or one dose of the viral-vector vaccine during pregnancy, 1% (n = 3) received one dose of an mRNA vaccine prior to pregnancy and the second during pregnancy, and 12% (n = 30) received one dose of an mRNA vaccine during pregnancy and the second after pregnancy. By trimester, 35 (14%) received their first dose during the first trimester, 105 (42%) received their first dose during the second trimester, 105 (42%) received their first dose during the third trimester, and 2 (1%) individuals had no date of vaccine initiation (but had received their second mRNA dose in pregnancy).The majority of vaccinated participants (97%) received an mRNA vaccine (n = 249), <1% received a viral-vector vaccine (n = 4), and < 1% did not have a vaccine brand specified (n = 3) (Supplemental table 3). Table 3 presents the results from the primary analysis, unadjusted and adjusted quantile and log-Poisson regression models adjusted for history of COVID-19, race and ethnicity, age at delivery, parity, pre-pregnancy hypertension, pre-pregnancy diabetes, pre-pregnancy BMI, enrollment center, and time between the first confirmed case of COVID-19 in NYC (March 1, 2020) and birth. In both the unadjusted and the adjusted models, we did not find an association of prenatal COVID-19 vaccination with birthweight, delivery gestational age, quantitative blood loss at delivery, mode of delivery and NICU admission.Table 3 Unadjusted and adjusted† quantile and log-Poisson regression of vaccination in pregnancy with delivery and neonatal outcomes, Mount Sinai Health System, April 2020 – November 2021 (N = 1,794). Outcome Unadjusted Estimate (95% CI) Adjusted†Estimate (95% CI) Birthweight (grams)* −34.9 (-114.2, 44.5) 12.2 (-90.5, 114.8) Delivery gestational age (days)* 0.0 (-0.8, 0.8) 0.2 (-1.1, 1.5) Quantitative blood loss at delivery (ml)* 0.02 (-45.5, 45.5) −50.6 (-107.0, 5.8) Caesarean delivery** 1.1 (0.9, 1.4) 0.8 (0.6, 1.1) NICU admission** 1.0 (0.7, 1.6) 0.9 (0.5, 1.7) †Adjusted for: SARS-CoV-2 infection history, race and ethnicity, maternal age at delivery, parity, pre-pregnancy hypertension, diabetes, pre-pregnancy BMI, enrollment center, and time between first case of COVID-19 in New York City and birth (months); NICU = Neonatal Intensive Care Unit. *Quantile regression at the median performed, beta (95% CI) presented. **Log-Poisson regression performed, risk ratio (95% CI) presented. Results from the secondary analysis, which examined our selected delivery and neonatal outcomes by trimester of COVID-19 vaccine initiation, showed no trimester-specific effects of the COVID-19 vaccine on birthweight, delivery gestational age, quantitative blood loss at delivery, or mode of delivery (Table 4 ).Table 4 Adjusted quantile regression and log-Poisson regression results by trimester of first dose of the COVID-19 vaccine in those who delivered after December 14, 2020, with delivery and neonatal outcomes (n = 1,789)†. Birthweight (grams)* Delivery gestational age (days)* Quantitative blood loss at delivery (ml)* Caesarean delivery** NICU admission** Trimester of vaccine initiation First (n = 35) 3,270 (3,020, 3,560) 275 (269, 278) 300 (157, 550) 14 (40) 5 (14) Second (n = 105) 3,210 (2,910, 3,560) 274 (268, 278) 359 (211, 604) 46 (44) 11 (10) Third (n = 105) 3,220 (2,965, 3,470) 274 (268, 279) 310 (200, 546) 37 (35) 9 (9) Adj. estimate: trimester of initiation vs unvaccinated First –22.3 (-227.3, 182.7) −1.5 (-4.1, 1.1) 83.1 (–32.5, 198.6) 0.7 (0.4, 1.4) 1.3 (0.5, 3.7) Second −12.3 (-148.1, 123.5) 0.4 (-2.1, 1.4) 36.5 (-40.1, 113.1) 0.9 (0.6, 1.3) 1.0 (0.5, 2.2) Third −9.7 (-133.0, 113.6) 0.1 (-1.4, 1.7) 63.1 (-6.4, 132.6) 0.8 (0.5, 1.1) 0.8 (0.4, 1.8) Adjusted for: SARS-CoV-2 infection history, race and ethnicity, maternal age at delivery, parity, pre-pregnancy hypertension, diabetes, BMI, enrollment center, and time between first case of COVID-19 in New York City and birth; NICU = Neonatal Intensive Care Unit; IQR = Interquartile range. †Excluding n = 3 with pre-pregnancy vaccine and n = 2 with unknown date of vaccine initiation. *Median (IQR) presented. Quantile regression at the median performed, beta (95% CI) presented. **N (%) presented. Log-Poisson regression performed, risk ratio (95% CI) presented. The sensitivity analysis, which was restricted to participants who delivered after December 14, 2020 (N = 603) to investigate pregnancy outcomes of those who delayed vaccination until after delivery to those who chose to be vaccinated during pregnancy and control for timing of the pandemic and vaccine availability, also found no effect of prenatal vaccination against COVID-19 on delivery or birth outcomes (Supplemental tables 4, 5, 6). 4 Discussion We found that COVID-19 vaccination during pregnancy was not associated with birthweight, gestational age, blood loss at delivery, Caesarean birth, or NICU admission in our prospective pregnancy cohort. We further did not find trimester-specific effects of the COVID-19 vaccine on these delivery and birth outcomes. Additionally, these findings remained the same when the sample was restricted to those who delivered after December 14, 2020, when the COVID-19 vaccine was first available to all pregnant persons in NYC. Vaccines are a key mitigation strategy for preventing and controlling the spread of COVID-19. Due to the increased risk of severe COVID-19 illness and adverse pregnancy outcomes affecting both the pregnant person and their child, vaccination during pregnancy is recommended by the CDC, ACOG, and SMFM [7], [8]. However, concerns regarding the safety of the COVID-19 vaccine during pregnancy have arisen partly because of the speed with which the vaccines were developed and because pregnant individuals were not included in the phase III trials of the vaccines [35]. Moreover, the desired goal of vaccines is to trigger a cell-mediated immune response against a virus (i.e., the pro-inflammatory cytokines tumor necrosis factor (TNF)α, interleukin (IL)-1, IL-6, and Type I and II interferons (IFNs)) [36], [37], in this case SARS-CoV-2. This immune response has been associated with adverse pregnancy outcomes in animal models [36], [37]. In human and mice studies, maternal immune activation has further been linked to an increased risk for preterm birth [38], [39] lower birthweight [40], and adverse offspring neurobehavioral outcomes [12], [41], [42]. Yet, our and several large prospective and registry-based retrospective cohort studies provide reassurance regarding the safety of the COVID-19 vaccine during pregnancy. Namely, we found no increased risk of adverse pregnancy outcomes associated with vaccination against COVID-19 and no trimester-specific effects. In line with previous findings, our research shows that vaccinated and unvaccinated pregnant individuals do not differ with regard to birthweight, gestational age at delivery, postpartum hemorrhage, and mode of delivery [21], [22], [23], [25], [26], [27]. While previous studies investigated the effects COVID-19 vaccination in the second and/or third trimesters, employed retrospective designs or compared pregnancy outcomes of vaccinated individuals to historical pregnancy data or non-pregnant persons, we examined the impact of COVID-19 vaccinations in all three trimesters in a large prospective pregnancy cohort [21], [22], [23], [24], [25], [26], [27], [28], [29], [30]. By including participants who were vaccinated at any point during pregnancy, we were able to elucidate trimester-specific effects of COVID-19 vaccination. Our prospective pregnancy cohort, which was established in NYC early in the COVID-19 pandemic, further allowed us to compare vaccinated and unvaccinated individuals who were pregnant during the pandemic. Thus, the current study substantially expands on available data that suggest no increased risk of adverse pregnancy outcomes among individuals vaccinated against COVID-19 during pregnancy. Our results suggest no increased risk of lower birthweight, preterm birth, increased blood loss at delivery, Caesarian birth or NICU admission and, therefore, provide support for the vaccination recommendations by the CDC, ACOG, and SMFM. A likely added benefit of receiving the COVID-19 vaccine during pregnancy, established in other studies, is that antibodies are transferred to the fetus [19], [43], [44], [45], offering the infant protection against the virus during the first months of life. This is particularly important since infants less than 6 months of age are not eligible for vaccination at the time of this analysis. Evidence regarding the efficacy and safety of COVID-19 vaccination during pregnancy is still emerging as vaccination and booster campaigns are ongoing worldwide. Consequently, more individuals vaccinated against COVID-19 during pregnancy will give birth in the future, allowing us to expand our sample sizes, as well as to investigate vaccine- and trimester-specific effects in more detail. Furthermore, future studies are needed to understand the longer-term effects of prenatal exposure to the COVID-19 vaccines on children’s immunity to COVID-19, as well as on their developmental milestones and neurobehavioral health. The strengths of our study include the use of 1) prospective data from a large diverse sample; 2) EMR data on COVID-19 vaccination to determine timing, and minimize exposure misclassification and researcher bias; 3) highly sensitive assays to distinguish between SARS-CoV-2 infection and COVID-19 vaccination, allowing us to control for SARS-CoV-2 infection history and potential sequalae; and 4) trimester-specific vaccination data allowing us to investigate the effects of vaccination timing on delivery and neonatal outcomes. The limitations of our study include potential sampling bias; our included and excluded participants differed on various characteristics and limited power to investigate the effects by race and ethnicity. Thus, our findings may not be generalizable to other populations. Second, we cannot preclude a healthy vaccination effect. Namely, it is conceivable that those who received a prenatal COVID-19 vaccine may be healthier and less prone to adverse delivery and/or neonatal outcomes. The healthy vaccination effect may be reflected in the smaller numbers of non-live births in the vaccinated group. Yet, in our analysis, we controlled for a range of covariates such as SARS-CoV-2 infection history, maternal age at delivery, pre-pregnancy hypertension, diabetes and pre-pregnancy BMI to account for maternal health. Due to the small number of events, non-live births were not included in the analyses and thus should be interpreted with caution. Additionally, exposure misclassification may result from our use of the New York Citywide Immunization Registry as the primary source of vaccination information. Individuals who were excluded due to unknown vaccination status may have received the COVID-19 vaccine outside of NYC or declined vaccination. However, those excluded from the analysis due to missing vaccination information (n = 475) did not differ from those included in the analytical sample (n = 1,796) with regard to zip code and NYC residency (supplemental table 7). Third, most of the unvaccinated individuals delivered prior to vaccine distribution in NYC (67%), in April-December 2020. Individuals who delivered in the earlier phases of the pandemic may have experienced various economic, social, and medical impacts of early public health measures introduced to mitigate the pandemic [45]. To address this issue, we included the time between the first confirmed case of COVID-19 in NYC (March 1, 2020) and birth and conducted a sensitivity analysis restricting our sample to those who delivered after the vaccine became available. Results of the main analysis and the sensitivity analysis were identical. Fourth, the sample sizes of certain subanalyses are relatively small (e.g., first-trimester vaccination). Thus, these analyses may be underpowered. Future research with larger samples is required to confirm our findings. Fifth, we were unable to control for the severity of COVID-19 in these analyses as these data were unavailable. Yet, we did include SARS-CoV-2 infection history in our model. Unlike previous research [21], [23], [24], [25], [26], [27], [28], [29], [30], we classified history of SARS-CoV-2 infection based on both SARS-CoV-2 PCR test data from the EMR and blood samples collected throughout pregnancy measuring SARS-CoV-2 anti-S IgG antibodies. The use of SARS-CoV-2 antibodies is a strength of this study as it detects asymptomatic cases as well as those who never underwent testing. Sixth, there were small numbers of pre-pregnancy SARS-CoV-2 infections in (7/36 SARS-CoV-2 positive and vaccinated; 12/336 SARS-CoV-2 positive and unvaccinated). We hope to assess how pre-pregnancy and trimester-specific SARS-CoV-2 infection and vaccination timing impacts delivery and neonatal outcomes in the future. Lastly, we are unable to assess the impact of the different types of vaccines due to the small number of participants who received the viral vector vaccine (n = 4). 5 Conclusion COVID-19 vaccination during pregnancy was not adversely associated with birthweight, gestational age at delivery, blood loss at delivery, mode of delivery, and NICU admission in a prospective cohort sample from New York City. Our study adds to the growing literature on the safety of COVID-19 vaccination in pregnancy and supports the continued recommendation of vaccination of pregnant individuals against SARS-CoV-2. Thus, it may help providers counsel pregnant patients on the safety and benefits of COVID-19 vaccination during pregnancy, which is particularly important considering the higher risk of severe COVID-19 during pregnancy. 6 Author contributions Conceptualization, Erona Ibroci, Veerle Bergink and Anna-Sophie Rommel; Data curation, Erona Ibroci and Elianna Kaplowitz; Formal analysis, Erona Ibroci; Funding acquisition, Elizabeth Howell, Siobhan Dolan, Joanne Stone and Veerle Bergink; Investigation, Erona Ibroci, Veerle Bergink and Anna-Sophie Rommel; Methodology, Erona Ibroci, Xiaoqin Liu, Veerle Bergink and Anna-Sophie Rommel; Project administration, Elizabeth Howell, Siobhan Dolan, Joanne Stone and Veerle Bergink; Resources, Erona Ibroci, Frederieke Gigase, Kyle Chung, Mara Graziani, Sophie Ohrn, Jezelle Lynch, Juliana Castro, Christina Marshall, Rushna Tubassum, Farida Mutawakil and Elianna Kaplowitz; Software, Erona Ibroci; Supervision, Veerle Bergink and Anna-Sophie Rommel; Validation, Erona Ibroci; Visualization, Erona Ibroci and Anna-Sophie Rommel; Writing – original draft, Erona Ibroci, Veerle Bergink and Anna-Sophie Rommel; Writing – review & editing, Erona Ibroci, Xiaoqin Liu, Whitney Lieb, Rebecca Jessel, Frederieke Gigase, Kyle Chung, Mara Graziani, Molly Lieber, Sophie Ohrn, Jezelle Lynch, Juliana Castro, Christina Marshall, Rushna Tubassum, Farida Mutawakil, Elianna Kaplowitz, Sascha Ellington, Nina Molenaar, Rhoda Sperling, Elizabeth Howell, Teresa Janevic, Siobhan Dolan, Joanne Stone, Lotje De Witte, Veerle Bergink and Anna-Sophie Rommel. All authors have read and agreed to the published version of the manuscript. Funding This study is partially funded (contract 75D30120C08186) by the US Centers for Disease Control and Prevention (CDC), who also provided technical assistance related to analysis and interpretation of data and writing the report. Institutional Review Board Statement. The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the Icahn School of Medicine (protocol IRB-20–00425, April 20, 2020). Informed Consent Statement. Informed consent was obtained from all subjects involved in the study. Data Availability Statement. Not applicable. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following are the Supplementary data to this article:Supplementary data 1 Data availability The data that has been used is confidential. Acknowledgements The authors would like to thank several members of the US Centers for Disease Control (CDC) that have contributed to the interpretation of the data and have provided their feedback on the manuscript: Romeo Galang, Kate Woodworth, Margaret C. Snead, Lauren Zapata. Appendix A The following supporting information can be downloaded at https://www.mdpi.com/xxx/s1, Table S1: Sample characteristics of participants included and excluded from analysis, Mount Sinai Health System, April 2020 – February 2022 (N = 3,116); Table S2: Summary demographics of participants with non-live birth outcomes with known vaccination status, Mount Sinai Health System, April 2020 – February 2022 (n = 55); Table S3: Detailed COVID-19 vaccination information of vaccinated participants with delivery and neonatal outcomes, Mount Sinai Health System, April 2020 – February 2022 (N = 250); Table S4: Sensitivity analysis sample characteristics restricted to participants who delivered after introduction of COVID-19 in New York City, Mount Sinai Health System, December 14, 2020 – February 2022 (N = 603); Table S5: Summary statistics for the sensitivity analysis restricted to participants who delivered after introduction of COVID-19 in New York City, Mount Sinai Health System, December 14, 2020 – February 2022 (N = 603); Table S6: Unadjusted and adjusted† quantile and log-Poisson regression of vaccination in pregnancy with delivery and neonatal outcomes, restricted to participants who delivered after introduction of COVID-19 in New York City, Mount Sinai Health System, December 14, 2020 – February 2022 (N = 603). Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.09.095. ==== Refs References 1 Centers for Disease Control and Prevention. Data on COVID-19 during Pregnancy: Severity of Maternal Illness. 2022. Available at https://covid.cdc.gov/covid-data-tracker/#pregnant-population. Accessed April 5, 2022. 2 Allotey J, Stallings E, Bonet M, et al. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: living systematic review and meta-analysis. BMJ 2020;370:m3320. 3 Khan D.S.A. Hamid L.-R. Ali A. Salam R.A. Zuberi N. Lassi Z.S. Differences in pregnancy and perinatal outcomes among symptomatic versus asymptomatic COVID-19-infected pregnant women: a systematic review and meta-analysis BMC Pregnancy Childbirth 21 1 2021 4 Metz T.D. Clifton R.G. Hughes B.L. Sandoval G. Saade G.R. Grobman W.A. Disease Severity and Perinatal Outcomes of Pregnant Patients With Coronavirus Disease 2019 (COVID-19) Obstet Gynecol 137 4 2021 571 580 33560778 5 Piekos S.N. Roper R.T. Hwang Y.M. Sorensen T. Price N.D. Hood L. The effect of maternal SARS-CoV-2 infection timing on birth outcomes: a retrospective multicentre cohort study The Lancet Digital Health 4 2 2022 e95 e104 35034863 6 Wei S.Q. Bilodeau-Bertrand M. Liu S. Auger N. The impact of COVID-19 on pregnancy outcomes: a systematic review and meta-analysis CMAJ 193 16 2021 E540–E548 7 American College of Obstetrics and Gynecologists. ACOG and SMFM recommend COVID-19 vaccination for pregnant individuals. 2022. Available at https://www.acog.org/news/news-releases/2021/07/acog-smfm-recommend-covid-19-vaccination-for-pregnant-individuals. Accessed April 5, 2022. 8 Centers for Disease Control and Prevention. COVID-19 Vaccines While Pregnant or Breastfeeding. 2022. Available at https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/pregnancy.html. Accessed April 5, 2022. 9 Centers for Disease Control and Prevention. COVID Data Tracker: COVID-19 vaccination among pregnant people aged 18-49 years overall, by race/ethnicity, and date reported to CDC - Vaccine Safety Datalink,* United States. 2022. Available at https://covid.cdc.gov/covid-data-tracker/#vaccinations-pregnant-women. Accessed on 05 April 2022. 10 Bettini E. Locci M. SARS-CoV-2 mRNA Vaccines: Immunological Mechanism and Beyond Vaccines (Basel) 9 2021 147 33673048 11 Cagigi A. Loré K. Immune Responses Induced by mRNA Vaccination in Mice Monkeys and Humans Vaccines (Basel) 9 2021 61 33477534 12 Estes M.L. McAllister A.K. Maternal immune activation: Implications for neuropsychiatric disorders Science 353 6301 2016 772 777 27540164 13 Bergdolt L. Dunaevsky A. Brain changes in a maternal immune activation model of neurodevelopmental brain disorders Prog Neurobiol 175 2019 1 19 30590095 14 Orsi N.M. Tribe R.M. Cytokine Networks and the Regulation of Uterine Function in Pregnancy and Parturition J Neuroendocrinol 20 4 2008 462 469 18266939 15 Nowicki S. Izban M.G. Pawelczyk E. Preterm Labor: CD55 in Maternal Blood Leukocytes Am J Reprod Immunol 61 2009 360 437 19341386 16 Garcia-Flores V. Romero R. Miller D. Xu Y.i. Done B. Veerapaneni C. Inflammation-Induced Adverse Pregnancy and Neonatal Outcomes Can Be Improved by the Immunomodulatory Peptide Exendin-4 Front Immunol 9 2018 17 Morgan J.A. Biggio J.R. Martin J.K. Mussarat N. Chawla H.K. Puri P. Maternal Outcomes After Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection in Vaccinated Compared With Unvaccinated Pregnant Patients Obstet Gynecol 139 1 2022 107 109 34644272 18 Halasa N.B. Olson S.M. Staat M.A. Effectiveness of Maternal Vaccination with mRNA COVID-19 Vaccine During Pregnancy Against COVID-19-Associated Hospitalization in Infants Aged <6 Months - 17 States, July 2021-January 2022 MMWR Morb Mortal Wkly Rep 71 2022 264 270 35176002 19 Gray K.J. Bordt E.A. Atyeo C. Deriso E. Akinwunmi B. Young N. Coronavirus disease 2019 vaccine response in pregnant and lactating women: a cohort study Am J Obstet Gynecol 225 3 2021 303.e1 303.e17 20 Dagan N. Barda N. Biron-Shental T. Makov-Assif M. Key C. Kohane I.S. Effectiveness of the BNT162b2 mRNA COVID-19 vaccine in pregnancy Nat Med 27 10 2021 1693 1695 34493859 21 Theiler R.N. Wick M. Mehta R. Weaver A.L. Virk A. Swift M. Pregnancy and birth outcomes after SARS-CoV-2 vaccination in pregnancy Am J Obstet Gynecol MFM 3 6 2021 100467 34425297 22 Blakeway H. Prasad S. Kalafat E. Heath P.T. Ladhani S.N. Le Doare K. COVID-19 vaccination during pregnancy: coverage and safety Am J Obstet Gynecol 226 2 2022 236.e1 236.e14 23 Bleicher I. Kadour-Peero E. Sagi-Dain L. Sagi S. Early exploration of COVID-19 vaccination safety and effectiveness during pregnancy: interim descriptive data from a prospective observational study Vaccine 39 44 2021 6535 6538 34600749 24 Bookstein Peretz S. Regev N. Novick L. Short-term outcome of pregnant women vaccinated with BNT162b2 mRNA COVID-19 vaccine Ultrasound Obstet Gynecol 58 2021 450 546 34198360 25 Goldshtein I. Nevo D. Steinberg D.M. Rotem R.S. Gorfine M. Chodick G. Association Between BNT162b2 Vaccination and Incidence of SARS-CoV-2 Infection in Pregnant Women JAMA 326 8 2021 728 34251417 26 Lipkind H.S. Vazquez-Benitez G. DeSilva M. Vesco K.K. Ackerman-Banks C. Zhu J. Receipt of COVID-19 Vaccine During Pregnancy and Preterm or Small-for-Gestational-Age at Birth - Eight Integrated Health Care Organizations, United States, December 15, 2020-July 22, 2021 MMWR Morb Mortal Wkly Rep 71 1 2022 26 30 34990445 27 Rottenstreich M. Sela H.Y. Rotem R. Kadish E. Wiener‐Well Y. Grisaru‐Granovsky S. Covid-19 vaccination during the third trimester of pregnancy: rate of vaccination and maternal and neonatal outcomes, a multicentre retrospective cohort study BJOG 129 2 2022 248 255 34554630 28 Wainstock T. Yoles I. Sergienko R. Sheiner E. Prenatal maternal COVID-19 vaccination and pregnancy outcomes Vaccine 39 41 2021 6037 6040 34531079 29 Shimabukuro T.T. Kim S.Y. Myers T.R. Moro P.L. Oduyebo T. Panagiotakopoulos L. Preliminary Findings of mRNA Covid-19 Vaccine Safety in Pregnant Persons N Engl J Med 384 24 2021 2273 2282 33882218 30 Magnus M.C. Örtqvist A.K. Dahlqwist E. Ljung R. Skår F. Oakley L. Association of SARS-CoV-2 Vaccination During Pregnancy With Pregnancy Outcomes JAMA 327 15 2022 1469 35323851 31 Mount Sinai Hospital. Facts and Figures. 2022. Available at https://www.mountsinai.org/about/facts. Accessed April 5, 2022. 32 Molenaar NM, Rommel AS, de Witte L, et al. SARS-CoV-2 during pregnancy and associated outcomes: Results from an ongoing prospective cohort. Paediatr Perinat Epidemiol 2021 [Eub ahead of print]. 33 Amanat F. Stadlbauer D. Strohmeier S. Nguyen T.H.O. Chromikova V. McMahon M. A serological assay to detect SARS-CoV-2 seroconversion in humans Nat Med 26 7 2020 1033 1036 32398876 34 Bradley T. Grundberg E. Selvarangan R. LeMaster C. Fraley E. Banerjee D. Antibody Responses after a Single Dose of SARS-CoV-2 mRNA Vaccine N Engl J Med 384 20 2021 1959 1961 33755375 35 Rasmussen S.A. Kelley C.F. Horton J.P. Jamieson D.J. Coronavirus Disease 2019 (COVID-19) Vaccines and Pregnancy: What Obstetricians Need to Know Obstet Gynecol 137 3 2021 408 414 33370015 36 Gómez-Chávez F. Correa D. Navarrete-Meneses P. Cancino-Diaz J.C. Cancino-Diaz M.E. Rodríguez-Martínez S. NF-κB and Its Regulators During Pregnancy Front Immunol 12 2021 679106 37 Casazza R.L. Lazear H.M. Miner J.J. Protective and Pathogenic Effects of Interferon Signaling During Pregnancy Viral Immunol 33 1 2020 3 11 31545139 38 Gomez-Lopez N. StLouis D. Lehr M.A. Sanchez-Rodriguez E.N. Arenas-Hernandez M. Immune cells in term and preterm labor CellMol Immunol 11 6 2014 571 581 39 Racicot K. Mor G. Risks associated with viral infections during pregnancy J Clin Investig 127 5 2017 1591 1599 28459427 40 Shafiq M. Mathad J.S. Naik S. Alexander M. Yadana S.u. Araújo-Pereira M. Association of Maternal Inflammation During Pregnancy With Birth Outcomes and Infant Growth Among Women With or Without HIV in India JAMA Network Open 4 12 2021 e2140584 34935918 41 Choi G.B. Yim Y.S. Wong H. Kim S. Kim H. Kim S.V. The maternal interleukin-17a pathway in mice promotes autism-like phenotypes in offspring Science 351 6276 2016 933 939 26822608 42 Zawadzka A. Cieślik M. Adamczyk A. The Role of Maternal Immune Activation in the Pathogenesis of Autism: A Review of the Evidence, Proposed Mechanisms and Implications for Treatment Int J Mol Sci 22 2021 11516 34768946 43 Gill L. Jones C.W. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibodies in Neonatal Cord Blood After Vaccination in Pregnancy Obstet Gynecol 137 2021 894 986 33684922 44 Paul G. Chad R. Newborn antibodies to SARS-CoV-2 detected in cord blood after maternal vaccination – a case report BMC Pediatrics 21 2021 138 33752624 45 Shuffrey LC, Firestein MR, Kyle MH, et al. Association of Birth During the COVID-19 Pandemic With Neurodevelopmental Status at 6 Months in Infants With and Without In Utero Exposure to Maternal SARS-CoV-2 Infection. JAMA Pediatrics 2022:e215563-e63 [Epub ahead of print].

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==== Front Environ Health Perspect Environ Health Perspect EHP Environmental Health Perspectives 0091-6765 1552-9924 Environmental Health Perspectives EHP12437 10.1289/EHP12437 Erratum Erratum: “Examining the Relationship between Climate Change and Vibriosis in the United States: Projected Health and Economic Impacts for the 21st Century” Sheahan Megan Gould Caitlin A. Neumann James E. Kinney Patrick L. Hoffmann Sandra Fant Charles Wang Xinyue https://orcid.org/0000-0002-7134-8317 Kolian Michael 14 12 2022 12 2022 14 12 2022 130 12 12900213 11 2022 23 11 2022 https://ehp.niehs.nih.gov/about-ehp/license EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Environ Health Perspect. 130(8): 087007 (2022), https://doi.org/10.1289/EHP9999a ==== Body pmcThe caption of Figure 1 contained extraneous text. These errors did not affect the figure. The corrected Figure caption reads: Figure 1. Trends identified in vibriosis cases in the United States reported to COVIS (2007–2018) by species. This figure does not present the number of total cases, which would account for underreporting and misdiagnosis. See Table S14 for the corresponding values presented in this figure. Note: ALG, V. alginolyticus; COVIS, Cholera and Other Vibrio Illness Surveillance (system); OTHER, other non V. cholerae Vibrio species, including V. cholerae non-O1 and non-O139, V. cincinnatiensis, V. damsela, V. fluvialis, V. furnissii, V. hollisae, V. metschnikovii, V. mimicus, V. species not identified, multiple V. species, and other; PAR, V. parahaemolyticus; VUL, V. vulnificus.

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==== Front Environ Health Perspect Environ Health Perspect EHP Environmental Health Perspectives 0091-6765 1552-9924 Environmental Health Perspectives EHP10321 10.1289/EHP10321 Research Maternal Metals Exposure and Infant Weight Trajectory: The Japan Environment and Children’s Study (JECS) https://orcid.org/0000-0002-2442-2406 Taniguchi Yu 1 Yamazaki Shin 1 https://orcid.org/0000-0001-7772-0389 Nakayama Shoji F. 1 https://orcid.org/0000-0002-2104-9140 Sekiyama Makiko 1 Michikawa Takehiro 2 https://orcid.org/0000-0001-9235-1227 Isobe Tomohiko 1 https://orcid.org/0000-0003-4275-548X Iwai-Shimada Miyuki 1 https://orcid.org/0000-0002-9221-4940 Kobayashi Yayoi 1 Nitta Hiroshi 1 Oba Mari 3 Kamijima Michihiro 4 the Japan Environment and Children’s Study Group 1 Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, Tsukuba, Japan 2 Department of Environmental and Occupational Health, Toho University School of Medicine, Ota, Japan 3 Department of Medical Statistics, Toho University School of Medicine, Ota, Japan 4 Department of Occupational and Environmental Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan Address correspondence to Shin Yamazaki, Japan Environment and Children’s Study Programme Office, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan. Email: [email protected] 14 12 2022 12 2022 130 12 12700514 9 2021 14 9 2022 14 10 2022 https://ehp.niehs.nih.gov/about-ehp/license EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Background: To our knowledge, the association of maternal exposure to metallic elements with weight trajectory pattern from the neonatal period has not been investigated. Objectives: The goals of this study were to identify infant growth trajectories in weight in the first 3 y of life and to determine the associations of maternal blood levels of lead, cadmium, mercury, selenium, and manganese with growth trajectory. Methods: This longitudinal study, part of the Japan Environment and Children Study, enrolled 103,099 pregnant women at 15 Regional Centres across Japan between 2011 and 2014. Lead, cadmium, mercury, selenium, and manganese levels were measured in blood samples collected in the second (14–27 wk gestational age) or third trimester (≥28wk). Growth trajectory of 99,014 children was followed until age 3 y. Raw weight values were transformed to age- and sex-specific weight standard deviation (SD) scores, and latent-class group-based trajectory models were estimated to determine weight trajectories. Associations between maternal metallic element levels and weight trajectory were examined using multinomial logistic regression models after confounder adjustment. Results: We identified 5 trajectory patterns based on weight SD score: 4.74% of infants were classified in Group I, very small to small; 31.26% in Group II, moderately small; 21.91% in Group III, moderately small to moderately large; 28.06% in Group IV, moderately large to normal; and 14.03% in Group V, moderately large to large. On multinomial logistic regression, higher maternal lead and selenium levels tended to be associated with increased odds ratios (ORs) of poor weight SD score trajectories (Groups I and II), in comparison with Group III. Higher levels of mercury were associated with decreased ORs, whereas higher levels of manganese were associated with increased ORs of “moderately large” trajectories (Groups IV and V). Discussion: Maternal lead, mercury, selenium, and manganese blood levels affect infant growth trajectory pattern in the first 3 y of life. https://doi.org/10.1289/EHP10321 Supplemental Material is available online (https://doi.org/10.1289/EHP10321). The authors declare they have no actual or potential competing financial interests. Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days. ==== Body pmcIntroduction The World Health Organization (WHO) estimates that 15%–20% of all births have low birth weight (LBW)1; in particular, 6% of infants born in East Asia and the Pacific, 13% in sub-Saharan Africa, and up to 28% in South Asia have LBW.2 LBW due to intrauterine growth restriction affects immune function,3 infectious disease hospitalization,4 illness, and mortality during the neonatal period.5 Negative effects of LBW can continue over a long period and cause adverse health outcomes, including cognitive deficits and altered brain structure in school-age children,6 mortality in adolescence,7 and disease and mortality in later life.8 Reducing LBW thus is a serious public health concern worldwide. Previous studies have reported an association between maternal exposure to several metallic elements, including iron,9 lead,10 cadmium,11 mercury,12 selenium,13 and manganese,14 and infant birth weight. In fact, a systematic review and meta-analysis identified an association of prenatal anemia and its most common cause—iron deficiency—with risk of LBW.9 As a consequence, two guidelines (WHO and Nordic Nutrition Recommendations) now recommend routine iron supplementation during pregnancy. However, other studies have reported that levels of several metallic elements, such as selenium and manganese, during pregnancy show only a marginal or no significant association with birth weight15,16; thus, dietary intake recommendations for these metallic elements during pregnancy require further consideration. According to the Developmental Origins of Health and Disease hypothesis,17 postnatal growth patterns, in addition to infant birth weight, also play a key role in neonatal prognosis. Previous studies examining infant growth trajectory patterns have done so by targeting specific groups based on, for example, adiponectin concentration,18 LBW status,19 birth weight quartile,20 and prepregnancy body mass index (BMI).21 A few studies, however, have examined entire populations to identify growth trajectory patterns. For example, Carling et al. examined weight-for-length trajectory using prenatal cohort data obtained from 595 subjects,22 Wang et al. investigated BMI growth trajectory from age 2 to 6 y using large cohort data (n=71,892),23 and Marinac et al. studied body shape trajectory from age 5 to 60 y using large cohort data (4,280,712 person-years of follow-up).24 Although weight standard deviation (SD) score is widely used in clinical settings to evaluate postnatal growth patterns, use of the weight SD score in longitudinal studies has to date been limited.20 To our knowledge, no study has used multiple-repeated-measures data obtained from a large birth cohort study to investigate trajectory patterns in weight SD scores calculated for the neonatal period and examined their associations with maternal levels of metallic elements. The ultimate goal of the nationwide birth cohort Japan Environment and Children’s Study (JECS) is to identify environmental factors that affect children’s health and development.25–27 Lead, cadmium, mercury, selenium, and manganese are considered to be important coexposures when examining the effect of other chemical substances on child development, and the JECS has provided basic data on the levels of these five metallic elements.25 In this prospective study, we examined data on maternal levels of blood metallic elements during pregnancy and repeated-measures data on infants’ weight SD scores from the neonatal period up to age 3 y. This study had two objectives: a) to identify weight SD score trajectories in the first 3 y of life and b) to examine the association of maternal levels of blood metallic elements (lead, cadmium, mercury, selenium, and manganese) with infant growth trajectories in weight. Methods Data Source and Study Population The data sets were sourced from a nationwide government-funded birth cohort study in Japan, the JECS. The design of the JECS has been published elsewhere.26,27 Briefly, a total of 103,099 women in early pregnancy were recruited and registered at 15 Regional Centres across Japan via co-operating health care providers and/or local government offices between 2011 and 2014. The profile paper reported that the children in the JECS covered approximately 45% of total live births within the Study Area,27 and it is assumed that the JECS participants are a representative sample of the Japanese population. Maternal blood samples for metallic element testing were collected during mid–late pregnancy.25 The present study used the jecs-ta-20190930 data set, released in October 2019, which contains follow-up data until the children were age 3 y (8 waves of follow-up). To be eligible for the study, individuals had to have repeated measurement data required for calculating weight SD score at least twice during the 8 waves of follow-up. Among 104,062 fetuses, the study excluded women who had miscarriages, stillbirths, or complications for unknown reasons at birth (n=758) and those who withdrew consent during the follow-up period (n=3,623). We further excluded those with missing data or single-measurement data for weight and height during the 8 waves of follow-up (n=663), those with logical contradiction in monthly age data at the time of measurement without 3 months of data, or those with missing data for sex (n=4). The data source for the present study was the 99,014 children who had their weight SD score. The average number (SD) of follow-up assessments was 6.1 (1.7), and the total number of observations during follow-up was 599,192 (Figure 1). Among 99,014 children, 97,164 were singletons, 1,810 were twins, and 40 were triplets; in addition, we used 99,014 samples to identify the infant growth trajectories in weight in all participants in the JECS. We additionally conducted preliminary analysis without twins and triplets (n=97,164). Figure 1. Flowchart of data processing to identify growth trajectory patterns in weight SD score in the first 3 y of life in the JECS study. Figure 1 is a flowchart that has six steps. Step 1: There are 104062 fetuses from the dataset jecs-ta-20190930, excluding 758 cases of miscarriages, stillbirths, or unknown. Step 2: There have been 103304 live births, excluding 3623 cases where consent was not obtained. Step 3: There are 99681 available subjects, excluding 663 cases of missing or only once measured data for weight and height during the 8-week follow-up. Step 4: There are 99018 subjects with repeated measured weight data, excluding 2 cases of logical contradiction for age at the time of measurement and 2 cases of missing data for sex. Step 5: There are 99014 subjects with repeated measured weight S D score data during the 8-week follow-up period (total of 599192 observations), excluding 4004 cases of missing data for maternal metallic element measurements. Step 6: There are 95010 subjects with repeated measure weight S D score data during an 8-week follow-up period and maternal metallic element measurements. The JECS protocol was reviewed and approved by the Ministry of the Environment’s institutional review board on epidemiological studies and the Ethics Committees of all participating institutions. The JECS was conducted in accordance with the Helsinki Declaration and other nationally valid regulations and guidelines. Written informed consent was obtained from all participants. Outcomes of Interest In this study, we collected children’s weight data from medical records transcribed at delivery and age 1 month. From 1 month after birth, self-administered questionnaires were continually mailed to participants every 6 months based on the children’s birthday.26 From age 0.5 to 3 y, we used children’s weight data provided on self-administered questionnaires by their mothers and/or guardians, and corresponding weight values were selected if they were measured within 3 months (highest frequency±1 month) at the time of each survey as follows: “0.5 years” included 4 to 6 months, “1 year” included 10–12 months, “1.5 years” included 16–18 months, “2 years” included 22–24 months, “2.5 years” included 28–30 months, and “3 years” included 34–36 months. We accepted weight data from the self-administered questionnaires that were within mean±5 SD. We transformed the raw weight values to age- and sex-specific weight SD scores28 and referred to the mean and SD of the reference population at each age in months provided in data from The Japanese Society for Pediatric Endocrinology.29 Finally, we obtained repeated-measures data on weight SD scores across the 8 waves of follow-up for the following age groups: 0 months (at delivery), 1 month, 0.5 y, 1 y, 1.5 y, 2 y, 2.5 y, and 3 y. Exposure Assessment In the JECS study, lead, cadmium, mercury, selenium, and manganese were measured as important coexposures when seeking to evaluate the effect of other chemical contaminants on child health.25 Blood samples (33mL) were collected by medical staff when the participants visited cooperating health care providers in the second (14–27 wk of gestational age) or third trimester (28 wk or more of gestational age). Approximately half of the blood samples used in this study were collected at the second trimester, and about half were collected at the third trimester. Whole blood samples for chemical analysis were collected into 3-mL tubes with sodium ethylenediaminetetraacetic acid (EDTA), transferred to a central laboratory within 48 h, divided into cryo-biobanking tubes, and stored at −80°C until analysis. Detailed information on measurement of metallic elements, including details on chemicals, reagents, sample preparation, instrument analysis, calculations, quality control, and comparison of each element with previous studies has been published elsewhere.25 Briefly, levels of metallic elements in this study were measured using inductively coupled plasma–mass spectrometry (ICP-MS) on an Agilent 7700 device (Agilent Technologies). Detection rate for measurement of metallic elements was 100%, and the method detection limits for each element were calculated based on Currie’s method using the following equation: t(n−1,0.05)×2×s, that t(n−1, 0.05) represents the Student’s t value under an α level of 0.05 with n−1 degrees of freedom, and s represents the standard deviation of blank measurements in n replicates (n≥7).25 The analysis was conducted in three different laboratories and quality control data indicated that all three laboratories showed good agreement, resulting in very high precision with differences of <10% relative SD between each measurement.25 Seven-point calibration curves had coefficients of determination higher than 0.9999. The method detection limits for lead, cadmium, mercury, selenium, and manganese were 0.129, 0.0234, 0.0490, 0.837, and 0.522 ng/g, respectively.25 Of the 99,014 children included in this study, maternal metallic element measurements were obtained for 95,010 (Figure 1). Covariates We used medical records transcribed during pregnancy and after delivery, and self-administered questionnaires conducted at the time of study registration, during mid- to late pregnancy and at child age 1 month to assess the following information: maternal age at delivery,30 infant sex, gestational duration,31 mode of delivery,32 primipara, medical and obstetric history,33,34 BMI (weight and height) before pregnancy, weight before pregnancy, weight at delivery, weight gain during pregnancy,35 smoking habit,36 drinking habits,37 household income,38 education,39 physical activity levels using The International Physical Activity Questionnaire short form (IPAQ),40,41 health-related quality of life using The Short Form-8 (SF-8),42 psychological distress using The Kessler Psychological Distress Scale (K6),43 long-term dietary intake of foods and nutrients using the Food Frequency Questionnaire (FFQ),44 and breastfeeding during the first month of life. Additionally, breastfeeding period and infant formula period were assessed in the self-administered questionnaires administered when the child was 1 y old. Medical and obstetric history in the present study included clinically relevant medical conditions: anemia, hypertension, hyperlipidemia, congenital heart disease, Kawasaki disease, pregnancy hypertension, bronchial asthma, atopic dermatitis, drug allergy, connective tissue disease, other immunological disease, type 2 diabetes mellitus, gestational diabetes, hyperthyroidism/Basedow disease, hypothyroidism/Hashimoto’s disease, depression, chronic nephritis (IgA nephropathy/glomerulonephritis), other kidney disease, paramenia/menoxenia, endometriosis, hysteromyoma, adenomyosis uteri, uterine deformity, ovarian tumor/ovarian cyst, polycystic ovary syndrome, other gynopathy, breast cancer, cervical cancer, uterine cancer, placental abruption, and other abnormal pregnancy/abnormal delivery. For each condition, participants were asked whether a physician had diagnosed the specific condition (yes or no). We used the food frequency questionnaire (FFQ) that formed part of the Japan Public Health Center-based Prospective Study for the Next Generation (JPHC-NEXT).44 In this study, an FFQ was used to assess estimated daily intake of energy (kcal), protein (grams per day), fat (grams per day), carbohydrate (grams per day), vitamin D (micrograms per day), vitamin K (micrograms per day), vitamin B1 (milligrams per day), vitamin B2 (milligrams per day), niacin (milligrams per day), vitamin B6 (milligrams per day), vitamin B12 (micrograms per day), folic acid (micrograms per day), pantothenic acid (milligrams per day), vitamin C (milligrams per day), saturated fatty acids (grams per day), monounsaturated fatty acid (grams per day), polyunsaturated fatty acid (grams per day), and cholesterol (milligrams per day). Categories of covariates in this study were mode of delivery (vaginal, induction of labor, vacuum extraction delivery, forceps delivery, cesarean); smoking habit (never, ex-smoker who quit before pregnancy, ex-smoker who quit from pregnancy, current); drinking habit (never, ex-drinker who quit before pregnancy, ex-drinker who quit from pregnancy, current); household income (<2 million, 2–4 million, 4–6 million, 6–8 million, 8–10 million, 10–12 million, 12–15 million, 15–20 million, ≥20 million Japanese yen); education (junior high school, high school, technical school, vocational school, junior college, university, graduate school); health-related quality of life, using the SF-8, which covers general health, physical function, role physical, bodily pain, vitality, social functioning, mental health, and role emotional, and physical component summary and mental component summary; and breastfeeding during the first month of life (mother’s milk only, mother’s milk and infant formula, infant formula only). The adjusted logistics regression model used to examine the associations of maternal intake of metallic elements with weight SD score trajectory pattern included covariates by accumulated evidence and comparison with maternal metallic element levels; however, correlation values above 0.4 were excluded to avoid multicollinearity, and medical and obstetric history with a prevalence below 1.00% was also subsequently excluded. Selected confounders were maternal age at delivery, infant sex, gestational duration, mode of delivery, primipara, medical and obstetric history at early pregnancy (anemia, pregnancy hypertension, bronchial asthma, atopic dermatitis, drug allergy, hyperthyroidism/Basedow disease, hypothyroidism/Hashimoto’s disease, depression, other kidney disease, paramenia/menoxenia, endometriosis, hysteromyoma, ovarian tumor/ovarian cyst, polycystic ovary syndrome, other gynopathy, other abnormal pregnancy/abnormal delivery), BMI and height before pregnancy, weight gain during pregnancy, smoking at early pregnancy, drinking at early pregnancy, household income at midpregnancy, education at midpregnancy, IPAQ at early pregnancy, SF-8 at early pregnancy (general health, bodily pain), K6 at midpregnancy, FFQ at early pregnancy (energy), breastfeeding during the first month of life, and breastfeeding period until 12 months; these selected confounders were also included as individual covariates. Statistical Analyses First, we identified trajectory patterns in weight SD scores calculated for the first 3 y of life using latent-class group-based trajectory models in SAS PROC TRAJ (SAS Institute, Inc.),45,46 which assumes that a study population comprises a mixture of finite latent groups within which people follow an approximately homogeneous growth trajectory. Cubic trajectory models were fitted for a fixed number of latent groups, and posterior probabilities for group membership assignment were calculated for each individual. Model selection for the number of trajectory groups and functional form were based on following criteria (Bayesian information criterion for the models and the precision of the group proportion, clinical trajectory interpretability, and the average posterior probability of assignment of the participants to their groups). Next, we compared maternal levels of metallic elements and covariates among the weight SD score trajectory groups using the χ2 test or analysis of variance (ANOVA). Finally, multinomial logistic regression models were fitted to examine the associations between maternal levels of metallic elements and weight SD score trajectory in the adjusted model. Based on several previous studies’ reported nonlinear associations of maternal metallic elements with birth weight, we categorized exposure variables into four levels. We confirmed the overall trend for the associations of maternal metallic elements with infant growth trajectories in weight by the test for linear trend. Bonferroni correction was used to adjust for multiple comparisons. All statistical analyses were conducted with SAS (version 9.4; SAS Institute, Inc.). The level of significance was set at p<0.05. Results Mothers included in the study had a mean (SD) age at delivery of 31.17 (5.05) y, gestational duration of 38.77 (1.65) wk, weight before pregnancy of 53.22 (8.98) kg, and weight at delivery of 63.49 (9.41) kg. Among the infants, 51.25% were male, 56.71% were born vaginally, and 20.05% were delivered by cesarean. The mean birth weight was 3,010.09 (430.54) g, mean birth height was 48.85 (2.37) cm, and mean gestational duration was 38.77 (1.65) wk (Table 1; Table S1). Maternal levels of blood metallic elements were shown in gravimetric units and volumetric concentrations.25 Median maternal blood levels were 5.85 ng/g and 0.62 μg/dL for lead, 0.66 ng/g and 0.69μg/L for cadmium, 3.64 ng/g and 3.82μg/L for mercury, 168.0 ng/g and 176.5μg/L for selenium, and 15.4 ng/g and 16.2μg/L for manganese, respectively (Table 2). Table 1 Characteristics of this study subjects in the Japan Environment and Children’s study (JECS) 2011–2014, stratified by weight SD trajectory group. Variableb Weight SD trajectory groupa Total p-Valuec Group I: Very small to small (4.74%) Group II: Moderately small (31.26%) Group III: Moderately small to moderately large (21.91%) Group IV: Moderately large to normal (28.06%) Group V: Moderately large to large (14.03%) Lead level (ng/g) (n=95,010) 6.59 (2.63) 6.37 (2.99) 6.46 (2.94) 6.21 (2.75) 6.32 (2.65) 6.35 (2.85) <.001 Cadmium level (ng/g) (n=95,010) 0.80 (0.43) 0.74 (0.38) 0.77 (0.39) 0.73 (0.37) 0.76 (0.38) 0.75 (0.38) <.001 Mercury level (ng/g) (n=95,010) 4.24 (2.49) 4.23 (2.48) 4.23 (2.54) 4.15 (2.45) 4.17 (2.49) 4.20 (2.49) <.001 Selenium level (ng/g) (n=95,010) 169.8 (21.1) 170.5 (20.2) 169.7 (20.2) 169.9 (20.2) 169.5 (21.0) 169.99 (20.33) <.001 Manganese level (ng/g) (n=95,010) 15.84 (4.86) 15.86 (4.67) 15.84 (4.63) 16.12 (4.67) 16.13 (4.67) 15.96 (4.67) <.001 Infant sex (% male) (n=99,014) 48.19 51.74 46.26 55.12 51.21 51.25 <.001 Infant birth height (cm) (n=98,541) 43.73 (3.98) 48.36 (1.75) 48.23 (1.79) 50.05 (1.59) 50.22 (1.76) 48.85 (2.37) <.001 Maternal age at delivery (y) (n=99,014) 31.76 (5.15) 31.15 (5.04) 31.50 (5.05) 30.89 (5.03) 31.06 (5.03) 31.17 (5.05) <.001 Gestational duration (wk) (n=98,830) 35.56 (3.55) 38.78 (1.27) 38.39 (1.45) 39.37 (1.07) 39.21 (1.19) 38.77 (1.65) <.001 Mode of delivery (n=98,588) — — — — — — <.001  Vaginal 34.55 58.97 54.12 60.49 55.61 56.71 —  Induction of labor 11.02 16.24 15.67 19.97 20.79 17.55 —  Vacuum extraction delivery 2.94 4.89 5.21 6.15 6.69 5.47 —  Forceps delivery 0.09 0.20 0.20 0.22 0.30 0.21 —  Cesarean 51.41 19.69 24.80 13.17 16.62 20.05 — Primipara (% yes) (n=99,014) 48.62 41.20 49.17 38.24 44.00 42.86 <.001 Medical and obstetric history at early pregnancy (n=97,872) — — — — — — — Anemia (% yes) 18.20 18.44 17.64 19.36 18.29 18.49 <.001 Pregnancy hypertension (% yes) 3.33 1.36 1.57 1.13 1.50 1.45 <.001 Bronchial asthma (% yes) 10.82 11.04 10.20 11.23 11.10 10.91 .005 Atopic dermatitis (% yes) 16.27 16.07 15.20 16.13 14.76 15.72 <.001 Drug allergy (% yes) 2.94 2.67 2.75 2.43 2.34 2.59 .023 Hyperthyroidism/Basedow disease (% yes) 1.21 1.13 1.18 0.93 0.96 1.07 .029 Hypothyroidism/Hashimoto’s disease (% yes) 1.23 0.96 1.22 0.90 0.87 1.00 .001  Depression (% yes) 3.85 3.06 3.00 2.92 2.88 3.69 .012  Other kidney disease (% yes) 1.49 1.60 1.58 1.43 1.23 1.49 .030  Paramenia/menoxenia (% yes) 14.30 11.35 11.60 10.93 11.22 11.41 <.001  Endometriosis (% yes) 4.35 3.66 4.14 3.23 3.52 3.66 <.001  Hysteromyoma (% yes) 8.20 5.73 7.27 5.28 6.07 6.10 <.001  Ovarian tumor/ovarian cyst (% yes) 3.48 3.47 3.75 3.20 3.48 3.46 .025  Polycystic ovary syndrome (% yes) 2.99 2.34 2.17 2.34 2.14 2.31 .010  Other gynopathy (% yes) 4.95 3.92 4.32 3.80 3.88 4.02 <.001  Other abnormal pregnancy, abnormal delivery (% yes) 2.90 2.30 2.22 1.69 1.72 2.06 <.001 BMI before pregnancy (kg/m2) (n=98,899) 20.89 (3.48) 20.78 (3.06) 21.09 (3.21) 21.49 (3.32) 22.08 (3.65) 21.23 (3.30) <.001 Weight before pregnancy (kg) (n=98,945) 51.20 (8.96) 51.19 (8.02) 52.88 (8.54) 54.09 (8.89) 56.57 (9.88) 53.13 (8.89) <.001 Height before pregnancy (cm) (n=98,952) 156.52 (5.38) 156.93 (5.21) 158.31 (5.28) 158.61 (5.17) 160.03 (5.34) 158.12 (5.35) <.001 Weight at delivery (kg) (n=96,747) 59.47 (9.13) 61.18 (8.61) 62.71 (8.52) 65.19 (9.71) 67.77 (9.65) 63.49 (9.41) <.001 Weight gain during pregnancy (kg) (n=96,685) 8.26 (4.21) 9.98 (4.85) 9.82 (4.08) 11.07 (5.71) 11.17 (4.25) 10.34 (4.91) <.001 Smoking at early pregnancy (% yes) (n=97,161) — — — — — — <.001  Never 59.55 60.10 58.35 57.44 55.53 58.30 —  Ex-smoker who quit before pregnancy 21.75 22.90 23.06 25.03 24.47 23.70 —  Ex-smoker who quit from pregnancy 12.19 12.21 13.10 13.59 15.29 13.22 —  Current 6.50 4.79 5.49 3.95 4.71 4.78 — Drinking at early pregnancy (% yes) (n=97,439) — — — — — — <.001  Never 35.93 35.83 33.85 34.60 32.48 34.58 —  Past 54.15 54.37 56.11 55.56 57.25 55.48 —  Current 9.91 9.80 10.03 9.85 10.27 9.94 — Household income at midpregnancy (% yes) (n=90,611) — — — — — — <.001 <2 million yen 6.64 5.57 5.46 5.69 5.63 5.64 —  2–4 million yen 35.15 34.83 33.32 35.18 34.09 34.50 —  4–6 million yen 31.93 33.39 32.93 32.93 33.13 33.06 —  6–8 million yen 15.18 15.95 16.64 15.64 15.78 15.96 —  8–10 million yen 6.67 6.20 7.01 6.54 6.68 6.56 —  10–12 million yen 2.60 2.27 2.80 2.17 2.65 2.43 —  12–15 million yen 0.84 0.94 1.05 0.91 0.95 0.95 —  15–20 million yen 0.58 0.56 0.52 0.53 0.68 0.56 —  ≥20 million yen 0.41 0.29 0.26 0.41 0.41 0.34 — Education at mid-pregnancy (% yes) (n=96,943) — — — — — — <.001  Junior high school 5.08 4.75 4.17 5.08 5.12 4.78 —  High school 31.07 30.99 30.75 31.88 32.32 31.38 —  Technical school 1.59 1.59 1.62 1.68 1.71 1.64 —  Vocational school 23.48 22.65 22.90 22.73 23.64 22.90 —  Junior college 17.55 18.34 17.59 17.33 16.13 17.54 —  University 19.96 20.26 21.46 19.85 19.45 20.28 —  Graduate school 1.28 1.41 1.50 1.44 1.63 1.47 — IPAQ at early pregnancy — — — — — — —  MET-minutes (n=96,020) 425.26 (762.09) 401.63 (722.96) 391.90 (699.26) 408.31 (716.09) 431.43 (758.51) 406.67 (723.04) <.001  kcal (n=96,015) 380.76 (708.13) 361.20 (693.38) 362.17 (662.69) 386.88 (764.90) 427.21 (773.43) 378.82 (720.27) <.001 SF-8 at early pregnancy — — — — — — —  General health  (n=97,509) 46.25 (7.52) 46.42 (7.69) 46.36 (7.69) 46.52 (7.67) 46.71 (7.68) 46.47 (7.67) <.001  Physical function  (n=97,154) 45.69 (8.32) 46.25 (7.64) 46.10 (7.73) 46.43 (7.41) 46.41 (7.52) 46.26 (7.62) <.001  Role physical  (n=97,230) 42.55 (9.87) 43.29 (9.21) 43.10 (9.34) 43.44 (9.13) 43.56 (9.19) 43.29 (9.25) <.001  Bodily pain  (n=97,430) 49.88 (8.32) 50.15 (8.24) 50.24 (8.25) 50.22 (8.31) 50.27 (8.23) 50.19 (8.26) .041  Vitality  (n=97,521) 46.72 (7.76) 46.97 (7.68) 46.95 (7.73) 47.11 (7.57) 47.17 (7.66) 47.02 (7.66) <.001  Social functioning  (n=97,166) 43.51 (9.77) 43.90 (9.48) 43.66 (9.59) 44.09 (9.41) 44.09 (9.44) 43.91 (9.50) <.001  Mental health  (n=97,401) 46.89 (7.06) 47.18 (7.11) 47.17 (7.02) 47.21 (6.99) 47.31 (7.15) 47.19 (7.06) .013  Role emotional  (n=97,083) 46.60 (8.61) 47.03 (8.03) 46.84 (8.24) 47.19 (7.82) 47.11 (7.96) 47.03 (8.04) <.001  Physical component summary  (n=96,024) 44.60 (7.62) 45.10 (7.33) 45.01 (7.39) 45.25 (7.24) 45.34 (7.24) 45.13 (7.32) <.001  Mental component summary  (n=96,024) 45.96 (7.46) 46.16 (7.28) 46.06 (7.34) 46.26 (7.14) 46.25 (7.25) 46.17 (7.26) <.001 K6 at midpregnancy (n=97,220) 3.87 (4.07) 3.45 (3.75) 3.47 (3.73) 3.43 (3.72) 3.44 (3.73) 3.47 (3.75) <.001 FFQ at early pregnancy (n=97,859) — — — — — — —  Energy (kcal/d) 1795.29 (842.24) 1808.6 (805.89) 1821.09 (796.46) 1835.81 (823.57) 1856.81 (940.89) 1825.13 (830.96) <.001  Protein (g/d) 61.92 (34.28) 62.18 (33.02) 62.86 (33.00) 62.90 (33.19) 63.94 (39.20) 62.77 (34.06) <.001  Fat (g/d) 60.75 (37.22) 61.25 (36.55) 61.77 (36.67) 61.83 (37.25) 62.15 (41.55) 61.63 (37.55) .052  Carbohydrate (g/d) 242.61 (105.70) 244.69 (102.49) 245.86 (101.21) 249.51 (105.06) 252.90 (115.76) 247.35 (105.09) <.001  Vitamin D (μg/d) 5.31 (4.88) 5.30 (5.21) 5.28 (5.48) 5.28 (4.43) 5.37 (5.24) 5.30 (5.06) .463  Vitamin K (μg/d) 197.57 (185.76) 196.64 (169.38) 202.04 (175.72) 201.91 (182.58) 207.93 (197.34) 200.94 (179.45) <.001  Vitamin B1 (mg/d) 0.85 (0.49) 0.86 (0.46) 0.86 (0.450) 0.87 (0.46) 0.88 (0.56) 0.87 (0.48) <.001  Vitamin B2 (mg/d) 1.19 (0.84) 1.19 (0.85) 1.21 (0.84) 1.21 (0.86) 1.24 (0.97) 1.21 (0.87) <.001  Niacin (mg/d) 14.96 (8.78) 14.81 (7.95) 15.06 (8.11) 14.88 (7.80) 15.15 (9.73) 14.94 (8.26) <.001  Vitamin B6 (mg/d) 1.07 (0.61) 1.07 (0.57) 1.08 (0.56) 1.08 (0.57) 1.10 (0.66) 1.08 (0.59) <.001  Vitamin B12 (μg/d) 4.62 (3.98) 4.57 (3.99) 4.62 (4.31) 4.61 (3.85) 4.72 (4.63) 4.62 (4.12) .009  Folic acid (μg/d) 278.98 (187.66) 276.37 (175.78) 278.64 (166.76) 279.99 (180.14) 286.29 (202.42) 279.40 (179.68) <.001  Pantothenic acid (mg/d) 6.12 (3.60) 6.15 (3.53) 6.23 (3.48) 6.26 (3.56) 6.39 (4.19) 6.23 (3.63) <.001  Vitamin C (mg/d) 95.45 (83.47) 95.35 (76.82) 95.66 (72.96) 97.41 (79.16) 99.30 (79.71) 96.56 (77.40) <.001  Saturated fatty acids (g/d) 19.25 (13.21) 19.53 (13.44) 19.76 (13.44) 19.79 (13.70) 19.89 (14.71) 19.69 (13.69) .007  Monounsaturated fatty acid (g/d) 22.51 (14.20) 22.70 (13.70) 22.85 (13.82) 22.87 (13.91) 22.90 (15.30) 22.80 (14.04) .262  Polyunsaturated fatty acid (g/d) 12.20 (7.13) 12.20 (6.78) 12.26 (6.79) 12.28 (6.93) 12.38 (8.31) 12.26 (7.07) .128  Cholesterol (mg/d) 273.86 (243.73) 273.42 (238.45) 276.12 (241.19) 277.81 (241.65) 284.93 (267.19) 276.88 (244.45) <.001 Breastfeeding during the first month of life (n=96,852) — — — — — — <.001  Mother’s milk only 29.70 44.28 35.96 46.51 40.28 41.85 —  Mother’s milk and infant formula 67.87 54.34 62.46 52.16 58.16 56.67 —  Infant formula only 2.43 1.38 1.58 1.33 1.57 1.48 — Breastfeeding period until 12 months (n=87,570) 8.79 (3.95) 9.84 (3.46) 9.34 (3.73) 9.88 (3.45) 9.43 (3.73) 9.63 (3.59) <.001 Note: —, no data; BMI, body mass index; FFQ, Food Frequency Questionnaire; IPAQ, The International Physical Activity Questionnaire short form; K6, Kessler Psychological Distress Scale; SD, standard deviation; SF-8, The Short Form-8. a Proportion, mean (SD). b Other variables are shown in Table S1. c p-Values were calculated using χ2 test or analysis of variance. Table 2 Maternal metallic element levels of this study subjects in the Japan Environment and Children’s Study (JECS). n Unit Mean SD 25th percentile Median 75th percentile Lead 95,010 ng/g 6.35 2.85 4.71 5.85 7.33 Cadmium 95,010 ng/g 0.75 0.38 0.50 0.66 0.90 Mercury 95,010 ng/g 4.20 2.49 2.54 3.64 5.19 Selenium 95,010 ng/g 169.99 20.33 156.00 168.00 182.00 Manganese 95,010 ng/g 15.96 4.67 12.60 15.40 18.70 Note: Metallic element levels were measured using whole blood samples. SD, standard deviation. Trajectory Patterns in Weight SD Score We identified five trajectories in weight SD score in the first 3 y of life: 4.74% of infants were classified in Group I, very small to small; 31.26% in Group II, moderately small; 21.91% in Group III, moderately small to moderately large; 28.06% in Group IV, moderately large to normal; and 14.03% in Group V, moderately large to large (Figure 2; Table S2). Children in Group I were born with a weight SD score of −2.28 (2,088g) and exhibited consistently light body weight throughout the follow-up period. Although children in Group II had weight SD scores that were similar to those in Group III [Group II, −0.34 (2,864g); Group III, −0.47 (2,813g)] at birth, their postnatal trajectory patterns differed. Children in Group II showed gradual body weight decline after birth such that they approached values observed in Group I at around age 2 y [Group II, −0.78 (10.6kg for boys and 10.1kg for girls); Group I, −1.03 (10.3kg for boys and 10.0kg for girls) at age 2 y]. Children in Group III reached the average weight until age 1 y and 0.48 weight SD score (14.4kg for boys and 13.9kg for girls) at age 3 y. Group III showed estimated weight SD scores that were comparable to standard values both at birth and age 3 y. In contrast, children in Group IV and Group V had the same weight SD score of 0.80 (3,300g) at birth and remained above the average weight throughout the follow-up period. We confirmed that preliminary analysis without twins and triplets showed similar trajectory patterns in weight SD score (Table S3). Figure 2. Growth trajectories in estimated weight SD score in the first 3 y of life: estimating latent-class group-based trajectory models. Note: Group I, very small to small (black, n=4,696); Group II, moderately small (gray dashed line, n=30,954); Group III, moderately small to moderately large (gray, n=21,696); Group IV, moderately large to normal (light gray dashed line, n=27,781); and Group V, moderately large to large (light gray, n=13,887). Figure 2 is a line graph titled Weight S D score trajectories, plotting S D score, ranging from negative 2.5 to 2 in increments of 0.5 (y-axis) across Time, across age rating from 0 month to 3 years (x-axis) of the SD score. Associations of Maternal Levels of Metallic Elements with Infant Growth Trajectory Patterns Multinomial logistic regression models showed significant linear trends in quartiles of maternal levels of metallic elements with weight SD trajectory groups (Table 3). Higher quartiles of lead levels tended to be associated with larger ORs of being in Group I, and smaller ORs of being in Group IV and Group V, in comparison with Group III (Table 3; Figure 3). For cadmium, the highest level Q4 showed the low OR of being Group IV, whereas no clear trend was observed with the other trajectories (Table 3; Figure 4). Higher quartiles of mercury levels tended to be associated with decreased ORs of being in Group IV and Group V (Table 3; Figure 5). Higher quartiles of selenium levels tended to be associated with higher ORs of being in Group I and Group II (Table 3; Figure 6), whereas ORs of being Groups IV and V did not show clear relationships. Meanwhile, higher quartiles of manganese levels tended to be associated with increased ORs of being in Group IV and Group V (Table 3; Figure 7). Table 3 Associations between maternal metallic element levels and weight SD trajectory groups. Weight SD trajectory group Group I vs. III Group II vs. III Group IV vs. III Group V vs. III Adjusted OR (95% CI) Adjusted OR (95% CI) Adjusted OR (95% CI) Adjusted OR (95% CI) Leada Q1 Ref Ref Ref Ref Q2 1.18 (1.04, 1.34) 0.95 (0.89, 1.00) 0.93 (0.87, 0.98) 0.90 (0.84, 0.97) Q3 1.15 (1.01, 1.30) 0.95 (0.90, 1.01) 0.86 (0.80, 0.91) 0.94 (0.87, 1.01) Q4 1.33 (1.18, 1.51) 0.93 (0.88, 0.98) 0.75 (0.71, 0.80) 0.80 (0.74, 0.86) Test for linear trend <.001 .118 <.001 <.001 Cadmiumb Q1 Ref Ref Ref Ref Q2 0.91 (0.81, 1.03) 1.03 (0.97, 1.09) 0.98 (0.92, 1.04) 1.01 (0.94, 1.08) Q3 0.96 (0.84, 1.08) 0.98 (0.92, 1.04) 0.99 (0.93, 1.06) 1.02 (0.95, 1.10) Q4 0.99 (0.87, 1.13) 0.96 (0.91, 1.02) 0.88 (0.83, 0.94) 0.99 (0.92, 1.07) Test for linear trend 1.000 .248 .005 1.000 Mercuryc Q1 Ref Ref Ref Ref Q2 1.08 (0.95, 1.22) 1.06 (1.00, 1.12) 0.95 (0.89, 1.01) 0.99 (0.93, 1.07) Q3 1.14 (1.00, 1.29) 1.07 (1.01, 1.13) 0.92 (0.87, 0.98) 0.93 (0.86, 1.00) Q4 1.04 (0.92, 1.18) 1.02 (0.97, 1.09) 0.88 (0.83, 0.94) 0.90 (0.83, 0.96) Test for linear trend 1.000 1.000 <.001 .002 Seleniumd Q1 Ref Ref Ref Ref Q2 1.00 (0.89, 1.13) 1.06 (1.00, 1.12) 0.96 (0.90, 1.02) 0.91 (0.85, 0.98) Q3 1.10 (0.98, 1.25) 1.10 (1.04, 1.16) 0.95 (0.89, 1.01) 0.89 (0.83, 0.96) Q4 1.19 (1.06, 1.35) 1.11 (1.04, 1.17) 0.98 (0.92, 1.04) 0.93 (0.86, 1.00) Test for linear trend .005 <.001 1.000 .154 Manganesee Q1 Ref Ref Ref Ref Q2 0.95 (0.84, 1.07) 1.10 (0.94, 1.05) 1.04 (0.98, 1.10) 1.05 (0.98, 1.13) Q3 0.90 (0.80, 1.02) 0.99 (0.93, 1.05) 1.04 (0.98, 1.10) 1.07 (0.99, 1.15) Q4 0.88 (0.77, 0.99) 1.04 (0.99, 1.11) 1.13 (1.06, 1.20) 1.16 (1.08, 1.25) Test for linear trend .112 .943 .002 <.001 Note: Multinomial logistic regression model (n=73,704). Dependent variable was weight SD trajectory group (vs. Group III). Independent variable was maternal metallic element. Adjusted for maternal age at delivery, infant sex, gestational duration, mode of delivery, primipara, medical and obstetric history (anemia, pregnancy hypertension, bronchial asthma, atopic dermatitis, drug allergy, hyperthyroidism/Basedow disease, hypothyroidism/Hashimoto’s disease, depression, other kidney disease, paramenia/menoxenia, endometriosis, hysteromyoma, ovarian tumor/ovarian cyst, polycystic ovary syndrome, other gynopathy, other abnormal pregnancy/abnormal delivery), BMI and height before pregnancy, weight gain during pregnancy, smoking at early pregnancy, drinking at early pregnancy, household income at midpregnancy, education at midpregnancy, IPAQ at early pregnancy, SF-8 at early pregnancy (general health, bodily pain), K6 at midpregnancy, FFQ at early pregnancy (energy), breastfeeding during the first month of life, breastfeeding period until 12 months. BMI, body mass index; CI, confidence interval; FFQ, Food Frequency Questionnaire; IPAQ, The International Physical Activity Questionnaire short form; K6, Kessler Psychological Distress Scale; OR, odds ratio; Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile; Ref, reference; SD, standard deviation; SF-8, The Short Form-8. a Each quartile for lead represents the following (nanograms per gram): ≤4.71, 4.72–5.85, 5.86–7.33, ≥7.34. b Each quartile for cadmium represents the following (nanograms per gram): ≤0.495, 0.496–0.662, 0.663–0.903, ≥0.904. c Each quartile for mercury represents the following (nanograms per gram): ≤2.54, 2.55–3.64, 3.65–5.19, ≥5.20. d Each quartile for selenium represents the following (nanograms per gram): ≤156.0, 157.0–168.0, 169.0–182.0, ≥183.0. e Each quartile for manganese represents the following (nanograms per gram): ≤12.6, 12.7–15.4, 15.5–18.7, ≥18.8. Figure 3. Associations of maternal lead levels with weight SD trajectory groups. Note: Multinomial logistic regression model. Independent variables of lead level are on the y-axis, and the odds ratios and 95% confidence intervals for weight SD trajectory group (vs. Group III) are on the x-axis. Each quartile for lead (nanograms per gram) represents the following: ≤4.71, 4.72–5.85, 5.86–7.33, ≥7.34. Corresponding numerical results presented in Table 3. Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile. Figure 3 is the plot of odds ratios and 95% confidence intervals titled Lead, plotting lowest quartile, second quartile, third quartile, and highest quartile of lead for weight S D trajectory group I, II, IV, and V vs Group III (y-axis). The odds ratios and 95% confidence, rating from 0.6 to 1.5 in increment of 0.1 (x-axis). Figure 4. Associations of maternal cadmium levels with weight SD trajectory groups. Note: Multinomial logistic regression model. Independent variables of cadmium level are on the y-axis, and the odds ratios and 95% confidence intervals for weight SD trajectory group (vs. Group III) are on the x-axis. Each quartile for cadmium (nanograms per gram) represents the following: ≤0.495, 0.496–0.662, 0.663–0.903, ≥0.904. Corresponding numerical results presented in Table 3. Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile. Figure 4 is the plot of odds ratios and 95% confidence intervals titled Cadmium, plotting lowest quartile, second quartile, third quartile, and highest quartile of cadmium for weight S D trajectory group I, II, IV, and V vs Group III (y-axis). The odds ratios and 95% confidence, rating from 0.6 to 1.5 in increment of 0.1 (x-axis). Figure 5. Associations of maternal mercury levels with weight SD trajectory groups. Note: Multinomial logistic regression model. Independent variables of mercury level are on the y-axis, and the odds ratios and 95% confidence intervals for weight SD trajectory group (vs Group III) are on the x-axis. Each quartile for mercury (nanograms per gram) represents the following: ≤2.54, 2.55–3.64, 3.65–5.19, ≥5.20. Corresponding numerical results presented in Table 3. Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile. Figure 5 is the plot of odds ratios and 95% confidence intervals titled Mercury, plotting lowest quartile, second quartile, third quartile, and highest quartile of mercury for weight S D trajectory group I, II, IV, and V vs Group III (y-axis). The odds ratios and 95% confidence, rating from 0.6 to 1.5 in increment of 0.1 (x-axis). Figure 6. Associations of maternal selenium levels with weight SD trajectory groups. Note: Multinomial logistic regression model. Independent variables of selenium level are on the y-axis, and the odds ratios and 95% confidence intervals for weight SD trajectory group (vs. Group III) are on the x-axis. Each quartile for selenium (nanograms per gram) represents the following: ≤156.0, 157.0–168.0, 169.0–182.0, ≥183.0. Corresponding numerical results presented in Table 3. Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile. Figure 6 is the plot of odds ratios and 95% confidence intervals titled Selenium, plotting lowest quartile, second quartile, third quartile, and highest quartile of selenium for weight S D trajectory group I, II, IV, and V vs Group III (y-axis). The odds ratios and 95% confidence, rating from 0.6 to 1.5 in increment of 0.1 (x-axis). Figure 7. Associations of maternal manganese levels with weight SD trajectory groups. Note: Multinomial logistic regression model. Independent variables of manganese level are on the y-axis, and the odds ratios and 95% confidence intervals for weight SD trajectory group (vs. Group III) are on the x-axis. Each quartile for manganese (nanograms per gram) represents the following: ≤12.6, 12.7–15.4, 15.5–18.7, ≥18.8. Corresponding numerical results presented in Table 3. Q1, lowest quartile; Q2, second quartile; Q3, third quartile; Q4, highest quartile. Figure 7 is the plot of odds ratios and 95% confidence intervals titled Manganese, plotting lowest quartile, second quartile, third quartile, and highest quartile of manganese for weight S D trajectory group I, II, IV, and V vs Group III (y-axis). The odds ratios and 95% confidence, rating from 0.6 to 1.5 in increment of 0.1 (x-axis). Discussion Although previous studies have used birth cohort data to report growth trajectory patterns,18,19,22 there are no data on weight SD score trajectory patterns that are comparable to those presented in this study. LBW is widely defined as a birth weight below 2,500g, with 6% of births in East Asia and the Pacific categorized as LBW.2 LBW prevalence in Japan has approximately doubled in the last three decades and was estimated to reach 5.3% in 2010.47 In the present study of a nationwide birth cohort in Japan, 4.74% of infants were classified in Group I (very small to small), representing those with LBW (weight SD score below −1.25), a finding that largely overlaps with those reported in earlier studies. An interesting finding was that estimating latent-class group-based trajectory models also led to the classification of 31.26% of infants in Group II (moderately small), who fell within the normal weight range at birth but subsequently showed a gradual decline in weight such that they approached values observed in Group I at around age 2 y. Group I and Group II showed weight SD score below 0 consistently in the first 3 y of life, and we considered the infants belonging to Group I or Group II as showing poor growth. These findings indicate that even infants with normal weight at birth may exhibit poor postnatal growth, as indicated by a negative SD score, and may in turn have increased risk of adverse health outcomes, such as postrenal morbidity, neurological impairment, and chronic disease, in later life. Our findings emphasize that a large proportion—approximately one-third of children in Japan—corresponded to such a group. After controlling for a comprehensive range of important confounders known to be risk factors for LBW, maternal levels of several metallic elements showed linear trend associations with infant growth trajectory patterns, even though live birth bias might lead to underestimation of the strength of exposure–outcome association.48 First, higher maternal lead and selenium levels were associated with higher ORs of being in Group I. Previous studies have reported that maternal lead exposure during pregnancy is inversely associated with fetal growth. Irgens et al.49 showed that the offspring of mothers who were occupationally exposed to lead were at higher risk of LBW than infants of women who were not. González-Cossío et al.50 demonstrated that maternal bone-lead burden is inversely related to birth weight. A recent study from the JECS reported that even at a maternal blood lead level below 1.0μg/dL, prenatal lead exposure was associated with decreased birth weight.51 The present results likewise showed that higher maternal lead levels were associated with increased risk of LBW and decreased risk of “moderately large” trajectories (Group IV and Group V) in offspring. Regarding selenium, earlier studies have reported inconsistent evidence. Makhoul et al.15 reported that higher umbilical cord selenium concentrations were significantly correlated with higher birth weight, whereas Nazemi et al.52 showed umbilical cord selenium concentrations that positively correlated with blood selenium concentrations were not different in low and adequate birth weight infants. Tsuzuki et al.53 examined the association of maternal serum iron, zinc, copper, and selenium with birth weight and showed that only maternal selenium concentrations were positively correlated with neonatal birth weight among 44 infants. Results from the present study suggest that maternal exposure to selenium may not only be associated with LBW (Group I) but also with delayed negative effects on postnatal growth among infants with close-to-standard weight values at birth (Group II). Because the window between optimal and toxic concentrations of selenium for most living organisms is narrow,54 cautious discussion is needed. The previous study reported that the median lead level in the JECS participants was similar to that of pregnant women in the United States and Canada, and the selenium level in the JECS was similar to that of pregnant Chinese women,25 whereas the median selenium level was slightly higher than pregnant women in the United Kingdom and Australia.25 Although the precise biological mechanisms of the effects of selenium are currently unclear, lead and selenium levels have strong positive associations with dietary intake,15,55,56 which highlights the importance of control measures for preventing excessive lead and selenium intake in pregnant women. Second, higher maternal mercury levels were negatively associated with the “moderately large” trajectories (Group IV and Group V) but had no clear association with LBW (Group I) in this study. Mercury is a well-known neurotoxicant, and exposure can cause adverse health effects. A previous studies reported that higher maternal mercury levels were associated with increased risk of small for gestational age (SGA),57,58 and LBW59,60; however, several studies reported nonsignificant association with birth weight.12,61–63 Our results showed a partial negative effect of maternal mercury exposure on moderately large trajectories groups in the first 3 y of life. The fetus is highly sensitive to the toxic effects of mercury. After occupational exposure, dietary intake is the most important source of mercury exposure. In Japan, nonoccupational mercury exposure tends to be higher with high levels of fish and seafood consumption.64 However, methylmercury was not measured in this study, and the source of mercury exposure cannot be identified. Fish and seafood are rich in protein, minerals, and omega-3 unsaturated fatty acids. Consuming seafood may be beneficial, despite potential contamination with methylmercury. Although the Ministry of Health, Labor and Welfare in Japan has created a pamphlet to call attention to excessive mercury intake among pregnant women,65 it will be necessary to continue outreach activities to promote appropriate intake of seafood. Third, the present study found that higher maternal manganese levels tended to be associated with increased ORs of the “moderately large” growth trajectory groups (Group IV and Group V). Although earlier studies have examined the association between maternal manganese levels and LBW, the results have been inconsistent, with some showing a linear association66,67 and others showing a nonlinear association.68–70 An earlier study from the JECS reported that nonlinear relationships between blood manganese level and birth weight only in male infants14 and maternal manganese levels were not related to risk of preterm birth.71 This study added evidence that higher maternal manganese levels contributed to the moderately large trajectory pattern in the first 3 y of life. Accumulating evidence indicates that manganese passes through the placenta via active transport and is necessary for fetal growth and development.67,72 We suggest two potential mechanisms by which maternal manganese levels may be associated with the “moderately large” trajectory groups. First, manganese is a component of manganese superoxide dismutase (Mn-SOD),14 which is the body’s primary reactive oxygen species (ROS) scavenger.73 Sufficient Mn-SOD may be needed to prevent ROS from affecting placental mitochondria, thereby enabling escape from placental cell damage and facilitating the transport of nutrients and oxygen to the fetus.14,74 Second, manganese deficiency reduces the circulating insulin-like growth factor I (IGF-1) levels.14 Conversely, higher manganese levels may increase circulating IGF-1 levels. Increased maternal IGF-1 levels might augment placental mitogenesis and/or transplacental transfer of glucose, leading to fetal growth.14,75 There is currently no domestic or international standard for blood manganese level. We suggest that defining a standard value, especially a lower limit of blood manganese and estimated average requirement for manganese during the gestation period, is a pressing task. Fourth, an earlier systematic review and meta-analysis demonstrated that elevated maternal cadmium levels are associated with decreased birth weight and higher LBW risk76; however, in the present study, we found that cadmium level had no clear linear trend association with weight SD trajectories. Previous study identified a negative correlation between maternal blood cadmium levels and birth weight in smoking pregnant women and highlights that cadmium may be a relevant biomarker for smoking toxicity on fetal development.77 Another study showed that the association between higher maternal urinary cadmium levels and risk of preterm LBW was more pronounced among female infants than male infants.78 Previous evidence suggests that maternal cadmium levels may affect infant growth among mothers with specific lifestyles and in infants with a specific gender. Future studies should examine the association between maternal cadmium levels and infant growth and trajectory patterns, stratified by characteristics such as mother’s lifestyle and infant’s gender. The present study has some potential limitations. First, maternal blood samples were collected during the second or third trimester, at a mean (SD) gestation period of 27.3 (3.1) wk. Future studies should examine the critical period for determining postnatal growth patterns during the gestational period. Second, potential mechanisms underlying the association of maternal blood metallic elements, including lead, mercury, selenium, and manganese, with weight SD score trajectory remain to be examined in detail. Future studies should conduct genome analysis with a focus on transport protein families and the direct effect of metallic element levels in umbilical cord blood to clarify the potential mechanisms. Third, previous studies have typically measured selenium in serum and plasma, and cadmium was measured in urine. Because we only examined the association of postnatal growth patterns with selenium and cadmium in whole blood to illustrate the current exposure status, further research is needed to determine whether the association holds true in serum, plasma, and urine. Fourth, the present study did not identify the source of the metallic elements. Further study should examine important diet source that affect excessive metallic elements such as lead and selenium intake in pregnant women. Finally, this study assessed children’s weight with medical record transcripts at delivery to 1 month and self-administered questionnaires from age 0.5 to 3 y. We selected children’s weight data if they were measured within 3 months (highest frequency±1 month) at the time of each survey and accepted the data from the self-administered questionnaires that were within five SDs. The weight SD score trajectories in this study were based on two different data resources. In conclusion, this nationwide government-funded birth cohort study identified five trajectories in weight SD score in the first 3 y of life, with 4.74% of infants classified in Group I, very small to small group; 31.26% in Group II, moderately small group; 21.91% in Group III, moderately small to moderately large group; 28.06% in Group IV, moderately large to normal group; and 14.03% in Group V, moderately large to large group. Multinomial logistic regression models showed that higher maternal lead and selenium levels tended to be associated with increased ORs of poor weight SD score trajectories, such as those observed in Group I and/or Group II, in comparison with Group III, highlighting the importance of control measures for preventing excessive lead and selenium dietary intake in pregnant women. Moreover, higher levels of mercury were associated with decreased ORs, and higher levels of manganese were associated with increased ORs of the “moderately large” trajectory groups (Group IV and Group V). We suggest that higher mercury levels may have negative effects on infant growth and that defining a standard value for blood manganese during the gestation period is a pressing task. Supplementary Material Click here for additional data file. Click here for additional data file. Acknowledgments The authors would like to extend our gratitude to all JECS participants and their families. The authors also thank M. Aya and J. Yonemoto (National Institute for Environmental Studies, Tsukuba, Japan) for technical assistance in summarizing the data, C.-R. Jung (National Institute for Environmental Studies, Tsukuba, Japan) for technical assistance in preparing documents, T. Sato (Kyoto University School of Public Health, Kyoto, Japan), J. B. Cologne (Radiation Effects Research Foundation, Hiroshima, Japan) for supervising statistical methods, and the JECS staff members for their daily support. Finally, the authors gratefully acknowledge their indebtedness to the previous principal investigators for the JECS, H. Satoh (Tohoku University Graduate School of Medicine, Sendai, Japan) and T. Kawamoto (University of Occupational and Environmental Health, Kitakyushu, Japan). The JECS was funded by the Ministry of the Environment, Japan. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The findings and conclusions of this article are solely the responsibility of the authors and do not represent the official views of the Ministry of the Environment, Japan. Members of the JECS Group as of 2021 are: M. Kamijima (principal investigator, Nagoya City University, Nagoya, Japan), S. Yamazaki (National Institute for Environmental Studies, Tsukuba, Japan), Y. Ohya (National Center for Child Health and Development, Tokyo, Japan), R. Kishi (Hokkaido University, Sapporo, Japan), N. Yaegashi (Tohoku University, Sendai, Japan), K. Hashimoto (Fukushima Medical University, Fukushima, Japan), C. Mori (Chiba University, Chiba, Japan), S. Ito (Yokohama City University, Yokohama, Japan), Z. Yamagata (University of Yamanashi, Chuo, Japan), H. Inadera (University of Toyama, Toyama, Japan), T. Nakayama (Kyoto University, Kyoto, Japan), H. Iso (Osaka University, Suita, Japan), M. Shima (Hyogo College of Medicine, Nishinomiya, Japan), Y. Kurozawa (Tottori University, Yonago, Japan), N. Suganuma (Kochi University, Nankoku, Japan), K. Kusuhara (University of Occupational and Environmental Health, Kitakyushu, Japan), and T. Katoh (Kumamoto University, Kumamoto, Japan). ==== Refs References 1. (WHO) World Health Organization. 2014. 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Fetal growth in relation to maternal and fetal IGF-axes: a systematic review and meta-analysis. Acta Obstet Gynecol Scand 92 (9 ):997–1006, PMID: , 10.1111/aogs.12192.23745729 76. Huang S, Kuang J, Zhou F, Jia Q, Lu Q, Feng C, et al. 2019. The association between prenatal cadmium exposure and birth weight: a systematic review and meta-analysis of available evidence. Environ Pollut 251 :699–707, PMID: , 10.1016/j.envpol.2019.05.039.31108303 77. Menai M, Heude B, Slama R, Forhan A, Sahuquillo J, Charles MA, et al. 2012. Association between maternal blood cadmium during pregnancy and birth weight and the risk of fetal growth restriction: the EDEN mother-child cohort study. Reprod Toxicol 34 (4 ):622–627, PMID: , 10.1016/j.reprotox.2012.09.002.23017269 78. Huang K, Li H, Zhang B, Zheng T, Li Y, Zhou A, et al. 2017. Prenatal cadmium exposure and preterm low birth weight in China. J Expo Sci Environ Epidemiol 27 (5 ):491–496, PMID: , 10.1038/jes.2016.41.27436694

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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)00975-0 10.1016/j.envres.2021.111681 111681 Article A post-pandemic sustainable scenario: What actions can be pursued to increase the raw materials availability? Zanoletti Alessandra Cornelio Antonella Bontempi Elza ∗ INSTM and Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123, Brescia, Italy ∗ Corresponding author. 15 7 2021 11 2021 15 7 2021 202 111681111681 22 4 2021 30 6 2021 8 7 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. On January 30, 2020, COVID-19 outbreak, detected for the first time in Wuhan (China), was declared by WHO a Public Health Emergency. In a strongly connected world, the consequent slowdown of the Chinese economy contributed to disrupt the global supply chains of several products. In a post-pandemic scenario, the expected rapid increase in demand of critical raw materials (associated with the transition to more green energy sources), coupled with the problems that some mining activities are relegated only in certain countries and regions, must be considered in a sustainable perspective. This work analyses the literature about (critical) raw materials and COVID-19, not only to present the impact of the pandemic on their supply, but also to propose some actions that should be pursued in a post-pandemic renaissance scenario, to increase raw materials availability, with great attention to most critical ones, in the frame of circular economy principles. The post-pandemic possibilities are evaluated and suitable actions are suggested to secure the raw materials availability for the foreseen increase of investments in crucial and strategic sectors, in accord with the UN Sustainable Development Goals (SDGs). The proposed actions can be summarized as policy, strategy, economy, and technology activities. Graphical abstract Image 1 Keywords Sustainability Critical raw materials (CRMs) Circular economy SDGs COVID-19 SARS-CoV-2 ==== Body pmc1 Introduction COVID-19 pandemic has created a global crisis with more than 178 millions of detected cases and more than 3,5 millions of reported deaths at the end of June 2021. It has disrupted economic, financial, political, and social structures all over the world. Undoubtedly, the main goal of almost all the political authorities was to limit the spread of the virus, with the consequence to put in place restriction measures that have impacted on the economic development. In particular, global GDP was evaluated to be decreased by 4.2% in 2020 (Gałas et al., 2021). Research activities to face the pandemic were mainly devoted to develop effective ways to reduce the infection and to limit exposure risks, also in view of possible future epidemics due to similar viral agents (Coccia, 2020). In this frame great efforts were made to evaluate the possible sources of virus spread, that were attributed not only to human-to-human interactions, but also to environmental (as for example air pollution agents (Coccia, 2021a) (Coccia, 2021b), meteorological (Anand et al., 2021), and socio-economic factors (as for example trade exchanges (Bontempi et al., 2021b, Bontempi et al., 2021a) (Bontempi and Coccia, 2021). However, despite that COVID-19 pandemic has globally caused major health, economic, and social difficulties, also some challenges and opportunities have been recognized (Shrestha et al., 2020) (Lurie et al., 2021) (Quitzow et al., 2021). As a consequence of containment measures, people have experimented the importance of technology diffusion, for information and communication, involving several typologies of activities, like working, learning, entertainment, and share news. The relevance of electronic devices was never so evident than during lockdown, when it was necessary to reduce (and often completely avoid) human interactions (Sarkis et al., 2020). Then, the pandemic was also recognized as an opportunity to develop new skills, adopt online education, employ telecommuting, and diffuse virtual meetings options, with also several consequent environmental advantages, associated to decrease of people transport and the reduction of derived emissions (Sarkis et al., 2020). The increase of digital connectivity is also one of the European Commission (EC) ambition for the next future, coupled with the aims to construct a more green and resilient society. In this context, it is fundamental to highlight that green and digital technologies are based on the use of several raw materials (RMs). Moreover, there are some natural resources, containing essential elements, with limited and/or restricted supply, that are defined critical raw materials (CRMs). They play a fundamental role mainly for industrialized regions in the world because several of these CRMs have contributed to revolutionary development of some recent technologies and are necessary for energy efficiency. Their economic importance is connected to their applications that are expected will be ulteriorly developed in green, defence, and high-tech sectors (Althaf and Babbitt, 2021). Considering some CRMs necessity in technological applications also for green energies and ecological transition, more than 30 metals are currently inserted in the list of critical raw materials (Akcil et al., 2020). Even if COVID-19 has highlighted some market distortions connected with CRMs (as better discussed in Section 3), in the past two decades, climate events, as for example droughts and floods, rainfall variability, and extreme storms have caused major impacts in mining activities around the world (Ford et al., 2010). EC has the aspiration to reach 2050 climate neutrality and recognizes that the access to resources is strategic to fulfil this ambition. As a consequence, CRMs that have been already considered crucial for society, are now even defined super-critical (Heffron, 2020). Fig. 1 reports the expected availability of major elements (in years), obtained by considering their world reserves divided by their annual world production (data were extracted from CES Selector database (Granta Design, 2019) referring to 2019, i.e. before COVID-19). These data are plotted versus the element's abundance in Earth's crust (in ppm), that accounts the elements availability in their minerals. Generally, CRMs are reported in the tables mainly highlighting their supply risks (European Commission, 2020a). Then, the results shown in Fig. 1 are very interesting because they don't account any risk correlated with the recyclability and/or geo-political situations where these materials are mined, that are the fundamental characters defining CRMs. As a consequence, only general considerations about the elements abundance and their use can be derived. For example, it is very interesting to highlight that, even if their low Earth concentrations, Rare Earth Elements (as for example Ce, Pr, and Nd) are expected to be available in mined sites for several years (more than 10000 years), based on their commercial interest.Fig. 1 Expected elements availability (in years), versus their abundance in the Earth's crust (ppm) not considering recycling. Data are extracted from CES Selector database – 2019 (Granta Design, 2019). Elements in the bottom may be not available in the next years if their supply will be based only on mining. Fig. 1 On the contrary, Fig. 1 shows that Sb, Au, and Ag are among the resources that may be still not available only by mining in the next years. As a consequence, considering the general perspective of limited time availability of some elements, it is clear that specific actions are necessary to guarantee the expected economic growth and prosperity in the post-pandemic. For example, it results evident that it is mandatory to invest efforts in developing sustainable ways to recycle CRMs from waste, considering that some available natural resources are inappropriate to support the raw materials need of the next future. Indeed, supply of resources is considered no longer able to meet demand (Henckens, 2021). In this frame, the linear economy principles, based on the raw materials transformation into final products, and their subsequent discharge as a waste, cannot be longer followed (Rocchi et al., 2021). The circularity is defined a sustainable model offering a basis for a new approach waste management, allowing to save costs and obtain environmental benefits on the long run (Van Straten et al., 2021). Making a circular economy transformation means to decouple value creation and the use of virgin raw materials (Valve et al., 2021). While the implementation of circular economy in local production (for example at firm-level, till to eco-industrial parks) is often considered, a massive gap exists in how the circular economy is perceived at the global level (Rahman and Kim, 2020), representing a fundamental issue in the raw materials market. However, it is also fundamental to guarantee the quality of the recycled material for direct re-employment (Oumarou Amadou et al., 2021). For example, global annual production of electronic waste was estimated to be approximatively 50 million tonnes (Larmer, 2018), and is expected to grow continuously due to the increased use of electronics, as it was also demonstrated during pandemic (that for example forced people to adopt smart-working). By 2030, more than 1 million of batteries is expected to reach the end of their first-life (Ribeiro et al., 2018). The metals present in these devices (like lithium, copper, cobalt, silver, and gold) often go into landfilling, despite their value, with the consequence to originate soil contamination and water pollution (Ribeiro et al., 2018). Then, the generated electronic wastes, that provide several concerns related to their toxicity, offer an opportunity for recycling precious (Larmer, 2018). The materials circularity is also a fundamental pillar of sustainable development. In particular, the strategies that will be chosen for these waste materials disposal or recover will be highly influential on the SDGs (mainly concerning SDG 12 for Responsible Consumption and Production) and the introduced circular economy agenda. However, despite that in recent literature several concerns have been focalized on different environmental issues related to COVID-19, ranging from pollution (Bontempi, 2020a), to critical resources related to batteries availability (Panda and Akcil, 2021), energy supply, and climate change, few attentions has been devoted to suggest possible actions concerning raw materials (with great attention mainly to CRMs) that should put in place in post-pandemic scenario to be synergic with the ecological transition. Indeed, it was recently shown (Bontempi et al., 2021a, Bontempi et al., 2021b) that sustainable materials are strictly related with most of SDGs and offer the opportunity to construct collaborative actions involving several of their targets. The mining sector, where raw materials are extracted, is very energy-intensive. The limits are co-determined by the large external costs of mining arising from the impact of ever deeper and remoter mining on climate change and on the mine's surrounding environment (Henckens, 2021). Then, it is necessary to revise some mining strategies and improve the recycling industry potentialities and support. In the next future, it will be fundamental to reallocate some resources to promote adaptation and development efforts (Church and Wuennenberg, 2019). As the basis of urban social-economic development, the resources are interrelated in the production and consumption activities of cities (Li et al., 2021). Increased raw materials recycling will contribute to reach the targets of SDGs 9 and 12, and to the adaptation-related objectives of SDG 13, by decreasing the reliance on mines, that are highly vulnerable to climate change. Despite the presence of some papers in literature devoted to the impact of pandemic on raw materials availability (Navon et al., 2021), only few are expressively devoted to CRMs (Akcil et al., 2020) (Gałas et al., 2021). In addition, current explorations lack a global vision of the problems deriving by pandemic and prepositive actions to face the crisis on CRMs availability. The aim of this paper is to show an analysis of the impact of COVID-19 on raw material supply, with particular attention to CRMs, and propose a combination of measures devoted to increase the raw materials accessibility, with great attention to CRMs. An accurate analysis of all available literature about CRMs (also connected with COVID-19) is used for the basis of analysis. Through this investigation, post-pandemic challenges and opportunities that should be pursued to increase the access to raw materials, are proposed, in the frame of SDGs. 2 Study design To provide a comprehensive assessment of the impacts of COVID-19 on raw materials, this work analyzed the articles related to this subject by a systematic literature review. Indeed, through the compression and integration of massive data analysis, literature investigation can provide the statistical mean in quantitative assessment of scientific information (Zhang et al., 2021). SARS-CoV-2 has attracted great attention in literature due to the exceptionality of the pandemic. Indeed, a search for COVID-19 (or SARS-CoV-2) with SCOPUS database showed the existence of more than 200,000 papers concerning to this argument. Currently, papers involving “critical raw materials” are 3876 (see Fig. 2 ). Pandemic had a great impact on materials availability and use, due to the economic crisis associated to COVID-19 global spread (Ibn-Mohammed et al., 2021). SCOPUS database allowed to identify 428 papers devoted to raw materials (RMs) and COVID-19 (or SARS-CoV-2). However, a similar search but concerning “critical raw materials” in all possible fields, has highlighted the existence of only 24 papers expressively devoted to this topic (see Fig. 2).Fig. 2 Summary of literature research about COVID-19 (or SARS CoV-2), raw materials and critical raw materials on SCOPUS database. Fig. 2 The information of the articles from SCOPUS platform included authors, title, keywords, abstract, and references for the data analysis, synthesis, and interpretation. In particular, an analysis of keywords co-occurrence network of papers devoted to RMs and COVID-19 (or SARS-CoV-2) allowed to perform a cluster analysis of the literature (see Fig. 3 ). The study design (with connected literature) was updated on June 10, 2021.Fig. 3 The keywords co-occurrence network of papers devoted to raw materials (RMs) and COVID-19 (or SARS-CoV-2). The study design was updated on June 10, 2021. Data analysis was performed by (“VOSviewer version 1.6.16,” 2020). Fig. 3 It results that the already available papers can be grouped in 5 clusters. The first one (49 items) is mainly devoted to circular economy principles, with great attention to sustainability and environmental impacts, involving waste management strategies. In this cluster raw materials extraction is also considered. The second cluster (27 items) is devoted to chemicals (compounds devoted to COVID-19) and their characterization. The third cluster (25 items) mainly concerns supply chain, with attention to raw materials commerce and manufacturing. The fourth one (20 items) essentially involves medical keyworks, related to pandemic disease. Finally, the last cluster (11 items) is centered on geographical locations (as for example China, Russian federation, India, and so on). It results that only few papers are expressly devoted to raw materials availability (they can be found in cluster 1). Then, based on the results of cluster analysis, the last step was realized thorough reading the selected full text papers from the perspectives of the consequence of pandemic on RMs and CRMs. These papers were carefully analyzed with great attention to the resulting 24 papers expressly devoted to CRMs and COVID-19. All the available literature about CRMs allowed also to propose possible future directions, which are presented in detail in the next section. The measures used in this study are the following:• Data about the current recovery efficiency of 4 CRMs (antimony, bismuth, indium, and tungsten), their proposed future recovery efficiencies and their substitutability, that are extracted from (Henckens, 2021). • Data about mining operations at risk on June 25, 2020, reporting the material, the number of respective involved mines and total revenue at risk, that are provided from S&P Global Market Intelligence (MacDonald et al., 2020). • Data about monthly price change (%) of lithium carbonate, cobalt, rock phosphate, iron ore, copper and aluminum and Commodity Metals Price Index (2005 = 100, includes Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U Price Indices) from January 2019 to February 2021, that were extrapolated from World Bank and Trading Economics. 3 Results and discussion 3.1 Summary about CRMs state of art The EU industrial strategy poses raw materials as key enablers for a green, competitive, and digital Europe. EC has identified some critical raw materials (CRMs) that are considered crucial for EU economy. The number of these minerals is increasing in the classifications that were updated after the first introduction. In 2011 CRMs were 14, in 2014 the list was extended to 20 materials, and in 2017 CRMs increased till to 27 (European Commission, 2017). Currently CRMs are 30 (European Commission, 2020a). These resources play a fundamental role in 81 countries that collectively account for a quarter of world GDP (Heffron, 2020). Moreover, it is expected an increase of CRMs demand strongly related to the world's capacity from green sources needs, under the 2015 Paris climate agreement (European Commission, 2020b). In particular, more energy needs to be stored, more CRMs are necessary (Hund et al., 2020). Regarding technological applications connected to green energy, several of the 30 CRMs are currently involved. For example, Al (extracted from bauxite) is used for turbine blades, wires and electrodes. Co, Ni, Fe, and Li are key metals for batteries (more than 60% of Co extracted from mines goes into rechargeable batteries). Rare metals like Ga and In are widely used in electronics components (to realize transistors and computer chips). Rare-earth elements (REEs) are widely used in high technology devices, including computer hard disks, flat screen televisions, smart phones, computer monitors, and digital cameras. They are also used in defence technologies and clean energy applications. For example, Nd and Dy are essential for magnets. The access to these resources, that are defined technology metals, is now fundamental and crucial for climate change combat. Indeed, it is estimated that 3 billion tonnes of metals and minerals will be mandatory to reach the objective to decarbonize the global energy system by 2050. In addition, other technologies development, such as 5G digital communications, is improving the pressures on other resources (Akcil et al., 2020). As a consequence, by 2030 the Li and Co demand is expected to increase up to 18 and 37 times respectively, considering the corresponding requests in 2015 (Jones et al., 2020). In particular, to satisfy its manufacturing batteries necessities, the Chinese market will increase the demand for both lithium and cobalt reaching about 68% of the global need (Jones et al., 2020). Also other alternative energies sources, based on solar cells, panels, and wind turbines will be more diffused in the next years, with an expected significant increase in demand of raw materials due to the both wind and solar photovoltaics technologies (Vidal et al., 2013), at global level. This also will contribute to produce strong concerns related to all CRMs associated with energy generation technologies. Considering data reported in Fig. 1, it is evident that an increase higher than 1 order of magnitude in the Li and Co demand, will risk limiting their availability to about 30 years for Li and less than 10 years for Co (only considering mining resources). In this frame it is then fundamental to evaluate the recovery efficiency and the possible substitutability of these materials, to evaluate the opportunities due to wastes valorisation and materials replacement. Fig. 4 reports the current recovery efficiency of some CRMs, their proposed future recovery efficiencies and their substitutability (Henckens, 2021). It results that some of these elements (as for example Sb) are recovered with efficiency higher than 80%. On the contrary, some elements are recovered with low efficiency (as for example In, that is recovered with an efficiency lower than 50%). Higher recovery efficiency may need too energy resulting a not sustainable process (Henckens, 2021), with an expected need of technological improvements aimed to enhance their recovery. Fig. 4 also shows the reported CRMs substitutability, i.e. the possibility to be replaced by another material. The results are very different considering Sb (that can be substituted for 90%) and W (that can be substituted only for 30% by another material).Fig. 4 The current recovery efficiency of some CRMs, their proposed future recovery efficiency and their substitutability (data were extracted from Henckens, 2021). Fig. 4 In this frame it is important to highlight that some concerns are also related to metals that aren't CRMs. Some researches, more focalized on specific elements, suggest, for example, a future increasing demand not only for Li, Co, and Al (it is extracted from bauxite), but also for Fe, Mn, Ni, and Pb (World Bank Group, 2017a). In particular, electrification is supposed to cause the increase the Ni and Mn request for new vehicles batteries by five times the 2015 level (Jones et al., 2020). In addition, there are metals that are necessary for vehicles productions, not considering batteries; they are for example steel and aluminium. Due to stainless-steel composition, for vehicle production also Cr, Mn, and Ni will be necessary. Al will be fundamental to reduce the vehicle weight (it is mainly used for car bodies and battery cases), allowing to meet the stringent emissions legislation, making possible also a gradual reduction of steel use for mobility. Key uses of this element not only include the transport sector, but also building industry (for example Al is used to realize windows, doors, and facades), and packaging (where Al is used for beverage cans, and for foil applications). Literature estimates an annual global demand increase in 2030 (then not considering batteries sector) by 30.4%, 8.4% and 6.3% respectively for Ni, Al and Cu, on the basis of 2017 production (Jones et al., 2020). Moreover, in 2050 the Al request is estimated to increase till to 40%. In addition to studies related to global necessities, that are summarized in this section, the request for the critical raw materials, associated with the transition to green technologies for low carbon economy, have been also analyzed in literature considering the specific frameworks of some countries (Ciacci et al., 2016) (Moss et al., 2011) (Hatayama and Tahara, 2015). Phosphate rocks (PR) have been also inserted in the CRMs list, due to their fundamental contribution for the P supply chain (European Commision, 2014). Although scarcity and provisions risk are common aspect for critical raw materials, when phosphorus is considered, it is fundamental to highlight that this element is linked to food production and thus its scarcity may compromise the access to food. This risk is then correlated to SDG 2. Literature about inorganic P production and connected use reports an 80% of phosphorus loss from mine to fork (Cordell et al., 2009) with the evaluation that only 10% of the processed fertilizers can be digested by humans (Scholz et al., 2014) and more than half of the losses from fertilizer application on soil to fork are in runoff from agricultural land (Scholz and Wellmer, 2015). The digested phosphorous is generally found in wastewater and can be also discharged outside of a wastewater collection system (Ducoli et al., 2021). The processes connected to PR mining and processing have several negative impacts on air and water qualities, and climate effects (Nedelciu et al., 2020). In addition to the pollution and eutrophication effects, the P supply chain produces large amount of waste deriving from the processing of PR to synthesize final products (as for example phosphoric acid) (Nedelciu et al., 2020). Several activities and technologies are proposed to recover phosphorus from waste, as for example from sewage sludge (Fahimi et al., 2021) (Pasquali et al., 2018) (Benassi et al., 2015) and poultry litter ashes (Fiameni et al., 2021) (Fahimi et al., 2020). Moreover, developing processes to recycle phosphate from the growing phosphogypsum stocks, derived from phosphoric acid synthesis, may also play a fundamental role in providing suitable qualities of fertilizers on the market (Nedelciu et al., 2020). Great attention should be also devoted in the loss reduction of P supply (Nedelciu et al., 2020). In this scenario some actions can be envisaged that can be linked to understanding and improve mining potential, recover resources, and secure job (Panda and Akcil, 2021). For example, an international network in raw materials i.e. EIT (“EIT Raw Material,” 2021) was instituted by EU and funded by the European Institute of Innovation and Technology (EIT). The aim of this network is to support innovation schemes addressed to mining, exploration, recycling, processing, and substitution of raw materials, in the frame of circular economy principles. 3.2 Pandemic impact on raw materials supply The analysis of the literature allowed to summarise the pandemic impact on raw materials supply (see Fig. 2). Indeed, the majority of available papers about this argument concerns the impact of pandemic on natural resources management (Laing, 2020) (Cai and Luo, 2020) (World Bank Group, 2017b). Some of these works show that the global international trade can be also associated to higher virus diffusion, then to higher negative effect (Bontempi, 2021) (Bontempi, 2020b) (Bontempi et al., 2020). On the basis of literature analysis (see Fig. 2) it results that: 1) exploration works for new mine sites were delayed (Gałas et al., 2021), 2) the metals demand was significantly reduced, 3) the international supply chain was almost completely destroyed, 4) the production sector stopped, due to reduced consumption, and 5) the price of several raw materials have been (also drastically) changed (some decrease and other increase). For example, it was reported that an unexpected price increase occurred for epoxy and polyester resins (Coating World, 2021) due to COVID-19 restrictions. Then, an economic shock was reflected on both the demand and the supply of raw materials (Panda and Akcil, 2021). In particular, considering the mining sector, COVID-19 caused several negative impacts across the world, with the consequence to several sites closure. For example, on June 25, 2020, it was found that 275 mining operations were globally disrupted as shown in Fig. 5 . Most of these mining sites were closed to comply with anti-contagion measures (MacDonald et al., 2020).Fig. 5 Representation of 275 mining operations at risk on June 25, 2020. Metals and the number of respective mines are reported. Fig. 5 It was found that the most impacted mines concerned gold, coal, copper, U308 and silver. In summary, the global impacted projects worldwide due to pandemic, connected to mining sector, was evaluated to be more than 7 billion of € (around US$ 9 billion) (MacDonald et al., 2020). Fig. 6 shows, according to (MacDonald et al., 2020), where the most endangered mining sites are located highlighting the countries that have suffered the greatest losses. The most affected country was Peru, with losses of more than 2 billion of €, followed by Mexico and Chile. Data analysis was performed by Qgis software (“Qgis,” 2018). Moreover, in the supplementary material of this work, all files necessary to create a Qgis map of mining sites and revenue at risk per country are available. With temporary closing of mining operations expected to reduce the 20% production in 2020 compared to 2019 (Habib et al., 2021).Fig. 6 Representation of the most endangered mining sites (divided into precious metals, specialty commodities, bulk commodities, and base metals) and the countries that have suffered the greatest losses. Data analysis was performed by Qgis software. Fig. 6 In addition, there were also some potential markets crisis that may be originated by the limited availability of some primary resource sectors to provide raw materials, when markets were closed to reduce safety risks. The prices of several metals can be associated to economic cycles: the decrease in some raw resource production is more likely associated to a decline of demand, rather than the falling of geological resources availability (Graedel et al., 2013). The metal market is extremely volatile, influenced by economic and political factors. The pandemic has exacerbated this trend, leading to a disruption of world balances which have had a major impact on world markets. In fact, when the COVID-19 has spread, to mitigate its impact, China introduced some strategic actions that have caused an important decrease in demand in the market of large consumers (Moore et al., 2020). The COVID-19 pandemic had a strong impact on industrial production. In the first quarter of 2020, the drop of 20% in the Chinese economy due to Wuhan's manufacturing shutdown generated a reduction in the raw material prices (Akcil et al., 2020). This trend, according to (Laing, 2020), shows an analogy with the Great Financial Crash of 2008–2009. The rapid decline in demand generated a reduction in the prices particularly for aluminium and copper (Laing, 2020). Fig. 7 shows the monthly price variation from September 2019 to February 2021 of some CRMs, such as lithium carbonate, cobalt, phosphate rock (PR), and other key metals for batteries such as iron ore, copper and aluminium. In addition, a commodity metals price index was inserted (Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U are considered). As reported in Fig. 7, in April 2020 the price of aluminium has dropped more than 9% respect to March 2020, while the price of copper slumped more than 8% between February and March 2020. In the same period the lithium carbonate price is reduced more than 4%, respectively. The closure of Mutanda mine site, the largest cobalt field in the world, in November 2019 (Mining weekly, 2020), generated a slight rise of Co price from December 2019 to January 2020. On the other hand, the COVID-19 pandemic caused a decrease in prices between February and March 2020 about 10%. Fig. 7 Monthly price change (%) of lithium carbonate, cobalt, rock phosphate, iron ore, copper and aluminum and Commodity Metals Price Index (2005 = 100, includes Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U Price Indices) from January 2019 to February 2021. The Wuhan lockdown period was from 23 January to April 8, 2020. Data source: World Bank and Trading Economics. Fig. 7 The exposure on the stock market of the metal sector can be defined by the Commodity Metal Price Index (CMPI). This index represents a general level of metals price. CMPI is a weighted average of the prices of some reference metals respect to their prices in a base year. In Fig. 7 is reported the Commodity Metals Price Index of Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U, from 2019 to February 2021. Following the droop in process between February and April due to COVID-19 pandemic, metal prices recovered strongly reflecting a recovery in global industrial demand largely driven by consumption in China (World Bank Group, 2017b) exciding the pre-pandemic values. Indeed, as reported in Fig. 7, the price of aluminium, copper, iron ore, PR and cobalt is increased in the third quarter of 2020 of 14%, 22%, 25%, 6% and 9%, respectively compared to second quarter of 2020. On the other hand, the price of lithium carbonate is decreased of 8% in the same period. However, from December 2020 to February 2021 lithium carbonate price increased due to the high demand of lithium ion batteries (Mining.com, 2021). Due to lockdown, the demand for electronic devices such as PCs and tablets, needed for smart working and distance learning, has strongly increased by 13.1% in 2020 and the trend is still growing (IDC, 2021). Some of the materials used for their manufacture, already present in limited quantities, are part of the sites at risk. Compared to previously economic crisis, the rolling spread of the pandemic has led to different outcomes. For example, in 2011 the prices of some metals peaked sharply after an abrupt and almost total supply disruption (Habib et al., 2021). On the contrary, after the pandemic diffusion, in 2020, the prices of some RMs initially went down slightly because demand was disrupted even more, and then they increased. 3.3 Proposed actions able to increase raw materials availability The dependence on natural resources extraction has shown several fragilities for local economies, that were enhanced during the pandemic crisis. However, over the next years an increase in the demand for some mineral resources is expected. To face the post-pandemic it is necessary to learn from past supply chain limits and propose active improvements to give a positive impulse to a sustainable renaissance. Indeed, even if COVID-19 pandemic has highlighted some fragilities in raw materials supply, this experience can also be used to suggest new strategies in their management, in view of a desired increased resilience. Literature analysis, mainly concerning the papers devoted to CRMs (see Fig. 2), allows to propose some actions, that may be pursued, to increase CRMs availability, also on the basis of past experiences. In this frame, for example, the strategies that were putted in place to better manage the Al supply can represent an example that may be followed for other natural resources, if possible. As reported in Section 3.1, Al represents a strategic element for the EU Green Deal policy. Currently Al industry is able to recover and reuse about 36% of secondary Al (European Aluminium, 2020). Al recycling involves only 5% of the energy that is used for primary production. To meet the next years increased request of this metal, both primary (mined) and secondary Al production will be necessary (European Aluminium, 2020). However, even if an increased demand is expected, a feasible scenario shows that EU can reduce its dependence on imported Al from 29% to 15% in 2030 by rising its domestic production. This will be possible if the right competences will be in place (European Aluminium, 2020), to improve collection and sorting, with the result to ensure higher recycling rates and better quality of the obtained output. It was already shown that a suitable scenario will require additional legal constrains and investments for better collection and sorting technologies, to limit Al components destined to incineration and/or landfilling. For example, the current shredding treatment process aimed to recover steel from vehicles, need to be improved to better recover also Al. Also considering this representative example, governments, industries, associations, and consumers have some possibilities to reduce the pressure on raw materials in a post-pandemic era. The actions that can be proposed, derived by analysing the available literature about CRMs (see Fig. 2), can be summarized as policy, strategy, economy, and technology activities, and they are summarized in Fig. 8 , with great attention to the involved sustainability pillars.Fig. 8 Summary of the proposed actions, grouped as policy, strategy, economy, and technology activities, proposed in the frame of sustainability pillars (Environmental, Social, Economic, and Cultural pillars). Fig. 8 3.3.1 Strategy actions 3.3.1.1 Monitor the mineral production and consumption Mineral extraction is an energy intensive activity, difficult to decarbonize (Ali et al., 2017). There is a compelling necessity to propose and adopt a framework for tracking mineral use along the entire value chain, from source to end of life, that should account the energies and emissions involved in all the lifecycle (Bontempi, 2017). This system may promote a notion of ‘metal miles’, aimed for example to reduce the transport costs of these resources, promoting the local products consumption (SDG 12). The framework should also consider transparency and ethical schemes: in such a monitoring also the social conditions of mine workers should be accounted, to consider all the pillars of sustainability in the frame of SDGs 5, 8, 10 and 16. For example, some natural resources (like cobalt minerals) are mined in the Democratic Republic of Congo, where also women and children often work in mines, without basic safety equipment, and where from years the population is plagued by armed conflicts (Amnesty International, 2016) (SDG 16). Even if we must be conscious that complete traceability schemes may be impossible and the proposed framework risks to be a pure exercise (Sovacool et al., 2020), the established public relations to compile this schema may transform in a support to improved outcomes for miners and better governance management. Mining Local Procurement Reporting Mechanism was already introduced to report information about local procurement of mining companies, as well as detail on mining procurement processes and due diligence practices (Geipe and Kaiser-Tedesco, 2018). 3.3.1.2 Revise globalized production systems The current production and consumption system are constructed onto on extremely interconnected value chains based on international exchanges and shipment of the basic components. This may increase the vulnerability to pandemic (Bontempi, 2020b), highlighting that more local supply configurations can contribute not only to decrease the local dependence on materials, but also increase local resilience (also to pandemic) (SDG 11). For example, for the polyester and epoxy resins, a complex mix of prices increase, high demand, supply problems also connected to the restricted possibilities of resources transport, had the consequence to increase the market uncertainty and contribute to the materials prices sharply growth (Coating World, 2021). The possibilities of the occurrence of these situations must be reduced, by the revision of the globalisation production system. 3.3.1.3 Explore the availability of new resources Even if it is mandatory to give primary emphasis on resource efficiency and recycling, it will be necessary to find additional primary resource mines (Sovacool et al., 2020). New resource streams may be found not only in new deposits or mines, but also in other matrices, such as groundwater (geothermal brines) and seawater (desalination). In this context new technologies for mineral exploration, from deep in the crust to the bottom of the ocean may be developed (SDGs 8). Geochemical and geophysical data must be shared in greater detail through dynamic databases (Ali et al., 2017). Moreover, also waste must be considered, as for example landfilling sites, to promote recovery and recycling of some resources, with great advantage in terms of environmental sustainability (SDGs 11, 12). 3.3.1.4 Enable the access to small local deposits Although large-scale mining is often economically efficient, it has several drawbacks (Sovacool et al., 2020). In a post-pandemic context, small deposit mining by small-scale operations may be more attractive, if compared to multiple mining operations (that probably may need additional capital costs to re-open mine sites), due to the lower investment required, to secure production, even if limited, in a market of low prices (Moore et al., 2020). Ore deposits are located on all continents including Europe (Goodenough et al., 2016), but in this case, the Europe strict regulation makes their use extremely complicated (Moore et al., 2020). EC must consider the possibility to take advantage of internal mines, with the result to increase its market resources. Domestic mineral extraction is fundamental to reduce critical raw materials import and increase the resilience of EU territories and their subsistence capability in extreme events, like pandemic (SDGs 8,12) (Bontempi, 2020b). In this frame, a focus on more local supply chains may be investigated and prioritized. 3.3.2 Technology actions 3.3.2.1 Redesign the technologies to support alternative materials use This necessity should be constantly pursued. However, it may result fundamental for CRMs. For example, cobalt abundance in Earth's crust is not sufficient to produce all the batteries that will be required by the markets in the next years (as shown in Section 3.1). Some manufacturers companies (for example Tesla and CATL in China) are pursuing new batteries, with the aim to make them cobalt-free (Akcil et al., 2020). In particular, in the last years, Tesla has reduced its dependency on Co for EV batteries by approximately 60% (Chen, 2018) (SDGs 7, 9, 12). Currently the proposed replacements consist in materials containing Ni or Fe, which still result less efficient, but decrease the pressure on Co. Na-ion batteries are also under study. 3.3.2.2 Recover and recycle waste In 2017, more than 10 million of tons of electric wastes were generated in Europe (Panda and Akcil, 2021). Unfortunately, only about 31% is currently recycled. Recycling for recovery of precious resources is one of the main activities with high potentialities, it but need to be more explored and supported. In addition to the landfill mining, the possibility to recycle devices before they are discharged must be encouraged and better investigated (SDG 12). Repair must be a valid option, with also the advantage to generate new jobs positions. On the other hand, obsolete devices and spent batteries have interesting amount of precious and rare metals, with high potential to allow to meet the growing demands for CRMs, making their extraction from waste potentially economical. Indeed, the activity could be implemented with low logistical and supply chain costs. For example, countries with high amount of electronic waste typologies may establish dedicated markets to reprocess some CRMs domestically (Işıldar et al., 2019). Hummingbird International estimated that materials recycling electronic waste may generate more than one order of magnitude jobs in comparison to those that are dedicated for traditional disposal activities (Sampson, 2015) thereby contributing to SDG 8, with also great advantage in terms of avoided GHG emissions (SDG 13). In this frame, the secondary Al management strategy can represent a valid example that must be promoted, also for the result that could be acquired in terms of avoided CO2 emissions (up to 39 million ton per year by 2050 (European Aluminium, 2020). To reach this aim new regulatory measures and improvements in legal constrains may be necessary, for example, to make electronics manufacturers responsible for recycling the products they make also from a legal point of view (SDG 12) (Akcil et al., 2020). Moreover, the recycling potential of a material used in a specific product depends on some factors, as for example its concentration and the product composition. Indeed, it is evident that a higher concentration generally results in a higher recycling potential. It also depends on eventual dissipative use of the material and possible contamination arising by its use (Henckens, 2021). 3.3.2.3 Increase the efficiency in materials extraction and use It was estimated that more than 70% of GHG emissions are originated in energy-intensive raw material production, and processing (McKinsey, 2020). It is evident that a higher efficiency in materials extraction, processing, and use is a fundamental step to address the climate neutrality (SDGs 7, 13). In a full-monitoring procedure of CRMs lifecycle it would be possible to highlight the sectors and steps that may be improved to limit materials loss and increase the efficiency in materials use (SDG 12). Concerning mining, for example, it is fundamental to minimize waste and the consumption of water during extraction processes, to maximize the amount of extracted materials, and reduce involved energies and emissions (SDG 14) (Lederer et al., 2020). Since 1980s, responsible mining has been addressed as a fundamental criterion in mining. Even if mining should be not defined sustainable, due to its nature that involve resources depleting, the attention towards less polluting technologies for metals extraction was increased in the last 20 years (Spooren et al., 2020). Ore grades is continuously reducing, increasing the necessities of more efficient extraction technologies, producing mineral residues that can be valorised (SDG 12). For example, many conventional separation technologies were considered inefficient for the treatment of low-grade ores, that need to be milled into very fine size grains to guarantee sufficient mineral separation (Dermont et al., 2008). The use of flotation technique (with the development of suitable floating agents) allowed to concentrate fine interest minerals, contributing to reach higher revenues in metals extraction (SDG 14) (Lopéz et al., 2019). Other possibilities to increase materials extraction efficiency will be dependent on the development of digitalization of some processes as for example sensor-based detection and separation of mineral streams (Robben and Wotruba, 2019), and the possibilities of machine learning implementation (SDG 12) (McCoy and Auret, 2019). Materials efficiency can be improved also by reducing their use in a product, assuring the required functional properties of the final component. This needs a suitable design strategy. In addition, also the reduction of the in-use dissipation (that involves the loss of a resource through and during its consumption, for example by corrosion) can be improved (Henckens, 2021). 3.3.3 Economy actions 3.3.3.1 Promote industrial symbiosis The possibility to recycle a material derived from a different supply chain sector must be better investigated and promoted. The circular economy principles require a gradually decoupling of economic activity from the natural resources consumption. Metals recycling from RAEE, for example, can offer the possibility to reduce the dependence from mined metals import. However, mineral recycling can be acquired also by using wastes derived from other sectors, such as for example municipal solid waste incineration ash, that can be reused in several other applications (SDGs 9, 12) (Bosio et al., 2013) (Benassi et al., 2017) (Benassi et al., 2015) (Assi et al., 2019) (Zanoletti et al., 2018). 3.3.3.2 Promote new business models The circular economy approach must be encouraged: materials must be retained within productive use, for as long as it is possible (SDG 9) (“Towards a Circular Economy: Business Rationale for an Accelerated Transition,” 2015). In this business model the waste must be limited and possibly re-introduced in the productive system. In a circularity scheme the waste assumes a new value, in a system that is designed to repair the previous damage (SDG 12) (Murray et al., 2017) (Zanoletti et al., 2018). The evolution in a different business models, that can be defined “circular business model” (Mohammad Ebrahimi and Koh, 2021), modifies the link between the members of supply chain: the cooperation between the actors is necessary and need their involved in long-term learning processes, to achieve the know-how necessary for the new industrial model (Krook and Baas, 2013), to support the urban mining process (Sharma et al., 2021). However, at the beginning of the activities, political intervention, with adequate supporting actions (as for example the provision of green certificates, with a similar mechanism that it is currently proposed for COVID-19 vaccinations certification) is fundamental to ensure that economic benefits outweigh costs (Van Passel et al., 2013). This is clearly evident in the current situation, where inter-regional flows of waste materials have been disrupted by lockdown restriction measures due to pandemic. However, the new business model initiatives, mainly involving small industries and start-ups have been estimated to be able realizing value higher than 230 billion € per year by 2030 (SDG 17) (Klevnäs et al., 2020). 3.3.3.3 Develop more efficient market strategies Sometimes consumers have the perception that recycled materials may not guarantee the same performances and qualities as virgin resources (Church and Wuennenberg, 2019), or that some devices can be considered old, even if they can be perfectly working. For example, in a recent experiment, considering 148 out-of-use laptops, it was shown that only few units were found to be completely unusable, with most batteries able to retain 89% of their original capacity (Furtkamp Julian, 2017). This may result in a barrier for the market of secondary materials, that can be overcome by the promotion of suitable dissemination campaigns. In addition, also incentives must be used to encourage consumers to address its electronic waste towards suitable collection and recycling schemes (SDG 9). It was shown that, for example, if in Japan all used mobile phones would be collected and recycled, the annual consumption of palladium, silver, and gold may be reduced by 2–3% (SDGs 11, 12) (Mishima et al., 2016). 3.3.4 Policy actions 3.3.4.1 Support the research The investments in research activities devoted to raw materials substitution and exploration, and aiming to find new production technologies, must be suitably addressed before to reach the feedstocks scarcity. Research activities must also cover the development and/or improvement of the extraction processes. Also the complete metals extractions must be fulfilled to avoid raw materials loss in ore (for example, indium or germanium can be recovered in zinc ores, or gallium can be recovered in bauxite) (SDGs 9, 12) (Ali et al., 2017). Finally, the priority must be addressed towards funding research activities devoted to secondary resources recycle, considering the increase of global amount of these wastes. The researchers role in developing suitable recovery technology is crucial. 3.3.4.2 Promote gender equality COVID-19 pandemic has caused several negative aspects. In particular, the effects of the pandemic have hit women the hardest. Concerning raw materials, in many countries mining laws and regulations neither fully mainstream the principle of gender equality nor acknowledge women as active participants in the sector (SDGs 5, 10) (Tekinbas and Deonandan, 2021). The gender equality is one of the SDGs (SDG 5). Post-pandemic conditions give the opportunity to revise legislation, priorities, and governance principles in raw materials sector, as proposed by Intergovernmental Forum on Mining, Minerals, Metals and Sustainable Development (Intergovernmental Forum on Mining, Minerals, 2020). 3.3.4.3 Improve legislation Legislation is currently in evolution to consider the most suitable instruments to address national investment strategies toward a post-pandemic reconstruction. The priority areas to address resources are in discussion. Literature has shown that best practices in responsible mining (related to environmental protection) need resources (Moore et al., 2020). Legislation has a fundamental role for example in promoting the raw materials traceability, with attention to sources that are responsibly acquired (SDG 16) (Young and Dias, 2012). In this frame, precautionary measures need attention to avoid illegal mining activities and secure the import and export of critical raw materials, associated with their sustainable supply (SDGs 10, 16) (Panda and Akcil, 2021). Clear regulatory actions must be designed to incentive eco-design and discourage linear business models in private industries. There is a strong necessity to support product manufacturers in the developing of new business models, taking advantage of economic opportunities due to circular solutions. As already suggested in this paper, governments can propose economic incentives to improve innovation and support new markets, including low-interest financing, tax abatements, tax revenue sharing, infrastructure assistance, and grants (Tilly, 2017) and ensure more certainty to investors. Extended producer responsibility (EPR) was introduced in the last years, in some sectors, as a governance mechanism destined to targeted producers, increasing their responsibility in collection, recycling, and waste disposal (Organisation for Economic Co-operation and Development (OECD)., 2016). Inclusion of mining waste treatment in EPR may be a fundamental action to promote the business for secondary raw material production and management as a fundamental part of the environmental cost of mining (Armstrong et al., 2019), allowing to take into account also environmental costs of raw materials extraction in economic planning (SDG 12). EPR has the aim to shift the responsibility for valuable resources collecting from consumers to companies that use these materials. This mechanism is expected to introduce some improvements in the materials use and recovery, like as increased eco-design strategies, higher opportunity of recover and repair, and extended product lifetimes and durability. 3.3.4.4 Encourage the Sustainable Materials Partnership for SDGs (Bontempi et al., 2021a, Bontempi et al., 2021b) SDG 17 (Partnership for the Goals) encourages activities devoted to promote actions across different sustainability goals. Collaboration between the public and private sectors, involving civil society must be envisaged. It was recently shown how the connection between sustainable development and materials is extremely strong. The institution of a “Sustainable Materials Partnership” involving all the stakeholders (from researchers, to industries, from clusters to people), may help to support SDGs achievement in all the countries, with great attention to the raw materials connected activities. A first basic example of a similar action may be found in the International Council on Mining and Metals guidelines, proposing the implementation of social actions of local populations of the territories where raw materials are extracted (Parra, 2020). Another example is the EIT RawMaterials initiative, established in 2018, with the mission to enable sustainable competitiveness of the European raw materials sector along all the value chain (“EIT Raw Material,” 2021). An informal accord, the “Green Recovery Alliance”, to accelerate the ecological transition in a post-pandemic, was recently launched by some members of European Parliament (Frédéric, 2020). However, more generally, the proposed partnership (SDG 17) should be devoted to all sustainable materials, and not only to CRMs. This will allow to establish a framework able to support all SDGs. 4 Conclusions CRMs play a key role for the progress of the industrialized regions in the world. They have contributed to recent technological development and energy efficiency improvement. They serve as essential RM for high-technology, sustainable, and green applications. COVID-19 disease has contributed to reduce the availability of several of these materials. However, the pandemic has also demonstrated the need of structured and global efforts to face crisis, because individual actions are pointless. The post-pandemic recovery period could be an opportunity to develop new prosperity models, based on green principles needs and priorities: a more resilient economy, able to catch the new opportunities of digitization, and meet the environment and climate targets. We have recently marked the 150th anniversary of the periodic table formulation and we are addressed towards SGDs. It is time to realize that a sustainable development needs more attention to some elements, and, in particular, to the minerals where necessary resources are embedded. Despite all the complexities, the humanity must be ready to face this challenge. After an analysis of reduced supply of RMs due to the pandemic, this work proposes some actions devoted to secure a more balanced resources distribution. This should happen in particular in terms of natural resources revenue that could be more equally shared across all the business value and supply chains (Heffron, 2020). The paper shows that the diversity of CRMs that are need for manufacturing future technologies necessitate a change in their extraction and manufacturing approaches. The materials production and consumption must be secured in a sustainable way, that probably will need a revision of giant ore deposits management strategies. Technology will be a fundamental player of this innovation, but also mining code and practices need strict revisions and improvements. Finally, increased materials recycle will help to fulfil some of the circular economy aims, by reducing reliance on finite resources and mitigating permanent waste disposal. The expected post-pandemic scenario will contribute to SDGs 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17 fulfil among others. This study presents some limits. One of the main limitations of this research is due to the data source, which was confined to articles collected from SCOPUS till a certain data (June 10, 2021). The information about COVID-19 and raw materials obtained from the following periods may provide additional data that are not considered in this work. Another limitation of this work concerns the social pillar of sustainability, that is not considered in the study: “social sustainability” is linked to the social outcomes and values, such as equality, social responsibility, children's work, gender equality, community resilience, freedom from poverty, that is often connected with population living and working in mining sites. Author contributions Conceptualization: E.B.; Data curation: E.B., A.C., A.Z.; Formal analysis: A.C., A.Z.; Investigation: E.B., A.C., A.Z.; Supervision: E.B.; Roles/Writing - original draft: E.B, A.C., A.Z; Writing - review & editing: A.C., A.Z. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)00975-0 10.1016/j.envres.2021.111681 111681 Article A post-pandemic sustainable scenario: What actions can be pursued to increase the raw materials availability? Zanoletti Alessandra Cornelio Antonella Bontempi Elza ∗ INSTM and Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, 25123, Brescia, Italy ∗ Corresponding author. 15 7 2021 11 2021 15 7 2021 202 111681111681 22 4 2021 30 6 2021 8 7 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. On January 30, 2020, COVID-19 outbreak, detected for the first time in Wuhan (China), was declared by WHO a Public Health Emergency. In a strongly connected world, the consequent slowdown of the Chinese economy contributed to disrupt the global supply chains of several products. In a post-pandemic scenario, the expected rapid increase in demand of critical raw materials (associated with the transition to more green energy sources), coupled with the problems that some mining activities are relegated only in certain countries and regions, must be considered in a sustainable perspective. This work analyses the literature about (critical) raw materials and COVID-19, not only to present the impact of the pandemic on their supply, but also to propose some actions that should be pursued in a post-pandemic renaissance scenario, to increase raw materials availability, with great attention to most critical ones, in the frame of circular economy principles. The post-pandemic possibilities are evaluated and suitable actions are suggested to secure the raw materials availability for the foreseen increase of investments in crucial and strategic sectors, in accord with the UN Sustainable Development Goals (SDGs). The proposed actions can be summarized as policy, strategy, economy, and technology activities. Graphical abstract Image 1 Keywords Sustainability Critical raw materials (CRMs) Circular economy SDGs COVID-19 SARS-CoV-2 ==== Body pmc1 Introduction COVID-19 pandemic has created a global crisis with more than 178 millions of detected cases and more than 3,5 millions of reported deaths at the end of June 2021. It has disrupted economic, financial, political, and social structures all over the world. Undoubtedly, the main goal of almost all the political authorities was to limit the spread of the virus, with the consequence to put in place restriction measures that have impacted on the economic development. In particular, global GDP was evaluated to be decreased by 4.2% in 2020 (Gałas et al., 2021). Research activities to face the pandemic were mainly devoted to develop effective ways to reduce the infection and to limit exposure risks, also in view of possible future epidemics due to similar viral agents (Coccia, 2020). In this frame great efforts were made to evaluate the possible sources of virus spread, that were attributed not only to human-to-human interactions, but also to environmental (as for example air pollution agents (Coccia, 2021a) (Coccia, 2021b), meteorological (Anand et al., 2021), and socio-economic factors (as for example trade exchanges (Bontempi et al., 2021b, Bontempi et al., 2021a) (Bontempi and Coccia, 2021). However, despite that COVID-19 pandemic has globally caused major health, economic, and social difficulties, also some challenges and opportunities have been recognized (Shrestha et al., 2020) (Lurie et al., 2021) (Quitzow et al., 2021). As a consequence of containment measures, people have experimented the importance of technology diffusion, for information and communication, involving several typologies of activities, like working, learning, entertainment, and share news. The relevance of electronic devices was never so evident than during lockdown, when it was necessary to reduce (and often completely avoid) human interactions (Sarkis et al., 2020). Then, the pandemic was also recognized as an opportunity to develop new skills, adopt online education, employ telecommuting, and diffuse virtual meetings options, with also several consequent environmental advantages, associated to decrease of people transport and the reduction of derived emissions (Sarkis et al., 2020). The increase of digital connectivity is also one of the European Commission (EC) ambition for the next future, coupled with the aims to construct a more green and resilient society. In this context, it is fundamental to highlight that green and digital technologies are based on the use of several raw materials (RMs). Moreover, there are some natural resources, containing essential elements, with limited and/or restricted supply, that are defined critical raw materials (CRMs). They play a fundamental role mainly for industrialized regions in the world because several of these CRMs have contributed to revolutionary development of some recent technologies and are necessary for energy efficiency. Their economic importance is connected to their applications that are expected will be ulteriorly developed in green, defence, and high-tech sectors (Althaf and Babbitt, 2021). Considering some CRMs necessity in technological applications also for green energies and ecological transition, more than 30 metals are currently inserted in the list of critical raw materials (Akcil et al., 2020). Even if COVID-19 has highlighted some market distortions connected with CRMs (as better discussed in Section 3), in the past two decades, climate events, as for example droughts and floods, rainfall variability, and extreme storms have caused major impacts in mining activities around the world (Ford et al., 2010). EC has the aspiration to reach 2050 climate neutrality and recognizes that the access to resources is strategic to fulfil this ambition. As a consequence, CRMs that have been already considered crucial for society, are now even defined super-critical (Heffron, 2020). Fig. 1 reports the expected availability of major elements (in years), obtained by considering their world reserves divided by their annual world production (data were extracted from CES Selector database (Granta Design, 2019) referring to 2019, i.e. before COVID-19). These data are plotted versus the element's abundance in Earth's crust (in ppm), that accounts the elements availability in their minerals. Generally, CRMs are reported in the tables mainly highlighting their supply risks (European Commission, 2020a). Then, the results shown in Fig. 1 are very interesting because they don't account any risk correlated with the recyclability and/or geo-political situations where these materials are mined, that are the fundamental characters defining CRMs. As a consequence, only general considerations about the elements abundance and their use can be derived. For example, it is very interesting to highlight that, even if their low Earth concentrations, Rare Earth Elements (as for example Ce, Pr, and Nd) are expected to be available in mined sites for several years (more than 10000 years), based on their commercial interest.Fig. 1 Expected elements availability (in years), versus their abundance in the Earth's crust (ppm) not considering recycling. Data are extracted from CES Selector database – 2019 (Granta Design, 2019). Elements in the bottom may be not available in the next years if their supply will be based only on mining. Fig. 1 On the contrary, Fig. 1 shows that Sb, Au, and Ag are among the resources that may be still not available only by mining in the next years. As a consequence, considering the general perspective of limited time availability of some elements, it is clear that specific actions are necessary to guarantee the expected economic growth and prosperity in the post-pandemic. For example, it results evident that it is mandatory to invest efforts in developing sustainable ways to recycle CRMs from waste, considering that some available natural resources are inappropriate to support the raw materials need of the next future. Indeed, supply of resources is considered no longer able to meet demand (Henckens, 2021). In this frame, the linear economy principles, based on the raw materials transformation into final products, and their subsequent discharge as a waste, cannot be longer followed (Rocchi et al., 2021). The circularity is defined a sustainable model offering a basis for a new approach waste management, allowing to save costs and obtain environmental benefits on the long run (Van Straten et al., 2021). Making a circular economy transformation means to decouple value creation and the use of virgin raw materials (Valve et al., 2021). While the implementation of circular economy in local production (for example at firm-level, till to eco-industrial parks) is often considered, a massive gap exists in how the circular economy is perceived at the global level (Rahman and Kim, 2020), representing a fundamental issue in the raw materials market. However, it is also fundamental to guarantee the quality of the recycled material for direct re-employment (Oumarou Amadou et al., 2021). For example, global annual production of electronic waste was estimated to be approximatively 50 million tonnes (Larmer, 2018), and is expected to grow continuously due to the increased use of electronics, as it was also demonstrated during pandemic (that for example forced people to adopt smart-working). By 2030, more than 1 million of batteries is expected to reach the end of their first-life (Ribeiro et al., 2018). The metals present in these devices (like lithium, copper, cobalt, silver, and gold) often go into landfilling, despite their value, with the consequence to originate soil contamination and water pollution (Ribeiro et al., 2018). Then, the generated electronic wastes, that provide several concerns related to their toxicity, offer an opportunity for recycling precious (Larmer, 2018). The materials circularity is also a fundamental pillar of sustainable development. In particular, the strategies that will be chosen for these waste materials disposal or recover will be highly influential on the SDGs (mainly concerning SDG 12 for Responsible Consumption and Production) and the introduced circular economy agenda. However, despite that in recent literature several concerns have been focalized on different environmental issues related to COVID-19, ranging from pollution (Bontempi, 2020a), to critical resources related to batteries availability (Panda and Akcil, 2021), energy supply, and climate change, few attentions has been devoted to suggest possible actions concerning raw materials (with great attention mainly to CRMs) that should put in place in post-pandemic scenario to be synergic with the ecological transition. Indeed, it was recently shown (Bontempi et al., 2021a, Bontempi et al., 2021b) that sustainable materials are strictly related with most of SDGs and offer the opportunity to construct collaborative actions involving several of their targets. The mining sector, where raw materials are extracted, is very energy-intensive. The limits are co-determined by the large external costs of mining arising from the impact of ever deeper and remoter mining on climate change and on the mine's surrounding environment (Henckens, 2021). Then, it is necessary to revise some mining strategies and improve the recycling industry potentialities and support. In the next future, it will be fundamental to reallocate some resources to promote adaptation and development efforts (Church and Wuennenberg, 2019). As the basis of urban social-economic development, the resources are interrelated in the production and consumption activities of cities (Li et al., 2021). Increased raw materials recycling will contribute to reach the targets of SDGs 9 and 12, and to the adaptation-related objectives of SDG 13, by decreasing the reliance on mines, that are highly vulnerable to climate change. Despite the presence of some papers in literature devoted to the impact of pandemic on raw materials availability (Navon et al., 2021), only few are expressively devoted to CRMs (Akcil et al., 2020) (Gałas et al., 2021). In addition, current explorations lack a global vision of the problems deriving by pandemic and prepositive actions to face the crisis on CRMs availability. The aim of this paper is to show an analysis of the impact of COVID-19 on raw material supply, with particular attention to CRMs, and propose a combination of measures devoted to increase the raw materials accessibility, with great attention to CRMs. An accurate analysis of all available literature about CRMs (also connected with COVID-19) is used for the basis of analysis. Through this investigation, post-pandemic challenges and opportunities that should be pursued to increase the access to raw materials, are proposed, in the frame of SDGs. 2 Study design To provide a comprehensive assessment of the impacts of COVID-19 on raw materials, this work analyzed the articles related to this subject by a systematic literature review. Indeed, through the compression and integration of massive data analysis, literature investigation can provide the statistical mean in quantitative assessment of scientific information (Zhang et al., 2021). SARS-CoV-2 has attracted great attention in literature due to the exceptionality of the pandemic. Indeed, a search for COVID-19 (or SARS-CoV-2) with SCOPUS database showed the existence of more than 200,000 papers concerning to this argument. Currently, papers involving “critical raw materials” are 3876 (see Fig. 2 ). Pandemic had a great impact on materials availability and use, due to the economic crisis associated to COVID-19 global spread (Ibn-Mohammed et al., 2021). SCOPUS database allowed to identify 428 papers devoted to raw materials (RMs) and COVID-19 (or SARS-CoV-2). However, a similar search but concerning “critical raw materials” in all possible fields, has highlighted the existence of only 24 papers expressively devoted to this topic (see Fig. 2).Fig. 2 Summary of literature research about COVID-19 (or SARS CoV-2), raw materials and critical raw materials on SCOPUS database. Fig. 2 The information of the articles from SCOPUS platform included authors, title, keywords, abstract, and references for the data analysis, synthesis, and interpretation. In particular, an analysis of keywords co-occurrence network of papers devoted to RMs and COVID-19 (or SARS-CoV-2) allowed to perform a cluster analysis of the literature (see Fig. 3 ). The study design (with connected literature) was updated on June 10, 2021.Fig. 3 The keywords co-occurrence network of papers devoted to raw materials (RMs) and COVID-19 (or SARS-CoV-2). The study design was updated on June 10, 2021. Data analysis was performed by (“VOSviewer version 1.6.16,” 2020). Fig. 3 It results that the already available papers can be grouped in 5 clusters. The first one (49 items) is mainly devoted to circular economy principles, with great attention to sustainability and environmental impacts, involving waste management strategies. In this cluster raw materials extraction is also considered. The second cluster (27 items) is devoted to chemicals (compounds devoted to COVID-19) and their characterization. The third cluster (25 items) mainly concerns supply chain, with attention to raw materials commerce and manufacturing. The fourth one (20 items) essentially involves medical keyworks, related to pandemic disease. Finally, the last cluster (11 items) is centered on geographical locations (as for example China, Russian federation, India, and so on). It results that only few papers are expressly devoted to raw materials availability (they can be found in cluster 1). Then, based on the results of cluster analysis, the last step was realized thorough reading the selected full text papers from the perspectives of the consequence of pandemic on RMs and CRMs. These papers were carefully analyzed with great attention to the resulting 24 papers expressly devoted to CRMs and COVID-19. All the available literature about CRMs allowed also to propose possible future directions, which are presented in detail in the next section. The measures used in this study are the following:• Data about the current recovery efficiency of 4 CRMs (antimony, bismuth, indium, and tungsten), their proposed future recovery efficiencies and their substitutability, that are extracted from (Henckens, 2021). • Data about mining operations at risk on June 25, 2020, reporting the material, the number of respective involved mines and total revenue at risk, that are provided from S&P Global Market Intelligence (MacDonald et al., 2020). • Data about monthly price change (%) of lithium carbonate, cobalt, rock phosphate, iron ore, copper and aluminum and Commodity Metals Price Index (2005 = 100, includes Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U Price Indices) from January 2019 to February 2021, that were extrapolated from World Bank and Trading Economics. 3 Results and discussion 3.1 Summary about CRMs state of art The EU industrial strategy poses raw materials as key enablers for a green, competitive, and digital Europe. EC has identified some critical raw materials (CRMs) that are considered crucial for EU economy. The number of these minerals is increasing in the classifications that were updated after the first introduction. In 2011 CRMs were 14, in 2014 the list was extended to 20 materials, and in 2017 CRMs increased till to 27 (European Commission, 2017). Currently CRMs are 30 (European Commission, 2020a). These resources play a fundamental role in 81 countries that collectively account for a quarter of world GDP (Heffron, 2020). Moreover, it is expected an increase of CRMs demand strongly related to the world's capacity from green sources needs, under the 2015 Paris climate agreement (European Commission, 2020b). In particular, more energy needs to be stored, more CRMs are necessary (Hund et al., 2020). Regarding technological applications connected to green energy, several of the 30 CRMs are currently involved. For example, Al (extracted from bauxite) is used for turbine blades, wires and electrodes. Co, Ni, Fe, and Li are key metals for batteries (more than 60% of Co extracted from mines goes into rechargeable batteries). Rare metals like Ga and In are widely used in electronics components (to realize transistors and computer chips). Rare-earth elements (REEs) are widely used in high technology devices, including computer hard disks, flat screen televisions, smart phones, computer monitors, and digital cameras. They are also used in defence technologies and clean energy applications. For example, Nd and Dy are essential for magnets. The access to these resources, that are defined technology metals, is now fundamental and crucial for climate change combat. Indeed, it is estimated that 3 billion tonnes of metals and minerals will be mandatory to reach the objective to decarbonize the global energy system by 2050. In addition, other technologies development, such as 5G digital communications, is improving the pressures on other resources (Akcil et al., 2020). As a consequence, by 2030 the Li and Co demand is expected to increase up to 18 and 37 times respectively, considering the corresponding requests in 2015 (Jones et al., 2020). In particular, to satisfy its manufacturing batteries necessities, the Chinese market will increase the demand for both lithium and cobalt reaching about 68% of the global need (Jones et al., 2020). Also other alternative energies sources, based on solar cells, panels, and wind turbines will be more diffused in the next years, with an expected significant increase in demand of raw materials due to the both wind and solar photovoltaics technologies (Vidal et al., 2013), at global level. This also will contribute to produce strong concerns related to all CRMs associated with energy generation technologies. Considering data reported in Fig. 1, it is evident that an increase higher than 1 order of magnitude in the Li and Co demand, will risk limiting their availability to about 30 years for Li and less than 10 years for Co (only considering mining resources). In this frame it is then fundamental to evaluate the recovery efficiency and the possible substitutability of these materials, to evaluate the opportunities due to wastes valorisation and materials replacement. Fig. 4 reports the current recovery efficiency of some CRMs, their proposed future recovery efficiencies and their substitutability (Henckens, 2021). It results that some of these elements (as for example Sb) are recovered with efficiency higher than 80%. On the contrary, some elements are recovered with low efficiency (as for example In, that is recovered with an efficiency lower than 50%). Higher recovery efficiency may need too energy resulting a not sustainable process (Henckens, 2021), with an expected need of technological improvements aimed to enhance their recovery. Fig. 4 also shows the reported CRMs substitutability, i.e. the possibility to be replaced by another material. The results are very different considering Sb (that can be substituted for 90%) and W (that can be substituted only for 30% by another material).Fig. 4 The current recovery efficiency of some CRMs, their proposed future recovery efficiency and their substitutability (data were extracted from Henckens, 2021). Fig. 4 In this frame it is important to highlight that some concerns are also related to metals that aren't CRMs. Some researches, more focalized on specific elements, suggest, for example, a future increasing demand not only for Li, Co, and Al (it is extracted from bauxite), but also for Fe, Mn, Ni, and Pb (World Bank Group, 2017a). In particular, electrification is supposed to cause the increase the Ni and Mn request for new vehicles batteries by five times the 2015 level (Jones et al., 2020). In addition, there are metals that are necessary for vehicles productions, not considering batteries; they are for example steel and aluminium. Due to stainless-steel composition, for vehicle production also Cr, Mn, and Ni will be necessary. Al will be fundamental to reduce the vehicle weight (it is mainly used for car bodies and battery cases), allowing to meet the stringent emissions legislation, making possible also a gradual reduction of steel use for mobility. Key uses of this element not only include the transport sector, but also building industry (for example Al is used to realize windows, doors, and facades), and packaging (where Al is used for beverage cans, and for foil applications). Literature estimates an annual global demand increase in 2030 (then not considering batteries sector) by 30.4%, 8.4% and 6.3% respectively for Ni, Al and Cu, on the basis of 2017 production (Jones et al., 2020). Moreover, in 2050 the Al request is estimated to increase till to 40%. In addition to studies related to global necessities, that are summarized in this section, the request for the critical raw materials, associated with the transition to green technologies for low carbon economy, have been also analyzed in literature considering the specific frameworks of some countries (Ciacci et al., 2016) (Moss et al., 2011) (Hatayama and Tahara, 2015). Phosphate rocks (PR) have been also inserted in the CRMs list, due to their fundamental contribution for the P supply chain (European Commision, 2014). Although scarcity and provisions risk are common aspect for critical raw materials, when phosphorus is considered, it is fundamental to highlight that this element is linked to food production and thus its scarcity may compromise the access to food. This risk is then correlated to SDG 2. Literature about inorganic P production and connected use reports an 80% of phosphorus loss from mine to fork (Cordell et al., 2009) with the evaluation that only 10% of the processed fertilizers can be digested by humans (Scholz et al., 2014) and more than half of the losses from fertilizer application on soil to fork are in runoff from agricultural land (Scholz and Wellmer, 2015). The digested phosphorous is generally found in wastewater and can be also discharged outside of a wastewater collection system (Ducoli et al., 2021). The processes connected to PR mining and processing have several negative impacts on air and water qualities, and climate effects (Nedelciu et al., 2020). In addition to the pollution and eutrophication effects, the P supply chain produces large amount of waste deriving from the processing of PR to synthesize final products (as for example phosphoric acid) (Nedelciu et al., 2020). Several activities and technologies are proposed to recover phosphorus from waste, as for example from sewage sludge (Fahimi et al., 2021) (Pasquali et al., 2018) (Benassi et al., 2015) and poultry litter ashes (Fiameni et al., 2021) (Fahimi et al., 2020). Moreover, developing processes to recycle phosphate from the growing phosphogypsum stocks, derived from phosphoric acid synthesis, may also play a fundamental role in providing suitable qualities of fertilizers on the market (Nedelciu et al., 2020). Great attention should be also devoted in the loss reduction of P supply (Nedelciu et al., 2020). In this scenario some actions can be envisaged that can be linked to understanding and improve mining potential, recover resources, and secure job (Panda and Akcil, 2021). For example, an international network in raw materials i.e. EIT (“EIT Raw Material,” 2021) was instituted by EU and funded by the European Institute of Innovation and Technology (EIT). The aim of this network is to support innovation schemes addressed to mining, exploration, recycling, processing, and substitution of raw materials, in the frame of circular economy principles. 3.2 Pandemic impact on raw materials supply The analysis of the literature allowed to summarise the pandemic impact on raw materials supply (see Fig. 2). Indeed, the majority of available papers about this argument concerns the impact of pandemic on natural resources management (Laing, 2020) (Cai and Luo, 2020) (World Bank Group, 2017b). Some of these works show that the global international trade can be also associated to higher virus diffusion, then to higher negative effect (Bontempi, 2021) (Bontempi, 2020b) (Bontempi et al., 2020). On the basis of literature analysis (see Fig. 2) it results that: 1) exploration works for new mine sites were delayed (Gałas et al., 2021), 2) the metals demand was significantly reduced, 3) the international supply chain was almost completely destroyed, 4) the production sector stopped, due to reduced consumption, and 5) the price of several raw materials have been (also drastically) changed (some decrease and other increase). For example, it was reported that an unexpected price increase occurred for epoxy and polyester resins (Coating World, 2021) due to COVID-19 restrictions. Then, an economic shock was reflected on both the demand and the supply of raw materials (Panda and Akcil, 2021). In particular, considering the mining sector, COVID-19 caused several negative impacts across the world, with the consequence to several sites closure. For example, on June 25, 2020, it was found that 275 mining operations were globally disrupted as shown in Fig. 5 . Most of these mining sites were closed to comply with anti-contagion measures (MacDonald et al., 2020).Fig. 5 Representation of 275 mining operations at risk on June 25, 2020. Metals and the number of respective mines are reported. Fig. 5 It was found that the most impacted mines concerned gold, coal, copper, U308 and silver. In summary, the global impacted projects worldwide due to pandemic, connected to mining sector, was evaluated to be more than 7 billion of € (around US$ 9 billion) (MacDonald et al., 2020). Fig. 6 shows, according to (MacDonald et al., 2020), where the most endangered mining sites are located highlighting the countries that have suffered the greatest losses. The most affected country was Peru, with losses of more than 2 billion of €, followed by Mexico and Chile. Data analysis was performed by Qgis software (“Qgis,” 2018). Moreover, in the supplementary material of this work, all files necessary to create a Qgis map of mining sites and revenue at risk per country are available. With temporary closing of mining operations expected to reduce the 20% production in 2020 compared to 2019 (Habib et al., 2021).Fig. 6 Representation of the most endangered mining sites (divided into precious metals, specialty commodities, bulk commodities, and base metals) and the countries that have suffered the greatest losses. Data analysis was performed by Qgis software. Fig. 6 In addition, there were also some potential markets crisis that may be originated by the limited availability of some primary resource sectors to provide raw materials, when markets were closed to reduce safety risks. The prices of several metals can be associated to economic cycles: the decrease in some raw resource production is more likely associated to a decline of demand, rather than the falling of geological resources availability (Graedel et al., 2013). The metal market is extremely volatile, influenced by economic and political factors. The pandemic has exacerbated this trend, leading to a disruption of world balances which have had a major impact on world markets. In fact, when the COVID-19 has spread, to mitigate its impact, China introduced some strategic actions that have caused an important decrease in demand in the market of large consumers (Moore et al., 2020). The COVID-19 pandemic had a strong impact on industrial production. In the first quarter of 2020, the drop of 20% in the Chinese economy due to Wuhan's manufacturing shutdown generated a reduction in the raw material prices (Akcil et al., 2020). This trend, according to (Laing, 2020), shows an analogy with the Great Financial Crash of 2008–2009. The rapid decline in demand generated a reduction in the prices particularly for aluminium and copper (Laing, 2020). Fig. 7 shows the monthly price variation from September 2019 to February 2021 of some CRMs, such as lithium carbonate, cobalt, phosphate rock (PR), and other key metals for batteries such as iron ore, copper and aluminium. In addition, a commodity metals price index was inserted (Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U are considered). As reported in Fig. 7, in April 2020 the price of aluminium has dropped more than 9% respect to March 2020, while the price of copper slumped more than 8% between February and March 2020. In the same period the lithium carbonate price is reduced more than 4%, respectively. The closure of Mutanda mine site, the largest cobalt field in the world, in November 2019 (Mining weekly, 2020), generated a slight rise of Co price from December 2019 to January 2020. On the other hand, the COVID-19 pandemic caused a decrease in prices between February and March 2020 about 10%. Fig. 7 Monthly price change (%) of lithium carbonate, cobalt, rock phosphate, iron ore, copper and aluminum and Commodity Metals Price Index (2005 = 100, includes Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U Price Indices) from January 2019 to February 2021. The Wuhan lockdown period was from 23 January to April 8, 2020. Data source: World Bank and Trading Economics. Fig. 7 The exposure on the stock market of the metal sector can be defined by the Commodity Metal Price Index (CMPI). This index represents a general level of metals price. CMPI is a weighted average of the prices of some reference metals respect to their prices in a base year. In Fig. 7 is reported the Commodity Metals Price Index of Cu, Al, iron ore, Sn, Ni, Zn, Pb, and U, from 2019 to February 2021. Following the droop in process between February and April due to COVID-19 pandemic, metal prices recovered strongly reflecting a recovery in global industrial demand largely driven by consumption in China (World Bank Group, 2017b) exciding the pre-pandemic values. Indeed, as reported in Fig. 7, the price of aluminium, copper, iron ore, PR and cobalt is increased in the third quarter of 2020 of 14%, 22%, 25%, 6% and 9%, respectively compared to second quarter of 2020. On the other hand, the price of lithium carbonate is decreased of 8% in the same period. However, from December 2020 to February 2021 lithium carbonate price increased due to the high demand of lithium ion batteries (Mining.com, 2021). Due to lockdown, the demand for electronic devices such as PCs and tablets, needed for smart working and distance learning, has strongly increased by 13.1% in 2020 and the trend is still growing (IDC, 2021). Some of the materials used for their manufacture, already present in limited quantities, are part of the sites at risk. Compared to previously economic crisis, the rolling spread of the pandemic has led to different outcomes. For example, in 2011 the prices of some metals peaked sharply after an abrupt and almost total supply disruption (Habib et al., 2021). On the contrary, after the pandemic diffusion, in 2020, the prices of some RMs initially went down slightly because demand was disrupted even more, and then they increased. 3.3 Proposed actions able to increase raw materials availability The dependence on natural resources extraction has shown several fragilities for local economies, that were enhanced during the pandemic crisis. However, over the next years an increase in the demand for some mineral resources is expected. To face the post-pandemic it is necessary to learn from past supply chain limits and propose active improvements to give a positive impulse to a sustainable renaissance. Indeed, even if COVID-19 pandemic has highlighted some fragilities in raw materials supply, this experience can also be used to suggest new strategies in their management, in view of a desired increased resilience. Literature analysis, mainly concerning the papers devoted to CRMs (see Fig. 2), allows to propose some actions, that may be pursued, to increase CRMs availability, also on the basis of past experiences. In this frame, for example, the strategies that were putted in place to better manage the Al supply can represent an example that may be followed for other natural resources, if possible. As reported in Section 3.1, Al represents a strategic element for the EU Green Deal policy. Currently Al industry is able to recover and reuse about 36% of secondary Al (European Aluminium, 2020). Al recycling involves only 5% of the energy that is used for primary production. To meet the next years increased request of this metal, both primary (mined) and secondary Al production will be necessary (European Aluminium, 2020). However, even if an increased demand is expected, a feasible scenario shows that EU can reduce its dependence on imported Al from 29% to 15% in 2030 by rising its domestic production. This will be possible if the right competences will be in place (European Aluminium, 2020), to improve collection and sorting, with the result to ensure higher recycling rates and better quality of the obtained output. It was already shown that a suitable scenario will require additional legal constrains and investments for better collection and sorting technologies, to limit Al components destined to incineration and/or landfilling. For example, the current shredding treatment process aimed to recover steel from vehicles, need to be improved to better recover also Al. Also considering this representative example, governments, industries, associations, and consumers have some possibilities to reduce the pressure on raw materials in a post-pandemic era. The actions that can be proposed, derived by analysing the available literature about CRMs (see Fig. 2), can be summarized as policy, strategy, economy, and technology activities, and they are summarized in Fig. 8 , with great attention to the involved sustainability pillars.Fig. 8 Summary of the proposed actions, grouped as policy, strategy, economy, and technology activities, proposed in the frame of sustainability pillars (Environmental, Social, Economic, and Cultural pillars). Fig. 8 3.3.1 Strategy actions 3.3.1.1 Monitor the mineral production and consumption Mineral extraction is an energy intensive activity, difficult to decarbonize (Ali et al., 2017). There is a compelling necessity to propose and adopt a framework for tracking mineral use along the entire value chain, from source to end of life, that should account the energies and emissions involved in all the lifecycle (Bontempi, 2017). This system may promote a notion of ‘metal miles’, aimed for example to reduce the transport costs of these resources, promoting the local products consumption (SDG 12). The framework should also consider transparency and ethical schemes: in such a monitoring also the social conditions of mine workers should be accounted, to consider all the pillars of sustainability in the frame of SDGs 5, 8, 10 and 16. For example, some natural resources (like cobalt minerals) are mined in the Democratic Republic of Congo, where also women and children often work in mines, without basic safety equipment, and where from years the population is plagued by armed conflicts (Amnesty International, 2016) (SDG 16). Even if we must be conscious that complete traceability schemes may be impossible and the proposed framework risks to be a pure exercise (Sovacool et al., 2020), the established public relations to compile this schema may transform in a support to improved outcomes for miners and better governance management. Mining Local Procurement Reporting Mechanism was already introduced to report information about local procurement of mining companies, as well as detail on mining procurement processes and due diligence practices (Geipe and Kaiser-Tedesco, 2018). 3.3.1.2 Revise globalized production systems The current production and consumption system are constructed onto on extremely interconnected value chains based on international exchanges and shipment of the basic components. This may increase the vulnerability to pandemic (Bontempi, 2020b), highlighting that more local supply configurations can contribute not only to decrease the local dependence on materials, but also increase local resilience (also to pandemic) (SDG 11). For example, for the polyester and epoxy resins, a complex mix of prices increase, high demand, supply problems also connected to the restricted possibilities of resources transport, had the consequence to increase the market uncertainty and contribute to the materials prices sharply growth (Coating World, 2021). The possibilities of the occurrence of these situations must be reduced, by the revision of the globalisation production system. 3.3.1.3 Explore the availability of new resources Even if it is mandatory to give primary emphasis on resource efficiency and recycling, it will be necessary to find additional primary resource mines (Sovacool et al., 2020). New resource streams may be found not only in new deposits or mines, but also in other matrices, such as groundwater (geothermal brines) and seawater (desalination). In this context new technologies for mineral exploration, from deep in the crust to the bottom of the ocean may be developed (SDGs 8). Geochemical and geophysical data must be shared in greater detail through dynamic databases (Ali et al., 2017). Moreover, also waste must be considered, as for example landfilling sites, to promote recovery and recycling of some resources, with great advantage in terms of environmental sustainability (SDGs 11, 12). 3.3.1.4 Enable the access to small local deposits Although large-scale mining is often economically efficient, it has several drawbacks (Sovacool et al., 2020). In a post-pandemic context, small deposit mining by small-scale operations may be more attractive, if compared to multiple mining operations (that probably may need additional capital costs to re-open mine sites), due to the lower investment required, to secure production, even if limited, in a market of low prices (Moore et al., 2020). Ore deposits are located on all continents including Europe (Goodenough et al., 2016), but in this case, the Europe strict regulation makes their use extremely complicated (Moore et al., 2020). EC must consider the possibility to take advantage of internal mines, with the result to increase its market resources. Domestic mineral extraction is fundamental to reduce critical raw materials import and increase the resilience of EU territories and their subsistence capability in extreme events, like pandemic (SDGs 8,12) (Bontempi, 2020b). In this frame, a focus on more local supply chains may be investigated and prioritized. 3.3.2 Technology actions 3.3.2.1 Redesign the technologies to support alternative materials use This necessity should be constantly pursued. However, it may result fundamental for CRMs. For example, cobalt abundance in Earth's crust is not sufficient to produce all the batteries that will be required by the markets in the next years (as shown in Section 3.1). Some manufacturers companies (for example Tesla and CATL in China) are pursuing new batteries, with the aim to make them cobalt-free (Akcil et al., 2020). In particular, in the last years, Tesla has reduced its dependency on Co for EV batteries by approximately 60% (Chen, 2018) (SDGs 7, 9, 12). Currently the proposed replacements consist in materials containing Ni or Fe, which still result less efficient, but decrease the pressure on Co. Na-ion batteries are also under study. 3.3.2.2 Recover and recycle waste In 2017, more than 10 million of tons of electric wastes were generated in Europe (Panda and Akcil, 2021). Unfortunately, only about 31% is currently recycled. Recycling for recovery of precious resources is one of the main activities with high potentialities, it but need to be more explored and supported. In addition to the landfill mining, the possibility to recycle devices before they are discharged must be encouraged and better investigated (SDG 12). Repair must be a valid option, with also the advantage to generate new jobs positions. On the other hand, obsolete devices and spent batteries have interesting amount of precious and rare metals, with high potential to allow to meet the growing demands for CRMs, making their extraction from waste potentially economical. Indeed, the activity could be implemented with low logistical and supply chain costs. For example, countries with high amount of electronic waste typologies may establish dedicated markets to reprocess some CRMs domestically (Işıldar et al., 2019). Hummingbird International estimated that materials recycling electronic waste may generate more than one order of magnitude jobs in comparison to those that are dedicated for traditional disposal activities (Sampson, 2015) thereby contributing to SDG 8, with also great advantage in terms of avoided GHG emissions (SDG 13). In this frame, the secondary Al management strategy can represent a valid example that must be promoted, also for the result that could be acquired in terms of avoided CO2 emissions (up to 39 million ton per year by 2050 (European Aluminium, 2020). To reach this aim new regulatory measures and improvements in legal constrains may be necessary, for example, to make electronics manufacturers responsible for recycling the products they make also from a legal point of view (SDG 12) (Akcil et al., 2020). Moreover, the recycling potential of a material used in a specific product depends on some factors, as for example its concentration and the product composition. Indeed, it is evident that a higher concentration generally results in a higher recycling potential. It also depends on eventual dissipative use of the material and possible contamination arising by its use (Henckens, 2021). 3.3.2.3 Increase the efficiency in materials extraction and use It was estimated that more than 70% of GHG emissions are originated in energy-intensive raw material production, and processing (McKinsey, 2020). It is evident that a higher efficiency in materials extraction, processing, and use is a fundamental step to address the climate neutrality (SDGs 7, 13). In a full-monitoring procedure of CRMs lifecycle it would be possible to highlight the sectors and steps that may be improved to limit materials loss and increase the efficiency in materials use (SDG 12). Concerning mining, for example, it is fundamental to minimize waste and the consumption of water during extraction processes, to maximize the amount of extracted materials, and reduce involved energies and emissions (SDG 14) (Lederer et al., 2020). Since 1980s, responsible mining has been addressed as a fundamental criterion in mining. Even if mining should be not defined sustainable, due to its nature that involve resources depleting, the attention towards less polluting technologies for metals extraction was increased in the last 20 years (Spooren et al., 2020). Ore grades is continuously reducing, increasing the necessities of more efficient extraction technologies, producing mineral residues that can be valorised (SDG 12). For example, many conventional separation technologies were considered inefficient for the treatment of low-grade ores, that need to be milled into very fine size grains to guarantee sufficient mineral separation (Dermont et al., 2008). The use of flotation technique (with the development of suitable floating agents) allowed to concentrate fine interest minerals, contributing to reach higher revenues in metals extraction (SDG 14) (Lopéz et al., 2019). Other possibilities to increase materials extraction efficiency will be dependent on the development of digitalization of some processes as for example sensor-based detection and separation of mineral streams (Robben and Wotruba, 2019), and the possibilities of machine learning implementation (SDG 12) (McCoy and Auret, 2019). Materials efficiency can be improved also by reducing their use in a product, assuring the required functional properties of the final component. This needs a suitable design strategy. In addition, also the reduction of the in-use dissipation (that involves the loss of a resource through and during its consumption, for example by corrosion) can be improved (Henckens, 2021). 3.3.3 Economy actions 3.3.3.1 Promote industrial symbiosis The possibility to recycle a material derived from a different supply chain sector must be better investigated and promoted. The circular economy principles require a gradually decoupling of economic activity from the natural resources consumption. Metals recycling from RAEE, for example, can offer the possibility to reduce the dependence from mined metals import. However, mineral recycling can be acquired also by using wastes derived from other sectors, such as for example municipal solid waste incineration ash, that can be reused in several other applications (SDGs 9, 12) (Bosio et al., 2013) (Benassi et al., 2017) (Benassi et al., 2015) (Assi et al., 2019) (Zanoletti et al., 2018). 3.3.3.2 Promote new business models The circular economy approach must be encouraged: materials must be retained within productive use, for as long as it is possible (SDG 9) (“Towards a Circular Economy: Business Rationale for an Accelerated Transition,” 2015). In this business model the waste must be limited and possibly re-introduced in the productive system. In a circularity scheme the waste assumes a new value, in a system that is designed to repair the previous damage (SDG 12) (Murray et al., 2017) (Zanoletti et al., 2018). The evolution in a different business models, that can be defined “circular business model” (Mohammad Ebrahimi and Koh, 2021), modifies the link between the members of supply chain: the cooperation between the actors is necessary and need their involved in long-term learning processes, to achieve the know-how necessary for the new industrial model (Krook and Baas, 2013), to support the urban mining process (Sharma et al., 2021). However, at the beginning of the activities, political intervention, with adequate supporting actions (as for example the provision of green certificates, with a similar mechanism that it is currently proposed for COVID-19 vaccinations certification) is fundamental to ensure that economic benefits outweigh costs (Van Passel et al., 2013). This is clearly evident in the current situation, where inter-regional flows of waste materials have been disrupted by lockdown restriction measures due to pandemic. However, the new business model initiatives, mainly involving small industries and start-ups have been estimated to be able realizing value higher than 230 billion € per year by 2030 (SDG 17) (Klevnäs et al., 2020). 3.3.3.3 Develop more efficient market strategies Sometimes consumers have the perception that recycled materials may not guarantee the same performances and qualities as virgin resources (Church and Wuennenberg, 2019), or that some devices can be considered old, even if they can be perfectly working. For example, in a recent experiment, considering 148 out-of-use laptops, it was shown that only few units were found to be completely unusable, with most batteries able to retain 89% of their original capacity (Furtkamp Julian, 2017). This may result in a barrier for the market of secondary materials, that can be overcome by the promotion of suitable dissemination campaigns. In addition, also incentives must be used to encourage consumers to address its electronic waste towards suitable collection and recycling schemes (SDG 9). It was shown that, for example, if in Japan all used mobile phones would be collected and recycled, the annual consumption of palladium, silver, and gold may be reduced by 2–3% (SDGs 11, 12) (Mishima et al., 2016). 3.3.4 Policy actions 3.3.4.1 Support the research The investments in research activities devoted to raw materials substitution and exploration, and aiming to find new production technologies, must be suitably addressed before to reach the feedstocks scarcity. Research activities must also cover the development and/or improvement of the extraction processes. Also the complete metals extractions must be fulfilled to avoid raw materials loss in ore (for example, indium or germanium can be recovered in zinc ores, or gallium can be recovered in bauxite) (SDGs 9, 12) (Ali et al., 2017). Finally, the priority must be addressed towards funding research activities devoted to secondary resources recycle, considering the increase of global amount of these wastes. The researchers role in developing suitable recovery technology is crucial. 3.3.4.2 Promote gender equality COVID-19 pandemic has caused several negative aspects. In particular, the effects of the pandemic have hit women the hardest. Concerning raw materials, in many countries mining laws and regulations neither fully mainstream the principle of gender equality nor acknowledge women as active participants in the sector (SDGs 5, 10) (Tekinbas and Deonandan, 2021). The gender equality is one of the SDGs (SDG 5). Post-pandemic conditions give the opportunity to revise legislation, priorities, and governance principles in raw materials sector, as proposed by Intergovernmental Forum on Mining, Minerals, Metals and Sustainable Development (Intergovernmental Forum on Mining, Minerals, 2020). 3.3.4.3 Improve legislation Legislation is currently in evolution to consider the most suitable instruments to address national investment strategies toward a post-pandemic reconstruction. The priority areas to address resources are in discussion. Literature has shown that best practices in responsible mining (related to environmental protection) need resources (Moore et al., 2020). Legislation has a fundamental role for example in promoting the raw materials traceability, with attention to sources that are responsibly acquired (SDG 16) (Young and Dias, 2012). In this frame, precautionary measures need attention to avoid illegal mining activities and secure the import and export of critical raw materials, associated with their sustainable supply (SDGs 10, 16) (Panda and Akcil, 2021). Clear regulatory actions must be designed to incentive eco-design and discourage linear business models in private industries. There is a strong necessity to support product manufacturers in the developing of new business models, taking advantage of economic opportunities due to circular solutions. As already suggested in this paper, governments can propose economic incentives to improve innovation and support new markets, including low-interest financing, tax abatements, tax revenue sharing, infrastructure assistance, and grants (Tilly, 2017) and ensure more certainty to investors. Extended producer responsibility (EPR) was introduced in the last years, in some sectors, as a governance mechanism destined to targeted producers, increasing their responsibility in collection, recycling, and waste disposal (Organisation for Economic Co-operation and Development (OECD)., 2016). Inclusion of mining waste treatment in EPR may be a fundamental action to promote the business for secondary raw material production and management as a fundamental part of the environmental cost of mining (Armstrong et al., 2019), allowing to take into account also environmental costs of raw materials extraction in economic planning (SDG 12). EPR has the aim to shift the responsibility for valuable resources collecting from consumers to companies that use these materials. This mechanism is expected to introduce some improvements in the materials use and recovery, like as increased eco-design strategies, higher opportunity of recover and repair, and extended product lifetimes and durability. 3.3.4.4 Encourage the Sustainable Materials Partnership for SDGs (Bontempi et al., 2021a, Bontempi et al., 2021b) SDG 17 (Partnership for the Goals) encourages activities devoted to promote actions across different sustainability goals. Collaboration between the public and private sectors, involving civil society must be envisaged. It was recently shown how the connection between sustainable development and materials is extremely strong. The institution of a “Sustainable Materials Partnership” involving all the stakeholders (from researchers, to industries, from clusters to people), may help to support SDGs achievement in all the countries, with great attention to the raw materials connected activities. A first basic example of a similar action may be found in the International Council on Mining and Metals guidelines, proposing the implementation of social actions of local populations of the territories where raw materials are extracted (Parra, 2020). Another example is the EIT RawMaterials initiative, established in 2018, with the mission to enable sustainable competitiveness of the European raw materials sector along all the value chain (“EIT Raw Material,” 2021). An informal accord, the “Green Recovery Alliance”, to accelerate the ecological transition in a post-pandemic, was recently launched by some members of European Parliament (Frédéric, 2020). However, more generally, the proposed partnership (SDG 17) should be devoted to all sustainable materials, and not only to CRMs. This will allow to establish a framework able to support all SDGs. 4 Conclusions CRMs play a key role for the progress of the industrialized regions in the world. They have contributed to recent technological development and energy efficiency improvement. They serve as essential RM for high-technology, sustainable, and green applications. COVID-19 disease has contributed to reduce the availability of several of these materials. However, the pandemic has also demonstrated the need of structured and global efforts to face crisis, because individual actions are pointless. The post-pandemic recovery period could be an opportunity to develop new prosperity models, based on green principles needs and priorities: a more resilient economy, able to catch the new opportunities of digitization, and meet the environment and climate targets. We have recently marked the 150th anniversary of the periodic table formulation and we are addressed towards SGDs. It is time to realize that a sustainable development needs more attention to some elements, and, in particular, to the minerals where necessary resources are embedded. Despite all the complexities, the humanity must be ready to face this challenge. After an analysis of reduced supply of RMs due to the pandemic, this work proposes some actions devoted to secure a more balanced resources distribution. This should happen in particular in terms of natural resources revenue that could be more equally shared across all the business value and supply chains (Heffron, 2020). The paper shows that the diversity of CRMs that are need for manufacturing future technologies necessitate a change in their extraction and manufacturing approaches. The materials production and consumption must be secured in a sustainable way, that probably will need a revision of giant ore deposits management strategies. Technology will be a fundamental player of this innovation, but also mining code and practices need strict revisions and improvements. Finally, increased materials recycle will help to fulfil some of the circular economy aims, by reducing reliance on finite resources and mitigating permanent waste disposal. The expected post-pandemic scenario will contribute to SDGs 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 and 17 fulfil among others. This study presents some limits. One of the main limitations of this research is due to the data source, which was confined to articles collected from SCOPUS till a certain data (June 10, 2021). The information about COVID-19 and raw materials obtained from the following periods may provide additional data that are not considered in this work. Another limitation of this work concerns the social pillar of sustainability, that is not considered in the study: “social sustainability” is linked to the social outcomes and values, such as equality, social responsibility, children's work, gender equality, community resilience, freedom from poverty, that is often connected with population living and working in mining sites. Author contributions Conceptualization: E.B.; Data curation: E.B., A.C., A.Z.; Formal analysis: A.C., A.Z.; Investigation: E.B., A.C., A.Z.; Supervision: E.B.; Roles/Writing - original draft: E.B, A.C., A.Z; Writing - review & editing: A.C., A.Z. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Academic Press S0022-4804(22)00001-4 10.1016/S0022-4804(22)00001-4 Article Table of content 29 1 2022 3 2022 29 1 2022 271 vvi 2019 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmc

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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)01005-7 10.1016/j.envres.2021.111711 111711 Article Assessment and mitigation of toddlers’ personal exposure to black carbon before and during the COVID-19 pandemic: A case study in Singapore Tran Phuong T.M. ab Adam Max G. a Balasubramanian Rajasekhar a∗ a Department of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore b Faculty of Environment, University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City, Viet Nam ∗ Corresponding author. 17 7 2021 11 2021 17 7 2021 202 111711111711 25 4 2021 13 7 2021 14 7 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Black carbon (BC), an important indicator of traffic-related air pollution (TRAP) in urban environments, is receiving increased attention because of its adverse health effects. Personal exposure (PE) of adults to BC has been widely studied, but little is known about the exposure of young children (toddlers) to BC in cities. We carried out a pilot study to investigate the integrated daily PE of toddlers to BC in a city-state with a high population density (Singapore). We studied the impact of urban traffic on the PE of toddlers to BC by comparing and contrasting on-road traffic flow (i.e., volume and composition) in Singapore in 2019 (before the COVID-19 pandemic) and in 2020 (during the COVID-19 pandemic). Our observations indicate that the daily BC exposure levels and inhaled doses increased by about 25% in 2020 (2.9 ± 0.3 μg m−3 and 35.5 μg day−1) compared to that in 2019 (2.3 ± 0.4 μg m−3 and 28.5 μg day−1 for exposure concentration and inhaled dose, respectively). The increased BC levels were associated with the increased traffic volume on both weekdays and weekends in 2020 compared to the same time period in 2019. Specifically, we observed an increase in the number of trucks as well as cars/taxis and motorcycles (private transport) and a decline in the number of buses (public transport) in 2020. The implementation of lockdown measures in 2020 resulted in significant changes in the time, place and duration of PE of toddlers to BC. The recorded daily time-activity patterns indicated that toddlers spent almost all the time in indoor environments during the measurement period in 2020. When we compared different ventilation options (natural ventilation (NV), air conditioning (AC), and portable air cleaner (PAC)) for mitigation of PE to BC in the home environment, we found a significant decrease (>30%) in daily BC exposure levels while using the PAC compared to the NV scenario. Our case study shows that the PE of toddlers to BC is of health concern in indoor environments in 2020 because of the migration of the increased TRAP into naturally ventilated residential homes and more time spent indoors than outdoors. Since toddlers’ immune system is weak, technological intervention is necessary to protect their health against inhalation exposure to air pollutants. Graphical abstract Image 1 Keywords Black carbon Personal exposure Mitigation Traffic COVID-19 Abbreviations BC, black carbon COVID-19, Coronavirus disease 2019 CB, circuit breaker GM, geometric mean PE, personal exposure PM, particulate matter ME, microenvironment NV, natural ventilation AC, air conditioning PAC, portable air cleaner TRAP, traffic-related air pollution OD, outdoor UFP, ultrafine particles ==== Body pmc1 Introduction Black carbon (BC), a major component of fine particulate matter (PM2.5, ≤ 2.5 μm in diameter), is primarily emitted from the incomplete combustion of biomass and oil/diesel (Bond et al., 2013; Vivanco-Hidalgo et al., 2018). BC concentrations are particularly pronounced in urban environments, leading to potentially elevated inhalation exposure to BC among urban dwellers (Dons et al., 2011; Li et al., 2015). A major source of BC in cities is vehicular traffic, especially from diesel engines (Targino et al., 2016; Tran et al., 2020a). Several studies have shown a strong association between pronounced exposure levels of BC (and related co-emissions of polycyclic aromatic hydrocarbons - PAHs) and adverse health impacts such as decreased cognitive function, respiratory ailments, cardiovascular diseases, and lung cancer (Grahame et al., 2014; Janssen et al., 2011; Lin et al., 2019; Niranjan and Thakur, 2017; Suglia et al., 2008). Among members of the public, young children, especially those less than 5 years old (comprising about 8.8% of the total of the world's population in 2019 (United Nations, 2019)) are particularly vulnerable to negative health effects of personal exposure (PE) to BC such as decreased cognitive function (Freire et al., 2010; Suglia et al., 2008) and neurodevelopment (Alemany et al., 2018; Perera, 2017). Furthermore, young children might experience chronic diseases later in their life because of higher inhaled doses relative to their lung size and body weight and immature immune system, lungs, tissues and brain compared to older children and adults (Buonanno et al., 2013; Sharma and Kumar, 2018; WHO, 2005; Yolton et al., 2019). It is, therefore, imperative to assess the PE levels of young children to BC in order to mitigate their exposure to BC and minimize potential health impacts. Children in the age group of 7–14 years have been the focus of several air quality PE studies in recent years (e.g., Cunha-Lopes et al., 2019; Jeong and Park, 2018; Jung et al., 2017). The findings indicate that their inhalation dose of PM2.5 and BC is high (up to 83%) in indoor microenvironments (MEs) within schools and homes (Cunha-Lopes et al., 2019; Jeong and Park, 2017a, b; Paunescu et al., 2017; Rivas et al., 2016) as the children spend a substantial amount of their time in indoors. In addition, they are exposed to pronounced short-term peaks in BC in transport environments while commuting to schools from their home and back (Cunha-Lopes et al., 2019; Jeong and Park, 2018; Rivas et al., 2016). It has been reported that toddlers (1–3 years old) could receive up to ~ 60% higher PE to air pollutants (e.g., PM2.5) than adults (Sharma and Kumar, 2018). The breathing height of toddlers is lower, ranging between 0.55 m and 0.85 m above the ground level, compared to older children. The breathing height depends on whether toddlers sit in a stroller or walk around. The PE of toddlers to PM emissions of vehicular traffic origin (Kumar et al., 2017) at a lower height is of serious health concern (Goel and Kumar, 2016). Also, they have distinct time-activity patterns and hence their PE is different from that of older children (Conrad et al., 2013; Fees et al., 2015; Wu et al., 2011). Limited previous studies on toddlers mainly focused on their PE to PM of different sizes (ultrafine particles - UFP, PM1, PM2.5, PM10) of vehicular traffic origin while on commuting with short-term exposure to elevated concentrations of PM (Buzzard et al., 2009; Galea et al., 2014; Kumar et al., 2017; Sharma and Kumar, 2020). However, to the best of our knowledge, no study has examined the integrated PE of toddlers to BC over 24 h and its spatio-temporal variations across outdoor and indoor microenvironments (MEs). Continuous measurement of toddlers’ PE to specific pollutants of emerging concern such as BC across different MEs will provide a basis for identifying where and how their exposure can be reduced (Assimakopoulos et al., 2018; Sharma and Kumar, 2018), so that appropriate mitigation strategies can be recommended to ensure their well-being. The Coronavirus Disease 2019 (COVID-19) global pandemic brought drastic changes in time-activity and lifestyle patterns on a day-to-day basis among city dwellers due to implementation of lockdown measures to contain the spread of the outbreak. These changes affected their integrated PE to air pollutants over 24 h because of reduction in the levels of outdoor air pollutants (Bao and Zhang, 2020; Bekbulat et al., 2020; Chen et al., 2020b; Kumar et al., 2020; Sharma and Kumar, 2020; Venter et al., 2021) and increased presence of city dwellers in their home environments (Domínguez-Amarillo et al., 2020; Kim et al., 2020; Nwanaji-Enwerem et al., 2020). Primary (i.e., direct) emissions of UFP (PM with diameter < 100 nm) and BC of traffic origin also decreased significantly due to a reduction in the number of on-road vehicles, especially passenger cars, in cities (Hudda et al., 2020). However, there was a notable change in the composition of on-road vehicles in 2020 during the COVID-19 period. For example, Hudda et al. (2020) reported a higher fraction of diesel-powered vehicles such as pick-up trucks and cargo vans during the lockdown compared to pre-lockdown periods because of the supply of essential services such as consumer goods deliveries, which in turn resulted in a higher ratio of BC to particle counts. Another notable change is that the PE to indoor PM2.5 levels was reported to have increased during the COVID-19 period due to the increased time spent at home and more frequent and intense indoor activities such as cleaning and cooking (Domínguez-Amarillo et al., 2020). In view of these changes in time-activity and lifestyle patterns, it is important to study the influence of COVID-19 pandemic on the PE of toddlers to air pollutants, given that they cannot wear face masks unlike older children and adults. This pilot study was conducted to assess and mitigate the PE of toddlers in general to BC, a pollutant of emerging health concern, in Singapore, a densely populated city-state with intense human activities. The study took place during July and August in 2019 (pre-COVID-19 period) and during the same time period in 2020 (during the COVID-19 period). The BC measurements were made using a microaethalometer over 24 h across diverse microenvironments (home, and non-home microenvironments) in 2019, but in mostly home environments in 2020. We estimated the contribution of each ME to the daily BC exposure. In addition, we assessed the effectiveness of BC exposure mitigation measures under different ventilation conditions in the home environment. The findings of this study provide insights into the role of COVID-19-induced air quality changes on the PE of toddlers to BC in dense cities and can be used to devise control strategies to protect toddlers’ health from the harmful effects of air pollution in cities. 2 Methodology 2.1 Site description and experimental design The integrated PE to BC was carried out in the south-western region of Singapore from July to August in 2019 and 2020 (see Table 1 ). Singapore is one of the most highly urbanized and densely populated city-states in the world (7804 people km− 2 within 722.5 km2 territory) (Department of Statistics Singapore, 2019). Traffic emission is a major source of air pollutants on the island accounting for about 56% of PM2.5 emissions (Zhang et al., 2017), raising concerns about the exposure to traffic-related air pollution (TRAP) for the local residents living near streets or highways (Sharma and Balasubramanian, 2019; Tran et al., 2020b). Its urban traffic fleet is characterized by a relatively high rate of in-use diesel vehicles (about 18.8% of the total motor vehicle population of 949,053), which substantially contribute to high emissions of BC in urban areas (LTA, 2020). There is no significant change (about +1.1%) in the total motor vehicle population in Singapore between 2019 and 2020.Table 1 Summary of meteorological conditions during the field studya. Table 1Date Ambient air temperature (oC) Daily Total Rainfall (mm) Mean Wind speed (km h−1) 24-7-2019 27.7 2.4 9.0 25-7-2019 28.5 0.0 9.0 26-7-2019 28.6 0.0 9.4 27-7-2019 28.9 0.0 9.4 28-7-2019 28.7 0.0 9.4 29-7-2019 29.2 0.0 10.1 13-8-2019 29.0 0.0 9.7 14-8-2019 29.1 0.0 9.7 15-8-2019 28.8 0.0 9.4 16-8-2019 29.2 0.0 10.8 16-7-2020 28.2 0.0 8.3 17-7-2020 29.0 0.0 8.3 18-7-2020 28.1 0.0 7.9 19-7-2020 28.4 0.0 9.4 22-8-2020 27.6 3.4 8.6 23-8-2020 27.8 3.0 7.6 24-8-2020 27.4 1.3 8.5 25-8-2020 26.6 7.6 8.7 26-8-2020 27.0 4.4 9.0 27-8-2020 27.9 0.0 10.6 28-8-2020 28.5 0.0 13.5 a Data was obtained from http://www.weather.gov.sg/climate-historical-daily. In response to the COVID-19 pandemic, Singapore implemented lockdown measures referred to as “circuit breaker” (CB), from 7 April to June 1, 2020, to curb the spread of the virus within the local community. During the CB period, all non-essential offices and businesses were closed, schools were transitioned to home-based learning, and food establishments were only allowed to offer take-away and delivery (Li and Tartarini, 2020; Lim et al., 2020). From June 1, 2020, Singapore migrated to the post-CB period with the resumption of routine activities, introduced cautiously in three phases (see Fig. 1 ). Our PE monitoring campaign was initially carried out from July to August 2019 (i.e., before the COVID-19 pandemic), which was then repeated in 2020 during the same time period (i.e., during the COVID-19 pandemic). Singapore was in phase 2 (safe transition) during the PE measurement campaign in 2020 when the entire local economy-related activities were almost resumed. However, telecommuting still continued to be the default option for the workforce. People were allowed to go out for limited outdoor activities such as physical exercise, shopping and recreation, but were restricted to a group size of no more than 5 persons at a time. Most of the public play areas designated for children were still kept closed. The overall aim of the PE assessment was to examine how the COVID-19 pandemic-related time-activity patterns influenced the PE to BC, compared to the pre-COVID-19 time period in 2019, especially when urban mobility restrictions were relaxed.Fig. 1 Overview of measurement timeline and safe distancing measures in Singapore. Fig. 1 A 2-year-old toddler was involved in this pilot study under the supervision of an adult. The child lived in a residential home (non-smoking household) on a low storey of a typical multi-storey naturally ventilated residential building situated near a major road that carries a large volume of vehicles in the south-western area of Singapore. During the first measurement period in 2019, the toddler's exposure to BC was monitored across several urban MEs (home and non-home MEs including restaurants, play areas, as well as transport-MEs). In contrast, the toddler mostly stayed at home in 2020. A detailed description of each ME is provided in Table 2 . The toddler was always seated in a stroller (a foldable 4-wheeler stroller with 103 cm height, 70 cm width and 5 kg weight) while commuting by different modes of transport (walking, bus, mass rapid transport - MRT) with the exception of being seated on an elevated platform (about 1 m) while co-riding an adult bicycle. To assess the effect of commonly maintained ventilation conditions in tropical residential apartments on the exposure of the toddler to BC, three exposure scenarios with different ventilation and intervention settings (natural ventilation – NV, air conditioning – AC and portable air cleaner + fan – PAC) were explored in a bedroom (3.5 m × 3.0 m × 2.4 m) in the home ME during the sleeping time (from 12:00 to 13:30, and from 21:30 to 8:00). Table 3 provides detailed characteristics and operation settings of the AC and PAC used.Table 2 Characteristics of micro-environments involved in the study. Table 2Micro-environment Measuring period Activity Description and location Ventilation and mitigation conditions Home Outdoor 24 h – Balcony of the living room on the low storey of a multi-storey residential building, at ≈100 m distance from a major road Ambient air conditions NV-SL From 12:00 to 13:30 and from 21:30 to 8:00 Sleeping Bedroom Windows opened AC-SL Windows closed, cooling by the air-conditioning system with recirculation mode PAC-SL Windows closed, using a portable air cleaner + Fan NV-Others Times other than sleeping at home and going out Eating, playing, personal hygiene Living room and bedroom Windows opened Non-home Play areas Rooftop 1 9:30–10:00 Playing, running, climbing, etc. Play area on a rooftop of a community centre at ≈ 50 m distance from a minor road Ambient air conditions Rooftop 2 9:30–10:00 Play area on a rooftop of a shopping mall in ≈100 m from a major road Ambient air conditions 17:00–18:00 Ground 1 9:00–9:30 Playground at ≈100 m distance from a minor road Ambient air conditions 16:55–17:50 Ground 2 9:00–9:30 Playground at ≈100 m distance from a major road Ambient air conditions 17:00–18:00 Indoor 1 17:00–18:00 Indoor play area in a shopping mall Cooling by the central air-conditioning system; mechanical ventilation Indoor 2 19:30–20:15 Indoor play area in a shopping mall Cooling by the central air-conditioning system; mechanical ventilation Bus stop 1 12:30–12:45 Sitting Total of 11 bus services. Minor road. Ambient air conditions 18:00–18:15 2 18:30–19:00 Total of 15 bus services. Major road. Ambient air conditions Walking (seated on a stroller) 10:00–11:00 Sitting Major, minor roads Ambient air conditions 18:00–19:00 20:15–21:00 Bicycle (seated on a bike seat) 8:00–8:30 Sitting Minor, major roads Ambient air conditions 10:00–10:30 18:00–19:00 Bus (seated on a stroller) 12:45–13:10 Sitting Stayed in the middle of single-deck buses Cooling by the central air-conditioning system 18:15–18:40 19:00–19:20 MRT (seated on a stroller) 17:00–17:15 Sitting Stayed in middle of trains Cooling by the central air-conditioning system 20:00–20:15 Restaurant Indoor 1 12:00–12:45 Sitting and eating Indoor restaurant in a commercial building at ≈100 m distance from a minor road; Western food dishes. Cooling by the central air-conditioning system; mechanical ventilation Indoor 2 12:30:13:30 Indoor restaurant in a shopping mall at ≈100 m distance from a minor road; Asian food dishes. Cooling by the central air-conditioning system; mechanical ventilation Indoor 3 19:15–20:05 Indoor restaurant in a shopping mall at ≈100 m distance from a major road. A whole range of food dishes. Cooling by the central air-conditioning system; mechanical ventilation Indoor 4 19:20–20:10 Indoor restaurant in a shopping mall at ≈200 m distance from a major road. A whole range of food dishes. Cooling by the central air-conditioning system; mechanical ventilation Outdoor 1 18:45–19:15 Open-air food court. A whole range of food dishes. About 30 food stalls. Natural ventilation 19:00–20:00 Outdoor 2 18:45–19:15 Open-air food court. A whole range of food dishes. About 20 food stalls. Natural ventilation 19:00–20:00 NV: national ventilation, AC: air conditioning, PAC: portable air cleaner, SL: sleeping. Table 3 Characteristics of the air conditioning unit and portable air cleaner used. Table 3Characteristics Air conditioning Portable air cleaner Brand Panasonic Camfil Model No.  Indoor CS-S12TKZW CamCleaner CITY M (WHITE)  Outdoor CU-3S27KKZ – Power input (W) (min-max)  Indoor 885 (260–1140) 6 (4–55)  Outdoor (for 1 room) 2060 (520–2830) – Air flow (m3 min−1) 11.0 (maximum air circulation) 1.56 (0.62–7.25) Air filter Polypropelene (material) 2 HEPA H13 One-touch (style) 2 Molecular Cooling capacity (W) (min-max)  Indoor 3230 (920–4000) –  Outdoor 27,000 (10,080-32400) – Dimensions (m) 0.295 (H) × 0.919 (W) × 0.199 (D) 0.70 (H) × 0.33(W) × 0.34 (D) Net Weight (kg) 9 15 Average service area (m2) – 75 Particle Clean Air Delivery Rate CADR (m3 h−1) – 433 Operation settings Cooling mode with temperature: 26 °C, fan speed: “level 3” in the range of 1–5 “Level 3” in the range of 1–6 A portable monitoring BC device (micro-Aethalometer AE51, Aethlabs, USA) with the air sampling inlet being placed within the breathing zone of the toddler was carried in the stroller and kept as closely as possible to the toddler throughout the day. This mode of BC monitoring allowed the PE assessment while the toddler was involved in different routine activities such as playing, eating and sleeping on a day-to-day basis under the supervision of an adult. Another micro-Aethalometer was located at the balcony of the home ME to simultaneously measure outdoor BC concentrations during the study periods. In addition, a mobile phone global positioning system (GPS) app (Sensor Play-IOS app) and time-activity diary were used to identify the specific time periods and the locations of the toddler across different MEs. During the PE assessment campaigns, the local traffic volume was monitored simultaneously by counting the hourly number of vehicles during traffic peak (07:30–08:30 and 18:00–19:00) and non-peak hours (10:00–11:00 and 14:00–15:00) on weekdays and weekends on a road closest to the home ME. Traffic composition was determined by classifying on-road vehicles into four categories of urban transport including motorcycles, gasoline-driven passenger cars/diesel-powered or hybrid taxis, diesel-powered buses (i.e., short transit buses, long transit buses (tandem buses), and private coaches) and diesel-powered trucks (i.e., light commercial trucks, haul trucks). The local traffic volume and composition were determined for 40 h during the two study periods. 2.2 Instrumentation and quality control and assurance The microAeth AE51 was used to assess the PM-bound BC mass concentration by measuring the attenuation of light transmitted at 880 nm through PM, which is continuously collected on a Teflon-coated borosilicate glass fiber filter. Filter strips of the two AE51 units were changed after every 12 h of exposure to outdoor or indoor air (observed attenuation coefficient (ATN) < 50) to prevent the filter loading effect (Virkkula et al., 2007). Negative or constant values in the recorded data, caused by instrumental noise at high logging intervals or very low BC values, were resolved by using the Optimized Noise reduction Averaging (ONA) algorithm tool (Hagler et al., 2011). To further test the performance of the two AE51 units, we collocated the two BC monitors with an Aethalometer AE33 (Magee Scientific, USA) over a period of 24 h and got a good agreement with R2 = 0.78 and 0.81, slopes = 1.029 and 1.002. The two micro-Aethalometer AE51 units were operated at a pre-calibrated flow rate of 100 mL min− 1 and data recorded every 1 min. We performed clock synchronization, battery and memory checks before each use of the BC measurement devices. Important observations (such as low airflow of the devices) were noted as part of our measurement protocol so that such spurious measurement data could be removed during data analysis. 2.3 Data processing and statistical analysis After each sampling section (24 h), the real-time BC measurements were immediately downloaded, inspected and archived to minimize data handling errors or recall bias. We used the R statistical software (R Studio, version 1.1.442) to import, synchronize, and combine datasets and to apply corrections, calibrations and perform statistical analysis. In this study, we consider the geometric mean (GM) rather than the arithmetic mean concentration to discuss the variations of BC concentration as the former fits the log-normal distribution of BC concentrations (Lee et al., 2015; Rivas et al., 2016; Targino et al., 2016), but all other descriptive statistics are also provided. A Pearson correlation test was used to examine relatedness between the traffic volume and the BC concentration. A non-parametric Kruskal-Wallis test was performed to test the difference in the PE to BC concentrations among different activities and MEs. The difference in the PE to BC levels and traffic volumes between 2019 and 2020 was checked based on the Mann-Whitney test. 2.4 Calculation of daily BC exposure contribution, inhaled dose and inhaled dose contribution The relative contribution of different MEs to daily exposure to BC was calculated by Eq. (1).(1)  Daily BC exposure contribution of MEi=Ci× ti∑i=1n(Ci× ti) where: MEi is the microenvironment i visited by the toddler; Ci (unit of μg m− 3) is the GM of exposure to BC concentration observed in MEi; ti (unit of hour day− 1) is the time spent in; n is the total number of MEs, ∑i=1nti=24 h. There are three potential exposure pathways of PM, including inhalation, ingestion and dermal contact (Phalen and Phalen, 2011; USEPA). Out of the three possible routes of exposure, inhalation emerged as the route that would pose the greatest risk to human health as compared to ingestion and dermal contact (Phalen and Phalen, 2011). Therefore, in this study, besides the exposure level of BC, we estimated the daily integrated inhaled dose (unit of μg day− 1) by integrating the BC concentrations (Ci) observed in each ME over the time spent (ti) in the corresponding ME and the inhalation rate (IR) (Eq. (2)).(2) Daily integrated inhaled dose=∑i=1n(Ci× IRi× ti) where: IR (unit of m3 h− 1) chosen for different activities were 0.277 m3 h− 1 for sleeping (in home-sleeping ME) and 0.72 m3 h− 1 for low-intensity activities (other activities apart from sleeping) for a child less than 3 years of age (USEPA, 2019). The relative contribution of different MEs to the daily inhaled dose was calculated by Eq. (3).(3)  Daily inhaled dose contribution of MEi=Ci×IRi× ti∑i=1n(Ci×IRi× ti) 3 Results and discussion 3.1 Diurnal variation of PE to BC concentrations Fig. 2 shows the diurnal variation of PE to BC concentrations for all days while using NV in 2019 and 2020. NV is commonly used in Housing & Development Board (HDB) apartments which are home to over 80% of Singapore's resident population. The diurnal variation of PE to BC concentrations on days with AC and PAC is shown in Fig. 3 . It should be noted that the PE to BC measurement was mostly conducted during weekdays (81% of days). Peaks in the average daily trend for BC are evident around 07:00–09:00 and 17:30–19:30 corresponding to morning and evening traffic peak hours in both observational time periods, indicating the impacts of road traffic on PE to BC for the toddler. The evening peak in BC was less pronounced compared to the morning one. This observation can be attributed to better dispersion of traffic emissions during the evening, caused by unstable atmospheric conditions in the presence of daytime intense incoming solar radiation, lasting for a relatively long duration in the tropical environment (Adam et al., 2020). In general, the geometric means of PE to BC concentrations during morning and evening traffic peak hours were about 3.0 ± 1.7 μg m−3 and 3.7 ± 1.9 μg m−3 in 2019 and 2020, respectively. Apparently, the PE to BC during traffic peak hours increased by 23.3% for the toddler in 2020 compared to 2019. At the night-time (around 22:00–06:00) which was corresponding to the period of the day with the lowest traffic emissions, the BC PE decreased to 2.2 ± 0.3 μg m−3 in 2019 and to 1.8 ± 0.2 μg m−3 in 2020 (with NV scenario). Representative time series data on the temporal variation of BC concentrations are shown in Fig. 4 . More details about the impacts of COVID-19 pandemic on changes in the traffic characteristics and the toddler's PE to BC levels in 2020 compared to 2019 are discussed in the following sections.Fig. 2 Diurnal cycle of PE to BC in 2019 and 2020 (days with natural ventilation, NV). Fig. 2 Fig. 3 Diurnal cycle of PE to BC in 2019 and 2020 (days with air conditioning, AC, and portable air cleaner, PAC). Fig. 3 Fig. 4 Representative time series of PE to BC concentration measured in: (a) 2019 and (b) 2020. Fig. 4 3.2 Impact of the COVID-19 pandemic on PE of the toddler to BC concentration In general, during both measurement periods, the toddler spent the majority of time indoors, especially in the home ME (more than 86% in 2019 and almost 100% in 2020) (see Fig. 5 ), indicating the importance of health risk assessment at this ME. Due to the difference in the toddler's BC exposure levels and activity levels, the home ME was divided into two parts, considering whether the toddler was sleeping or involved in other general activities such as playing, eating, etc. The results show that the greatest amount of time was spent on sleeping (52.1% for both years), followed by being involved in other general activities at home (34.0% in 2019 and 47.9% in 2020), and 13.9% for non-home MEs in 2019 (including commuting, playing in play areas and eating in restaurants). Our results on time-activity are fairly similar to those found in Chen et al. (2019) and Chen et al. (2020a), who studied the physical activity and behavior among young children in Singapore.Fig. 5 Contributions of the home micro-environment to the BC exposure in the year 2019 and 2020 under different exposure scenarios. Different bars mean the average BC exposure in days with natural ventilation (NV), air conditioning (AC) and portable air cleaner (PAC) while the child was sleeping (SL). Fig. 5 The PE to BC measured in different MEs in 2019 and 2020 is shown in Table 4 . The BC GM concentrations in the home ME were 2.2 ± 1.6 μg m−3 and 2.8 ± 1.9 μg m−3 during the overall sleeping time (from 12:00 to 13:30, and from 21:30 to 8:00) with the NV scenario, and 2.6 ± 1.5 μg m−3 and 3.1 ± 1.9 μg m−3 during other general activities in 2019 and 2020, respectively. The toddler's BC exposure in 2019 in the non-home MEs was 2.4 ± 2.2 μg m−3. The PE to BC measured in the home ME in our study in both years is relatively higher than the results reported in several BC measurement studies in Spain, Portugal (≈ 0.9 μg m−3 (Cunha-Lopes et al., 2019; Rivas et al., 2016)) and Korea (≈ 1.2 μg m−3 (Jeong and Park, 2018)), but lower than those measured in India (5.4–34.9 μg m−3 (Ravindra, 2019)). The differences in the BC PE levels of the aforementioned studies are likely due the variation of indoor BC emission sources (e.g., cooking, smoking, candles and incense burning) and infiltration from outdoor sources of BC (i.e., the difference in nearby traffic volume related to the home location).Table 4 PE to BC (μg m−3) and ratio of PE and outdoor (OD) BC concentration (simultaneous measurement, the ratios are calculated pairwise) in different MEs. Table 4Year Micro-environment BC (μg m−3) PE/OD GM GSD AM SD Max Min AM SD Max Min 2019 Home NV-SL 2.17 1.55 2.35 0.93 17.37 0.03 0.98 0.33 3.02 0.27 AC-SL 1.23 1.10 1.36 0.93 5.46 0.04 0.70 0.21 1.87 0.06 PAC-SL 0.75 1.04 0.76 0.19 1.70 0.52 0.25 0.13 0.66 0.19 NV-Others 2.59 1.48 2.78 1.07 21.50 0.15 1.08 0.46 3.50 0.06 Non-home 2.40 2.23 3.26 3.50 44.55 0.13 1.25 1.07 11.05 0.03 2020 Home NV-SL 2.77 1.90 3.52 1.88 7.62 0.61 0.99 0.36 3.85 0.24 AC-SL 1.79 1.65 2.02 1.10 7.05 0.81 0.69 0.31 2.68 0.13 PAC-SL 0.77 1.00 0.93 0.40 3.13 0.20 0.26 0.16 1.44 0.03 NV-Others 3.13 1.85 3.76 1.69 29.86 0.66 1.17 0.52 7.42 0.29 GM: geometric mean, GSD: geometric standard deviation, AM: arithmetic mean, SD: standard deviation, NV: national ventilation, AC: air conditioning, PAC: portable air cleaner, SL: sleeping. During the COVID-19 pandemic in the year 2020, family members mostly stayed at home and, therefore, generally spent more time on cooking leading to slightly higher exposure to BC (see in Fig. 4). Jeong and Park (2017a) had revealed that cooking with gas stoves and food rich in fat at a high temperature increased BC exposure. It was recently reported that an increase of 12% daily PM2.5 levels and 37–559% of total volatile organic compound (TVOC) concentrations were also observed in the home MEs during the lockdown period in Spain compared to the period before lockdown (Domínguez-Amarillo et al., 2020). This increase was attributed to more intense as well as frequent cooking and other indoor activities of building occupants than usual. However, BC emissions from indoor cooking in our study were likely to be low (Fig. 4) as cooking types involved only boiling, steaming and light-frying on a liquefied petroleum gas stove (See and Balasubramanian, 2008; Sharma and Balasubramanian, 2020). It should be noted that there were no other major sources of BC emission (e.g., candles or incense burning) during the two study periods. The simultaneous measurement of PE to BC in different MEs and outdoor (OD) BC in the home ME also allowed us to calculate their ratios, i.e., PE/OD (see Table 4). The ratios were highest (mean of 1.3) for non-home MEs, implying that the toddler was exposed to higher BC concentrations in those MEs compared to the home ME. Moreover, the pairwise comparisons between PE and OD measurements when the toddler was at home in the NV scenario (≈1.0) indicated that the PE to BC was very similar to those measured outdoors. In addition to the NV scenario, we investigated BC levels for two other commonly maintained ventilation scenarios (AC and PAC) in the tropics during the sleeping time (the most prolonged and vulnerable exposure) in the home ME. The average PE/OD ratios were found to be approximately 0.70 and 0.25 for AC and PAC scenarios, respectively, for both years 2019 and 2020, showing the effectiveness of the PAC in mitigating the exposure to indoor BC levels (through the filtration of indoor air by high-efficiency particulate air - HEPA filters) (Tran et al., 2020b). Fig. 5 and Table 5 presents the relative contributions of different activities/MEs to the toddler's daily exposure to BC concentration and inhaled dose, respectively. The overall daily PE to BC concentration of the toddler was 2.3 ± 0.4 μg m−3 in 2019 and was enhanced significantly by approximately 25.5% (p-value <0.05) to 2.9 ± 0.3 μg m−3 in 2020 with the NV scenario. Using PE mitigation measures such as AC and PAC during the sleeping time at home helped to reduce the daily exposure to BC to 1.9 ± 0.4 μg m−3 (≈ 20.8% reduction compared to the NV scenario) and 1.6 ± 0.3 μg m−3 (≈ 31.6% reduction) in 2019, and 2.4 ± 0.1 μg m−3 (≈ 17.4% reduction) and 1.9 ± 0.3 μg m−3 (≈ 35.0% reduction) in 2020, respectively. On the other hand, the daily BC inhaled dose was equal to 28.5 μg for NV scenario in 2019 and increased by 24.5% to 35.5 μg in 2020. The total daily inhaled doses recorded in 2019 were reduced to 25.2 μg and 23.6 μg, while the numbers were 32.1 μg and 28.6 μg in 2020 for AC and PAC scenarios, respectively.Table 5 Daily integrated inhaled dose in different MEs and for different activities. Table 5Scenario Home - Sleeping Home - NV - Others Non-home Sum μg day−1 % μg day−1 % μg day−1 % μg day−1 % 2019 NV-SL 7.48 26.28 15.24 53.51 5.76 20.22 28.48 100 AC-SL 4.24 16.81 15.24 60.38 5.76 22.81 25.24 100 PAC-SL 2.57 10.92 15.24 64.65 5.76 24.43 23.57 100 2020 NV-SL 9.56 26.97 25.89 73.03 0.00 0.00 35.45 100 AC-SL 6.17 19.24 25.89 80.76 0.00 0.00 32.06 100 PAC-SL 2.67 9.34 25.89 90.66 0.00 0.00 28.56 100 Due to the largest amount of time spent at home and high BC concentrations measured there, the home ME had the dominant contribution to the overall daily exposure and inhaled dose to BC (in total ≈ 80% in 2019 and almost 100% in 2020). The highest contribution to the daily exposure to BC was observed while the child was sleeping (in NV scenario) (48.2%), followed by the active time period in the home ME (37.6%) and non-home MEs (14.2%) in 2019, whereas relatively equal contributions (about 50%) were observed for sleep and active periods in 2020 when mobility restrictions prevented people from participating in outdoor activities. Nevertheless, the home ME while the child was active contributed mostly to the daily inhaled dose (about 15.2 μg ≈ 53.5% in 2019 and 25.9 μg ≈ 73.0% in 2020 (Table 5) due to the highest inhalation rate and high exposure concentration of the child while it was in this ME. 3.3 Impact of the COVID-19 pandemic on traffic volume/composition and PE to BC Vehicular traffic emissions have been shown to influence the ambient and PE exposure to BC levels in Singapore in several previous studies (Tran et al., 2020a, 2020b, 2020c, 2020d). In order to ascertain the impact of traffic emissions on the PE of the toddler to BC, we also simultaneously monitored the local traffic characteristics on the road closest to the home ME in addition to BC. Fig. 6 shows the traffic volume and composition during traffic peak and non-peak hours on weekdays and weekends prior to and during the COVID-19 pandemic. In general, passenger cars/taxis accounted for a major fraction of on-road vehicles (about 66.0%), followed by trucks (20.0%), motorcycles (9.0%) and buses (5.0%) in both years, which is in agreement with the findings by Tran et al. (2020a). Interestingly, compared to the same period in 2019, the overall traffic volume was 11.1% (p-value = 0.13), and 14.1% (p-value < 0.05) higher in 2020, on weekdays and weekends, respectively. Specifically, there was an increase of the hourly numbers of trucks (27.7% and 14.1%, significantly with p-value < 0.05), cars/taxis (10.1%, p-value = 0.33 and 16.6%, p-value < 0.05), motorcycles (9.9%, p-value = 0.50 and 15.0%, p-value = 0.12) on weekdays and weekends, respectively. In contrast, the bus volume declined by 4.0% (p-value = 0.06) on weekdays and 17.1% (p-value = 0.09) on weekends.Fig. 6 Traffic volume and composition during peak and non-peak traffic hours on weekdays (WD) and weekends (WK) in 2019 and 2020. Fig. 6 The increase in private transport (cars/taxis and motorcycles) and the decline of public transport (buses) may be explained by the impact of the COVID-19 pandemic on the preferred mode of transport by city dwellers. The concern over the spread of the infection among travellers and the perceived health risk was believed to shift the mode of travel from public transport to private transport and non-motorized modes in many countries in the world (Abdullah et al., 2020; Orro et al., 2020). An online questionnaire survey conducted in June 2020 found that 39% of respondents in Singapore were more willing to commute by cars, and among those who used to drive before the health crisis, 61% of respondents were more willing to use a car (Tan, 2020). Conversely, people were less willing to take public transport (buses and trains) to “minimize contact with crowds” as there was a perceived higher risk of infection with COVID-19 in these modes of transport (Tan, 2020). Also, delivery services (e.g., delivery of food and consumer goods by motorcycles, cars, trucks) increased in sales in 2020 compared to the year 2019 as more people worked at home as part of business continuity plans or stayed indoors due to concerns over the coronavirus (Abdullah, 2020). Additionally, after lifting mobility restrictions in phase 2 after the CB period, Singapore moved toward the economic ramp-up with the resumption of economic activities including businesses in the retail, entertainment and leisure sectors apart from manufacturing and delivery of goods and services. Several other cities in Canada (Tian et al., 2021) and Spain (Orro et al., 2020) were also observed with the rapid recovery in traffic volume after the opening policy was announced. Moreover, we observed strong correlations between BC exposure measured in the home ME and total traffic volume (Pearson correlation r = 0.59, p-value < 0.05), in particular with the number of diesel vehicles (trucks: r = 0.68, p-value < 0.05) in both years. These results, together with the observed elevated PE to BC in the home ME, confirm the significance of the BC infiltration and impact of the traffic emissions on residential buildings in highly densified cities such as Singapore. Overall, the results of our study clearly point to the dominant role of vehicular traffic emissions (especially from diesel vehicles) in the BC exposure experienced by the toddler. The COVID-19 pandemic led changes in the magnitude of traffic-related emissions and also the toddler's mobility pattern in the year 2020 when compared to the same time period in 2019, which significantly influenced the toddler's PE to BC. Therefore, proper air pollution exposure mitigation actions should be taken by urban dwellers to protect the vulnerable groups in societies. We do acknowledge that our pilot study has limitations: we performed the PE assessment to BC of a toddler living in Singapore. Therefore, the PE to BC could be different in other cities due to the difference in geographical and cultural backgrounds. Our main goal was to conduct a comparative investigation of the PE of young children to BC, a representative toxic air pollutant of combustion origin, before and during COVID-19 pandemic. The related goal was to quantify the direct impacts of changes in traffic emissions on neighbourhood-scale air quality with specific reference to the PE to traffic-related air pollutants, which is currently not captured by the fixed ambient air quality monitoring stations. Further investigations involving a larger sample size with toddlers of different ages (1–3 years old) at multiple locations with additional traffic information (e.g., on different street types, vehicle fleet age distributions) are warranted. Nevertheless, the practical implications of our study as stated above provide the impetus for conducting more research on PE to BC of vulnerable groups in societies in cities. 4 Conclusions This pilot study describes a comparative investigation of the PE to BC of a toddler and traffic characteristics in Singapore before and during the COVID-19 pandemic. The BC exposure measurements were conducted in July and August in 2019 (considered as before COVID 19 pandemic) and the same period in 2020 (during COVID-19 pandemic) during which Singapore had moved to the re-opening phase after a strict lockdown. The daily PE to BC concentration and inhaled dose of the toddler was observed to be enhanced by approximately 25% in the year 2020 compared to that in 2019. This increase in BC exposure during the COVID-19 pandemic was accompanied by a simultaneous increase in traffic volume of 11.1% and 14.1% on weekdays and weekends, respectively, compared to the same period in the previous year. In particular, we observed an increase in the number of trucks as well as cars/taxis and motorcycles (private transport) and a decline in the number of buses (public transport) during the study periods. Strong correlations were also reported between PE to BC concentration measured in the home ME and total traffic volume, especially the number of diesel vehicles. This implies the significant influence of traffic-related emissions on the BC levels in ambient air. Among the different MEs visited by the toddler, the home ME was the main contributor to the overall daily BC exposure and inhaled dose with a total of about 80% in 2019 and almost 100% in 2020. Therefore, this work highlights that measures to reduce toddlers’ exposure to BC should focus on urban planning to reduce the traffic around residential areas. In addition, the phasing out of diesel-driven vehicles, which are significant emitters of BC, would contribute towards improved overall air quality in urban environments. In the home environment, using a PAC equipped with high-grade air filters showed significant reductions in the exposure to BC compared to the NV and AC scenarios. Therefore, to protect the vulnerable populations, proper mitigation actions such as using PACs or centralized air conditioning systems with high-grade PM filters should be considered, in particular during severe air pollution instances such as severe traffic pollution during peak hours. We observed the changes in traffic characteristics and the toddler's mobility patterns resulting in the differences in PE to BC before and during COVID-19 pandemic in this study. Currently, among the COVID-19 hotspot regions, several of them (e.g., certain countries in Europe) have still not eased lockdown measures, while some regions have shown progress in tackling the virus and recently lifted their lockdowns. As lockdown restrictions are eased and economic recovery sets in, air pollution levels, especially traffic-related air pollutants, are expected to return to pre-pandemic levels or even get worse (Ding et al., 2020; Tian et al., 2021; Zheng et al., 2020), which may impact toddlers' PE to air pollutants. Therefore, the results of our study merit serious consideration in light of exposure to BC health impacts and mitigation strategies in other cities or such a pandemic in the future. Author contributions Phuong T.M. Tran: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing - original draft. Max G. Adam: Writing - original draft, review & editing. Rajasekhar Balasubramanian: Conceptualization, Supervision, Writing - original draft, Writing - review & editing, Funding acquisition. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This research was financially supported by the 10.13039/501100001352 National University of Singapore (grant number R-302-000-236-114). ==== Refs References Abdullah M. Dias C. Muley D. Shahin M. Exploring the impacts of Covid-19 on travel behavior and mode preferences Trans. Res. Interdis. Perspect. 8 2020 100255 Abdullah Z. Delivery services see spike in business because of Covid-19 Channelnewsasia 2020 Adam M.G. Chiang A.W.J. Balasubramanian R. 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Satellite-based estimates of decline and rebound in China’s CO2 emissions during Covid-19 pandemic Sci. Adv. 6 2020 eabd4998

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==== Front Int J Nurs Stud Int J Nurs Stud International Journal of Nursing Studies 0020-7489 1873-491X Elsevier Ltd. S0020-7489(21)00019-5 10.1016/j.ijnurstu.2021.103887 103887 Article The “nurse as hero” discourse in the COVID-19 pandemic: A poststructural discourse analysis Mohammed Shan a⁎ Peter Elizabeth a Killackey Tieghan ab Maciver Jane a a Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, 155 College Street, Suite 130, Toronto, Ontario M5T1P8, Canada b Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, 686 Bay Street, Room 069715, Toronto, Ontario M5G 0A4, Canada ⁎ Corresponding author. 26 1 2021 5 2021 26 1 2021 117 103887103887 6 10 2020 18 1 2021 19 1 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Nurses have been labelled “heroes” by politicians, the mass media, and the general public to describe their commitment to providing front-line care to people with COVID-19, despite the risks of exposure and lack of clinical resources. Few studies have examined the implications of the hero discourse to nurses’ professional, social, and political identities. Objective To critically examine the effects of the hero discourse on nurses who are contending with the ongoing COVID-19 crisis and to consider the political, social, cultural, and professional impact of this discourse on nursing work. Methods A poststructural discourse analysis, employing the theoretical ideas of truth, power, knowledge, subjectivity, and normalization, was conducted to explore the mass media's constructions of nurse as hero in the contexts of COVID-19. Media electronic databases were searched between March 1, 2020 to August 1, 2020 to locate newspaper and magazine articles, corporate advertisements, videos, social media postings, and institutional/corporate websites. Setting Data sources included English language media accounts that originated from Canada, the USA, and the UK. Results Three main elements of the hero discourse include: 1. Nurses as a “necessary sacrifice” - portraying nurses as selfless, sacrificing, and outstanding moral subjects for practicing on the front-line without adequate protective gear and other clinical resources; 2. Nurses as “model citizens” - positioning nurses as compliant, hardworking, and obedient subjects in contrast to harmful individuals and groups that ignore or resist COVID-19 public health measures. 3. Heroism itself as the reward for nurses - characterizing hero worship as a fitting reward for nurses who were unappreciated pre-pandemic, as opposed to supporting long-term policy change, and highlighting how heroism reconfigures nursing work from the mundane and ordinary to the exciting and impactful. Conclusions The hero discourse is not a neutral expression of appreciation and sentimentality, but rather a tool employed to accomplish multiple aims such as the normalization of nurses’ exposure to risk, the enforcement of model citizenship, and the preservation of existing power relationships that limit the ability of front-line nurses to determine the conditions of their work. Our study has implications for approaching the collective political response of nursing in the ongoing COVID-19 crisis and formalizing the ongoing emotional, psychological, ethical, and practice supports of nurses as the pandemic continues. Keywords Discourse analysis COVID-19 Nursing Nurses’ role Professional image Mass media Performative allyship Poststructuralism Discourse Subjectivity ==== Body pmc What is already known about the topic? • Nurses have been publicly labelled “heroes” to describe their commitment to providing care to people with COVID-19, despite the risks of front-line practice and the lack of clinical resources such as adequate personal protective equipment. • Few studies have examined the implications of the hero discourse on nurses’ professional, social, cultural, and political identities. What this paper adds • The hero discourse results in the normalization of risk for nurses to justify the need for a timely and committed clinical response to COVID-19 in the contexts of uncertainty, political divisiveness, and unprepared healthcare systems. • Heroic nurses are positioned as productive subjects who form an archetype for how the public should think and behave in COVID-19, thereby forming a device to both enforce the compliance of nurses and to enact disciplinary power over the public. • The hero discourse is a tool for politicians, leaders, and decision makers to publicly demonstrate their support for nurses while concealing the preservation and extension of existing power relations that limit nurses such as racism, gender discrimination, austerity measures, and managerialism. 1 Background The novel coronavirus (COVID-19) pandemic coincides with the 200th anniversary of Florence Nightingale's death, arguably nursing's most iconic historical and heroic figure. Nightingale's innovation in hygiene and sanitation represents the shift away from the “angel of mercy” nursing persona toward the need for scientific knowledge, training, and appropriate clinical resources in nursing practice (Wildman and Hewison, 2009). No one nurse has emerged as emblematic of the global healthcare response to COVID-19. Rather, the discourse of “nurse as hero” has emerged to describe the collective response of the thousands of front-line nurses who continue to compromise their own personal safety to provide direct care to the millions of people infected with the virus. The direct risk of the virus to nurses is real. The International Council of Nurses (2020, October 28) estimated that approximately 1500 nurses have died globally from COVID-19. Perhaps the most recognizable nursing figure of COVID-19 is not a real nurse at all, but rather Banksy's acclaimed black and white painting of a “supernurse”, who is female, masked, and wearing a traditional nursing cape and is depicted as specially selected by a small child from a toybox of other superheroes (Einboden, 2020). The nurse as hero discourse has found public expression through community performances (e.g. singing from balconies, clapping, and banging pots and pans), corporate visibility (e.g. TV commercials, marketing campaigns, and promotional offers to healthcare staff), and governmental displays (e.g. military tributes, politician speeches, and light shows on public buildings). Although part of the public consciousness, heroism in nursing remains understudied. The few studies on the nurse as hero focus on the positive effects of this discourse on enhancing the visibility and moral fortitude of the profession. MacDonald et al. (2018) suggested that the sharing of stories of nurse heroes, which are primarily enacted in everyday front-line practice, may encourage other nurses to “internalize heroic attributes” (p. 139), bolster professional self-identity, and foster professional activism. Darbyshire (2011) argued that nursing should more visibly highlight its heroic legacy of responding to stigmatized health issues in the past, such as HIV/AIDS, to rally a contemporary response to the health inequities of today. Heroism in nursing, particularly from an American perspective, has also been linked to national pride, the fortification of victories in war efforts, and dedication to religious healthcare orders (Kenny et al., 2020). Few studies, however, have problematized the hero discourse in nursing or examined the effects of this discourse on nurses’ professional, social, and political identities (Einboden, 2020; Morin and Baptiste, 2020). Using a poststructural discourse analysis of the media, we call attention to the effects of the nurse as hero discourse on nurses who are contending with the ongoing COVID-19 crisis and examine the political, social, cultural, and professional impact of this discourse on nursing work. To accomplish our objectives, we draw on a poststructural perspective, which is a branch of French philosophy concerned with the politics of knowledge and power. In particular, we utilize Foucault's (1972; 1980) notion of discourse, truth, power, knowledge, subjectivity, and normalization in our analysis. Few studies have explored the interrelationships between the hero discourse and poststructural thought (Guevarra, 2009). To supplement our main theoretical framework, we also draw on the more recent concept of performative allyship (Green and Barbara, 1997), which refers to “someone from a nonmarginalized group professing support and solidarity with a marginalized group, but in a way that is not helpful” (Kalina, 2020, p.478). This concept is increasingly used in public discourse to problematize the performative element of corporations, social media “influencers,” and celebrities in supporting social causes such as anti-Black police violence. Despite the growing public discussion of performative allyship, there is a lack of academic work about this concept and no study to date has examined the interconnection between performative allyship and the public support for nurses. We will explore three main effects of the nurse as hero discourse in our analysis: 1. Nurses as a “necessary sacrifice;” 2. Nurses as “model citizens;” and 3. Heroism itself as the reward for nurses. 2 Theoretical framework A central theoretical idea in poststructuralism, discourse refers to a system of knowledge, ideas, beliefs, attitudes, actions, and practices that systematically construct the objects of which they speak (Foucault, 1972). Not only does it describe an area of knowledge, discourse also functions to constitute, produce, and generate particular systems of understanding (Foucault, 1972). Since knowledge and power are joined together through discourse (Foucault, 1966), discourse is often used by authority figures and other social actors as an instrument of power (Hall, 2001). Each discourse has its own politics of “truth/untruth” which, through practices of exclusion, distinguish what some consider to be true and false knowledge (Foucault, 1980). Certain discourses are dominant (for example, medicine, experimental science, capitalism, etc.) because they are largely accepted and are employed to “shut down” the possibilities of writing, speaking, and thinking in ways that challenge this authority (Hook, 2001). Through discourse, social actors can both constitute, and ensure the reproduction of, existing and dominant social systems, using different forms of knowledge selection, exclusion, and domination (Hook, 2001). In addition, discourses exist as various practices, also known as discursive practices, which Foucault (1972) claims “systematically form the object of which they speak” (p. 49) and shape how people and groups act, think, behave, and act. As opposed to a universal identity, poststructuralism suggests that the self is made up of multiple and concomitant subjectivities that shift with changing discursive conditions and different contexts of power and knowledge (Foucault, 1976). A subjectivity is produced (i.e. the process of subjectification) through the crystallization of different forms of social, political, and cultural discourses (Mansfield, 2000). Subjectivities are rarely self-determinant in poststructuralism, rather authority figures use subjectivity as a technique of power to govern people's thoughts, choices, desires, behaviours, and actions. People are often made the subjects of disciplinary discourses that may generate norms and expectations for thinking and acting. This process of normalization involves the construction and enforcement of idealized norms of conduct in which people internalize these regulations and modify their behaviour to meet normal parameters (Foucault, 1977). Social institutions, such as prisons, armies, factories, and schools, have historically employed normalization as a political technique in the production of “docile bodies” to subdue dissent and direct people towards a more “productive” aim (Foucault, 1977). Foucault (1976), however, noted “where there is power, there is resistance” (p. 95) and these forms of resistance are diverse, multiple, and move across different social relations. Foucault's (1963; 1972) examination of medical discourse, which spanned across his career, provides an example of how discourse analysis can illuminate multiple discursive practices and describe the social, political, and cultural effects on the people and groups that are subject to these discourses. Foucault (1963) conceptualized medicine not only as an empirical study of the pathology of disease, but also a series of discursive practices in which individuals are constituted as both objects and subjects of the medical gaze and professional knowledge. Medicine accomplishes its normalizing and disciplinary aims through different techniques to enact power and knowledge relations that are taken up by people who adapt their everyday behaviours to fit within medical advice, requirements, and parameters of health, treatment, and prevention. Although some productive elements many result from these guidelines, people are governed by the internalization of medical discourse, such as normal parameters of blood pressure or sexual activity, and often modify their behaviours to meet these regulations. Later poststructural scholars have examined how people resist medical discourse and authority to challenge professional control of their bodies and medical constructions of identity (Rose, 2007). For example, discourse analyses of medical advice and public health measures against obesity have examined the growing resistance to certain lifestyle interventions, attributing individual blame for poor eating habits, and the lack of understanding of the social conditions that limit access to health resources (Warin, 2011; Mulderrig, 2011). 3 Methods We conducted a poststructural discourse analysis, employing Foucault's main theoretical ideas of power, knowledge, truth, normalization, discourse, and subjectivity as analytical devices, to examine the media's constructions of nurse as hero in the contexts of COVID-19 (Yates and Hiles, 2010; Graham, 2011; Arribas-Ayllon and Walkerdine, 2008). Discourse analysis is a critical and qualitative method of inquiry that employs poststructural concepts to characterize and examine certain discourses and to explore the impact and effects of these discourses. A central aim of this method is to describe the formation of subjectivity, or how people become the subjects of discourses, and how certain discursive practices shape people's actions, thoughts, and behaviors (Arribas-Ayllon and Walkerdine, 2008). Scholars often use discourse analysis to focus on the consequences and costs of uneven relations of power and the enforcement of the so-called truth for those who are the subject of dominant discourses (Yates and Hiles, 2010). In addition to an exploration of how subjects of discourses are situated in relationships of power and knowledge, discourse analysis is also concerned with different practices of resistance or how people can challenge dominant constructions of the truth/untruth and knowledge authority. We initiated data collection by locating key media reports in a systematic and rigorous way. Using the period of March 1, 2020 to August 1, 2020, we searched the following electronic databases: Factiva, Communication Abstracts, Canadian Business & Current Affairs Database, US Newsstand, Canadian Newsstream, and Global Newsstream. These databases were selected because of their comprehensiveness, scope, link to multiple sources and media outlets, ability to screen results, and full text access. Since qualitative media analysis relies on the researchers’ close engagement with documents to select their relevance to a research topic (Altheide and Schneider, 2013), we conducted a preliminary analysis of the results of our early searches before continuing to narrow our selection of data sources included in the study. Our early search showed that media from certain geographic areas covered the hero discourse more prominently and in-depth (i.e. UK and North America), often to report the public lobbying of politicians, state sponsored campaigns to endorse the heroism of health professionals, and high-profile corporate responses. Since our research aims were to examine the political and social implications of the hero discourse to nursing, we utilized “progressive theoretical sampling” (Altheide and Schneider, 2013 p. 56) to select materials to best inform our emergent analytical impressions of the topic and to iteratively shape the conceptualization of our final analysis. We therefore limited data sources to media reports that originated from Canada, USA, and the UK in order examine these local contexts in more analytical detail. Inclusion criteria included English language newspaper and magazine articles, corporate advertisements, videos, social media postings, and institutional/corporate websites. Exclusion criteria included personal social media and blogs (i.e. from individuals versus larger news outlets or corporations). We included data from the same outlet (i.e. newspaper or magazine title) since different authors and/or articles may offer different data. We employed a flexible and iterative approach to sampling in keeping with our qualitative methodological approach (Patton, 2015). In addition to theoretical sampling to select documents to test and develop our early analytical findings, we utilized purposeful sampling to identify and select information rich sources that encouraged an in-depth examination of our phenomenon of interest and were of central interest to our research aims (Patton, 2015; Coyne, 1997). Criteria for purposeful sampling included sources that were typical of the media discussions of the topic, offered a variety of perspectives (i.e. endorsed or critiqued heroism), manifested the phenomenon intensely (i.e. took a strong stance on hero worship), and provided outliers that offered novel information (i.e. described a local policy related to heroism) (Patton, 2015). Our goal was not to archive every media report on nurse as hero, which is beyond the scope and intent of our study, but to examine the expression of this discourse in the news media sources examined in our study and to engage in a rich description of the research phenomenon. 4 Data analysis Study data included 71 documents that were stored and coded using the qualitative data management software NVivo 12. The first author initiated data analysis by becoming immersed in the data through full text reviews of the documents, memoing of early analytical impressions, and discussing the early analyses with the research team. Coding began with an inductive approach, using categories and terms found in the documents, and then moved to later rounds of deductive coding to encourage creative thought about the data (Coffey and Atkinson, 1996). The primary author led the coding process, however, the abstraction and conceptualization of data were performed collaboratively. A dialogical process, coding moved between the empirical data, poststructural theory, and emerging analytical concepts. For example, certain early codes (e.g. corporate use of heroes, clapping, military tributes, etc.) were combined and collapsed with key theoretical ideas (e.g. disciplinary power, productive subjectivities etc.) to develop more finalized codes and themes. The analytical process involved an iterative engagement between Foucault's writings and our data. To locate discourses in the data, we looked for patterns of knowledge, power, authority, legitimacy, moralism, and discipline. In addition to examining how this discourse is constructed in the media, our analysis also considered the effects of the nurse as hero discourse on the subjectivities of nurses and the broader political, social, cultural, and professional impact on nursing. We were concerned with discourse and subjectivity as political techniques that are used to exercise power and preserve existing social hierarchies (Yates and Hiles, 2010). We were conscious not to depict discourses and subjectivity in a universal or continuous way, but considered how these concepts are dynamic and fluid depending on different contexts (Graham, 2011). We also examined the data for different forms of resistance as a counterpoint to the nurse as hero discourse. In addition to our main analytical framework of poststructuralism, we drew on performative allyship as a concept to examine instances in the data in which the hero discourse was used to conceal the preservation of dominant discourses and potentially harmful relations of power (Kalina, 2020). Theoretical saturation was achieved when the research team determined that there were no new theoretical insights gained through the analytical process (Charmaz, 2006). As part of methodological rigor, we promoted trustworthiness to ensure the study findings could be traced back to the theoretical perspective and data analysis strategies (Manning, 1997). The research team regularly met to discuss the quality of data analysis, emergent conceptual ideas, and the final study results. Since the researcher is the primary instrument in poststructural research (Mohammed et al., 2015), we reflexively considered how our own subjectivities as nurses, researchers, academics, and people who live in a geographic area with one of the highest COVID-19 infection rates in Canada impacted our relationship to the data. All members of the research team are nurses, doctorally prepared, have scientific training in critically-informed qualitative research, and are working as academics and researchers. Our interest in the research topic extended from our professional and personal concern with how COVID-19 impacted the well-being of nurses, the long-term implications of clinical work in the pandemic, and our assumptions about the value of heroism to nursing. As researchers using a critical perspective, we, therefore, developed a reflexive awareness of our positionality and our location within the very discourses (for example, nursing as a profession) that we were interrogating (McCabe and Holmes, 2009). Since the data is part of the public domain and people referred to in the presentation of findings have no reasonable expectation of privacy, our study was exempt from research ethics board review (Tri-Council of Canada, 2018). 5 Results The 71 data sources included in our study are listed in Table 1 . Types of media include newspaper articles (n = 37), news websites (n = 16), videos (n = 6), corporate websites (n = 6), magazine articles (n = 4), and medical information websites (n = 2). Geographic area of origin included USA (n = 37), Canada (n = 17), and UK (n = 17). All sources were from 2020, including the months of March (n = 10), April (n = 27), May (n = 23), June (n = 5), and July (n = 6). Although the majority of sources included high profile and well circulated news outlets/corporations, such as The New York Times and The Guardian, some less circulated local sources, such as The Toronto Star and Richmond News, were also included. Although all data sources were coded, analytically examined, and informed study themes, we present only representative quotes and segments of the data in the presentation of our findings for the sake of brevity.Table 1 List of documents. Table 1Author Title Date Source Aldrich, J. Picture of poise: Shared Health's chief nurse is perfect public face of COVID-19 response May 12, 2020 The Winnipeg Sun Arthur, B. ‘I didn't sign up to die on my job’: Fear and anger among Ontario nurses battling COVID-19 pandemic March 30, 2020 The Toronto Star Atkinson, L. Help us crown your NHS hero July 6, 2020 Daily Mail Austen, I. In Detroit she's a hero. In Canada she's seen as a potential risk April 10, 2020 New York Times Bailey, L Suddenly, I'm not ‘just a nurse’ May 11, 2020 The Toronto Star Baker, M. A rare look inside the hospital where 15 coronavirus patients have died March 11, 2020 The New York Times Barr, S. England's Chief Nursing Officer states nurses 'are expert professionals, not heroes' May 12, 2020 The Independent Bettiza, S. Italy's medical workers: 'We became heroes but they've already forgotten us' May 26, 2020 BBC Blank, D. World Health Day honors nurses on the front lines. Meet the heroes dealing with coronavirus April 7, 2020 CNN Brody, B. Stars and striped, still forever: A coronavirus scene from Forest Hills April 27, 2020 New York Daily News Buiser Schnur, M. Honoring unsung heroes: Home health nurses May 11, 2020 Lippincott Nursing Center Bull, T. Hero NHS staff on frontline 'slapped in the face' as pay promise leaves out nurses July 22, 2020 Daily Star Butt, S. The NHS is doing an amazing job but is it at risk of being white-washed? April 6, 2020 HR Magazine Campbell, J. Hull nursing expert says calling NHS staff heroes and angels is 'unhelpful' April 30, 2020 Hull Daily Mail Cedars-Sinai Healthcare Heroes: Nurses April 10, 2020 YouTube Corbella, L. Sports idols have been replaced by health heroes — for now May 27, 2020 Calgary Herald Coyer, C. #ClapBecauseWeCare: World cheers for frontline workers April 7, 2020 The Christian Science Monitor DeMont, J. Coming out of retirement to join the war on COVID-19 April 6, 2020 Chronicle-Herald Dickinson, M. 'We are called heroes, but I am scared and so are my colleagues' May 11, 2020 The Times Dohrenwend, P. Nurses are the coronavirus heroes March 30, 2020 The Wall Street Journal Dove US Courage is Beautiful April 8, 2020 YouTube Elliot, J. ‘Truly heroes’: Tributes pour in for doctors, nurses fighting coronavirus pandemic March 18, 2020 Global News Ekram, T. 4 reasons why nurses are heroes May 6, 2020 lumahealth Faith Ho, A. Doctors and nurses are heroes on-duty, 'lepers' off-duty April 10, 2020 MedicineNet Ferguson, R. Nurses outraged at one percent raise under wage-cap law while Doug Ford calls them ‘heroes’ in the COVID-19 fight June 12, 2020 The Toronto Star Hamm, A. I'm a nurse. But no, I don't want to be a hero April 9, 2020 Quillette Hess, A. In praise of quarantine clapping April 9, 2020 The New York Times Hess, A. & O'Neill, S. In coronavirus advertising, you're the hero May 28, 2020 The New York Times Higgin, C. Why we shouldn't be calling our healthcare workers 'heroes' May 27, 2020 The Guardian Hodge, B. Celebrating Nurses Week through the voice of our modern-day super-heroes May 7, 2020 Nuance Inside Edition Hero Nurses Are Risking Their Lives to Save Others March 16, 2020 YouTube Johnson & Johnson By nurses to nurses: A letter to healthcare heroes April 1, 2020 Johnson & Johnson Nursing Karlamangla, S. A last selfless act; A nurse with no N95 mask treated a COVID-19 patient who couldn't breathe May 10, 2020 Los Angeles Times Kane, J. ‘Do not call me a hero.’ Listen to an ICU nurse's plea for fighting the coronavirus April 24, 2020 PBS News Kilraine, L. Nurse heroes to protest this evening about paltry pay rise – at hospital which saved Boris Johnson's life July 29, 2020 South London Press King, K. Daily cheers give morale boost to medical workers fighting coronavirus April 18, 2020 Wall Street Journal Knowles, M. NHS Heroes: The faces behind our masked nurses April 11, 2020 Daily Express Kondi, E. Health care ‘hero’ nurses being forgotten by province June 15, 2020 The Toronto Star Kotsis, J. U of W study reveals discrimination against nurses commuting to Michigan July 15, 2020 Windsor Star Kuper, S. How health workers replaced soldiers as society's heroes March 26, 2020 Financial Times Lamothe, D. Pentagon plans to dispatch Blue Angels and Thunderbirds in coronavirus tribute April 22, 2020 Washington Post Leung, V. ‘Nurses are everyday heroes’ says Trudeau May 12, 2020 Richmond News Lewis, M., Willete, Z., & Park, B. Calling health care workers 'heroes' harms us all May 21, 2020 STAT The Lincoln Project Two Americans April 24, 2020 YouTube Logan, C. Vancouver Island nurse honoured as ‘Unsung Hero’ by Canucks, BC Hockey July 19, 2020 Tofino-Ucluelet Westerly News Marcus, R. These are the heroes of the coronavirus pandemic March 27, 2020 The Washington Post Matthers, J., & Kitchen, V. NHS ‘heroes’ should not have to risk their lives to treat coronavirus patients April 20, 2020 The Conversation Mattel A new kind of hero has arrived May 29, 2020 Mattel Maxwell, M. Martin Maxwell on COVID-19: This generation's great war May 4, 2020 The National Post Moeslein, A Nurses have always been heroes—But we need them now more than ever march 30, 2020 Glamor Morris, N. Ethnic minority medics are ‘being whitewashed’ out of celebrations of the NHS April 3, 2020 Metro UK National Nurses United Nurses endorse the House Stimulus HEROES Act May 13, 2020 National Nurses United Neal-Boylan, L. Nurses on the front lines: A history of heroism from Florence Nightingale to coronavirus May 11, 2020 The Conversation Nguyen, L. Thunderbirds fly overhead as a salute to first responders for COVID-19 May 15, 2020 Los Angeles Times Palus, S. A nurse explains who can call her a hero and what she thinks of all the applause April 23, 2020 Slate Papworth, A. Meet 11 Suffolk nurses doing YOU proud on International Nurses Day May 12, 2020 East Anglican Daily Times Patterson, K. Front-line health-care workers are heroes. We should celebrate them as such March 12, 2020 The Globe and Mail Payne, E. Waiting for the deluge: Frontline workers deal with anxiety, fear as pandemic worsens March 29, 2020 Ottawa Citizen Peter, M. The nightly ovation for hospital workers may be New York's greatest performance April 6, 2020 The Washington Post Picheta, R. 'She is blown away': World leaders and families praise two nurses who cared for Boris Johnson in ICU April 13, 2020 CNN Simpson, J. Migrants helped build our NHS July 1, 2020 Eastern Eye Sodha, S. NHS heroes … and targets of racists April 5, 2020 The Guardian Temkar, A. Coronavirus heroes: I thought Filipino nurses were 'sellouts.' I was wrong May 27, 2020 USA Today Thomas, T. & Greene, L. Coronavirus nurse finally locates NYC firefighter who pulled her from burning building 37 years ago May 26, 2020 New York Daily News Time Magazine Please, God, just cover me.' health care workers are risking their lives daily in the fight against coronavirus April 9, 2020 Time Magazine UW Health Healthcare Heroes: Frontline Nurses April 9, 2020 YouTube Volmers, E. Calgary singer-songwriter brings hope as he honours all the front-line workers June 4, 2020 Calgary Herald Wallis, H. Nurses say they don't want to be called heroes during the coronavirus pandemic April 28, 2020 Teen Vogue Watson, C. 'Nurses are not heroes - they're just finally beginning to be recognized as they should' May 12, 2020 The Telegraph Winfield, N. Pope hails Italy virus doctors, nurses as heroes at Vatican June 20, 2020 ABC News Xing, L. 1% pay increase under public-sector wage cap a 'slap in the face,' Ontario registered nurses say June 11, 2020 CBC The results of our analysis of the media suggest that there are three main elements of the hero discourse in COVID-19 that have unforeseen but potent effects on nurses: 1. Nurses as a “necessary sacrifice;” 2. Nurses as “model citizens;” and 3. Heroism itself as the reward for nurses. 5.1 Nurse as hero as a “necessary sacrifice” Our analysis of nurse as hero revealed a discursive pattern that culturally positioned nurses as a “necessary sacrifice” to contend with the pandemic. Depictions in the media often drew on religious notions of martyrdom to describe nurses’ selflessness in uncertain and, at times, dangerous conditions. For example, an American article highlighted Pope Francis’ use of religious archetypes to describe how acute care nurses in Northern Italy were transformed into “literal angels” after they died from exposure to coronavirus (Winfield, 2020, June 20). A Financial Times article mapped out the “cult of the medic” and noted that “the Christ who dies for our sins is the health worker” (Kuper, 2020, March 26). By using interviews with nurses who worked in highly impacted areas without adequate protective equipment, other reports offered a counterbalance to the religious imagery: “Please don't call me a hero. I am being martyred against my will.” (Palus, 2020, April 23). The valorization of nurses’ sacrifices to work without proper equipment was additionally conveyed through symbols of war and nationalism. An editorial, written by a WWII veteran, employed the analogy of battle: “Those brave women and men who, when a code sounds in a hospital, run into battle with this vicious virus every day… And they do it bravely without even a guarantee of the supplies they need” (Maxwell, 2020, May 4). The militaristic-like sacrifices of nurses were publicly represented by “Operation America Strong,” a Trump-endorsed display of fighter jets that flew over US cities most impacted by COVID-19 (Lamothe, 2020, April 22). Although publicly applauded for fostering national resolve, this militaristic spectacle was challenged by some nurses: “It makes it almost excusable, like we went to war and fought for you. But we went to war without a gun, and that's not what I was asking for” (Palus, 2020, April 23). The hero discourse often characterized nurses as outstanding moral subjects, who often placed their commitment to patients, public safety, and professional duty over their fears of personal safety and anxieties over constrained clinical resources. A Los Angeles Times article, entitled “A Last Selfless Act,” recounted the story of 61-year-old nurse, originally from the Philippines, who resuscitated a patient in respiratory distress despite not having access to an N95 mask (Karlamangla, 2020, May 10). The nurse later died in the same hospital where this heroic act occurred. Other articles described nurses’ resourcefulness and ingenuity in the face of inadequate protective gear: “Supplies were so strained that nurses turned to menstrual pads to buttress the padding in their helmets” (Baker, 2020, March 11). Later in the article, the unit manager reported that rather than refusing to work because of risks, nurses have repeatedly said, “If you need me, I'm available” (Baker, 2020, March 11). For healthcare organizations and corporations, the growing visibility of nurses’ sacrifices provided a window for nurses to enact moral values such as benevolence and justice:Despite the risks and unknowns, one thing is for certain: Nurses always show up to help provide safe, timely, effective and equitable healthcare. That is our legacy, our privilege, our honor. Now with the eyes of the world upon us, we have the opportunity for a defining moment… (Johnson and Johnson, 2020, April 1) The notion of COVID-19 as a “once in a lifetime” opportunity for moral action and sacrifice circulated throughout the media coverage, which often reassured the public about nurses’ professional commitment. For example, an intensive care nurse reported in Glamour magazine: “As nurses, we signed up for being there for our patients, their families, and the general public no matter what. We've been training for moments like this our entire careers…” (Moeslein, 2020, March 30). 5.2 Nurses as “model citizens” The hero discourse often constructed nurses as “model citizens” in a rapidly evolving crisis that required responsibility, action, and, depending on one's political perspective, obeying public authority. Nurses were often depicted as compliant with their role as the “last line of defense” in pandemic management, particularly in the uncertain early phases of the crisis. For example, a nursing executive stated, “I've never had such respect for the profession as I do today, watching how our nurses have quickly adapted without question or hesitation to a rapidly changing healthcare landscape” (Hodge, 2020, May 7). As a counterbalance, other media accounts characterized nurses’ uncertainty and ambivalence of being thrust into important but dangerous roles: “There's a narrative that says that doctors and nurses must ‘answer the call.’ That's why I'm uneasy about the nightly cheering sessions. Some of us don't feel like trying to become heroes” (Hamm, 2020, April 9). A discursive technique emerged in the mass media where nurses, positioned as hardworking and productive subjects, were contrasted with “harmful” individuals and groups that denied the severity of the pandemic or resisted public COVID-19 measures. For example, The Lincoln Project (2020, April 24), an anti-Trump American political organization, produced the YouTube video “Two Americans” to emphasize the upstanding role of nurses: “There are two types of Americans that have emerged through this pandemic; Those who sacrifice and those who demand.” The video begins with images of nurses in full protective gear, some comforting patients and appearing physically exhausted, and then cuts to images of anti-lockdown protesters, including those screaming in front of public buildings and brandishing guns (The Lincoln Project, 2020, April 24). During interviews, nurses reinforced the message of civic responsibility: “It's really upsetting to be driving to the hospital and see groups of people having picnics outside or getting their nails done at the local salon despite the closure of nonessential businesses” (Moeslein, 2020, March 30). Our analysis suggested that the hero discourse and the model citizen subjectivity may not have been evenly applied to all nurses but appeared to be delineated according to the racialized, ethnic, and migrant identities of nurses. Widespread marketing campaigns in Britain to honor healthcare providers as heroes, sponsored by National Health Service (NHS), came under scrutiny over their lack of racial representation and authentic reflection of NHS staff. Some authors employed the term “white-washing” to suggest that the NHS purposefully excluded people of color from #ClapForCarers, a campaign focused on applauding healthcare workers as a public ritual (Morris, 2020, April 3; Butt, 2020, April 6). In another instance, Morris (2020, April 3) called attention to an NHS sponsored video to promote handwashing as a civic duty and build public solidarity that featured 20 health professionals who were all were white. Other articles suggested that the lack of media recognition negates the 40 years of contribution that racialized and migrant nurses have played as “hidden architects” to the modern NHS (Simpson, 2020, July 1). In an editorial from The Guardian, Sonia Sodha (2020, April 5) described the double burden of racialized NHS nurses, who faced both heightened racism on the job and the policing of the acknowledgement of their contributions by “self-appointed white gatekeepers.” Reflecting the divisiveness of the issue of representation, Sodha (2020, April 5) goes on to describe the avalanche of racist tinged backlash they received from readers after acknowledging the ethnic background of workers on a British news show. 5.3 Heroism itself as the reward for nurses A discursive pattern in which the attribution of the hero subjectivity became a social and cultural reward for nurses, who were positioned by the media as a group whose contributions were unrecognized by the public before COVID-19, was also evident. The hero discourse was often characterized by a transformative process in which the public association with nurses and nursing work moved from the mundane and unappreciated to the exceptional and valorized. This trajectory was conveyed through news headlines that described how nurses moved from being “unsung” (Logan, 2020, July 19; Zielinski, 2020, May 5) and “everyday” (Leung, 2020, May 12) to temporarily replacing “sports idols” (Corbella, 2020, May 27) and even “holding the torch of freedom” (Maxwell, 2020, May 6). The notion of hero worship as reconfiguring the gendered identities of nurses circulated throughout the media coverage. In an opinion piece, Bailey (2020, May 11) declares, “suddenly, I'm not ‘just a nurse,’” but then later questions, “Why does it take a pandemic to recognize that nursing, a predominantly female dominated profession, is important?” In a USA Today article, Temkar (2020, May 27), describes their assumptions about mostly female family members who immigrated to the US from the Philippines to become nurses:When I was growing up, if you had asked me what I thought about nursing, I might have said it was a “sellout” profession. A sellout, to a punk teenager like me, was the worst thing a person could be. Selling out meant that you lacked authenticity and imagination. You followed the herd. You were a cliché. Later in the article, Temkar (2020, May 27) described how social media images of nurses “preparing resolutely to do battle on the front lines,” people cheering, and “stories of nurses becoming like family to their isolated patients” initiated a transformative process in which they became proud of their nursing lineage. Other voices, such as an emergency room nurse from New York, offered a more skeptical take on the long term rewards of the public's shifting view of nurses: “I fear that once the worst is over, everybody is going to forget and go back on to life as usual” (Wallis, 2020, April 28). The media coverage often highlighted the outpouring of corporate generosity, such as free hotels, delivered meals, and discounted shoes, to help nurses manage the long hours and travel to endemic areas, particularly early in the pandemic. Several companies incorporated the hero discourse as part of both demonstrating support for nurses and marketing their products in an economic downturn. For example, Dove US (2020, April 8) developed a YouTube video entitled, “Courage is Beautiful” that depicted the bruised and lacerated faces of masked healthcare professionals alongside its corporate logo, an association to its restorative skincare products. Whereas some felt rewarded by corporate recognition, other nursing voices were more skeptical and reflected a growing concern with being exploited: “You're making money off of me while you're handing me more work by not protecting your workers and not giving them what they need” (Wallis, 2020, April 28). Similarly, other media reported that rituals such as clapping and pot banging were not only intended to honor nurses, but had performative benefits to participants. For example, a New York Times editorial suggested: “But the more the ritual is repeated, the more it feels as if it's for the rest of us, too. We used to go out to concerts or movies or plays and clap for the performances. Now the clapping is the performance” (Hess, 2020, April 9). Nursing voices, however, placed public rituals within concerns for longer-term systemic change: “We appreciate the support that we're getting from the community… But I do challenge people who are clapping, writing in sidewalk chalk, to go a step further” (Palus, 2020, April 23). Although the rewards to nurses were emphasized, other media reports suggested that the hero discourse failed to materialize long standing policy changes to nurses’ workload, input in decision making, or financial renumeration. The UK and Canadian media highlighted several stories of nurses being denied pandemic pay and salary increases, despite politicians labelling them as COVID-19 heroes. Early in the pandemic British Prime Minister Boris Johnson, who became infected with COVID-19, publicly thanked two nurses who provided him 48-hour constant care: "The reason, in the end, my body did start to get enough oxygen was because for every second of the night, they were watching and they were thinking and they were caring…” (Picheta, 2020, April 13). One of these nurses, Jenny McGee, who is originally from New Zealand, was later thanked by Prime Minister Jacinda Ardern on social media and reported feeling “blown away” by the praise from international leaders (Picheta, 2020, April 13). As the pandemic evolved, however, McGee's nursing colleagues from the hospital where Johnson was treated staged a protest against the government refusal to approve a pay rise. An intensive care nurse at the protest voiced their anger: “After eight weeks of clapping, I feel completely betrayed and as though what myself and my colleagues went through was just expected of us as our duty” (Kilraine, 2020, July 29). In Ontario, Canada, the notion of heroism and gender equity surfaced in the media coverage of Bill 124, a provincial bill that restricts public service workers, including nurses, to salary increases of one percent annually. Other frontline professionals, such as police officers and firefighters, are not subject to this bill. Certain media outlets provided a platform for the provincial nursing union, who critiqued politicians for employing the hero discourse throughout the pandemic, while simultaneously restricting the ability of nurses to bargain for an equitable wage: “it has widened the gender pay-equity gap and the impact on the morale of our dedicated RNs…” (Ferguson, 2020, June 12). Nurses impacted by Bill 124 expressed their sense of betrayal and frustration through using violent imagery, such as a “slap in the face,” and often questioned the sincerity of politicians who employ the hero rhetoric: “You're called front-line and you're called essential but you're really a body that can replaced” (Xing, 2020, June 11). 6 Discussion In this study, we conducted a poststructural discourse analysis of the mass media to examine the effects of the hero discourse on nurses who are contending with the ongoing crisis and the impact of this discourse as a political, social, and cultural tool to further eroding nursing work. We argued that the hero discourse has created the conditions in which nurses are vulnerable to subjectification or the production of new forms of subjectivity that emerge from this expanding public discourse. The hero discourse is not a neutral expression of appreciation and sentimentality, but rather a political, social, and cultural technique employed to accomplish multiple aims such as the normalization of nurses’ exposure to risk, the enforcement of model citizenship, and the preservation of existing power relationships that limit the ability of front line nurses to determine the conditions of their work. Our analysis of the media also uncovered multiple points of resistance, often from nurses themselves in various media reports, to the discursive construction of hero worship and its use as a tool to enact relationships of power with nurses. The hero discourse culturally positions nurses to become "necessary sacrifices" in order to respond to an emerging crisis. Enacted through war and military imagery, the moral characterization of nurses as self-sacrificing heroes aligns with a previous media analysis of the SARS crisis (McGillis Hall et al., 2003). Our study also identified religious metaphors to highlight nurse's selflessness, which parallels the historical construction of nurses as innately virtuous and self-sacrificing (Gordon and Nelson, 2005). These idealized subjectivities fail to acknowledge the emotional complexities and sense of conflict experienced by front-line nurses. Interview studies with nurses who worked on the early COVID-19 front lines described their ambivalence as they navigated the tensions between their professional responsibilities and the desire to flee high-intensity, under-resourced, and dangerous work (Sun et al., 2020; Liu et al., 2020). The failure of the hero discourse to acknowledge the emotional, psychological, moral, and physical stressors of pandemic work has implications for the future expectations that will placed on nurses as the crisis continues. We argue that the hero discourse has resulted in the normalization of risk for nurses to justify the need for a timely and committed response in the contexts of uncertainty, political divisiveness, and unprepared healthcare systems. This normalizing process has made the unacceptable, such as nurses wearing garbage bags as protective gear and leaving older patients to die alone, more palatable to a broader audience. Defining the “new normal” of nurses’ risk occurred through the normalizing processes of euphemisation, endowing unpalatable practices with positive attributes, and legitimization, representing the taking on of bodily hazards as a moral act (Krzyżanowski, 2020). The discursive construction of nurses’ sacrifices was also supported through the normalizing process of naturalization (Krzyżanowski, 2020), in which dangerous occupational conditions have been justified as an inevitable event in nurses work. The hero discourse, therefore, functions to conceal the political contributors to risk that were caused not only by the biological realities of the virus, but also the lack of government assurance of protective equipment, uneven public health measures, and inadequate organizational staffing (Smith, 2020; Monteverde and Gallagher, 2020). The public fascination with the sacrifice of nurses may provide reassurance that regardless of government policies and unsafe working conditions, nurses will sacrifice themselves to save everyone. Heroism has been employed as a disciplinary political device in the creation of the docile bodies of nurses. Our analysis parallels other poststructural studies that have examined the effects of disciplinary power on dictating the everyday conduct of nursing work (Dillard-Wright, 2019; McIntyre et al., 2019; McIntyre et al., 2020). Returning to Banksy's image of the “supernurse,” who is depicted as being played with as a child's toy which is open to manipulation, nurses may be similarly susceptible to pressure to perform, comply, and endure (Einboden, 2020). Nurses who refused to care for people with COVID-19 or failed to show up at work were noticeably absent from media coverage. Media accounts, however, did not render nurses as powerless but routinely highlighted multiple forms of nurse-led resistance such as political protests and lobbying from nursing unions. In an analysis of the French language Canadian media, Gagnon and Perron (2020) found that the Quebec Interprofessional Health Federation, the province of Quebec's nursing union, engaged in highly publicized “whistleblowing” about pandemic working conditions and used social media to highlight the ways that COVID-19 destabilized an already vulnerable and under-resourced nursing workforce. Our analysis revealed a discursive pattern in which nurses were constructed as model citizens and workers during a rapidly evolving crisis. Based on an analysis of literature and mythology, Campbell (1949) suggests the cultural power of the hero myth lies in its ability to provide a general pattern for the everyday person to act in a moral sense. Nicholson (2011) suggests that the heroic stories of an era may present an idealized cultural model of active selfhood, that is resourceful, embodies bravery, and desires to conquer the enemy. We argue that nurses have been employed as social and moral models who formed an archetype for how the public should think and behave in the context of COVID-19, thereby forming a tool to enact disciplinary power over the general population. Disciplinary power, which may have productive aims, is often enacted by those in authority to subdue dissent and enforce certain types of behavior (Foucault, 1977), such as compliance with socially responsible measures to mitigate the evolving pandemic. The construction of nurses as productive subjectivities has occurred within the politically and socially divisive climate of responding to the pandemic that has been characterized by the politicization of mask wearing, public resistance over lockdowns, and doubts about the scientific credibility of medical officials (Harsin, 2020). Our analysis suggests that, at certain points in the pandemic, the hero discourse has crossed political divides because of its widespread public support and its sentimentalization of nursing work. Employing hero worship, therefore, has become a “low stakes” technique used by politicians and other leaders to convey public messages about idealized citizenship and collective resolve. Our analysis, for example, described how Donald Trump and Boris Johnson, both criticized for their inaction and poor decision making, still employed the hero discourse as a rallying cry to foster national unity early in the pandemic. The hero discourse was not evenly applied to every nurse, but rather was shaped by racialized and migrant identities of particular nurses described in the media reports. The construction of hero subjectivities occurred alongside prevailing racist assumptions about the causation and spread of COVID-19 such as the othering of immigrants and blaming certain racialized groups (Devakumar et al., 2020). Our analysis of the lack of racial and ethnic representation in public NHS campaigns suggest that the hero discourse was shaped by long standing patterns of discrimination experienced by nurses of color. Subject to the effects of colonialism and gender discrimination, NHS nurses who are female and racialized experience greater disparities in promotions and pay when compared to others (Milner et al., 2020). In an interview study, Isaac (2020) suggested that Black British-born nurses were socialized to employ a defence of their “British cultural capital” (p. 97), comprised of their British identity and positive social contributions, to override systemic racism in the NHS. Although additional theoretical work by critical race scholars is needed in this area, particularly in the contexts of Brexit and the rise of nationalistic populism, the hero discourse may perpetuate the idealization of certain archetypes of nurses based on whiteness and acceptable nationality. The widespread attribution of the hero subjectivity was often constructed as social and cultural reward for nurses and this public recognition was often positioned as sufficient to sustain nursing through a tumultuous period. Some media reports highlighted how hero worship initiated a transformative process that raised the profile of a profession that was considered to be unacknowledged pre-pandemic. The hero discourse was used to distance nursing work from the everyday, mundane, dirty, and gendered caring work (Ceci, 2004; Bolton, 2005; Lawler, 2006), thereby temporarily reconfiguring nursing to a healthcare practice that is dynamic, admirable, and vital to surviving the pandemic. At the same time, the gendered division of comfort and caring, historically labelled as women's work (Davies, 2003), was sometimes depicted as continuous with the hero narrative. Nurses were routinely applauded as heroes for their meticulous round the clock monitoring of critically ill people and their intimate therapeutic work with isolated and dying people with the virus. Associating heroism with comfort and caring work, traditionally associated with the feminine and gendered work, opens up the need for future studies on the contemporary construction of the female heroic archetype. The female hero, traditionally depicted as less than their male hero counterpart (Nicholson, 2011), may take on new possibilities for social representation in a post-pandemic world. We explored the political, social, and cultural strategy of being seen as an ally to nurses through the hero discourse, but often without a meaningful commitment to long term transformative change. For example, our analysis highlighted the use of allyship as a public relations strategy to appear sympathetic to the plight of front-line healthcare staff, as a marketing strategy to align one's brand with a moral identity, and as a performative exercise that provided an accessible outlet for public participation. In many cases, the hero discourse provides a public face to conceal the eroding conditions of nursing work and to justify further cuts to staffing, pay raises, and clinical resources in an economic downturn. The nurse as hero discourse may function to preserve and extend existing power relations that limit nurses such as racism, gender discrimination, austerity measures, and managerialism. Some media reports suggested that corporations, politicians, and even the general public used the heroization of nurses’ suffering for their own political, economic, and cultural ends. We argue that this strategy often amounts to a form of performative allyship in which people with decisional and economic power signal their support, but fail to engage in the ongoing educational, self-reflexive, policy, and structural changes inherent in more genuine forms of allyship (Erskine and Bilimoria, 2019; Krause and Miller, 2020). For example, a recent survey of the American and Canadian public (n = 3551) suggests that participation in heroic rituals (e.g. clapping and cheering) was unrelated to holding stigmatizing attitudes towards healthcare professionals such as the belief they should be isolated from their families and communities (Taylor et al., 2020). Forms of hero worship, such as those described in our study, are performative because they are enacted in highly visible ways, provided to appear legitimate, and given to illicit self-congratulations or a “virtual pat on the back” (Jacobson Frey, 2020 p. 72). 7 Strengths and limitations Strengths of the study include the rigor of the discourse analysis method, the richness of the document data, the quality of the analysis, and the close use of Foucault's theoretical framework to open up a new conceptual understanding of this often taken for granted phenomenon. Limitations include our exploratory and qualitative approach, which does not allow the generalizability of our findings to make universal claims about this discourse. Since our data sources were limited by date, country of origin, and language, it is possible that an analysis of different media reports would have resulted in different forms of discursive patterns and subjectivities. Since the positionality and subjectivity of the researcher is central to research informed by poststructuralism (McCabe and Holmes, 2009), it is also possible that researchers with different backgrounds and objectives would have yielded alternative or divergent results. Although we reflexively considered the effects of our subjectivities on the research process, our identities as nurses and people concerned about the effects of COVID-19 on healthcare workers may have shaped the findings. 8 Implications to nursing and conclusion Using a poststructural perspective, we critically examined the effects of the hero discourse as a political, social, and cultural device to uphold expectations for nurses to make sacrifices for the common good, enforce subjectivities of model citizenship, and to position public adulation as the main reward for nurses. Although there were some productive elements of this discourse, such as the public recognition of nursing work and the provision of short-term conveniences, our analysis suggests that nursing should view hero worship with caution and trepidation. The pandemic has made visible the challenges of nursing work, but also the accumulated consequences of managerialism, financial austerity, gender and racial inequities, and inadequate governmental support. The hero discourse and heroic subjectivities do little to rectify these barriers and often comprise a form of performative allyship. Recognizing the destructive effects of hero worship will help nurses resist and move past its effects. Our study has implications for formalizing the ongoing emotional, psychological, ethical and practice supports of nurses as they contend with the ongoing pandemic. Although there is some level of being a hero that is voluntary, many nurses were involuntarily placed into such roles, especially through practices such as mandatory re-deployment to COVID units or testing centers. The costs of heroism to nurses, many who need their jobs to survive economically, remains uncertain. Heroic nurses are meant to face and overcome any circumstances individually, thereby lessening the responsibility of government to enact systemic changes, the only way to properly address this crisis. Left unchallenged, the limits of the hero discourse remain unclear and open to manipulation by those in power. As the pandemic continues and healthcare systems struggle to prevent financial collapse, will leaders and decision makers continue to placate nurses with the hero discourse in order for them to work in even more perilous and inequitable conditions? The long-term impact of the stress and strain of practicing nursing in COVID-19 times will likely be dire and will ultimately cost society who needs a healthy, resilient, and sustainable nursing work force. Broader considerations include the retention of nursing staff, particularly in high acuity areas and long-term care, emotional burnout, occupational induced illnesses, and nurses permanently leaving the profession. Both nursing and society cannot allow the hero discourse to sideline their ongoing quest to secure formal supports for nurses, including practical supports such as limits on patient to nurse ratios and opportunities for respite from exhausting work (Maben and Bridges, 2020). Our study also has implications for the collective political response of nursing in the contexts of COVID-19. As nurses lead political activism to secure much needed clinical resources in a post-pandemic world, they cannot assume that their hero status will provide them with enough political clout to obtain a seat at the policy table or sway decision makers. Nurses need to be attuned to the multiple ways that hero worship legitimizes threats to nurses and the erosion of the conditions of nursing work, thereby challenging this approach as a strategy of power. Nurses themselves were not necessarily the authors or orchestrators of the hero discourse; nor were they rendered powerless, but often were located as multiple points of resistance across media coverage. Nurses consistently requested tangible support for their work (i.e. increased access to protective gear, improved staffing, etc.) as opposed to accepting or perpetuating the title of hero. When faced with the complexities of direct clinical care during a pandemic, the hero label lost much of its meaning. The resistance to the hero discourse and other harmful forms of political strategizing will be essential as the emergency continues and nursing work evolves to meet these demands. Rather than participate in the superficial sentimentalizing of hero worship as form of performative allyship, nursing must instead engage in meaningful political activism, continue its professional commitment to advocacy, and articulate a strong voice of knowledge and reason in COVID-19 (Morin and Baptiste, 2020). CRediT authorship contribution statement Shan Mohammed: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Project administration. Elizabeth Peter: Conceptualization, Validation, Formal analysis, Resources, Writing - original draft. Tieghan Killackey: Conceptualization, Validation, Formal analysis, Resources, Writing - original draft, Writing - review & editing. Jane Maciver: Conceptualization, Validation, Formal analysis, Resources, Writing - original draft, Writing - review & editing. 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Foucault, subjectivity and discourse analysis Theory Psychol. 20 2010 52 75 10.1177/0959354309345647 Zielinski L. Another Voice: Nurses are Unsung Heroes of Health Care May 5 2020 The Buffalo News https://buffalonews.com/opinion/another-voice-nurses-are-unsung-heroes-of-health-care/article_5dc10d93-7bc9-51a1-ae4b-83ae2b08e692.html

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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(21)01255-2 10.1016/j.molstruc.2021.131125 131125 Article A novel manganese(II) bisthiocarbohydrazone complex: Crystal structures, Hirshfeld surface analysis, DFT and molecular docking study with SARS-CoV-2 Hashim K.K. Mohammed a Manoj E. a⁎ Kurup M.R. Prathapachandra b⁎ a Department of Applied Chemistry, Cochin University of Science and Technology, Kochi, Kerala 682022, India b Department of Chemistry, School of Physical Sciences, Central University of Kerala, Tejaswini Hills, Periye, Kasaragod 671320, India ⁎ Corresponding authors. 23 7 2021 15 12 2021 23 7 2021 1246 131125131125 10 6 2021 8 7 2021 14 7 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. A novel Mn(II) complex [Mn(H2L)Cl2]•H2O (1) of a ditopic ligand 1,5-bis(2-benzoylpyridine) thiocarbohydrazone (H2L) was synthesized and characterised physico-chemically. A part of the mother solution of the complex 1 and THF yielded single crystals in a triclinic space group and are found same from the crystals obtained from another mixture of the mother solution and ethyl acetate. Single crystal XRD studies have confirmed the mononuclear complex formation and absence of any interactions between the Mn(II) centers. A solution of the complex 1 in chloroform, conversely, yielded a crystallographically different complex [Mn(H2L)Cl2]•CHCl3 (1a) in monoclinic and is also characterised with single crystal XRD. The ligand is coordinated through thione sulfur atom to form a square pyramidal geometry around Mn(II) center in both the complexes. The molecular packing of the complexes is found influenced by the nature of solvent inclusion, and are stabilized by different non-covalent interactions in the lattice. The intermolecular interactions are quantified by Hirshfeld surface analyses, which reveal that H•••Cl interactions has maximum contribution to the total Hirshfeld surface in the complex 1a. This is the first crystal structure study of a manganese(II) complex of a bisthiocarbohydrazone ligand. The molecular and electronic structures of the complexes are studied by DFT quantum chemical calculations. The band gap (Eg) of the complex 1 was estimated as 2.45 eV using Kubelka-Munk model and is in agreement with the electronic spectral calculations of the complex at TD-DFT level. Molecular docking studies of both the ligand and the complex reveal their greater propensity towards SARS-CoV-2 main protease compared to B-DNA dodecamer. Also, the binding potential of the ligand and the complex with SARS-CoV-2 main protease is found higher than that with chloroquine and hydroxychloroquine. Graphical abstract Two crystallographically different complexes of a novel Mn(II) bisthiocarbohydrazone complex are investigated with a comparative structural and intermolecular interaction study. The experimental results are supported by theoretical DFT study. Both the thiocarbohydrazone ligand and the Mn(II) complex are subjected to the molecular docking studies with the crystal structure of B-DNA dodecamer and SARS-CoV-2 main protease. The Mn(II) complex exhibit excellent docking score and greater propensity towards SARS-CoV-2 main protease.Image, graphical abstract Keywords Thiocarbohydrazone DFT X-ray diffraction Hirshfeld surface analysis Molecular Docking SARS-CoV-2 ==== Body pmc1 Introduction The coordination chemistry of thiocarbohydrazones, the higher homologues of the well-known thiosemicarbazones, and their biological potentials are very little known and yet to be explored. The extra hydrazine moiety provides the ligands variable metal binding modes, structural diversity and promising biological implications including antiviral and anticancer applications [1]. Thiosemicarbazones and their complexes are known for their various antiviral properties and transition metal complexes have recently been identified as potential tools against SARS-CoV-2 [2]. The molecular docking study of a platinum based thiosemicarbazone complex with SARS-CoV-2 have revealed its potentials for the inhibition of RNA dependent RNA polymerase RdRp, which is comparably higher with respect to WHO recommended repurposed drugs like chloroquine, Remdesivir, Galidesivir, Tenofovir, Sofosbuvir, Ribavirin, etc. [2]. Similarly, Pd-thiosemicarbazone complexes have also been reported as having better binding potentials with SARS-CoV-2 main protease 6Y2F compared with chloroquine and hydroxychloroquine [3]. Also, Mn(II) thiosemicarbazone complexes are known for their versatile biological implications [4]. As various studies are progressing in several directions to develop novel medicines for the treatment of dangerous COVID-19 disease that is spreading all over the world [5–8], the nature of the interaction between a bisthiocarbohydrazone and its Mn(II) complex with a main protease of SARS-CoV-2 would be worthwhile to study. Besides the biological potentials [1,9], the coordination chemistry of ditopic bisthiocarbohydrazones and their oxygen analogue carbohydrazones are getting increasing attention as they are considered as potential building blocks for controlled self-assembly of metallosupramolecular squares [[10], [11], [12], [13]–18] of high-tech applications. Conformationally rigid tetranuclear [2 × 2] square grid complexes remain a popular research area in synthetic coordination chemistry and the products are very stable by non-covalent interactions. The inclusion of magnetic metal ions and formation of nanometer-sized magnetic clusters like single molecule magnets added another dimension to carbohydrazone based complexes [17]. There are only few reports on the Mn(II) complexes of biscarbohydrazones [[14], [15]–16] and are all molecular squares. The structural aspects and the chemistry of Mn(II) complexes of thiocarbohydrazones are yet to be explored, which might be due to susceptibility of the resultant species to decompose or difficulty in yielding single crystals. There are no reports of crystal structure study of Mn(II) bisthiocarbohydrazone complex so far, to the best of our knowledge. Mn(II) ions are considered as hard and show maximal stability with hard oxygen donors, which is reflected in their self-assembled oxygen bridged carbohydrazone square grid formation. Thiocarbohydrazones, however, have soft sulfur atom in place of oxygen, which may be less favourable for bridging Mn(II) centres compared to their carbohydrazone counterparts. Here, we have successfully isolated single crystals of two crystallographically different Mn(II) complexes from three different sources of solutions of a novel Mn(II) complex. As a continuation of our study on (thio)carbohydrazone complexes and their anticancer potentials, we report the structural aspects of the Mn(II) complexes derived from a ditopic bisthiocarbohydrazone ligand. The density functional theory calculations of both the ligand and the complexes are performed to support the experimental study. Moreover, in silico molecular docking studies of the ligand and the complex with B-DNA dodecamer d(CGCGAATTCGCG)2 and SARS-CoV-2 main protease are investigated. 2 Experimental 2.1 Materials 2-Benzoylpyridine (Aldrich), thiocarbohydrazide (Aldrich), MnCl2• 4H2O (Fluka), HCl (Rankem), methanol (Merck), DCM (S.D. Fine), chloroform (S.D. fine), etc. were used as received. 2.2 Synthesis The ligand H2L was synthesized by the condensation between thiocarbohydrazide with 2-benzoylpyridine in 1:2 molar ratio as reported previously [18–20]. The synthesis of the complex [Mn(H2L)Cl2]• H2O (1) was done by the reaction between an equimolar ratio of ligand and metal salt in neutral medium. A solution of MnCl2• 4H2O (0.99 g, 0.5 mmol) in 10 ml of methanol was added to a solution of H2L (0.218 g, 0.5 mmol) in 10 ml chloroform and refluxed for 2 h and allowed to stand at room temperature. Orange coloured product separated out after two days was filtered, washed with ether and dried. Recrystallized in ethyl acetate and dried over P4O10 in vacuo. Yield: ~ 64%. Elemental Anal. Found (Calc.): C, 51.64 (51.74); H, 3.16 (3.82); N, 14.55 (14.48); S, 6.12 (5.52)%. FT-IR: 1233 (ν(C=S)), 3560 (ν(H2O)), 3280, 3450 (ν(N–H)), 1630, 1595, 1515, 1468, 1440 (ν(C=N)+ ν(C=C)), 1350, 1392 (ν(C–N)/ νheterocyclic), 1148, 1072 (ν(N–N)), etc. The crystals of the complex [Mn(H2L)Cl2]•CHCl3 (1a) were formed from a solution of the complex 1 in chloroform on slow evaporation. FT-IR spectra of the complexes 1 and 1a are given in the supporting file as Fig. S1. 2.3 Methods and instrumentation CHNS analysis of the compound was carried out using an Elementar Vario EL III CHNS analyzer at SAIF, Kochi, India. Electronic spectra (200–900 nm) were recorded on a UV-Thermo scientific evolution 220 spectrometer. Infrared spectra in the range 4000–400 cm−1 were recorded on a JASCO FT-IR 4100 spectrometer with KBr pellets at the Department of Applied Chemistry, CUSAT, Kochi. 2.4 X-ray crystallography The single crystal X-ray diffraction data of the complexes 1 and 1a were collected at 296(2) K using Bruker SMART APEXII CCD diffractometer, equipped with graphite-crystal incident beam monochromator, and a fine focus sealed tube with Mo Kα (λ = 0.71073 Å) and the X-ray source at the SAIF, Kochi, India. The Bruker SMART software and Bruker SAINT software were used for data acquisition and data reduction, respectively. The structures were solved by direct methods and refined by full-matrix least-square calculations with the SHELXL-2018/3 software package [21]. All non-hydrogen atoms were refined anisotropically, and all hydrogen atoms on carbon were placed in calculated positions, guided by difference maps and refined isotropically. One of the Cl atoms of the complex 1 is disordered with occupancies 64 and 36% and the disorder was resolved using PART instruction. Also, we could not locate the hydrogen atoms of the water molecule. The molecular and crystal structures were plotted using ORTEP [22], PLATON [23], Mercury [24] and Diamond 3.2 k [25] programs. The crystallographic data along with details of structure refinement parameters of both the complexes are given in the Table 1 . Selected bond lengths and bond angles are listed in the Table 2 .Table 1 Crystal refinement parameters of the complexes [Mn(H2L)Cl2]•H2O (1) and [Mn(H2L)Cl2]•CHCl3 (1a). Table 1Parameters [Mn(H2L)Cl2]•H2O (1) [Mn(H2L)Cl2]•CHCl3 (1a) CCDC number Empirical Formula 2086573 C25H20Cl2MnN6OS 2044372 C26H21Cl5MnN6S Formula weight (M) 578.37 681.74 Temperature (T) 296(2) K 296(2) K Wavelength (Mo Kα) 0.71073 Å 0.71073 Å Crystal system triclinic monoclinic Space group P1¯ P21/n Unit cell dimensions a = 9.2111(7) Å α = 63.927(3)° b = 12.3698(8) Å β = 70.802(3)° c = 13.6122(10) Å γ = 87.115(3)° a = 14.2205(11) Å α = 90° b = 14.0542(9) Å β = 100.466(3)° c = 15.4581(13) Å γ = 90° Volume V, Z 1307.52(17) Å3, 2 3038.0(4) Å3, 4 Calculated density (ρ) 1.469 g/cm3 1.491 g/cm3 Absorption coefficient, μ 0.820 mm−1 0.971 mm−1 F(000) 590 1380 Crystal size 0.30 × 0.20 × 0.15 mm 0.35 × 0.30 × 0.20 mm Limiting Indices -12 ≤ h ≤ 12, -12 ≤ k < = 16, -16 ≤ l < = 18 -18 ≤ h ≤ 18, -18 ≤ k < = 16, -20 ≤ l < = 19 Reflections collected 16128 24880 Independent Reflections 6480[R(int) = 0.0273] 7532[R(int) = 0.0280] Refinement method Full-matrix least-squares on F2 Full-matrix least-squares on F2 Data / restraints / parameters 6481/2/343 7532/2/360 Goodness-of-fit on F2 1.027 1.000 Final R indices [I > 2σ (I)] R1 = 0.0437, wR2 = 0.1181 R1 = 0.0430, wR2 = 0.1091 R indices (all data) R1 = 0.0713, wR2 = 0.1363 R1 = 0.0842, wR2 = 0.1323 Largest difference peak and hole 0.506 and -0.551 e Å−3 0.621 and -0.428 e Å−3 R1 = Σ||Fo| - |Fc|| / Σ|Fo|                       wR2 = [Σw(Fo2-Fc2)2 / Σw(Fo2)2]1/2 Table 2 Selected bond lengths (Å) and bond angles (˚) of [Mn(H2L)Cl2]•H2O (1) (bond parameters involving minor component of atom Cl2 is ignored) and [Mn(H2L)Cl2]•CHCl3 (1a). Table 2Bond lengths (Å) Bond angles (˚) [Mn(H2L)Cl2]•H2O (1) [Mn(H2L)Cl2]•CHCl3 (1a) [Mn(H2L)Cl2]•H2O (1) [Mn(H2L)Cl2]•CHCl3 (1a) S1–C7 1.679(2) 1.683(2) N1–Mn1–N2 70.83(7) 70.41(8) N2–C6 1.282(3) 1.287(3) N1–Mn1–Cl1 98.33(6) 97.56(6) N5–C8 1.283(3) 1.294(3) N1–Mn1–Cl2 100.68(14) 101.78(6) N2– N3 1.355(3) 1.363(3) N1–Mn1–S1 141.02(6) 140.26(6) N4–N5 1.373(3) 1.367(3) N2–Mn1–Cl1 147.54(6) 145.63(6) N3–C7 1.335(3) 1.339(3) N2–Mn1–Cl2 96.4(4) 101.25(5) N4–C7 1.339(3) 1.341(3) N2–Mn1–S1 74.51(5) 74.61(6) Mn1–S1 2.6115(7) 2.6311(9) S1–Mn1–Cl1 101.42(3) 100.80(3) Mn1–N1 2.244(2) 2.259(3) S1–Mn1–Cl2 100.35(16) 102.99(3) Mn1–N2 2.246(2) 2.2489(19) Cl1–Mn1–Cl2 115.9(4) 112.85(3) Mn1–Cl1 2.3350(7) 2.3325(8) Mn1–Cl2 2.306(4) 2.3445(8) 2.5 Theoretical study The DFT calculations were performed using Gaussian 09 program package [26] and GaussView 5.09 molecular visualization programs [27] at computational chemistry facility lab, DAC, CUSAT, Kochi, India. Geometry optimizations and frequency calculations of the ligand and the complex were performed at B3LYP/6-311G (d,P) and LanL2DZ level of theories, respectively using Becke's three-parameter hybrid functional [28] with the Lee et al. correlation functional [29], a combination that gives rise to the well-known B3LYP method. The electronic absorption wavelengths were calculated by time-dependent density functional theory (TD-DFT) [30] in chloroform/methanol media for the first hundred singlet states and was modelled by conductor-like polarizable continuum model (CPCM) [31]. 2.6 Molecular docking study The molecular docking studies were performed using AutoDock Tool (ADT) version 1.5.6 software [32]. The cif format files were converted to pdb files using mercury software. The receptor binding sites of B-DNA dodecamer d(CGCGAATTCGCG)2 (Protein Data Bank (PDB) ID: 1BNA) [33], and SARS-CoV-2 main protease (PDB ID: 6LU7) [34] were obtained from the Protein Data Bank [35]. Visualization of docked poses were done by using Discovery Studio and Pymol softwares. 3 Results and discussion An equimolar ratio of the ligand 1,5-bis(2-benzoylpyridine) thiocarbohydrazone and the metal salt resulted into the formation of the mononuclear complex [Mn(H2L)Cl2]• H2O (1), which was confirmed by single crystal X-ray diffraction studies and supported by elemental analysis, infrared and electronic spectral studies. As the possible sulfur bridged Mn(II) thiocarbohydrazones are not very stable like their oxygen counterparts, we tried to get single crystals from the mother solution itself. However, the single crystals obtained from mother solutions of the complex 1 layered with THF or ethyl acetate were found triclinic and same and confirms the mononuclear complex formation only. Though, a solution of the complex 1 in chloroform yielded crystals in monoclinic, single crystal XRD studies reveal a crystallographically different, but mononuclear complex [Mn(H2L)Cl2]•CHCl3 (1a). FT-IR spectra of the complexes 1and 1aexhibited a band at 1233 cm−1 attributed to ν(C=S) frequency (calculated 1272 cm−1) indicating thione coordination and absence of bands at ~ 2600 cm−1 is indicative of absence of thiol tautomer in free or coordinated forms [20]. The complex 1ashows additional bands at 2985 (calculated 3128), 1070 cm−1 (calculated 1280 cm−1) indicating ν(C–H) and δ(C–H) of chloroform. For the complex 1, an additional band at 3560 cm−1 (calculated 3658 cm−1) is observed in the broadened region and is attributed due to lattice water. The DFT calculated vibrational frequencies are as expected, since they are normally higher than that of experimental solid state IR spectrum and the optimization is done in gas phase and not involving hydrogen bonding and other intermolecular interactions in the crystal lattice. The comparatively large variation for the vibrational bands of chloroform is because of the C26–H26···Cl2 hydrogen bonding, as evidenced by the crystal structure results. The solution phase electronic spectra of the complex 1 as methanol, chloroform and dichloromethane solutions (0.25 × 10−5 M) were recorded (Fig. 1 ). The free ligand H2L as a methanol solution exhibit λmax at 346 nm, assigned as intraligand n→π* transition mainly due to thiocarbonyl group. This band is slightly shifted to 350 nm for complex 1 in methanol solution, and have suffered more red shift (to 358 nm) in chloroform and dichloromethane solutions. The UV-Vis spectrum of 1 as methanol solution shows bands, λmax (ε) at 250sh (262400), 275sh (214800), 350 (342400) and 425 nm (196800 M−1 cm−1). The intensity of the band at 425 nm is indicative of possible charge transfer transition in the complex. The ground state for an Mn(II) ion having d5 configuration is 6 A 1g and the transitions from this state is doubly forbidden. The crystal field of any symmetry is unable to split this ground state. The molar absorptivity of the band at 425 nm is higher compared to possible 4 T 2g←6 A 1g transition [36], characteristic of Mn(II) complexes. The band is not obscured by intraligand transition [37] also. The band is thus assigned due to metal to ligand charge transfer transition, most probably, to thione sulfur atom of the ligand. This band is absent in the dichloromethane and chloroform solution spectra of the complex 1, probably indicating weakening of Mn(II)–S coordinate bond of the complex 1 in these solutions.Fig. 1 Electronic spectra of the complex 1 (0.25 × 10−5 M in methanol, chloroform, and dichloromethane solutions) and that of the free ligand in methanol solution. Fig 1 Solid state UV-Vis diffuse reflectance spectrum of the complex 1 is plotted with Kubelka-Munk model (Fig. 2 ), which shows a very high intense band at ca 320 nm. Intraligand transition is expected in this region, but the enhanced intensity may be due to charge transfer transition most probably originating from chloride to the coordinated ligand H2L. The band at ca 420 nm may be attributed to the charge transfer originating from Mn(II) ion to thione sulfur atom. This is in agreement with the methanol solution electronic spectrum of the complex 1. The direct band gap (Eg) of the complex was estimated as 2.45 eV using the graph plotted with (hνF(R))1/2 versus photon energy (hν), where F(R) is Kubelka-Munk function [F(R) = (1-R)2/2R]. This low band gap of the complex is indicative of semi-conductor characteristics and probably the material would be useful for photovoltaic applications.Fig. 2 UV-Vis diffuse reflectance spectrum of the complex 1. The inset shows the band gap from Kubelka-Munk as a function of energy (eV). Fig 2 3.1 Crystal structures of [Mn(H2L)Cl2]•H2O (1) and [Mn(H2L)Cl2]•CHCl3 (1a) Single crystals of the complex 1 were isolated by slow evaporation from a mixture of the mother solution layered with THF or ethyl acetate. Suitable crystals of complex [Mn(H2L)Cl2]•CHCl3 (1a) was obtained from a solution of the complex 1 in chloroform on slow evaporation. Both the complexes adopt a five coordinated geometry around the Mn(II) ion center. The molecular structure of the complex 1a and the relevant atom numbering scheme used for both the complexes is given in Fig. 3 . Though one of the chlorido ligand in complex 1 is disordered, the inner coordination sphere of both the complexes are similar. The Mn(II) centre is coordinated by the neutral ligand using cis pyridyl nitrogen N1, trans azomethine nitrogen N2 and cis thione sulfur S1 atoms. The thiocarbohydrazone moiety shows a E, Z configuration about the C6–N2 and C8–N5 double bonds with the two pyridine rings, which is different from the Z, Z form of the free ligand [18], [19]. This change in conformation of the ligand facilitates the NNS coordination to the Mn(II) center.Fig. 3 ORTEP diagram of the complex [Mn(H2L)Cl2]•CHCl3 (1a) in 50% probability ellipsoids, showing intramolecular hydrogen bonding interactions also. Fig 3 The geometry index values calculated for the complex 1 (τ = 0.1080) and 1a (τ = 0.0895) indicate square pyramidal geometry around Mn(II) ions [38]. The coordination of thione form of sulfur was confirmed by the double bond nature of the C7–S1 (1.679(2) Å for the complex 1 and 1.683(2) Å for 1a) [39,40] and single bond nature of N3–C7 (1.335(3) Å and 1.339(3) Å for the complexes 1 and 1a, respectively) and N4–C7 (1.339(3) Å and 1.341(3) Å for complexes 1 and 1a, respectively) bonds. The C–S bond distance is not much varied from the C=S distance (1.649(4) Å) of the free ligand [18] also. The Mn–Nazo and Mn–Npy lengths are comparable with previously reported Mn(II) thiosemicarbazone complexes [40–42]. The Mn–S distances (2.6115(7) Å and 2.6311(9) Å for the complexes 1 and 1a, respectively) are in agreement with thione coordination with Mn(II) ions [40] and longer than the Mn–S bonds observed in the Mn(II) thiosemicarbazones with deprotonated thiolate form of sulfur [41–43]. The N1, N2 and S1 atoms along with a chlorine atom Cl1 constitute the square base, while the chlorine atom Cl2 occupies the axial site in both the complexes. The basal plane shows a very slight distortion and is more in complex 1a as Mn1 is -0.5023(4) Å out of the same plane towards the chlorine Cl2 atom. The metal centre is shared by two fused five membered chelate rings in both the complexes. The planes comprising Mn1, S1, C7, N3, N2 and Mn1, N1, C5, C6, N2, with a maximum deviation from the mean plane of 0.1501(19) Å for N2 and 0.030(2) Å for N1, respectively, make an angle of 15.81(8) in the complex 1a. In the complex 1, these deviations are maximum for N2 (0.178(2) Å) and C6 (-0.045(3) Å), respectively as the same planes make an angle of 15.35(10). Two intramolecular hydrogen bonds N3–H3A···N5 and N4–H4A···N6 are seen in both the complexes, which provide additional stability to the complex molecules. Relevant hydrogen bonding interactions and the significant C–H···π interaction (Fig. 4 ) in the packing of the crystal lattice of the complex 1 are listed in Table S1. In the complex 1a, the Cl2 atom is engaged in hydrogen bonding with the hydrogen atom H26 of the solvent chloroform with H···Cl distance of 2.54 Å and C26–H26···Cl2 angle 161. The complex 1a molecules are connected in the crystal lattice through intermolecular hydrogen bonds C11–H11···Cl2 (symmetry code: 3/2-x, 1/2+y,-1/2- z; with a H···Cl distance of 2.78 Å and angle 153°) and C12–H12···Cl1 (symmetry code: -1+x, y, z; with a H···Cl distance of 2.80 Å and angle 136°) and leading to three dimensional network (Fig. S2). The hydrogen bonds and significant C–H···π and C–Cl···π interactions found in complex 1a (Fig. S3) are listed in Table S2. Here, both the chlorido ligands and the chlorine atoms of the solvent chloroform play a significant role in the crystal structure cohesion. For the complex 1, however, only one intermolecucular C–H···Cl interaction (C3–H3···Cl2 (symmetry code: 1+x, y, z; with a H···Cl distance of 2.76 Å and angle 144°) is present along with a C23–H23···O1S (with a H···O distance of 2.49 Å and angle 143°) interaction involving lattice water. The π···π interactions observed are all weak in the lattice of both the complexes, also reinforces the crystal structure cohesion in their packing.Fig. 4 The C–H···π and relevant inter and intra molecular hydrogen bonding interactions in the crystal lattice of the complex [Mn(H2L)Cl2]•H2O (1). Fig 4 3.2 Hirshfeld surface analysis of [Mn(H2L)Cl2]•H2O (1) and [Mn(H2L)Cl2]•CHCl3 (1a) The Hirshfeld surface characteristics of both the complexes are analyzed and the intermolecular interactions in the crystal packing are quantified using the Crystal Explorer 17.5 [44] software. Hirshfeld surface of [Mn(H2L)Cl2]•H2O (1) was generated after modelling the structure by manually removing the disordered component of Cl2 and putting full occupation for the major Cl2A component in the CIF. Hirshfeld dnorm, shape index and curvedness surfaces of the complexes 1 and 1a are given in Figs. 5 and 6 , which provide an insight about the electron density distribution among the molecular fragments. In the dnorm Hirshfeld surface, contacts with distances equal to the sum of the van der Waals radii are represented as white regions and the contacts with distances shorter than and longer than van der Waals radii are shown as red circles and blue areas, respectively. The dnorm surface is used to identify very close intermolecular interactions and are observed as red coloured spots [45,46]. The blue region around H19 on the bottom middle and the red regions near phenylene rings over the mapped Hirshfeld shape index properties of 1a (marked regions in Fig. 6) indicate C–H···π interactions in the crystal lattice. Similarly, for the complex 1 the blue region around H25 and red region on the phenylene ring as marked in Fig. 5 indicate strong C–H···π interactions. Hirshfeld surfaces with high curvedness, which is highlighted as dark-blue edges, reveals absence of significant π···π stacking in the lattice of both the complexes. However, very weak π···π stacking interactions present may be seen as red and blue triangles on the same region of the shape index surface and flat regions on the curvedness surfaces. Fig. 7 illustrates the dominant interacting surfaces observed for the complexes, which are due to isotropic van der Waals forces (H···H interactions) for the complex 1 and H···Cl/Cl···H intermolecular hydrogen bonding interactions for the complex 1a. These measures of curvature provide clear insight on molecular packing in the crystal lattice [46] of both the complexes.Fig. 5 dnorm surface view, shape index and curvedness of the complex [Mn(H2L)Cl2].H2O (1). Fig 5 Fig. 6 dnorm surface view, shape index and curvedness of complex [Mn(H2L)Cl2].CHCl3 (1a). Fig 6 Fig. 7 The dominant interacting Hirshfeld surfaces observed for the complexes due to (a) H···Cl/Cl···H intermolecular connections for the complex 1a and (b) isotropic H···H van der Waals forces for the complex 1. Fig 7 To analyse and to quantify the nature of different intermolecular interactions inside the crystal the 2D fingerprint plots are mapped for both the complexes and are given in Figs. 8 and 9 . The plots reveal the percentage of contacts contributed to the total Hirshfeld surface area of that complex. For complex 1a, it is found that H···Cl/Cl···H contacts are the main intermolecular interaction (33.1%) and is reflected on both sides of the scattered point of the 2D finger print plot. The isotropic van der Waals forces (H···H interactions, 27.9%) in the middle of the scattered point of the 2D fingerprint plot are one of the most significant contacts, which is followed by C···H/H···C interactions (15.2%). The relative contribution of other significant interactions observed are C···Cl/Cl···C (5.0%), S···H/H···S (4.0%), N···H/H···N (3.8%), N···Cl/Cl···N (3.2%), etc. C···C interactions are not significant and indicate lack of π···π stacking interactions. However, the significant C···H/H···C interactions can be considered as a measure of C–H···π interactions and is found very strong, reinforces the crystal structure cohesion. For the complex 1, however, the van der Waals forces play the dominant role as evidenced by greater H···H interactions (31.3%) contribution to the total Hirshfeld surface. The relative contributions from C···H/H···C (20.1%) and Cl···H/H···Cl (16.6%) interactions are found very significant among other contributions like N···H/H···N (6.8%), O···H/H···O (6.3%), S···H/H···S (4.7%), Cl···O/O···Cl (2.7%), C···C (2.6%), etc. in the crystal packing. The role of H⋯H interactions in the stabilization of crystal structure is, however, quite small in importance because these interactions are between the same species. The C···H/H···C interactions, a measure of C–H•••π interactions, are found very strong in the complex 1 and reinforces the crystal structure cohesion, though the weak C···C interactions indicating that the π•••π stacking is not very significant. Also, the analyses reveal the absence of any kind of intermolecular contacts involving the central metal atom in both the complexes.Fig. 8 2D finger print plots of compound 1a showing the percentage of significant contacts contributed to the total Hirshfeld surface area. Fig 8 Fig. 9 2D finger print plots of the complex 1 showing the percentage of significant contacts contributed to the total Hirshfeld surface area. Fig 9 3.3 Theoretical study of the ligand and the complexes [Mn(H2L)Cl2] 3.3.1 Frontier molecular orbital (FMO) analysis The geometry optimisation of the ligand H2L and its Mn(II) complexes was performed by B3LYP/6-311G(d,p) and B3LYP/LanL2DZ levels of DFT using the geometric coordinates of the crystal structures. The FMO energies and the FMO energy gap (EHOMO–ELUMO: ΔE) are the important parameters of molecular electronic structure. Relevant frontier molecular orbital energies and the calculated band gap of the compounds along with the chemical reactivity parameters [47] like electron affinity, electronegativity, ionization energy, chemical hardness, softness, chemical potential, electrophilicity and nucleophilicity are listed in Table 3 . Also, the relevant occupied and unoccupied MO electron densities calculated for the ligand and the complex [Mn(H2L)Cl2] are given in Fig. 10, Fig. 11 .Table 3 The Frontier molecular orbital energies and calculated chemical reactivity parameters for the compounds. Table 3Energy parameters (eV) H2L [Mn(H2L)Cl2] [Mn(H2L)Cl2]• H2O (1) [Mn(H2L)Cl2]•CHCl3 (1a) [Mn(H2L)Cl2]• H2O (1) in chloroform HOMO -5.43 -5.52 (α) -5.47 (α) -5.70 (α) -7.81(α) HOMO-1 -5.57 -5.62 (α) -5.54 (α) -5.81 (α) -7.89(β) LUMO -2.24 -3.04 (β) -3.06 (β) -3.16 (β) -1.93(β) LUMO+1 -1.73 -2.99 (α) -3.03 (α) -3.11 (α) -1.87(α) EHOMO–ELUMO: ΔE 3.19 2.48 2.41 2.54 5.88 Ionization Energy, I 5.43 5.52 5.47 5.70 7.81 Electron Affinity, A 2.24 3.04 3.06 3.16 1.93 Chemical hardness, η 1.60 1.24 1.21 1.27 2.94 Electronegativity, χ 3.84 4.28 4.27 4.43 4.87 Chemical potential, μ -3.84 -4.28 -4.27 -4.43 -4.87 Electrophilicity, ω 4.60 7.38 7.55 7.73 4.03 Total Energy -1691.69 -1437.08 -1513.49 -1520.59 -1512.60 Global softness, σ (eV−1) 0.63 0.81 0.83 0.79 0.34 Nucleophilicity, ε (eV−1) 0.22 0.14 0.13 0.13 0.25 Dipole moment (Debye) 7.80 18.37 19.47 18.54 23.86 Fig. 10 Relevant frontier molecular orbitals of the thiocarbohydrazone H2L. Fig 10 Fig. 11 Relevant frontier molecular orbitals of the complex [Mn(H2L)Cl2] in gas phase. Fig 11 The HOMO electron densities of H2L are distributed mainly over the sulfur atom and partially over the nitrogen atoms of the thiocarbohydrazide moiety, while the LUMO electron densities are distributed over the entire thiocarbohydrazone including the thiocarbonyl carbon. The relatively higher HOMO energy characterizes the electron giving ability of H2L and energy gap of 3.19 eV between HOMO and LUMO indicates the molecular chemical stability. Also, the chemical hardness (η = 1.60 eV) and small HOMO-LUMO gap indicate that the molecule is soft. The total energy of the complex [Mn(H2L)Cl2] in gas phase is only -1437.08 eV and is not very favorable compared to that of the free ligand (-1691.69 eV). Also, the HOMO energy of the Mn(II) complex is calculated as -5.52 eV and the LUMO energy is calculated as -3.04 eV. The small energy gap between HOMO and LUMO (2.48 eV) indicates that charge transfer may occurs within the Mn(II) complex, and Mn(II) complex can easily be polarized (global softness, σ = 0.81 eV−1). Also, TD-B3LYP/CPCM calculations indicate that the Mn(II) complex is more stable (chemically hard) in methanol or chloroform solutions than the complex in gas phase, which may attributed due to solvation. 3.3.2 Molecular electrostatic potential (MEP) surfaces The calculated molecular electrostatic potential surfaces of the ligand H2L and the complex [Mn(H2L)Cl2] are given in Fig. 12, Fig. 13 , which illustrates the three-dimensional charge distributions within the molecules [48]. The positive and negative charged electrostatic potential regions in the molecule provide an insight about the various interactions between the molecules. Also, these electrostatic potential mapping onto the iso-electron density surface displays molecular size and shape and is a very useful tool in the correlation study of molecular structure of bio molecules or drugs and their physiochemical properties [49]. A high electrostatic potential or the blue coloured region indicates the relative absence of electrons or partially positive charge, light blue region indicates slight electron deficiency, green shows neutral (zero potential), yellow shows slight electron richness, and a low electrostatic potential or red coloured region indicates an abundance of electrons [50,51].Fig. 12 (a) Molecular electrostatic potential (MEP) and (b) the contour over the electrostatic potential map of the ligand H2L. Fig 12 Fig. 13 (a) The MEP surface and (b) spin density distribution of the complex [Mn(H2L)Cl2]. Fig 13 The red and yellow coloured negative region of the MEP surface of the ligand H2L is associated with the lone pair of electronegative atoms, indicate the Lewis base region and is prone to electrophilic attack or coordination to metal centers. The positive slight blue region is seen near the NH protons and is indicative of relative absence of electrons there. The ESP surfaces also indicate the soft nature of the ligand as well as a preference for NNS coordination. For the Mn(II) complex the most negative potential are over the electronegative chloride ligands, then on sulfur atoms as evidenced by yellow coloured region. The most positive regions are over the hydrogen atoms, while the carbon atoms are seem to have zero potential. The spin density distribution plot of the complex demonstrates that the central Mn(II) atom carries the main spin density along with the donor atoms, which are directly bonded to it. The spin density delocalisation occurs through the metal- ligand bonds, as expected, to the nearby region of the complex. However, most of the regions around the carbon atoms carry a negative spin density and only the coordinating atoms play a significant part in the propagation of spin density. 3.3.3 Theoretical UV-Vis spectral study The theoretical calculation of UV-Vis spectra of both the ligand and the complex were carried out and are found to exhibit the main characters of the experimental spectra. An interesting aspect in the simulated gas phase UV-Vis spectrum for the complex [Mn(H2L)Cl2] observed is that the electronic transition corresponding to 443.25 nm (2.79 eV) is found to have a significant oscillator strength and α contributions only (with major transition types (HOMO)α→(LUMO)α and (HOMO)α→(LUMO+1)α). This may be attributed to the band observed at λmax ~ 420 nm for the solid state and methanol solution experimental electronic spectra. The disappearance of this transition with CPCM modelling may be indicative of weakening of coordinate bond, mainly Mn(II)–S, due to solvation. The calculated major electronic transitions, oscillator strength (f), major type of transition and their coefficient for both the ligand H2L and the complex [Mn(H2L)Cl2]• H2O (1) in solutions are listed in Table 4 . The theoretical λmax values are found corroborating with the experimental results.Table 4 The calculated major electronic transitions, oscillator strength (f), major type of transition and their coefficient for the ligand H2L (in methanol) and the complex (in chloroform) calculated at UCAM -B3LYP TD FC (6-311G (d,P) and LanL2DZ basis sets for H2L and the complexes, respectively). Table 4Compound E(eV)/ λ(nm) f Major Type Coefficient Theoretical λmax (nm) Experimental λmax (nm) H2L 3.61/ 343.04 0.80 (HOMO)α→(LUMO)α (HOMO)β→(LUMO)β (HOMO-1)α→(LUMO)α (HOMO-1)β→(LUMO)β 0.52 0.52 0.32 0.32 337 346 3.67/ 337.99 0.18 (HOMO-2)α→(LUMO)α (HOMO-2)β→(LUMO)β (HOMO)α→(LUMO)α (HOMO)β→(LUMO)β 0.54 0.54 0.33 0.33 3.91/ 316.73 0.33 (HOMO-1)α→(LUMO)α (HOMO-1)β→(LUMO)β (HOMO-2)α→(LUMO)α (HOMO-2)β→(LUMO)β (HOMO)α→(LUMO)α (HOMO)β→(LUMO)β 0.53 0.53 0.26 0.26 0.25 0.25 270 5.12/ 242.12 0.23 (HOMO-2)α→(LUMO+1)α (HOMO-2)β→(LUMO+1)β (HOMO-6)α→(LUMO)α (HOMO-6)β→(LUMO)β 0.35 0.35 0.25 0.25 235 242 5.50/ 225.39 0.15 (HOMO)α→(LUMO+4)α (HOMO)β→(LUMO+4)β (HOMO-1)α→(LUMO+4)α (HOMO-1)β→(LUMO+4)β 0.37 0.37 0.34 0.34 [Mn(H2L)Cl2]• H2O (1) 3.67/ 338.18 0.76 (HOMO)β→(LUMO)β (HOMO-1)α→(LUMO)α (HOMO)α→(LUMO)α 0.58 0.38 0.34 335 350 3.69/ 335.95 0.29 (HOMO-2)α→(LUMO)α (HOMO-18)α→(LUMO)α 0.47 0.20 3.79/ 327.45 0.22 (HOMO-1)β→(LUMO)β (HOMO-2)α→(LUMO)α 0.57 0.41 247 275 5.00/ 247.73 0.17 (HOMO-10)β→(LUMO)β (HOMO-11)α→(LUMO)α 0.36 0.36 250 3.4 Molecular docking study of H2L and the complex [Mn(H2L)Cl2] Molecular docking is generally considered for sensible design of drug entities that recognise the preferred binding sites of nucleic acids and proteins, which anticipates the presence of various noncovalent interactions. A comparative study of the docking of the ligand and the Mn(II) complex are investigated here for knowing their biological potentials. 3.4.1 Interaction with B-DNA dodecamer d(CGCGAATTCGCG)2 As DNA is the primary intracellular target of an anticancer drug [52], the docking studies were conducted to predict the binding efficiency of the H2L and Mn(II) complex with the active site residues of B-DNA dodecamer d(CGCGAATTCGCG)2. The crystal structure of 1BNA [33] obtained from protein data bank at a resolution of 1.9 Å is taken and the receptor was prepared by deleting all the heteroatoms including water and by adding polar hydrogen atoms. The Mn(II) complex shows better binding capability with docking energy (-9.31 kcal/mol) than the ligand H2L (-8.87 kcal/mol) and probably a useful candidate for therapeutic purposes. Docking study suggest that the DNA is efficiently binds with H2L mainly through various hydrogen bonding interactions, of which one of the hydrogen bonds is very strong (d = 1.86 Å) with 153.3o donor-hydrogen-acceptor (D–H··· A) angle. For Mn(II) complex, however, various electrostatic interactions play the crucial role in DNA binding. The docking studies reveal that both the compounds fit comfortably in the curved shape of the DNA target and exhibit a minor groove mode of binding (Fig. 14 ). Many of the anticancer candidates bind with grooves of DNA double-strand, which leads to interrupt DNA function and alter the cell division by interfering with replication and transcription [53]. Relevant hydrogen bonding, hydrophobic/electrostatic interactions are listed in the Table 5 .Fig. 14 Molecular docking interactions of (a) Mn(II) complex and (b) H2L with 1BNA. Fig 14 Table 5 Docking energy and interactions of the compounds with 1BNA. Table 5Compound Docking energy (kcal/mol) Hydrogen bonding interactions Hydrophobic/electrostatic interactions H2L -8.87 DG4(N2): N2 DG22(N2): N5 DC23(O4′): H4(N4) DG22(C4′): N1 DC23(C4′): Py2 (C-H‧‧‧π) DC21: Phe1 (π‧‧‧π) [Mn(H2L)Cl2] -9.31 DA17(O3): S1 DC11(OP1): N4 DC11(O4’): H4(N4) DA17(OP1): Py1 DA18(OP2): Cl1 DG12(OP2): Cl2 Py1 = Pyridyl ring 1, Py2 = Pyridyl ring 2, Phe1 = Phenyl ring 1 3.4.2 Interaction with SARS-CoV-2 main protease The main protease of coronaviruses is an essential enzyme Mpro or 3CLpro , which has a pivotal role in the replication and transcription of virus, and constitutes one of the best characterised drug targets for SARS-CoV-2 [34]. Studies are progressing in the development of new drug candidates other than chloroquine (CQ) and its hydroxyl analog hydroxychloroquine (HCQ) [3,5,6] in several directions for different type of patients [7]. Here, the in silico molecular docking computational tool identifies the binding potentials of H2L and its Mn(II) complex with the main protease of SARS-CoV-2. The ligand binding efficiency of the enzyme-H2L (-9.76 kcal/mol) is found comparable with that of inhibitor cocrystal ligand (-9.48 kcal/mol) and better than those of CQ and HCQ (-7.04 kcal/mol and -7.58 kcal/mol, respectively). Mn(II) complex showed, however, excellent binding energy (-10.46 kcal/mol) by exhibiting a hydrogen bonding (GLU166(O): H4(N4)) and other nonbonding interactions with active site residues HIS-41, MET-49, CYS-145, MET-165, LEU-167, PRO-168 and GLN-189 (Table 6 ). Interactions of cocrystal ligand, HCQ, CQ, thiocarbohydrazone H2L and the Mn(II) complex at the active site of the protein are displayed in Fig. 15 . Also, the 2D diagram for the interaction of H2L and the Mn(II) complex with SARS-CoV-2 main protease are presented in Fig 16 . The study reveals that the novel Mn(II) bisthiocarbohydrazone complex may be considered as a useful drug candidate against SARS-CoV-2.Table 6 Docking energy and interactions of the compounds with 6LU7. Table 6Compound Docking energy (kcal/mol) Hydrogen bonding interactions Groups involving hydrophobic/ electrostatic interactions H2L -9.76 CYS145(SG): N1 HIS164(O): H3(N3) GLN189(OE1): H4(N4) MET-49, CYS-145, HIS-163, MET-165, GLU-166, PRO-168. [Mn(H2L)Cl2] -10.46 GLU166(O): H4(N4) HIS-41, MET-49, CYS-145, MET-165, LEU-167, PRO-168, GLN-189. Fig. 15 Binding mode of (a) co-crystal ligand, (b) HCQ, (c) CQ, (d) H2L and (e) Mn(II) complex at the active site of SARS-CoV-2 main protease. Fig 15 Fig. 16 2D representation of (a) H2L and (b) Mn(II) complex at the active site of SARS-CoV-2 main protease. Fig 16 4 Conclusion A novel Mn(II) bisthiocarbohydrazone complex [Mn(H2L)Cl2]•H2O (1) is synthesized and characterised. Crystallization of the complex yielded crystals of [Mn(H2L)Cl2]•CHCl3 (1a) in monoclinic form and is found different from the triclinic crystal system of the complex 1 obtained from mother solution. This is the first crystal structure report of a manganese(II) complex of a bisthiocarbohydrazone ligand. The intermolecular interactions are studied and quantified using Hirshfeld surface analysis. It is found that the nature of the solvent inclusion plays a major role in the supramolecular assembly in the crystal lattice. To support the experimental data, quantum-chemical DFT studies of the bisthiocarbohydrazone ligand and the complexes are carried out. The ligand is found more stable than the Mn(II) complex in the gas phase, though the complex is more stable and chemically hard in solution phase through solvation. The electronic spectra of the ligand and the complex in solutions are also calculated using CPCM modelling at RTD-B3LYP-FC level of theory and is found corroborating with the experimental results. The solution phase and solid state electronic spectral results are indicative of weakening of Mn(II)–S coordinate bond in solutions, which is in accordance with DFT and single crystal XRD studies. Moreover, the thiocarbohydrazone H2L and the Mn(II) complex are subjected to molecular docking studies with the crystal structures of B-DNA dodecamer d(CGCGAATTCGCG)2 and SARS-CoV-2 main protease. The docking energy of the SARS-CoV-2 main protease with the novel Mn(II) complex (-10.46 kcal/mol) is found much better than that of chloroquine (-7.04 kcal/mol) and hydroxychloroquine (-7.58 kcal/mol) and the study offers a new drug candidate against COVID-19. Supplementary material: CCDC 2086573 and 2044372 contain the supplementary crystallographic data for the complexes 1 and 1a, which can be accessed free of charge from the Cambridge Crystallographic Data Centre via http://www.ccdc.cam.ac.uk/structures. Declaration of Competing Interest There are no conflicts to declare. Appendix Supplementary materials Image, application 1 Acknowledgements We thank Sophisticated Test and Instrumentation Centre (STIC), Cochin University of Science and Technology (CUSAT) for analysis. EM is thankful to CUSAT for SMNRI Grant (No.CUSAT/PL(UGC).A1/1112/2021) and MHKK is thankful to CSIR-India (CSIR File No.09/239(0565)/2020-EMR-I) for the award of JRF. Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.molstruc.2021.131125. ==== Refs References 1 Bonaccorso C. Marzo T. La Mendola D. 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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(21)01295-3 10.1016/j.molstruc.2021.131165 131165 Article Synthesis and DFT computations on structural, electronic and vibrational spectra, RDG analysis and molecular docking of novel Anti COVID-19 molecule 3, 5 Dimethyl Pyrazolium 3, 5 Dichloro Salicylate Dexlin X.D. Divya ae Tarika J.D. Deephlin be Kumar S. Madhan c Mariappan A. de Beaula T. Joselin de⁎ a Research Scholar, Register No: 19213082132004, Department of Physics and Research Centre, Malankara Catholic College, Mariagiri - 629153, Tamil Nadu, India b Research Scholar, Register No: 19213082132003, Department of Physics and Research Centre, Malankara Catholic College, Mariagiri - 629153, Tamil Nadu, India c Department of Chemistry and Research Centre, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, India d Department of Physics and Research Centre, Malankara Catholic College, Mariagiri - 629153, Tamil Nadu, India e Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli - 627012, Tamil Nadu, India ⁎ Corresponding author at: Department of Physics and Research Centre, Malankara Catholic College, Mariagiri - 629153, Tamil Nadu, India. 22 7 2021 15 12 2021 22 7 2021 1246 131165131165 5 6 2021 15 7 2021 21 7 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Prospective Anti-viral compound 3, 5 Dimethyl Pyrazolium 3, 5 Dichloro Salicylate (DPDS) was synthesized and characterized using FT-IR, FT-Raman, UV and NMR spectra. To escort the experimental results, computational methods were performed using B3LYP with 6-311G (d, p) basis set expending Gaussian09w package to attain geometry of the molecule. Vibrational assignments for all the vibrational modes have been made of PED results obtained from SQM method. On contrary, FMO analysis, global chemical reactivity descriptors, Aromaticity and Natural charge analysis were studied. Molecular stability and bond strength have been inquired by executing NBO analysis. Topological features of DPDS were intended by MEP, ELF and LOL maps. UV–vis spectrum was predicted by TD-DFT method in gaseous phase and compared with the experimental spectrum for displaying the involved electronic transitions in the compound. The interactions within the DPDS molecule were investigated via RDG analysis. Molecular docking was performed with SARS-CoV-2 proteins and docking parameters were obtained. Drug likeness was carried out based on Lipinski's rule of five and the ADMET factors were also predicted. Graphical abstract Image, graphical abstract Keywords ELF RDG DFT IR Docking ADMET ==== Body pmc1 Introduction In recent times, viral infections are reflected as one of the prime extortions to social existence and health global. Controlling of such viral infections epitomizes a rich field of scientific research because of viruses’ mutability that springs new drug-resistant strains. Pyrazole derivatives have attracted more attention due to their expediency in the pharmaceutical research. Naturally acquired pyrazoles exhibits organic activities and used as efficient chemotherapeutics. Among azoles, pyrazoles are rarely found in nature probably due to their difficulty in formation of N-N bond by living organisms [1]. Pyrazole derivatives are also important in treating muscle pain, fever and arthritis but the recent literature shows these derivatives are not only analgesic and anti-inflammatory but also parades anti-viral, anti-bacterial, anti-tumor and anti-microbial activities.The competency to kill infectious agents and preventing their proliferation inside the living organisms and environment is the anti-microbial properties. Thus, multitude of compounds is investigated for their potential applications as therapeutic agents in treating infectious diseases especially those caused by multi-resistant virus and bacteria [2]. Salicylic acid is an organic acid used in pharmaceutical laboratories and is the formative material for making drugs like aspirin [3] and is the natural and safest chemical used for postharvest quality maintenance in the field of horticulture [4]. 3, 5-dichloro salicylic acid has extensive applications and has been recognized as a potential enzyme inhibitor and burgeoning usage in cosmetic industries [5]. Effect of pyrazole is studied and reveals that the pyrazolium anion is less reactive towards nucleophiles and is more to electrophiles whereas it has broad spectrum of biological activity comprising anti-viral, anti-bacterial and anti-tumor to engender novel primes possessing pyrazole nucleus with high efficacy. Since pyrazole a heterocyclic moiety, it epitomizes the fundamental configuration for the number of drugs and establishes a pertinent synthetic path in curative diligence [6]. Literature screening divulges that the crystal structure of 3, 5-Dimethyl Pyrazolium 3, 5-Dichloro Salicylate (DPDS) has not been reported so far and this meagerness observed in the literature stimulated us to make vibrational spectroscopic research based on the molecule, to give a precise consignment of the fundamental bands in FT-IR and FT-Raman spectra. Geometry of the organic structures requires diverse techniques and approaches en route for their strength in gaseous state. Redistribution of electron density was designed using NBO (Natural Bond Orbital) analysis. MO (Molecular Orbital) analysis was accomplished to intend the biological activity of the molecule and the impact of transition of electrons from π →π* were studied using UV-vis (Ultraviolet Visible) spectrum. Further Aromaticity and Natural Charge Analysis were also counted in order to determine the aromatic character and charge distribution of the molecule correspondingly. Distribution of electrons and reactive sites on the surface were analyzed using ESP (Electrostatic potential), ELF (Electron localization function) and LOL (Localized orbital locator). Repulsive, attractive, and van der Waals strong and weak interactions in DPDS were investigated by RDG (Reduced Density Gradient) analysis. Drug likeness and molecular docking methodology were empowered to form drug potential and bioactivity of DPDS. Insilico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicology) analysis was also anticipated to crisscross whether the compound is a vigorous drug to treat SARS-CoV-2. 2 Experimental details 2.1 Synthesis Slow evaporation- solution growth technique was used to grow single crystal. 3, 5 –Dichloro Salicylic acid (DS) and 3, 5-Dimethyl Pyrazole (DP) were taken in 1:1 stoichiometric proportions and dissolved homogeneously using methanol as a solvent. Their reaction mixture was thoroughly mixed together using mechanical stirrer up to 3 h at room temperature to get a clear solution. The synthetic scheme of DPDS was depicted in Scheme 1 . This solution was filtered through a quantitative filter paper (Whatman no.40) and filtrate was kept aside without any disturbance for the growth of crystal in a dust free environment at ambient temperature. Well defined, transparent crystals were collected at the end of 6th day and the collected crystals were recrystallized using dry methanol to get good quality crystals.Scheme 1 Synthetic scheme for DPDS. Scheme 1 2.2 Characterization techniques FT-IR spectrum of DPDS was recorded on a Perkin Elmer FT-IR 8000 spectrophotometer in the range of 4000–400 cm−1using KBr pellet technique at room temperature. FT-Raman spectrum has been recorded on a Bruker RFS 27: Standalone FT-Raman Spectrometer with Nd: YAG laser source at 1064 nm and resolution 2 cm−1 in the region 4000–50 cm−1. Electronic absorption spectrum was measured in methanol using JASCO UV-Vis spectrophotometer in the range of 200–800 nm.13Cand 1H NMR spectra were recorded employing a Bruker AV III 400 MHz spectrometer in deuterated dimethyl sulphoxide (DMSO) as solvent using TMS as an internal standard. 3 Computational details Theoretical calculations for DPDS were carried out with the aid of Gaussian09w package [7] using B3LYP method in conjunction with 6–311G (d, p) basis set. Natural bond orbital (NBO) analysis [8] was also achieved and the molecular visualization was ended by the Gauss view6 program [9]. MOLVIB programming was preferred for the vibrational spectral assignments by utilizing potential energy distribution (PED) analysis [10]. MO analysis and Molecular electrostatic potential (MEP) mapping were calculated using the same B3LYP level of theory. Auto Dock Suite 4.2.1 was used to find the minimum binding energy, inhibition constant and various parameters of the ligand-protein docking interactions [11].The ligand-protein binding sites have been visualized using PYMOL graphic software [12]. ELF, LOL and the RDG and sign (λ2)ρ functions were computed using Multiwfn [13] and the RDG scatter graph was drawn with the VMD (Visual Molecular Dynamics) program [14]. 4 Results and discussions 4.1 Geometry optimization Optimized molecular structures of DS, DP & DPDS with atomic symbols and labels are shown in Fig. 1, respectively.Fig. 1 Optimized Molecular Structures of DS, DP and DPDS. Fig 1 The self-consistent field energy of DPDS -1720.34 a.u is combined with the energy of DS and DP refers to -1415.40 a.u and -304.92 a.u, respectively. BSSE and counterpoise corrected energy for DPDS have been calculated as 0.004 a.u and -1720.34 a.u, respectively. Wavenumbers in the solid phase must be higher than the gaseous phase due to the isolation of molecule in the solid state. Optimized Bond length with bond angles of DPDS are tabulated in Table 1 and dihedral angles of DPDS are tabulated in Table S1 which are compared with the XRD data of 3,5 Dichlorosalicylic acid [16] and Dichlorobis(3,5-dimethylpyrazole)copper(II) [18] besides their RMS values are calculated.Table 1 Comparison of optimized bond length and bond angle of DPDS with the XRD data of 3,5 Dichlorosalicylic acid and Dichlorobis(3,5-dimethylpyrazole)copper(II). Table 1Bond length Theoretical value (Å) Expt (Å) RMS (Å) Bond Angle Theoretical value (°) Expt (°) RMS (°) DPDS DS/DP DPDS DS/DP C1-C2 1.401 1.409 1.393 0.00006 C2-C1-C6 120.58 120.58 120.22 0.1296 C1-C6 1.415 1.415 1.399 0.00025 C2-C1- C13 120.70 120.40 119.83 0.1296 C1-C13 1.482 1.485 1.474 0.00006 C6-C1- C13 118.70 118.66 119.93 1.5129 C2-C3 1.380 1.381 1.376 0.00001 C1-C2-C3 119.77 119.69 119.82 1.5129 C2-H8 1.080 1.055 0.930 0.02250 C1-C2- H8 119.18 119.13 120.11 0.8649 C3-C4 1.395 1.394 1.376 0.00036 C3-C2- H8 121.04 121.17 120.07 0.8649 C3-Cl9 1.760 1.745 1.733 0.00072 C2-C3-C4 120.86 120.80 121.12 0.0676 C4-C5 1.384 1.386 1.380 0.00001 C2-C3-Cl9 120.03 120.00 119.01 0.0676 C4-H7 1.081 1.088 0.931 0.02250 C4-C3-Cl9 119.10 119.18 119.87 0.5929 C5-C6 1.408 1.398 1.390 0.00032 C3-C4-C5 119.52 119.51 118.65 0.5929 C5-Cl10 1.748 1.745 1.737 0.00012 C3-C4- H7 120.61 120.63 120.65 0.0016 C6-O11 1.332 1.331 1.342 0.00010 C5-C4-H7 119.85 119.81 120.7 0.0016 O11-H12 0.989 0.982 0.921 0.00462 C4-C5-C6 121.47 121.82 122.2 0.5329 H12-O14 1.689 1.689 1.912 0.04972 C4-C5-Cl10 119.55 119.54 119.43 0.5329 C13-O14 1.232 1.239 1.232 0.00000 C6-C5-Cl10 118.97 118.13 118.37 0.3600 C13-O15 1.316 1.315 1.305 0.00012 C1-C6-C5 117.77 117.62 117.98 0.3600 O15…H21 1.013 - 0.820 0.03724 C1-C6- O11 123.08 122.46 123.85 0.5929 C16-C17 1.381 1.380 1.390 0.00008 C5-C6- O11 119.14 118.10 118.16 0.5929 C16-N20 1.35 1.350 1.368 0.00048 C6- O11-H12 106.51 106.91 106.42 0.0081 C16-C23 1.494 1.494 1.496 0.00000 C1- C13-O14 122.03 122.42 122.3 0.0081 C17-C18 1.415 1.419 1.352 0.00396 C1- C13-O15 114.82 114.82 114.7 0.0144 C17-H24 1.078 1.078 0.931 0.02160 O14- C13-O15 123.13 123.30 123 0.0144 C18-N19 1.331 1.329 1.347 0.00003 C13-O15…H21 111.75 - 109.54 4.8841 C18-C25 1.496 1.497 1.501 0.00002 C17- C16-N20 105.56 105.59 106.28 0.5184 N19-N20 1.355 1.354 1.342 0.00016 C17- C16-N23 131.75 131.85 130.75 1.0000 N19-H21 1.690 - - - N20- C16-N23 122.68 122.65 121.94 1.0000 N20-H22 1.007 1.007 2.010 1.00600 C16- C17-C18 106.17 106.17 107.01 0.7056 C23-H26 1.094 1.094 0.960 0.01795 C16- C17-H24 126.56 126.87 126.45 0.7056 C23-H27 1.094 1.090 0.959 0.01822 C18- C17-H24 127.26 127.68 126.53 0.5329 C23-H28 1.089 1.084 0.960 0.01664 C17-C18-N19 110.00 110.58 110.91 0.5329 C25-H29 1.093 1.093 0.959 0.01795 C17-C18-C25 128.70 128.73 128.31 0.1521 C25-H30 1.089 1.084 0.960 0.01664 N19- C18-C25 121.28 121.96 121.78 0.1521 C25-H31 1.093 1.093 0.959 0.01795 C18-N19-N20 105.61 105.60 105.29 0.1024 C18-N19-H21 134.62 134.53 134.85 0.1024 N20-N19-H21 119.75 119.65 - - C16-N20-N19 112.63 112.68 111.5 1.2769 C16-N20-H22 127.94 127.96 127.81 0.0169 N19-N20-H22 119.41 119.60 120.57 0.0169 O15…H21-N19 168.23 - - - C16-C23-H26 111.57 111.61 110.5 1.1449 C16-C23-H27 111.57 111.71 109.45 0.0144 C16-C23-H28 109.97 109.71 109.55 0.0144 H26-C23-H27 107.85 107.02 109.51 0.1156 H26-C23-H28 107.85 107.70 109.4 0.1156 H27-C23-H28 107.85 107.75 109.4 0.2025 C18-C25-H29 110.72 110.75 109.36 0.2025 C18-C25-H30 110.49 111.40 109.38 1.2321 C18-C25-H31 110.73 110.66 109.4 1.2321 H29-C25-H30 108.69 108.40 109.58 0.7921 H29-C25-H31 107.39 107.41 109.63 0.7921 H30-C25-H31 108.69 108.41 109.47 0.0484 Deliberated bond length O15…H21 is feeble than other wave numbers, indicates the formation of intermolecular hydrogen bonding O15…H21-N21 with improved stabilization. Hydrogen bond length O15…H21 is obtained as 1.013 Å which is emphatically deficient than that of van der Waals radii (2.75 Å) [15] and is extraordinary compared to the experimental value 0.820 Å [16] with a RMS value surpassed around 0.03724 Å at very high resolution. Also geometrical bond angles O15...H21-N19 spectacles extreme of 168.23 ° which is terribly amplified from the trigonal angle 120 ° owed to intermolecular interactions between pyrazole and benzene ring moieties. These results boot the contingency of intermolecular hydrogen bond formation which gets a closer comprehension and enterprise the molecules with enhanced biological contour. Bond angles C1-C6-O11 (123.08 °) and C5-C6-O11 (119.14 °) with experimental values 123.85 ° and 118.16 °, respectively are diverged from the intended trigonal angle owing to the influence of hydroxyl group in the phenyl ring. This divergence scanted the bond length O11-H12 (0.9898 Å) and bond angle C6- O11-H12 (106.51 °) which are deflated to radical values due to the deprotonation of salicylic acid to form hydrogen bond between oxygen in the carboxylate anion and hydrogen in the alcohol group, thereby forming intramolecular interaction H12…O14 with bond length 1.689 Å with a RMS value 0.04972 Å at high resolution stimulates the bioactive nature of DPDS molecule [17]. RMS value for the bond angle C13-O15…H21 gets increased to 4.8841 ° due to the formation of salicylate anion at very low resolution leads to the inter-molecular interaction. Adjoining these interactions C3-Cl9 (1.760 Å) & C5-Cl10 (1.748 Å) holds the extreme bond length ensuring their experimental values 1.733 Å & 1.737 Å, respectively which are in line for the virtue of electron withdrawing nature of chlorine which increases the double bond character by redistributing π electrons in maximum shared orbitals, thus deteriorate the bond length of chlorine as compared to other bond lengths. Bond length assigned to C18-C25 (1.4962 Å) & C16-C23 (1.4941 Å) remains shrinking owing to the resonance of dimethyl group annexed to the pyrazole ring. Bond length for C18-N19 (1.3319 Å) is subordinate to the bond length C16-N20 (1.3573 Å) which designates the effect of double bond in the pyrazole ring. RMS value increases to 1.0 Å amidst N20-H22 with bond distance 1.007 Å and C16-N20-N19 with bond angle 112.63 ° obligating experimental value 2.010 Å and 111.5 °, respectively [18] which is hefty and random since the diffraction terms ought to slight or no impact on the geometry at low resolution [19]. Bond lengths C18-N19 (1.331 Å) and C16-N20 (1.357 Å) were stretched while equated to their experimental values 1.347 Å and 1.368 Å, respectively designates substantial electron delocalization in the pyrazole ring structure. Bond angles H21-N19-C18 (134.62 °) and H21-N19-N20 (119.75 °) are exaggerated distortedly in line for the incidence of nitrogen in the pyrazole ring whereas scale down in bond angles C17- C16-N20 (105.56 °) and C18-N19-N20 (105.56 °) stand owed to the steric interaction in virtue of intermolecular hydrogen bonding interactions. Dihedral angle is an imperative constraint to unfold the conformation of voluminous organic and bioorganic molecules. Furthermost of the dihedral angles of phenyl ring are about virtually -179.99 ° and 0 ° point towards structure of salicylate ion is planar. Carboxylate anion devoted to the phenyl ring has antiperiplanar conformations which are flagged by the torsional angles C2-C1-C13- O 14 (-179.98 °) & C6-C1- C13-O 15 (-179.98 °). The dimethyl groups attached to the pyrazole ring are found to be out of plane are indicated by the torsional angles C17-C16-C23-H26 (-119.97 °), C17-C16-C23-H27 (-119.31 °), N20-C16-C23-H26 (-60.06 °), N20-C16-C23-H27 (60.65 °), C17-C18-C25-H29 (59.32 °), C17-C18-C25-H31 (-59.68 °), N19-C18-C25-H29 (-120.68 °) and N19-C18-C25-H31 (120.30 °) [20]. 4.2 NBO profile NBO analysis contributes the proficient schemes to examine the chemical characteristics of the bonding and different properties like intra and intermolecular charge transfer, basicity, stability, reactivity and correlation between donor and acceptor. The interactions between filled and vacant orbitals are tabulated in Table 2 .Table 2 Second order perturbation theory analysis of Fock matrix. Table 2Donor (i) ED(i) (e) Acceptor (j) ED(j) (e) E(2)a (kcal/mol) E(j)-E(i)b (a.u) F(ij)c (a.u) σ(C1 – C2) 1.96303 σ*(C1 – C13) 0.06263 2.25 1.12 0.045 σ*(C3 – Cl9) 0.03318 5.60 0.84 0.061 LP(1)O14 1.95987 σ *(O11 - H12) 0.05796 4.30 1.10 0.062 π (C1 – C2) 1.65916 π*(C5 – C6) 0.44564 24.93 0.26 0.074 σ(C1 – C6) 1.96778 σ*(C1 – C2) 0.02188 4.29 1.26 0.066 π(C5 – C6) 1.60297 π*(C3 – C4) 0.42826 27.78 0.28 0.080 σ(O11 – H12) 1.98573 σ*(C5 – C6) 0.04282 5.82 1.25 0.077 LP (1)Cl9 1.99215 σ*(C3 – C4) 0.03087 1.51 1.47 0.042 LP (3)Cl9 1.93777 π*(C3 – C4) 0.42826 11.21 0.33 0.060 LP (2)Cl10 1.96664 σ*(C4 – C5) 0.02608 4.02 0.88 0.053 LP (3)Cl10 1.92703 π*(C5 – C6) 0.44564 11.60 0.32 0.060 LP (1)O11 1.97308 σ*(C1 – C6) 0.03779 7.79 1.10 0.083 LP (2) O11 1.81257 π*(C5 – C6) 0.44564 40.21 0.31 0.107 LP (2) O14 1.85119 σ*(C13 – O15) 0.06444 24.60 0.72 0.121 LP (1) O15 1.96086 σ*(C13 – O14) 0.02806 8.82 1.11 0.089 LP (2) O15 1.74384 π*(C13 – O14) 0.33800 64.50 0.30 0.126 σ*(C13 –O15) 0.02806 LP (1) H21 0.56515 13.93 1.16 0.133 LP (3) O15 1.65021 LP*(1) H21 0.56515 437.42 0.67 0.511 π* (C18 - N19) 1.90252 π* (C16 - C17) 0.05796 89.77 0.03 0.071 LP (1) N19 1.73150 LP*(1) H21 0.56515 286.67 0.61 0.404 a E(2) means energy of hyper conjugative interaction (stabilization energy). b E(j) - E(i) is the energy difference between donor i and acceptor j. c F(i,j) is the Fock matrix element between i and j NBO orbital's. Scope of hyper conjugation is governed by the contributing capability of Lewis type orbitals and the acquiescent capability of non-Lewis type orbitals which is measured in terms of interaction energy E(2). Inordinate hyper conjugative interaction energy advance the level of delocalization which consequently grander the stability of molecular system [21]. Overlapping of orbitals σ(C1 – C2) to σ*(C1 – C13) and σ*(C3 – Cl9) leads to the interaction energy of 2.25 kcal/mol and 5.60 kcal/mol, respectively. Utmost pivotal interactions in DPDS is stuck between the lone pairs LP(3)O15 and LP*(1) H21 ensuing very high stabilization energy 437.42 kcal/mol which forms intermolecular hydrogen bonding O15…H21-N19 resulting in intermolecular charge transfer. This loftier energy shows that the hyper conjugative interaction transpires amongst the electron donating and the acceptor group which enriches the bioactivity of the DPDS molecule [22]. Anti-bonding orbital σ*(C13 –O15) overlaps with the lone pair orbital LP (1) H21 outcomes with a negative hyper conjugation with a stabilization energy 13.93 kcal/mol and is illuminated using inductive effect of oxygen atom. Interaction between two lone pair electrons LP (1) N19 and unfilled p-orbital LP*(1) H21 grades to stronger stabilization with an interaction energy 286.67 kcal/mol and leads to reduction in hybridization and the non-periodic power of these bonds ensure inferences for the exposure of molecules [23]. Orbital overlapping between lone pair LP (1) O14 to anti-bonding orbital σ *(O11 - H12) results in the stabilization energy of 4.30 kcal/mol triggering intra-molecular charge transfer and leads to hyper conjugative intra-molecular hydrogen bonding interaction [24]. Rehybridization plays a dominant role in weakening and elongation of hydrogen bond observed in Table 3 . NBO analysis of DPDS in comparison with DS and DP clearly shows the sign for the formation of strong H-bonded interactions.Table 3 Composition of H-bonded NBOs in terms of natural atomic hybrids. Table 3Bond (A-B) DS/DP DPDS EDA % EDB % spn A B EDA % EDB % spn A B s% p% s% p% s% p% s% p% C13-O15 66.79 33.21 sp2.41 25.08 74.72 35.03 64.90 66.74 33.26 sp2.16 29.31 70.49 34.35 65.57 C18-N19 59.35 40.65 sp1.97 29.61 70.28 38.85 61.06 61.19 38.81 sp1.94 28.75 71.15 41.25 58.69 N19- N20 45.30 54.70 sp2.86 30.59 69.33 22.39 77.46 47.24 52.76 sp2.85 22.96 76.92 29.83 70.09 The influence of rehybridization results in destructive effects, leads to contraction and strengthening of C13-O15 bond by the impact of s-character of the hybrid orbital decreases from sp2.41 to sp2.16. Though the contraction and elongation of bonds is due to the effect of rehybridization and hyper conjugation, rehybridization dominates and overshadows the latter. This is well reflected in the geometry as bond C13-O15 which contracts by 0.25 Å with respect to DS and manifests the delocalization of electron density. The s-character of C18-N19 hybrid orbital falls from sp1.97 to sp1.96 hints to the contraction of C18-N19 bond due to the reduction in s-character of carbon and polarization of the C-N bond in the progression of N-H…O inter-molecular hydrogen bond formation. Polarization promulgates over bonds leads to rehybridization with hybrid orbital declines from sp2.86 to sp2.85 and p-character increases to 69.33% with s-character 30.59% in the N-N hybrid bond. Because the total s- and p-characters at every nitrogen atom are sealed, this shrinkage in the s-character leads to a reflex upsurge in the other hybrid orbitals at N including the N-H bond. This escalation leads to inter-molecular hydrogen bonding by shortening and strengthening N-H bonds. 4.3 Electronic properties 4.3.1 Frontier molecular orbitals Highest occupied molecular orbitals (HOMO) and Lowest unoccupied molecular orbitals (LUMO) are the Frontier molecular orbitals which are vibrant to depict kinetic stability and chemical reactivity by their energy gap. The positive and negative has shown in red and green congealed segments, respectively. HOMO is related to the more reactive molecule with electrophiles whereas the lower energy profile related to less reactive molecule with nucleophiles [25]. A molecule with small frontier orbital gap is more polarisable and is generally associated with high chemical reactivity [26]. Ordinarily the conjugated molecules are characterised by less H→L separation, which is significant to the gradation of intra-molecular charge relocation from the end-capping electron-donor groups to the proficient electron-acceptor group [27]. Fig. 2 illustrates the Frontier Molecular Orbital transitions within the molecule. It is inferred from the figure that HOMO localised over the phenyl ring and hydroxyl group and LUMO localised over the phenyl ring and carboxylic acid group besides DS. HOMO→LUMO transition implies that electron density has spread over and charge transfer occurs from hydroxyl group to the carboxylic acid group, thereby no charge interactions in DP moiety. Calculated HOMO and LUMO energies are -9.12983 eV and -4.89177 Ev, respectively while the energy gap serves as a stability index. In fact, a small HOMO-LUMO gap implies high molecular stability in the sense of virtuous reactivity in chemical reactions [28]. The figured band gap energy is 4.23806 eV which embodies the low energy electronic transitions inherently belongs to π→π* transitions and is chemically more reactive [29] affords biological delineation with a trifling band gap. The overlying orbital loops has occurred in the benzene ring of DS moiety and the HOMO LUMO transition endorses the revelation of resonance expedited with hydrogen bonding in course of electron density transfer. HOMO-1→ LUMO+1 and HOMO-2→LUMO+2 transitions were also implemented with their band gap energies 5.38738 eV and 5.84955 eV, respectively.Fig. 2 Plot of Frontier Molecular Orbitals of DPDS. Fig. 2 4.3.2 Global reactivity descriptors Molecular descriptors of global reactivity act as mediators between the kinetic stability and chemical reactivity of the molecule and were calculated from Frontal Orbital energy using Koopman's theorem [30].The global descriptors are Ionisation Potential (IP), Electron Affinity (EA), Electronegativity (χ), Global hardness (ɳ), Softness (σ), Chemical Potential (µ) and Global Electrophilicity index (ω) [31] can be calculated using B3LYP/6-311G (d, p) and listed in the Table S2. Following Parr and Pearson [32] Chemical potential describes the emerging inclination of electron from a firm system and is -7.01080 eV which notifies the stability does not disintegrate impulsively into its fundamental at its least possible assessment. The hardness insinuates the resistance towards the dislocation of electron cloud in the chemical systems under slight perturbations that stumble during chemical course of action. DPDS molecule is more reactive and highly polarizable due to lanky hardness of 7.01080 eV and stumpy softness value 0.14263 eV [33]. Global electrophilicity index is the better descriptor of global chemical reactivity which is 3.50539 eV describes the biological activity of DPDS compound [34] for DPDS and measures the equilibrium in energy when the system acquires additional electronic charge from the entourage. It also encompasses the dexterity of an electrophile to acquire additional electronic charge and the resistance of the system to swap electronic charge with the entourage. 4.4 UV-visible spectral analysis UV absorption spectra for the optimized molecule DPDS were calculated with the aid of computational method to determine the low-lying excited states of DPDS theoretically [35] and recorded in gas phase. The electronic transitions confirm the absorption peaks experimentally and theoretically are shown in Fig 3 . NBO analysis indicates that molecular orbitals are generally composed of π and σ atomic orbitals but in UV-vis region molecules allows strong π →π* transitions with the high extinction coefficients and higher the coefficients, more wavelength is absorbed [36]. The absorption spectra exhibits an intense peaks at 270 nm which affirms the protonation of nitrogen in the pyrazole ring due to π→π* transitions.Fig. 3 UV spectra of DPDS. Fig. 3 Excitation energies, absorbance and oscillator strength (f) for DPDS molecule were tabularized in Table 4 using Gauss Sum [37]. Absorption peak (λmax) in the UV-vis spectrum predicts electronic transition supreme at 265 nm with an oscillator strength f = 0.0576 with a major contribution of 86% from HOMO to LUMO shows good agreement with the experimental data at 270 nm. Other wavelengths obtained via computational method are 235.88, 232.97, 229.33, 225.12 and 219.05 nm (in gas). In view of calculated absorption spectra the maximal oscillator strength is 0.0722 at the wavelength 225 nm with a major contribution of about 52% from HOMO to LUMO+1 transitions. Minimal oscillator strength is zero at wavelength 235 nm with a major contribution 60% from HOMO to LUMO+4 electronic transition.Table 4 UV-vis excitation energy and oscillator strength for DPDS. Table 4:Experimental Energy Theoretical Oscillator strength Symmetry Major Contributions λmax (nm) Band gap (eV) λmax (nm) Band gap (eV) 270 4.23806 37720 265 4.67924 0.0576 Singlet-A H-1→L+1 (12%), HOMO→LUMO (86%) 42394 235 5.27659 0.0000 Singlet-A HOMO→L+2 (25%), HOMO→L+3 (12%), HOMO→L+4 (60%) 42922 232 5.34482 0.0008 Singlet-A HOMO→L+3 (83%), HOMO→L+4 (12%) 43603 229 5.41484 0.0060 Singlet-A HOMO→L+1 (16%), HOMO→L+2 (56%), HOMO→L+4 (26%) 44420 225 5.51111 0.0722 Singlet-A H-1→LUMO (30%), HOMO→L+1 (52%), HOMO→L+2 (15%) 45650 219 5.66210 0.0014 Singlet-A H-4→L+3 (31%), H-1→L+3 (63%) 4.5 Vibrational spectral analysis Calculated frequencies of molecular structures are paralleled with the observed frequencies by NCA based on SQMFF (Scaled Quantum Mechanical force field) calculations recommended by Rauhat and Pulay [38]. Root mean square (RMS) error of scaled wavenumber is 8 cm−1 and this scaling factor is used to correct the anharmonicity and neglected part of electron correlation [39]. The detailed assignments stipulated by the potential Energy Distribution (PED) are depicted in Table S3 and the experimental and theoretical FT-IR & FT-Raman spectra are shown in the Figs. 4 and 5 .Fig. 4 FT-IR spectra of DPDS. Fig. 4 Fig. 5 FT-Raman spectra of DPDS. Fig 5: 4.5.1 C-H vibration Aromatic hetero structure offers C-H stretching vibrations in the region 3100–3000 cm−1 [40]. In DPDS, the C-H stretching vibration emerges at the medium range 3067 cm−1 in the Raman spectrum where it shows the stretching mode flashes with strong Raman intensity and 99% PED contribution. Vibrations assigned to aromatic C-H stretching in the region 3076 cm−1 predicted theoretically are in good alliance with the experimental assignment 3067 cm−1. DPDS molecule possesses a dimethyl group in the pyrazole ring and the CH stretching expose at lower frequencies for about 3000–2800 cm−1 than those of the aromatic ring are typically downshifted owing to electronic possessions [41]. CH3 in-plane and out-of-plane stretching displays around medium region 2960 cm−1 in the FT-IR and at the weak 2840 cm−1 in the Raman region. Scaled frequencies for the in-plane and the out-of-plane stretching occur at 2960 & 2955 cm−1, respectively and are correlated with the experimental values. 4.5.2 C – C and C=C vibrations C-C and C=C stretching modes are expected in the range from 1650–1200 cm−1 [42]. The strong bands at 1586 and 1448 cm−1 belongs to tetra-substituted benzene ring which reveals the hyper conjugative interaction between the rings with the C – C stretching. The weak band 1220 cm−1 in the FT-Raman region shows C-C stretching which contributes 91% PED. The bands 1548, 1382 & 1256 cm−1 belong to the C=C stretching in the pyrazole group effects to hyperconjugation. C=C in-plane deformation prevails in the range 760–600 cm−1 and out-of-plane deformation turns below 600 cm−1 endorses by Shimanouchi et.al [43] which is higher frequencies than the out-of-plane vibrations. C-C-C in-plane bending vibration is observed at 752 cm−1 and out-of-plane bending vibrations of the ring is observed as a strong peak at 395 cm−1 with 86% PED contribution. In addition, these frequencies show the substitutions in the ring to some extend which affect the ring modes of vibration with good agreement by the literature. 4.5.3 C-Cl vibration C-Cl stretching vibrations are expected in the range 765–505 cm−1 [44] and authorizes that the CH bond has the sign of polarity opposite to that of the carbon‐halogen bonds. In DPDS, strong IR spectral band is observed at 731 cm−1 and the medium Raman bands observed at 730 and 593 cm−1which is well correlated with scaled wavenumbers at 757, 725 and 592 cm−1 with 69% PED contribution. C-Cl in-plane bending vibrations are assigned as strong Raman bands at 284 and 144 cm−1 and the C-Cl out-of-plane bending vibration is assigned at medium band 361 cm−1 which are good parallelism with the scaled wavenumbers. 4.5.4 C-N & N-N vibrations For the aromatic compound which bears a C-N group, a band of good intensity has been absorbed in the region 1400–1200 cm−1 [45] where the identification of C-N stretching is very much entangled since the mixing of bands is possible in this region [46]. For the DPDS, the bands observed at 1383 and 1256 cm−1 in IR region and at 1382 and 1257 cm−1 in Raman region while scaled values at 1378 and 1247 cm−1 coincides well with the experimental values. This result affirms the values are hard up in the direction of the higher end of the range which is due to the interaction of C-C modes. Similarly for the C=N stretching, Demir et.al observed the C=N stretching in pyrazole ring is predictable at 1610 cm−1 [47]. In DPDS, C=N stretching mode for the pyrazole ring is observed in the region 1548 cm−1. This is rather least in their wavenumber and may be due to the intermolecular interaction C=N-H…O between the rings. N-N stretching vibrations transpires at the weak bands 1112 cm−1 in IR and Raman spectra and its scaled frequency has supposed at 1125 cm−1 with 87% PED contribution and are in good line with experimental. 4.5.5 COO− vibrations Carbonyl stretching frequency is very sensitive to the factors that disturb the nature of carbonyl group and its precise frequency is characteristic to the type of the carbonyl compound being studied. C=O group of saturated aromatic carboxylic acid absorbs strongly in the region 1730–1680 cm−1 [48]. In DPDS, theoretically predicted wave number 1724 cm−1 is identified as C=O stretching vibration and equated with the experimental band as a very strong at 1700 cm−1 in FT-IR spectrum and 1710 cm−1 in FT-Raman spectrum gets correlated. Broadening and red-shifted nature of this band depicts the level of intermolecular hydrogen bonding N-H…O with the formation carboxylate anion by donating hydrogen atom to the pyrazole moiety [49]. For the DPDS, the aromatic C-O band observed in the FT-IR region at 1275 cm−1 and the scaled at 1276 cm−1 and related to the expected in the range 1320–1210 cm−1. C=O in-plane deformation is observed as medium at 360 cm−1in FT-IR and strong in FT-Raman at 361 cm−1 and is correlated by the scaled frequency at 362 cm−1 with 72% PED contribution. C=O out-of-plane deformation is weakly observed in FT-IR at 752 cm−1 and scaled frequency 757 cm−1 which is inactive in Raman spectrum due to the C=O in the vicinity of halogen substituted aromatic structure. 4.5.6 N-H vibration For secondary amine formation in pyrazole ring, N-H stretching and N-H bending vibration occurs in the range 3500–3300 cm−1 and 1580–1490 cm−1, respectively [49]. For DPDS, symmetric stretching mode of NH group is observed at 3449 cm−1 as a medium in Raman and scaled value at 3450 cm−1 with 97% PED contribution. O…H-N bending occurs as a strong peak at 869 cm−1 in IR and 870 cm−1 in Raman spectrum with the scaled value at 872 cm−1. N23-H12 stretching and bending proceed in the range below 100 cm−1 with a contribution of 58% and 42%, respectively and this lower magnitude be existent due to the intermolecular N-H…O bonding between two ring moieties. 4.5.7 OH vibration Hydroxyl bonds are very important in dipole interactions to stabilize the molecular structures and have higher stretching frequencies when its relative intensity increases due to the differences in force constants. [50]. For the DPDS, intramolecular hydrogen bonding occurs in the hydroxyl group and their stretching vibration expected at 3590–3400 cm−1 [51]. Therefore stretching occurs at band 3448 cm−1 in FT-IR spectrum which is strong and unaffected by concentration changes. In-plane deformation the band occurs in 1586 cm−1 which is strong in IR and medium in Raman spectra related to the scaled values 1588 cm−1 with 35% PED contribution. This deformation exceeds the expected range due to the intramolecular interaction O-H…O. 4.6 Electrostatic potential map analysis ESP surfaces show the charge distributions of molecules three dimensionally and their interaction of molecules with one another can be determined. It can also be used to determine the nature of the chemical bond [52]. ESP is to probe the relative electron density in a molecule and to understand the concepts of Lewis acids and bases on top of hydrogen bonded interactions amidst the molecules [53]. ESP contour mapping is a substantial tool in molecular modeling studies to predict the interactions of distant geometries. ESP view is mapped up with the optimized geometry using B3LYP computational method and shown in Fig. 6 with colour code mapped in the range between -0.06882e and 0.06882e. Electron rich zone is the negative region concentrated over the carboxylic oxygen atoms and indicate that oxygen atom is surrounded by greater surface of positive charge and is a site for electrophilic attack. Positive potential sites is the electron poor zone mainly focused over the hydrogen atoms of methyl group attached to the carbon atom of the pyrazolium moiety and be susceptible to nucleophile attack. The nucleophilic and electrophilic reactive sites illustrate the binding site [54] of the molecule besides act as a tool for giving information about intermolecular hydrogen interaction amongst salicylate and pyrazolium ions over and above leads to biological activity of the molecule.Fig. 6 Electrostatic potential for DPDS. Fig. 6 4.7 Aromaticity Aromatic compounds have a cyclic and conjugated set of p-orbitals that embodies the π-system. Aromaticity quantification is positioned by the geometric indexes that are agitated in the drift of aromatic compounds making equal distances of the bonds in an aromatic ring [55]. There are many structural criteria to evaluate the aromaticity for the six-membered and five-membered heterocycles. For DPDS HOMA (Harmonic Oscillator Model of Aromaticity) index is preferred to estimate the delocalization of the molecules. HOMA divided into geometric and energetic terms that are not correlated with each other and the index of aromaticity is described [56]. Energetic and Geometric terms are in least number explains the decrease of resonance energy and slight increase of bond length alteration which is caused by the contingency of C-H through hydroxyl and carboxylic acid group, which derange the electron density distribution within the ring and inhibits the aromaticity. The HOMA value for supreme benzene molecule is unity but the phenyl ring shows lesser striction in this value. Experimental and theoretical HOMA indexes values of phenyl ring are 0.9873 and 0.9778, respectively which emphasize benzene belongs to aromatic compound. By virtue of magnetic and geometric criteria, pyrazoles and triazoles are 80–85% aromatic relative to benzene in the midst of heterocyclic compounds [57] and for the pyrazole derivatives the disparity of supremacy is perceived. Analysis of the geometric data reveal that experimental HOMA value of the pyrazole derivatives is 0.4729 and the theoretical value is 0.8693. The theoretical HOMA exhibits pyrazole is an electron withdrawing substituent which attract π-electrons from the phenyl ring, prominent to the formation of structures not gratifies the Huckel's rule 4n + 2 [58]. The depreciate HOMA value on trial could not be premeditated as non-aromatic [59]. Owing to the fact they undergo other chemical reactions such as dimethyl substitution and intermolecular interaction N-H…O. It is witnessed that π-electron delocalization is very sensitive to the nature of the substituent and to intermolecular interactions. By this the observed HOMA value emphasizes pyrazole ring belongs to aromatic compound. 4.8 Natural charge analysis To study the charge distribution of DPDS, it is better to use Natural Charge Analysis since it do not exhibit dependence on basis set [60]. The atomic charges are calculated by natural population analysis by using B3LYP/6-311G (d, p) method are plotted in Fig. 7 and tabulated in Table S4.Fig. 7 Natural charge Distribution for DPDS. Fig. 7 C13 shows maximum positive charge (0.83017 e) than other atoms due to the charge formation over the electronegative oxygen atoms. In carboxylic acid group, carbon atom progress a partial positive charge and oxygen atom develops partial negative charge. Deprotonation takes place in the acid group and becomes carboxylate anion which makes the carbon atom more positive. Minimum positive charge over Cl10 is due to the substitution reaction in which hydrogen atom is replaced by chlorine atom in the benzene ring which grades the polarization of chlorine by the Lewis base. Maxima negative charge (-0.70089 e) is stated in the atom O15 of the carboxylate anion which interacts with nitrogen of pyrazole ring through intermolecular interactions. Since oxygen and nitrogen are electronegative atoms, they are electrostatically attracted to the hydrogen atom and bear a large negative charge. The minima negative charge (-0.00196 e) is observed in the atom Cl9 and is more electronegative than carbon which pulls the bonded electrons closer to itself. Substitution of hydrogen atom by chlorine atom makes the carbon more electropositive and befalls minima negative charge. 4.9 NMR chemical shift analysis 1H and 13C NMR spectra contribute a tectonic data with disparate hydrogen and carbon atoms in a molecule and their chemical shifts are shown in Fig. 8. For reliable calculation of magnetic properties it is acknowledged that literal prognosis of molecular geometries are important [61]. Chemical shifts are very advantageous to afford the facts of diverse protons and carbons extant in the molecule. Chemical shifts in the spectrum are due to the de-shielding and shielding by electrons.Fig. 8 Experimental NMR spectra of DPDS. Fig. 8 1H NMR spectrum of DPDS advertises distinct type of protons in which signal at δ 2.164 ppm is is due to proton impurity in the DMSO – d6 solvent. Hydrogen peaks in the 1H spectrum develops in the range 6.95–7.40 ppm which reciprocates to the aromatic region whereas greater shielding is owing to the anisotropy of aromatic ring π electrons [62]. In DPDS, 1H spectrum of pyrazole ring reveals a wide-ranging signal at δ 5.091 ppm which is the symptomatic of two identical hydrogen atoms in amino group (H22 and H21), whereas it appears in deshielded region because of adjacent amino group (NH+). The carboxylate group primarily arises in the range 10–12 ppm [63] however in DPDS H21 exhibits a peak at δ 5.866 ppm which is deshielded in supreme because of the electronegative oxygen atom in the carboxylate anion. 1H spectrum shows doublet signals at δ 7.68 ppm and δ 7.618ppm which are assigned to H8 and H7 , respectively. These signals are deshielded due to the adjacent chlorine atoms in the salicylate group moiety. The signals at δ 2.55 ppm, δ2.35 ppm and δ2.164 ppm are attributed to the methyl groups attached to the pyrozolium moiety where it acquires electron donating resonance effect and shows a substantial lowering in chemical shift. 13C NMR spectrum of DPDS advertises ten distinct carbon signals to indicate carbon points present in the compound. The signal for DMSO solvent is assigned at δ 39.40 ppm. The presence of C13 signal at δ 170.73 ppm in the highly deshielded region delivers the emergence of carboxylate anion in the compound. The C6 (δ 157.72 ppm) and C16 (δ 143.74 ppm) signals are intensely deshielded by virtue of O-H grouping and the transacted methyl group in the pyrazole ring moiety, respectively. The emergence of prime intensity signals in the region δ 120–130 ppm is indicative of the carbons in the aromatic ring. The signals in the low field region (– 20–50 ppm) manifest the being of methyl, methylene and methane carbons present in the compound [64]. Signals at C3 (δ 133.25 ppm) and C5 (δ 128.46 ppm) are granted to phenyl ring bordening on the electronegative chlorine atom which leads to deshielding of carbon by attracting all electron clouds towards chlorine atom and results in the increase of chemical shift value. Correspondingly signal at δ 118.81 ppm is due to the aromatic carbon C1 and the downfield signal at δ 104.37 ppm is due to the carbon atoms attached to the methyl groups in the pyrazolium moiety. The upfield signal at δ 12.04 ppm is attached with methyl group of base moiety of C23 and C25. 4.10 Thermal (TG/DTA) analysis Thermal characteristics of DPDS crystal was studied by using TG and DTA under nitrogen atmosphere at a heating rate of 10 °C /min from 30 to 500 °C. TG-DTA thermo gram is shown in the Fig. 9 . The compound DPDS was stable up to 95 °C whereas the endothermic dip at 105 °C in DTA indicates the melting point of the compound. Single stage decomposition is observed between 90 °C and up to 230 °C, which corroborate the bulk degradation of the material and was further established by a broad endothermic dip at 200 °C in DTA. The weight loss in the bulk decomposition is around 80% when the temperature was increased from 160 to 250 °C. Volatile fragments such as CO, CO2, NO, CH4 are the decomposition products. Thus, the thermal analysis validates the applicability of DPDS for any biotic purpose.Fig. 9 TG/DTA spectrum of DPDS. Fig 9 4.11 ELF and LOL analysis ELF (electron localization function) and LOL (localized orbital locator) maps concede the zones of molecular space and their colour shade maps are presented in Fig. 10 . This topological surface analysis is based on the covalent bonds where the revelation of electron pair is high. The chemical content of LOL is analogous to that of ELF, as both counts upon the kinetic-energy density. ELF is dredged up owing to the fact of electron pair density and LOL decodes the gradients of localized orbitals inflamed when overlap [65].Fig. 10 (a) ELF and (b) LOL colour filled maps for DPDS. Fig. 10 The value of ELF, τ(r) ranges from 0.0 to 1.0, where relatively comprehensive values in the interval 0.5 and 1.0 indicate the contours containing bonding and nonbonding localized electrons. The diminutive values (< 0.5) describe the contours where electrons are intended to be delocalized [66]. LOL, ƞ(r), attains large values (> 0.5) in contours where the electron density is dominated by electron localization [67]. High localization of electrons is due to the endurance of covalent bond in the contours with comprehensive values [68]. From the Fig. 10a, the high ELF regions are seen around hydrogen atoms H27and H29 indicating the presence of highly localized bonding and nonbonding electrons and the blue regions around few chlorine atoms Cl9 and Cl10 show the delocalized electron cloud around it. From the Fig. 10b, it is seen that the central region of a hydrogen atom is white; indicating that electron density exceeds the upper limit of colour scale (0.80 au.) and the blue circles around hydrogen atoms (H27 and H29) shows the electron depletion region between inner shell and valance shell. The most part of the covalent region present between the carbon and chlorine atoms are indicated by green colour. 4.12 Reduced density gradient na A visual approach known as RDG analysis is implemented to make the non-covalent interactions more specific and to explore intra and inter non-bonded interactions in a molecular system [69]. The gradient isosurfaces and scatter graphs of DPDS molecule are shown in Fig. 11 .Fig. 11 Gradient isosurfaces and scatter graphs of DPDS. Fig. 11 RDG scatter graph is generated between RDG versus sign (λ2)ρ, where sign(λ2)ρ is the second Eigen value of the electron density which provides useful information regarding the strength and nature of the interactions. The value and sign of sign (λ2)ρ are used to explain the nature of interactions,sign (λ2)ρ > 0 for repulsive interaction, sign (λ2)ρ < 0 for attractive interaction and sign (λ2)ρ nearly zero for vander Waals weak interaction [70]. The function of λ2(r) ranges between −0.035 and0.02 0 a.u in RDG scatter spectra which is divided in to three colours red, green and blue. In the RDG isosurfaces, the red spike shows the steric repulsion observed in the centres of aromatic and pyrazole rings. The RDG scatter graph manifests the red contour between 0.02 and 0.05 au clarifies the higher repulsive exchange contribution. This plot illustrates the steric repulsive force between the aromatic carbon atoms between the rings. The blue spike manifests the strong attraction between N-H…O constitutes inter-molecular hydrogen bonding. In the RDG scatter graph blue contour between -0.05 and -0.03 au confirms the presence of strong hydrogen bonding. The red-green mixed spikes are observed near the C-Cl interactions in the aromatic ring. The red-green mixed flaky area between 0.00 and 0.015 au confirms the absence of hydrogen bonding and the C-H formation in the aromatic ring are due to the interaction between hydrogen atoms and the π-electrons. The RDG graph results confirm the interacting regions in the molecular structure DPDS. 4.13 Molecular docking analysis Molecular docking leads to further drug development with the bio molecular interactions for the rational drug design [71,72]. It is an enticing framework to be acquainted which is used to predict the binding energy, free energy and stability of the complexes. PDB structures of the target proteins are downloaded from RCSB (Research Collaboratory for Structural Bioinformatics) protein data bank. The ID's for downloaded SARS-CoV-2 hydrolase inhibitor proteins are 7JRN, 6XBI, 6XMK and 6XFN with various drug molecules. Fig. 12 represents the Ramachandran plots for the four centred proteins and the preparation of ligand with minimal energy for docking was done.Fig. 12 Ramachandran plots of SARS-CoV-2 hydrolase inhibitor proteins. Fig. 12 By observing the Ramachandran plots for all the four proteins, 85–95% of the amino acids lie in the allowed region (red) and only very few lie in the partially allowed vicinity (yellow) and therefore 7JRN, 6XBI, 6XMK and 6XFN have 88.3%, 90.6%, 90.7% and 91.7% of the amino acids in the allowed region, respectively. Majority of the amino acids in the allowed regions indicate the stability of the proteins chosen for the binding interaction. Auto Dock Tools is used to remove the ligand and water molecules present in the target proteins, which was also used to add the polar hydrogen bond and Geisteger charges. The docking parameters such as binding energy and refRMS of the molecule with respect to the targeted proteins are listed in Table 5 . The interactions of ligand DPDS with SARS-CoV-2 proteins are shown in Fig. 13 and the yellow dotted interactive line in the figure indicates the formation of intermolecular hydrogen bond between DPDS and SARS-CoV-2 hydrolase inhibitor proteins.Table 5 Docking parameters of DPDS docked into SARS-CoV-2 hydrolase inhibitor proteins. Table 5Protein (PDB ID) Bonded residues Bond distance (Å) Estimated inhibition constant (milli Molar) Binding energy (kcal/mol) Inter molecular energy (kcal/mol) Reference RMSD 7JRN LEU`80 2.1 48.22 -5.89 -7.38 31.92 PRO`77 2.5 ARG`65 2.2 6XBI THR`198 2.1 242.51 -4.93 -6.42 33.88 LEU`208 2.2 PHE`219 2.7 6XMK GLN`189 2.8 377.41 -4.67 -6.16 36.69 6XFN GLY`146 1.7 465.11 -4.55 -6.04 18.93 Fig. 13 Molecular Docking of SARS-CoV-2 hydrolase inhibitor proteins. Fig. 13. Amongst the targeted protein inhibitors, 7JRN protein exhibits least binding energy -5.89 kcal/mol with three amino acids LEU`80, PRO`77 and ARG`65 encompassing N-H…O and O…H interactions with an intermolecular energy -7.38 k cal/mol and prominent RMSD value 31.92. At this juncture, the energy released due to the bond formation or relatively interaction of ligand and the protein is termed as the binding energy whereas higher the binding energy, the stronger the interaction. The intermolecular energy is estimated for the combination of ligand and protein in their bound conformation which shows that higher the interaction energy lowers the bond stiffness. This shows the relation between binding energy and the interaction energy between ligand and the protein [73,74]. It can be seen that the hydrogen bond is formed between the hydrogen atom and oxygen atom from the ligand and the hydrolase inhibitor. Looking into the docking conformation of the ligand with the proteins reveals that the atoms N19, H21 and O15 are the only active sites undergoing hydrogen bond formation. In general hydrogen bonds are formed between the hydrogen which is bound to a more electronegative atom like nitrogen or oxygen and another atom bearing a lone pair of electron. This similarity has been observed between the DPDS and the SARS-CoV-2 hydrolase inhibitor proteins. Ligand DPDS shows good binding affinity towards all the four targeted proteins, indicating anti-viral features and the reactive site analysis with topological studies complements DPDS assured towards amino acids and awards to be a noble antiviral drug. 4.14 Drug likeness To unmask an active drug, reckoning of drug likeness is efficacious for which Lipinski's rule of 5 was used. In consonance with this rule, either ligand is considered as a pharmaceutic drug if it is apt for marked preconditions counting molecular weight < 500 Dalton, number of H-bond acceptor < 10, number of H-bond donors < 5 and lipophilicity laid out as log P < 5 [75]. Utterly these limitations deputize the drug likeness trial and the values akin to the antiviral drug DPDS is tabulated in Table 6 . Resultantly, the number of hydrogen bond donors and hydrogen bond acceptors for DPDS found to be 3 and 4 for each. Values of log P was perceived to be 1.32, which is a shade of lipophilic feature and Molar refractivity raised as 72 and settled within the appropriate range. DPDS ligand accede this rule and firmly established as a vigorous anti-viral drug.Table 6 Drug likeness parameters of DADS. Table 6:Descriptors Calculated Expected Molecular mass(Dalton) 301 <500 Hydrogen bond donor 3 <5 Hydrogen bond acceptor 4 <10 Log P 1.32 <5 Molar refractivity 72 40-130 4.15 ADMET contour To uncover a neoteric drug and its buildup, researchers must assay the liveliness of a drug in the body to gage welfare and toxicity. Drug assimilation and pharmacokinetics studies, such as ADME and toxicology studies, are the perilous phase in this process. Records poised states if a drug is doable and affords explicit goals for the future study and progression [76] and ADMET factors are tabulated in Table 7 .Table 7 ADMET Factors. Table 7:ADMET Factors DPDS Absorption Water solubility (log mol/L) -2.987 Caco-2 permeability (log Papp in 10−6cm/s) 0.909 Human intestinal absorption (%) 98.692 P-glycoprotein substrate YES P-glycoprotein I/II inhibitors NO Distribution CNS permeability (log PS) -2.426 Human VDss (log L/kg) -2.018 Fraction unbound (human)(Fu) 0.307 Metabolism CYP substrates/inhibitors NO Excretion Total clearance (log ml/min/kg) 0.2 Toxicity AMES toxicity NO Max. tolerated dose (human)(log mg/Kg/day) 1.177 Oral rat Acute Toxicity (LD50)(mol/kg) 2.651 hERG l/ll inhibitors NO Hepatotoxicity NO Amidst absorption all the constituents of the ligand trot out water solubility (log S) greater than -5, which reflect their solubility in water at 25 ˚C [77] and the predicted water solubility of a compound results as -2.987 (log mol/L). Alike absorbance in the small intestine is one of the cardinal evolution to incline bioavailability of drug in the rear oral profit [78]. Thus the prediction for the fraction of DPDS that absorbed by virtue of the human small intestine is 98.6%. Conjointly Caco-2 permeability value presumes the logarithm of permeability coefficient 0.909 in Caco -2 monolayer of the cells (log Papp in 10−6 cm/s). DPDS compound is not a p-glycoprotein I/II inhibitors and act as substrates which functions as a biological barrier by extruding toxins and xenobiotics out of cells. While distributing the drug throughout the body, most drugs in plasma abide in serenity by unbounding or bounding to serum proteins. Bounding to the serum proteins in less would transverse cellular membranes [74]. DPDS compound results less bound to the serum proteins and is impotent to infiltrate the CNS. The steady state volume of distribution (VDss) is the complete dose of a drug needed to be circulated homogeneously in blood and plasma. It is considered low if log VDss is less than -0.15 and high if it is more than 0.45. Higher VDss value grants better drug distribution in tissue rather than plasma and cause renal failure and dehydration [78].DPDS spectacles VDss IS -2.081 (log L/kg) which is less and scattered homogeneously in blood plasma and tissues. Total clearance enumerates the amputation of drug from blood or plasma where the drug riddance practice grades from kidney and liver [79]. ADMET result foresees the total clearance as 0.2 (log ml/min/kg). Toxicity of the compound can be tested by AMES toxicity which predicts if the compound has mutagenic potential or not [77]. ADMET result predicts that the compound has flimsy inhibitor and non-inhibitor of hERG inhibition and is non-mutagenic which does not act as a carcinogen. Also the compound is not hepatotoxic as it does not intrude the normal function of liver. Maximum tolerated dose is freaked out and is about 1.177 (log mg/Kg/day) which is an estimate of the toxic dose inception in humans. ADMET results interpreted that DPDS compound is an innocuous incitation to treat SARS-CoV-2. 5 Conclusion DPDS was synthesized by slow evaporation method and their molecular structure has been optimized using the DFT technique and the calculated bond length, bond angle and dihedral angle sustains a good stability with good agreement with the experimental values. Rehybridization overshadows hyper conjugation and is well reflected in the geometry of DPDS as bond O11-H12 contracts with respect to DS due to on intra-molecular hydrogen bonding O11-H12…O14. Charge transfer interactions were dissected to scrutinize hydrogen bonding networks and the strong N-H…O inter-molecular hydrogen bonding get earlier perception into these interactions to project the molecules with biotic profile. Second order perturbation theory results the transfer of electron density from lone pair nitrogen atom to the anti-bonding orbital of O-H bond which produces a strong evidence for inter-molecular hydrogen bonding. HOMO-LUMO energy gap (ΔE) value is demoted and directs that DPDS molecule yields virtuous biological activity of the molecule. Electron distribution and reactive sites on the surface of the DPDS were analysed using ESP, ELF and LOL contour maps. Colour code for the ESP was mapped and confirmed the concepts of Lewis acids and bases with in the molecule. Electronic spectra was evaluated using DFT method displays that the absorption spectra exhibits an intense peaks at 360 transitions. Aromaticity evaluated and agreed the phenyl ring and the pyrazole ring belongs to the aromatic compounds while Natural Charge analysis confirmed that C13 and O15 are the most electropositive and electronegative atoms respectively. Thermal analysis showed that the DPDS crystal can retain its stability up to 95 °C which is recognized using TG/DTA curve. Structure elucidation of DPDS using IR, Raman and NMR spectroscopic techniques were used for the data analysis and spectral interpretation which contributes the outcome of the functional groups in DPDS. In this context, predictions of 1H and 13C NMR chemical shifts and scaled frequencies have been demonstrated to be a viable strategy for the relative configuration of new molecules with good linearity amongst scaled and experimental vibration frequencies was also observed. Broadening and red-shifted nature of the bands depicts the level of intermolecular hydrogen bonding O-H…N with the molecular structure in gaseous nature. RDG approach allowed analyzing the weak attractive interactions, strong attraction, and steric repulsion existed in DPDS. Molecular docking study with SARS-CoV-2 hydrolase inhibitor protein 6XA4 shows substantial anti-viral activity exhibiting least binding energy -5.89 kcal/mol in the active sites. From docking result, it is evident that nitrogen atom in the pyrazole moiety involved in hydrogen bond formation with the target enzyme and concludes pyrazolium moiety acts as a good anti-viral medication. Drug Likeness and ADMET analyses analyzes revealed that DPDS is superlative in absorption and total clearance rate which high spot the consequential aptitude for antiviral activity counter to SARS-CoV-2. These consummations propound supplemental powerful assays for the declaration of activity of antiviral constituents contrast to SARS-CoV-2 affords evidence concerning drug pharmacokinetics. CRediT authorship contribution statement X.D. Divya Dexlin: Conceptualization, Data curation, Formal analysis, Writing – original draft. J.D. Deephlin Tarika: Investigation, Data curation. S. Madhan Kumar: . A. Mariappan: Methodology, Supervision. T. Joselin Beaula: Supervision, Writing – review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Supplementary materials Image, application 1 Acknowledgement The authors thank Dr. I. Hubert Joe, Associate Professor, Department of Physics, University of Kerala for providing laboratory for the DFT based calculations using Gaussian’09 software package. Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.molstruc.2021.131165. ==== Refs References 1 Kumar V. Kaur K. Gupta G.K. Sharma A.K. Pyrazole containing natural products: Synthetic preview and biological significance Eur. J. Med. Chem. 69 2013 735 753 10.1016/j.ejmech.2013.08.053 24099993 2 Mocanu A.M. Luca C. Potential Antimicrobial activity of some new 3,5- dimethyl pyrazole derivatives Eur. J. Med. Chem. 3 2 2018 03 07 ISSN: 2537-4338 3 Schror K. Acetylsalicylic Acid 2nd ed. 2016 John Wiley and sons New York 4 Ding C.K. Wang C.Y. The dual effects of methyl salicylate on ripening and expression of ethylene biosynthetic genes in tomato fruit Plant Sci. 164 2003 589 596 10.1016/S0168-9452(03)00010-4 5 Paul B.K. Ray D. Guchhait N. Spectral deciphering of the interaction between an intramolecular hydrogen bonded ESIPT drug, 3,5-dichlorosalicylic acid, and a model transport protein J. Phys.Chem, Chem. Phys. 14 2012 8892 8902 10.1039/C2CP23496C 6 Faisal M. Saeed A. Hussain S. Dar P. Larik F.A. Recent developments in synthetic chemistry and biological activities of pyrazole derivatives J. Chem. Sci. 131 70 2019 1 30 10.1007/s12039-019-1646-1 7 Frisch M.J. Trucks G.W. Schlegel H.B. Scuseria G.E. Roobb M.A. Cheeseman J.R. Scalmani G. Barone V. Mennucci B. Petersson G.A. Nakatsuji H. Caricato M. 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Intermolecular energy transfer in real time J. Chem. Theory Comput. 14 10 2018 5339 5349 10.1021/acs.jctc.8b00540 30141921 75 Lipinski C.A. Lead-and drug-like compounds: the rule-of-five revolution Drug Discov. Today: Technol. 1 2004 337 341 10.1016/j.ddtec.2004.11.007 24981612 76 https://www.technologynetworks.com/drug-discovery/articles/what-is-adme-336683. 77 Pires Douglas E.V Blundell T.L. Ascher D.B. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph based signatures J. Med. Chem. 58 2015 4066 4072 10.1021/acs.jmedchem.5b00104 25860834 78 Radchenko E. Dyabina A. Palyulin V. Zefirov N. Prediction of human intestinal absorption of drug compounds Russ. Chem. Bull. 65 2016 576 580 10.1007/s11172-016-1340-0 79 Smith D.A. Beaumont K. Maurer T.S. Di L. Clearance in drug design: mini perspective J. Med. Chem. 62 2018 2245 2255 10.1021/acs.jmedchem.8b01263 30281973

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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Elsevier Inc. S0022-4804(21)00045-7 10.1016/j.jss.2021.01.018 Shock/Sepsis/Trauma/Critical Care A Dual Pandemic: The Influence of Coronavirus Disease 2019 on Trends and Types of Firearm Violence in California, Ohio, and the United States Donnelly Megan R. BS a Grigorian Areg MD b Inaba Kenji MD b Kuza Catherine M. MD c Kim Dennis MD d Dolich Matthew MD a Lekawa Michael MD a Nahmias Jeffry MD, MHPE a∗ a Division of Trauma, Burns and Surgical Critical Care, Department of Surgery, University of California Irvine, Orange, California b Department of Surgery, University of Southern California, Los Angeles, California c Department of Anesthesiology, University of Southern California, Los Angeles, California d Department of Surgery, Harbor-UCLA, Torrance, California ∗ Corresponding author. University of California Irvine, 333 City Blvd West, Suite 1600, Orange, CA 92868. Tel.: +1 (714) 456 5890; fax: +1 (855) 209 8413. 2 2 2021 7 2021 2 2 2021 263 2433 20 11 2020 15 1 2021 22 1 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background This study sought to determine the impact of coronavirus disease 2019 stay-at-home (SAH) and reopening orders on trends and types of firearm violence in California, Ohio, and the United States, hypothesizing increased firearm violence after SAH. Materials and methods Retrospective data (January 1, 2018, to July 31, 2020) on firearm incidents/injuries/deaths and types of firearm violence were obtained from the Gun Violence Archive. The periods for SAH and reopening for the US were based on dates for California. Ohio dates were based on Ohio's timeline. Mann–Whitney U analyses compared trends and types of daily firearm violence per 100,000 legal firearm owners across 2018-2020 periods. Results In California, SAH and reopening orders had no effect on firearm violence in 2020 compared with 2018 and 2019 periods, respectively. In Ohio, daily median firearm deaths increased during 2020 SAH compared with 2018 and 2019 and firearm incidents and injuries increased during 2020 reopening compared with 2018, 2019 and 2020 SAH. In the United States, during 2020, SAH firearm deaths increased compared with historical controls and firearm incidents, deaths and injuries increased during 2020 reopening compared with 2018, 2019 and 2020 SAH (all P < 0.05). Nationally, when compared with 2018 and 2019, 2020 SAH had increased accidental shootings deaths with a decrease in defensive use, home invasion, and drug-involved incidents. Conclusions During 2020 SAH, the rates of firearm violence increased in Ohio and the United States but remained unchanged in California. Nationally, firearm incidents, deaths and injuries also increased during 2020 reopening versus historical and 2020 SAH data. This suggests a secondary “pandemic” as well as a “reopening phenomenon,” with increased firearm violence not resulting from self-defense. Keywords Firearm violence Accidental gunshot wounds COVID-19 Pandemic Stay-at-home Reopening ==== Body pmcIntroduction By the end of August 2020, the United States (US) had a case fatality rate of 3.1% and a total coronavirus disease 19 (COVID-19) death count of over 180,000 Americans.1 During this time, California had nearly 700,000 confirmed cases and nearly 13,000 fatalities,2 with some calling it the new COVID-19 “epicenter.”3 Many measures have been taken to try to eliminate the threat of the virus and slow its transmission. For instance, California, the most populous state, implemented the first statewide stay-at-home (SAH) order in the US on March 19, 2020.4 However, as a consequence of social distancing, quarantining, and the SAH orders themselves, the social and economic well-being of many Americans has been profoundly impacted. Furthermore, preliminary studies have shown that as a result of SAH, quarantine, and travel ban orders,5 many individuals have found that their social networks have been depleted.6 Feelings of isolation because of these national and statewide orders have not only exacerbated preexisting mental health conditions,7 but these measures are provoking new diagnoses of alcohol and substance abuse disorders.7 , 8 In addition, the rate of domestic violence has also increased based on some reports,9, 10, 11, 12 as have rates of self-harm and suicide.13, 14, 15 Born out of these pandemic-related stressors, one additional public health concern that has emerged is the drastic increase in the sales of firearms. These sales are considered “panic purchases,”16, 17, 18, 19 with owners purporting that they will be needed for self-defense. Some states have even deemed firearm retailers to be “essential businesses.”20 A recent midpandemic survey study performed by Kravitz-Wirtz et al. found that, in the state of California, there were an estimated 110,000 adults who acquired a firearm in response to the pandemic. Of these, 47,000 (43.0%) were first-time owners.21 This may at least partially explain why Hatchimonji et al. demonstrated that firearm-related injuries have persisted unabated throughout the pandemic22 despite orders to SAH. Commentary from other US trauma centers has postulated that firearm violence is paradoxically increasing despite overall crime rates dropping during the COVID-19 pandemic.23, 24, 25 In addition, the authors of this article have recently published a study detailing the effects of the pandemic on firearm purchases and firearm violence in both the US and New York State during the initial phase of the pandemic.26 The purpose of this study was to quantify the impact of SAH and reopening orders on trends and particular types of firearm violence in California, Ohio, and the US. Despite intuition leading one to initially believe that SAH orders might decrease the rates of firearm violence, as a result of the aforementioned reports we hypothesize overall increased rates of firearm violence during SAH that may continue to increase during reopening compared with 2018 and 2019 historical controls. In addition, we hypothesize a decrease in defensive forms of firearm violence (i.e., victim stopping a crime) across the nation with a simultaneous increase in accidental shootings during SAH compared with 2018 and 2019. Materials and methods This study was deemed exempt by the Institutional Review Board, and as such, no consent was needed. No funding was provided for this study. Firearm violence data were obtained retrospectively (January 2018 to July 2020) from the Gun Violence Archive (GVA). The GVA is a not-for-profit, independent organization whose goal is to provide accurate, evidence-based research to the American public on gun-related violence in the US. The GVA uses automated Internet queries in addition to manual investigations to parse through over 7500 sources reporting on firearm violence each day. The sources used by the GVA include police reports, news and media, online databases, as well as government and other resources. The data generated from these investigations are then organized into incidents, deaths, and injuries as well as into categories such as accidental shootings, hate crimes, home invasions, domestic violence, defensive gun use, and more. However, real-time data on firearm suicides and armed robberies with no injuries are not reported.27 Dates for SAH (March 19, 2020, to May 24, 2020) and reopening orders (May 25, 2020, to July 31, 2020) for California were used for both California and US analyses.4 , 28 This was because of the highly variable timing of implementation and cessation of SAH orders for states, as well as the fact that eight states never implemented statewide SAH orders. The states that never implemented SAH were North Dakota, South Dakota, Nebraska, Iowa, and Arkansas and the states that only implemented partial SAH were Wyoming, Utah, and Oklahoma.4 , 28 These eight states were excluded from analysis, and as such, only data for the remaining 42 states and the District of Columbia were included. California was selected as the focus for this article, as the authors' own anecdotal experiences were of increased firearm violence across Southern California. In addition, California has the strictest gun laws in the country (rated first out of 50 states) per the Giffords Law Center.29 Ohio, which is ranked 24th out of 50, was selected for comparison to California, as the state's gun laws are much less stringent, with no requirements for universal background checks and no regulation of untraceable firearms.29 However, regarding pandemic policies, both states had relatively similar, strict SAH orders and phased reopenings, making Ohio an ideal control. Ohio's SAH period was defined as March 23, 2020, to May 19, 2020, and its reopening was defined as May 19, 2020, to July 31, 2020.30 Data on the number of legal firearm owners by state were obtained from the World Population Review. This is an independent organization with no political affiliations whose goal is to make important data accessible and easy to understand.31 These numbers were used to weight daily firearm violence by the number of legal firearm owners in each state to allow for comparison between California, Ohio, and the US. Mann–Whitney U tests were run to quantify the effects of SAH and reopening orders on daily firearm violence per 100,000 legal firearm owners compared with 2018 and 2019 historical control data for California, Ohio, and the US. An additional analysis for types of firearm incidents for the nation was performed. Only trends that were consistent when comparing 2020 to both 2018 and 2019 were considered to be significant. Because of the very low occurrence of daily firearm violence in California and Ohio, analysis of trends or changes in types of firearm violence was not performed. Statistics were performed on IBM SPSS Statistics, version 26 (IBM Corp, Armonk, NY). Statistical significance was set as P < 0.05. Results Trends in firearm violence: California, Ohio, and the US In California, there were no consistent increases in daily firearm violence per 100,000 legal firearm owners during 2020 SAH compared with 2018 and 2019 control periods, respectively. In addition, California did not experience a consistent increase in firearm incidents, deaths and injuries during 2020 reopening compared with 2018, 2019, and 2020 SAH control data. In contrast, Ohio had increased daily firearm deaths per day in 2020 SAH compared with 2018 (maximum: 2.88 versus 4.04; P = 0.033) and 2019 (maximum: 2.88 versus 4.04; P = 0.031). Moreover, Ohio had increased median daily firearm incidents and injuries per 100,000 legal gun owners between 2020 reopening and 2018, 2019, and 2020 SAH (all P < 0.05). Nationally, the US experienced increased firearm deaths in 2020 SAH compared with both 2018 (0.78 versus 0.89; P = 0.001) and 2019 (0.78 versus 0.89; P < 0.001). The median daily firearm incidents, deaths and injuries also increased during 2020 reopening in the US compared with 2018, 2019, and 2020 SAH (all P < 0.05) (Table 1, Table 2, Table 3, Table 4, Table 5 ).Table 1 2018 historical control versus 2020 SAH, per 100,000 licensed firearm owners. Outcome measure 2018 control SAH P value California  Firearm incidents per day, median (min, max) 2.61 (0.87, 6.09) 2.61 (0.58, 4.93) 0.096  Firearm deaths per day, median (min, max) 0.87 (0.00, 3.77) 0.87 (0.00, 3.19) 0.117  Firearm injuries per day, median (min, max) 1.16 (0.00, 3.77) 1.45 (0.00, 3.77) 0.014 Ohio  Firearm incidents per day, median (min, max) 4.04 (1.73, 9.23) 3.46 (0.58, 7.50) 0.048  Firearm deaths per day, median (min, max) 0.58 (0.00, 2.88) 0.58 (0.00, 4.04) 0.033  Firearm injuries per day, median (min, max) 1.73 (0.00, 7.50) 1.73 (0.00, 8.07) 0.197 The United States  Firearm incidents per day, median (min, max) 3.18 (2.59, 4.01) 2.82 (1.93, 4.37) <0.001  Firearm deaths per day, median (min, max) 0.78 (0.34, 1.32) 0.89 (0.51, 1.46) 0.001  Firearm injuries per day, median (min, max) 1.57 (0.95, 2.38) 1.65 (1.06, 2.97) 0.103 Table 2 2019 historical control versus 2020 SAH, per 100,000 licensed firearm owners. Outcome measure 2019 control SAH P value California  Firearm incidents per day, median (min, max) 2.61 (0.58, 4.35) 2.61 (0.58, 4.93) 0.386  Firearm deaths per day, median (min, max) 0.87 (0.00, 2.61) 0.87 (0.00, 3.19) 0.442  Firearm injuries per day, median (min, max) 1.45 (0.00, 4.93) 1.45 (0.00, 3.77) 0.571 Ohio  Firearm incidents per day, median (min, max) 4.04 (0.58, 8.07) 3.46 (0.58, 7.50) 0.209  Firearm deaths per day, median (min, max) 0.58 (0.00, 2.88) 0.58 (0.00, 4.04) 0.031  Firearm injuries per day, median (min, max) 1.73 (0.00, 5.19) 1.73 (0.00, 8.07) 0.227 The United States  Firearm incidents per day, median (min, max) 2.93 (2.25, 3.69) 2.82 (1.93, 4.37) 0.331  Firearm deaths per day, median (min, max) 0.78 (0.36, 1.19) 0.89 (0.51, 1.46) <0.001  Firearm injuries per day, median (min, max) 1.61 (0.93, 2.74) 1.65 (1.06, 2.97) 0.215 Table 3 2018 historical control versus 2020 reopening, per 100,000 licensed firearm owners. Outcome measure 2018 control Reopening P value California  Firearm incidents per day, median (min, max) 2.90 (1.16, 5.22) 2.90 (1.16, 6.96) 0.453  Firearm deaths per day, median (min, max) 1.16 (0.00, 2.90) 1.45 (0.00, 3.77) 0.056  Firearm injuries per day, median (min, max) 1.45 (0.29, 5.80) 1.74 (0.00, 5.51) 0.031 Ohio  Firearm incidents per day, median (min, max) 4.61 (1.73, 8.65) 5.19 (1.15, 13.84) 0.028  Firearm deaths per day, median (min, max) 1.15 (0.00, 3.46) 1.15 (0.00, 3.46) 0.060  Firearm injuries per day, median (min, max) 2.31 (0.58, 8.07) 4.04 (0.58, 12.11) <0.001 The United States  Firearm incidents per day, median (min, max) 3.14 (2.48, 5.09) 3.89 (2.95, 7.28) <0.001  Firearm deaths per day, median (min, max) 0.86 (0.59, 1.29) 1.18 (0.59, 2.44) <0.001  Firearm injuries per day, median (min, max) 1.76 (1.08, 3.33) 2.72 (1.80, 6.92) <0.001 Table 4 2019 historical control versus 2020 reopening, per 100,000 licensed firearm owners. Outcome measure 2019 control Reopening P value California  Firearm incidents per day, median (min, max) 2.90 (1.16, 5.22) 2.90 (1.16, 6.96) 0.960  Firearm deaths per day, median (min, max) 1.16 (0.00, 3.48) 1.45 (0.00, 3.77) 0.031  Firearm injuries per day, median (min, max) 1.74 (0.29, 6.67) 1.74 (0.00, 5.51) 0.776 Ohio  Firearm incidents per day, median (min, max) 4.33 (1.15, 10.96) 5.19 (1.15, 13.84) 0.021  Firearm deaths per day, median (min, max) 1.15 (0.00, 4.04) 1.15 (0.00, 3.46) 0.082  Firearm injuries per day, median (min, max) 2.31 (0.00, 6.34) 4.04 (0.58, 12.11) <0.001 The United States  Firearm incidents per day, median (min, max) 3.31 (2.23, 5.26) 3.89 (2.95, 7.28) <0.001  Firearm deaths per day, median (min, max) 0.92 (0.53, 1.61) 1.18 (0.59, 2.44) <0.001  Firearm injuries per day, median (min, max) 1.82 (1.17, 3.71) 2.72 (1.80, 6.92) <0.001 Table 5 2020 SAH versus 2020 reopening, per 100,000 licensed firearm owners. Outcome measure SAH Reopening P value California  Firearm incidents per day, median (min, max) 2.61 (0.58, 4.93) 2.90 (1.16, 6.96) 0.025  Firearm deaths per day, median (min, max) 0.87 (0.00, 3.19) 1.45 (0.00, 3.77) 0.001  Firearm injuries per day, median (min, max) 1.45 (0.00, 3.77) 1.74 (0.00, 5.51) 0.094 Ohio  Firearm incidents per day, median (min, max) 3.46 (0.58, 7.50) 5.19 (1.15, 13.84) <0.001  Firearm deaths per day, median (min, max) 0.58 (0.00, 4.04) 1.15 (0.00, 3.46) 0.007  Firearm injuries per day, median (min, max) 1.73 (0.00, 8.07) 4.04 (0.58, 12.11) <0.001 The United States  Firearm incidents per day, median (min, max) 2.82 (1.93, 4.37) 3.89 (2.95, 7.28) <0.001  Firearm deaths per day, median (min, max) 0.89 (0.51, 1.46) 1.18 (0.59, 2.44) <0.001  Firearm injuries per day, median (min, max) 1.65 (1.06, 2.97) 2.72 (1.80, 6.92) <0.001 Table 6 Types of firearm violence 2018 historical control (March 19, 2018, to May 24, 2018) versus 2020 SAH (March 19, 2020, to May 24, 2020), per 100,000 licensed firearm owners. Outcome measure 2018 control SAH P value Accidental shooting, median (min, max)  Incidents 0.08 (0.00, 0.30) 0.11 (0.02, 0.25) 0.056  Deaths 0.00 (0.00, 0.08) 0.02 (0.00, 0.17) 0.007  Injuries 0.06 (0.00, 0.23) 0.06 (0.00, 0.25) 0.144 Child-involved incidents, median (min, max)  Incidents 0.04 (0.00, 0.08) 0.04 (0.00, 0.21) 0.077  Deaths 0.00 (0.00, 0.13) 0.02 (0.00, 0.13) 0.024  Injuries 0.02 (0.00, 0.17) 0.04 (0.00, 0.17) 0.054 Home invasion, median (min, max)  Incidents 0.11 (0.00, 0.21) 0.06 (0.00, 0.19) <0.001  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.664  Injuries 0.04 (0.00, 0.13) 0.02 (0.00, 0.15) 0.012 Officer involved incident, median (min, max)  Incidents 0.25 (0.11, 0.45) 0.25 (0.11, 0.47) 0.448  Deaths 0.08 (0.00, 0.28) 0.08 (0.00, 0.25) 0.507  Injuries 0.08 (0.00, 0.38) 0.08 (0.00, 0.28) 0.864 Defensive use, median (min, max)  Incidents 0.11 (0.02, 0.30) 0.06 (0.00, 0.17) <0.001  Deaths 0.02 (0.00, 0.19) 0.02 (0.00, 0.08) 0.594  Injuries 0.06 (0.00, 0.17) 0.04 (0.00, 0.15) 0.013 Gang involvement, median (min, max)  Incidents 0.04 (0.00, 0.17) 0.02 (0.00, 0.17) 0.002  Deaths 0.00 (0.00, 0.06) 0.00 (0.00, 0.06) 0.378  Injuries 0.02 (0.00, 0.15) 0.02 (0.00, 0.25) 0.909 Drug involvement, median (min, max)  Incidents 0.28 (0.06, 0.55) 0.08 (0.02, 0.21) <0.001  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.13) 0.537  Injuries 0.02 (0.00, 0.15) 0.02 (0.00, 0.11) 0.146 Domestic violence, median (min, max)  Incidents 0.17 (0.04, 0.34) 0.19 (0.04, 0.36) 0.273  Deaths 0.11 (0.00, 0.30) 0.13 (0.00, 0.25) 0.581  Injuries 0.06 (0.00, 0.17) 0.06 (0.00, 0.28) 0.320 Table 7 Types of firearm violence 2019 historical control (March 19, 2019, to May, 24, 2019) versus 2020 SAH (March 19, 2020, to May 24, 2020), per 100,000 licensed firearm owners. Outcome measure 2019 control SAH P value Accidental shooting, median (min, max)  Incidents 0.06 (0.02, 0.21) 0.11 (0.02, 0.25) <0.001  Deaths 0.02 (0.00, 0.13) 0.02 (0.00, 0.17) 0.001  Injuries 0.04 (0.00, 0.17) 0.06 (0.00, 0.25) 0.003 Child-involved incidents, median (min, max)  Incidents 0.02 (0.00, 0.08) 0.04 (0.00, 0.21) 0.031  Deaths 0.00 (0.00, 0.11) 0.02 (0.00, 0.13) 0.084  Injuries 0.02 (0.00, 0.15) 0.04 (0.00, 0.17) 0.241 Home invasion, median (min, max)  Incidents 0.08 (0.02, 0.21) 0.06 (0.00, 0.19) 0.001  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.648  Injuries 0.02 (0.00, 0.19) 0.02 (0.00, 0.15) 0.295 Officer involved incident, median (min, max)  Incidents 0.19 (0.08, 0.40) 0.25 (0.11, 0.47) <0.001  Deaths 0.06 (0.00, 0.17) 0.08 (0.00, 0.25) 0.053  Injuries 0.06 (0.00, 0.28) 0.08 (0.00, 0.28) 0.048 Defensive use, median (min, max)  Incidents 0.08 (0.00, 0.17) 0.06 (0.00, 0.17) 0.001  Deaths 0.02 (0.00, 0.15) 0.02 (0.00, 0.08) 0.102  Injuries 0.04 (0.00, 0.15) 0.04 (0.00, 0.15) 0.189 Gang involvement, median (min, max)  Incidents 0.02 (0.00, 0.13) 0.02 (0.00, 0.17) 0.987  Deaths 0.00 (0.00, 0.08) 0.00 (0.00, 0.06) 0.483  Injuries 0.00 (0.00, 0.21) 0.02 (0.00, 0.25) 0.168 Drug involvement, median (min, max)  Incidents 0.23 (0.04, 0.47) 0.08 (0.02, 0.21) <0.001  Deaths 0.02 (0.00, 0.11) 0.02 (0.00, 0.13) 0.156  Injuries 0.02 (0.00, 0.11) 0.02 (0.00, 0.11) 0.185 Domestic violence, median (min, max)  Incidents 0.15 (0.06, 0.32) 0.19 (0.04, 0.36) 0.024  Deaths 0.08 (0.00, 0.28) 0.13 (0.00, 0.25) 0.040  Injuries 0.06 (0.00, 0.13) 0.06 (0.00, 0.28) 0.147 Table 8 Types of firearm violence 2018 historical control (May 25, 2018, to July 31, 2018) versus 2020 reopening (May 25, 2020, to July 31, 2020), per 100,000 licensed firearm owners. Outcome measure 2018 control Reopening P value Accidental shooting, median (min, max)  Incidents 0.08 (0.02, 0.23) 0.13 (0.04, 0.36) 0.004  Deaths 0.02 (0.00, 0.19) 0.04 (0.00, 0.19) 0.001  Injuries 0.06 (0.00, 0.28) 0.11 (0.02, 0.34) <0.001 Child-involved incidents, median (min, max)  Incidents 0.04 (0.00, 0.15) 0.06 (0.00, 0.28) <0.001  Deaths 0.02 (0.00, 0.11) 0.02 (0.00, 0.17) 0.283  Injuries 0.04 (0.00, 0.28) 0.08 (0.00, 0.34) <0.001 Home invasion, median (min, max)  Incidents 0.11 (0.02, 0.23) 0.06 (0.00, 0.17) <0.001  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.646  Injuries 0.04 (0.00, 0.17) 0.02 (0.00, 0.11) 0.024 Officer involved incident, median (min, max)  Incidents 0.23 (0.08, 0.47) 0.25 (0.11, 0.72) 0.020  Deaths 0.06 (0.00, 0.23) 0.08 (0.00, 0.21) 0.481  Injuries 0.08 (0.00, 0.49) 0.08 (0.00, 0.57) 0.472 Defensive use, median (min, max)  Incidents 0.11 (0.00, 0.21) 0.08 (0.02, 0.23) 0.004  Deaths 0.04 (0.00, 0.11) 0.04 (0.00, 0.13) 0.432  Injuries 0.06 (0.00, 0.19) 0.04 (0.00, 0.32) 0.704 Gang involvement, median (min, max)  Incidents 0.04 (0.00, 0.13) 0.02 (0.00, 0.13) 0.001  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.547  Injuries 0.02 (0.00, 0.40) 0.02 (0.00, 0.34) 0.504 Drug involvement, median (min, max)  Incidents 0.25 (0.02, 0.47) 0.06 (0.00, 0.23) <0.001  Deaths 0.02 (0.00, 0.08) 0.01 (0.00, 0.13) 0.330  Injuries 0.03 (0.00, 0.19) 0.02 (0.00, 0.13) 0.016 Domestic violence, median (min, max)  Incidents 0.17 (0.04, 0.34) 0.19 (0.02, 0.32) 0.235  Deaths 0.08 (0.00, 0.25) 0.08 (0.00, 0.25) 0.754  Injuries 0.06 (0.00, 0.19) 0.06 (0.00, 0.25) 0.373 Table 9 Types of firearm violence 2019 historical control (May 25, 2019, to July 31, 2019) versus 2020 reopening (May 25, 2020, to July 31, 2020), per 100,000 licensed firearm owners. Outcome measure 2019 control Reopening P value Accidental shooting, median (min, max)  Incidents 0.11 (0.02, 0.28) 0.13 (0.04, 0.36) 0.074  Deaths 0.02 (0.00, 0.11) 0.04 (0.00, 0.19) 0.021  Injuries 0.08 (0.00, 0.30) 0.11 (0.02, 0.34) 0.014 Child-involved incidents, median (min, max)  Incidents 0.04 (0.00, 0.15) 0.06 (0.00, 0.28) <0.001  Deaths 0.00 (0.00, 0.13) 0.02 (0.00, 0.17) 0.003  Injuries 0.02 (0.00, 0.38) 0.08 (0.00, 0.34) <0.001 Home invasion, median (min, max)  Incidents 0.08 (0.00, 0.25) 0.06 (0.00, 0.17) 0.005  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.666  Injuries 0.03 (0.00, 0.21) 0.02 (0.00, 0.11) 0.309 Officer involved incident, median (min, max)  Incidents 0.21 (0.11, 0.40) 0.25 (0.11, 0.72) 0.005  Deaths 0.08 (0.00, 0.36) 0.08 (0.00, 0.21) 0.834  Injuries 0.08 (0.00, 0.42) 0.08 (0.00, 0.57) 0.103 Defensive use, median (min, max)  Incidents 0.08 (0.02, 0.19) 0.08 (0.02, 0.23) 0.355  Deaths 0.02 (0.00, 0.11) 0.04 (0.00, 0.13) 0.060  Injuries 0.04 (0.00, 0.15) 0.04 (0.00, 0.32) 0.603 Gang involvement, median (min, max)  Incidents 0.02 (0.00, 0.13) 0.02 (0.00, 0.13) 0.604  Deaths 0.00 (0.00, 0.06) 0.02 (0.00, 0.08) 0.214  Injuries 0.00 (0.00, 0.30) 0.02 (0.00, 0.34) 0.439 Drug involvement, median (min, max)  Incidents 0.23 (0.04, 0.47) 0.06 (0.00, 0.23) <0.001  Deaths 0.02 (0.00 0.13) 0.01 (0.00, 0.13) 0.001  Injuries 0.02 (0.00, 0.13) 0.02 (0.00, 0.13) 0.040 Domestic violence, median (min, max)  Incidents 0.17 (0.04, 0.32) 0.19 (0.02, 0.32) 0.178  Deaths 0.11 (0.00, 0.30) 0.08 (0.00, 0.25) 0.613  Injuries 0.06 (0.00, 0.25) 0.06 (0.00, 0.25) 0.369 Table 10 Types of firearm violence 2020 SAH (March 19, 2020, to May 24, 2020) versus 2020 reopening (May 25, 2020, to July 31, 2020), per 100,000 licensed firearm owners. Outcome measure SAH Reopening P value Accidental shooting, median (min, max)  Incidents 0.11 (0.02, 0.25) 0.13 (0.04, 0.36) 0.044  Deaths 0.02 (0.00, 0.17) 0.04 (0.00, 0.19) 0.021  Injuries 0.06 (0.00, 0.25) 0.11 (0.02, 0.34) 0.001 Child-involved incidents, median (min, max)  Incidents 0.04 (0.00, 0.21) 0.06 (0.00, 0.28) <0.001  Deaths 0.02 (0.00, 0.13) 0.02 (0.00, 0.17) 0.170  Injuries 0.04 (0.00, 0.17) 0.08 (0.00, 0.34) <0.001 Home invasion, median (min, max)  Incidents 0.06 (0.00, 0.19) 0.06 (0.00, 0.17) 0.998  Deaths 0.02 (0.00, 0.08) 0.02 (0.00, 0.08) 0.349  Injuries 0.02 (0.00, 0.15) 0.02 (0.00, 0.11) 0.946 Officer involved incident, median (min, max)  Incidents 0.25 (0.11, 0.47) 0.25 (0.11, 0.72) 0.584  Deaths 0.08 (0.00, 0.25) 0.08 (0.00, 0.21) 0.172  Injuries 0.08 (0.00, 0.28) 0.08 (0.00, 0.57) 0.256 Defensive use, median (min, max)  Incidents 0.06 (0.00, 0.17) 0.08 (0.02, 0.23) 0.017  Deaths 0.02 (0.00, 0.08) 0.04 (0.00, 0.13) 0.002  Injuries 0.04 (0.00, 0.15) 0.04 (0.00, 0.32) 0.157 Gang involvement, median (min, max)  Incidents 0.02 (0.00, 0.17) 0.02 (0.00, 0.13) 0.545  Deaths 0.00 (0.00, 0.06) 0.02 (0.00, 0.08) 0.019  Injuries 0.02 (0.00, 0.25) 0.02 (0.00, 0.34) 1.000 Drug involvement, median (min, max)  Incidents 0.08 (0.02, 0.21) 0.06 (0.00, 0.23) 0.048  Deaths 0.02 (0.00, 0.13) 0.01 (0.00, 0.13) 0.015  Injuries 0.02 (0.00, 0.11) 0.02 (0.00, 0.13) 0.033 Domestic violence, median (min, max)  Incidents 0.19 (0.04, 0.36) 0.19 (0.02, 0.32) 0.500  Deaths 0.13 (0.00, 0.25) 0.08 (0.00, 0.25) 0.207  Injuries 0.06 (0.00, 0.28) 0.06 (0.00, 0.25) 0.431 Types of firearm violence during SAH Median daily accidental shooting deaths per 100,000 legal firearm owners increased in 2020 SAH compared with 2018 (0.00 versus 0.02; P = 0.007) and 2019 (maximum: 0.13 versus 0.17; P = 0.001). Meanwhile, the median daily home invasion, defensive use, and drug-involved incidents all decreased in 2020 SAH compared with 2018 (home invasion: 0.11 versus 0.06, P < 0.001; defensive use: 0.11 versus 0.06, P < 0.001; drug involved: 0.28 versus 0.08, P < 0.001) and 2019 (home invasion: 0.08 versus 0.06, P = 0.001; defensive use: 0.08 versus 0.06, P = 0.001; drug involved: 0.23 versus 0.08; P < 0.001) (Table 6, Table 7). Types of firearm violence during reopening There was an increase in 2020 reopening median daily accidental shooting deaths and injuries as well as median daily child-involved shooting incidents and injuries per 100,000 legal firearm owners compared with 2018 (accidental shooting deaths: 0.02 versus 0.04, P = 0.001; accidental shooting injuries: 0.06 versus 0.11, P < 0.001; child-involved shooting incidents: 0.04 versus 0.06, P < 0.001; child-involved shooting injuries: 0.04 versus 0.08, P < 0.001), 2019 (accidental shooting deaths: 0.02 versus 0.04, P = 0.021; accidental shooting injuries: 0.08 versus 0.11, P = 0.014; child-involved shooting incidents: 0.04 versus 0.06, P < 0.001; child-involved shooting injuries: 0.02 versus 0.08, P < 0.001) and 2020 SAH (accidental shooting deaths: 0.02 versus 0.04, P = 0.021; accidental shooting injuries: 0.06 versus 0.11, P = 0.001; child-involved shooting incidents: 0.04 versus 0.06, P < 0.001; child-involved shooting injuries: 0.04 versus 0.08, P < 0.001), whereas there was a decrease in 2020 reopening drug-involved incidents compared with 2018 (0.25 versus 0.06, P < 0.001), 2019 (0.02 versus 0.01, P = 0.001), and 2020 SAH (0.02 versus 0.01, P = 0.015) (Table 8, Table 9, Table 10). Discussion This retrospective database study indicates a national increase in firearm violence during the 2020 SAH and reopening periods of the COVID-19 pandemic. Although the authors could not report consistently increased firearm violence in California during the two 2020 periods compared with control data, it is important to note that SAH orders were unable to decrease daily rates of firearm violence in this state. Perhaps because of California's strict gun laws, only Ohio saw increased firearm violence during SAH and during phased reopening. With regard to national data, although most firearm purchases were reportedly made for reasons of self-defense, defensive use and home invasions involving firearms decreased nationally during SAH. Instead, accidental shooting deaths increased during this time. In addition, a “reopening phenomenon” of increased firearm violence compared with a corresponding 2018 and 2019 historical control and to 2020 SAH was also observed across the US. SAH orders were implemented in 42 US states and the District of Columbia to mitigate the transmission and effects of COVID-194. Although the reports have shown that these methods appear effective for combating the pandemic,32 preliminary data demonstrate that SAH orders may have unintended consequences. For instance, this study demonstrated that firearm deaths in the US increased after SAH orders. Furthermore, SAH orders were unable to decrease the median number of firearm incidents, deaths, and injuries per day even in the first state to enact this order, California. Most intriguingly, although purchasers of firearms intended to use their weapons for defensive purposes, defensive firearm incidents and home invasions actually decreased during SAH. Contrarily, the types of firearm-related deaths that increased were related to accidental shootings. Given that a recent survey study found that around 40% of individuals have been storing at least one firearm unlocked in their home during the pandemic33 and that other reports have shown a spike in first-time gun owners,21 our results suggest that pandemic-related firearm ownership may be doing more harm to owners and their families during SAH than good. This suggests that the determination that firearm retailers are essential businesses may merit future discussion. In addition, a “reopening phenomenon” of increased firearm violence compared with the corresponding historical timeframe in 2018 and 2019 as well as compared with SAH baselines was observed in the US. In terms of types of violence, accidental shootings and child-involved shootings increased nationally during reopening compared with these periods. This suggests that heightened awareness and increased measures by law enforcement and civilians to mitigate a firearm-related “reopening phenomenon” following a pandemic is needed. Unlike California, Ohio experienced consistent increases in firearm violence during SAH as well as during phased reopening compared with historical data. Although California had isolated increases in firearm violence throughout these comparison periods, these overall findings suggest that the strength of a state's gun regulations may affect firearm violence seen during the COVID-19 pandemic. This finding may help guide future policy surrounding how to control firearm-related incidents, deaths, and injuries during a pandemic. Because of the use of retrospective databases, this study is subject to multiple limitations, including missing data and reporting bias. For instance, the GVA is a database that uses multiple sources, such as the media, to track firearm incidents, deaths, and injuries. Because media coverage may be spotty in some areas because of geographic limitations or scarce resources, these reports may not accurately represent all firearm violence that occurs across the US. In addition, many incidents of firearm violence, particularly domestic violence involving a firearm, are likely to go unreported to the authorities or to the media, and thus, this study's results probably represent underestimates of daily firearm violence. It warrants repeating that although the cutoff points for SAH and reopening orders are accurate for California, these date ranges were also applied to US data. This is because there were no clear cutoff points for US SAH and reopening orders, as each state declared its plans separately and eight states, which were excluded from analysis, never implemented full statewide orders.4 Although the strength of state gun laws and legislation was compared between California and Ohio, it should be acknowledged that the processes and restrictions to acquire a firearm vary state by state. Thus, in the national analysis of the 42 states plus the District of Columbia, this is a significant potential confounder. Finally, because of the retrospective nature of this study, we cannot draw conclusions regarding cause and effect. Therefore, the associations uncovered within this study may not be solely related to COVID-19 and the orders implemented during the pandemic. In support of this concern, there were numerous confounders we were unable to control for, such as the global economic crisis, the forceful police and federal responses to the killings of Breonna Taylor and George Floyd that heightened largely peaceful protests against systemic racism and oppression across the country21 and the growing anticipation and anxiety surrounding the 2020 presidential election. To date, there is no scientific literature available discussing the impact of these major current events on trends and the types of firearm violence. Regardless, there was no suggestion of increased use for self-defense independent of any findings related to domestic or nondomestic violence. Despite these limitations, to the knowledge of the authors this is the first large national analysis to quantify the changes in the types of firearm violence surrounding the COVID-19 pandemic. Conclusions Although the fears surrounding the COVID-19 pandemic have created a surge in the perceived need for protective firearm ownership, particularly among first-time buyers, this study found that defensive use of firearms decreased nationally during SAH orders compared with 2018 and 2019 control data. Instead, a spike in accidental shooting deaths occurred during SAH compared with 2018 and 2019 historical data. Future discussion regarding the status of firearm retailers as essential businesses may be warranted. In addition, a “reopening phenomenon” of further increased firearm violence was notable in Ohio and the US when comparing reopening to 2018 control data, 2019 control data, and 2020 SAH. The strength of gun laws may have an effect on trends in firearm violence during the COVID-19 pandemic; however, this requires further study before any definitive conclusions. Finally, because of the substantial firearm violence noted across the US, the authors recommend that even during a pandemic, public health efforts should continue to focus on firearm safety. Disclosure The authors reported no proprietary or commercial interest in any product mentioned or concept discussed in this article. Acknowledgment This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions: M.D., A.G., and J.N. were involved in conception and design, analysis and interpretation of the data, drafting the article, critically revising the article, and providing final approval. K.I., C.M.K., D.K., M.D., and M.L. were involved in interpretation of the data, critically revising the article, and providing final approval. ==== Refs References 1 Johns Hopkins University & Medicine. Mortality analyses. Coronavirus Resource Center Available at: coronavirus.jhu.edu/data/mortality 2020 2 California Department of Public Health Available at: Cdph.ca.gov/Programs/CIS/DCDC/Pages/Immunization/ncov2019.aspx 2020 3 Poston B. Los Angeles Times Available at: Latimes.com/California/story/2020-08-06/newest-covid-19-cases-surge-in-southeast-la-county 2020 4 Mervosh S. Lu D. Swales V. See which states and cities have told residents to stay at home. The New York Times Available at: Nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html 2020 5 Boserup B. McKenney M. Elkbuli A. Alarming trends in US domestic violence during the COVID-19 pandemic Am J Emerg Med 38 2020 2753 2755 32402499 6 Ramalho R. Alcohol consumption and alcohol-related problems during the COVID-19 pandemic: a narrative review Australas Psychiatry 2020 1 3 7 Kalin N.H. COVID-19, substance use, anorexia nervosa, 22q11.2 deletion syndrome, and stress Am J Psychiatry 177 2020 7 31892296 8 Finlay I. Gilmore I. Covid-19 and alcohol- a dangerous cocktail BMJ 369 2020 m1987 32434792 9 Mazza M. Marano G. Lai C. Janiri L. Sani G. Danger in danger: interpersonal violence during COVID-19 quarantine Psychiatry Res 289 2020 113046 32387794 10 Usher K. Bhullar N. Durkin J. Gyamfi N. Jackson D. Family violence and COVID-19: increased vulnerability and reduced options for support Int J Ment Health Nurs 29 2020 549 552 32314526 11 Viveiros N. Bonomi A.E. Novel coronavirus (COVID-19): violence, reproductive rights and related health risks for women, opportunities for practice innovation J Fam Violence 2020 [Epub ahead of print] 12 Duncan T.K. Weaver J.L. Zakrison T.L. Domestic violence and safe storage of firearms in the COVID-19 era Ann Surg 272 2020 e55 e57 32675496 13 Devitt P. Can we expect an increased suicide rate due to COVID-19? Irish J Psychol Med 2020 1 5 14 Mannix R. Lee L.K. Fleegler E.W. Coronavirus disease 2019 (COVID-19) and firearms in the United States: will an epidemic of suicide follow? Ann Intern Med 173 2020 228 229 32320463 15 Lange C. Probst C. Rehm J. Coronavirus disease 2019 crisis and intentional injuries: now is not the time to erode alcohol control policies Can J Public Health 111 2020 466 468 32757121 16 Caputi T.L. Ayers J.W. Dredze M. Suplina N. Burd-Sharps S. Collateral crises of gun preparation and the COVID-19 pandemic: infodemiology study JMIR Public Health Surveill 6 2020 e19369 32437329 17 Dutheil F. Baker J.S. Navel V. Firearms or SARS-CoV-2: what is the most lethal? Public Health 183 2020 44 45 32422439 18 Dutheil F. Baker J.S. Navel V. To fight SARS-CoV-2: putting your guns down Can J Public Health 111 2020 411 412 32542509 19 Schleimer J.P. McCort C.D. Pear V.A. Firearm purchasing and firearm violence in the first months of the coronavirus pandemic in the United States medRxiv 2020 Preprint 20 Pomeranz J.L. Firearm industry groups are using COVID-19 to expand gun rights www.JPHMP.com 26 4 2020 320 321 21 Kravitz-Wirtz N. Aubel A. Schleimer J. Pallin R. Wintemute G. Violence, firearms, and the coronavirus pandemic: findings from the 2020 California Safety and Wellbeing Survey. medRxiv preprint Available at: https://www.medrxiv.org/content/10.1101/2020.10.03.20206367v1.full.pdf 2020 22 Hatchimonji J. Swendiman R. Seamon M. Nance M. Trauma does not quarantine: violence during the COVID-19 pandemic Ann Surg 272 2020 e53 e54 32675495 23 Mohler G. Bertozzi A.L. Carter J. Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis J Criminal Justice 68 2020 101692 24 Boman J.H. Gallupe O. Has COVID-19 changed crime? Crime rates in the United States during the Pandemic Am J Criminal Justice 45 2020 537 545 25 Hodgkinson T. Andresen M.A. Show me a man or a woman alone and I’ll show you a saint: changes in the frequency of criminal incidents during the COVID-19 pandemic J Criminal Justice 69 2020 101706 26 Donnelly M.R. Barie P.S. Grigorian A. New York State and the Nation: trends in firearm purchases and firearm violence during the COVID-19 pandemic Am Surg 2020 10.1177/0003134820954827 [Epub ahead of print] 27 Gun Violence Archive Available at: https://www.gunviolencearchive.org/query 28 Lee J.C. Mervosh S. Avila Y. Harvey B. Matthews A.L. See how all 50 states are reopening (and closing again). The New York Times Available at: Nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html 2020 29 Annual Gun Law Scorecard Giffords law center https://giffords.org/lawcenter/resources/scorecard/#OH 2020 30 Documenting Ohio's reopening and path to recovery from the coronavirus (COVID-19) pandemic, 2020. Ballotpedia Available at: https://ballotpedia.org/Documenting_Ohio%27s_reopening_and_path_to_recovery_from_the_coronavirus_(COVID-19)_pandemic,_2020 2020 31 Gun Ownership by State World Population Review Available at: https://worldpopulationreview.com/state-rankings/gun-ownership-by-state 2020 32 Lyu W. Wehby G.L. Stay-at-home orders reduced COVID-19 mortality and reduced the rate of growth in hospitalizations Health Aff (Millwood) 39 2020 1615 1623 32644825 33 Lyons V.H. Haviland M.J. Azrael D. Firearm purchasing and storage during the COVID-19 pandemic Inj Prev 27 2020 87 92 32943492

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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(21)01308-9 10.1016/j.molstruc.2021.131178 131178 Article Recent advances on the development of plasmon-assisted biosensors for detection of C-reactive protein Nagy-Simon Timea a Hada Alexandru-Milentie ab Suarasan Sorina a Potara Monica a⁎ a Nanobiophotonics and Laser Microspectroscopy Center, Interdisciplinary Research Institute in Bio-Nano-Sciences, Babes-Bolyai University, T. Laurian 42, 400271, Cluj-Napoca, Romania b Faculty of Physics, Babes-Bolyai University, M. Kogalniceanu 1, 400084, Cluj-Napoca, Romania ⁎ Corresponding author: 27 7 2021 15 12 2021 27 7 2021 1246 131178131178 28 5 2021 21 7 2021 23 7 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Human C-reactive protein (CRP), an early clinical indicator of infectious or inflammatory conditions has been recently identified as a key biomarker associated with the development of COVID-19. The rapid and accurate determination of CRP level in blood serum is an urgent need to predict timely the risk of disease worsening. The emergence of nanotechnological tools has provided an attractive perspective in designing portable bioanalytical assays with fast response time, high sensitivity and specificity, and multiplexing capability for accurate, on-site disease diagnosis and monitoring. Due to their versatile optical properties, plasmonic nanoparticles (PNPs) are appealing candidates for biosensing applications. This review summarizes the advances in the application of PNPs for CRP detection and quantification. Particularly, we review the improvements attained in the detection of CRP using aggregation-based colorimetric, localized surface plasmon resonance (LSPR), plasmon-assisted fluorescence and chemiluminescence, and surface-enhanced Raman scattering (SERS) spectroscopic methods. Graphical Abstract Image, graphical abstract Keywords C-reactive protein plasmonic nanoparticles colorimetric detection LSPR MEF SERS ==== Body pmc1 Introduction As morbidity and mortality associated with local organ infections have meaningfully increased in the past years, early diagnosis has become a major challenge in view of prevention, disease management, and prognosis of inflammatory infections. Inflammation is considered as a non-specific immune response to different harmful stimuli such as pathogens, irritants, or damaged cells as a part of the body's defence mechanism. Diagnosis, prognosis, and treatment of inflammation or inflammatory infections are related to the measurement of biomarkers in biological specimens. Human C-reactive protein (CRP) is an early clinical indicator of infectious or inflammatory conditions related to various diseases and pathological conditions (e.g. sepsis, cardiovascular diseases, viral infections, etc.) [1], which has been also recently identified as a key biomarker associated with the development of coronavirus disease 2019 (COVID-19) [2]. CRP is a plasma protein composed of five identical monomeric sub-units with cyclic pentameric symmetry [3], mainly synthesized in the liver upon an inflammatory stimulus [4]. After reaching the region of the inflammatory reaction through the blood flow, CRP dissociates into the monomers that play a direct role in the inflammatory response. The CRP levels in blood plasma for humans with no inflammation are lower than 10 mg/l, in case of viral infection between 10–40 mg/l, active inflammation and bacterial infection produce levels of 40–200 mg/l, while in severe bacterial infection and burn its level raises above 200 mg/l. However, in response to trauma, necrosis of tissue and acute inflammatory events the CRP blood level can suddenly increase up to 1000-fold over the baseline by 48 h after an acute event [5]. On the other hand, chronic low-level of CRP below 5 mg/l has been also shown to play an essential role in the development of coronary heart disease, ischemic stroke, and acute myocardial infarction, therefore CRP can be used also as a predictive marker for the future development of these diseases [6]. Moreover, in the case of COVID-19 patients, the blood level of CRP may predict timely the risk of disease worsening and helps in triage of patients. Although cannot be the sole basis for accurate COVID-19 diagnosis, the CRP level can provide valuable quantitative information on the severity or critical trends of COVID-19 infection. In particular, the patients with high levels of CRP (≥26.9 mg/l) had significantly elevated risks of developing into severe cases when compared with patients with low levels [7], [8], [9]. Since different diseases have different corresponding CRP detection ranges and CRP is also present at trace levels in plasma, there is a need to develop clinical tests with a broad linear detection range and high detection sensitivity to ensure accurate CRP analysis which could be helpful in making accurate clinical decisions for appropriate medication administration. The current methods for the determination of CRP level, based on immunochemical laboratory techniques such as ELISA, immunoturbidimetric and chemiluminescent assay, are complex, expensive, time-consuming and require experienced personnel, therefore not suitable for point-of-care (POC) clinical diagnosis. As an emerging approach, the development of smart biosensing protocols for fast, routine, accurate, low sample volume, on-site, real-time identification and quantification with high specificity and sensitivity of biological entities (e.g. molecules, viruses, bacteria, etc.) related to pathological conditions represents one of the “holy grails” of modern medical diagnostics. Due to the continuous progress in the field of nanotechnology, several nanomaterials with enhanced optical, mechanical, electrical and electrochemical properties have been successfully developed and applied in biosensing applications [10]. Among them, plasmonic nanoparticles (PNPs) are very attractive candidates for such applications since they integrate into a single system several desirable properties [11]. For example, the surface chemistry of PNPs allows their functionalization with target molecules by both chemical conjugation methods or physical adsorption strategies [12]. To confer recognition function against CRP, PNPs can be functionalized with CRP-specific antibodies, aptamers or phosphocholine groups. On the other hand, PNPs feature outstanding optical properties determined by their localized surface plasmon resonance (LSPR) [13,14]. LSPR is an optical phenomenon occurring when the electromagnetic field of the light interacts with metallic NPs smaller than the wavelength of light [15]. This interaction induces a collective coherent oscillation of the surface conduction electron in resonance with the frequency of light, manifesting as a well-defined plasmon band in the UV-Vis-NIR spectrum. Noble metal NPs which exhibit well defined optical properties on account of their LSPR are widely exploited in sensing applications [16]. For instance, the interaction of biomolecules with PNPs can induce aggregation or shape transformation (etching) of the particles, both phenomena being accompanied by a drastic color change of the colloidal solution, enabling their use as colorimetric sensors simply by a standard UV-Vis spectrometer or even by the naked eye. Another way to explore PNPs as nanosensors is related to their high sensibility to the refractive index changes occurring in their close vicinity as a consequence of binding with analyte molecules, inducing a spectral shift in their LSPR maximum [16]. In this case, the detection can be spectroscopically monitored via the LSPR peak shift as a function of the biomarker concentration. The high electromagnetic field created around NPs, especially at the hot-spots of the PNPs (in the gaps created by aggregation or at the corners and edges of PNPs with anisotropic shape) is recognized to have considerable effects on the organic molecules in their closed vicinity. This can give rise to a number of outstanding plasmon-enhanced optical phenomena, which can be exploited in biosensing applications in order to achieve high sensibility [17]. For instance, metal enhanced fluorescence (MEF) may occur when fluorophores are placed at an optimal distance from the surface of PNPs in the near-field, characterized by an increased fluorescence intensity and a decreased fluorescence lifetime [18]. Surface-enhanced Raman scattering (SERS) is another highly sensitive spectroscopic technique taking advantage of the highly amplified electromagnetic fields of PNPs [19]. Besides its high sensibility, allowing the detection of SERS signal even from a single molecule, this technique is also able to identify molecular species and provide structural information. Both surface-enhanced spectroscopic techniques have attracted considerable research interest to implement them into biosensing applications. In this review, we present an overview of the recent developments of high-sensitivity biosensing assays for the detection and quantification of CRP based on PNPs. Specifically, we focused onto the colorimetric and LSPR-based approaches relying on the modification of the optical properties (colour, LSPR) of PNPs induced by the specific interaction with the CRP. Two other high sensitivity spectroscopic detection methods were also reviewed, based on the plasmon-enhanced optical phenomena occurring due to the amplified electromagnetic fields around PNPs, namely MEF and SERS. Summary of the discussed assays for CRP detection based on PNPs is presented in Table 1 .Table 1 Analytical characteristics for different CRP biosensors reported in the literature. The lowest LOD reported for each detection principle is highlighted. Table 1Optical Technique/ Principle Plasmonic platform CRP recognition element Linear Dynamic Range LOD Ref. Colorimetric AuNPs O-phosphorylethanolamine 50-450 ng/ml 50 ng/ml [28] AuNPs poly(2-methacryloyloxyethyl phosphorylcholine)-b-poly(N-methacryloyl-(L)-tyrosine methylester) <10 nM-> 100 nM 20 - 40 nM [29] AuNPs antibody 10 ng/ml-5 µg/ml - [31] AuNPs aptamer 0.889-20.7 μg/ml 1.2 µg/ml [32] AuNPs Detection and capture antibodies - 1 ng/ml [33] AuNPs Detection and capture antibodies 0-100 μg/ml 1.15 μg/ml [34] AuNPs Antibody, antigen and single-stranded DNA - 326 pg/ml [35] AuNPs antibodies 0-0.5 ng/ml 32.0 pg/ml [36] AuNPs antibodies 0-2 µg/ml 54 ng/ml [37] AuNPs biotinylated antibodies - 3•10−8 g/ml [38] LSPR triangular Ag nanoplates Phosphocholine (PC) 5 ng [42] Au-edge-coated triangular Ag nanoplate cytidine 5’-diphosphocholine 3.310−3 mg/l [43] AuNR single chain variable fragment (scFv) 1 ng/ml [44] AuNS MUA linkers 41-124.2 ng/ml 41 ng/ml [45] Au nanobipyramids CRP antibody 100 pg/ml-100 ng/ml 87 pg/ml [46] Au deposited nanostructured anodicaluminum oxide substrates CRP antibody 100 ag/ml [47] AuNP deposited onto transparent substrates Anti-CRP 0.001μg/ml [48] Au truncated icosahedra NPs assembled in an array Anti-CRP 2-160 mg/l [49] plasmonic Au nanohole array antibody 36 pg/ml [50] Fluorescence AuNPs Antibody - - [53] Au chip Antibody 33.3 zM-800 pM 33.3 zM [54] AgNPs Antibody - 0.24 µg/l [55] AgNPs Antibody 0.1-10 ng/ml 30 pg/ml [56] Ti-Ag-Ti-SiO2 chip Polymer - 10 pM [57] AuNPs Aptamer 3 pM-6 nM 1.77 pM [58] AuNPs Antibody 3.5-455 nM - [59] Au-Fe3O4NPs Antibody 9.5-2375 pM 2.5 pM [60] AgNPs Antibody 7 ×10−7-0.07 mg/ml 0.05 ng/ml [61] SERS AuNPs antiCRP 0.2 ng/ml [64] Ag nano aggregates Label-free 0.01 ng/ml [69] Au-coated magnetic nanostars antibodies 27 pM [76] Core-shell Au@AgAuNPs Detection and capture antibodies 7.7 pM [70] AgNPs antibodies 1.56-25 ng/ml 1.09 ng/ml [66] AuNPs antibodies 1-1000 ng/ml [65] core-shell Ag@Au antibodies 5 pg/ml-10 μg/ml 478 fg/ml [71] Au nanoplates anti-CRP 10−17 M [78] Fe3O4@Au core-shell antibodies 0.01 ng/ml [74] Au-Ag core-shell antibodies 0.01-1000 ng/ml 53.4 fg/ml [77] Au nano-bridged nanogaps particles + Ag coated magnetic NPs aptamers 10 fM-10 nM 10 fM (1.14 pg/ml) [75] 2 Aggregation-based colorimetric detection of CRP Colorimetric detection based on the aggregation of PNPs is one of the most powerful, rapid, and sensitive methods for real-time and on-site detection of various biomarkers simply by observing the color change of the colloidal solution with the naked eye [20], [21], [22]. The principle of this technique relies on the target-mediated aggregation of colloidal PNPs modified with a (bio)recognition element upon specific receptor-analyte binding. As the color of colloidal PNPs is strongly dependent on their morphology and electromagnetic coupling, the aggregation of PNPs is accompanied by an obvious color change of the colloidal solution detectable with the naked eye. On the other hand, the aggregation of the NPs leads to the emergence of a new plasmonic band supported by interconnected NPs, which can be evidenced by UV-Vis-NIR spectroscopy. Therefore, the concentration of the targeted (bio)analyte can be quantified by monitoring the ratio between the LSPR absorption maximum of aggregated and individual NPs. Due to their ultrahigh extinction coefficients (e.g., 2.7×108 M−1cm−1 for gold NPs (AuNPs)), PNPs enable an ultrasensitive colorimetric detection of target biomarkers with a limit of detection (LOD) in the nanomolar-picomolar range [23], [24], [25], [26], [27]. Among PNPs, spherical NPs, especially those made from gold (Au) are the most well-known sensing units in colorimetric assays based on the aggregation of NPs. In the following, we summarize some successful colorimetric assays proposed for the specific detection and quantification of CRP. A good example is the approach reported by Raj and Sreenivasan [28]. In their method, citrate-capped spherical AuNPs with a diameter of 39±3 nm were first functionalized with 16-mercaptohexadecanoic acid. The NPs affinity towards CRP was assured by tethering O-phosphorylethanolamine (PEA) onto their surface via carbodiimide chemistry. The colorimetric detection of CRP was achieved through specific molecular interaction between CRP and PEA which results in a color change of the colloidal solution due to NPs aggregation, detectable with naked eyes. As the aggregation of AuNPs is consistent with the shift and broadening of the plasmonic resonant band, UV-Vis-NIR spectroscopy was also used to evaluate the performance of the sensing method. The results showed that the developed colorimetric assay provides combined colorimetric and spectroscopic detection and quantification of CRP with the LOD of 50 ng/ml and a linear range of detection from 50 to 450 ng/ml. Besides CRP determination in aqueous solution, the designed approach enables the measurement of CRP in blood samples in the nanogram range. At the same time, the study conducted by Reed and co-workers provided important findings on biomolecular interaction between CRP and lipid-coated AuNPs [29]. Specifically, their work demonstrates that CRP can recognize and bind to the lipid-coated AuNPs via a reversible calcium-bridging mechanism. Although this study does not focus on CRP detection, the information it provides is useful for the subsequent design of CRP sensors. For instance, the calcium dependent binding of CRP to some biomolecules was later exploited by Yusa et al. to construct a colorimetric sensor for label-free detection of CRP in aqueous solution [30]. In this work, biomimetic block copolymer-protected AuNPs were prepared using a thiol-terminated biomimetic block copolymer, poly(2-methacryloyloxyethyl phosphorylcholine)-b-poly(N-methacryloyl-(L)-tyrosine methylester) as both, reducing and stabilizing agent. The obtained NPs exhibit remarkable colloidal stability over a wide range of pH and at a high salt concentration. The results showed that the prepared NPs facilitate the sensitive detection of CRP with a LOD between 20 and 40 nM. Significantly, the dynamic range for CRP quantification corresponds to the clinically relevant upregulation of CRP from normal (<10 nM) to acute-phase levels (>100 nM). Antibody-conjugated AuNPs were proposed by Kim et al. as sensing units to develop a homogeneous colorimetric immunoassay for qualitative analysis of CRP in aqueous solution and serum samples [31]. In their concept, the authors exploit the pentameric structure of CRP and the immunoreaction of CRP with AuNP probes to promote the aggregation of the NPs as a function of CRP concentration. Specifically, one of the monomeric units of CRP binds to antibody-modified AuNPs, while the rest of the tetrameric portion of CRP promotes the aggregation of the NPs, leading to a red-shift of the LSPR band and a visible color change of the colloidal solution. To overcome the limitations caused by the hook and improve the linear range of CRP assay, the authors intentionally induced the aggregation of AuNPs by pre-spiking the serum sample with CRP. The designed colorimetric assay achieved a dynamic range for CRP detection of 10 ng/ml to 5 µg/ml. Recently, Daniel-da-Silva and co-workers have demonstrated a facile, fast, sensitive, and selective colorimetric assay for CRP detection using as sensing elements citrate–capped AuNPs modified with a guanine (G)-rich single-stranded DNA (ssDNA) aptamer [32]. The specificity of the designed sensor toward CRP was achieved through the high affinity of the aptamer against CRP. The ability of the aptamer to switch its conformation upon specific binding to targeted CRP was exploited here to induce the CRP-mediated aggregation of the aptamer-modified PNPs in buffer salts. CRP detection was performed by naked eye assessment of the aggregation induced color change of the colloidal AuNPs, while the concentration of CRP was quantified via UV-Vis-NIR extinction spectroscopy by monitoring the absorbance ratio between A670 nm and A520 nm. The proposed strategy for CRP determination yielded a linear sensing range of 0.889–20.7 μg/ml and a LOD of 1.2 µg/ml. The suitability of the designed colorimetric assay for practical applications was also demonstrated by the determination of CRP in diluted human urine. Initially used as colorimetric transducers for quantification of a specific biomarker, PNPs were later integrated into various colorimetric immunoassays that allow simultaneous detection of multiple biomarkers with high specificity and selectivity. In this matter, Long et al. proposed an enzyme-free alternative to enzyme-linked immunosorbent assay (ELISA) for selective detection of CRP, prostatic specific antigen (PSA) and α-fetoprotein (AFP) [33]. In their method, called metal-linked immunosorbent assay (MeLISA), alkyne-functionalized AuNPs were exploited as colorimetric sensing units, while silver NPs (AgNPs) of spherical shape were introduced to promote a signal amplification mechanism that replaces the enzyme element in ELISA. Remarkable, the developed MeLISA strategy enables the fastest AuNPs-based colorimetric assay ever reported until 2016, yielding a LOD of 1 ng/ml for CRP, 0.1 ng/ml for PSA and 0.1 ng/ml for AFP, respectively. Later, Jiang et al. fabricated a colorimetric immunoassay that enables the simultaneous detection of three inflammatory markers (CRP, procalcitonin (PCT) and interleukin-6 (IL-6)) by the naked eye with a broad detection range [34]. The proposed assay involves the use of negatively charged AuNPs, a series of phosphorylated short peptides and alkaline phosphatase (ALP), a widely used labeling enzyme in immunoassays. The principle of peptide-ALP-AuNPs immunoassay (PAAI) for simultaneous detection of CRP, PCT, and IL-6 is schematically illustrated in Fig. 1 (A).Fig. 1 Principle of peptide-ALP-AuNPs immunoassay (PAAI) for simultaneous detection of the inflammatory markers (IL-6, PCT, and CRP). (A) The scheme of AuNPs-based signal readout. Negatively charged phosphotyrosine or phosphoserine in the short peptides acts as a molecular switch for the AuNPs aggregation. When ALP removes the negatively charged phosphate group from the peptide, AuNPs aggregation is triggered after addition of the resulting dephosphorylated peptide. (B) The scheme of PAAI based on three kinds of peptides for simultaneous detection of IL-6, PCT, and CRP. (C) The color, spectra change, and corresponding relationship between the concentrations of CRP, IL-6, PCT and the A600nm/A530nm. Fig. 1Reprinted with permission from ref. [34]. Copyright (2018) American Chemical Society According to their concept, ALP removes the negatively charged phosphotyrosine or phosphoserine groups in the short peptides thus yielding a positively charged product. This, in turn, causes the aggregation of negatively charged AuNPs accompanied by a visible color change of the colloidal solution from red to blue and the emergence of a new plasmonic band supported by interconnected NPs. The quantitative analysis of CRP, PCT, and IL-6 in aqueous solutions and serum samples was performed by monitoring the aggregation ratio (A600 nm/A530 nm). By using three peptides with different sensitivities (the most sensitive, a moderately sensitive, and the most insensitive) quantitative analysis of multiple biomarkers in serum samples with a broad and controllable detection range from pg/ml to μg/ml was accomplished (Fig. 1(B) and (C)). The proposed colorimetric immunoassay provides reproducible, selective detection of CRP, PCT, and IL-6 with the LOD of 1.15 μg/ml for CRP, 0.24 ng/ml for PCT and 12.51 pg/ml for IL-6. Owing to its high specificity, sensibility, tunable detection range and multiplexing capacity, the designed colorimetric immunoassay could be promising for applications in clinical diagnostic. Recently, Mao et al. fabricated a multiplexed colorimetric immunoassay for the simultaneous detection of CRP and IL-6 with high specificity and selectivity [35]. The design of this colorimetric immunoassay involves the use of bifunctional spherical AuNPs decorated with antibodies and single-stranded DNA (ssDNA) as detection probes, AuNPs conjugated with complementary ssDNA as signal amplification probes and antibody microarrays. The mechanism of detection relies on dual signal amplification to improve the sensitivity of the protein microarray. Specifically, AuNPs labeled with antibodies and ssDNA bind to the targeted molecules and then it hybridized with complementary ssDNA conjugated AuNPs, thus increasing the signal of the protein microarrays. On the basis of this, AuNPs initiated Au reduction and subsequent deposition, leading to the second amplification of the output signal of the immunoassay. Finally, the colorimetric visualization was performed with a microscope. and the data were analyzed using a Gray analysis 5.0 software. The principle of detection is illustrated in Fig. 2 .Fig. 2 Schematic representation of the detection process of CRP and IL-6. Fig. 2Reprinted with permission from ref. [35]. Copyright (2020) Elsevier. By exploiting the cooperation of the two mechanisms of signal amplification a highly sensitive analysis of CRP and IL-6 in aqueous solution could be accomplished with the LOD of pg/ml (326 pg/ml for CRP and 8 pg/ml for IL-6). The clinical applicability of the designed multiplexed immunoassay was evaluated in 28 clinical serum samples and the results showed a good agreement with those obtained by the ELISA kit. At the same time, Park et al. developed a colorimetric immunoassay using AuNPs conjugated with 6X-histidine (6X-his) peptide as colorimetric transducers, and nickel-horseradish peroxidase (Ni2+-HRP) as enhancer of the colorimetric signal in ELISA [36]. The performance of the developed assay was assessed by quantifying CRP in standard and serum samples and comparing the results with conventional ELISA assay. The results showed that the developed colorimetric immunoassay enables an increase of 12-fold sensitivity for detecting CRP compared to ELISA, yielding a LOD of 32 pg/ml in human serum samples and a wide linear range of detection (0-0.5 ng/ml). Microfluidic immunoassays were also fabricated and tested for colorimetric determination of CRP with the aim to fulfill the requirements for POC diagnosis. For instance, Lin et al. demonstrated a proof-of-concept for a new, innovative paper-based microfluidic immunoassay employing AuNPs integrated into a lateral flow assay [37]. In their design, the color changes caused by NPs aggregation were detected using a commercial smartphone. The fabricated colorimetric immunoassay enables qualitative and quantitative analysis of CRP in aqueous solution, plasma, and whole blood with a LOD of 54 ng/ml and a linear range of detection up to 2 µg/ml. Later, Russell and de la Rica designed a novel type of colorimetric transducer consisting of a piece of paper printed in toner with a specially designed pattern [38]. The results showed that the fabricated paper transducer can detect the plasmon variations caused by the aggregation of AuNPs using a mobile device as the reader. The suitability of the proposed printable colorimetric transducer toward practical applications was demonstrated by quantitative determination of CRP with a similar LOD compared to a competitive ELISA (3×10−8 g/ml). 3 Localized Surface Plasmon Resonance (LSPR)-Based Detection of CRP Besides their own size, shape, and dielectric properties, the LSPR of a metallic NPs also depends on the local refractive index (RI) change leading to some well-quantifiable spectral shifts of the LSPR extinction peak. The principle of LSPR sensors is based on the quantification of LSPR peak modifications upon binding of analyte molecules to the surfaces of NPs conjugated with specific receptors. The local RI sensitivity of metal NPs defines the sensing depth of the LSPR sensor, therefore the dependence of RI sensitivity on the properties of NPs has been extensively investigated. It was found that the RI sensitivity increases for nanocrystals with higher curvatures [39], larger polarizabilities [40], and longer plasmon wavelengths [41]. In general, NPs with high shape anisotropy (eq. nanoplates, bipyramids, etc) or increased aspect ratio (width/height) results in LSPR red-shifted to longer wavelengths. To date, various nanostructures with high RI sensitivity have been developed and applied in the LSPR-based detection of CRP. For instance, Fournet et al. developed highly sensitive solution phase LSPR bioassays based on triangular silver nanoplates (TSNPs) for the detection of CRP. Their results revealed that TSNPs with the highest aspect ratios exhibit the highest LSPR sensitivities, thus the lowest detection level of CRP was reached (5 ng) [42]. Another approach to increase the RI sensitivity of plasmonic NP was addressed by Zhang et al. [43]. Namely, TSNPs with various lengths with LSPR from 600 nm to 1197 nm were subjected to galvanic replacement reaction to obtain Au-edge-coated-TSNP. Detailed characterization regarding the stability and ensemble RI sensitivity over figures of merit was examined using discrete dipole approximation calculations and single nanostructures dark field microscopy measurements. The obtained results reveal that Au-edge-coated-TSNPs possess higher sensitivity and stability by hindering NPs etching in saline solutions. The highest ensemble RI sensitivity value of 1816 nmRIU−1 was measured for an Au-edge-coated TSNPs sol with a LSPR maximum at 1197 nm. This Au-edge-coated-TSNP based nowash detection method allowed for rapid and sensitive detection of high sensitive CRP (hs-CRP) at concentrations as low as 3.3×10−3 mg/l. As the sensitivity of LSPR sensing drastically decreases with the distance between the surface of the NP and the target binding event, Byun et al. have chosen as recognition element for CRP detection a short single-chain variable fragment (scFv) instead of a full-length antibody [44]. The synthesised scFv contains cysteine tags that enable conjugation to Au nanorods (AuNR) surface, but also feature recognition sites for CRP target binding. The assay relies on the LSPR peak-shift of scFv-functionalized AuNR induced by the binding of CRP. Due to the shortness of the antibody fragment (3 nm instead of 15 nm in the case of full-length antibody), the obtained system was found to display a much higher sensitivity compared to one using a full-length antibody as a capture receptor. CRP in human serum was quantitatively detected at concentrations lower than 1 ng/ml. The LSPR detection systems are also influenced by the employed surface chemistry method, therefore its optimization for maximum protein immobilization and retention is relevant as well. Garifullaina et al. conducted a detailed study and compared four representative Au surface functionalization methods in attaching biomolecules to nonspherical plasmonic Au nanostructures (AuNS): simple physical adsorption, microcontact printing, and two thiol linkers 11-mercaptoundecanoic acid (MUA method) and thiolated PEG acid linkers (HS−PEG−COOH method), respectively [45]. The efficiencies of the different surface chemistry methods were estimated based on the LSPR shifts before and after incubation of AuNS in anti-hCRP. The study concluded that binding via the MUA linkers resulted in the most efficient and reproducible antibody immobilization, retaining the specificity toward hCRP giving a LOD and LOQ of 41.0 and 124.2 ng/ml, respectively. Another study uses Au nanobipyramids (AuNBP) to create a sensitive sandwich LSPR immunosensor for CRP detection [46]. Here an enzyme catalysed precipitation reaction was applied to overcome the limitation of sensing distance in the case of large proteins. 4-chloro-1-naphthol (4-CN) was precipitated onto the surface of CRP antibody functionalized AuNBP substrate which induced additional large change of local RI, manifested in a strong LSPR-shift after the sandwich structure formed. By monitoring the LSPR-shift, this sensing method enabled the quantitative analysis of CRP from 100 pg/ml to 100 ng/ml with good linearity, reaching a LOD of 87 pg/ml. Yeom et al. also applied the sandwich assay approach to increase the sensitivity of their LSPR sensor system [47]. The reported sensor consists of Au deposited nanostructured anodicaluminum oxide (AAO) substrates functionalized with CRP antibodies. The binding of the CRP antigen induces changes in the RI of the sensing membrane, and the LSPR-shift in the reflection spectrum of the membrane can be measured in real-time. By applying the sandwich structure using AuNP-labeled CRP antibodies, the sensitivity of the sensor increased 1.84 times reaching a LOD of 100 ag/ml. Portability and rapidity of such infections marker detection sensors are a key requirement, particularly in the management of life-threatening infections and sepsis. Therefore, the development of POC testing devices is needful for early diagnosis of diseases. Oh et al. recently developed a selective portable LSPR sensor chip for the sensitive detection of CRP using a cuvette cell system, as shown in Fig. 3 [48]. The sensor is based on AuNP deposited onto transparent substrates in form of stripes functionalized with anti-CRP. The detection sensitivity of the sensor chip was evaluated for various CRP concentrations, with the low detection limit of 0.001 μg/ml confirming the superiority of the detection platform developed in this study.Fig. 3 Schematic illustration of the anti-CRP-based LSPR sensor chip for CRP detection. Fig. 3Reprinted with permission from ref. [48]. Copyright (2019) Frontiers Media SA. Furthermore, James-Pemberton et al. recently developed a multiplexed biophotonic assay platform for a clinically useful triage of CRP detection from diluted whole blood sample, requiring no preparation and giving result in only 8 minutes [49]. The assay is based on the LSPR of Au truncated icosahedra NP with a diameter of approximately 60 nm assembled in an array and functionalised with a self-assembled monolayer to allow EDC–NHS coupling of anti-CRP to the surface. Control samples functionalised with BSA or FBR were also evaluated in order to correct for variations in temperature, non-specific binding and variations in the illumination field. The detection technique is based on the variation in the intensity of the scattered light from the array elements when illuminated in total-internal reflection mode, calibrated for refractive index sensitivity. The accuracy and precision of the CRP sensor assay were assessed with 54 blood samples containing spiked CRP in the range 2-160 mg/l. The mean accuracy was 0.42 mg/l with Confidence Interval (CI) at 95% from 14.7 to 13.8 mg/l and the precision had a Coefficient of Variation (CV) of 10.6% with 95% CI 0.9% - 20.2%. Another portable digital NP-enhanced plasmonic imager for rapid detection of the inflammatory sepsis-related biomarkers, PCT and CRP from blood serum has been recently developed by Belushkin et al. [50]. The detection principle is based on a plasmonic imaging mechanism based on antibody-conjugated AuNP binding in the presence of the biomarker to plasmonic Au nanohole array surface functionalized with complementary antibodies (Fig. 4 (A)). Individual AuNP bound inside or close to the nanoholes create strong local intensity contrast allowing digital detection of single analyte molecules. The bioassay is performed in a single step without signal amplification or washing procedures and enables quantification of individual molecule binding on the sensor surface in complex media. This compact and low-cost device, a prototype DENIS reader (Fig. 4(B)), can identify biomarker level in less than 15 min, achieving an outstanding limit of detection of 21.3 pg/ml for PCT and 36 pg/ml for CRP.Fig. 4 Portable digital NP-enhanced plasmonic imager for biomarkers detection. (A) PCT and CRP, which are blood-circulating protein biomarkers secreted by the host body in response to systemic inflammation, are detected using DENIS. A single-step bioassay directly in human serum enables rapid molecular results, critical for the early diagnosis of sepsis, by detecting individual Au-NPs binding to Au-NHA. (B) A prototype DENIS reader developed for highly sensitive and multiplexed detection of biomarkers. The device uses a CMOS camera and a narrow-band LED source to record the transmitted images from a nanoplasmonic chip. (C) SEM image of a Au-NHA area after a bioassay showing the bound NPs. Inset shows a single NP bound inside a nanohole. (D) Plasmonic image of a Au-NHA area with bound NPs. The binding of Au-NPs on Au-NHAs causes local transmission suppression through distortion of plasmonic excitations in the Au-NHA and can be digitally detected using far-field imaging. The inset shows a normalized intensity contrast induced by a single NP trapped in a nanohole. Fig. 4Reprinted with permission from ref [50]. Copyright (2020) John Wiley & Son. 4 Plasmon-assisted fluorescence and chemiluminescence based detection of CRP Fluorescence is the emission of electromagnetic radiation generated by the radiative relaxation of excited electrons inside a material. Particularly, once the material absorbs light, electrons transition to the first (S1) or second excited singlet level. After an extremely short period of time, they relax to the ground vibrational state of S1. Afterwards, the electrons return to their fundamental electronic state through a radiative process, causing energy release under the form of fluorescence emission. Fluorescence-method is one of the most often applied analytical approaches for the detection of biomarkers [51]. The sensing mechanism is based on linking the analyte concentration with the emission intensity. The relationship can be direct through fluorescence amplification (higher analyte concentration, higher emission intensity) or indirect through fluorescence quenching (higher analyte concentration, lower emission intensity). However, fluorescence-based sensors present limitations when it comes to sensitivity in the CRP molecule's clinical region of interest due to low quantum efficiency, photobleaching and autofluorescence [52]. Therefore, it is absolutely necessary the development of new methods that could solve this deficiency of fluorescence-based immunoassays. Lately, it was found that the addition of PNPs in the proximity of fluorophores resulted in two possible processes: metal enhanced fluorescence (MEF) when an ideal distance between the plasmonic NP and the fluorophore is obtained (10-90 nm) or fluorescence quenching when the fluorophore-NP distance is too short (0-10 nm). Both phenomena are able to enhance the sensitivity of fluorescence-based immunoassays if they are used under proper experimental conditions. In 2008, Hong et. al. developed the first MEF-based biosensor for the quantification of CRP molecules [53]. Briefly, they formed a sandwich protein complex captured by its specific antibody to metal NPs (Au and Ag) on one side and a fluorophore (Cyanine 5 or Alexa 647) on the other side. Next, they thoroughly studied how the NPs metal type (Au and Ag) and the solvent can enhance the sensitivity of fluorophore-mediated biosensors, superior enhancements being obtained when AuNPs dissolved in 1-butanol were used. In the end, they translated the sensor into an automated prototype for the simultaneous detection and quantification of four cardiac markers (CRP, myoglobin, cardiac Troponin I, and B-type natriuretic peptide). The preliminary results demonstrated that their system could quantify all the markers within 10 minutes with the same precision as single-detection sensors. Beside precision, the detection limit of an immunoassay should be as low as possible. The detection of a single CRP molecule was obtained using a 20 nm Au-nanopatterned biochip proposed by Heo et. al. [54]. The target molecule was identified via a total internal reflection fluorescence microscope based on evanescent field-enhanced fluorescence imaging. The same antibody-sandwich method was used to capture the CRP molecule between the Au chip and the Alexa 488 fluorophore. By reducing the CRP concentrations, the relative fluorescence intensities linearly decreased in the range of 33.3 zM - 800 pM, with a correlation coefficient of 0.9925. The determined LOD was 33.3 zM, the equivalent of a single CRP molecule. Noteworthy, they observed that for CRP concentration below 500 zM, the optimum incubation time between the CRP antigen and the antibody needs to be increased from 1 to 4 h. Moreover, their technique was successfully implemented on the detection of low CRP concentrations even in human serum samples. The NPs geometry is another factor that has a huge impact on the MEF effect and, in consequence, on the detection's sensitivity. Spherical NPs were found by Zhang et. al. to exhibit improved fluorescence enhancement compared to triangular ones, due to increased scattering [55]. Therefore, spherical AgNPs were deposited on a microplate and the platform was used to perform, for the first time, solid-phase sandwich assay via MEF for the detection of human CRP. In this case, the protein sandwich-capture was performed with mouse-specific antibodies between the AgNPs and a DY-647 fluorophore. The solid-phase sandwich assays revealed a 0.24 µg/l LOD when enhanced by AgNPs and a 4.5 µg/l one in the absence of NPs. In order to obtain these results, a complex purification was performed after the deposition of each component, which could have led to system destabilization. An easier purification process was described by Zhao et. al., who managed to combine magnetic beads and AgNPs functionalized with specific antibodies (Fig. 5 ) to detect CRP molecules via a Ag+ turn “on” fluorescence method [56]. The use of physically and chemically stable magnetic beads provided the possibility of easy and efficient purification, separation and concentration processes, while AgNPs triggered and enhanced the fluorescence emission. Once the sandwich-type immune-complex was formed, it was magnetically separated and dissolved in a solution containing an inactivated Rhodamine B-based fluorophore dissolved in hydrogen peroxide (H2O2). The interaction between AgNPs and H2O2 generated numerous Ag+, turning “on” the fluorophore emission, whose intensity was correlated with the detected CRP concentration. Using this novel method, they obtained good linear response range between 0.1-10 ng/ml, while the calculated LOD was just 30 pg/ml. Moreover, the same device was able to detect specifically even α-fetoprotein in real human samples with extremely high accuracy, but cheaper than the commercial methods, demonstrating great promise even in clinical applications.Fig. 5 Schematic illustration of Ag+ triggered fluorescence detection for protein biomarkers. Fig. 5Reprinted with permission from ref [56]. Copyright (2017) Ivyspring International Publisher. All the aforementioned immunoassays use antibodies as an antigen-capture agent. Even though antibodies are extremely specific, they are unstable and expensive. A cheaper bio-chemical hybrid CRP sensing device was reported by Matsuura et. al. [57]. They used a synthetic polymer instead of antibodies on one side of the protein-sandwich as a CRP-specific ligand, making it a more affordable method. The artificial polymer Poly(2-methacryloyloxyethyl-phosphorylcholine) was grafted on a four layers chip (Ti, Ag, Ti, SiO2) using controlled radical polymerization and the CRP molecule was captured between the polymer and the specific antibody. Afterwards, by adding a biotin-streptavidin complex between the antibody and the Cy5 fluorophore to increase the NP-fluorophore distance, they managed to reduce the background fluorescence making the system more sensitive and obtaining a LOD of 10 pM. This work was the first one to use a ligand as a substitute of the specific antibody to capture and detect via MEF the CRP molecules, making a step forward towards the development of more affordable CRP fluorescence-based sensing devices. Another substitute for antibodies is aptamers. They exhibit lower development time, smaller sizes, and higher stability. The first DNA aptamer-based optical turn “off” nanosensor was released by Ghosh et. al in 2019 [58]. The device was composed of a deoxyribonucleic acid aptamer linked on the 3’ terminus to a AuNP and on the 5’ one to a quantum dot (QD). The principle used in this work was fluorescence resonance energy transfer (FRET) and, therefore, increasing the CRP concentration resulted in a decrease of the QD's photoluminescence. The linear quenching behaviour was obtained between 3 pM and 6 nM, while the LOD was calculated to be 1.77 pM. Remarkably, only 5 µl of sample was required to perform the measurement. Moreover, the nanosensor showed promising results even against CRP spiked human samples, exhibiting 10% fluorescence quenching at 10 pM of target molecule. Unfortunately, at high concentrations, a limitation called the Hook effect is encountered due to the total saturation of the capture agent. Bravin et. al. developed a multi-component sandwich immunoassay, which could detect a wide range of CRP concentrations without being affected by the Hook effect [59]. The sandwich system was based on an antiCRP-functionalized AuNPs and an antiCRP-conjugated FRET donor-acceptor complex. In consequence, the resulting FRET fluorescence is quenched by the AuNPs through the nanomaterial-surface energy transfer phenomenon. The linear quenching effect was obtained in the clinical range of interest between 3.5-455 nM. Furthermore, the assay showed high accuracy and reproducibility when tested on real human serum samples, obtaining an error percentage of just 3 ± 3%. Another method that is widely used in the detection of biomarkers is Chemiluminescence (CL). CL is electromagnetic radiation, released under the form of light after a chemical reaction. This method presents fast times of detection combined with low costs. Therefore, Xing et. al developed a CL immunoassay for the detection of CRP based on antibody functionalized Au-Fe3O4 core-shell NPs as magneto-plasmonic nanocarriers and glycerophosphoryl (GPC) as blocking agent [60]. When ultrasensitive detection is wanted, non-specific protein adsorption on the sensing platform could be a big problem due to the increased noise which reduces the diagnose performance. By eliminating non-specific adsorption of CRP molecules, the LOD of the device decreased by 3.8 times from 9.5 (without GPC) to 2.5 pM (with GPC). The linear response was obtained between 9.5 to 2375 pM with a correlation coefficient as high as 0.9980. The sensing device exhibited great efficiency also against clinical samples, even surpassing the Immunoturbidimetric Assay which was approved by the US Food and Drug Administration for clinical use. However, CL-based immunoassays present sensitivity limitations due to low reaction yield and low quantum yield. Thus, a novel metal enhanced CL (MEC) signal tag was designed by Zong et. al. to be used as an ultrasensitive immunoassay for the detection of CRP [61]. The target molecule was captured in an antibody-sandwich between a CL substrate and a plasmonic complex with the role of CL enhancer. The plasmonic part was provided by the hybridization of two kinds of AgNPs probes: (A) DNA-hemin/DNA-A/biotin-DNA-AgNPs and (B) DNA-hemin/DNA-B-AgNPs. Evaluating the CL intensity against CRP concentrations, lead to a wide linear detection range from 7×10−7 to 7×10−2 mg/ml. The LOD was estimated to be around 0.05 ng/ml. In interaction with different markers such as CRP, human myoglobin, and myoglobin isoenzyme of creatine kinase, the proposed immunosensor chip presented high CRP selectivity. Furthermore, when tested against real human samples the MEC immunoassay obtained great results with a relative error of just 4.94%. 5 Surface Enhanced Raman Scattering (SERS) based detection of CRP The enhanced electromagnetic field around PNPs generated as a consequence of collective oscillations of conduction electrons, known as surface plasmon resonances, is recognized to have considerable effects on the organic molecules in their closed vicinity, giving rise to a number of remarkable phenomena with particular interest in imaging and detection applications. Specifically, the Raman scattering from molecules located in the amplified electromagnetic field near PNPs can be drastically enhanced, termed as Surface Enhanced Raman Scattering (SERS) [62], allowing even single-molecule detection [63]. Compared to the aforementioned spectroscopic detection techniques, Raman spectroscopy excels by its high specificity, with the ability to identify and provide structural information about molecular species from their unique vibrational Raman fingerprint. Therefore, SERS-based biosensing approaches not only enables ultralow detection, but also can identify molecular species and provide additional information about conformation etc. The SERS technique was also widely explored in the detection of infection biomarkers such as CRP, using different plasmonic nanostructured optimized for high enhancement. The first SERS bioassay for quantitative human CRP analysis was reported in 2008 by Campbell et al. [64]. In this study, SERS was used with the aim of improving CRP detection in an ELISA system by investigating the coloured label generated by the enzymatic reaction. A common substrate for alkaline phosphatase (AP) is converted in SERS active species upon the action of AP following the detection of CRP by AP-antiCRP antibodies. By replacing the colorimetric detection step in ELISA with SERS, the limit of detection for CRP is highly improved, from 7 ng/ml to 0.2 ng/ml. An enzymatic strategy to activate reduction caged reporters in SERS bioassays for CRP detection was also reported by Guo et al. [65]. Through enzymatic activation of a Raman inactive reporter using horseradish peroxidase (HRP), leucomalachite green (LMG), new MG Raman active agents that give a strong SERS signal are obtained. For CRP detection immunoassay, CRP biomarker is captured with antibodies attached on agarose beads which form a sandwich with HRP conjugated detection antibodies. HRP activates the SERS reporters in the presence of H2O2 which mixed with AuNPs generates a reliable signal with a strong peak at 1615 cm−1 and a linear dependence on the CRP concentration in the 1-1000 ng/ml range. Due to their versatility, metallic NPs can also be successfully used as artificial enzymes, called nanozymes. For CRP detection, the catalytic activity of AgNPs was exploited in a surface based Ag-linked immunosorbent assay (SLISA) [66]. Specifically, AgNPs were functionalized with antibodies specific for CRP target antigen to replace the usual enzymes used in a traditional ELISA. These bind to the CRP immobilised on the substrate by the capture antibody and when 3,3′,5,5′-tetramethylbenzidine (TMB) substrate is added, the AgNPs catalyse the oxidation of TMB by H2O2. The reaction product, analysed by SERS, is dependent on the AgNPs concentration which is related to the detected CRP. Therefore, by replacing the conventional enzymes with AgNPs, a low LOD of 1.09 ng/ml for CRP is achieved. Aggregated PNPs present a high density of hot-spots at the junctions and gaps created between interconnected NP with enhanced electromagnetic fields which can drastically enhance the Raman signal of entrapped molecules, therefore are widely explored as SERS substrates in sensing applications. For instance, Benford et al. used SERS active aggregated AuNPs to detect CRP as a cardiac biomarker together with b-type natriuretic peptide (BNP) and cardiac troponin (cTn) for detection of acute coronary syndrome, employing a nanofluidic device [67]. Later, the same authors improved this platform to specifically capture CRP using agarose beads functionalized with an anti-CRP antibody [68]. The captured biomarker displaces a peptide fragment that contains the binding epitope of the antibody labeled with Rhodamine-6G (R6G). In the presence of the analyte, an increase in the SERS signal of R6G is noticed, proportional to the amount of the detected analyte. Ag nanoaggregates were also exploited by Kim and co-authors [69] to develop a label-free SERS detection chip for CRP detection in serum. Specifically, they functionalized a glass coverslip with phosphocholine coated Ag nano aggregates to selectively capture CRP in a capillary gap created to control the flow of the CRP solutions. Due to a less than 4 nm distance between the nanoaggregates and CRP, and a small difference between the radius of the aggregated AgNPs (20 nm) and the size of CRP (12 nm), this chip presents a high sensitivity with increased chances of protein attachment to the interparticle hotspots with maximized field enhancement. The minimum detection amount of CRP was 0.01 ng/ml in buffer and 0.1 ng/ml in 1% serum when aggregated AgNPs were layered in a 200-300 nm thickness. Interestingly, higher sensitivity and resolvability were recorded for the 2800-3000 cm−1 range, compared to fingerprint (1000-1600 cm−1) and low frequency (<900 cm−1) regions. The highest sensitivity was reported for asymmetric C–H stretching mode at 2930 cm−1. The cross-reactivity was also tested, and the chip proved an excellent selectivity and specificity for CRP. A key issue in SERS-based detection bioassays represents the development of SERS-substrates which provide highly amplified, reproducible, stable and quantifiable SERS signals. To respond to these queries, researchers proposed to synthesize core-shell metallic nanostructures with well-defined narrow interior nanogaps. Raman reporters localized in these built-in hot spots in the core-shell nanostructure junction experience extremely enhanced local electromagnetic fields providing a strong SERS signal. Moreover, reporter molecules are well-retained in these gaps, protected by the shell from leakage, degradation and uncontrolled aggregation-induced enhancement, therefore they offer stable and quantitative SERS signal. This approach was also recently employed in the development of sensing SERS nanoplatforms for CRP detection, reaching very low LOD values. For instance, Li et al. [70] reported a new SERS-active core-shell PNP-based on an etching-assisted approach for multiplex analyte detection. They labelled Au cores with Rhodamine B as Raman reporter and formed a Ag shell on top by depositing Ag atoms on the Au cores in the presence of Pluronic F127. Then, the Ag shell was etched with HAuCl4 to form Au@AgAuNPs with nanometric gaps inside which induces an increased Raman signal of trapped reporters, reaching a LOD of 7.7 pM. These SERS tags were also successfully employed for multiplex quantitative detection of CRP and PSA and as cancer cell imaging agents after conjugation with specific aptamers. Another core-shell SERS nanotag-based biosensor was also developed by Liu et al. [71] for CRP detection using photonic crystal beads (PCBs) as carriers. SERS nanotags were built on a Au core with a Ag shell and Nile blue A dye embedded at their interface to provide a strong Raman signal. The SERS tags were conjugated with detection antibodies to form an immunocomplex with capture antibodies loaded on PCBs when CRP is present. In this way, the signal from the nanotags can be recorded under excitation with a 785 nm laser line and the detected CRP quantified based on the Raman intensity of 595 cm−1 peak. A high sensitivity and a LOD much better than previously reported values of 478 fg/ml can be obtained by using this biosensor. Additionally, the biosensor shows high stability, reproducibility, reliability, and a high signal-to-noise ratio. Also, it performs well over a wide range of clinical CRP concentrations, ranging from 5 pg/ml to 10 μg/ml, and has a correlation coefficient with the ELISA clinical reference method of 99.82% when is tested on five real human serum samples with CRP concentration ranging from 70.2 pg/ml to 7.7 μg/ml. Raman reporter-embedded Au-core Ag-shell NPs were also employed by Cong et al. for the design of a rapid and accurate POC SERS-based lateral flow assay for selective quantitative detection of CRP [72]. The specificity of the fabricated SERS nanotags toward CRP was achieved by conjugating them with a CRP antibody. CRP quantification was performed by monitoring the SERS intensity of a characteristic Raman reporter band following the exposure of NPs to different concentrations of CRP. The designed SERS-based lateral flow assay provides reproducible, selective and high-sensitive detection of CRP with the LOD of 0.01 ng/ml. The suitability of the presented SERS-assay for practical applications was also demonstrated by the determination of CRP in plasma samples of irradiated nonhuman primates. By endowing PNPs with magnetic properties has brought new advances in SERS-based detection of CRP, enabling rapid separation enrichment of targets from a complex solution. For example, a smart, label-free SERS immunosensor for rapid and hypersensitive analysis of CRP was demonstrated by Yang et al. using porous magnetic Ni@C nanospheres and calcium carbonate (CaCO3) microcapsules as the SERS sensing platform and AgNP-coated silicon wafer as an enhancer of the SERS signal [73]. The fabricated immunosensor enables specific capture of CRP via antibody-antigen interactions, yielding a LOD of 0.01 pg/ml and a linear range for CRP quantification from 0.1 pg/ml to 1 μg/ml. Liu et al have recently proposed a novel Fe3O4@Au core-shell NP based SERS - lateral flow immunoassay biosensor (Fig. 6 ) for simultaneous and quantitative detection of two infection biomarkers: serum amyloid A (SAA) and C-reactive protein (CRP), respectively [74]. The Fe3O4@Au nanotag is composed of three main elements, specifically, a superparamagnetic Fe3O4@Au core as the active SERS substrate, a layer of Raman reporter molecules 5,5-dithiobis-(2-nitrobenzoic acid) (DTNB) adsorbed on the Au shell and surface-modified monoclonal antibodies which specifically recognize SAA/CRP (Fig. 6(A)). The superparamagnetic component served as a separation tool for magnetic enrichment of SAA/CRP in unprocessed blood samples. SERS-based quantified analysis of target infection biomarkers revealed a 100- and 1000-fold sensitivity enhancement by the proposed sensor compared to those of standard colloidal Au-based lateral flow assays, reaching a LOD of 0.1 ng/ml and 0.01 ng/ml for SAA and CRP, respectively.Fig. 6 Schematic illustration of (a) the preparation of antibody-conjugated Fe3O4@Au SERS nanotags, and (b) the detection principle of the Fe3O4@Au-based SERS-LFA strip for simultaneous quantification of SAA/CRP. Fig. 6Reprinted with permission from ref [74]. Copyright (2020) American Chemical Society. By combining Ag coated magnetic NPs (Ag MNPs) and Au nano-bridged nanogaps particles (Au NNPs) an innovative aptamer-based SERS biosensor has been recently developed by Hu et al showing high affinity to CRP [75]. As schematically presented in Fig. 7 (A) and (B), the sensor is composed of 4-ATP-labeled Au NNPs with narrow nanogap as SERS signal probe and Ag MNPs as a capture substrate, respectively. The approach of using Ag MNPs instead of conventional Fe3O4 NP, also served for magnetic separation, Au NNPs-CRP-Ag MNPs complex enhanced the intensity of the SERS signal 13 times compared to the Au NNPs-CRP-Fe3O4 complex. For specific recognition of CRP, both SERS tag and magnetic capture substrate were modified with aptamers against CRP. Selective and specific detection of CRP was accomplished by SERS measurement based on sandwich complex strategy (Fig. 7(C)), reaching an ultra-low LOD of 10 fM (1.14 pg/ml) and exhibits high accuracy in the detection of actual human serum samples.Fig. 7 Synthesis of (A) Au NNPs SERS tag and (B) Ag MNPs magnetic capture substrate. (C) Schematic illustration of protein detection via Ag MNPs-CRP-AuNNPs "sandwich" structure by SERS. Fig. 7Reprinted with permission from ref [75]. Copyright (2021) Elsevier. Another common approach for the preparation of SERS substrates relies on the use of mesoporous templates greatly increasing active sites and enlarging adsorption capacity. Owning to its outstanding biochemical properties, a biomaterial template, M13 bacteriophage, was used to fabricate a novel SERS substrate to develop a biosensor for sepsis related biomarker detection (CRP, procalcitonin (PCT) and soluble triggering receptor expressed on myeloid cells-1 (sTREM-1)) [76]. The SERS substrate developed on M13 phages was decorated with Au-coated magnetic nanostars and functionalized with specific antibodies to capture sepsis biomarkers. Three different SERS tags were prepared by bioconjugating antibodies and Raman reporter molecules on the AuNPs surface using EDC/NHS method. In the presence of specific biomarkers, a sandwich immunoassay is formed, and the SERS signal appears after excitation with a 785 nm laser line. The recorded SERS spectra show distinct peaks for all corresponding tags of biomarkers. In the case of CRP detection, a 4-ATP SERS signal is detected at 1132 cm−1 with good specificity and sensitivity and a limit of detection of 27 pM. By combining the advantages offered by an ordered nanoporous template and core-shell SERS nanotags, Chen et al. have recently reported multiplexed SERS-based vertical flow immunoassay system for detection of CRP along with three other inflammation biomarkers, interleukin-6 (IL-6), serum amyloid A (SAA), and procalcitonin (PCT) [77]. As presented in Fig. 8 , the sensor substrate is composed of a nanoporous anodic aluminum oxide (AAO) with 350 nm pore size with double through nanochannels functionalized with multiplex capture antibodies, the resulting structure allowing the analytes to penetrate through the vertical pores. The second component, the Raman dyes encoded Au-Ag core-shell SERS nanotags are used as labels for biomarker detection. The detection assay was performed by dropping the sample and the functionalized SERS nanotags onto the AAO substrate, followed by washing and measuring the SERS signal of nanotags captured in the nanochannels. As also sustained by theoretical analysis, the electromagnetic field of the encoded core-shell SERS nanotags is enhanced in the presence of AAO template. Furthermore, the vertical channels improve the reaction binding kinetics and the washing efficiency. Consequently, the four biomarkers were detected with LODs of 53.4, 4.72, 48.3, and 7.53 fg/ml for the simultaneous detection of CRP, IL-6, SAA, and PCT, respectively covering in all cases a linear dynamic range of at least five orders of magnitude.Fig. 8 Schematic illustration of nanoporous AAO-based multiplex vertical flow assay (VFA) for the detection of four inflammatory biomarkers with Raman dyes encoded core-shell SERS nanotags. Characteristic Raman peaks of NBA at 593 cm−1, 4-MBA at 1075 cm−1, DNTB at 1341 cm−1, and MB at 1621 cm−1, are used to encode CRP, IL-6, SAA, and PCT, respectively. Fig. 8Reprinted with permission from ref [77]. Copyright (2020) John Wiley & Son. An innovative PNP architecture, ultraflat and ultraclean single-crystalline Au nanoplates with no grain boundaries, has been reported by Hwang et al. for completely specific and attomolar detection of CRP [78]. The high sensing performance of the nanoplatforms relies on the carefully optimized immobilization of the anti-CRP onto Au nanoplate in order to maximize the binding capacity for CRP and to eliminate the nonspecific binding of other proteins or Au NPs. Anti-CRP-attached Rhodamine B isothiocyanate (RBITC) tagged AuNP served as a probe in CRP detection assay providing SERS signal only when the NPs-on-nanoplate architecture is formed upon specific binding with CRP. The optimized anti-CRP-immobilized Au nanoplate achieved a LOD of 10−17 M in CRP detection. 6 Conclusions and perspectives The literature reports a large number of plasmon-assisted biosensors able to quantify CRP with the aim to improve the performances compared to the existing analytical methods, regarding sensibility, accuracy, rapidity and portability. Aggregation-based colorimetric assays are simple, rapid and the color change of the colloidal solution can be also observed with the naked eye. This strategy was used both for the specific detection of a target biomarker, both for multiplexed detection and signal amplification in ELISA immunoassays. The best LOD for CRP as low as 32 pg/ml using colorimetric assays was reached by Park et al. using AuNPs conjugated with 6X-histidine peptide as colorimetric transducers, and nickel-horseradish peroxidase as enhancer of the colorimetric signal in ELISA [36]. For LSPR-based detection of CRP various nanostructures have been also developed with increased RI sensitivity in order to enhance the sensibility of the biosensor. The lowest LOD reported using LSPR sensor for CRP detection was of 100 ag/ml, achieved by Yeom et al., using Au deposited nanostructured anodicaluminum oxide substrates and applying the sandwich structure to increase the sensitivity of the sensor [47]. PNPs were also successfully used as enhancers of the fluorescence signal leading to the development of fluorescent-based assays with very low LOD for CRP detection. For instance, the detection of a single CRP molecule (33.3 zM) was obtained using a 20 nm Au-nanopatterned biochip proposed by Heo et. al. [54]. Different plasmonic nanostructured optimized for high SERS enhancement were also widely explored in the detection of CRP. Among them, the best LOD of 10−17 M was yielded by Hwang et al. using a completely specific detection nanoplatform based on ultraflat and ultraclean single-crystalline Au nanoplates with carefully optimized immobilization of the anti-CRP on their surface. Portability and rapidity of CRP detection assays have particular importance in the management of life-threatening infections, therefore special attention has been accorded to the development of POC testing devices using PNPs. Such POC assays seem to be promising for clinical evaluation from real samples, some of them providing equivalent performance to gold-standard laboratory assays in considerably faster time. However, some of these devices still require qualified personnel to perform the detection assay. A lot of research is now dedicated to overcome this limitation and some progress is achieved in the development of simple, fast, easy-to-use and low-cost assays for clinical diagnosis in resource-limited areas. For translation of these devices into clinical application, user friendly software interfaces able to accurately correlate the optical signal with the CRP concentration are also needed to be developed, which could facilitate introduction of these devices in medical offices and clinics. We strongly believe that the current PNPs-based sensing strategies and portable devices for CRP detection will open new opportunities for easy, fast, accurate and real-time diagnosis and monitoring of inflammatory infections. CRediT authorship contribution statement Timea Nagy-Simon: Conceptualization, Writing – original draft, Writing – review & editing. Alexandru-Milentie Hada: Writing – original draft. Sorina Suarasan: Writing – original draft. Monica Potara: Conceptualization, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS/CCCDI – UEFISCDI, project number PN-III-P4-ID-PCE-2020-1592, within PNCDI III. This work is dedicated to honor Prof. Dr. Simion Astilean on the occasion of his birthday. The authors thank Prof. Dr. Simion Astilean for introducing them in the field of nanotechnology, for his valuable scientific guidance and constructive criticism. ==== Refs References 1 Ansar W. Ghosh S. C-reactive protein and the biology of disease Immunol. Res. 56 2013 131 142 10.1007/s12026-013-8384-0 23371836 2 Garg M. Sharma A.L. Singh S. Advancement in biosensors for inflammatory biomarkers of SARS-CoV-2 during 2019–2020 Biosens. Bioelectron 171 2021 112703 10.1016/j.bios.2020.112703 3 Ngwa D.N. 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2022-12-16 23:24:09

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J Mol Struct. 2021 Dec 15; 1246:131178

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J Mol Struct

10.1016/j.molstruc.2021.131178

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==== Front Nurs Womens Health Nurs Womens Health Nursing for Women's Health 1751-4851 1751-486X AWHONN S1751-4851(22)00266-5 10.1016/j.nwh.2022.11.004 In Practice Childbearing COVID-19 and Pregnancy: Risks and Outcomes Holland Cindra ∗ Hammond Crystal Richmond Misty M. ∗ Address correspondence to: 14 12 2022 14 12 2022 17 11 2022 © 2022 AWHONN. 2022 AWHONN Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The normal physiologic changes of pregnancy are known to increase susceptibility to respiratory illness. Individuals who are pregnant are more likely to acquire a SARS-CoV-2 infection and develop COVID-19 than the general population; they are at increased risk for hospitalization; ventilator-assisted breathing; and other subsequent maternal, fetal, and neonatal health issues. Although the incidence of infection and subsequent morbidity is increased in pregnancy, mortality does not seem to be increased. Individuals who are vaccinated against COVID-19 before childbirth can pass antibodies to their fetuses via the placenta during pregnancy and to their neonates during breastfeeding. It is important for health care providers to be cognizant of the potential impacts of COVID-19 on pregnant individuals and their offspring. Understanding the effects of SARS-CoV-2 infection during and after pregnancy can help health care providers implement interventions to improve outcomes for pregnant individuals and their newborns. Keywords antibodies breastfeeding COVID-19 infection long COVID pregnancy SARS-CoV-2 vaccination ==== Body pmc Photo © Fly View Productions / gettyimages.com Clinical Implications ▪ Coronaviruses infect a human host primarily via air droplets inhaled through the respiratory tract. ▪ Normal physiologic changes that occur during pregnancy increase the risk of morbidity and mortality related to respiratory illnesses. ▪ COVID-19 vaccination in pregnancy has been beneficial in preventing serious illness in pregnancy. ▪ Pregnant individuals who receive the COVID-19 vaccination can transmit antibodies to their fetuses, which can continue for months after birth. ▪ COVID-19 antibodies are found in the breast milk of lactating vaccinated women. SARS-CoV-2 and the disease it causes, COVID-19, (Katopodis et al., 2022), has claimed lives around the world since it first appeared in late 2019. The numbers of cases worldwide have waxed and waned in the ensuing years, with viral mutations producing new variants. The ongoing development and distribution of vaccines and the discovery of new treatments have affected its spread. Scientific research has been intense throughout the COVID-19 pandemic, and much has been learned about the impact of the virus on the body, including its effect on multiple body systems. One area of particular interest is its unique impact on pregnancy. The combination of COVID-19 pathophysiology and normal pregnancy changes in the immune and respiratory systems and coagulation pathways are confounding. According to the Centers for Disease Control and Prevention (CDC), pregnant individuals who contracted COVID-19 had increased risk of hospitalization, ICU admission, and the need for mechanical ventilation compared with those who were nonpregnant, especially with the delta variant (CDC, 2022e; Ellington et al., 2020). However, mortality rates did not seem to be any greater in pregnancy than in the general population (Ellington et al., 2020). As of July 22, 2022, 225,656 pregnant women in the United States have been diagnosed with COVID-19, resulting in 34,693 hospitalized cases, of which 673 patients required intensive care admissions, 148 required invasive ventilation, and 42 required extracorporeal membrane oxygenation; there were 306 reported deaths (CDC, 2022e). Understanding the impact of COVID-19 and how it affects pregnant individuals and newborns is the focus of this article. (Note: because of the continually evolving conditions of the pandemic and of the variants of virus circulating, some of the information contained herein may become outdated). Historical Impact of Viral Illnesses on Pregnancy Past pandemics provide a glimpse into the impact of viral infections during pregnancy. Lifelong adverse health effects have been identified in the offspring of infected individuals, including increased rates of diabetes, heart, and kidney disease. During the 1918 Spanish influenza outbreak, these complications were found to be related to the gestational age of the fetus at the time of infection during pregnancy (McCarthy et al., 2021). Increased development of adult-onset cardiovascular disease could be traced to those individuals whose mothers were infected during the first trimester. Individuals with chronic kidney disease were found to have been exposed to the virus during the third trimester when the kidneys were maturing (McCarthy et al., 2021). Inflammation from a viral infection during pregnancy has the potential to affect fetal brain development, potentially leading to neurological and psychological problems later in life (Mor et al., 2017; Shook et al., 2022b). An increase in the diagnosis of schizophrenia in hospitalized adults was noted in those who were born during the 1957 influenza pandemic (Mendick et al., 1988). A significant increase in the incidence of autism spectrum disorder and schizophrenia were noted in offspring related to the rubella pandemic in 1964 (Patterson, 2009). We know from the 2009 H1N1 influenza pandemic in the United States that pregnant individuals had greater morbidity and mortality rates than the general population (Mor et al., 2017). More recently, Zika virus infection during the first trimester of pregnancy was found to result in fetal microcephaly and other brain and eye deformities (CDC, 2022f, 2022g; Mor et al., 2017). Furthermore, even those without obvious birth defects were more likely to have seizures, movement disorders, feeding difficulties, and developmental delays than those not exposed to Zika during pregnancy (CDC, 2022f, 2022g). Brief Review of COVID-19 On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic (Cucinotta & Vanelli, 2020). Internationally renowned virus pioneer June Almeida and her colleagues first identified coronaviruses in a London laboratory in 1968 (Almeida et al., 1968). Corona refers to the characteristic fringe seen around the virus when viewed under an electron microscope that is similar to the solar corona (Almeida et al., 1968; Almeida & Tyrrell, 1967; Tyrrell et al., 1975). Unlike many viruses, SARS-CoV-2 is highly virulent, has a tendency to mutate, and is able to use multiple host cell mediators to gain access to bodily organs (Katopodis et al., 2022). It is also able to infect other animals, raising the further threat of economic, veterinary, and public health concerns (V’kovski et al., 2021). In humans, coronaviruses infect the host primarily via air droplets inhaled through the respiratory tract (Katopodis et al., 2022). Infected individuals do not have symptoms in the early stage of infection; some will go on to develop symptoms when the virus actively spreads throughout the body (Cordon-Cardo et al., 2020). According to the European Centre for Disease Prevention and Control (2021), the most common symptoms are headache, loss of taste and smell, fatigue, muscle aches, cough, nasal symptoms, sore throat, shortness of breath, and fever. A subset of those individuals will go on to experience severe symptoms and widespread organ damage to the lungs, heart, kidneys, brain, and liver, usually prompting hospitalization (Cordon-Cardo et al., 2020). The most severe symptoms occur in the last stage, when diffuse endothelial injury occurs from severe hyperinflammation and dysregulated thromboinflammatory pathways, resulting in microthrombus formation and systemic microvascular dysfunction. There is some suggestion that the underlying pathophysiology of severe disease is primarily driven by the inflammatory response and coagulopathy rather than direct viral injury at this point in the illness (Cordon-Cardo et al., 2020). COVID-19 and Pregnancy Immune Response Among the most important changes to normal physiologic processes that occur during pregnancy are those to the immune system to accommodate the growing and developing fetus and maintain the pregnancy. The body’s ability to adapt the immune system typically provides the balance between the growth and development of the fetus and the ability to fight off invading pathogens. During the initial stages of pregnancy, the immune system is in a proinflammatory state in which immune cells at the implantation site support embryo and placental development. Once the pregnancy is established, the maternal immune system and the fetal–placental unit’s trophoblasts take active roles in secreting anti-inflammatory cytokines that remove dying trophoblastic cells and protect fetal cells. This prevents a maternal immune response against the fetus and promotes its growth and development. During the third trimester, the immune system once again becomes proinflammatory to initiate and maintain labor and promote the separation of the placenta after birth (Mor et al., 2017). These changes make pregnancy more vulnerable to infection from COVID-19 and its hyperinflammatory state. Respiratory Response Hormonal and physiologic changes to the respiratory system further increase maternal susceptibility to respiratory infections. Progesterone causes a relaxation of the ribs, and changes to the hypothalamus result in increased tidal volume of the lungs (X. Zhao et al., 2020). The elevation of the diaphragm decreases chest wall compliance and lung residual capacity, resulting in a functional maternal hypoxia. The body compensates by taking deeper breaths and hyperventilating, increasing the risk of inhaling respiratory droplets or aerosols. Pathogens adhere more easily to the mucous membranes in the upper airways and are more difficult to clear because of mucosal dryness from progesterone and edema from estrogen (X. Zhao et al., 2020). These normal physiologic changes make pregnant individuals more prone to respiratory infections, including SARS-CoV-2. Coagulation Response Pregnancy is considered a hypercoagulation state, which ultimately assists with homeostasis after childbirth. Hormonal changes result in a five- to sixfold increased risk of developing thromboembolic disease beginning early in the gestational period and continuing until 12 weeks after childbirth (Antony et al., 2021). Among the coagulation changes in pregnancy, there is a significant rise in von Willebrand factor. Fibrinogen levels also increase up to twofold from prepregnancy levels (Antony et al., 2021). Multiple procoagulants are increased, venous stasis escalates, and endothelial injury can occur, resulting in damage to the vascular system (Antony et al., 2021). This can further cause placental infarcts, decreased placental perfusion, fetal growth restriction, and hypertensive disorders such as preeclampsia. The presence of antiphospholipid syndrome or other preexisting coagulopathies are known to increase the incidence of placental infarcts and recurrent miscarriages, as well as placental pathologies (Merriam & Pettker, 2021).It is important for anyone who develops symptoms of COVID-19 to be tested so that early diagnosis can be made when the test result is positive Early in the COVID-19 pandemic, it became clear that thromboembolic events and disseminated intravascular coagulation triggered high morbidity and mortality for individuals affected with severe disease (Aires et al., 2022; Iba et al., 2020; Munro et al., 2020). Although the precise process for these events was unclear, one proposed mechanism seemed to be initiated by the high production of proinflammatory cytokines (Aires et al., 2022; Iba et al., 2020). In addition, levels of von Willebrand factor antigen, a known coagulation factor and endothelial injury marker, were found to be 300% of normal or greater and were highly predictive of death and prolonged hospital stays (Cotter et al., 2022). Development of emboli in those with active COVID-19 has been implicated in respiratory compromise; acute coronary syndrome; limb or digit compromise; and damage to major organs, including the brain, heart, liver, and kidneys (Munro et al., 2020). Katsoularis et al. (2022) further found that the risk for deep vein thrombosis, pulmonary embolism, and bleeding (especially in the brain) were much greater for up to 3, 6, and 2 months, respectively, after recovery from COVID-19. Placental Changes Because the placental blood barrier protects the fetus from some diseases and environmental insults, vertical transmission of SARS-CoV-2 is rare, occurring in only 2.8% of neonates born to individuals with COVID-19 (AbdelMassih et al., 2021). Despite minimal vertical transmission of the virus, individuals who have contracted COVID-19 during pregnancy are at greater risk for developing placental infarcts because of the increased proinflammatory cytokines and von Willebrand factors found during pregnancy and SARS-CoV-2. These infarcts in placental circulation can result in placental insufficiency, decreased oxygen and nutrient delivery, fetal growth restriction, impaired fetal brain development, increased risk for preterm birth, and even death (AbdelMassih et al., 2021; A. Chen, et al., 2022; Hsu et al., 2021; Seymen, 2021). Placentitis was a noted feature in examinations of stillbirth placentas; it may account for increased stillbirth rates in those with COVID-19 (Konstantinidou et al., 2022; Stenton et al., 2022). Understanding these increased risks helps health care providers be more aware of potential complications when pregnant individuals acquire COVID-19. As with previously described viral infections, the gestational age at which the pregnant individual become infected may determine outcomes. In a small study early in the pandemic, researchers found that individuals who were diagnosed during the first or second trimesters had significant chronic ischemic placental changes; however, the neonates tested negative for the virus at birth and were healthy at 6 months (Y. Zhao et al., 2021). It was theorized that the maternal systemic inflammatory response within the first and second trimesters was responsible for injury to the placentas (Y. Zhao et al., 2021). Other researchers conducted multicenter studies and examined the placentas of individuals who tested positive for SARS-CoV-2 during the second and third trimesters and had live births. Although some microscopic changes were noted, they were not statistically significant compared to the placentas of individuals who did not have COVID-19 (Celik et al., 2022; Tasca et al., 2021). Placentitis has been a noted feature in the examination of stillbirth placentas, which may account for increased stillbirth rates in those with COVID-19 (Konstantinidou et al., 2022; Stenton et al., 2022). Further research is needed in this area to fully understand the impact of SARS-CoV-2 on placental development and function. Researchers have documented the potential for acute and chronic adverse neurodevelopmental outcomes in neonates exposed to SARS-CoV-2 during pregnancy (Shook et al., 2022b). Early signs of insufficient neurodevelopment in social–emotional growth and developmental delays were noted in infants as early as 3 months of age and continued to be an issue at later intervals (Shook et al., 2022b). Researchers have identified that definitive causation is difficult to establish because of small sample sizes and the need for longevity studies. Research in this area will be ongoing as affected offspring grow into adulthood. Preeclampsia Preeclampsia is a multisystem disorder that can negatively affect the fetus and the pregnant individual during pregnancy and the puerperium. Before COVID-19, researchers noted a trend for increasing rates of preeclampsia in the United States. Between 2005 and 2014, rates of preeclampsia increased by 21% (Fingar et al., 2017), and Cameron et al. (2022) further found a more rapid increase in rates of preeclampsia after 2014, particularly in urban areas. The exact etiology of the increased rates has not been determined, although increased obesity rates and increased comorbidities are thought to be contributing factors. Some researchers note that since the discovery of SARS-CoV-2, the rate of preeclampsia in individuals who have tested positive for the virus has increased dramatically, even up to a fourfold incidence (Papageorghiou et al., 2021; Villar et al., 2021). Those individuals diagnosed with severe preeclampsia often have preexisting comorbidities such as obesity, diabetes, hypertension, and other metabolic diseases that affect metabolism and are also at increased risk of negative outcomes from COVID-19 (CDC, 2022c, 2022d). COVID-19 and preeclampsia result in increased inflammatory markers, making it a challenge for providers to diagnose and treat appropriately (Naeh et al., 2022). It is unclear whether there is a causal relationship between COVID-19 and preeclampsia (Naeh et al., 2022). Because the inflammatory symptoms of both are similar, mistaken diagnoses may occur. Some providers are now identifying a preeclampsia-like syndrome in women who have COVID-19 that imitates the signs and symptoms of preeclampsia (Naeh et al., 2022). Further research into identifying the potential biomarkers that distinguish between COVID-19 and preeclampsia is needed. Psychosocial Impacts Childbirth in the United States is a social event. The consequences of quarantine, isolation, social distancing, and hospital visitor restriction policies during the pandemic changed how families approached and experienced childbirth (Gutschow & Davis-Floyd, 2021; Jackson et al., 2021). Although many women voiced lost independence and self-identity, felt more isolated, and had subsequent feelings of guilt when breaking social distancing guidelines to meet emotional needs, some women also sensed increased partner interaction and relief from social obligation pressures (Jackson et al., 2021). Women also voiced fears of isolation, being infected with SARS-CoV-2, being denied chosen adequate labor support, passing along the virus to their newborn, and being separated from their newborn after birth (Gutschow & David-Floyd, 2021). Interruption in face-to-face prenatal care practice also increased anxiety levels about the pregnancy (Gutschow & Davis-Floyd, 2021). Mollard and Whittmaack (2021) found that of the 885 women they surveyed who gave birth during the pandemic, 61% expressed inadequate childbirth support, 33.8% had high levels of anxiety, 18.6% reported depression, and 20.5% perceived that it was unsafe to give birth in the hospital. Women overall reported higher anxiety, depression, and stress levels related to the changes implemented during the pandemic (Fallon et al., 2021; Gutschow & Davis-Floyd, 2021; Jackson et al., 2021; Mollard & Whittmaack, 2021; Morris et al., 2022). When unexpected events occur or there are preexisting comorbidities, there is a greater risk for anxiety and depression during the antenatal and postpartum periods (Holland & Richmond, 2022). Implications for Practice Nurses and advanced practice providers can have a significant impact on educating the public on evidence-based practice at every point of care. Inaccurate or biased news reporting, social media, and general fear about SARS-CoV-2 and COVID-19 have greatly influenced personal and public responses to the crisis. Nurses have a responsibility to investigate the evidence for best practice to affect positive outcomes for individuals, their families, and the public. Prevention of COVID-19 At the time of this writing, the CDC (2022c, 2022d) continues to recommend that pregnant individuals and others who are at increased risk of acquiring COVID-19 wear masks when indoors in public places, especially in those areas of the country with high transmission rates. Furthermore, it is important for anyone who develops symptoms of COVID-19 to be tested so that early diagnosis can be made when the test result is positive. Anyone who contracts COVID-19 while pregnant should communicate with their obstetric provider to the ensure initiation of appropriate treatment as soon as possible. Vaccination Because of the increased risk of hospitalization and ICU admissions, the CDC (2022c, 2022d) recommends that those who are pregnant or who are planning to become pregnant receive the full series of COVID-19 vaccinations and stay up to date on all other immunization recommendations. American College of Obstetricians and Gynecologists, 2020, American College of Obstetricians and Gynecologists, 2022a, American College of Obstetricians and Gynecologists, 2022b; Society for Maternal Fetal Medicine (SMFM; 2022); American College of Nurse-Midwives (2021); National Association of Nurse Practitioners in Women’s Health (2021); and Association of Women’s Health, Obstetric, and Neonatal Nurses (2022) all support COVID-19 vaccination during pregnancy. Lack of inclusion of pregnant women in early vaccine trials and significant vaccine hesitancy resulted in decreased vaccine coverage in pregnant women (Hosokawa et al., 2022; Stock et al., 2022). Vaccination during pregnancy decreases morbidity and mortality related to COVID-19 (Kalafat et al., 2022). Vaccination has found to be safe, with no increases in vaccine-related maternal or fetal complications (Association of Women’s Health, Obstetric, and Neonatal Nurses, 2022; Blakeway et al., 2021; F. Chen et al., 2022; Kalafat et al., 2022). Vaccination prepregnancy has not been shown to affect fertility, conception, or rates of spontaneous abortion (F. Chen et al., 2022; Wesselink et al., 2022). Researchers who conducted a prospective cohort study of more than 2,000 couples found that the timing of the vaccination; type of vaccine received; and other identified variables, including socioeconomic, lifestyle, and medical considerations, had no impact on either partner’s ability to conceive (Wesselink et al., 2022). Men who tested positive for SARS-CoV-2 were less likely to conceive within 60 days, possibly because the presence of fever, a common symptom of COVID-19, causes a reduction in sperm count and sperm motility (Wesselink et al., 2022).Photo © Branden McCrea / gettyimages.com As new research is published about COVID-19, interventions and treatment plans can be tailored to each unique individual for the best potential outcome Emerging information obtained from researchers supports the efficacy of COVID-19 vaccination during pregnancy. Shook et al. (2022a) compared individuals who were fully vaccinated (received two doses of a messenger RNA vaccine) with those who were infected with the virus between 20 and 32 weeks’ gestation. Individuals and fetal umbilical cords were tested after birth; vaccinated individuals had higher antibody levels (Shook et al., 2022a). Infants born to individuals in this study were then tested at 2 and 6 six months of age. Again, infants whose mothers were vaccinated before childbirth had significantly higher levels of immunoglobulin G antibodies (Shook et al., 2022a). Breastfeeding Before COVID-19, researchers established the many benefits of breastfeeding. Researchers suggest that antibodies in breast milk may in fact coat the mucosal lining of the neonate’s mouth, throat, and gut, providing valuable protection against many diseases (Atyeo & Alter, 2021; Lyons et al., 2020). Researchers identified that COVID-19 antibodies were found in the breast milk of lactating vaccinated women (Fox et al., 2020; Hand & Noble, 2020). Infants who exclusively breastfed after their mother received the COVID-19 vaccination had higher levels of SARS-CoV-2 antibodies for at least 6 months of age or longer because of continual exposure to the antibody-rich breast milk (Narayanaswamy et al., 2021). Additional researchers found that individuals who received two doses of a messenger RNA COVID-19 vaccine while lactating attained adequate antibody levels in breast milk for up to 23 months after vaccination (Narayanaswamy et al., 2021; Ramírez et al., 2021; Vale et al., 2021). Narayanaswamy et al. (2021) also found higher levels of antibodies in the stools of infants whose mothers were vaccinated. Infants who receive breast milk from vaccinated mothers continued to receive antibodies during their early vulnerable months. These results are continuing to be investigated and studied. Encouraging women to breastfeed when possible and offering appropriate support might contribute to the prevention of COVID-19 in infants. Further Management It is important to be aware of the specific physiologic risks of COVID-19 during pregnancy. Nurses can be instrumental in monitoring the respiratory status of pregnant individuals, who are at increased risk of infection, acquiring COVID-19, and negative consequences related to hypoxia. Gathering a detailed health history and completing a thorough assessment can help identify those women who already have an increased risk for blood clotting disorders that may be compounded by COVID-19. Although ACOG and the SMFM do not offer any specific guidance regarding the use of daily low-dose aspirin prophylactically, in light of the potential relationship between COVID-19, preeclampsia, and thrombophilia, they have stated that it is appropriate to use aspirin when clinically indicated (Eslamian et al., 2021). Some providers are considering SARS-CoV-2 a risk factor when contemplating initiating use of aspirin for prophylaxis of preeclampsia and its complications (ACOG, 2020; Eslamian et al., 2021). Because many patients do not consider over-the-counter medications significant enough to report, it is vital that nurses include questions about aspirin therapy when gathering a detailed health history. The inflammation response related to COVID-19 has been shown to potentially cause long-term negative health effects. This may include problems for individuals who acquire COVID-19 during pregnancy as well as for their offspring. As the COVID-19 pandemic is becoming more endemic and affecting several aspects of daily life, nurses have a challenge in keeping up with all the new information that continues to be released daily about the virus, treatment, and potential long-term effects. As new research is published about COVID-19, interventions and treatment plans can be tailored to each unique individual for the best potential outcome. COVID-19 Treatment in Pregnancy Throughout the COVID-19 pandemic, treatment plans have changed based on best evidence and the response to each newly identified variant. The National Institutes of Health (NIH; 2021), ACOG (2022a), and SMFM (2022) have all recommended that vaccinations and treatment for pregnant women with COVID should not be withheld and that other treatment options should be offered, even though there may be theoretical risks involved. So far, research has supported that early decision. As with all treatment management, individual patient concerns, comorbidities, preexisting risk factors, disease presentation, and risk for progression should be discussed between the care provider and patient so that informed, joint decision-making can occur to promote optimal outcomes. Regardless of the treatment option chosen, any patient with COVID-19 symptoms should have supportive care. As with any high-risk condition in pregnancy, appropriate intrapartum care and interventions for suspected preterm and high-risk pregnancies should be implemented based on current evidence-based recommendations for the care of pregnant individuals with COVID-19. Monoclonal antibodies When SARS-CoV-2 first began, emergency use authorization was given by the U.S. Food and Drug Administration for various intravenous monoclonal antibodies (mAbs), which must be initiated within 7 days of symptom onset. Bamlanivimab plus etesevimab, casirivimab plus imdevimab, and sotrovimab were mAbs recommended for use with the delta variant (NIH, 2022a). However, because of newer mutations to SARS-CoV-2, bebtelovimab is the only currently recommended mAb for omicron. Bebtelovimab is not considered a first-line treatment but should be reserved when other options are not available or reasonable (NIH, 2022b). Because pregnant individuals are at greater risk for developing respiratory illnesses, they are eligible to receive mAb treatment (CDC, 2022b; NIH, 2022). Researchers have not found mAb therapy in pregnancy to have serious side effects (for the pregnant individual or the fetus) and have found it effective for preventing COVID-19 progression (Folkman et al., 2022; Hirshberg, 2021; Thilagar et al., 2022). Antivirals Remdesivir is a broad-spectrum antiviral drug that has received emergency use U.S. Food and Drug Administration approval for preventing progression of COVID-19. The drug is also used to treat those with mild to moderate symptoms in outpatient settings as well as for severe COVID-19 hospitalized adults and some children in the United States (NIH, 2022c). Like the mAb drugs, remdesivir should be initiated early within 7 days of symptom onset. Remdesivir is only given intravenously but requires three doses to be effective. Pregnant individuals were eligible initially to receive remdesivir under compassionate use, and it is continued to be used for this population. Researchers in small studies have typically found improved clinical outcomes in severe COVID-19 with minimal adverse effects (Budi et al., 2022; Burwick et al., 2021). The results of the NIH-sponsored study (NCT04582266) that began in 2021 to investigate remdesivir use during pregnancy has been completed, but the results have yet to be published (NIH, 2022a). More recently, Paxlovid (Pfizer, Inc., New York, NY), a combination of antivirals nirmatrelvir and ritonavir, received emergency use authorization and has become the current drug of choice (at the time of this writing) for those high-risk COVID-19–positive individuals, including those who are pregnant (NIH, 2022d). Again, the goal of treatments is to prevent disease progression and prevent hospitalization for high-risk individuals. Paxlovid, oral tablets taken twice a day for 5 days, should be initiated within the first 5 days of symptom onset (NIH, 2022d). Ritonavir has previously been studied in pregnancy for HIV treatment and was not found to be teratogenic (Roberts et al., 2009; U.S. Department of Health and Human Services, 2021b); nirmatrelvir has not been studied, but it is thought to be relatively safe (NIH, 2022d). Paxlovid may be problematic for some patients because it is a strong cytochrome P-450 inhibitor and has many interactions with other drugs. The dose must also be adjusted for those with decreased kidney function. The SMFM (2022) and ACOG (2022a) both support the use of Paxlovid, remdesivir, and bebtelovimab for outpatient treatment of COVID-19, especially if a pregnant individual has more than one high-risk factor for disease progression. Paxlovid is the preferred drug of choice unless there are contraindications to its use or there are issues with access. The goals of all three antiviral treatments are to help manage symptoms and to reduce hospital admissions and disease progression, especially for those in high-risk categories. Long COVID Long COVID (also referred to as postacute sequelae of SARS-CoV-2) occurs in individuals who continue to have symptoms for more than 4 weeks after onset of the virus (CDC, 2022a); females have been found to have a threefold risk of continued symptoms (Bai et al., 2022). The persistent health problems experienced by those who have prolonged COVID symptoms have resulted in long COVID now being considered an accepted diagnosis under the Americans With Disabilities Act (U.S. Department of Health and Human Services, 2021a). Because of the increasing numbers of those with long COVID, this complication must be taken into consideration as well.Photo © Fly View Productions / gettyimages.com Nurses who are well informed regarding current research are in the best position to offer support and to assist in pursuing appropriate interventions for this population at all levels of care Del Brutto et al. (2022) compared Montreal Cognitive Assessment (MoCA) scores of those with COVID-19 and healthy individuals. They found a significant reduction in MoCA scores in infected individuals 6 months after the onset of symptoms; there was no difference in MoCA scores between the two groups 1 year later (Del Brutto et al., 2022). The researchers theorize that symptoms of long COVID related to cognition may improve over time; however, the science in this area is continually evolving. Limited reports suggest that SARS-CoV-2 can interfere with the blood–brain barrier and affect brain function; this might explain increased “brain fog” and may affect future cognitive functioning in individuals who have had COVID-19 (Shook et al., 2022b). This may also need to be a consideration of care if the symptoms are severe. Staying Current in An Evolving Situation The coronavirus has been known to readily mutate, with new variants being identified worldwide with increasing frequency (Katopodis et al., 2022). With each new variant, symptoms and treatment recommendations may change. There is also growing concern that initial vaccinations designed to decrease mortality from SARS-CoV-2 may not be as effective against the emerging variants. Providing education about the virus and the sequalae can help promote the best potential outcomes for all involved. Nurses can readily access up-to-date information and COVID-19 guidelines from professional organizations that promote the health and well-being of women and newborns (see Box 1 ). These organizations provide evidence-based information for consumer and professional audiences and frequently update treatment and nursing care recommendations on their websites. It is important for health care providers and health agencies to provide care that is the most up to date and effective.Box 1 Selected Professional Organizations And Their Covid Information American College of Nurse-Midwives (ACNM) www.midwife.org www.midwife.org/covid-19-vaccine-information American College of Obstetricians and Gynecologists (ACOG) https://www.acog.org https://www.acog.org/clinical-information/physician-faqs/covid-19-faqs-for-ob-gyns-obstetrics Association for Women’s Health, Obstetric and Neonatal Nurses (AWHONN) https://www.awhonn.org https://www.awhonn.org/novel-coronavirus-covid-19/ Centers for Disease Control and Prevention (CDC) https://www.cdc.gov https://www.cdc.gov/coronavirus/2019-ncov/hcp/inpatient-obstetric-healthcare-guidance.html National Association of Nurse Practitioners in Women’s Health (NPWH) https://www.npwh.org https://cdn.ymaws.com/npwh.org/resource/resmgr/news/npwhandwhorecommendvaccinaio.pdf Society for Maternal–Fetal Medicine (SMFM) https:www.smfm.org https://www.smfm.org/covidclinical Conclusion The novel coronavirus SARS-CoV-2 has caused loss of life on a global scale, with an unprecedented impact on human behavior and society. The virus is known to be particularly virulent and to possess an increased rate of mutation. New variants continue to be reported worldwide. Ongoing research will be needed as we move from a pandemic to an endemic status. Known negative risks for pregnant individuals and their offspring include increased likelihood of acquiring SARS-CoV-2 infection; threat of developing severe COVID-19, resulting in increased risk for hospitalization; ICU admission; need for ventilation; and increased rates of stillbirth and preterm birth. Further potential sequalae may involve risks for placental damage, development of preeclampsia or preeclampsia-like syndrome, maternal anxiety and depression, maternal and neonatal neurological insults, and long COVID. Given their increased risk from COVID-19, as well as increased vulnerability in general, it is understandable that pregnant individuals will likely be concerned about the potential negative short- and long-term effects for themselves and their offspring. Because of the increased risk for these negative outcomes, it is important for health care professionals to remain current. Nurses who are well informed regarding current research are in the best position to offer support and to assist in pursuing appropriate interventions for this population at all levels of care. Cindra Holland, DNP, RNC-OB, C-EFM, ACNS-BC, is an associate professor in the School of Nursing, Kinesiology, and Health Sciences at Wright State University in Dayton, OH; https://orcid.org/0000-0003-4529-6493. Crystal Hammond, MSN, CNM, FNP, is a clinical assistant professor in the School of Nursing, Kinesiology, and Health Sciences at Wright State University in Dayton, OH; https://orcid.org/0000-0002-3629-5774. Misty M. Richmond, PhD, PMHNP-BC, is an associate professor in the College of Nursing at Texas Women’s University in Denton, TX; https://orcid.org/0000-0002-1566-0509. Author Disclosures The authors report no conflicts of interest or relevant financial relationships. Funding None. ==== Refs References AbdelMassih A. Fouda R. Essam R. Negm A. Khalil D. Habib D. Tadros M.A. COVID-19 during pregnancy should we really worry from vertical transmission or rather from fetal hypoxia and placental insufficiency? A systematic review Egyptian Pediatric Association Gazette 69 2021 10.1186/s43054-021-00056-0 Article 12 Aires, R. B., Soares, A. A. de S.M., Gomides, A. P. M., Nicola, A. M., Teixeira-Carvalho, A., da Silva, D. L. M.,... da Mota, L. M. H. (2022). Thromboelastometry demonstrated endogenous coagulation activation in nonsevere and severe COVID-19 patients and has applicability as a decision algorithm for intervention. PLOS ONE, 17(1), Article e0262600. 10.1371/journal.pone.0262600 Almeida J.D. 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==== Front Geoforum Geoforum Geoforum; Journal of Physical, Human, and Regional Geosciences 0016-7185 1872-9398 Elsevier Ltd. S0016-7185(21)00117-2 10.1016/j.geoforum.2021.04.018 Forum Indigenous livelihood portfolio as a framework for an ecological post-COVID-19 society Matias Denise Margaret S. ⁎ Center for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany Non-Timber Forest Products Exchange Programme (NTFP-EP) Asia, 20A Maaralin St., Barangay Central, Diliman Quezon City 1100, Philippines Institute for Social-Ecological Research (ISOE), Hamburger Allee 45, 60486 Frankfurt am Main, Germany ⁎ Address: Center for Development Research (ZEF), University of Bonn, Genscherallee 3, 53113 Bonn, Germany. 6 5 2021 7 2021 6 5 2021 123 1213 12 4 2021 22 4 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The present economic system is geared towards increasing specialization and infinite growth. This orientation may have led to efficiency and new ways of increasing wealth but it has also led to unsustainable practices and, in some cases, loss of traditional knowledge. Many a systems thinker like the Limits to Growth’s Club of Rome have suggested ways to avoid the negative consequences of the current economic system but these entail radical changes that cannot be afforded by deeply-entrenched practices of the worldwide economy. In this paper, another alternative is proposed, which may not only be desirable to an envisioned ecological society but also may also be logical to the unsustainable society of today. Looking at rural indigenous livelihoods may show us how an ecological society should be like. Exemplifying collectivism, indigenous peoples continue to cultivate empathy while at the same time inculcating sense of responsibility. Before “multi-hyphenated” became fashionable, indigenous peoples were already engaged in different occupations that, in turn, result to a diversified livelihood portfolio similar to what banks today advise clients on their investments. However, the difference lies in the indigenous tradition of only having enough for what is needed and rarely hoarding to the point of exhausting resources. This paper proposes that the diverse indigenous livelihood portfolio can be a valuable economic framework for an ecological society. It does not limit growth, but it makes sure growth happens in a sustainable manner. Keywords Sustainable development Livelihood Diverse portfolio ==== Body pmc1 Introduction Most societies of today have been built around an economic system that is geared towards infinite growth. This is partly driven by the prevailing use of the gross domestic product (GDP) as an indicator of progress, wealth, and well-being. The GDP measures the total economic production of a country, but can only capture the well-being of its citizens through material wealth. An increasing awareness of the insufficiency of GDP called for the use of the Human Development Index (HDI), which includes mortality and literacy as additional determinants of well-being (UNDP, 2019). This is a welcome step towards moving beyond GDP as a main economic indicator, but it is still insufficient insofar as it only assesses the impact of economic activity on consumers and not on capital resources. These indicators, therefore, do not capture all the costs involved in pursuing well-being. This is why externalities and unsustainable practices are often overlooked, as long as GDP and HDI remain high and economic growth is sustained. The Industrial Revolution of the late 18th century laid out the blueprint for increased economic production, which stems from efficiency brought about by specialization (Kim, 1989). In a labor economy, this means dedicating one worker to only one task among many in a product assembly line. In a factory producing a pair of pants, one person may be tasked with sewing buttons while another may be tasked with sewing brand tags, repeating the same tasks day in and day out. In principle, dedication to one single task or topic for a prolonged period of time develops expertise; however, not all expertise is valued the same way. Expertise tends to be valued more in the knowledge economy than the labor economy, but in both cases, expertise makes members of society more dependent on each other. This is not an undesirable consequence as long as dependencies are mutually beneficial and everyone involved are properly compensated. However, this is currently not the case, as reflected by the disparity in compensation between low-skilled workers versus high-skilled workers (Freeman and Oostendorp, 2000). High-skilled workers, most often constituting the knowledge economy, choose to have more time for skilled work than labor work and instead pay low-skilled workers to do labor-intensive jobs for them. It has been said that labor workers enable high-skilled workers to do their work and, in the process, subsidize them through the low cost of labor services. High-skilled workers with good pay could easily pay for labor services; the value of doing simple labor work by one’s self is lost as incomes rise. For example, households with extra income may opt to hire a helper to cook meals or clean the house for the family. Children growing up in such households do not learn how to cook or use tools in cleaning the house. These can be considered traditional skills and the term “use it or lose it” applies in their transmission, which generally occurs through practice. If these skills are not used, they become lost. 1.1 A framework for an ecological society The prevailing economic system is a vicious cycle of growth, wealth accumulation, inequality, and loss of traditional skills. Ultimately, the adoption of this economic system leads to unsustainable societies. Profit is valued more over people and the planet, interpersonal relationships are reduced to business transactions, and the planet’s resources are exhausted beyond their regeneration point. For all its modernity, the current economic system seems to be bringing only short-lived prosperity and well-being. I propose a framework for an ecological society post-COVID-19 that is similar to the traditional livelihood portfolio of indigenous peoples. These are usually diversified, as they are sensitive to seasonal and ecological changes and usually capitalize on the strengths of each community member (Choueifaty et al., 2013). Often based on natural resource extraction, traditional indigenous livelihoods are foremost used for subsistence and, subject to surplus, for trading (Behrens, 1992). Following the seasonal availability of resources, for e.g. wild honey bee colonies can only be found during the summer season in South and Southeast Asia, the livelihood portfolio of indigenous peoples become diverse and do not predestine community members to a lifetime of doing only one labor task unlike the specialized labor of industrial workers (Matias et al., 2017). Ceteris paribus, indigenous peoples have more freedom of choice than industrial workers in work tasks and are not subject to boredom or monotony of working on only one task repeatedly. Once the season for one livelihood strategy is over, they can move to another livelihood strategy. These varieties of work tasks not only contribute to more work satisfaction, but also in preserving skills proficiency as skills for different types of work are continually practiced year in and year out (Ericsson and Charness, 1994). On the community level, the indigenous peoples are traditionally collectivists and work in a communal manner (Choueifaty et al., 2013). Fierce competition for resources is rare and trading is conducted in an empathic manner, which is unlike transactions in the current economic system where differences in financial capabilities are mostly overlooked, making expensive commodities inaccessible to poorer citizens. Moreover, as a subsistence strategy and based on natural resources with minimal processing or value addition (hence, products have shorter shelf life or cannot be stored), indigenous livelihoods do not encourage hoarding. In the current economic system, hoarding in the guise of saving up for future purposes is highly encouraged and this contributes to the widening gap between the rich and the poor. This leads to unsustainable practices due to increasing demand for resources beyond what is needed. The Earth Overshoot Day (formerly known as the Ecological Debt Day) demonstrates the unsustainable demand for resources; it calculates if and when people’s resource consumption exceeds the Earth’s natural resources generated for the year (Wackernagel and Pearce, 2018). Traditional livelihood strategies of indigenous peoples respect the limits of natural resources, with resource consumption commensurate to resource regeneration. Barring gender balance issues, traditional indigenous livelihoods have streamlined tasks according to the respective capabilities of each community member. In the example above on wild honey bee colonies in South and Southeast Asia, gathering of wild honey is conducted by males since most tasks entailing multi-day trips to deep forest areas are conducted by males (Matias et al., 2017). Women, on the other hand, are involved in the consolidation of the resources gathered by their male counterparts. Children have tasks as well, but these are limited to simple tasks such as gathering leaves from nearby trees or, in the case of wild honey bees, gathering wild honey from non-aggressive honey bee species. A sense of responsibility is inculcated early on among members of the community, with tasks distributed among different genders and different ages. Highlighting this sense of responsibility is not to critique women empowerment or gender balance advocacy, but to show that indigenous traditional livelihood strategies have community members contributing to a whole, which may make their tasks more meaningful for them. This personal connection or engagement with their livelihood strategies can, therefore, contribute to a feeling of fulfillment, unless efforts are not financially compensated properly. Through this feeling of fulfillment, livelihood strategies become appreciated alongside natural resources, which serve as capital. 2 Conclusion Taken altogether, the features of indigenous livelihoods can form an economic framework for an ecological post-COVID-19 society where many have lost their fulltime jobs. Such a framework is not entirely a novelty, but rather a rediscovery of an ancient economic system of our roots. The most important feature of traditional indigenous livelihoods is gathering of only the right amount of resources. The excesses of the current economic system lead to ecological challenges. However, it is not easy to eliminate this system and limit economic growth as prescribed by several thinkers (Meadows et al., 1974). The indigenous livelihood portfolio demonstrates that sustainable growth is possible. Economic growth is pegged with resource consumption and resource regeneration, thereby incorporating externalities. In addition, applying diverse livelihood strategies may contribute to the well-being of people. With limited to no pressure to pursue excessive lifestyles or specialization (i.e., being an expert), people have more time to pursue what is called work-life balance. Millennials, the largest generation of workers after the baby boomers, are primary proponents of work-life balance (Calk and Patrick, 2017). It is high time that our society and the current economic system change if we would like future generations to benefit from what we have experienced so far. ==== Refs References Behrens C.A. Labor specialization and the formation of markets for food in a Shipibo subsistence economy Human Ecology 20 1992 435 462 Calk R. Patrick A. Millennials through the looking glass: workplace motivating factors The Journal of Business Inquiry 16 2017 131 139 Choueifaty Y. Froidure T. Reynier J. Properties of the most diversified portfolio Journal of Investment Strategies 2 2013 49 70 Ericsson K.A. Charness N. Expert performance: its structure and acquisition American Psychologist 49 1994 725 747 Freeman R.B. Oostendorp R.H. Wages around the world: pay across occupations and countries 2000 National Bureau of Economic Research Cambridge Kim S. Labor specialization and the extent of the market Journal of Political Economy 97 1989 692 705 Matias DMS, Tambo JA, Stellmacher T, Borgemeister C, von Wehrden H (2017) Commercializing traditional non-timber forest products: An integrated value chain analysis of honey from giant honey bees in Palawan, Philippines. Forest Policy and Economics 97: 223-231. Meadows D.H. Meadows D.L. Randers J. Behrens W.W. III Limits to Growth 1974 Universe Books New York UNDP (2019) Human Development Report 2019. Beyond income, beyond averages, beyond today: Inequalities in human development in the 21st century. UNDP, New York. Wackernagel M. Pearce F. Day of reckoning New Scientist 239 2018 20 21 10.1016/S0262-4079(18)31389-7

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00470-2 10.1016/j.jpsychires.2021.07.037 Article COVID-19 related moral injury: Associations with pandemic-related perceived threat and risky and protective behaviors Khan A.J. abc∗ Nishimi K. bc Tripp P. c Maven D. c Jiha A. c Woodward E. bc Inslicht S. bc Richards A. bc Neylan T.C. bc Maguen S. bc1 O'Donovan A. bc∗∗1 a Department of Psychiatry, University of California San Diego, La Jolla, CA, USA b Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA c San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA ∗ Corresponding author. Department of Psychiatry, University of California, San Diego, 9500 Gillman Drive, La Jolla, CA, 92093, USA. ∗∗ Corresponding author. 4150 Clement Street, Building 16 (116C-1), San Francisco, CA, 94121, USA. 1 Authors share senior authorship. 22 7 2021 10 2021 22 7 2021 142 8088 25 2 2021 28 6 2021 21 7 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The coronavirus-2019 (COVID-19) pandemic is associated with increased potential for morally injurious events, during which individuals may experience, witness, or learn about situations that violate deeply held moral beliefs. However, it is unknown how pandemic risk and resilience factors are associated with COVID-related moral injury. Methods Individuals residing in the U.S. (N = 839; Mage = 37.09, SD = 11.06; 78% women; 63% White; 33% PTSD) participating in an online survey reported on COVID-19 related moral injury (modified Moral Injury Events Scale), perceived current and future threat of pandemic on life domains (social, financial, health), and COVID-19 risky and protective behaviors. Multivariate linear regressions examined associations of perceived threat and risky and protective behaviors on type of COVID-19 related moral injury (betrayal, transgression by others, self). Results Participants endorsed MI betrayal (57%, N = 482), transgression by other (59%, N = 497), and by self 17% (N = 145). Adjusting for sociodemographics, only future threat of COVID-19 to health was significantly associated with betrayal (B = 0.21, p = .001) and transgression by other (B = 0.16, p = .01), but not by self. In contrast, high frequency of risky behaviors was associated with transgressions by self (B = 0.23, p < .001). Sensitivity analyses showed PTSD did not moderate the observed effects. Conclusions Betrayal and transgression by others was associated with greater perceived future threat of COVID-19 to health, but not financial or social domains. Stronger endorsement of transgression by self was associated with more frequently engaging in risky behaviors for contracting COVID-19. These findings may suggest the need for individual, community, and system level interventions to address COVID-19 related moral injury. Keywords Coronavirus Moral injury Perceived threat Risky behaviors Pandemic ==== Body pmc1 Introduction The coronavirus 2019 (COVID-19) pandemic has had strong adverse effects on public health and economic well-being around the globe (e.g., Congressional Research Service, 2021; Salari et al., 2020). In the United States (U.S.), in addition to the possible individual life threat posed by COVID-19, the pandemic is associated with increasing levels of depression, anxiety, and substance misuse (Czeisler et al., 2020; Ettman et al., 2020). Another possible mental health sequelae from COVID-19 is moral injury, which refers to the biopsychospiritual suffering stemming from participating, witnessing, or learning about events that transgress one's deeply held moral beliefs (Litz et al., 2009; Shay, 2014). Moral injury is in essence a social wound, predicated on the morals and values constructed and shaped by communities and society (Scheder et al., 1987). In a time when individual behavior is paramount to the health and well-being of the population (Center for Disease Control, 2020a, b), examining the relationship between pandemic factors and COVID-19 related moral injury is critical to understanding the intricate web of morality, mental health, and public safety. Moral injury is not a psychiatric diagnosis (Farnsworth et al., 2017; Jinkerson, 2016), but it can include feelings of guilt, shame, anger, disgust, and sadness, thoughts of personal regret and systemic failures, and avoidance and self-handicapping behaviors (Ang, 2017). Moral injury is associated with significant impairment in relational, health, and occupational functioning as demonstrated by poorer trajectories in these areas (e.g., Maguen et al., 2020; Purcell et al., 2016). Largely studied in the context of military experiences (see Griffin et al., 2019a for review), researchers have generally bifurcated potentially morally injurious events into transgressions (by others and self) and betrayal (Bryan et al., 2016; Nash et al., 2013). During COVID-19, significant attention has been directed to the potential of moral injury in healthcare workers, who are having to make challenging ethical decisions about resource allocations, face complex ethical decisions, and grapple with balancing work and personal/family health (Chen et al., 2020; Harper et al., 2020; Lai et al., 2020; Litam and Balkin, 2020; Maguen et al., 2020). Importantly, many other individuals are also likely exposed to potential transgression or betrayal-related events during COVID either professionally (see Williamson et al., 2018) or personally (e.g., Bachem, et al., 2020; Landry et al., 2020). Across the nation, employers have had to layoff large numbers of employees with families to support (Frontstin and Woodbury, 2020), spiritual leaders and therapists are experiencing significant burnout (e.g., Greene et al., 2020; Sammons et al., 2020), and people are having to choose separation over caregiving for sick family members. Moreover, disease spread is contingent on societal compliance with public safety guidelines (Centers for Disease Control, 2020a, Centers for Disease Control, 2020b), and as such, an individual's adherence or lack thereof to those guidelines may put one's own and others' health at stake. Consequently, witnessing others' behaviors and discrepancies between local, state, and country level ordinances may foster feelings of betrayal, immorality, or contempt towards community members and governments or public health systems (Mohsin et al., 2020). However, an individual's perception of the risk of COVID-19 due to personal (e.g., use of protective behaviors such as masks, extent of concern over pandemic) and environmental factors (e.g., work related risks, others' use of protective measures) may influence the presence and degree of moral injury (de Bruin and Bennett, 2020; Harper et al., 2020). Importantly, this relationship may likely be bidirectional. For example, witnessing behaviors or acting in ways that increase risk for COVID-19 can serve as potentially morally injurious events that lead to moral injury. But moral injury can also increase self-punishing behaviors and as such, individuals may take more risk or engage in fewer precautions. The first aim of the current empirical investigation was to assess levels of COVID-19 related moral injury. Secondly, we sought to examine the relationship between COVID-19 related moral injury and perceived threat of COVID-19 to different life domains. We hypothesized greater perceived current and future threat would be associated with higher levels of moral injury. Finally, we explored whether risky and protective behaviors for contraction of COVID-19 were associated with COVID-19 related moral injury. We expected risky behaviors to be inversely and protective behaviors to be positively (e.g., Usset et al., 2020) associated with COVID-19 related moral injury. We hypothesized protective behaviors to be positively related to moral injury because greater protective behaviors may reflect stronger connection to morals or values (or higher health risk), therefore individuals may have been more likely to perceive certain actions/inactions as transgressions. 2 Method 2.1 Participants and procedures The current study leveraged a pre-existing participant pool (N = 3631) from a previous entirely remote (online) study from 2017 to 2018, which was focused on posttraumatic stress (Niles et al., 2020); thus, our sample is enriched for trauma exposure and posttraumatic stress disorder (PTSD) symptoms. Participants were community-dwelling adults (≥18 years) living in the US. For the current study, participants from the recruitment pool were re-contacted via email and invited to participate in the current study. If participants consented to participate, they were directed first to a brief, 30-min online Qualtrics survey assessing psychological experiences during the pandemic. Upon completion of the full survey, participants were compensated $5 with Amazon e-gift cards. Data was collected from August 4 through September 19, 2020. Of those contacted, 1000 individuals started the online survey, 78 stopped the survey prior to consenting, 25 did not complete the demographic questions at the start of the survey, and one person declined to consent. The final sample was comprised of 896 individuals. Of note, COVID-19 vaccinations were not yet available when data were collected. The study was reviewed and approved by the Institutional Review Board at the University of California, San Francisco. 2.2 Measures 2.2.1 Demographics Demographic variables included age, gender identity, race/ethnicity (Non-Hispanic White, Black, Asian, Latinx, other or bi/multiracial), sexual orientation, highest level of education achieved, current employment status, changes to employment or income due to COVID-19, marital status, annual household income, and US Census Bureau region of residence (e.g., northeast). 2.2.2 COVID-19 exposures and vulnerabilities Participants self-reported whether they had COVID-19 and whether anyone in their household had COVID-19 (yes, diagnosed with a test; probably yes, diagnosed by clinician without test; maybe, suspected COVID-19/presence of some symptoms; no, did not have COVID-19). Participants reported whether they had a COVID-19 test (yes/no), had a condition that increases vulnerability to COVID-19 (yes/no), or knew anyone that had COVID-19 (yes/no). 2.2.3 COVID-19 perceived threat For the purpose of the current study, we created a 7-item measure to assess the perceived threat of COVID-19 to three life domains: health (2 items, physical and emotional), financial well-being (3 items, work life, financial security, housing), and social well-being (2 items, inside and outside of the home). Participants rated how much threat COVID-19 has presented to each of these areas (current) and for the next 12 months (future). Items were rated on a 5-point Likert scale (0 = no threat, 1 = mild threat, 2 = moderate threat, 3 = severe threat, 4 = extreme threat). Mean scores per life domain were created for analyses. 2.2.4 COVID-19 protective and risky behaviors For the purpose of the current study, we created a measure to assess risky and protective behaviors in contracting COVID-19. Participants rated the frequency of their engagement in 18 behaviors (10 protective, 8 risky) over the past 30 days. Items were rated of a 5-point Likert scale (0 = never, 1 = rarely, 2 = sometimes, 3 = often, 4 = always). Protective behaviors included maintaining 6-feet social distance, mask wearing, washing hands, using hand sanitizer, sanitizing packages, staying up to date on COVID-19 news, isolating or quarantining, stocking up on food or supplies, changing clothes after being outside, and taking immune supplements. Risky behaviors included taking flight for leisure, going to indoor restaurants or bars, attending events with large crowds, socializing indoors, going to outdoor restaurants or bars, socializing outdoors, taking public transportation, and going to grocery stores.2 Composite scores were created for the average frequency per type of behavior (protective, risky). 2.2.5 COVID-related moral injury We adapted the 9-item Moral Injury Events Scale (MIES; Nash et al., 2013) with permission from the developing author (Nash) to capture COVID-19 related moral injury (modified by Khan and Maguen, 2020). The MIES measures exposure to and feelings of three types of moral injury: betrayal (3 items), transgression by others (2 items), and transgression by self (4 items). Instructions were adapted to anchor moral injury to COVID-19 (e.g., “During the coronavirus pandemic, some individuals may experience, witness, or learn about situations that go against their deeply held moral beliefs), provide a brief example (e.g., “having to lay people off, failing to isolate”), and specify time frame (“since the coronavirus pandemic began”). Individual items were not modified except for the three betrayal items. These items are typically anchored to military experiences and for the current study, were modified to assess betrayal by “leaders from the government,” “other community members,” and “healthcare or public health organizations.” Items are rated on a 6-point Likert scale (1 = strongly disagree, 6 = strongly agree). Sum totals and averages were created for the three subscales. For regressions, averages for each subscale were used. Internal consistency in the current sample was good (α = 0.84 for total score; 0.82 for transgression by others; 0.94 for transgression by self; 0.75 for betrayal). Inter-item correlations are shown in Supplementary Table 1. For descriptive purposes, MIES average subscale scores were also dichotomized (yes ≥ 4.00, corresponding to slightly agree or greater; no≤ 3.99). We chose to use all three subscales rather than collapsing the transgressions (by self, others) subscale into one because of evidence that witnessing versus perpetrating transgressions are differentially associated with mental health outcomes (e.g., Bryan et al., 2016; Maguen et al., 2020). 2.2.6 Posttraumatic stress disorder (PTSD) Past month PTSD severity was assessed using the PTSD Checklist-5 (PCL-5; Weathers et al., 2013). The PCL-5 is a widely used self-report questionnaire of PTSD symptoms in adults with good psychometric properties (Bovin et al., 2016). Participants rated the severity of 20 symptoms corresponding to the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) PTSD criteria on a five-point Likert scale (0 = not at all, 4 = extremely). A total symptom severity score was derived through summation, with potential scores ranging from 0 to 80. Internal consistency in the current sample was excellent (α = 0.96). In line with current guidelines, we defined probable PTSD as a symptom severity score cutoff of ≥33 (Bovin et al., 2016) and meeting this threshold was used as a moderator for sensitivity analyses. 2.3 Statistical analyses Of the eligible sample (N = 896), 94.3% had complete data (N = 845). Of those, 6 participants were missing MIES data. One participant was missing all MIES items and was excluded from analyses. The remaining five participants were missing only one item and included in analyses. To derive a score for the missing item, we first calculated an average of the subscale score (e.g., betrayal) and used this to replace the missing value. Next, we calculated the new average and sum for the subscale. The final sample used for analyses was comprised of 839 participants. Data distributions of primary variables of interest and covariates were examined for normality and descriptives were derived. We performed bivariate correlations to preliminarily examine the associations between perceived threat, risk and protective behaviors, and COVID-related moral injury. We also performed t-tests to examine whether COVID-19 related moral injury subtype severities differed based on history of having COVID-19 oneself or someone in the home having COVID-19 (0 = no or maybe; 1 = yes confirmed with test and/or doctor diagnosis), knowing anyone with COVID-19 (0 = no, 1 = yes), and having a condition that makes one vulnerable to contracting COVID-19 (0 = no, 1 = yes). Research shows associations between mental health and moral injury vary based on type of moral injury (e.g., Maguen et al., 2020; Yeterian et al., 2019). Therefore, each series of multivariate linear regressions were performed for each type of COVID-19 related moral injury (betrayal, transgression by others, transgression by self). To examine the effect of perceived threat of COVID-19, we performed multivariate linear regressions with all six average perceived threat variables entered simultaneously (both current and future financial, social, and health). Tests to see if perceived threat data met assumption of collinearity indicated that multicollinearity was not a concern (Tolerance range = 0.36–0.25; VIF range = 3.96–2.79). We also performed multivariate regressions examining the effect of average frequency of COVID-19 risky and protective behaviors (included simultaneously). To improve interpretability, COVID-19 related moral injury scale scores were transformed into z-scores for regressions. All models were adjusted for possible cofounding sociodemographics that were significantly associated with or different on COVID-19 related moral injury at p < .05 (using parametric or non-parametric continuous and categorical tests), which were: age, gender (0 = woman and non-binary, transgender, other; 1 = man), sexual orientation (0 = heterosexual, 1 = LGBQ+), marital status (0 = married, 1 = all else), and employment (0 = unemployed; 1 = all else)). We also conducted sensitivity analyses repeating primary regressions further adjusting for the COVID-19 experiences that differed significantly on at least one moral injury scale (which was knowing someone who had COVID-19 and having a condition that made one vulnerable to COVD-19). Finally, because the current sample was PTSD-enriched (33%, N = 277), we performed a sensitivity analyses to determine whether PTSD moderated the relationships between significant perceived threat and behavior variables and COVID-19 related moral injury. All analyses were conducted in SPSS, version 26. 3 Results 3.1 Demographics and preliminary analyses The majority of the sample identified as women (78%) and college educated (63%), with average age of 37 years (SD = 11.1; see Table 1 for full demographics). The sample was somewhat diverse with regards to race/ethnicity, with approximately 59% identifying as White, 14% as Black or African American, 10% Latinx, 9% Asian, and 8% as other or more than one race. A small proportion of the sample endorsed working in a role that provided either direct (N = 33, 3.9%) or supportive (N = 54, 6.4%) care for COVID-19. Approximately 18% reporting losing their job and 35% reporting losing hours or income (not mutually exclusive). Approximately 33% reported having a COVID-19 test, but rates of confirmed or diagnosed COVID-19 were low (1.4% and 1.1% respectively). Notably, rates of suspecting having had COVID-19 were higher (16%) which may be related to test availability earlier in the pandemic. In examining individual COVID-19 contraction behaviors, the majority of the sample reported high frequency engagement in certain protective behaviors (e.g., 82% always wore masks, 57% always washed their hands) and avoidance of certain risky behaviors (e.g., 83% never flew for leisure, 82% never attended a large event). Average engagement in protective behaviors (M = 2.73, SD = 0.70) was higher than the average engagement in risky behaviors (M = 0.91, SD = 0.70), and they were significantly inversely correlated (see Table 2 ).Table 1 Sociodemographics, COVID-19 experiences and behaviors, and moral injury. Table 1Characteristic Whole Sample (N = 839) Mean (SD) or N (%) Age (in years) 37.09 (11.1) Gender Man 168 (20.0) Woman 650 (77.5) Non-Binary, Transgender, Other 21 (2.5) Race/Ethnicity Non-Hispanic White 495 (58.6) Black or African American 115 (13.5) Asian 79 (9.4) Latinx 86 (10.2) Other or 2+ races 71 (8.4) Sexual Orientation Heterosexual 665 (79.3) LGBQ+ 173 (20.6) Education </ = High School Degree 78 (9.3) Some College or Associate's 228 (27.2) College Degree or Graduate School 532 (63.4) Employment Employed Full Time 467 (55.7) Employed Part Time 133 (15.9) Unemployed 171 (20.4) Student 34 (4.1) Retired 19 (2.3) Furloughed 14 (1.7) COVID-19 Employment Changes Lost Job 153 (18.2%) Lost Hours or Income 296 (35.3%) Work in Unsafe Conditions 113 (13.5%) Laid off or Furloughed 35 (4.2%) Increased Workload 209 (24.9%) Gained a Job 88 (10.5%) Difficulty Working Due to Caregiving 117 (13.9%) None of the Above 194 (23.1%) Annual Household Income ≤$50,000 per year 344 (41.0) $50,001-$100,000 per year 334 (39.8) $100,001-$150,000 per year 103 (12.3) >$150,000 per year 57 (6.8) Marital status Married 285 (34.0) Single, In a Relationship 249 (29.7) Single, No Relationship 235 (28.0) Separated/Divorced/Widowed 69 (8.2) Living Situation Living Alone 182 (21.7) Living with Others 657 (78.3) Region of Residence West 240 (28.6) Midwest 138 (16.4) Northeast 167 (19.9) South 291 (34.6) Had a COVID-19 Test Yes 271 (32.3) Had COVID-19 Yes, diagnosed with test 13 (1.5) Probably, diagnosed without test 9 (1.1) Maybe, suspected COVID-19 137 (16.3) No, did not have COVID-19 680 (81.0) Vulnerable Conditions Yes 294 (35.0) Household Member with COVID-19 Yes, diagnosed with test 31 (3.7) Probably, diagnosed without test 7 (0.8) Maybe, suspected COVID-19 80 (9.5) No, did not have COVID-19 721 (85.0) Know Anyone with COVID-19 Yes 510 (60.8) Current Perceived Threat Total (average) 1.71 (0.81) Financial 1.67 (1.02) Relational 1.78 (1.05) Health 2.11 (1.03) Future Perceived Threat Total (average) 1.70 (0.87) Financial 1.70 (1.06) Relational 1.69 (1.02) Health 2.10 (1.07) COVID-19 Behaviors Total (average) 3.33 (0.49) Protective 2.72 (0.66) Risky 0.93 (0.66) COVID-19 related Moral Injury Total (average) 3.15 (1.10) Betrayal 3.91 (1.44) Transgression by Others 3.87 (1.60) Transgression by Self 2.23 (1.44) Note. Other race includes Native Hawaiian, Pacific Islander, American Indian, Alaska Native, and Middle Eastern. LGBQ + includes Gay/Lesbian, Bisexual, Queer, Pansexual, and Other. COVID-19 employment changes are not mutually exclusive. All scores are raw. COVID-19 Total Behaviors is average frequency of protective behaviors and risky behaviors (reverse scores). All COVID-19 related moral injury subscale scores ranged from 1.00 to 6.00 in the sample. Table 2 Bivariate correlations between COVID-19 related moral injury, perceived threat, and behaviors. Table 2 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 1. Betrayal – .53** .22** .12** .12** .23** .17** .17** .27** .03 -.05 2. Transgression by Others – .29** .11** .09* .12** .13** .11** .16** .05 -.05 3. Transgression by Self – .13** .09* .14** .13** .12** .11** -.08* .16** 4. Current Financial Threat – .47** .48** .82** .48** .46** .19** .10* 5. Current Relational Threat – .54** .43** .76** .49** .22** .01 6. Current Health Threat – .50** .53** .81** .23** -.05 7. Future Financial Threat – .56** .58** .16** .05 8. Future Relational Threat – .61** .18** .03 9. Future Health Threat – .20** -.10** 10. COVID-19 Protective Behaviors – -.11** 11. COVID-19 Risky Behaviors – Note. *p < .01, **p < .01. All scores reflect raw averages. Betrayal and transgression by others and by self refer to COVID-19 related moral injury from adapted Moral Injury Events Scale. Threat is perceived threat of COVID-19 to life domain. Protective and risk reflect frequency of engagement with behaviors. Regarding COVID-19 related moral injury (see Fig. 1 ), dichotomized MIES averages showed approximately 57% (N = 482) of participants endorsed MI betrayal, 59% (N = 497) endorsed transgression by other (N = 497), and 17% (N = 145) endorsed transgression by self (see Fig. 2 ). Bivariate correlations revealed all three types of COVID-19 related moral injury were significantly positively associated with both current and future perceived threat of COVID-19 to health and financial and relational well-being (see Table 2). However, only transgression by self was significantly correlated with risk (r = 0.16, p < .001) and protective (r = −0.08, p < .01) behaviors.Fig. 1 Raw averages of COVID-related moral injury items from adapted Moral Injury Events Scale. Fig. 1 Fig. 2 Raw averages of COVID-related moral injury. Fig. 2 T-tests comparing COVID-19 related moral injury averages across COVID exposures and vulnerabilities revealed no significant differences on betrayal, transgression by others, or self based on having had COVID-19 or someone in the household having COVID-19 (ps > .20). However, COVID-19 related moral injury did differ significantly based on whether a participant knew someone who had COVID-19 or had a condition that increased their vulnerability for contraction. Specifically, individuals who knew someone who had COVID-19 endorsed significantly greater betrayal (M = 4.11, SD = 1.38; t (837) = −5.01, p < .001) and transgression by others (M = 3.97, SD = 1.61; t (837) = −2.38, p = .017) than those who did not know anyone who had COVID-19 (M = 3.61, SD = 1.46, Cohen's d = 0.35; M = 3.70, SD = 1.58, Cohen's d = 0.17, respectively). Similarly, individuals who had a condition that made them vulnerable to COVID-19 endorsed significantly greater betrayal (M = 4.22, SD = 1.36; t (837) = −4.59, p < .001) and transgression by others (M = 4.07, SD = 1.61; t (837) = −2.69, p = .007) than those who not have any conditions (M = 3.75, SD = 1.45, Cohen's d = 0.33; M = 3.76, SD = 1.59, Cohen's d = 0.19, respectively). There were no significant associations between any COVID-19-related experiences and severity of transgression by self. 3.2 Primary analyses Linear regressions adjusting for age, gender, sexual orientation, marital status, and unemployment (see Table 3 ), showed only future perceived threat of COVID-19 to health was significantly associated with betrayal (β = 0.21, 95% CI 0.09, 0.33, p = .001). More specifically, those who endorsed MI due to betrayal perceived COVID-19 as a greater threat to their future health. Similarly, adjusted regression showed only future perceived threat to health was significantly related to transgression by others3 (β = 0.16, 95% CI 0.04, 0.28, p = .036). No perceived threat of any kind was significantly related to transgression by self. In contrast, neither average frequency of risky or protective behaviors were significantly associated with betrayal or transgression by others (see Table 4 ).4 However, adjusted linear regressions revealed more risky behaviors for contracting COVID-19 were significantly positively associated with transgression by self (β = 0.23, 95% CI 0.12, 0.33, p < .001). More specifically, those who felt like they had crossed a line related to their own morals or values related to the pandemic were more likely to engage in risky behaviors.Table 3 Linear regressions with perceived COVID-19 threat predicting COVID-19 related moral injury. Table 3Variable B SE β t 95% CI Lower 95% CI Upper Betrayal ΔF = 10.52** Age −0.00 0.00 −0.03 −0.94 −0.01 0.00 Gender −0.00 0.07 0.00 −0.01 −0.14 0.14 Sexual Orientation 0.38 0.09 0.15 4.39** 0.21 0.55 Marital Status 0.03 0.07 0.01 0.37 −0.12 0.17 Employment 0.04 0.08 0.02 0.45 −0.13 0.20 Current Threat Financial −0.03 0.06 −0.03 −0.43 −0.17 0.09 Relational −0.07 0.05 −0.07 −1.28 −0.17 0.04 Health 0.03 0.06 0.04 0.58 −0.08 0.15 Future Threat Financial 0.03 0.06 0.03 0.47 −0.09 0.15 Relational 0.06 0.06 0.06 0.10 −0.06 0.17 Health 0.21 0.06 0.22 3.49** 0.09 0.33 Transgression by Others ΔF = 3.50** Age −0.00 0.00 −0.02 −0.43 −0.01 0.01 Gender 0.13 0.07 0.07 1.87 −0.01 0.27 Sexual Orientation 0.34 0.09 0.14 3.84** 0.17 0.52 Marital Status 0.11 0.07 0.05 1.49 −0.04 0.26 Employment 0.10 0.09 0.04 1.19 −0.07 0.27 Current Threat Financial 0.04 0.06 0.04 0.60 −0.08 0.16 Relational −0.01 0.05 −0.01 −0.14 −0.11 0.10 Health −0.07 0.06 −0.07 −1.09 −0.18 0.05 Future Threat Financial 0.02 0.06 0.02 0.23 −0.11 0.14 Relational 0.02 0.06 0.02 0.28 −0.10 0.13 Health 0.16 0.06 0.17 2.55** 0.04 0.28 Transgression by Self ΔF = 3.41** Age −0.01 0.00 −0.07 −1.86 −0.01 0.00 Gender 0.14 0.07 0.07 2.02* 0.00 0.28 Sexual Orientation 0.11 0.09 0.05 1.24 −0.06 0.29 Marital Status 0.07 0.08 0.03 0.96 −0.08 0.22 Employment −0.04 0.09 −0.01 −0.40 −0.20 0.13 Current Threat Financial 0.05 0.06 0.05 0.86 −0.07 0.17 Relational −0.05 0.05 −0.05 −0.86 −0.15 0.06 Health 0.12 0.06 0.12 1.97 0.00 0.24 Future Threat Financial 0.02 0.06 0.02 0.35 −0.10 0.15 Relational 0.08 0.06 0.08 1.37 −0.04 0.20 Health −0.06 0.06 −0.06 −0.90 −0.18 0.07 Note. *p < .05, **p < .01. Regression reflects step 2 of model. COVID-19 related moral injury scores are standardized (M = 0, SD = 1). Table 4 Linear regressions with COVID risk and protective behaviors predicting COVID-19 related moral injury. Table 4Variable B SE β t 95% CI Lower 95% CI Upper Betrayal ΔF = 1.04 Age −0.00 0.00 −0.04 −0.98 −0.01 0.00 Gender 0.00 0.07 0.00 0.06 −0.14 0.14 Sexual Orientation 0.40 0.09 0.16 4.46** 0.22 0.58 Marital Status 0.02 0.08 0.01 0.31 −0.12 0.17 Employment 0.10 0.09 0.04 1.17 −0.07 0.27 COVID Behaviors Protective 0.05 0.05 0.03 0.97 −0.05 0.15 Risky −0.05 0.05 −0.03 −0.96 −0.16 0.05 Transgression by Others ΔF = 2.31 Age −0.00 0.00 −0.02 −0.62 −0.01 0.00 Gender 0.15 0.07 0.07 2.05* 0.01 0.29 Sexual Orientation 0.34 0.09 0.14 3.75** 0.16 0.51 Marital Status 0.13 0.07 0.06 1.79 −0.01 0.28 Employment 0.14 0.09 0.06 1.61 −0.03 0.30 COVID Behaviors Protective 0.09 0.05 0.06 1.77 −0.01 0.20 Risky −0.05 0.05 −0.04 −1.02 −0.16 0.05 Transgression by Self ΔF = 11.46** Age −0.01 0.00 −0.06 −1.54 −0.01 0.00 Gender 0.15 0.07 0.07 2.06* 0.01 0.28 Sexual Orientation 0.17 0.09 0.07 1.86 −0.01 0.34 Marital Status 0.16 0.07 0.02 0.69 −0.09 0.20 Employment 0.05 0.08 0.02 0.46 −0.13 0.20 COVID Behaviors 0.04 Protective −0.08 0.05 −0.06 −1.62 −0.19 0.02 Risky 0.23 0.05 0.15 4.31** 0.12 0.33 Note. *p < .05, **p < .01. Regression reflects step 2 of model. COVID-19 related moral injury scores are standardized (M = 0, SD = 1). 3.3 Sensitivity analyses Linear regressions were repeated further adjusting for history of knowing someone who had COVID-19 and having a condition that increases vulnerability for contracting COVID-19. The pattern of results were unchanged. Perceived threat to one's future health, but not financial or relational well-being, remained significantly associated with betrayal (β = 0.19, 95% CI 0.08, 0.31, p = .001) and transgression by others (β = 0.15, 95% CI 0.03, 0.27, p = .016). Greater frequency of risky behaviors also remained significantly associated transgressions by self (β = 0.23, 95% CI 0.12, 0.33, p < .001). An additional sensitivity analyses was performed to examine whether currently having PTSD moderated the relationships between (1) perceived future health threat and betrayal and transgression by others, and (2) risky behavior frequency and transgression by self. We performed a moderation analysis using the macro PROCESS (Hayes, 2013), mean-centering product variables and adjusting for sociodemographics, COVID-19 vulnerability, and knowing someone who had COVID-19. Models revealed PTSD was significantly associated with transgression by others (b = 0.16, se = 0.07, p = .03, 95 CI 0.01–0.31) and self (b = 0.36, se = 0.08, p < .001, 95 CI 0.21–0.51). However, PTSD did not moderate the effect of perceived health on betrayal or transgression by self, or the effect of risky behaviors on transgression by self (interaction CIs included 0). 4 Discussion Although the impact of the COVID-19 pandemic on mental health will continue to unfold for some time, the current study sheds light on reported moral distress and its relationship to COVID-19 perceived threat and safety precaution behaviors. Over half of the sample endorsed betrayal and transgressions by others, and stronger feelings of betrayal and transgression by others were associated with higher perceived threat of COVID-19 to one's future physical and mental health. Although transgressions by self was less frequently endorsed, it was significantly related to engaging in behaviors that elevate risk for contracting COVID-19. Although future longitudinal research is needed, these findings may suggest targeting individual, group, and system level responses to the pandemic could help mitigate long-term moral distress. The current PTSD-enriched sample of community dwelling individuals endorsed COVID-19 related moral injury at average levels and rates higher than those reported in two recent studies of moral injury (not COVID-19 related) in veterans (Maguen et al., 2020; Wisco et al., 2017). Dichotomizing MIES averages, we found approximately 57% of participants endorsed MI betrayal, 59% endorsed transgression by others, and 17% endorsed transgression by self. In contrast, endorsements were 26% betrayal, 26% transgression by others, and 11% transgression by self in combat veterans (Wisco et al., 2017) and 41% betrayal, 28% transgression by others, and 19% transgression by self in a veteran sample with more female representation (Maguen et al., 2020). Although difficult to compare averages across studies, the current findings reflect moral injury endorsements were higher than those in healthcare workers at the onset of COVID-19 (Hines et al., 2020). Differences in samples and reporting styles notwithstanding, these findings suggest ethically challenging situations during the pandemic are having substantive effects on psychological functioning. Further, endorsements were highest for betrayal, especially by leaders of government and other community members, and transgression by others. Our results highlight the connection between perceptions of how community members and institutions are behaving in response to COVID-19 and individual well-being. We also found that even when adjusting for being more vulnerable to contracting COVID-19, betrayal and transgression by others were associated with greater perceived threat to future health. This is in line with findings from an international study that found institutional betrayal was associated with greater COVID-19 related fear (Bachem et al., 2020). In considering implications for early intervention, the most effective fear-reduction strategies will likely come as a result of systemic action that actually increases safety through vaccinations (e.g., Oliver et al., 2020), antibody treatments, and restoring structural and social functioning. In the interim however, efforts to boost individual resilience through engaging in moral or value-affirming activities, seeking psychospiritual support, and stress management techniques may help mitigate COVID-19 related moral injury (e.g., Borges et al., 2020; Harris et al., 2015; Williams et al., 2020). COVID-19 related moral injury, specifically with transgression by self, was significantly associated with more frequent risky behaviors. That transgression by self was associated only with COVID-19 risky behaviors could suggest this type of COVID-19 related moral injury is more related to actual threat behavior than threat perception. Thus, actions that increase risk for COVID-19 are associated with feelings of having transgressed one's own morals. Although longitudinal research is needed, an alternative interpretation is that individuals who violate their own morals could also be engaging in self-punishment through increased COVID-19 risky behaviors (Maguen et al., 2020). Regardless, moral reasoning research suggests following a moral violation by self, engaging in moral, prosocial behavior can assist in regaining injured self-worth (Sachdeva et al., 2009). Thus, interventions for transgression by self-related moral injury during the pandemic may benefit from incorporating amend-making or value-reorientation as well as self-compassion and self-forgiveness (e.g., Forkus et al., 2019; Griffin et al., 2019; Purcell et al., 2018). In contrast to expectations, frequency of protective behaviors was not significantly related to any type of COVID-19 related moral injury. These data could suggest COVID-19 protective behaviors neither buffer (through engagement) or confer risk (through omission) for moral injury. Alternatively, this could suggest those with moral injury are not changing COVID-19 protective behaviors patterns even if they are increasing risky ones. It is worth noting that the sample endorsed generally high frequencies of protective behaviors and therefore we may not have had enough variability to find associations. Notably, several important considerations warrant discussion. First, the sample endorsed generally high frequencies of protective behaviors and much lower frequencies of risky behavior. This pattern may imply an underlying moral or values system or moral identity (Reynolds and Ceranic, 2007) or reflect interpretations of public health advice, which may not generalize across all U.S. inhabitants. Importantly, the majority of the sample were employed full-time (55%), had a college degree (63%), and denied losing their job due to COVID-19 (82%). Although 35% reporting losing hours or income, these sample characteristics may have influenced why only future perceived health, but not financial or social, threat was associated with COVID-19 related moral injury. As a result of the pandemic, millions have lost stable employment and job loss is more heavily concentrated in those who do not have a 4-year degree (Center for Budget and Policy Priorities, 2020). Consequently, millions are still behind on housing payments and are reporting food insecurity (Center for Budget and Policy Priorities, 2020). This has been disproportionately true for persons with minority identities (e.g., Fortuna et al., 2020), who also have disproportionally higher rates of COVID-19 (American Public Media Research Lab, 2020). Future research that samples more widely across levels of race, socioeconomic status, education, and job type is critical to better characterize how the negative impacts of COVID-19 on financial and relational well-being are associated with COVID-19 related moral injury. Furthermore, ability to comply with safety guidelines is also reflective of privilege (e.g., being able to have groceries delivered vs. going into the store, driving one's own car vs. needing to take public transportation) and participants may have been employed in risky contexts (e.g., grocery stores), thus findings should be contextualized within this cultural reality. Finally, LGBQ + identity was significantly related to betrayal and transgression by others in regressions, possibly reflective of the impact of different within-group responses to COVID-19 or discrimination during the pandemic. 4.1 Limitations Despite notable strengths including a large sample size, rapid empirical investigation of novel constructs, national reach, and thorough data on COVID-19 experiences, several limitations should be noted. First, the data are cross-sectional and thus we are unable to determine causality in associations between perceived pandemic threat and behaviors and COVID-19 related moral injury. As the eligible sample included only 25% of those initially contacted from the recruitment pool, there is also possible non-response bias and all of our measures were based on self-report and thus subject to social desirability biases. Subsequent analyses revealed non-responders were significantly (p < .001), albeit modestly, younger (M = 31.4(SD = 10.5)) and had worse PTSD severity (M = 43.4(SD = 19.7)) than responders (Mage = 34.7(SD = 11.1); MPTSD = 40.5(SD = 19.4)). The samples did not differ on gender, trauma count, prevalence of trauma exposure, or PTSD diagnosis. However, it is important to note that participants were recruited from a PTSD-enriched sample, and PTSD is associated with moral injury. Therefore, the current findings may reflect associations attributable to higher PTSD prevalence and co-occurrence, and therefore may not generalize to the general public. Additionally, the MIES was originally developed for veterans and assesses subjective appraisals of potentially morally injurious events versus objective events, and the scales collapse across both exposure to events and psychological reactions to those events. Participants may thus have referenced different events and used different thresholds for what qualifies as a moral transgression. We also adapted the MIES without validation testing and there remains a lack of consensus on both the definition and phenomenology of moral injury, thus the current measure may be missing important construct facets. Furthermore, it is critical that we better understand all of these aspects of moral injury and their manifestations in non-veteran samples to better study other groups, especially in light of the pandemic. The sample was comprised predominately of women, limiting the generalizability to men and gender diverse persons. Notably though, the sample covered nearly all U.S. states and generally was largely representative of the racial/ethnic distribution of the U.S. (U. S. Census Bureau, 2020). 5 Conclusions The current study provides novel information on moral injury in response to COVID-19 in a sample of individuals spread across the U.S. Our data indicate that ethical challenges associated with the COVID-19 pandemic are being felt as moral distress. Betrayal and transgressions by others were more highly endorsed than transgressions by self. Both types of moral injury that pertain to others' behaviors are associated with how much a person perceives threat of COVID-19 to their future physical and mental health. However, moral injury from one's own actions or inactions was most strongly associated with how frequently a person engages in behaviors that increase risk for contracting COVID-19. Although longitudinal research is needed, intergroup and system level reconciliation (Enright et al., 2020; Griffin et al., 2019a, Griffin et al., 2019b) in addition to interventions focused on self-forgiveness may be needed to facilitate moral healing from this pandemic. Author contributions AJK: Study Design, Conceptualization, Planned and Conducted Analyses, Table and Figure Presentation, Writing – original draft, Writing-Original Draft Preparation; KN: Study Design and Implementation, Database Creation, Writing-Reviewing and Editing; PT: Study Design and Implementation; DV: Study Design and Implementation; AJ: Study Design and Implementation; EW: Study Design and Implementation, Writing-Reviewing and Editing; SI: Writing-Reviewing and Editing; AR: Writing-Reviewing and Editing; TCN: Writing-Reviewing and Editing; SM: Supervision; Writing-Reviewing and Editing; AOD: Study Design and Conceptualization, Funding acquisition, Supervision, Writing-Reviewing and Editing. Role of funding source This work was supported by the UCSF Department of Psychiatry Rapid Award (AOD) and the National Institutes of Mental Health (AOD; K01MH109871). AJK is supported by the VA Office of Academic Affiliations in conjunction with the Advanced Fellowship Program in Mental Illness Research and Treatment, Department of Veterans Affairs. KN is supported by the Department of Veterans Affairs San Francisco Data Science Fellowship. These funding sources did not have any role in the conduct of the research or the preparation of this article. The views expressed in this article are those of the authors and do not necessarily reflect the views of the Department of Veterans Affairs or the United States Government. Declaration of competing interest The authors declare no conflicts of interest. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 2 Confirmatory factor analysis was performed to examine the underlying structure. Model provide adequate fit, confirming a priori groupings of risk and protective COVID-19 behaviors. 3 Regressions were repeated using standardized average total MIES scores and collapsing transgressions by others and self into one standardized scaled score. For the total MIES score, average perceived future threat to health approached significance (β = 0.10, 95% CI -0.01, 0.2, p = .085). For the collapsed transgression scale, there was no significant effect of perceived threat. 4 Regressions were repeated using the standardized average total MIES score and collapsing transgressions by self and other into one standardized scaled score. For the total MIES score, frequency of risky behaviors approached significance (β = 0.09, 95% CI -0.01, 0.20, p = .08). 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Psychospiritual developmental risk factors for moral injury Religions 11 10 2020 484 Weathers F.W. Litz B.T. Keane T.M. Palmieri P.A. Marx B.P. Schnurr P.P. The PTSD Checklist for DSM-5 (PCL-5) – Standard [Measurement instrument] Available from https://www.ptsd.va.gov/ Williams R.D. Brundage J.A. Williams E.B. Moral injury in times of COVID-19 J. Health Soc. Pol. 46 2020 65 69 Williamson V. Stevelink S.A.M. Greenberg N. Occupational moral injury and mental health: systematic review and meta-analysis Br. J. Psychiatry 212 6 2018 339 346 29786495 Wisco B.E. Marx B.P. May C.L. Martini B. Krystal J.H. Southwick S.M. Pietrzak R.H. Moral injury in U.S. combat veterans: results from the national health and resilience in veterans study Depress. Anxiety 34 4 2017 340 347 Yeterian J.D. Berke D.S. Carney J.R. McIntyre-Smith A. St Cyr K. King L. Kline N.K. Phelps A. Litz B.T. Members of the moral injury outcomes project consortium Defining and measuring moral injury: Rationale, design, and preliminary findings from the Moral Injury Outcome Scale Consortium. J Trauma Stress 32 3 2019 363 372 30947372

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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(21)02053-6 10.1016/j.molstruc.2021.131932 131932 Article Crystal structure, DFT studies, Hirshfeld surface and energy framework analysis of 4-(5-nitro-thiophen-2-yl)-pyrrolo [1, 2-a] quinoxaline: A potential SARS-CoV-2 main protease inhibitor Divya K.M. ab Savitha D.P. a Krishna G. Anjali a Dhanya T.M. a Mohanan P.V. a⁎ a Department of Applied Chemistry, Cochin University of Science and Technology, Kerala, India b Department of Chemistry, N.S.S College, University of Kerala, Cherthala, Alappuzha, India ⁎ Corresponding author. 15 11 2021 5 3 2022 15 11 2021 1251 131932131932 3 8 2021 7 10 2021 12 11 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The title compound 4-(5-nitro-thiophen-2-yl)-pyrrolo[1,2-a] quinoxaline (5NO2TAAPP) was obtained by a straightforward catalyst-free reaction of 5-nitro-2- thiophene carboxaldehyde and 1-(2-aminophenyl) pyrrole in methanol and was structurally characterized by FT IR, UV–Vis, NMR spectroscopic techniques and elemental analysis. The structure of the compound has been confirmed by the single-crystal X-ray diffraction technique. The compound crystallizes in a monoclinic crystal system with space group P21/c. Unit cell dimensions: a = 12.2009(17) A0, b = 8.3544(9) A0, c = 13.9179(17) A0 and β = 104.980(5) A0. Hirshfeld surface analysis was carried out to understand the different intermolecular interactions. The two-dimensional fingerprint plot revealed the most prominent interactions in the compound. Theoretical calculations were executed using Density functional theory (DFT) by Gaussian09 software to develop optimized geometry and frontier molecular orbital analysis. Molecular docking studies revealed that the title compound is a potent inhibitor of Main protease 3CLpro with PDB ID: 6LU7, the viral protease which is responsible for the new Corona Virus Disease (COVID-19). Keywords Pyrrolo[1,2-a] quinoxaline Crystal structure Hirshfeld surface DFT ==== Body pmc1 Introduction SARS-CoV-2 outbreak poses a serious threat to humanity all over the world today. It was declared as a pandemic by World Health Organization on 11 March 2020 [1]. Currently, there are no effective drugs developed for the treatment of this disease. Researches are going on for developing vaccines, druggable molecules, monoclonal antibodies, and cell-based therapies [2]. The chymotrypsin-like cysteine protease also known as Main protease 3CLpro plays an important role in viral replication of SARS-CoV-2 [3]. 3CLpro is the key target in anti-COVID-19 drug design due to its non-similarity with human proteins [3,4]. Compounds with quinoxaline ring systems are extensively studied for the last two decades due to their wide range of biological properties. Several quinoxaline derivatives have been reported to possess remarkable pharmacological properties which fight against several diseases with few side effects. They have been described as anti-inflammatory [5], antimalarial [6], antidepressants [7], antiviral [8], antimicrobial [9,10] as antifungal and antibacterial agents. Pyrrolo [1, 2-a] quinoxalines, an important heterocyclic compound bearing quinoxaline moiety is characterized by a broad range of biological properties [11]. Derivatives of these tricyclic systems have received a great deal of attention for their miscellaneous use in the medicinal field as antileukemic [12], anti-tuberculosis [13], antimalarial [14], anti–Leishmania [15] antidiabetic [16,17] and antibacterial [18] agents. Also, well-established evidence showed that several pyrrolo [1,2-a] quinoxaline-based compounds act as inhibitors against human protein kinase CK2, AKT kinase [19]. Furthermore, suitably functionalized quinoxaline derivatives can act as promising antiviral drugs [8]. At present, research on the synthesis, characterization, and development of new candidates which can effectively bind the Covid 19 Main protease is flying up [4]. In this context, we have been enthused to screen, in silico, the interaction between the main protease (6LU7) active site with a pyrrolo [1, 2-a] quinoxaline-based compound. Methods to synthesize pyrrolo [1,2-a] quinoxalines often involve the use of functionalized aniline precursors [20], transition metal catalysts [21], [22], [23], and harsh reaction conditions. We aimed to develop a simple method that avoids the use of expensive transition metal catalysts and dangerous oxidizers. In this paper, we have described the synthesis of a new pyrrolo [1, 2-a] quinoxaline compound, 4-(5-nitro-thiophen-2-yl)-pyrrolo[1,2-a] quinoxaline (5NO2TAAPP) in good yield through the simple coupling of 1-(2- aminophenyl) pyrrole and 5-nitro-2- thiophene carboxaldehyde. We also report the single-crystal X-ray structure, spectral characterization, Hirshfeld Surface analysis, and from the docking studies, predicted the binding affinity of this compound to the active site of 6LU7. 2 Experimental 2.1 General characterization techniques 1-(2- aminophenyl) pyrrole and 5-nitro-2- thiophene carboxaldehyde were purchased from Sigma Aldrich. All the chemicals and reagents were of analytical grade and used without further purification. Electronic spectra of the compound were recorded in DMF on a Thermo electron Nicolet evolution 300 UV–Vis spectrophotometer. FT-IR spectra of the compound were recorded as KBr pellets with a JASCO-8000 FT-IR spectrophotometer in the 400–4000 cm−1 range. Elemental analyzes of the compound were done using an Elementar Vario EL III CHN analyzer at Sophisticated Test and Instrumentation Centre (SAIF), Cochin University of Science and Technology, Kochi, India. 1H and 13C NMR spectra were recorded in CDCl3 on a Burker Advance DRX 300 FT-NMR spectrometer with TMS as the internal standard. 2.2 Synthesis of 5NO2TAAPP A methanolic solution (10 mL) of 5-nitro-2- thiophene carboxaldehyde (2 mmol) and 1-(2-amino phenyl) pyrrole (2 mmol) was refluxed in an oil bath at 60 °C for 12 h. On completion of the reaction as observed using TLC (20:80; Ethyl acetate: Hexane), the solvent was evaporated. The crystalline precipitate formed was collected through filtration using a vacuum pump, washed with cold methanol, dried, and recrystallized from methanol. The light-yellow single crystals were collected. The yield and melting point of the product were determined. Yield 80%; m. p 125–127 °C; IR (KBr, cm−1): 3070(CH), 1607(C=N), 1495(NO2); UV Vis (λ, nm): 228, 315; 1H NMR (400 MHz, DMSO‑d6, δ ppm): 7.9(1H, J = 4.4 Hz, d), 7.4(1H, J = 12 Hz, d), 7.1(1H, J = 4 Hz, d), 6.7–6.9(4H, m), 6.2(1H, J = 3.2 Hz, t), 6.01(1H, J = 2.8 Hz, d); 13C NMR (DMSO‑d6, δ ppm): 157.9, 149.9, 145.1, 134.8, 130.4, 126.8, 125.5, 125.2, 124.6, 119.4, 116.2, 115.8, 115.2, 110.7, 106.1; Anal. Calcd. For: C15H9N3O2S (295.04) C, 61.01; H, 3.07; N, 14.23; S, 10.86, Found: C, 60.80; H, 3.12; N, 14.16; S, 10.52. 2.3 Crystal structure determination and refinement Single crystal of the compound 5NO2TAAPP, suitable for X-ray structure analysis was obtained by slow evaporation at room temperature from its methanolic solution over 24 h. The single-crystal X-ray diffraction studies were carried out using a Bruker AXS Kappa Apex 2 CCD diffractometer, with graphite monochromator Mo Kα radiation (k = 0.71073 Å). The unit cell dimensions and intensity data were recorded at 296 K. The structure was solved with the direct method using SIR 92 [24] and refinement was carried out by full-matrix least-squares on F2 using SHELXL-97 [25]. The program SAINT/XPREF was used for data reduction and APEX2/ SAINT for cell refinement [26]. Software used for computing molecular graphics ORTEP 3 [27] and Mercury [28]. Software used to prepare material for publication SHELXL-97. 2.4 DFT and Hirshfeld surface analysis The density functional theory calculations were performed with Gaussian 09 package [29] with the B3LYP exchange-correlation functional and the 6–31 G (d, p) basis set. For the 5NO2TAAPP optimized structure, Frontier molecular orbitals (HOMO, LUMO) and Mulliken atomic charges were generated using GaussView05 [30]. Hirshfeld surfaces were created using the Crystal Explorer 17 program [31]. The energy framework and interaction energies of the 5NO2TAAPP molecule were calculated using the TONTO program [32], which is inherent in Crystal Explorer 17 software. The initial geometries were taken from the X-ray data CIF file of 5NO2TAAPP and used as input files for DFT and Hirshfeld surface analysis. 2.5 Molecular docking The structure of 3CLpro protein having PDB ID: 6LU7 was retrieved from RCSB Protein Data Bank [33]. An online server, Expasy protparam which provides all information regarding the protein was used for its characterization. Molecular docking was performed using Autodock 4.2.6 [34]. Both protein and ligand were prepared in pdbqt format. Polar hydrogens and Gasteiger charges were added to the receptor. Grid centerd at −19.173, 20.969, 68.039 A0 along x, y, z axes was prepared with 60 Χ 60 Χ 60 A0 3 with spacing 0.375 A0. The genetic algorithm was employed as a search parameter with 50 runs, 300 population size and 27,000 number of generations, respectively. 3 Results and discussion 3.1 Characterization The title compound 4-(5-nitro-thiophen-2-yl)-pyrrolo [1, 2-a] quinoxaline (5NO2TAAPP) was synthesized (Scheme 1 ) and characterized. The synthesized compound was crystalline, non-hygroscopic, insoluble in water but soluble in methanol, DMF and DMSO. The compound was characterized by elemental analysis, IR, UV-Visible spectra and NMR analysis. UV-Visible, IR and NMR (1H and 13C) spectra of the title compound are shown in Figs. S1, S2, S3, S4, respectively in the supporting information file.Scheme 1 Synthesis of 5NO2TAAPP Scheme 1 3.2 Crystal structure of 5NO2TAAPP The compound could be crystallized from the slow evaporation of its methanolic solution at room temperature. ORTEP diagram of the compound 5NO2TAAPP with atom numbering scheme is given in Fig. 1 (a). Crystal data and refinement parameters of the compound are given in Table 1 . X-ray crystallographic analysis revealed that the compound crystallizes in a monoclinic crystal system with space group P21/c. Unit cell dimensions of the crystal are a = 12.2009(17) Aº, b = 8.3544(9) Aº, c = 13.9179(17) Aº and β = 104.980(5)º. Bond lengths and bond angles obtained from crystal data are summarized in Table 2 .Fig. 1 (a) ORTEP diagram of the compound with thermal ellipsoids drawn at 50% probability level (b) The optimized structure of the compound Fig. 1 Table 1 Crystal data and details of the structure refinement for the title compound. Table 1Parameters Compound CCDC number Empirical formula Formula weight Temperature (K) Crystal system Space group a (Å) b (Å) c (Å) Volume (Å3) Z Calculated density (mg/cm3) μ (mm−1) F(000) Crystal size (mm3) Θ range for data collection (°) Index ranges T min, T max Reflections collected/unique Completeness to theta Data/restraints/parameters Goodness-of-fit on F2 Final R indexes [I ≥ 2σ (I)] Final R indexes [all data] Largest diff. peak/hole (e Å−3) 2,093,318 C15H9N3O2S 295.32 296(2) Monoclinic P 21/c 12.2009(17) 8.3544(9) 13.9179(17) 1370.5(3) 4 1.431 0.244 608 0.300 × 0.200 × 0.200 1.728 to 28.420 deg −16<=h<=11, −10<=k<=10,18<=l<=18 0.931,0.953 11,100 / 3405 [R (int) = 0.0335] 25.242,99.9% 3405 / 0 / 190 1.130 R1 = 0.0576, wR2 = 0.1683 R1 = 0.0855, wR2 = 0.1924 0.631 and −0.360 Table 2 Selected bond lengths (Å) and angles (°) of the crystal structure of the compound. Table 2Bond Length (Aº) Bond Angle (o) C(1)-S(1) 1.701(3) N(3)-C(5)-C(4) 108.88(17) C(4)-C(5) 1.502(3) N(3)-C(5)-C(6) 108.40(17) C(4)-S(1) 1.711(2) C(6)-C(5)-C(4) 111.32(18) C(5)-N(3) 1.474(3) C(5)-C(4)-S(1) 120.24(16) C(6)-N(2) 1.369(3) C(3)-C(4)-S(1) 112.24(19) C(9)-N(2) 1.375(3) C(14)-N(3)-C(5) 116.96(17) C(14)-N(3) 1.398(3) C(6)-N(2)-C(15) 122.75(19) C(15)-N(2) 1.406(3) C(1)-S(1)-C(4) 89.55(13) N(1)-O(1) 1.221(4) O(1)-N(1)-C(1) 117.2(3) N(1)-O(2) 1.217(4) O(2)-N(1)-C(1) 118.5(4) In the crystal structure of the compound, all C—C and C—H bond lengths in the benzene ring are in the normal range and bond angles are approximately 120º. The bond lengths and bond angles in the pyrrole and thiophene ring are comparable to other previously reported crystal structures [35]. The angle between the benzene ring and quinoxaline ring C(13)-C(14)-N(3) and C(10)-C(15)-N(2) are 120.6º and 122.7º, respectively. The thiophene ring joined to the quinoxaline ring through the bonds N(3)-C(5)-C(4), C(6)-C(5)-C(4) at an angle of 108.88º, 111.32º, respectively. The torsion angle between the quinoxaline ring and the thiophene ring is −25.3º and −85.1º for the atoms S(1)-C(4)-C(5)-N(3)and C(3)-C(4)-C(5)-C(6), respectively. CCDC deposition number 2,093,318 contains supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/structures. 3.3 Geometry optimization, frontier molecular orbitals and mulliken charges distribution The optimized geometry of the compound is given in Fig. 1(b). Frontier molecular orbital analysis was carried out which provide an insight into its biological potential and chemical reactivity [36,37]. The highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energies were calculated to be EHOMO = −6.0504 eV and ELUMO = −3.2446 eV. The HOMO – LUMO energy gap is 2.8058 eV. By using frontier molecular orbital energy values, the global reactivity descriptors such as hardness (η), chemical potential (μ), softness (S), electronegativity (χ) and electrophilicity index (ω) have been defined [38, 39]. The frontier molecular orbital and their energy gap for the compound is shown in Fig. 2 . Table 3 lists the energy gap and the other global reactivity descriptors for the title compound.Fig. 2 HOMO-LUMO and energy gap of the title compound Fig. 2 Table 3 HOMO –LUMO and global reactivity descriptors of the title compound. Table 3Parameter Value EHOMO ELUMO Energy gap (∆E) Ionization potential (I) Electron Affinity (A) Chemical Hardness (η) Global softness (σ) Eletronegativity (X) Chemical potential (μ) Eletrophilicity (ω) −6.0504 (eV) −3.2446 (eV) 2.8058 (eV) 6.0504 (eV) 3.2446 (eV) 1.4029 (eV) 0.3564 (eV−1) 4.6475 (eV) −4.6475 (eV) 7.698 (eV) Mulliken charge distribution within the molecule is very significant because it affects the overall stability, reactivity, electronic structure and more properties of the molecular system [40]. The net atomic charges 5NO2TAAPP molecule obtained by using Mulliken population analysis, are plotted in Fig. 3 . The result indicates that the S atom has the highest positive charge, with the corresponding value of charge +0.599. The two nitrogen atoms in the quinoxaline ring have the highest electronegativity and the lowest net atomic charge values (−0.848 e and −0.477 e). Therefore, it is anticipated that the quinoxaline ring as a whole or the individual nitrogen atoms within the ring can interact through hydrogen bonds or other intermolecular forces with the suitably positioned amino acid residue in the target protein. In addition, the carbon atoms in the thiophene ring (−0.321, −0.039, −0.074, −0.079 e) and oxygen atoms of the nitro group (−0.302, −0.295 e) also have relatively low atomic charges, may also be involved in prolific binding interactions with the enzymes.Fig. 3 Mulliken charge distribution for the optimized compound Fig. 3 3.4 Hirshfeld surface analysis Hirshfeld surface analysis enables us to understand the packing modes, the surface of the molecular system and intermolecular interactions in crystals. Hirshfeld surface volume and surface area of the compound are 335.91Aº3 and 312.47 Aº2, respectively. The globularity and the asphericity for the shape are 0.748 and 0.213, respectively. In the Hirshfeld surface mapped over normalized contact distance dnorm (Fig. 4 ), the white surface area indicates the contact with distances equal to the sum of van der Waals radii, and the red and blue colours indicate the distances shorter (in close contact) or longer (distinct contact) than the van der Waals radii, respectively [41].Fig. 4 Three-dimensional Hirshfeld surface of the title compound plotted over dnorm in the range -0.1039 to 1.4949 a. u. Fig. 4 The 2D fingerprint plot permit us to calculate the percentage contribution of each type of contacts for the total Hirshfeld surface area. The H . . . H contacts are responsible for the largest contribution (30.6%) to the Hirshfeld surface. Beside these contacts, H. . .O/O. . .H (24.8%), H . . . C/C . . . H (14.4%), H. . .S/S. . .H (7.3%), H . . . N/N . . .H (5.6%), C. . .C (5.5%), N. . .C/C. . .N (4.6%) and S. . .C/C. . .S (3.1%) interactions contribute considerably to the total Hirshfed surface area. The 2D fingerprint plot for all major contacts are depicted in Fig. 5 . The contributions of other contacts are only minor and add to N . . .N (0.1%), S. . .O/O. . .S (0.1%) and S. . .N/N. . .S(0.1%).Fig. 5 2D fingerprint plots for all major contacts on Hirshfeld surface. di and de denote the closest internal and external distances in A° from a point on the surface. Fig. 5 Hirshfeld surface plotted over shape index, curvedness and electrostatic potential map (Fig. 6 .) give more details about the shape and molecular packing in crystals. The shape index map (Fig. 6a.) of the title compound was generated in the range of −1 to 1 Aº. The surface around the acceptor atoms are indicated by the blue colour regions and the surface around the donor atoms are indicated by red colour regions in the shape index map. The curvedness map (Fig. 6b.) of the title compound was generated in the range −4 to 4 Aº. The large green regions indicate a planar surface area, while the blue regions reveal the areas of curvature. The presence of π-π stacking interactions is also evident as the flat regions around the thiophene and benzene ring on the Hirshfeld surface plotted over curvedness. In the electrostatic potential map (Fig. 6c.) over the Hirshfeld surface, the electropositive regions (around hydrogen bond donor) are indicated by blue colour; whereas red colour regions are electronegative regions (around hydrogen bond acceptor) [42].Fig. 6 Hirshfeld surface mapped over (a) shape index (b) curvedness (c) electrostatic potential Fig. 6 3.5 Energy framework analysis The intermolecular interaction energies for the title compound was calculated using CE-B3LYP/6–31 G(d, p) energy model available in crystal explorer with scale factors k_ ele = 1.057, k_ pol = 0.740, k_ disp = 0.871, k_ rep = 0.618, respectively [31]. The different interaction energies, coulombic interaction energy (red), dispersion energy (green), total interaction energy (blue) of the compound are depicted in Fig. 7 . The magnitude of the interaction energy is proportional to the radii of the corresponding cylinder. Table 4 list the result of different interaction energies of the title compound, rotational symmetry operations to the reference molecule (Symop), the centroid to centroid distance between the reference molecule and the interacting molecule (R) and the number of pairs of the interacting molecule to the reference molecule (N).Fig. 7 Graphical representation of electrostatic interactions (a) coulomb interaction energy (b) dispersion energy (c) total energy of the title compound Fig. 7 Table 4 Different interaction energies of compound in KJ/mol. Table 4N Symop R (A0) Eele Epol Edis E rep Etot 1 2 2 2 1 2 1 2 1 1 -x, -y, -z x,-y + 1/2, z + 1/2 x, y, z -x,y + 1/2,-z + 1/2 -x, -y, -z -x,y + 1/2,-z + 1/2 -x, -y, -z x,-y + 1/2, z + 1/2 -x, -y, -z -x, -y, -z 13.42 12.65 12.20 13.80 7.21 4.29 7.20 7.43 11.56 9.76 2.1 −15.6 5.4 0.5 −12.4 −8.7 1.0 −6.1 −7.9 −3.3 −0.5 −3.7 −2.4 −0.2 −2.9 −4.0 −4.0 −2.1 −2.2 −0.3 −3.1 −8.3 −10.1 −2.0 −15.8 −71.2 −36.7 −18.8 −9.6 −3.5 0.0 5.3 5.7 0.0 5.3 34.5 20.5 9.8 1.5 0.1 −0.9 −21.4 −0.6 −1.4 −24.5 −47.6 −18.0 −16.7 −16.9 −6.7 The calculated electrostatic, polarization, dispersion, repulsion energies are −45.0 KJ/mol, −22.3 KJ/mol, −179.1 KJ/mol and 82.7 KJ/mol, respectively. The calculated total energy of the molecule is −154.7 KJ/mol. The result revealed that dispersion energy is predominant over the other interaction energies and have a key role in the total forces in the crystal packing. 3.6 Docking analysis The purpose of this docking analysis was to assess the binding affinity of the amino acids within the 3CLpro .active site to the target ligand 5NO2TAAPP. The Auto Dock tool was used to perform the docking of the ligand into the catalytic binding site of SARS-CoV-2 3CLpro with PDB ID: 6LU7 (Fig. 8 ). The selected protein was characterized by primary and secondary structure analysis as shown in Table 5 .Fig. 8 The pictorial representation of the compound 5NO2TAAPP binded inside the pocket of SARS-CoV-2 main protease 3CLpro Fig. 8 Table 5 Characterization of protein. Table 5Properties Values molecular weight energy Number of amino acids Theoretical pI Instability index Aliphatic index GRAVY Resolution 333,797.64 KDa −16,473.465 kJ mol−1 306 5.95 27.65 82.12 −0.019 2.16 Å The isoelectric point (pI value) 5.95 indicates that the protein is slightly acidic. The relative volume of a protein occupied by its aliphatic side chains is termed as the aliphatic index (AI). The aliphatic index plays role in protein thermal stability. A high aliphatic index specifies that the protein is thermally stable over a wide temperature range. Aliphatic index in the range of 66.5 to 84.33 indicate high thermal stability and hydrophobicity of protein, which help them for biological membrane perturbation [43]. The Grand average of hydropathy (GRAVY) value is a measure of the hydrophilic nature of protein [44]. The value obtained for GRAVY is −0.019, which is close to zero, indicate the hydrophobic nature of the protein. The instability index is an assessment of the stability of a protein experimentally. A protein whose instability index is smaller than 40 is predicted as stable [45]. These results indicated that 6LU7 is a stable, hydrophobic protein. The binding affinity of the inhibitor (ligand or drug) is an important parameter, which determines the strength of its binding interaction with the target protein or the biomolecule. These interactions can be of many kinds such as hydrogen bonding, electrostatic interactions, hydrophobic and van der Waals forces. The 2D interaction diagram of the compound with the target 6LU7 is shown in Fig. 9 . Compound forms two hydrogen bonds with GLY 143 and CYS 145 through an oxygen atom of the nitro group; van der Waals interactions with GLU 166, HIS 164, GLN 189, ASP 187, ARG 188, PRO 52, TYR 54, HIS 41, SER 144, ASN 142; π-sulphur interaction by Sulphur atom of CYS 44 with the π electron cloud of benzene ring; and a π-donor hydrogen bond interaction with the –SH group of CYS 145. MET 165 and MET 49 form π-alkyl bonds with the compound.Fig. 9 Interaction of 5NO2TAAPP with SARS-CoV-2 main protease. Fig. 9 The binding affinity of a ligand with a protein is measured in terms of binding energy. The negative value of binding energy indicates a release of energy while forming a protein−ligand docked complex, which imparts stability. The more negative the binding energy, the higher will be the stability and binding affinity [46]. The binding energy obtained for the compound 5NO2TAAPP with 6LU7 is −7.95 Kcal mol−1. Hydroxychloroquine and remdesivir, which were approved drugs as inhibitors to SARS- CoV-2 having binding affinity values −6.06 and −4.96 Kcal mol−1, respectively [47]. In general, molecular docking results reveals that there should be effective hydrogen bonding and other hydrophobic interactions between the compound and the target protein. These interactions together with the predicted binding affinity, indicating that the compound 5NO2TAAPP should show significant SARS-Cov-2 inhibitory activity. 4 Conclusion The new biologically active compound, 4-(5-nitro-thiophen-2-yl)-pyrrolo[1,2-a] quinoxaline (5NO2TAAPP) was synthesized and structurally characterized by FT IR, UV–Vis, NMR and elemental analysis techniques. Single crystal X-ray diffraction analysis was used to confirm the three-dimensional conformation of the compound. Hirshfeld surface analysis revealed the important intermolecular interactions and contacts within the crystal structure. Using DFT calculations, its molecular structure was optimized, frontier molecular orbitals were deduced. Molecular docking experiments disclosed that the compound formed important binding interactions with the amino acid residues within the active site of 3CLpro, the main protease of SARS-CoV-2. Hydrogen bonding and many other hydrophobic interactions are responsible for the binding affinity of the compound with the target protein. The result revealed that the studied compound has a comparable binding affinity for 3CLpro to that of approved drugs for COVID-19 such as remdesivir and favipiravir. This suggests that the compound can be chosen for further studies as a potential therapeutic candidate for COVID-19. CRediT authorship contribution statement K.M. Divya: Writing – original draft, Formal analysis, Visualization. D.P. Savitha: Formal analysis, Writing – review & editing. G. Anjali Krishna: Formal analysis, Visualization. T.M. Dhanya: Formal analysis. P.V. Mohanan: Project administration, Writing – review & editing. Declaration of Competing Interest The authors have declared no conflicts of interest. Acknowledgments The author wishes to thank SAIF-STIC, CUSAT, Kerala for their help in single-crystal XRD analysis. ==== Refs References 1 Astuti I. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): an overview of viral structure and host response Diabetes Metab. Syndr. Clin. Res. Rev. 14 2020 407 412 10.1016/j.dsx.2020.04.020 2 Lythgoe M.P. Middleton P. Ongoing clinical trials for the management of the COVID-19 pandemic Trends Pharmacol. Sci. 41 2020 363 382 10.1016/j.tips.2020.03.006 32291112 3 ul Qamar M.T. Alqahtani S.M. Alamri M.A. Chen L.L. Structural basis of SARS-CoV-2 3CLpro and anti-COVID-19 drug discovery from medicinal plants J. Pharm. Anal. 10 2020 313 319 10.1016/j.jpha.2020.03.009 32296570 4 Jin Z. Du X. Xu Y. Deng Y. Liu M. Zhao Y. Zhang B. Li X. Zhang L. Peng C. Duan Y. 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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 The Author(s). Published by Elsevier Inc. S0013-9351(21)00221-8 10.1016/j.envres.2021.110927 110927 Article Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions Sannigrahi Srikanta a∗ Kumar Prashant bc Molter Anna ad Zhang Qi ef Basu Bidroha a Basu Arunima Sarkar a Pilla Francesco a a School of Architecture, Planning and Environmental Policy, University College Dublin Richview, Clonskeagh, Dublin, D14 E099, Ireland b Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, GU2 7XH, United Kingdom c Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, Dublin, Ireland d Department of Geography, School of Environment, Education and Development, The University of Manchester, USA e Department of Earth and Environment, Boston University, Boston, MA, 02215, USA f Frederick S. Pardee Center for the Study of the Longer-Range Future, Frederick S. Pardee School of Global Studies, Boston University, Boston, MA, 02215, USA ∗ Corresponding author. 4 3 2021 5 2021 4 3 2021 196 110927110927 15 9 2020 7 2 2021 19 2 2021 © 2021 The Author(s) 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Clean air is a fundamental necessity for human health and well-being. Anthropogenic emissions that are harmful to human health have been reduced substantially under COVID-19 lockdown. Satellite remote sensing for air pollution assessments can be highly effective in public health research because of the possibility of estimating air pollution levels over large scales. In this study, we utilized both satellite and surface measurements to estimate air pollution levels in 20 cities across the world. Google Earth Engine (GEE) and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) application were used for both spatial and time-series assessment of tropospheric Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) statuses during the study period (1 February to May 11, 2019 and the corresponding period in 2020). We also measured Population-Weighted Average Concentration (PWAC) of particulate matter (PM2.5 and PM10) and NO2 using gridded population data and in-situ air pollution estimates. We estimated the economic benefit of reduced anthropogenic emissions using two valuation approaches: (1) the median externality value coefficient approach, applied for satellite data, and (2) the public health burden approach, applied for in-situ data. Satellite data have shown that ~28 tons (sum of 20 cities) of NO2 and ~184 tons (sum of 20 cities) of CO have been reduced during the study period. PM2.5, PM10, and NO2 are reduced by ~37 (μg/m3), 62 (μg/m3), and 145 (μg/m3), respectively. A total of ~1310, ~401, and ~430 premature cause-specific deaths were estimated to be avoided with the reduction of NO2, PM2.5, and PM10. The total economic benefits (Billion US$) (sum of 20 cities) of the avoided mortality are measured as ~10, ~3.1, and ~3.3 for NO2, PM2.5, and PM10, respectively. In many cases, ground monitored data was found inadequate for detailed spatial assessment. This problem can be better addressed by incorporating satellite data into the evaluation if proper quality assurance is achieved, and the data processing burden can be alleviated or even removed. Both satellite and ground-based estimates suggest the positive effect of the limited human interference on the natural environments. Further research in this direction is needed to explore this synergistic association more explicitly. Keywords Air pollution Google Earth Engine COVID-19 Lockdown Human mobility TROPOMI ==== Body pmc1 Introduction As per the Ecosystem Services (ESs) definition of Millennium Ecosystem Assessment (MA, 2005), clean air is one of the fundamental needs of human lives (Ash et al., 2010; Baró et al., 2014; Schirpke et al., 2014; Charles et al., 2020). Air pollution has been reduced substantially during the COVID-19 lockdown period. Venter et al. (2020) had examined both tropospheric and ground air pollution levels using satellite data and a network of >10,000 air quality stations across the world and found 29% reduction in NO2 (with 95% confidence interval −44% to −13%), 11% reduction in Ozone (O3), and 9% reduction in PM2.5 during the first two weeks of lockdown (Venter et al., 2020). Kerimray et al. (2020) study at Almaty, Kazakhstan, found that the city-scale lockdown (effective on March 19, 2020) has resulted in 21% reduction in PM2.5. The CO (49% reduction) and NO2 (35% reduction) concentration has also been reduced substantially (Kerimray et al., 2020). In the same period, an increase (15%) in O3 levels is also observed in Almaty, Kazakhstan (Kerimray et al., 2020). Mahato et al. (2020) had reported a sharp reduction in air pollution in Delhi, one of the most polluted cities in the world. The author found that the concentration of PM10 and PM2.5 in Delhi was reduced to 60% and 39%, compared to the air pollution levels in 2019. The concentration of other pollutants, such as NO2 (−53%) and CO (−30%), have also been reduced substantially during the lockdown period. In addition to this, Mahato et al. study had observed a 40%–50% improvement in air quality in Delhi within the first week of lockdown. Bao and Zhang (2020) study combined air pollution and Intracity Migration Index (IMI) data for 44 cities in northern China and found that restriction on human mobility is strongly associated with the reduction of air pollution in these cities. The author found that the air quality index (AQI) in these cities is decreased by ~8%, as the concentration of five key air pollutants, i.e., SO2, PM2.5, PM10, NO2, and CO have decreased by ~7%, ~6%, ~14%, ~25%, and ~5%, respectively. Sicard et al. (2020) had observed that due to COVID-19 lockdown, NO2 mean concentrations were reduced substantially in all European cities, which was ~53% at urban stations. During the same period, the mean concentrations of O3 was reported to be increased at the urban stations in Europe, i.e., 24% increases in Nice, 14% increases in Rome, 27% increases in Turin, 2.4% increases in Valencia and 36% in increases in Wuhan (China). Otmani et al. (2020) study at Morocco using three-dimensional air mass backward trajectories and the HYSPLIT model found that PM10, SO2, and NO2 has been reduced up to 75%, 49%, and 96% during the lockdown period. In the southeast Asian (SEA) countries, Kanniah et al. (2020) study found that in Malaysia, PM10, PM2.5, NO2, SO2, and CO concentrations have been decreased by 26–31%, 23–32%, 63–64%, 9–20%, and 25–31% during the lockdown period. Kumar et al. (2020a) examined the impacts of COVID-19 mitigation measures on the reduction of PM2.5 in five Indian cities (Chennai, Delhi, Hyderabad, Kolkata, and Mumbai), using in-situ measurements from 2015 to 2020, and termed it as an ‘anthropogenic emission switch-off’ experiment, allowing to understand the baseline concentrations across various cities. Kumar et al. study found that during the lockdown period (25 March to 11 May), the PM2.5 concentration in the selected cities has been reduced by 19–43% (Chennai), 41–53% (Delhi), 26–54% (Hyderabad), 24–36% (Kolkata), and 10–39% (Mumbai), respectively. This study also found that cities with higher traffic volume exhibited a more significant reduction of PM2.5. The level of air pollution has a severe impact on human health and overall well-being. Air pollution is responsible for nearly 5 million deaths each year globally (IHME 2018). In 2017, air pollution had contributed to 9% of total deaths, ranges from 2% in the highly developed country to a maximum of 15% in low-developed countries, especially in South and East Asia (IHME, 2018). Based on Disability-Adjusted Life Years (DALYs) statistics, which demonstrate of losing one year of good health due to either premature mortality or disability caused by any factors, it has been estimated that air pollution is the 5th largest contributor to overall disease burden, only after high blood pressure, smoking, high blood sugar, and obesity, respectively (IHME, 2018). The adverse impact of air pollution on human health is not only limited to (low)developing countries. In the European regions, nearly 193,000 deaths in 2012 were attributed to airborne particulate matter (Ortiz et al., 2017). In addition, it has been found that air pollution in China is accountable for 4000 deaths each day, i.e., 1.6 million casualties in 2016 (Wang and Hao, 2012; Rohde and Muller, 2015). Chen et al. (2020) found that reduction in PM2·5 during the lockdown period helped to avoid a total of 3214 PM2·5 related deaths (95% CI 2340–4087). Chen et al. (2020) also estimated that COVID-19 lockdown and resulted cut down of air pollution brought multi-faceted health benefits to non-COVID mortalities. Several research studies (Crouse et al., 2015; Dutheil et al., 2020a; He et al., 2020) have echoed the surmountable effects of air pollutants on human lives and found that an increase in 10 μg/m3 of NO2 per day will be responsible for a 0.13% increases of all-cause mortality (He et al., 2020). The mortality rate would be around 2% when the 5-day average NO2 level would reach 10 μg/m3 (Monica et al., 2011). It is now well-established by many data-driven experiments that the accelerated rate of air pollution can have a substantial impact on overall human well-being. Many previous studies have examined the synergistic association between limited human activity and improved air quality across the scale (Chen et al., 2020; Kumar et al., 2020; Mahato et al., 2020; Ogen, 2020). These studies collectively suggested that temporary or periodic cessation of human activity could be a temporary solution for battling air pollution. However, the substantial reduction in air pollution during the lockdown is obvious and does not convey any revelation. Therefore, the co-benefits of this reduced anthropogenic emission need to be evaluated with an evidence-based approach to allow for the results to be used as a reference for future decision making and policy development. In addition to this, most of the previous studies have relied on ground-based measurements, and hence, strongly depend on the availability of publicly available data, which often creates obstacles while upscaling the approaches for larger scales. Therefore, this research work has made an effort to assess the air pollution levels of many key air pollutants after combining both satellite and ground measurements. This work aims to estimate the spatiotemporal variations in air pollution levels during the lockdown period from 1 February to 11 May in 2020 using a reference of the same period in 2019. The avoided premature mortality due to the reduction of air pollution levels and the corresponding economic benefits were also assessed using multiple economic valuation approaches. Finally, each city's population-weighted average air pollution concentration was estimated for the considered study period. 2 Materials and methods 2.1 Data source and data preparation A total of 20 cities have been selected for evaluating the effect of lockdown on air quality. These cities are Antwerp, Barcelona, Brussels, Chicago, Cologne, Denver, Frankfurt, London, Los Angeles, Madrid, Milan, New York, Paris, Philadelphia, Rotterdam, Sao Paulo, Tehran, Turin, and Utrecht. These cities have been considered based on two criteria: high air pollution and high COVID-19 casualties. Most of the cities listed here are from European and American countries. These countries reported more COVID-19 casualties compared with the Asian and Latin American countries (as of May 11, 2020) (Sannigrahi et al., 2020; WHO, 2020). Both satellite remote sensing and ground air pollution data were utilized for evaluating the positive effects of lockdown on the air quality levels of these cities. For comparison purposes, the satellite-based air pollution was measured from 1 February to 11 May for both 2019 (lockdown equivalent period) and 2020 (lockdown period). The concentration of two key air pollutants, nitrogen dioxide (NO2) and carbon monoxide (CO) was computed for both 2019 and 2020 using Sentinel 5 P data. Human mobility data Our World in Data including driving and transit for the selected cities, were collected from Apple (https://covid19.apple.com/mobility/) (city-scale) and Google (https://www.google.com/covid19/mobility/) (country-scale) mobility reports. In addition, the gridded human settlement data and population density data (pixel format) were collected from the Socio-Economic Data Application Center, National Aeronautics and Space Application data center (SEDAC, NASA). For evaluating the total air pollution reduction of these 20 cities in a more accurate way, the Geographical Information System (GIS) enabled city boundary (shapefile format) was extracted from the OpenStreetMap (OSM) application. Two consecutive steps were followed to get the boundary of these cities. First, the OSM relation identifier number (OSM id) was generated for all the 20 cities using Nominatim, a search engine for OpenStreetMap data. Then, the OSM relation id of each city was used as an input in the OSM polygon creation application interface, which generates the geometry (both actual and simplified) of the relation id in poly, GeoJSON, WKT, or image formats. The formatted image geometry of the cities was then imported in the ArcGIS Pro software, and the city boundary was extracted using an automatic digitization function. 2.2 Estimation of air pollution 2.2.1 Sentinel 5 P TROPOMI data and TROPOMI Explorer Application The ESA (European Space Agency) Sentinel-5 Precursor (S 5 P) is an example of low earth Sun-synchronous Orbit (SSO) polar satellite that provides information of tropospheric air quality, climate dynamics, and ozone layer concentration for the time period 2015–2022 (Veefkind et al., 2012). The ESA S 5 P mission is one of the few missions that is intended to measure air and climatic variability from the space-borne application. The S 5 P mission is associated with the Global Monitoring of the Environment and Security (GMES) space programme. The TROPOspheric Monitoring Instrument (TROPOMI) payload of S 5 P mission was designed to measure the tropospheric concentration of few key air pollutants, i.e., Ozone (O3), NO2, SO2, CO, CH4, CH2O, and aerosol properties in line with Ozone Monitoring Instrument (OMI) and SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY) programme (Veefkind et al., 2012). TROPOMI measures the concentration of key tropospheric constituents at a 7 × 3.5 km2 spatial unit. This default spatial scale was downscaled into 1 km × 1 km scale for city-scale analysis and subsequent interpretation. In this study, the spatial and temporal variability of two key air pollutants was extracted and mapped from the TROPOMI measurements using the Google Earth Engine cloud platform. For this purpose, an interactive application called TROPOMI Explorer Application (https://showcase.earthengine.app/view/tropomi-explorer), was utilized to facilitate quick and easy S5P data exploration and to examine the changes in air pollution in both cross-sectional and longitudinal scale. Spatial visualization and time series charts for the selected air pollutants were prepared with the help of the TROPOMI Explorer application. The other accessories of this application, such as NO2 time series inspector, NO2 temporal comparison, NO2 time-series animation, were also utilized for different computational purposes. An extensive body of research has examined the applicability of satellite remote sensing in air pollution assessment from regional to global scale (Meng et al., 2016; Fernández-Pacheco et al., 2018; Alvarez-Mendoza et al., 2019; Basu et al., 2019; Zhang et al., 2019). Many studies have focused on monitoring Aerosol Optical Depth (AOD) using Moderate Resolution Imaging Spectroradiometer (MODIS) data to predict ambient fine particulate matter concentration (Zhang et al., 2010; Mehta et al., 2016). Since MODIS data has a low spatial resolution that often limits its application at fine-scale air pollution assessment, several moderate to high-resolution satellite data products, such as Landsat and Sentinel emerged to be an efficient alternative to measure pollution levels at city scale (Meng et al., 2016; Basu et al., 2019). Several approaches, including land use regression (Kloog et al., 2012; Basu et al., 2019), machine learning approaches – random forest (Fernández-Pacheco et al., 2018), stepwise regression, partial least squares regression (Alvarez-Mendoza et al., 2019), artificial neuronal network (Zhang et al., 2019), have been established to retrieve AOD and particulate matter concentration from Landsat and Sentinel data. 2.2.2 In-situ air pollution data Ground monitored air quality data was collected from different governmental sources and open data repositories, including U.S. Environmental Protection Agency (https://www.epa.gov) (for Chicago, Denver, Detroit, Los Angeles, New York, and Philadelphia), European Environmental Agency (https://www.eea.europa.eu/) (for Antwerp, Barcelona, Brussels, Frankfurt, London, Madrid, Milan, Paris, Rotterdam, Utrecht), and OpenAQ (https://openaq.org/) (for Detroit and Los Angeles). The in-situ data was collected for three key air pollutants, i.e., NO2, PM2.5, and PM10, for a fixed time period (1 February to 11 May) of both 2019 and 2020. The average concentration of different air pollutants was calculated to perform comparative assessment and subsequent interpretation. Since the in-situ air pollution data was not adequate for thorough spatial assessment, the same had not been used for validating satellite pollution estimates. The time series (2000–2020) air quality index (AQI) of the US cities (time-series historical data is not available for other cities) was also generated using the multilayer time plot function of EPA. The overall AQI values were sub-divided into six groups, i.e., good, moderate, unhealthy for sensitive population groups, unhealthy, very unhealthy, and hazardous, respectively. In addition, the single year AQI data was also extracted for the selected cities from the EPA. The number of unhealthy days for each pollutant was measured using the EPA AQI plot function. The combination of two different pollutants, such as CO and NO2, PM10, and PM2.5, was used to assess the yearly AQI status of the cities. As several studies reported the increment of O3 due to the reduction of GHG emissions, this study also evaluated the O3 exceedances for the current year compared to the average O3 concentration of the last 5 and 20 years. This particular task was implemented using the EPA Ozone exceedances plot function (EPA, 2020). Table S1 provides the criteria of categorization for each index. 2.3 Environmental significance of improving air quality status The accelerating increases of air pollution in cities is a major concern across the world (Mayer, 1999; Kim Oanh et al., 2006; Chan and Yao, 2008; Guttikunda et al., 2014; Pilla and Broderick, 2015; Abhijith et al., 2017; Rai et al., 2017; Zhu et al., 2020; Kumar et al., 2021; Rodríguez-Urrego and Rodríguez-Urrego, 2020). Various policies have been implemented for managing the city-based air pollution that mainly originated from anthropogenic activities from specific sources and sectors (Baró et al., 2014; Kumar et al., 2015, 2016, 2019a; Feng and Liao, 2016; Zhang et al., 2016). These include the Directive 2010/75/EU on industrial emissions, initiated by European Commission to define “Euro standards” for measuring the road vehicle emissions and the Directive 94/63/EC for calculating volatile organic compounds emissions from petrol storage (Baro et al., 2014). The reduction of these gaseous pollutants by green canopy has significant economic importance (Kumar et al., 2019). Two main ecosystem services, such as air quality regulation and climate/gas regulation, are mainly associated with air quality ecosystem services (Zhang et al., 2018, Zhang et al., 2020). Several studies have calculated the economic values of NO2, SO2, CO reductions using various valuation approaches such as carbon tax, the social cost of carbon, shadow price method, marginal cost method. (Guerriero et al., 2016; Castro et al., 2017; Jeanjean et al., 2017; Bherwani et al., 2020). Since this study has considered the air pollution reduction at the city scale, the public health burden (utilized for in-situ data-based economic valuation) and mean externality valuation (utilized for satellite data-based economic valuation) approaches were utilized for estimating economic damage due to air pollution and for calculating the economic values of improved air quality (Matthews and Lave, 2000; Baro et al., 2014). Unit social damage price due to air pollution was estimated for 2020 using the US consumer price index (CPI) inflation calculator (U.S. Bureau of Labor Statistics, 2020). Additionally, using the most updated price conversion factors, the mean externality values for the key pollutants were estimated as 5149 and 956 US$ ton−1 for NO2 and CO, respectively. The public health burden valuation approach has been utilized in many studies for health impact assessment (COMEAP, 2010; Hu et al., 2015; Sahu and Kota, 2017; Etchie et al., 2018; Kumar et al., 2020a; Sharma et al., 2020). The calculation of public health burden and the associated economic burden was conducted by following three subsequent steps: first, estimation of population-weighted average concentration; second, estimation of health burden or a number of premature mortality attributable to air pollution; and third, the economic burden due to excess air pollution and economic benefits subject to the reduction of air pollution levels during the lockdown period. The population-weighted average concentration (PWAC) (Ivy et al., 2008; Etchie et al., 2018; Park et al., 2020) was measured as follows:(1) PWAC=∑x(Popx×Cx)∑xPopx where Popxis the population count of a pixel, Cxis the average concentration of NO2, PM2.5, PM10 (101 days, 1 February to 11 May in 2019 and 2020), ∑xPopxis the total population count of the city, PWAC(μg/m3) is the population-weighted average concentration. The PWAC was estimated using the ArcPy Python module. Gridded population data from SEDAC, NASA was utilized for this task. Pollution and gridded population data for the same time period were used for estimations of PWAC. Following, the health burden (HB), which refers to premature deaths attributable to short-term exposure to air pollutants, was estimated for the study period. The reduction in HB (ΔHB) was also measured by calculating the difference between the previous and later HB estimates.(2) HBx=AF×Bx×∑xPopx (3) AF=(RRx−1RRx) (4) ΔHB=HB2019−HB2020 (5) RRi=e[βi(Ci−Ci,0)],Ci>0 (6) ER=RR−1 where HBxis the health burden of city x, AFis the attributable fraction associated with the relative risk of each pollutant, RRiis the relative risk of pollutant i, Bxis the baseline cause-specific mortality rate per 100,000 population. For calculating Bx, the country-wise cardiovascular and chronic respiratory baseline mortality rate was collected from the Global Burden of Disease study of 2017 (IHME, 2018).Popxis the population of city x derived from the SEDAC, NASA gridded population count data. ΔHBis the difference in health burden (or avoidance of premature death due to the reduction in air pollution) from 1 February to May 11, 2020 compared to the same period in 2019. HB2019and HB2020is the health burden estimates in 2019 and 2020 (estimated for 1 February to 11 May time period). βiis the exposure-response relationship coefficient, indicates the excess risk of health burden (such as mortality) per unit increase of pollutants. βis calculated 0.038%, 0.032%, 0.081%, 0.13%, and 0.048% per 1 μg/m3 increases of PM2.5, PM10, SO2, NO2, and O3, respectively (Hu et al., 2015; Chen et al., 2020; Kumar et al., 2020a; Sharma et al., 2020). βis calculated 3.7% per 1 mg/m3 increases of CO. Ciis the concentration of pollutant i, Ci,0is the threshold concentration, below which the pollutant exhibits no obvious adverse health effects (i.e., RR = 1). The economic burden (EB) and economic benefits of the reduced air pollution concentration were estimated using the value of statistical life (VSL) approach (Hu et al., 2015; Etchie et al., 2018). The VSL represents an individual's willingness to pay for a marginal reduction in the risk of dying. The economic benefits due to avoided premature mortality were estimated as follows:(7) EBx=HBx×VSLx where EBx is the economic benefit attributed to the reduction of air pollution and resulted in estimates of avoidable mortality, HBx is the health burden estimates of city x, VSLxis the value of statistical life of the country x that corresponds to the city. Since this study considers cities that cover many diversified economic setup and development background, a uniform income elastic global VSL estimates measured by Viscusi et al. (2017) was considered for the economic valuation and subsequent analysis. As city-specific VSL data is not available for many cities, the VSL estimates for the corresponding countries were taken for the analysis. The 2017 VSL values were converted to 2020 unit price for adjusting price fluctuation. The income adjusted VSL (Million US$) was estimated as Belgium (8, was used for Antwerp and Brussels), Spain (5, was used for Barcelona, Madrid), the USA (10, was used for Chicago, Denver, Detroit, Los Angeles, New York, and Philadelphia), Germany (8, was used for Cologne, Frankfurt), the UK (8, was used for London), Italy (6, was used for Milan and Turin), France (7, was used for Paris), the Netherlands (9, was used for Rotterdam and Utrecht), Brazil (2, was used for Sao Paulo), and Iran (1, was used for Tehran), respectively (Viscusi et al, 2017). 2.4 Examining human mobility and its connections with air pollution status Due to the emergence of the COVID-19 pandemic, countries across the world imposed mandatory lockdowns to restrict human-mobility. This triggers the reduction of motorized traffic, which is one of the key sources of urban air pollution (Chinazzi et al., 2020; De Brouwer et al., 2020). Human mobility could accelerate the transmission of contagious diseases, especially when a larger fraction of the population daily commutes used public transport to sustain their essential daily journey (Sasidharan et al., 2020). Troko et al. (2011) study noted a statistically significant association between human mobility that is mainly attributed to public transport and transmissions of acute respiratory infections (ARI). Troko et al. (2011) also found that the use of public transport within the five days of symptom onset (Influenza) in the UK has increased the risk of ARI infection by six-times. To evaluate the effects of reduced human mobility on air pollution, the study presented in this paper utilized the human mobility data provided by Apple and Google (Drake et al., 2020; Wang and Yamamoto, 2020; Wellenius et al., 2020; Yilmazkuday, 2020). Apple mobility data includes three mobility components, i.e., driving, walking, and transit (public transport), respectively. The reduction of human mobility during the lockdown period was calculated from the baseline (13 January). Both positive and negative changes in human mobility were recorded in percentage form to eliminate calculation bias. Among the three mobility components, driving and transit was considered for the evaluation. Google mobility data was also used in this study which has six components (retail and recreation, grocery and pharmacy, parks, transits, workplace, and residential). This data is available from February 15, 2020 to recent date. Since Google mobility data is not available for city scale, the smallest scale (county/state) was taken for the analysis for which the mobility counts are available. This data is also prepared in percentage format to handle the calculation bias. 3 Results 3.1 Spatiotemporal changes in air pollution in different cities Spatial distribution of tropospheric NO2 and CO column (derived from Sentinel TROPOMI data) is analyzed and presented in Fig. 1 and Fig. S1. During the observation period, a sharp reduction in NO2 and CO (μmol/m2) is observed in all 20 cities. This could be due to the lockdown and resultant reduction of transportation and industrial emission. The maximum reduction in NO2 is found for the European cities, such as Paris, Milan, Madrid, Turin, London, Frankfurt, Cologne, and American cities, such as New York, Philadelphia, etc. (Fig. 1). Moreover, the highest and lowest NO2 reduction is found in Tehran and Sao Paulo. The CO concentration has also been reduced significantly during the study period. The highest reduction is recorded in Detroit, followed by Barcelona, London, Los Angeles, etc. (Fig. S1). On the other hand, during the same period, CO was increased in Cologne and Denver (Fig. S1).Fig. 1 Spatiotemporal variation (panel a and b) and changes in NO2 tropospheric column (μmol/m2) (panel c) in 20 cities during February 1 to May 11 derived from Sentinel 5 P TROPOMI sensor. Fig. 1 Fig. 2 shows the average tropospheric NO2 and CO column values from 1 February to 11 May. The average NO2 (μmol/m2) in 2019 and 2020 was found highest in Tehran, followed by Milan, New York, Paris, Turin, Chicago, Cologne, Philadelphia, etc. The lowest NO2 column values (μmol/m2) are found in Sao Paulo, Brussels, and Denver, respectively. The average CO values (μmol/m2) was found highest in American cities, i.e., New York, Philadelphia, Detroit, Chicago, Los Angeles, while a comparably low tropospheric CO column (μmol/m2) values are seen in Sao Paulo, Denver, Madrid, Barcelona, and Brussels (Fig. 2). Except for a few cities, NO2 and CO column values have been reduced substantially during the study period (Fig. 3 , Table 1 ). For NO2, the highest reduction was detected in Paris (46%), followed by Detroit (40%), Milan (37%), Turin (37%), Frankfurt (36%), Philadelphia (34%), London (34%), and Madrid (34%), respectively. At the same time, a comparably lower reduction in NO2 is observed in Los Angeles (11%), Sao Paulo (17%), Antwerp (24%), Tehran (25%), and Rotterdam (27%), respectively (Fig. 3). For CO, the maximum reduction was recorded for New York (4.24%), followed by Detroit (4.09%), Sao Paulo (3.88%), Philadelphia (3.45%), Milan (3.17%), Barcelona (2.86%), respectively. At the same time, a positive (increase) changes in CO were observed in Denver (1.92%), Cologne (0.49%), and Rotterdam (0.01%) (Fig. 3). The temporal variability of NO2 is presented in Fig. 4 , Fig. 5 . Both median and interquartile range (IQR) values in Figs. 4 and 5 suggesting that NO2 was decreased substantially.Fig. 2 NO2 and CO tropospheric column (μmol/m2) in 20 cities during February 1 to May 11 in 2019 and 2020, derived from Sentinel TROPOMI sensor. Fig. 2 Fig. 3 Changes (%) in NO2 and CO during February 1 to May 11 in 2019 and 2020, derived from Sentinel 5 P TROPOMI data. Fig. 3 Table 1 Total emission (ton) of different air pollutants in 2019 and 2020 derived from Sentinel TROPOMI. Table 1City NO2 CO 2019 2020 Difference (%) 2019 2020 Difference (%) Antwerp 1.73 1.31 −24.14 215.83 212.80 −1.40 Barcelona 0.82 0.58 −29.49 104.91 101.91 −2.86 Brussels 1.20 0.86 −27.95 167.84 167.68 −0.10 Chicago 5.55 3.88 −30.09 656.87 652.04 −0.74 Cologne 3.62 2.47 −31.77 426.27 428.37 0.49 Denver 2.97 1.98 −33.43 336.58 343.06 1.92 Detroit 3.16 1.89 −40.29 409.95 393.18 −4.09 Frankfurt 2.14 1.36 −36.36 263.32 261.39 −0.73 London 12.45 8.20 −34.15 1644.88 1627.06 −1.08 Los Angeles 10.63 9.51 −10.54 1405.58 1390.45 −1.08 Madrid 5.18 3.42 −34.03 555.52 547.84 −1.38 Milan 2.15 1.36 −36.85 196.23 190.00 −3.17 New York 8.73 6.21 −28.86 899.48 861.33 −4.24 Paris 1.00 0.54 −45.94 112.10 109.37 −2.43 Philadelphia 3.17 2.08 −34.45 411.40 397.21 −3.45 Rotterdam 2.50 1.83 −26.72 342.27 342.31 0.01 Sao Paulo 8.39 6.95 −17.17 1139.46 1095.22 −3.88 Tehran 25.09 18.93 −24.54 786.14 773.67 −1.59 Turin 1.23 0.78 −36.83 136.12 132.54 −2.63 Utrecht 0.74 0.49 −33.70 104.73 104.32 −0.40 Fig. 4 Temporal variation in NO2 tropospheric column (μmol/m2) in the selected cities, derived from Sentinel 5 P TROPOMI data. Fig. 4 Fig. 5 Variation in NO2 tropospheric column (μmol/m2) in the selected cities during the study period in 2019 and 2020, derived from Sentinel 5 P TROPOMI data. Fig. 5 Using the in-situ air pollution data, the concentration (μg/m3) of NO2, PM2.5, and PM10 was evaluated and presented in Fig. 6 and Table 2 . NO2 concentration (μg/m3) was found highest in American cities. On the contrary, the concentration of particulate matter (PM2.5 and PM10) was found highest in European cities. The changes (%) in NO2, PM2.5, and PM10 concentration from reference values (NO2, PM2.5, and PM10 concentration from 1 February to 11 May in 2019), was also measured (Fig. 7 ). The in-situ data suggest that the reduction in NO2 was maximum in Brussels, followed by Paris and London. At the same time, PM2.5 and PM10 were decreased substantially in London, Frankfurt, and Rotterdam. Additionally, PM2.5 and PM10 concentration were found to be increased in Los Angeles. The possible reason for this increment is discussed in the Discussion section.Fig. 6 NO2, PM2.5, and PM10 concentration (μg/m3) in selected cities during February 1 to May 11 in 2019 and 2020, derived fromin-situ data. Fig. 6 Table 2 NO2, PM2.5, and PM10 concentration (μg/m3) during the study period (1 February to 11 May) in 2019 and. Table 2 NO2 (μg/m3) PM10 (μg/m3) PM2.5 (μg/m3) 2019 2020 Δ NO2 (%) 2019 2020 Δ PM10 (%) 2019 2020 Δ PM25 (%) Antwerpen 33.83 22.76 −32.72 31.76 25.40 −20.02 18.96 12.81 −32.44 Barcelona 35.77 22.71 −36.50 24.23 19.74 −18.52 – – – Brussels 31.04 18.32 −40.98 20.73 15.69 −24.28 14.48 9.36 −35.32 Chicago 52.35 46.56 −11.08 25.73 20.99 −18.42 9.21 8.56 −7.03 Denver 60.18 53.41 −11.26 27.48 20.91 −23.92 8.44 6.35 −24.81 Detroit 50.68 40.54 −20.01 17.93 16.06 −10.44 9.18 8.15 −11.22 Frankfurt 28.37 22.14 −21.95 20.84 14.86 −28.71 13.55 9.47 −30.10 London 41.83 25.39 −39.31 25.40 16.33 −35.73 15.58 9.88 −36.56 Los Angeles 48.81 48.73 −0.16 18.55 21.28 14.70 7.42 8.46 13.98 Madrid 35.69 24.16 −32.32 16.78 14.95 −10.88 9.00 8.69 −3.40 Milan 44.14 34.45 −21.96 31.44 31.79 1.13 21.21 22.61 6.57 New York 50.98 43.94 −13.80 – – – 7.44 6.30 −15.33 Paris 44.16 26.25 −40.55 25.03 18.17 −27.40 16.87 13.07 −22.55 Philadelphia 42.35 43.67 3.11 17.34 14.84 −14.40 7.99 7.76 −2.96 Rotterdam 31.96 21.20 −33.66 23.73 18.33 −22.78 13.61 8.71 −36.01 Utrecht 24.29 17.06 −29.77 21.18 17.11 −19.21 13.98 9.79 −29.95 Fig. 7 Changes in NO2, PM2.5, and PM10 concentration (%) during February 1 to May 11 in 2019 and 2020, derived from in-situ data. Fig. 7 3.2 Changes in human mobility Using the Apple human mobility data, the driving, and transit driven mobility was calculated and presented in Fig. 8 . Mobility on January 13 was taken as a baseline, and further changes in human mobility during the study period was calculated from the baseline mobility. The driving counts reduced most substantially in Paris, followed by Madrid, London, Antwerp, and Brussels (Fig. 8, Table S2). Whereas, such changes were comparably low in Chicago, Cologne, Denver, Los Angeles, New York (Fig. 8). Transit counts also reduced considerably in Paris, followed by Utrecht, Sao Paulo, New York, Milan, Chicago, Antwerp, and Brussels (Fig. 8). Google mobility records, which has six mobility components, i.e., retail and recreation, grocery and pharmacy stores, transit, parks and outdoor, workplace visitor, and time spent at home, were also utilized for country-wise assessment of human mobility changes (Fig. S2). Transport related mobilities were reduced most substantially in the Latin American countries, followed by a few Middle East and Southeast Asian countries, and American countries (Fig. S2; Fig. S3). Parks and outdoor activities were found to be reduced maximum in the Latin American countries and South Asian countries (Fig. S3). At the same time, outdoor activities are seen to be increased in a few European countries as well (Fig. S3). The highest reduction in retail and recreation was found in India, Turkey, the UK, and few Latin American countries due to lockdown and associated restrictive measures. (Fig. S4). Considering grocery and pharmacy-related mobilities, the highest reduction is being observed in the Latin American countries and a few European countries. Whereas grocery related mobility was found to be increased in the USA, few African and European countries (Fig. S4). Workplace related mobility is reduced considerably in Peru, Bolivia, India, Spain, Turkey, Saudi Arabia, USA, and Canada (Fig. S5). At the same time, such changes were positive in a few African countries (Mali, Niger, Mozambique, Zambia), Venezuela, and a few island countries (Fig. S5). Finally, using the Google real-time mobility information, another mobility component, i.e., time spent at home, was calculated (Fig. S5). As expected, due to lockdown and mandatory restrictive measures on human activities, people tend to spend more time at home, which also suggests that at most of the countries have taken timely decisions to control the pandemic. Except for a few European countries, peoples around the world limited their outdoor activities, which is supported by the results shown in Fig. S5.Fig. 8 Changes in mobility due to lock down and resulted in restriction in the selected cities. Fig. 8 3.3 Lockdown and improving status of air quality Both public health burden (applied for in-situ data) and externality valuation (applied for Sentinel TROPOMI pollution data) approaches were utilized for assessing economic benefits and economic burden attributed to air pollution led cause-specific mortality (Table 3 , Table 4 , Table 5 , Table S3; Table S4; Table S5). For satellite data-based economic assessment, the default unit (μmol/m2) was converted to a mass unit (Ton) using the standard mass conversion approach (Borsdorff et al., 2018; Ialongo et al., 2020; Liu et al., 2020). Also, to estimate the total economic benefits of air pollution reduction, the difference in pollution concentration between the current year (1 February to 11 May in 2020) and the preceding year (1 February to 11 May in 2019) was computed. The per-unit economic benefits (US$) due to the reduction of air pollution was found maximum in Sao Paulo (49,709), followed by New York (49,447), Tehran (43,625), London (38,928), Detroit (22,585), Los Angeles (20240), Philadelphia (19188), Madrid (16,413), Chicago (13,222), Milan (10,034), Frankfurt (5854), Turin (5749), Antwerp (5039), Paris (4971), Barcelona (4116), Cologne (3915), Rotterdam (3401), Brussels (1876), and Utrecht (1675) (Table 4). The health burden and associated economic impacts of COVID-19 led reduction in NO2, PM2.5, PM10 concentrations across selected 20 cities were analyzed and presented in Table 5. Health impacts are presented in terms of economic burden (indicates the increased levels of air pollution and resulted in cause-specific mortality) and economic benefits (related to reducing air pollution levels and avoided premature deaths). For NO2, economic benefits (Million US$) were found highest in London, followed by New York, Paris, and Chicago. In comparison, monetary benefits were found minimum in Los Angeles and Utrecht. The economic benefits attributed to the reduction of PM2.5 and PM10 were found highest in London, followed by New York, Paris, Chicago, Frankfurt, respectively. The city-wise population-weighted average concentration (μg/m3), attributed to NO2, PM2.5, and PM10, were estimated and presented in Fig. 9 . The high PWAC value denotes the higher level of long term exposure to NO2, PM2.5, and PM10 and vice versa. Among the cities, the higher PWAC values (for NO2) were estimated for the US cities (Denver, Detroit, New York, Los Angeles), compared to the European cities considered in this study. For both PM2.5 and PM10, the higher level of exposure was computed for the European cities (Milan, London, Paris, Antwerp, Barcelona). Due to the reduction of air pollution concentration, the average population exposed to PM2.5, PM10, and NO2 was reduced substantially for all cities, except few US cities (Los Angeles and Philadelphia). The reduced level of exposure during the study period suggesting a strong synergistic association between controlled human interference and improved air quality across the world. The health burden (HB) estimates suggest that due to the reduction of air pollution, a total of ~1310 (NO2), ~401 (PM2.5), and ~430 (PM10) premature cause-specific deaths have been averted. The economic benefits of this avoided mortality were measured as ~10, ~3.1, and ~3.3 Billion US$ for NO2, PM2.5, and PM10.Table 3 Per unit ecosystem service equivalent value (US$) of different. Table 3Pollutants Min Median Mean Max CO 2 956 956 1931 NOX 404 1949 5149 17,468 PM10 1747 5149 7907 29,788 Table 4 Economic benefits of reduced anthropogenic emission estimated using median. Table 4City NO2 CO Overall Economic Benefit (US$) Economic Benefit (US$) Economic Benefit (US$) Antwerp 2145 2894 5039 Barcelona 1251 2866 4116 Brussels 1720 156 1876 Chicago 8606 4616 13,222 Cologne 5924 −2009 3915 Denver 5115 −6190 −1075 Detroit 6550 16,036 22,585 Frankfurt 4007 1847 5854 London 21,888 17,040 38,928 Los Angeles 5771 14,469 20,240 Madrid 9073 7340 16,413 Milan 4083 5951 10,034 New York 12,976 36,471 49,447 Paris 2362 2608 4971 Philadelphia 5625 13,563 19,188 Rotterdam 3437 −36 3401 Sao Paulo 7415 42,294 49,709 Tehran 31,702 11,923 43,625 Turin 2328 3421 5749 Utrecht 1279 396 1675 Table 5 Economic burden (EB) and benefits (Million US$) of reduced anthropogenic emission in NO2, PM2.5, PM10. Table 5City NO2 PM2.5 PM10 EB 2019 EB 2020 Economic Benefit EB 2019 EB 2020 Economic Benefit EB 2019 EB 2020 Economic Benefit Antwerp 616 437 179 413 289 124 442 362 80 Barcelona 1381 933 448 – – – 742 615 127 Brussels 296 186 110 167 111 56 155 120 35 Chicago 5486 5002 484 1326 1237 89 2289 1900 389 Denver 1475 1346 128 295 225 70 587 458 130 Detroit 1339 1120 219 331 295 35 411 371 40 Frankfurt 1107 890 217 636 455 181 631 460 171 London 12,815 8395 4420 6034 3955 2078 6319 4200 2119 Los Angeles 8098 8087 10 1682 1906 −224 2643 3001 −358 Madrid 2515 1797 717 810 784 26 964 865 99 Milan 1720 1403 318 1023 1082 −59 984 994 −10 New York 16,907 15,028 1879 3406 2903 502 – – – Paris 2742 1770 972 1329 1052 277 1277 951 326 Philadelphia 2778 2847 −70 694 675 20 954 824 130 Rotterdam 337 236 102 175 116 60 196 154 42 Utrecht 318 232 87 216 155 61 211 173 38 Fig. 9 Population Weighted Average Concentration (PWAC) (μg/m3) for different pollutants in the selected cities. Fig. 9 4 Discussion 4.1 Relevance of satellite remote sensing in air pollution mapping The ESA Sentinel 5 P TROPOMI real-time pollution data was successfully utilized for evaluating linkages between the temporary cessation of human interferences and improving air quality across the cities. Sentinel 5 P satellite mission is one of the finest space-borne applications that provide the crucial key information of air quality, Ozone, ultra-violate radiation, and climate monitoring and forecasting (ESA, 2020). TROPOMI widens the application of the satellite air pollution observation and works in line with other global missions, i.e., SCIAMACHY (2002–2012), GOME-2 (since 2007), and OMI (since 2004) (Lorente et al., 2019). This data has been used for many purposes, including air pollution measurement (Borsdorff et al., 2018; Zheng et al., 2019; Shikwambana et al., 2020), epidemiological studies (Chen et al., 2020; Dutheil et al., 2020b; Gautam, 2020; Muhammad et al., 2020; Ogen, 2020; Shehzad et al., 2020); monitoring global volcano (Valade et al., 2019), demographic analysis (Kaplan and Yigit, 2020), evaluating sun-induced chlorophyll fluorescence (SIF) (Guanter et al., 2015), estimation of volcanic sulfur dioxide emission (Theys et al., 2019), etc. In addition, the advent of Google Earth Engine cloud-based functionality in handling the large volume of spatial data facilitates the application of satellite images for timely decision making and offering cost-benefit solutions to many environmental problems. Evaluating the reliability of remote sensing data is always a matter of concern. Many studies across the world have evaluated the reliability of Sentinel 5 P pollution data with ground measurements. Lorente et al. (2019) have examined the reliability of Sentinel TROPOMI tropospheric column NO2 with in-situ (ground NO2 boundary layer height over the Eiffel Tower was used in this purpose) data and found a very good agreement (R2 = 0.88) between the two estimates. Griffin et al. (2019) study on validating TROPOMI data with aircraft and surface in situ NO2 observations over the Canadian oil sands found that the TROPOMI vertical NO2 column values are strongly correlated (R2 = 0.86) with the aircraft and ground in situ NO2 observations with a low bias (15–30%). 4.2 Reduced anthropogenic emission and improving air quality status across the cities Air pollution levels in the urban region are mainly influenced by the local emission of pollutants. For example, Zeng et al. (2018) found that ~75% of the daytime O3 concentration in Wuhan in summer 2016 was caused by localized photochemical formation. In cities, the main air pollutants of concern to public health are particulate matter (PM₂.₅ and PM₁₀), NO₂, and tropospheric ozone (O₃). Both satellite and in-situ data suggest a considerable reduction in air pollution in all the 20 cities considered in this study. However, such reduction is not consistent among the cities: it was found to be very high over the European cities (for all three pollutants, i.e., PM₂.₅ and PM₁₀, NO₂), and comparably low over the US cities. NO2 concentration was reduced most significantly (>-40%) in Brussels and Paris, followed by −35% to −40% in Barcelona and London, −25% to −35% in Rotterdam, Antwerp, Madrid, Utrecht, −15%–25% in Milan, Frankfurt, Detroit, −5%–15% in New York, Denver, Chicago, and less than −5% in Los Angeles, respectively. During the same period, an incremental trend of NO2 was observed in Philadelphia. Several studies also noted that NO2 declined substantially during the COVID-19 time compared to historical years. Berman and Ebisu (2020) observed a statistically significant reduction in NO2 (25.5% reduction with an absolute decrease of 4.8 ppb) in the continental USA from March 13 to April 21 in 2020 compared to average NO2 concentration (μg/m3) during 2017–2019. Baldasano (2020) estimated that reduction in NO2 concentrations in Barcelona and Madrid (Spain) during the lockdown (March 2020) was 50% and 62%, which is in line with the findings of the present research. Using both satellite and in-situ data, Chen et al. (2020) observed that NO2 concentration had been reduced substantially in China (12·9 μg/m3). Venter et al. (2020) have analyzed ground-level measurements from >10,000 air quality stations in 34 countries and recorded a substantial NO2 reduction (60%, 11 μg/m3 in absolute terms) during the COVID lockdown dates. In the urban region, NO2 is mainly produced by human activities, including traffic emission, fuel combustion, and partly from industrial emission. EEA (2019) documented that in Europe, transport sector (road transport contributed to 39% of total emission, non-road transport contributed to 8% of total emission) is the most significant contributor to NOx emissions, followed by commercial, institutional and households emission, contributed to 14% of total NO2 emission (EEA, 2019). Additionally, in urban regions, the higher level of NO2 concentration is mostly evident in cities with higher motorized traffic share, industrial regions, and densely populated areas (Zoran et al., 2020). Since we observed a drastic reduction in human mobility, including driving and transit during the observation period, the reduced level of traffic emission could therefore be linked to the decreasing trend of NO2 observed in the European cities. The reduction of other sectorized emissions, such as industrial emission and commercial, institutional, and household emission, can also be associated with changes in NO2 observed in these European cities. Kumar et al. (2020a) study on Indian cities noted that among all the influencing factors, including lockdown strictness, switch-off time to halt human activities, local meteorological condition, reduction in road traffic volume was found to be the most influential factor for explaining the variation in air pollution across cities. Among the cities, the highest NO2 reduction (−41%) was recorded in Brussels. Fierens et al. (2011) reported that air pollution in Brussels is strongly associated with traffic-related pollutants. Apple's mobility report also shows a substantial reduction in road traffic (−47% in driving and −25% in transit) in Brussels. Thus, the reduced level of road traffic volume could explain the noteworthy reduction of NO2 concentration observed in Brussels. The second highest reduction in NO2 concentration was recorded in Paris (−40.6%). In Paris, traffic and residential sectors are the main source of NO2 pollution (Connerton et al., 2020). Traffic corresponds to 69% of NOx emissions, 36% of PM10 emissions, and 35% of PM2.5 emission (Connerton et al., 2020). At the same time, the other contributing factors, such as the residential sector, contributes to 21% of total NOx emissions, 41% of total PM10 emissions, and 49% of total PM2.5 emissions (Connerton et al., 2020). Other European capital cities, such as London, Barcelona, Madrid have also witnessed a drastic reduction in NO2 concentration during the studied period. Reduction in road traffic and meteorological factors could be linked with this reduced NO2 concentration observed in this study (Berman and Keita, 2020; Muhammad et al., 2020; Sharma et al., 2020). The NO2 concentration in the US cities has not been changed significantly during the study period. Among the six US cities, the negative NO2 changes were found lowest in Los Angeles (0.16%), followed by Chicago (~11%), Denver (~11%), New York (~14%), and Detroit (~20%), respectively. During the same period, NO2 concentration was found to be increased in Philadelphia (~3%). Meteorological factors could be responsible for these irregularities detected in these cities (Chauhan and Singh, 2020; Goldberg et al., 2020; Kumar et al., 2020a). Similar to NO2, a substantial reduction in particulate matter (PM2.5 and PM10) was observed for all the selected cities. The highest reduction (>35%) in PM2.5 was recorded for London, Rotterdam, and Brussels, followed by 25%–35% reduction in Antwerp, Frankfurt, Utrecht, 15%–25% reduction in Denver, Paris, New York, 5%–15% reduction in Detroit, Chicago), and <5% reduction in Madrid and Philadelphia, respectively. On the other hand, PM2.5 was recorded to be increased in Los Angeles and Milan. While, for PM10, the maximum decline was observed over the European cities, with ranges >35% (London), 25%–35% (Paris, Frankfurt), 15%–25% (Brussels, Denver, Rotterdam, Antwerp, Utrecht, Barcelona, Chicago), 5%–15% (Detroit, Madrid, Philadelphia). In Milan and Los Angeles, PM10 concentration was found to be increased during the studied period. Urrego and Urrego (2020) study analyzed the PM2.5 concentration in the 50 most polluted capital cities in the world. Urrego et al. stated that in Asian cities, the highest reduction in PM2.5 was recorded in Delhi (40% reduction during the quarantine week), followed by Tehran (39%), Kabul, Colombo and Tashkent (28%), Dhaka (24%), and Astana (18%), respectively. Wang et al. (2020) examined the effect of lockdown in PM2.5 concentration in Beijing, Shanghai, Guangzhou, and Wuhan and found a marked reduction in PM2.5 emissions, which was mainly attributed to the partial/complete closure of transportation and industries across the cities. Guevara et al. (2020) study on time-resolved emission reductions in Europe during the COVID-19 lockdown period found that during the most severe lockdown period, average PM2.5 emission was reduced −7% at the EU-30 level. Sicard et al. (2020) study on analyzing air pollution reduction on four European and one Asian city have found that at all stations, PM10 concentrations were decreased by 5.9% in Nice, 8.9% in Turin, 32.1% in Valencia, and 48.7% in Wuhan during the lockdown period, while a slight increase (1.8%) in PM10 concentration was observed in Rome. This study has observed substantial differences in PM2.5 and PM10 reduction across the cities. This can be associated with many factors, including the starting date of lockdown in different cities (Venter et al., 2020), the strictness of the lockdown measures (Singh et al., 2020), traffic volume (Kumar et al., 2020a), uses and mode of domestic energy (EEA, 2019), industrial emission (EEA, 2019), meteorological determinants (Goldberg et al., 2020), etc. Additionally, the reduced level of particulate matter emission can also be linked with the reduction of NO2, as the indirect conversion from NO2 to PM2.5 was temporarily ceased during the lockdown period. An increased level of PM2.5 and PM10 concentration was observed in this study in Los Angeles. Chauhan and Singh (2020) observed a similar pattern of air pollution concentration in Los Angeles. Chauhan et al. recorded a 4% reduction in PM2.5 in Los Angeles during March 2020, comparing to the baseline period (March 2019), and also noted that such changes are mainly associated with the meteorological factors, i.e., wind speed, wind direction, rainfall, etc. Due to the strong external effects of these confounding factors, air pollution status has been improved in coastal US cities, including the few considered in this study (Goldberg et al., 2020). The health and economic benefits of the COVID pandemic led to the reduction of air pollution were thoroughly examined across the selected cities. Since we utilized both satellite and in-situ data in the assessment, two relevant valuation approaches, i.e., median externality and public health burden, were implemented for handling the valuation bias and uncertainty. The health impact was presented in terms of ER (excess risk due to exceeding level of air pollution) and health burden (avoided premature mortality due to the reduction of air pollution). The details of RR, ER are given in Table S3, Table S4, Table S5. Combinedly, a total of ~1310 (NO2), ~401 (PM2.5), and ~430 (PM10) cause-specific premature deaths were averted during the study period, which valued ~16 Billion US$. HB was sharply declined from the previous year's baseline. This demonstrates the harmonious association between limited anthropogenic appropriations and resulting economic and health (co)benefits. Kumar et al. (2020a) observation in five Indian cities also found a strong positive association between lockdown restrictions and improved health benefits across the cities. However, Kumar et al. also stated that complete/partial closes of business and industries should not be the optimal way of handling air pollution problems; instead, such an estimate can be treated as a mere supposition to reveal the synergistic association between limited human interferences and associated health/economic (co)benefits. 4.3 Human mobility and its association with air pollution The connection between human mobility and air pollution levels in selected cities were also examined in this research. Results derived from both Google and Apple mobility report suggested that due to the mandatory lockdown and resulted in limited outdoor human activities, mobility has been reduced significantly across the world. This drastic reduction of human mobility could contribute to the reduced level of air pollution observed in the last few months. For most of the cities analyzed, human mobility has been reduced up to 80% from the baseline mobility. The highest reduction in mobility was found in the European cities. To prevent the spread of the pandemic, the authorities in these cities implemented strong preventive measures, which included partial lockdown in different sectors, including restricted outdoor social activities. This mandatory imposition of lockdown has resulted in a reduced level of traffic volume in cities (Fig. 8, Table S2). The mobility analysis thus suggests that by introducing sustainable transport plans and policies, air pollution in the urban regions can be minimized to a certain extent. The periodic and temporary lockdown can also be adopted in highly polluted cities if no other alternatives are feasible to adopt. A similar strategy has already been adopted by New Delhi Government by introducing an “odd/even” transport scheme where private vehicles with odd digit (1, 3, 5, 7, 9) registration numbers will be allowed on roads on odd dates and vehicles with even digit (0, 2, 4, 6, 8) registration numbers can use the vehicles on even dates. Additionally, Mahato et al. study had observed a 40%–50% improvement in air quality in Delhi within the first week of lockdown. He et al. (2020) study on short-term impacts of COVID-19 lockdown on urban air pollution has found that within a week, the AQI in the locked-down cities in China has been reduced by 19.84 points (PM2.5 goes down by 14.07 μg/m3) compared to the cities where lockdown has not been implemented strictly. The findings suggest an increased clean air ecosystem services in cities under the cessation of human activities. 5 Conclusion We made an effort to investigate the positive effects of COVID-19 lockdown restrictions on the reduction of NO2, PM2.5, and PM10 concentration. Different valuation methods, including median externality and public health burden, were incorporated into the economic valuation to assess the health impact and economic benefits of avoided mortalities. Both satellite and ground-based estimates are exhibiting an affirmative association between controlled human interference and improved air quality. The outcome of this research demonstrates the strong connection between the decline in traffic volume and reduction of NO2, PM2.5, and PM10 emission across the cities. This also suggests that the controlled motorized traffic pollution and limiting other unsustainable human activities could be the most effective ways of improving the air quality status of a city. Though the selected pollutants have shown a substantial reduction in all the 20 cities analyzed, there has been an irregularity in the reduction of air pollutants found. Many factors, including meteorological factors, start time of lockdown restrictions, the strictness of lockdown measures, volume of road traffic, other point sources of localized emission, could be linked to this varying concentration and reduction of air pollution observed in the selected cities. The outcome of this study can be a reference to introduce new public policies for promoting adaptive socio-ecological models to understand the synergies and trade-offs between the reduced human interventions and the environmental health of cities systematically. Further research in this direction is needed to explore this synergistic association more explicitly. Credit author statement Srikanta Sannigrahi, Conceptualization, Data curation, Formal analysis, Writing – review & editing. Prashant Kumar, Conceptualization, Supervision, Writing – review & editing. Anna Molter, Conceptualization, Writing – review & editing. Qi Zhang, Conceptualization, Writing – review & editing. Bidroha Basu, Conceptualization, Supervision, Writing – review & editing. Arunima Sarkar Basu, Conceptualization, Writing – review & editing. Francesco Pilla, Conceptualization, Supervision, Writing – review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Acknowledgments The authors wish to thank the editor and two anonymous reviewers for their constructive comments and suggestions. 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Divergent socioeconomic-ecological outcomes of China's conversion of cropland to forest program in the subtropical mountainous area and the semi-arid Loess Plateau Ecosystem Services 45 2020 101167 32953433 Zheng Z. Yang Z. Wu Z. Marinello F. Spatial variation of NO2 and its impact factors in China: an application of sentinel-5P products Rem. Sens. 11 2019 1 24 10.3390/rs11161939 Zhu Y. Xie J. Huang F. Cao L. Association between short-term exposure to air pollution and COVID-19 infection: evidence from China Sci. Total Environ. 727 2020 138704 10.1016/j.scitotenv.2020.138704 32315904 Zoran M.A. Savastru R.S. Savastru D.M. Tautan M.N. Assessing the relationship between surface levels of PM2. 5 and PM10 particulate matter impact on COVID-19 in Milan Italy. Sci. Total Environ. 738 2020 139825 32512362

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==== Front Geoforum Geoforum Geoforum; Journal of Physical, Human, and Regional Geosciences 0016-7185 1872-9398 Elsevier Ltd. S0016-7185(21)00026-9 10.1016/j.geoforum.2021.01.018 Forum Essential or dismissible? Exploring the challenges of waste pickers in relation to COVID-19 Carenbauer Mary Greene Washington, DC, United States 28 1 2021 3 2021 28 1 2021 120 7981 2 7 2020 10 1 2021 17 1 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Waste plays an essential role during the COVID-19 crisis. This includes increased attention to the amount of waste produced, concerns with how hazardous materials are discarded and handled, or unease that sustainability and recycling efforts are derailed. However, there is a human side to waste: the people who work directly with these materials. Waste pickers are the men, women, and children around the world who rely on tossed away items for their livelihoods. Across dynamic and generally informal networks, waste pickers collect, transport, and separate our discarded materials. They are recyclers, entrepreneurs, and a key component of solid waste management systems in many countries. However, they are also subject to discrimination and unsafe working conditions. The pandemic has shed light on the nuances between vulnerability and opportunity for waste pickers. This intervention considers economic and societal structures during and beyond COVID-19 to highlight underlying concepts of health, hygiene and sustainability and how these may shape experiences of waste pickers. Keywords Waste pickers Waste workers Circular economy COVID-19 Hygiene Health ==== Body pmc1 Realities of waste work Waste pickers have been identified as the “invisible heroes” of informal solid waste management due to their positive contributions to both the local environment and the local economy (Gall et al., 2020, 2). Yet, they often receive little recognition in the form of social standing, wages and protections. Waste workers are time and again referred to as vulnerable, which can be understood through the prisms of economics, health and sustainability. The pandemic disrupted the economics of waste work in several ways. Social distancing measures put in place by governments around the world may have halted ability to go to work. Additionally, as global oil demand lowered, prices of oil dropped, and as such, cost of virgin plastics dropped; this impacts the recycling market (Kaza, 2020). Limited transnational movement may prompt some countries to dispose of their waste instead of recycling (Kaza, 2020). These disruptions pose a larger threat to waste pickers due to the informal nature of the work as many are without social and economic protections. There are physical vulnerabilities to the work, too. Waste picking is a job that can place workers in direct contact with toxic or dangerous substances. Health susceptibilities of waste pickers are related both to the nature of the work and worksites, and the often-low-income living situations, sometimes without proper sanitation or sufficient water, poor air quality and food insecurity (Schenck et al., 2019, 3). In some settings, this includes acts of using bare hands to sort waste containing sharp and rusted objects, animal parts, and medical instruments, such as syringes. However, the motivation for not using gloves may include cost, discomfort, or maintaining a certain decorum or standards shared between waste pickers (Carenbauer, 2015, 35). For example, waste pickers in Bangladesh noted cultural stigmatization with the use of protective gear, with one waste picker proclaiming the use of this gear implies one is “not friends with the waste” (Casey, 2016, 7). This furthers how waste workers need to be included in the design of safety measures, rather than being assigned to protocols “assumed to fit their needs” (Casey, 2016, 7). 2 Health and Hygiene during COVID-19 Now, with COVID-19 there is evolving understanding about how the virus lives and is transmitted via different materials. Some studies suggest that the virus can remain active on plastics for as long as three days (Woodward and Gal, 2020). Waste pickers work directly with recyclable materials, increasing their exposure compared with many other workers, and as mentioned, are often conducting this work without gloves or masks (Dias, 2020). In the Global South, much waste work is done on the side of the road, along informal landfills. In many countries, due to the unregulated and informal nature of the work, city administrators often do not intervene for “safer working conditions” (Gutberlet, 2016, 56). When sorting waste materials in indoor settings, the working conditions are often without proper distance between workers or ventilation (Dias, 2020). This may produce conditions in which a virus can spread more readily. This brings us to social distancing, perhaps an unacknowledged luxury. “Social distancing” quickly entered the public vocabulary for many. According to the Center for Disease Control and Prevention (CDC), social distancing practices involve keeping physical space between oneself and others outside one’s home; and is considered one of the “best tools” available for slowing the spread of the virus locally and across nations (CDC, 2020). The CDC provides additional protocols and suggestions for those living in close quarters and shared housing; however, these may not be feasible for all dependent upon country, income and access to resources. As explained by Wasdani and Prasad (2020) in India, stringent social distancing measures set in place by the government are not always attainable and particularly exclude the countries’ most socially vulnerable. This is due to several reasons. The very densely populated living arrangements in slums are “natural conduits” for the contagious virus (Wasdani and Prasad, 2020, 416). Many workers and laborers rely on daily wages; this includes waste pickers. Without the ability to complete their work and limited access to social welfare, some workers have to choose between the threat of contracting COVID-19 or the threat of hunger (Wasdani and Prasad, 2020). This reveals several cracks in societal structure, including social protection. There are also interesting questions about what work is considered “essential” and how we value work based on necessity, culture and cleanliness. 3 Sustainability: Plastic as pollution or protection During the pandemic, plastic plays an important role. Personal protective equipment (PPE), such as masks, gloves and face shields are made of plastic and protect front line workers (Hughes, 2020). This may or may not include waste pickers. Meanwhile, single use plastic is surging in popularity (Kaplan, 2020); for example, single-use water bottles, plastic bags and increased packaging are rising due to safety concerns. While these trends, and current or future environmental implications, may persist in both high- and low-income nations, it is noteworthy to consider this in low-income countries with regard to the waste picker population. The COVID-19 pandemic has situated plastic in a new way: as a necessity for protection. This is not exactly new but goes against trends shifting away from the nonbiodegradable materials. Evidence of this trend includes full or partial bans of plastic bags in countries in both the Global Northand the Global South. However, the pandemic has increased the need for plastic for certain professions and individuals. While this protection is required, it does not erase the numerous health and environmental issues related to the life cycle of plastics, both at local and global scales (Akenji and Bengtsson, 2019, 17-18). Medical waste is a new driver of environmental degradation and is already posing to overwhelm countries like Bangladesh, where hospitals generated around 250 tons of medical waste during May 2020 (Chowdhury, 2020). Without the ability to manage this waste, the spread of the virus could be worsened as sanitation workers lack the necessary protective gear (Chowdhury, 2020). Plastic is connected to several environmental and health concerns, locally and globally (Akenji and Bengtsson, 2019, 17-18). These concerns include disrupting ecosystems, worsened flooding and increased hazardous contaminants which harm human and soil health (Akenji and Bengtsson, 2019, 17-18). Quite interestingly, during COVID-19, plastic and waste are reaffirmed as a source of burden and of livelihood; of challenge and of opportunity. 4 Circular economy: Its meaning to waste pickers The concept of circular economy has gained traction in recent years, both among academics and practitioners (Kirchherr et al., 2017). While definitions of the concept vary, typically the concept engages reduction, reuse and recycling of materials (Kirchherr et al., 2017). Shifts towards a circular economy could tactfully involve waste workers, and the Global South may have a unique opportunity. Waste pickers’ role in sorting and recycling waste, including plastics is critical. They are often the first processors of the waste. As mentioned, plastic poses health and environmental problems. The COVID-19 pandemic has accentuated tension between plastic as a protector or polluter (Hughes, 2020). The recycling pillar of a circular economy is particularly dependent upon dealing with plastic, as this allows opportunity to address challenges confronting the “economic, the environmental, and the social sphere alike” (Gall et al., 2020, 9). The ways in which the pandemic may or may not offer occasion to restructure societal relations to plastic and waste and labour needs to be considered in future efforts within the context of a circular economy located in the socio-economic settings of low and middle-income nations (Gall et al., 2020). Circular economy efforts should be modified locally and socially oriented in the Global South, with a specific focus on waste picker communities (Gall et al., 2020, 9). 5 Essential work: What and who is needed? Collecting mixed waste is generally considered as a needed service, as discontinuing this may pose the risk of generating an outbreak of infectious diseases in addition to COVID-19 (Global Alliance of Waste Pickers, 2020). However, the additional services of collecting and sorting recyclable materials may be halted or postponed. The official advice given by the Global Alliance for Waste Pickers (2020, sec. 4, para. 2) was that “if waste pickers who are not providing essential mixed waste collection services can afford to skip work and stay at home, then they should”. While it is understandable to prioritize health and safety, which cannot at this time be guaranteed in the work environments of waste pickers, there are interesting notions to unpack around how we consider and label work as essential. The terms “essential work” and “essential worker” have emer been associated with the pandemic, along with terms such as social distancing. While these are widely used, there is not yet an agreed upon global definition of essential work. Naming - or not naming - waste work as an essential service varies across the world. For example, according to the National Waste and Recycling Association, in the United States, the Department of Homeland Security named solid waste collection as essential; however, at the time of writing, the designation did not include recycling options (Wright, 2020). Defining what constitutes essential work across the Global South was not available at the time of writing. However, collecting and removing potentially dangerous materials from public space does seem essential - especially during an outbreak of a virus that is overwhelming hospitals and paralyzing economies. Waste work is essential. Waste pickers are essential. Without romanticizing the work and erasing the real physical threats and stigmatization that pervade this livelihood, the work is not always done out of desperation or in dehumanizing contexts. Although waste picking is arguably an undesirable occupation, and certainly one that poses levels of insecurity or exposure to harm, not all waste pickers serve this role “as a last resort, or with resentment” (Carenbauer, 2015, 41). Waste pickers in Dhaka, Bangladesh and in Beijing, China revealed that this career allowed them to earn more money than other similar informal work and to work independently (Carenbauer, 2015, Landsberge, 2019). Landsberger (2019, 100) goes so as far as to reject that waste pickers are “down and out”; deconstructing this language is an important step in reframing waste pickers and their essential role. Waste work conditions have been described as “extremely dehumanizing” (WIEGO, 2005, 6). Word choice of dehumanizing implies that improving physical working conditions may make the work more humane. However, this approach is problematic in that not all waste pickers expressed finding the conditions dehumanizing (Carenbauer, 2015, Rathore, 2020). Waste pickers are aware of the conditions of the work, and the physical health hardships. Rathore (2020, 181), suggests that is not “acute poverty” that steers individuals to “stumble” into e-waste work, a particlar type of waste work which deals with electronic materials. Rather, the e-waste sector is a more established community and livelihood. The safety conditions and health of workers could, and should, be improved upon. However, not all engagement with waste pickers suggest that the conditions of the work environment led waste pickers to view the job as something less-than work, as suggested by some literature (WIEGO, 2005, Damasio, 2014). 6 Valuing essential work(ers) and the future At the time of writing, COVID-19 continues to take lives, halt or devastate economies and produce waste. For waste pickers, the impact is felt physically and financially, with high risk of exposure to the virus and high risk of losing livelihood. In the article “A pandemic gives permission for change”, Davidsen (2020) explains exactly that: a pandemic bares global vulnerabilities, including weaknesses of health, economic and social structures. The vulnerability of poor and marginalized populations has been forced into focus, along with the vulnerability of our planet (Davidsen, 2020). The pandemic may present an opportunity to rebuild economies and services, particularly in urban environments. As we consider what is essential, public health, hygiene and environmental sustainability or circular economy are emerging. Relatedly, the need for increased social and economic protections for the huge number of informal workers, including those in the waste sector, has become urgent. Across the world, people are adapting. People are changing their work conditions to remain relevant when possible; whether this means working from home or limiting work at a centralized office or increasing safety precautions by wearing PPE. Building upon this, there may be the prospect for more permanent improvement to safety conditions and deepened social protections for waste workers. Reframing and revaluing waste workers as agents of environmental and economic protection may increase their protections. There may be hence numerous reasons, including access, as to why efforts to encourage use of safety protections, such as gloves did not work on waste sites. As norms shift and awareness and acceptance of masks expands, the cultural and practical adaptation of protective gear may resonate with waste pickers. This may come from the waste pickers themselves and an increased desire for physical protection, and from a broader societal understanding of contagions and contaminants and desire for shared protection. At the risk of inappropriately applying what is perhaps a narrative more prominently used in the Global North to the Global South, discussion around work and essential work are evolving. Some valuation of work has been tied to education level or income. However, increased attention has been given to health workers, fire fighters, grocery clerks, mail services, transportation services, and waste workers. Dealing with waste - which potentially poses risk for human and environmental health - is essential. The essential nature of this work may appeal to opportunities for greater formalization and more sanitary work conditions. Legal framework and public policies, which ensure decent working conditions and safe worksites, along with guaranteed access to recyclable materials, are key to continued protection of these essential workers (Gutberlet, 2016, 61). Understanding waste picking as essential, for human and environmental health, may allow the profession to receive greater attention which could be leveraged for improved health and social protections under increasingly globalized lenses and standards. Waste work is not just dismal and dangerous; for many, this is their livelihood and “a viable career” (Carenbauer, 2015, 38; see also Rathore, 2020). This is important as there is the potential to reframe waste work from desperate to opportunistic; from dirty to sustainable; from vulnerable to empowered; from secondary to essential. ==== Refs References Akenji and Bengtsson, 2019. Circular Economy and Plastics: A Gap-Analysis in Asean Member States. Institute for Global Environmental Strategies, https://www.jstor.org/stable/resrep21872.6. Carenbauer, Mary, 2015. Dhaka’s Unseen Green: An Analysis of the Roles and Opportunities of Informal Waste Workers. MSc diss. University of Edinburgh. Casey, Jonathan, 2016. Technology and the future of work: Experiences of informal waste workers and street vendors in Dhaka, Lima, and Nairobi. Practical Action Publishing. http://dx.doi.org/10.3362/9781780446585/. Center for Disease Control and Prevention, N.d. Social Distancing: Keep Your Distance to Slow the Spread (accessed May 1 2020). https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html#:~:text=Social%20distancing%2C%20also%20called,)%20from%20other%20people. Chowdhury, Tanvir, 2020. Can Bangladesh deal with mountains of COVID-19 medical waste?. Al Jazeera. June 2 2020. https://www.aljazeera.com/news/2020/06/bangladesh-deal-mountains-covid-19-medical-waste-200602084636302.html. Damasio, João, 2014. Waste pickers' urban environmental services and sustainability. In: Moksnes, Heidi, Melin, Mia (Eds.), Claiming the City: Civil Society Mobilisation by the Urban Poor. Uppsala University, Uppsala, pp. 109–116. Davidsen, Elizabeth, 2020. A pandemic gives permission for change. United Nations Development Programme, April 17, 2020. https://www.undp.org/content/undp/en/home/blog/2020/a-pandemic-brings-permission-for-change.html. Dias, Sonia, 2020. Waste Pickers and COVID-19: An Interview with WIEGO’s Sonia Dias. Interview by Carlin Carr. WIEGO. April 26, 2020. Audio, 24:24. https://www.wiego.org/blog/waste-pickers-and-covid-19-interview-wiegos-sonia-dias. Gall et al., 2020. Building a circular plastics economy with informal waste pickers: Recyclate quality, business model, and societal impacts. Resour., Conserv. Recycling 156, 104658. Global Alliance of Waste Pickers, 2020. Coronavirus (COVID-19) and Waste Pickers. Global Alliance of Waste Pickers. https://globalrec.org/covid19/. Gutberlet, Jutta, 2016. Ways out of the Waste Dilemma: Transforming Communities in the Global South. Rachel Carson Center, RCC Perspectives, No. 3. Hughes, Kristin, 2020. Protector or polluter? The impact of COVID-19 on the movement to end plastic waste. World Economic Forum, May 6, 2020. https://www.weforum.org/agenda/2020/05/plastic-pollution-waste-pandemic-covid19-coronavirus-recycling-sustainability/. Kaplan, Rob, 2020. COVID-19 Underscores The Need To Invest In Local Waste Management And Recycling To Combat Ocean Plastic. Forbes, April 3, 2020. https://www.forbes.com/sites/robkaplan/2020/04/03/covid-19-underscores-the-need-to-invest-in-local-waste-management-and-recycling-to-combat-ocean-plastic/#325766f24bb1. Kaza, Silpa, 2020. Waste workers are protecting our communities during COVID-19. World Bank Blogs. April 9, 2020. https://blogs.worldbank.org/sustainablecities/waste-workers-are-protecting-our-communities-during-covid-19. Kirchherr, Reike, Hekkert, 2017. Conceptualizing the circular economy: an analysis of 114 definitions. Resour., Conserv. Recycling, 127. Landsberge, Stefan, 2019. The Human Factor-Gabrage Pickers. In: Beijing Garbage: A City Besieged by Waste. University of Amsterdam Press. https://www.jstor.org/stable/j.ctvhrcz2t.7. Rathore, Gayatri Jai Singh, 2020. Circulating waste, circulating Bodies? A critical review of e-waste trade. Geoforum 110, 180–182. Schenck, Catherina J., Blaauw, Phillip, Viljoen, Jacoba M.M., Swart, Elizabeth C., 2019. Exploring the potential health risks faced by waste pickers on landfills in South Africa: a socio-ecological perspective. Int. J. Environ. Res. Public Health 16(11), https://doi.org/10.3390/ijerph16112059. Wasdani, Kishinchand Poormini, Prasad, Anjesh, 2020. The impossibility of social distancing among the urban poor: the case of an Indian slum in the times of COVID-19. Local Environ. 25(5). WIEGO, 2005. Organising the Unorganised: A Case Study of the Kagad Kach Patra Kashtakari Panchayat (Trade Union of Waste-pickers.) WIEGO. https://www.wiego.org/resources/organising-unorganised-case-study-kagad-kach-patra-kashtakari-panchayat-trade-union-waste-. Woodward, Aylin, Gal, Shayanne, 2020. One chart shows how long the coronavirus lives on surfaces like cardboard, plastic, wood, and steel. Business Insider, April 7, 2020. https://www.businessinsider.com/coronavirus-lifespan-on-surfaces-graphic-2020-3. Wright, Brandon, 2020. DHS designates waste collection “essential critical infrastructure” at request of NWRA. National Waste and Recycling Association. https://wasterecycling.org/news/494863/DHS-DESIGNATES-WASTE-COLLECTION-ESSENTIAL-CRITICAL-INFRASTRUCTURE-AT-REQUEST-OF-NWRA.htm.

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PMC9749928

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2022-12-16 23:24:10

no

Geoforum. 2021 Mar 28; 120:79-81

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Geoforum

10.1016/j.geoforum.2021.01.018

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==== Front J Pediatr Surg J Pediatr Surg Journal of Pediatric Surgery 0022-3468 1531-5037 Elsevier Inc. S0022-3468(22)00195-6 10.1016/j.jpedsurg.2022.03.005 Editorial Report of 54th Annual Meeting of the Pacific Association of Pediatric Surgeons Brindle Mary E ab⁎ Chung Patrick Ho-Yu c a Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary AB, Canada b Ariadne Labs, Harvard TH Chan School of Public Health, Brigham and Women's Hospital, Boston, MA c Department of Surgery, Li Ka Shing Faculty of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong Special Administrative Region ⁎ Corresponding author. 15 3 2022 7 2022 15 3 2022 57 7 11871188 1 3 2022 2 3 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcIn 2021, the Pacific Association of Pediatric Surgery (PAPS) met virtually for the second time, one and a half years into the COVID-19 pandemic at its 54th annual meeting. Waves of new COVID variants made travel next to impossible and nearly resulted in the cancelation of the annual conference. PAPS members across the globe experienced upheaval in their clinical work and research. The decision to proceed with a virtual conference was made with trepidation. Despite the uncertainties and a short timeline, the virtual program was a success. John Meehan chaired a dynamic program. Distances between colleagues were bridged, and 157 presentations from across the globe were presented to an audience of 273. The Coe Medal, recognizing an individual who has made substantial contributions to pediatric surgery, was awarded to the new PAPS president, Professor Hong-Shiee Lai from the National Taiwan University Hospital. The keynote speakers this year were an excellent reflection of the PAPS community. The curiosity and enthusiasm of PAPS members (as well as our love of singing) was apparent in John Meehan's interview of Phillipa Soo, a star Broadway performer in “Hamilton”. Dunya Moghul's harrowing description of her flight from Afghanistan was a reminder of the strength, resilience, and steadfast support within the PAPS community and is published as a narrative in this edition of JPS. PAPS membership is truly global, and global crises such as war, disease, and societal disparities impact our patients and our surgical community and continue to be reflected in the presentations and experiences of the PAPS members and the Global Alliance Partnership (GAP) fellows. There were four trainee research awards presented this year at PAPS. The presentations and publications of these trainees highlight the richness of the PAPS research community and the strength of up-and-coming researchers. In this year's report, we reflect on the four manuscripts produced by the award-winning trainees and published in the PAPS JPS issue. Hageman and colleagues from Royal Children's Hospital (RCH), Melbourne, Australia, perform a broad examination of the use of opioids in pediatric inguinal hernia operations through a systematic review and complementary exploration of the opioid prescribing practices at their own site [1]. Hernia surgery remains one of the most common procedures in children, and the findings of this study are highly relevant to our daily practice. The harms of opioids in children are numerous. Despite efforts to reduce their use, opioids are frequently prescribed for peri operative pain control even for relatively minor operations. In this study, the authors performed a comprehensive systematic review of opioid use in inguinal hernia surgery. This paper reviews 15 articles, including 12 high quality randomized controlled trials and a total of 1166 patients. The authors report a huge variation in opioid prescribing practices. Fentanyl was the most common opioid used intra operatively but was not commonly prescribed at discharge. In addition to the systematic review, the authors performed a retrospective review of 150 patients who underwent open inguinal hernia repair at RCH, a larger and more contemporary study than the articles in the systematic review. Similar to the findings from the review, there was significant variability in opioid administration between cases at RCH. Significantly fewer patients (20%) were treated with peri operative opioids at RCH than was observed in the systematic review. Factors influencing opioid administration included patient age and gender. The use of regional analgesia was associated with a lower rate of opioid use. We agree with the authors that this paper supports a call for a more robust strategy to guide the judicious use of opioids. Hagemen and colleagues shine a light on the inconsistency and inadequacy of our current opioid prescribing practices and suggest opportunities to decrease opioid use through greater awareness, standardized pain assessment, and the use of regional blocks. A second study in this issue that focuses on post operative pain relief was performed by Rettig and colleagues from Los Angeles Medical Center in the United States [2]. This study describes the use of intercoastal nerve cryoablation accompanying a modified Ravitch procedure for chest wall deformity. Chest wall reconstruction is associated with significant pain. Thoracic epidurals have long been used for chest wall surgery analgesia. Intercoastal nerve cryoablation is a relatively new approach that has been demonstrated to be highly effective for the Nuss procedure in recent years. However, the role of this approach in the Ravitch procedure has not been described. It should not be assumed that the benefits of the intercoastal nerve cryoablation would necessarily translate from the Nuss procedure to the Ravitch. The Nuss procedure is performed thoracoscopically and involves the placement of a shaped metal bar to reshape the chest wall without physically dividing the ribs or cartilage. The Ravitch is performed through an open incision and requires the division of muscle, breaking and removing multiple costal cartilages, and often dividing the sternum. Understanding whether adding a thoracoscopic approach with the additional time of intercoastal nerve cryoablation can improve the pain outcomes of patients undergoing a Ravitch procedure and could help surgeons and their patients. In this small study, patients undergoing a modified Ravitch repair with intercostal nerve cryoablation were compared with historical patients who received thoracic epidural. Several advantages in terms of decreased hospital stay and cost were found to be associated with the use of intercoastal nerve cryoablation. Although this approach is associated with an increased operative time, this drawback was overshadowed by other merits including the reduction of opioid usage. Long term negative effects were not apparent. Larger and longer term studies will be needed to give a definitive answer as to the long term impact of this approach. To reduce the pain and associated opioid use that frequently accompanies chest wall reconstruction, it is critical for surgeons to explore new methods of pain control. The expanded use of intercoastal nerve cryoablation beyond the Nuss procedure offers the opportunity to provide superior pain management for these complex patients. An additional PAPS prize winning presentation by Sophie Carr on behalf of her authorship team at British Columbia Children's Hospital in Vancouver, Canada, further examines how our care pathways can improve the outcomes of our patients [3]. In this study, Chlorhexidine skin preparation is explored as part of a strategy to reduce surgical site infections in neonates. This paper examines a focus of neonatal surgical care that has been plagued by low quality and sparse evidence. Despite the fact that Chlorhexidine has been found to significantly diminish skin bacterial counts and reduce infections in older patients, the potential risks of skin injuries and significant burns has led to a cautious approach to its use in neonates. Surgical site infections in neonates are common, and the sequelae of these infections are significant. The Enhanced Recovery after Surgery Society neonatal surgery guideline did not include recommendations related to skin preparation owing to the uncertainty of the data. The authors of this current study performed both a focused evidence review and a prospective analysis of 50 consecutive neonates (greater than 24 weeks ant 1500 g) treated with 70% chlorhexidine prep compared to a historic cohort. The authors demonstrated a 8% rate of SSIs in the cohort treated with Chlorhexadine skin preparation compared to 14% in the historical cohort (although not statistically significant) and demonstrated an absence of skin reaction in these neonates. The authors did not set out to examine the impact of Chlorhexidine on the rate of surgical site infection, although the results are promising, nor did they examine the safety of Chlorhexidine prep in premature or small for gestation infants. Carr's study targeted a demonstrated knowledge gap in order to offer new evidence supporting the safety of Chlorhexidine skin preparation in neonates greater than 34 weeks gestation and greater than 1500 g. The authors acknowledge that there is more work to be done. There are opportunities for further prospective studies with larger number of patients to compare the effectiveness of different skin preparations. In this study, the authors have uncovered data with meaningful impact that can be integrated into care pathways including enhanced recovery protocols for term and near term neonates. Basic science research is increasingly challenging to sustain for pediatric surgeons. And yet, these investigations represent some of the most foundational work done within our research community. PAPS awardee Wang and colleagues from Children's Hospital of Fudan University in China presented their work at PAPS, published in this issue, on the effects of Rapamycin on Kaposiform Hemangioendothelial (KHE) cells in vitro [4]. To best understand the mechanism by which Rapamycin could be used to treat Kaposiform Hemangiomas, the authors explored the impact of this therapy at the cellular level. This approach required the development of KHE cells lines and an in vitro analysis of the impact of Rapamycin on cell proliferation and apoptosis. The authors build a clear story that describes the impact of Rapamycin on KHE cells. The viability of Rapamycin-exposed KHE cells was determined using immunofluorescence with the addition of flow cytometry to explore cell cycle and apoptosis. The authors identified both decreased proliferation and increased apoptosis of exposed cells. To understand the mechanism of apoptosis, the team used Western blot to identify the phosphorylation of mammalian target of rapamycin. This work presents an elegant description of the impact of Rapamycin at the cellular level on KHE and supports the potential of a novel therapeutic pathway for treatment of Kaposiform Hemangiomas. The trainees presenting at the 54th annual PAPS meeting continue to reflect the excellence of PAPS. PAPS 2021 was distinguished by compelling stories and groundbreaking research. The upcoming PAPS conference is planned to occur in person in Quito, Ecuador, and promises to further showcase the curiosity, warmth, and research excellence of the PAPS community.

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PMC9749962

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2022-12-16 23:24:10

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J Pediatr Surg. 2022 Jul 15; 57(7):1187-1188

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J Pediatr Surg

10.1016/j.jpedsurg.2022.03.005

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==== Front Industrial Marketing Management 0019-8501 0019-8501 Elsevier Inc. S0019-8501(21)00095-X 10.1016/j.indmarman.2021.05.003 Research Paper Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context Dubey Rameshwar a⁎ Bryde David J. a Blome Constantin b Roubaud David c Giannakis Mihalis d a Liverpool Business School, Liverpool John Moore's University, Merseyside, Liverpool L3 5UG, UK b School of Business Management and Economics, University of Sussex, Sussex House, Falmer, Brighton BN1 9RH, United Kingdom c Montpellier Business School, Montpellier Research in Management, 2300 Avenue des Moulins, 34185 Montpellier, France d Audencia Business School, Nantes, France ⁎ Corresponding author. 23 5 2021 7 2021 23 5 2021 96 135146 2 2 2021 16 5 2021 17 5 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic has disrupted global supply chains and exposed weak links in the chains far beyond what most people have witnessed in their living memory. The scale of disruption affects every nation and industry, and the sudden and dramatic changes in demand and supply that have occurred during the pandemic crisis clearly differentiate its impact from other crises. Using the dynamic capabilities view, we studied alliance management capability (AMC) and artificial intelligence (AI) driven supply chain analytics capability (AI-SCAC) as dynamic capabilities, under the moderating effect of environmental dynamism. We tested our four research hypotheses using survey data collected from the Indian auto components manufacturing industry. For data analysis we used Warp PLS 7.0 (a variance-based structural equation modelling tool). We found that alliance management capability under the mediating effect of artificial intelligence-powered supply chain analytics capability enhances the operational and financial performance of the organization. Moreover, we also observed that the alliance management capability has a significant effect on artificial intelligence-powered supply chain analytics capability under the moderating effect of environmental dynamism. The results of our study provide a nuanced understanding of the dynamic capabilities and the relational view of organization. Finally, we noted the limitations of our study and provide numerous research directions that may help answer some of the questions that arise from our study. Keywords Artificial intelligence Supply chain analytics Alliance management Environmental dynamism Dynamic capability view ==== Body pmc1 Introduction “Thanks to the explosive expansion and advances of digital technologies, such as smart mobile phones, social media platforms, e-commerce, and so on, data are around in every organization. As the analytics capabilities of organizations develop rapidly, artificial intelligence tools, big data analytics, blockchain, and so on are all tools available and being used in the industry (Araz, Choi, Olson, & Salman, 2020, p. 1316). Supply chain analytics (SCA), via the use of cognitive technologies, such as artificial intelligence (AI), helps improve complex supply chain process decisions (Akter, Michael, Uddin, McCarthy, & Rahman, 2020; Asmussen & Møller, 2020; Boehmke, Hazen, Boone, & Robinson, 2020). Cognitive technologies capability enables machines to understand complex situations at high speed, whilst processing large amounts of data, and to learn and interact like humans (Duan, Edwards, & Dwivedi, 2019; Dwivedi et al., 2021; Gupta, Kar, Baabdullah, & Al-Khowaiter, 2018; Kelly, 2015) and Artificial intelligence-powered supply chain analytics (AI-SCA) has gained increased momentum during a pandemic crisis (Cankurtaran & Beverland, 2020; Ivanov, 2020). Motivated by the perceived importance of AI-SCA capability (AI-SCAC), we undertook a theory-driven study to examine antecedents of AI-SCAC and the effects of AI-SCAC on performance during the COVID-19 crisis. In recent times, AI-SCAC has been touted as a game-changer, especially as a means of dealing with the pandemic, with its use increasing significantly across all functional departments of the organization during this period of crisis (PYMTS, 2020; Sheng, Amankwah-Amoah, Khan, & Wang, 2020; Sharma, Adhikary, & Borah, 2020; Ivanov & Dolgui, 2020; The State of BI and Business Analytics Report, 2020). However, despite the rich body of literature on the use of AI-SCAC, empirical study is scant. The COVID-19 crisis has affected customers' ability to pay for their goods and services and vendors are unable to produce and supply raw materials to meet demand (Queiroz, Ivanov, Dolgui, & Wamba, 2020). It has also significantly affected the accounts payable (AP), accounts receivables (AR) and days of sales outstanding (DSO). In a way, most organizations have experienced serious working capital management (WCM) issues that have been resolved largely via data analytics capability (PYMTS, 2020). Supply chain management scholars have noted that SCA capability has the potential to revolutionize the next generation of business (Hazen, Boone, Ezell, & Jones-Farmer, 2014; Schoenherr & Speier-Pero, 2015; Waller & Fawcett, 2013). The pandemic resulting from COVID-19 has disrupted the entire supply chain, leading to shortages of essential items (Craighead, Ketchen Jr, & Darby, 2020; Ketchen Jr & Craighead, 2020; Ritter & Pedersen, 2020). In order to survive in such extreme uncertain times, organizations have been making significant efforts to adapt to new norms via the leveraging of relationships (Colombo, Piva, Quas, & Rossi-Lamastra, 2020; Crick & Crick, 2020) and by harnessing analytics capability (Ivanov, 2020). During the pandemic we have observed organizations having superior capabilities of managing alliances, demonstrating the successful use of analytics capability (Crick & Crick, 2020; Hanelt, Bohnsack, Marz, & Antunes Marante, 2020; Sheng et al., 2020). With the motives for forming such alliances including inter-organizational learning, accessing technology and complementary resources, and fostering innovation (Leischnig, Geigenmueller, & Lohmann, 2014; Rothaermel & Deeds, 2006), alliance management capability (AMC) is considered as a source of competitive advantage (Dyer and Singh, 1998; Schreiner, Kale, & Corsten, 2009; Sluyts, Matthyssens, Martens, & Streukens, 2011; Schilke, 2014). Although it is well understood that AMC has a strong influence on SCAs, the evidence for such influence is mostly anecdotal (Zhang, Meng, de Pablos, & Sun, 2019). Therefore, our study is one of the first to examine the effect of AMC on SCA capability. Furthermore, we argue that theory in this area remains underdeveloped, lacking grounding in established theoretical perspectives. Hence, we posit our first research question (RQ1): what are the effects of AMC on AI-SCAC? The insights derived via processing large data can be utilized to improve both operational performance (Srinivasan & Swink, 2018; Dubey et al. 2019a; Kar & Dwivedi, 2020) and financial performance (Gupta, Drave, Dwivedi, Baabdullah, & Ismagilova, 2020; Mikalef et al., 2019a, Mikalef et al., 2019b; Sena, Bhaumik, Sengupta, & Demirbag, 2019). Yet despite high levels of enthusiasm among practitioners, exploiting AI-SCAC for enhanced operational and financial performance is still a major challenge for supply chain managers, due to dealing with the complexities associated with utilizing big data (Gunasekaran et al., 2017; Dubey et al. 2019; Kinra, Hald, Mukkamala, & Vatrapu, 2020). Hazen et al. (2014) cautioned that if the quality of the data is not properly controlled then the outcome generated via processing large unstructured datasets might have a negative consequence on decision-making. So, despite the opportunities, management scholars have expressed caution related to the potential use of data analytics capability in their decision-making process (see, Agarwal & Dhar, 2014; Albergaria & Jabbour, 2020; Brown, Chui, & Manyika, 2011; Ross, Beath, & Quaadgras, 2013; Simsek, Vaara, Paruchuri, Nadkarni, & Shaw, 2019). Chen, Chiang, and Storey (2012) argue that in most cases organizations aim to use data analytics capability to improve their decision-making abilities to satisfy their stakeholders. Although, there exists a rich body of the literature on the effects of analytics capability on organizational performance (see, for example, Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016; Fosso Wamba et al., 2017; Wang & Wang, 2020; Bag, Gupta, Kumar, & Sivarajah, 2020), research on SCA capability on performance is limited (Srinivasan & Swink, 2018). This is a clear research gap, which needs to be addressed. We, therefore, posit our second research question (RQ2): what are the effects of AI-SCAC on operational/finance performance? Analyzing direct effects, as is the focus of our first two RQs, is necessary, but the direct effects on their own often fail to fully explain complex relationships in business situations (Boyd, Takacs Haynes, Hitt, Bergh, & Ketchen Jr, 2012; Eckstein, Goellner, Blome, & Henke, 2015). To explain the differential effects of capabilities, scholars have assumed specific conditions that may influence the direct effects. This view is well captured by contingency theory (Sousa & Voss, 2008). Conceptual and empirical study of the effect of higher-order capability on lower-order capability is scant (Fainshmidt, Pezeshkan, Lance Frazier, Nair, & Markowski, 2016). Furthermore, the moderating effect of environmental dynamism (ED) on the paths joining higher-order capability and lower order capability, to address ill-defined boundary conditions and the confounding effects of the dynamic capabilities is limited (Fosso Wamba, Dubey, Gunasekaran, & Akter, 2020; Schilke, 2014). Schilke (2014) argues that in the case of dynamic capabilities, environmental conditions are often equated with a high degree of ED. In recent times, some scholars have expressed their reservations related to the notion of dynamic capabilities theory and its usefulness in practice (Eisenhardt & Martin, 2000). Advocates of contingency theory argue that the potential benefits of the dynamic capabilities of any organization depends not only on the organizational structure but also on the context in which these capabilities are exploited (Hitt, Ireland, & Palia, 1982; Schilke, 2014; Sirmon & Hitt, 2009). We recognize the need for an adaptation of the dynamic capabilities, which are to a certain extent explained by environmental forces (Eckstein et al., 2015; Hrebiniak & Joyce, 1985; Schilke, 2014). In recent times scholars have increasingly identified ED as an important contextual variable in building organizational capabilities and enhancing performance i.e., Helfat and Winter (2011), Schilke (2014) and Fosso Wamba et al. (2020). Most studies to date have focused on the moderating influence of ED on the paths joining dynamic capabilities and organizational performance. However, the existing literature is silent on the moderating effect of ED on the paths joining higher-order capabilities and the lower order capabilities (Fainshmidt et al., 2016). To address this research gap, we posit our third research question (RQ3): what is the effect of ED on the path joining alliance management capability and AI-SCAC? To address our three RQs we have used data collected from the Indian auto components manufacturing sector. Our theoretical model is grounded in the dynamic capability view of the firm (Akter et al., 2016; Eisenhardt & Martin, 2000; Fosso Wamba et al., 2017; Hossain, Akter, Kattiyapornpong, & Dwivedi, 2020; Schilke, 2014; Teece, Pisano, & Shuen, 1997) and contingency theory (Lawrence & Lorsch, 1967; Tosi Jr & Slocum Jr, 1984). The main contributions of our study are threefold. Firstly, we make a theoretical contribution by examining the direct effect of the higher-order organizational dynamic capability on the lower order dynamic capability. Secondly, we attempt to explain the effect of higher-order dynamic capability on lower-order dynamic capability under the moderating effect of ED. Thirdly, we provide a nuanced understanding of how AMC affects the operational and financial performance of the organization under the mediating effect of SCA. We have organized our paper into six sections. In the next sections, we present our underpinning theories, theoretical model, and hypotheses development. In the third section, we discuss our research design, outlining how we developed our measuring instrument, the sampling design, and the data collection strategy. We further present the demographic profile of our respondents and the results of the non-response bias test. In the fourth section, we present our data analysis using PLS-SEM. In the fifth section, we discuss the findings of our statistical analyses. In this section, we highlight our main contributions to theory and practice. We also outline the limitations of our study, which leads us to set out areas for further study and research questions which remain un-addressed. Finally, we draw the main conclusions from our study. 2 Underpinning theories, theoretical model and research hypotheses 2.1 Underpinning theories 2.1.1 Dynamic capability view (DCV) Since the seminal work by Teece et al. (1997), scholarly interest in DCV has increased in management research. The DCV is regarded as an extension of the popular resource-based view (RBV) (Barney, 1991). Helfat and Peteraf (2003, p. 997) argue that “the RBV provides an explanation of competitive heterogeneity based on the premise that close competitors differ in their resources and capabilities in important and durable ways. These differences in turn affect competitive advantage and disadvantage. Nothing in this premise necessarily implies a static approach to the resource-based view, notwithstanding some controversy in this regard”. Helfat and Peteraf further argue that the DCV element of the RBV involves adaptation and change, because they build, integrate, or reconfigure the strategic resources and capabilities to generate a competitive advantage. Following Teece (1997, p. 516), we define DCV as “the firm's ability to integrate, build and reconfigure internal and external competencies to address rapidly changing environments”. In the context of highly uncertain environments, dynamic capabilities are simple, experiential, unstable processes that are based purely on the quick learning gained from a given situation to produce unexpected results (Eckstein et al., 2015; Eisenhardt & Martin, 2000; Fosso Wamba et al., 2020; Mikalef, Krogstie, Pappas, & Pavlou, 2020). It may refer to specific process or routines that enable integration, conversion, or renewal of tangible and intangible resources into new competencies as markets evolve (Eckstein et al., 2015; Eisenhardt & Martin, 2000; Teece, 2007). Based on preceding discussions, we see that DCV has covered a long distance since the seminal work by Teece et al. (1997). The basic notion of the DCV converges around two main tenets: (1) the effects of dynamic capabilities on organizational performance, (2) the value of dynamic capabilities are more visible in the case of technologically dynamic industries (see, Fainshmidt et al., 2016). However, despite the high popularity of DCV and growing body of literature on the topic, we note the absence of an explanation as to how the hierarchical ordering of dynamic capabilities and the economic context serve as contingencies producing differential outcomes. Fainshmidt et al. (2016) found that higher-order dynamic capabilities are significantly more related to performance than lower-order dynamic capabilities. Schilke (2014) notes that the lower-order dynamic capabilities partially mediate the relationship between higher-order dynamic capabilities and performance. Hence, for our study, we conceive AMC as a higher-order dynamic capability and the AI-SCAC as a lower-order dynamic capability. 2.1.2 Contingency theory (CT) Contingency theory (CT) is a mid-range theory based on the notion of fit (Sousa & Voss, 2008). Eckstein et al. (2015) argue that CT assumes organizations adapt based on specific situations they find themselves in and this adaptation generates competitive advantage. Thus, managers must carefully analyse their firm's external and internal environment and decide on the fit of alternative actions (Volberda, van der Weerdt, Verwaal, Stienstra, & Verdu, 2012). CT is a key theoretical lens for understanding the context under which higher-order dynamic capabilities effect lower-order ones (Fainshmidt et al., 2016; Schilke, 2014). Looking through such a lens provides enhanced theoretical understanding of the role of dynamic capabilities (Fainshmidt et al., 2016). Hence, we argue that in CT-related research, different concepts of fit can be employed and should be explicitly considered when conducting such studies (Sousa & Voss, 2008). Informed by CT, we argue that ED is a contingent variable, which offers a better understanding of how AMC affects AI-SCAC in the extremely uncertain environment resulting from the COVID-19 pandemic. 2.1.3 Environmental dynamism (ED) Schilke (2014) argue that ED has two main characteristics: volatility (rate and amount of change) and uncertainty. For instance, the COVID-19 crisis has led to significant change in industry structures due to stringent measures taken by national governments to control the spread of the virus (de Haas, Faber, & Hamersma, 2020). These measures have significantly affected the consumption behaviour of citizens (Sheth, 2020). This sudden change in behaviour has resulted in the instability of market demand (Oehmen, Locatelli, Wied, & Willumsen, 2020). Thus, we can argue that environments with little dynamism are characterised by little change and the market behaviour is almost predictable (Sirmon, Hitt, & Ireland, 2007). In contrast, highly dynamic environments are characterised by highly turbulent environments, which often experience rapid and continuous change (Schilke, 2014). The effect of ED on the path joining dynamic capabilities and the organizational performance has led to two schools of thoughts. In the first school scholars advocate change, in the order to gain significant positive outcomes from utilizing the dynamic capabilities of organizations (Helfat et al., 2009; Weerawardena & Mavondo, 2011). The second school of thought argue that routine-based dynamic capabilities are not always sufficient for achieving beneficial change, although there is a significant need for the reconfigurations of resources (Eisenhardt & Martin, 2000). Following Schilke (2014) arguments, we understand that the environmental dynamism affects both the extent of opportunities to change and the organization abilities to exploit these available opportunities through routine-based change. Hence, we argue that when ED is low, the effectiveness of organizational dynamic capabilities are low, as there are hardly any occasions when these capabilities are properly utilized. In such situations, dynamic capabilities have limited usefulness. On the other hand, when ED is high, the usefulness of dynamic capabilities increases. In such case the impact of dynamic capabilities on organizational performance is high. In our study we posit that the effect of ED on the path joining AMC and the AI-SCAC will be significant. 2.1.4 Alliance management capability (AMC) In a dynamic and highly uncertain environment, AMC holds great promise in terms of resolving complications that may prevent stakeholder's abilities to productively share their strategic resources in the form of activities and information (Schilke, 2014). Existing literature provides rich evidence in support of the significant role played by AMC in enhancing organizational performance (Schilke, 2014; Sluyts et al., 2011). Schilke (2014, p.183-184) argues that “organizations with a strong alliance management capability possess routines that support various alliance-related tasks, such as partner identification and inter-organizational learning, that facilitate an effective execution of inter-firm relationships”. Hence, we argue that alliance management may occur over one or more projects within the B2B context, for example, information exchange, context, and capacity analysis need assessment, resource mobilization, joint risk assessment, or sharing of logistics facilities. Nevertheless, organizations face challenges in maintaining an alliance with their partners. These challenges stem from poor alignment (Dubey et al., 2018; Lee, 2004). Management scholars have attempted to examine the extent to which an organization should invest to build AMC and the effect on organizational performance (Forkmann, Henneberg, & Mitrega, 2018; Kohtamäki, Rabetino, & Möller, 2018). 2.1.5 Artificial intelligence powered supply chain analytics capability (AI-SCAC) In recent years, as technology has moved forward, information systems are necessary but not sufficient to achieve desired levels of organizational performance (Fosso Wamba & Akter, 2019; Jeble et al., 2018). With the rapid proliferation of the internet, smartphones, and other emerging technologies (RFID, sensors, Internet of Things, Cloud Computing, etc.), we have reached a new phase where large volumes of data are collected in real-time in structured, semi-structured and unstructured formats (Agarwal & Dhar, 2014; Fisher, DeLine, Czerwinski, & Drucker, 2012). Therefore, it is imperative for firms to develop analytics capabilities, on top of existing IT capability, to convert this data into useful information and to retain competitive advantage (Davenport, 2014). AI-SCAC is an all-encompassing term for techniques to handling large complex data, as well as encompassing the inherent challenges of such data handling (Fosso Wamba & Akter, 2019). Critical challenges are related to data capture, storage, transfer & sharing, related to system architectures and search, analysis, and visualization related to data analytics methods (Dubey et al., 2020; Srinivasan & Swink, 2018; Venkatesh, 2021). Srinivasan and Swink (2018) argue that SCAC is an extension of traditional analytics capability that enables organizations to increase their information processing capability. Hence, firms collect data from various sources, which is analysed to provide insights to guide managers in making the right decisions related to supply chain processes. Extending Srinivasan and Swink (2018) arguments, we posit that the use of cognitive technology, along with SCAC, will lead to the decisions taken by the managers being more effective than in the past. So, for example, supply chain managers will process complex information, with the help of cognitive technology, to forecast changes in supply or demand patterns, especially during pandemic crises (Cortez & Johnston, 2020; He, Zhang, & Li, 2021). 2.2 Theoretical model and hypotheses development Our theoretical model is shown in Fig. 1 . From the DCV perspective, AMC and AI-SCAC is the dynamic capabilities of an organization, which Eisenhardt and Martin (2000) argue manifest themselves in different identifiable business processes. Hence, instead of quantifying vague dynamic capabilities, management scholars have started exploring the set of processes within which these dynamic capabilities exist (Schilke, 2014). Motivated by the theoretical arguments offered by Eisenhardt and Martin (2000, p. 1108), empirical study of specific types of dynamic capabilities, “sheds light not only on these specific processes, but also on the generalized nature of dynamic capabilities”.Fig. 1 Theoretical Model. Fig. 1 Our research hypotheses are grounded in two contingent dynamic capabilities: AMC and AI-SCAC. We conceive these as higher-order and lower order dynamic capabilities, respectively, and posit that they are ways to reconfigure the organizational resource base during a pandemic crisis. AMC helps the organization to sense the fluctuations in the market, as well as provide access to resources that lie beyond their reach (Crick & Crick, 2020; Das & Teng, 2000; Schilke, 2014). AI-SCAC enables organizations to process complex information to make effective and efficient supply chain decisions (Cortez & Johnston, 2020; He et al., 2021). Secondly, motivated by the arguments offered by Fainshmidt et al. (2016, p. 1349), who argue that “just as there are different classes of resources, there are different levels of dynamic capabilities”, we suggest the impact of higher-order dynamic capability on organizational performance takes place under the mediating effect of lower-order dynamic capability. Hierarchical ordering of dynamic capabilities into different levels is an important aspect, yet remains underdeveloped as a concept (see, Ambrosini, Bowman, & Collier, 2009; Fainshmidt et al., 2016). Hence, we argue that the interaction of dynamic capabilities at different levels impacts on organizational performance. We differentiate, both conceptually and empirically, between AMC and AI-SCAC as being at different levels; with the former generating enhanced performance, both directly and indirectly, via AI-SCAC. In this way we analyse how the hierarchical ordering of dynamic capabilities makes a difference to organizational performance. Furthermore, we seek clarity regarding the role of ED in the dynamic capabilities-organizational performance link, by including ED as a contextual moderating factor (Fainshmidt et al., 2016; Fosso Wamba et al., 2020; Schilke, 2014). 2.2.1 Alliance management capability (AMC) and AI powered supply chain analytics capability (AI-SCAC) AI-SCAC processes the complex information required to decision making (Srinivasan & Swink, 2018). However, its success depends upon the quality of information derived from various sources (Hazen et al., 2014). In such a situation, the role of AMC can be crucial. Prasad, Zakaria, and Altay (2018) argue that, in context to humanitarian efforts, high levels of transparency and effective information-sharing capabilities position organizations to develop and deploy systems and processes for supporting analytics capabilities. In complex environments like a crisis, information sharing among partners is often considered critical for better alliance management (Altay & Labonte, 2014; Altay & Pal, 2014). In addition, organizations that develop AI-SCAC are also likely to invest in AMC, as strong alliances provide data and other technical support upon which analytics systems and processes operate (Kamalaldin, Linde, Sjödin, & Parida, 2020). Kamalaldin et al. (2020, p. 306) further argue that “digitalization is viewed as a source of future competitiveness due to its potential for unlocking new value-creation and revenue-generation opportunities. To profit from digitalization, providers and customers tend to move away from a transactional product-centric model to relational service-oriented engagement”. This suggests that AMC can enhance AI-SCAC, which, in turn, helps achieve competitive advantage. Based on these preceding discussions, we hypothesize the following: H1: AMC has positive and significant effect on AI-SCA. 2.2.2 AI-SCAC and operational/financial performance (OP/FP) Most of the early studies devoted to OP is rooted in classical economic theory (Dubey et al. 2019a), with OP being regarded as one of the most important variables in management research, as “the market competition for customers, inputs, and capitals make organizational performance essential for the survival” (Richard, Devinney, Yip, & Johnson, 2009, p. 719) Hence, we argue that OP is the sum of accomplishments achieved by all businesses. These accomplishments are measured in terms of meeting an organizational goal within a given period (Lee & Huang, 2012). A competitive advantage with superior performance has become a vital element of an organization's ability to survive (Schilke, 2014). Management scholars argue that by using rich and up-to-date current information to inform operational decisions and by developing better solutions quickly, organizations can avoid expensive courses of action, such as overtime production, lost sales, and excess inventories (Srinivasan & Swink, 2018; Dubey et al. 2019a). Bayraktar, Demirbag, Koh, Tatoglu, and Zaim (2009) found a positive and significant relationship between information system practices and OP; and Srinivasan and Swink (2018) found a positive association between SCAC and OP under the moderating effect of organizational flexibility. Further, Ayinder et al. (2019a, 2019b) found a significant association between the level of big data analytics adoption and overall business/firm performance, via the operations of its business processes. Because of these suggested links between variables, we argue that AI-SCAC enables supply chain managers to reduce working capital, maximise return on capital employed, improve inventory turnover ratio, enhance product quality, and improve product delivery. Hence, we hypothesize it as:H2a : AI-SCAC has positive and significant effect on OP. H2b : AI-SCAC has positive and significant effect on FP. 2.2.3 Moderating role of environmental dynamism (ED) Schilke (2014) argues that building and maintaining an AMC requires significant investments, for instance, in creating a dedicated team to support the alliance operations and that the extent of alliance opportunities is contingent on ED. Rosenkopf and Schilling (2007) suggest that when the ED is low, organizations score relatively lowly in terms of alliance opportunities. So, we postulate that the impact of AMC on organizational performance is low in the case of low ED. Conversely, high ED may reduce the value creation opportunities in the supply chain network because the alliance management capability rests on routinized practices that utilize the lessons drawn from previous experiences (Anand & Khanna, 2000; Schilke, 2014). Hence, we believe that the role of AMC in improving the AI-SCAC, under the moderating influence of ED, is worth investigating. Environmental dynamism (ED) requires changes in an organization's resource base to align with the external changes in the environment (Fainshmidt et al., 2016; Fosso Wamba et al., 2020; Mikalef et al., 2019a, Mikalef et al., 2019b; Schilke, 2014). Although organizations may derive potential benefits from their dynamic capabilities (Fosso Wamba & Akter, 2019), benefits are more likely realized in technologically dynamic industries (Schilke, 2014). Weerawardena et al. (2007, p. 294) argue that dynamic capabilities allow organizations to “develop cutting-edge knowledge-intensive products, paving the way for their accelerated market entry”. Thus, in the face of frequent change in the external environment, dynamic capabilities should have more value; because such a context increases the opportunity to exercise dynamic capabilities (Schilke, 2014). Following, Fainshmidt et al. (2016) and other arguments, we argue that the impact of higher-order dynamic capabilities (i.e., AMC) impact on lower-order dynamic capabilities (i.e., AI-SCAC) increases when high ED is present. This aspect of the dynamic capabilities view has received less attention in prior literature; thus, we hypothesize: H3: High ED has a significant and positive effect on the path joining AMCs and AI-SCAC; Consistent with various management scholars' arguments, we consider organization size and age as appropriate control variables for our study (see, Schilke, 2014; Srinivasan & Swink, 2018; Dubey et al. 2019a; Fosso Wamba et al., 2020). In addition, we have controlled for the organization's alliance portfolio size (Schilke, 2014). 3 Research design We used the three-staged research design, as suggested by Schilke (2014). Firstly, we conducted interviews to understand various types of organizational capabilities relevant to organizational resource configuration and their effects on organizational performance. Secondly, we developed a survey-based instrument. Thirdly, we gathered and analysed data for the dependent and independent variables of our study from appropriate organizations. 3.1 Qualitative interviews We conducted 26 interviews, via Zoom/ Microsoft Teams, with senior-level supply chain managers from the auto components manufacturing industry. Each interview lasted between 30 and 45 min. In the first part of the interview, we asked managers to share their understanding of types of routine activities that enable their organization to adapt to rapid external changes. In particular we asked them about activities taken in response to the COVID-19 crises. Managers highlighted the important role of AMC and AI-SCAC. In the second part, we confirmed the appropriateness of our research hypotheses by asking these managers how critical the activities were for achieving high levels of operational and financial performance. Moreover, we asked how a change in the environment influence the AMC on AI-SCAC. There was considerable agreement among interviewees as to the relevance of our proposed hypotheses. Some managers also suggested that the COVID-19 crisis has accelerated their digitalization programs and that top leaders in their companies are now more positive to invest in their supply chain analytics capability and associated training programs. 3.2 Survey We chose auto component manufacturing organizations registered on the database of the Auto Components Manufacturers Association of India (ACMA). This industry sector was chosen for reasons: (1) alliances are common in this industry (Dussauge, Garrette, & Mitchell, 2004); (2) the supply chain analytics capability plays a key role in the industry (Jeble et al., 2018); (3) the ACMA is the apex body in India that represents automotive components manufacturing industry both nationally and globally. We procured the assistance of a professional marketing firm that provides services related to data collection and consulting to many organizations in India and abroad. Prior to collecting data, we pre-tested our questionnaire to assure that respondents understood and our wordings and to avoid any confusion. We identified respondents with similar profiles as those from the main survey for the pre-testing. Although, it was very time intensive process to collect responses from several senior supply chain managers in automobile manufacturing companies, especially during pandemic crisis as many managers were not willing to participate in the process. However, despite these challenges, we were determined to gather such inputs, as we believe pre-testing is an essential step to identify and fix any issues related to language of statements, clarity or use of technical terms prior to the launch of main survey. In view of these considerations, a survey was pretesting with a group of fifteen supply chain managers working in manufacturing firms in the Pune region of India (a hub of auto-component manufacturing firms). Short interviews via Zoom/Microsoft Teams were conducted to discuss problems encountered in interpreting questions as and when needed. Minor changes were done to the wording of questions, as per feedback received, and a final survey was launched. Our questionnaire was initially sent out by professional marketing team on our behalf, via e-mail, to 656 organizations in the ACAMA database, which contains details of over 800 firms. After two waves of data collections, using the key informant method to ensure diversity in the respondents (Capron & Mitchell, 2009), we finally received 167 usable responses. The response rate of 25.46% is consistent with previous studies of a similar nature i.e., Srinivasan and Swink (2018), Dubey et al. (2019), Fosso Wamba and Akter (2019) and Gupta et al. (2020). We provide the characteristics of participating organizations and key informants in Table 1 . To examine the appropriateness of the key respondents, we included an item in the questionnaire to know about their tenure and job title (Kumar, Stern, & Anderson, 1993). Overall, 67% of the participants in the final data set had been associated with their organization for more than six years (see Table 1).Table 1 Sample characteristics (N = 167). Table 1 Sample (Wave 1) Sample (Wave 2) Industry Auto component manufacturing 93 74 Firm Size <100 employees 17 16 100–249 employees 22 14 250–499 employees 18 18 500–999 employees 15 11 1000–4999 employees 13 8 ≥5000 employees 8 7 Firm age (years) <5 8 9 5–9 7 6 10–19 34 23 20–29 26 22 >30 18 14 Job title of respondents Procurement Head 32 28 Logistics Head 25 22 Head of Production & Quality 23 13 Head of R&D 13 11 Tenure of the respondent in the organization (years) <1 10 9 2–5 20 16 6–10 45 38 ≥10 18 11 3.3 Nonresponse bias We checked for non-response bias in three ways. Firstly, by comparing the responses from the two waves of data collection, using Student's t-test: an early wave and a late wave (Armstrong & Overton, 1977). The results are shown in Table 1. We observed no significant difference between respondents and non-respondents (p > 0.05) across the means for each respondent. Secondly, we examined whether the non-respondents were different from those that returned the questionnaire, in terms of organization size. Here we found no significant differences in responses (p > 0.05). Finally, following Mentzer, Flint, and Hult (2001), we randomly selected people from the non-respondents' sample and asked them to answer one item for each of the constructs, as shown Fig. 1. Based on a sample of 28 non-respondents, the Student's t-tests of group means yielded no significant differences between respondents and non-respondents for any question (p > 0.05). We therefore drew an inference that non-response bias is not a potential issue in our study. 3.4 Measures We adopted multi-item scales to measure our constructs (see Fig. 1). We adapted our measures from existing literature. Following the suggestions of DeVellis (2016) we further refined the questionnaire items via in-depth interviews with 17 senior managers. We further pre-tested our instrument with 23 managers. To assure reliability we triangulated the inputs obtained from the managers with complementary data sources (Homburg, Klarmann, Reimann, & Schilke, 2012; Schilke & Cook, 2015). The next sections describe the measures. 3.4.1 Alliance management capability (AMC) We used a five-dimensions, reflective construct to measure AMC, as developed by Schilke (2014) and Schilke and Goerzen (2010). The dimensions are: (a) inter-organizational coordination; (b) alliance portfolio coordination; (c) inter-organizational learning; (d) alliance pro-activeness; and (e) alliance transformation (Schilke, 2014, p. 191). 3.4.2 AI powered supply chain analytics (AI-SCAC) For AI-SCAC we modified the measures developed by Srinivasan and Swink (2018). This is a five items reflective construct. We included items to understand how organizations used advanced techniques powered by cognitive technology to process useful information related to supply chain decisions from large and complex data sets. From a visualization point of view, we included items to measure the extent to which managers use dashboards to interpret the extracted information to gain insights from other managers involved in their supply chain networks. Further, we measured how the information enables managers take alternative decisions, in cases of supply shortages and demand fluctuations resulting from the COVID-19 crisis. 3.4.3 Environmental dynamism (ED) To capture ED, we adapted measures developed by Schilke (2014) and we further confirmed our items based on the scale developed by Miller and Friesen (1982), which resulted in a five-item reflective construct. The items include measuring whether: a change in production modes is present, a changing external environment is continuously impacting the demand for products, digitalization is rapidly changing business practices, disasters like COVID-19 are highly unpredictable and, finally, in the current pandemic, organizations are rapidly changing their business models. 3.4.4 Organizational performance We measured OP outcomes using items developed by Srinivasan and Swink (2018) and Dubey et al. (2019). For FP we took the items from Cochran and Wood (1984), Vickery, Jayaram, Droge, and Calantone (2003) and Richard et al. (2009). All constructs and their measuring items are listed in Appendix A. 3.5 Control variables 3.5.1 Organization size (OS) Management scholars suggest that OS might play an important role in enhancing organizational performance, by facilitating the access to a lower cost of capital, whilst simultaneously reducing operational risk (Chang & Thomas, 1989; Schilke, 2014; Srinivasan & Swink, 2018; Dubey et al. 2019a). Schilke (2014) further argues that OS influences the organization's dynamic capabilities, with larger organizations being able to invest in resources to develop their change routines. Hence, we use OS as a control variable, which we measured in terms of number of full-time employees. 3.5.2 Alliance portfolio size (APS) In addition to OS, we used APS as a control variable, reflecting the fact that as well as the size of the individual organization, the size of the alliances formed could also facilitate enhanced performance - for the same reasons as outlined in the previous section. The past research has found significant association between the number of firm's alliances and the organizational performance (Powell, Koput, & Smith-Doerr, 1996). Following Jiang, Tao, and Santoro (2010) and Schilke (2014) suggestions we measured APS as the organization's total number of alliances. We used the logarithmic value to reduce the skewness in answers. 4 Data analysis We used Warp PLS 7.0 software to analyse our data (see, Dubey et al., 2021; Kock, 2019), which is based on Partial Least Squares (PLS) method. Moshtari (Moshtari, 2016, p. 1549, c.f. Peng & Lai, 2012, p. 468) argue that “PLS is a prediction oriented statistical tool that helps researchers to understand the predictive validity of the exogenous constructs”, which is appropriate, as our study examines the effect of AMC on AI-SCAC and the effects of AI-SCAC on OP/FP. Where there is no empirical evidence anticipating a relationship, as is the case with AMC and AI-SCAC, PLS-Structured Equation Modelling (SEM) is highly recommended (see, Akter, Fosso Wamba, & Dewan, 2017; Hult et al., 2018; Peng & Lai, 2012; Rigdon, Sarstedt, & Ringle, 2017). We followed Peng and Lai (2012) and Kock (2019) suggestions to evaluate the proposed model in two stages: (a) checking the validity and the reliability of the measurement model; (b) analyzing the structural model. 4.1 Measurement properties of constructs Table 2 reports scale composite reliability (SCR) and average variance extracted (AVE) for our multi-item constructs (see, Fig. 1). Based on the SCR values we confirm that our constructs possess desired convergent validity (i.e., λi ≥ 0.5; SCR ≥ 0.7 & AVE ≥ 0.5) (Fornell & Larcker, 1981). We examined the discriminant validity of the constructs following Fornell and Larcker (1981) suggestions. We found that the square root of AVE (see the leading diagonal of Table 3 ) is greater in magnitude than all the correlated values in the same row and column. Further, using criterion test, the HTMT values (see, Table 4 ) are much below the cut off value (0.9). Hence, we confirm that our constructs possess sufficient discriminant validity (Henseler, Ringle, & Sarstedt, 2015). Overall, the tests undertaken show our constructs possess sufficient reliability and validity and are sufficiently strong to enable structural estimates.Table 2 Measurement properties (N = 167). Table 2Constructs Items Λi Variance Error Scale composite reliability (SCR) Average Variance Extracted (AVE) IC AMC1a 0.75 0.56 0.44 0.85 0.58 AMC1b 0.75 0.56 0.44 AMC1c 0.77 0.60 0.40 AMC1d 0.77 0.60 0.40 APC AMC2a 0.89 0.80 0.20 0.95 0.83 AMC2b 0.94 0.89 0.11 AMC2c 0.91 0.83 0.17 AMC2d 0.90 0.81 0.19 IL AMC3a 0.74 0.54 0.46 0.92 0.73 AMC3b 0.87 0.76 0.24 AMC3c 0.89 0.79 0.21 AMC3d 0.92 0.84 0.16 AP AMC4a 0.90 0.81 0.19 0.93 0.77 AMC4b 0.90 0.81 0.19 AMC4c 0.70 0.49 0.51 AMC4d 0.98 0.95 0.05 AT AMC5a 0.97 0.95 0.05 0.94 0.83 AMC5b 0.97 0.95 0.05 AMC5c 0.77 0.59 0.41 AI-SCAC AI-SCAC1 0.66 0.44 0.56 0.91 0.67 AI-SCAC2 0.77 0.60 0.40 AI-SCAC3 0.77 0.59 0.41 AI-SCAC4 0.93 0.86 0.14 AI-SCAC5 0.94 0.88 0.12 ED ED1 0.80 0.63 0.37 0.88 0.61 ED2 0.77 0.60 0.40 ED3 0.78 0.60 0.40 ED4 0.67 0.45 0.55 ED5 0.87 0.75 0.25 OP OP1 0.92 0.84 0.16 0.96 0.85 OP2 0.95 0.91 0.09 OP3 0.93 0.86 0.14 OP4 0.89 0.79 0.21 FP FP1 0.96 0.93 0.07 0.98 0.93 FP2 0.97 0.94 0.06 FP3 0.97 0.93 0.07 Notes: IC, inter-organizational coordination; APC, alliance portfolio coordination; IL, inter-organizational learning; AP, alliance pro-activeness; AT, alliance transformation; AI-SCAC, artificial intelligence powered supply chain analytics capability; ED-environmental dynamism; OP, operational performance; FP, financial performance; λi, factor loadings; SCR, scale composite reliability; AVE, average variance extracted. Table 3 ConstructPlease provide a definition for the significance of bold in Table 3. correlations (N = 167). Table 3 AMC AI-SCAC ED OP FP AMC 0.87 AI-SCAC 0.01 0.82 ED 0.10 0.14 0.78 OP −0.22 −0.31 −0.36 0.92 FP −0.07 −0.09 −0.15 0.02 0.96 Notes: AMC, alliance management capability; AI-SCAC, artificial intelligence powered supply chain analytics capability; ED-environmental dynamism; OP, operational performance; FP, financial performance. Table 4 HTMT values (N = 167). Table 4 AMC AI-SCAC ED OP FP AMC AI-SCAC 0.27 ED 0.25 0.29 OP 0.21 0.11 0.21 FP 0.31 0.36 0.56 0.17 Notes: AMC, alliance management capability; AI-SCAC, artificial intelligence powered supply chain analytics capability; ED-environmental dynamism; OP, operational performance; FP, financial performance. 4.2 Common method bias (CMB) As survey-based cross-sectional data may suffer from common method bias (CMB) (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Podsakoff & Organ, 1986), we followed strict procedures to minimize the CMB effect. Firstly, we undertook the traditional single factor Harman's test (single factor explained nearly 26.2% of the total variance). However, some management scholars believe that Harman's single factor test is not sufficient and may not be treated as conclusive evidence. Hence, we undertook the second procedure, suggested by Lindell and Whitney (2001), which is popularly known as the correlation marker technique. We adopted an unrelated variable to partial out correlations that were a result of CMB. Additionally, we further extracted the significant values of correlations, as suggested by Lindell and Whitney (2001). There are minimal differences between the adjusted and unadjusted correlations. Hence, based on these statistical findings, we infer that CMB does not seriously influence our remaining results. Following Kock's (2019) suggestion we determined the nonlinear bivariate causality direction ratio (NLBCDR). “The NLBCDR measures the extent to which bivariate nonlinear coefficients of association provide support for the hypothesized directions of the causal links in the proposed theoretical model “(Kock, 2012, p.52–53). We observed a NLBCDR of 0.91, which is significantly above the threshold value ≥0.7. Hence, we argue that causality is not an issue. We further provide the values for model fit and quality indices supporting this conclusion [see, average R2 = 0. 51; Tenenhaus GoF = 0.67]. 4.3 Hypotheses testing We examined our four research hypotheses as H1, H2a, H2b and H3. Table 5 provides the β co-efficient of the paths and corresponding p-values. Firstly, we found support for H1, which examines the effect of AMC on AI-SCAC (AMC → AI-SCAC) (β = 0.32; p < 0.0001). Secondly, we found support for H2a (AI-SCAC→OP) (β = 0.28; p < 0.0001). Addressing H2b (IA-SCAC→FP), we found support in the results (β = 0.17; p < 0.05). These findings are all consistent with previous literature (see, Kamalaldin et al., 2020; Srinivasan & Swink, 2018)). We further tested the interaction effect of ED on the path joining AMC and IA-SCAC (H3). We found support for H3 (β = 0.27; p < 0.0001). Our findings here support Fainshmidt et al. (2016)’s arguments.Table 5 Structural Estimates (N = 167). Table 5Hypothesis Effect of Effect on β p-value Results H1 AMC IA-SCAC 0.32 <0.0001 supported H2a IA-SCAC OP 0.28 <0.0001 supported H2b IA-SCAC FP 0.17 <0.005 supported H3 ED*AMC IA-SCAC 0.17 <0.05 supported Control variables OS OP 0.027 >0.05 Not supported OS FP 0.013 >0.05 Not supported APS OP 0.17 <0.005 Supported APS FP 0.21 <0.005 Supported Notes: AMC, alliance management capability; AI-SCAC, artificial intelligence powered supply chain analytics capability; ED-environmental dynamism; OP, operational performance; FP, financial performance; OS, organizational size; APS, alliance portfolio size. Based on our results we argue that the effect of higher-order dynamic capability on lower-order dynamic capability is enhanced in the presence of high environmental dynamism. We note that the control variable organizational size (OS) does not have a significant effect on our study model. We interpreted these observations during the pandemic crisis and conclude that the size of the organization does not affect the motivation of organizations to invest in AMC and AI-SCAC. Furthermore, the alliance portfolio size (APS) has a positive and significant effect on our study model. 5 Discussion The response to the pandemic crisis confirms dynamic capabilities as being simple, experiential, and unstable processes that are the outcome of the learning process (Colombo et al., 2020). The tenets of the DCV revolves around two key perspectives: (1) the effects of dynamic capabilities on competitive advantage, (2) the value of dynamic capabilities are more visible in the case of technologically dynamic industries (see, Fainshmidt et al., 2016). Despite its popularity in literature, DCV remains silent on how the hierarchical ordering of dynamic capabilities and the external environment context serve as contingencies producing different performance outcomes. Fainshmidt et al. (2016) argue that higher-order dynamic capabilities are significantly more linked to performance than lower-order dynamic capabilities. Similarly, Schilke (2014) notes that the lower-order dynamic capabilities partially mediate the relationship between higher-order dynamic capabilities and performance. Fainshmidt et al. (2016) further argue that the effect of higher-order dynamic capabilities on lower-order dynamic capabilities is more pronounced in the presence of high ED. Schilke (2014) observes that the relationship is not linear, with the performance outcome higher in the case of medium ED. We took these scientific debates as the foundation of our study. We recognize that despite the increasing use of DCV, the boundaries are yet to be understood. Our study was motivated by the significant use of data analytics capability to minimize the supply chain disruptions resulting from COVID-19. Despite increasing in volume, the existing literature has failed to provide theory-driven empirical results, with studies being purely conceptual or anecdotal in nature. Hence, we posited three guiding research questions to address research gaps and we addressed the questions with the help of data gathered from the Indian auto component manufacturing industry. The results paint an original and interesting picture of DCV during a pandemic (see Table 2, Table 3, Table 4, Table 5). Table 5 provides a summary of the hypotheses testing. Based on Table 5, we see which statements of our study are supported and which are not supported. In totality, the findings generated in our study offer some useful contributions to theory and provide rich guidance to supply chain managers, especially during such a pandemic crisis. We further believe that our study may open new avenues for research. In the remainder of this section, we elaborate on implications for theory, practice, and limitations/further research directions. 5.1 Implications to theory Firstly, our study enhances understanding of how dynamic capabilities are distinct and cannot all be grouped into one homogeneous category. Previous studies have not provided a clear understanding of how dynamic capabilities behave and under what conditions they generate better results. Previously scholars have conceptualized big data analytics capability as dynamic in nature (see, Akter et al., 2016; Gupta & George, 2016; Mikalef et al., 2019a, Mikalef et al., 2019b). All these studies have viewed big data analytics capability as a higher-order reflective construct or as a combination of both reflective and formative constructs. Srinivasan and Swink (2018) further conceptualized supply chain analytics as a reflective construct. However, among the rich debate on the topic, we found that DCV theory has not been developed to explain the antecedent of AI-SCAC. To address this and building on previous studies (see, Fainshmidt et al., 2016; Schilke, 2014) we extend Srinivasan and Swink's (2018) theoretical contribution to understand how AMC, as a higher-order dynamic capability, influences AI-SCAC, as a lower order dynamic capability, under the presence of high volatility caused by the pandemic. Hence, our findings provide a nuanced understanding of DCV boundaries and contribute to addressing the gap noted by some scholars (see, Eisenhardt & Martin, 2000; Fainshmidt et al., 2016; Schilke, 2014). Secondly, our study provides empirical evidence that AMC acts as an antecedent to AI-SCAC. The existing literature rarely acknowledges AMC as a causal element of analytics capability. We argue that our statistical results lend weight to the contingent view of DCV, which is regarded as higher-order organizational capability. Our findings contribute to theory by identifying that AMC, under the mediating effect of the AI-SCAC, enhances operational and financial performance, despite poor demand and restrictions imposed by governments on the movement of products. Hence, we provide further evidence that dynamic capabilities may produce excellent results if the stakeholders invest in alliance management capability during such a crisis. Thirdly, our study is the first to test the relationship between AMC, AI-SCAC, and organizational performance. Most of the previous studies have tested a direct causal relationship to study organizational performance (Akter et al., 2016; Fosso Wamba et al., 2017) or under the moderating effect of organizational flexibility (Srinivasan & Swink, 2018). Based on an extensive review of salient literature, we highlight that, despite immense popularity, AMC has not attracted much attention from the organization researchers (Rothaermel & Deeds, 2006), which is mainly due to methodological constraints. Despite these constraints we have examined how AMC has a significant role in building AI-SCAC, which is yet unexplored by organizational scholars. Whilst we recognize that our attempt towards conceptualizing AMC is in its early stage, we believe that our efforts to date raise some new questions related to the AMC and, specifically, its influence on AI-SCAC. 5.2 Managerial implications In terms of managerial implications, our results suggest that, when considering investments in building higher-order capabilities and lower-order capabilities, senior managers need to understand the details in terms of the what, how and when. In this respect the results provide directions to managers engaged in exploiting analytics capability to enable them to extract useful information to inform decision making related to managing complex supply chain networks. For instance, many organizations invest in building AI-SCAC, yet despite these, often substantial, investments, most do not yield strong positive returns. Our results suggest that AMC is a higher-order capability. Hence, in the absence of AMC, organizations may face enormous challenges to translate AI-SCAC into the successful outcomes which they initially expected. Moreover, in high ED, due to volatility in the market, organizations may fail to make sense of the demand and supply uncertainties. Our results offer guidance to policymakers involved in formulating policies for developing countries to understand how dynamic capabilities can be exploited to gain superior outcomes during a pandemic crisis. They further inform managers, as well as policymakers, of the important contingent role external conditions play. These results are explicit and particularly useful to managers engaged in the automotive sector. They are also conceptually stimulating and may be transferred to manufacturing organizations in other sectors. Furthermore, they provide guidance to managers engaged alliance management activities, as to the how alliance management capability can be an important antecedent of AI-powered supply chain analytics capability. Hence, they show how the organization must invest in building important capabilities, such as: inter-organizational coordination, alliance portfolio coordination, inter-organizational learning, alliance pro-activeness, and alliance transformation. Similarly, training managers must prepare comprehensive training and development programs to improve organizational learning and knowledge management capabilities; and senior managers must empower the right people to make a significant positive difference and deliver a return on investment in relation to AI-SCAC. The APS has significant effect on the model which suggests that the partnering capabilities and the number of alliance partners significantly influences the benefits realized from the AI-SCAC. Our results support the previous findings of scholars that during a period of high environmental dynamism, the efforts of organizations to interact with their partners should be re-doubled to maintain a high degree of transparency. Moreover, there should be continuous interactions with partners to improve collaboration, which is an essential success factor. The results show that alliance management capability is difficult to build, due to the complexities and uncertainties that exist across organizational boundaries. Hence, it is not surprising, therefore, that most alliances among partners fail to generate expected outcomes, especially in context to leveraging the potential of AI-SCAC during pandemic crisis. Following, the arguments of Levitt and March (1988) relating to the “experience curve”, AMC is built over the time via repeated engagements. The learning effects literature has shaped the operations management literature (Yelle, 1979), and the arguments made then remain true in the present case. AMC is the outcome of the continuous investment; however, in the wake of the sudden pandemic crisis, the importance of swift trust has been identified as an important driver of AMC (Tatham & Kovács, 2010; Dubey et al. 2019b; Schiffling, Hannibal, Fan, & Tickle, 2020). Our results provide a framework that may act as a blueprint for the manufacturing sector to assess and improve alliance management capability and AI-powered supply chain analytics capability, as well as increased organizational and financial performance. 5.3 Limitations and further research directions Like any other previous studies, our study has its limitations. These limitations and unanswered research questions raise new questions that may help advance the theoretical boundaries. The limitations and future research directions are outlined below. Firstly, our study utilized cross-sectional data to test the research hypotheses. As is common with such research designs, our study used single-informant data. Such data contributes to common-method bias (see, Ketokivi & Schroeder, 2004). Moreover, it is difficult to assess the causality among the hypothesized relationships using cross-sectional data. Therefore, due to the nature of the data, we could not further assess the variable effects of ED on the path joining AMC and AI-SCAC, as this requires collecting data via a longitudinal study (see Schilke, 2014). Hence, we strongly recommend such a study to comprehensively address unanswered questions relating to causality and common-method bias. Further, following Ketokivi and Schroeder (2004) suggestions, we recommend the use of a multi-informant instrument to gather data. This will help minimize the common method bias in the data. Secondly, we examined the role of AMC and the AI-SCAC on organizational performance. However, other capabilities may be studied in this context i.e., strategic alliances and new product development capabilities may further explain variations in organizational performance, as they are essential ways to reconfigure organizational resources. The external resources may be obtained via strategic alliances, whereas the new product development capabilities may help organizations to update their product portfolio. Thirdly, our data were gathered from the Indian auto components manufacturing industry. Hence, we caution readers that the results of our study should be interpreted in the context of this industry and generalization needs be doing with caution. We, therefore, encourage future replication studies that may test our findings in other settings, possibly incorporating a greater number of different industries, countries, and/or time periods to ensure a higher level of variance of the AMC and the analytics capability. Finally, our study adopted a rather narrow definition of the contingent DCV, focused on experience-based, rather static routines and excluded more flexible forms of organizational change. Hence, we recommend the use of a qualitative approach to understanding the interplay of alliance management, analytics capability, and environmental changes, to understand the differential outcomes during a crisis. We believe, therefore, there are several unanswered and new questions that warrant further theorizing and empirical investigation. 6 Conclusions In conclusion, we suggest that DCV, which is one of the most popular theories among management scholars, requires further development in some areas, which is the rationale for our study. Specifically, the behaviour of dynamic capabilities and the effect of ED on their performance outcomes are yet to be fully understood. We believe that emerging technologies as dynamic capabilities, such as AI, are far more complex in terms of their management, than capabilities based on traditional and well-established technologies. Hence, our findings suggest that future organizational scholars seeking to expand the boundaries of DCV theory ought to focus on explaining how some dynamic capabilities yield superior results beyond expectations, whilst other such capabilities produce poor outcomes. To do this we believe a more integrated approach, supported by other organizational theories, may be a fruitful avenue for further research. Appendix A Operationalisation of constructs Unlabelled TableConstructs Items Statement Source IC AMC1a We maintain strong coordination with our alliance partner during crisis. Schilke (2014, p. 189) AMC1b We assure that our tasks fit well with our alliance partner during crisis. AMC1c We assure that our work is well synchronized with our alliance partner during crisis. AMC1d We have regular interaction with our alliance partner despite lockdown. APC AMC2a We assure good coordination with all our partners during crisis. Schilke (2014, p. 189) AMC2b Maintain good synergy among our partners portfolio during crisis AMC2c We have accurately defined our interdependencies during crisis AMC2d We identify any overlaps between us IL AMC3a We assure that we learn from our partners during pandemic crisis Schilke (2014, p. 189) AMC3b We have desired level of competence to absorb new knowledge to navigate pandemic crisis AMC3c We have adequate routines to analyse the information obtained from alliance partners during pandemic crisis AMC3d We can successfully integrate our existing knowledge with the information's that we have obtained from each partner to navigate pandemic crisis AP AMC4a We do not compete with our partners especially during pandemic crisis Schilke (2014, p. 189) AMC4b We often take initiatives to reach out to our partners with a strong proposal to navigate pandemic crisis AMC4c We are far more proactive in comparison to our competitors in terms of exploring possible opportunities for alliance with the partners to minimize the negative consequences of pandemic crisis AMC4d We actively monitor environments to explore possibilities of new partnership with our partners AT AMC5a We do not give much importance to contractual agreements if it act as a barrier in our partnerships. Schilke (2014, p. 190) AMC5b We continuously modify our agreement based on the environment AMC5c We are flexible to change based on partners request especially during crisis AI-SCAC AI-SCAC1 We use cognitive computing to improve our decision making to navigate pandemic crisis Adapted from Srinivasan and Swink (2018) & Dubey et al. (2020) AI-SCAC2 We often make cognitive interpretation of the information extracted using data analytics to mitigate the disruption resulting from pandemic crisis AI-SCAC3 We often integrate our data gathered from multiple sources to extract meaningful information AI-SCAC4 Our dashboard helps managers to understand the cognitive computing outputs of complex data to make effective decision AI-SCAC5 We have installed dashboard applications to our managers communication devices ED ED1 We have changed our production capacity based on demands Schilke (2014, p. 189) ED2 The current demand during crisis is changing continuously ED3 Marketing strategies are changing rapidly during crisis ED4 The crisis creates high degree of unpredictability in terms of demand and supply ED5 We are rapidly changing our business models to respond to the crisis OP OP1 Delivery on time Srinivasan and Swink (2018); Dubey et al. (2019) OP2 Order fulfilment lead time OP3 Inventory turnover ratio OP4 Capacity utilization FP FP1 EBIDTA (Earnings Before Interest, Depreciation, Taxation and Amortization Schilke (2014, p. 189) FP2 ROI (Return on Investment) FP3 ROS (Return on Sales) ==== Refs References Agarwal R. Dhar V. Big data, data science, and analytics: The opportunity and challenge for IS research Information Systems Research 25 3 2014 443 448 Akter S. Fosso Wamba S. Dewan S. Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality Production Planning & Control 28 11−12 2017 1011 1021 Akter S. Michael K. Uddin M.R. McCarthy G. Rahman M. Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics Annals of Operations Research 2020 1 33 10.1007/s10479-020-03620-w Akter S. Wamba S.F. Gunasekaran A. Dubey R. Childe S.J. 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The application of “swift trust” to humanitarian logistics International Journal of Production Economics 126 1 2010 35 45 Teece D.J. Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance Strategic Management Journal 28 13 2007 1319 1350 Teece D.J. Pisano G. Shuen A. Dynamic capabilities and strategic management Strategic Management Journal 18 7 1997 509 533 Tosi H.L. Jr. Slocum J.W. Jr. Contingency theory: Some suggested directions Journal of Management 10 1 1984 9 26 Venkatesh V. Adoption and use of AI tools: A research agenda grounded in UTAUT Annals of Operations Research 2021 1 12 10.1007/s10479-020-03918-9 Vickery S.K. Jayaram J. Droge C. Calantone R. The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships Journal of Operations Management 21 5 2003 523 539 Volberda H.W. van der Weerdt N. Verwaal E. Stienstra M. Verdu A.J. Contingency fit, institutional fit, and firm performance: A metafit approach to organization–environment relationships Organization Science 23 4 2012 1040 1054 Waller M.A. Fawcett S.E. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management Journal of Business Logistics 34 2 2013 77 84 Wang W.Y.C. Wang Y. Analytics in the era of big data: The digital transformations and value creation in industrial marketing Industrial Marketing Management 85 2020 12 15 Weerawardena J. Mort G.S. Liesch P.W. Knight G. Conceptualizing accelerated internationalization in the born global firm: A dynamic capabilities perspective Journal of world business 42 3 2007 294 306 Weerawardena J. Mavondo F.T. Capabilities, innovation and competitive advantage Industrial Marketing Management 40 8 2011 1220 1223 Yelle L.E. The learning curve: Historical review and comprehensive survey Decision Sciences 10 2 1979 302 328 Zhang X. Meng Y. de Pablos P.O. Sun Y. Learning analytics in collaborative learning supported by slack: From the perspective of engagement Computers in Human Behavior 92 2019 625 633

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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Published by Elsevier Inc. S0022-4804(21)00417-0 10.1016/j.jss.2021.06.046 Article 2021 Association for Academic Surgery Ethics Committee Essay and Cover Art Contest 31 7 2021 10 2021 31 7 2021 266 A1A2 © 2021 Published by Elsevier Inc. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcCOVID-19 exerted a profound psychologic impact on the world and the medical community. The Ethics Committee of the Association for Academic Surgery (AAS) saw an opportunity to offer our members a means to express their feelings and emotions through art with respect to this pandemic. Contestants were asked to compose essays or offer a visual depiction describing the most challenging ethical issue, personal or professional, they encountered in the COVID era. The winners of this contest are featured here in the October 2021 edition of the Journal of Surgical Research, and winners of both categories received cash prizes. We had several powerful entries for both contests. All were reviewed by an independent panel of judges drawn from AAS membership. Runners-up were voted to be published monthly in the AAS Ethics Committee Blog. Essay Winner: Shruti Hegde, MD Title: “Self-neglect is NOT Selfless” Shruti Hegde, MD, was raised in Plano, Texas, and developed a passion for medicine at a young age. She received her Bachelor of Arts degree in Biochemistry from Austin College in Sherman, Texas. She then obtained her medical degree from the University of Texas Medical Branch in Galveston, Texas. She is currently a fourth-year general surgery resident embarking on her professional development time at the University of Texas Southwestern (UTSW) Medical Center in Dallas, Texas. She is an advocate for maternal resident wellness as a member of the UTSW Surgery Maternal Task Force. Her research interests include addressing health care disparities in the surgical patient population related to health literacy and integrating artificial intelligence and machine learning in surgical training. Her goal is to become an academic general surgeon and eventually practice globally. Art Winner: Vivian Hsiao, MD, and Madhuri Nishtala, MD Title: “Critical Capacity” Dr. Vivian Hsiao is a Chinese-American General Surgery Resident at the University of Wisconsin, Madison, currently completing her NIH/NIDCD-funded postdoctoral fellowship focusing on health informatics and Endocrine Surgery mentored by Dr. David F. Schneider and Dr. David O. Francis. She is also interested in quality improvement, ethics, and diversity. Vivian grew up in California and received her undergraduate and medical education at Brown University. Ever since childhood, Vivian has enjoyed telling stories using art. Dr. Madhuri Nishtala is an Indian-American immigrant who obtained her undergraduate degree at The University of Chicago before completing her MD at Case Western University School of Medicine. She is currently a General Surgery resident at the University of Wisconsin-Madison and is completing her NIH/TL1 funded postdoctoral fellowship in Dr. Ben Zarzaur's lab. She is studying the role of the economizing behaviors of financial hardship in mediating the health outcomes of injured patients. Her long-term goals include pursuing health services research with a focus on health equity and health economics. Madhuri enjoys writing and performing poetry, pottery, and engaging with local politics in her free time. The AAS Ethics Blog, currently run by Dr. Krista Haines, Director of the AAS Ethics Committee and an Assistant Professor at Duke University Department of Surgery, will publish entries from both the art and essay contests monthly. https://www.aasurg.org/blog/ethics-comm-contest/ Please visit the blog site to support these incredible submissions! For twitter: @AcademicSurgery @DrKristaHaines

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==== Front Industrial Marketing Management 0019-8501 0019-8501 The Authors. Published by Elsevier Inc. S0019-8501(21)00021-3 10.1016/j.indmarman.2021.01.013 Research Paper “Can we build it? Yes, we can!” complexities of resource re-deployment to fight pandemic. Elsahn Ziad a⁎ Siedlok Frank b a Northumbria University, Newcastle Business School, City Campus East, NE1 8ST, Newcastle upon Tyne, United Kingdom b University of Auckland, 12 Grafton Road, Auckland, New Zealand ⁎ Corresponding author. 28 1 2021 2 2021 28 1 2021 93 191207 8 6 2020 14 12 2020 20 1 2021 © 2021 The Authors. Published by Elsevier Inc. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. During the COVID-19 pandemic, several countries asked their domestic firms to produce various medical equipment. Many firms promised to do so, including redesigns of existing ventilators or designing new ones. Despite these firms' enthusiasm, however, many of their attempts at being resourceful- through deploying their resources in activities beyond their current use- were unsuccessful. Our study attempts to explain why the success of these efforts varied. We integrate concepts of resourcefulness, managerial cognition, and product architecture to develop a typology of resourcing approaches, using a firm's characteristics and resources, its interpretative frames, and the technical and regulatory characteristics of the product being resourced for as boundary conditions. We illustrate our theorizing through case studies on the manufacturing of face shields, hand sanitiser, face masks, and medical ventilators. Our study provides important implications for firms attempting to deploy their resources in new contexts. Keywords Resourcing strategy Resourcefulness Capability re-deployment COVID-19 Innovation ==== Body pmc "Eric Humphreys began building a DIY breathing machine. “I literally used Christmas parts,” he says. But he and his boss, Manu Sawkar, the founder of Standard Transmission, also realised that this “DIY MacGyver creation,” as Sawkar puts it, wasn't even vaguely ready for prime time. Real ventilators require considerable testing for reliability. They have to monitor patients and set off alarms if too much or too little air is going to the lungs. They have sophisticated algorithms to regulate flow depending on how well the patient is inhaling. Even if Standard Transmission did create something usable, Sawkar says, it would never be able to manufacture enough units to interest the city. So Humphreys's creation will go no farther than a well-meaning gesture." (Levy, 2020) 1 Introduction As COVID-19 became increasingly widespread, governments worldwide realised their healthcare systems risked being overwhelmed. Most countries lacked adequate hospital capacity, ICU units, ventilators, and personal protective equipment (PPE). In the UK, for example, early estimates suggested that the NHS would be short of 20,000 ventilators (Davies & Rankin, 2020). In response, several governments called on private-sector firms to help produce PPE and ventilators.1 Many organisations, including LVMH, Airbus, Dyson, GM, and Ford, offered to deploy their resources, some individually and others jointly, to produce the needed items, including hand sanitiser, face shields or simple fabric face masks, medical-grade face masks,2 and ventilators. These efforts were supported by individuals and organisations sharing relevant information, designs, and design blueprints (Chesbrough, 2020; Crick & Crick, 2020) and by relaxing some requirements and rules about producing these goods. Many attempts at being resourceful- through deploying resources in activities beyond their current use- were, however, unsuccessful or deficient: some products were of unacceptable quality, could not be produced at scale, had limited clinical effectiveness (e.g., only short emergency use), or failed to secure regulatory clearance.3 It is crucial that we understand why some initiatives succeeded while others failed. We address these questions by integrating the concepts of resourcefulness and resourcing (Baker & Nelson, 2005; Korsgaard, Anderson, & Gaddefors, 2016; Sonenshein, 2015) with literature on innovation types (Henderson & Clark, 1990; (McDermott & O'Connor, 2002; Tushman, Smith, Wood, Westerman, & O'Reilly, 2010). Resourcefulness denotes an ability to redeploy existing resources in novel ways to address an issue or create a new opportunity (Baker & Nelson, 2005; Korsgaard et al., 2016; Sonenshein, 2014). It explains how relationships among existing objects (or capabilities) that are redeployed, interpretive frames, and environmental limitations influence success. But its boundary conditions are not well understood (Williams et al., 2019). The innovation literature suggests product complexity is one boundary condition: for product development to succeed, product characteristics such as architecture (Henderson & Clark, 1990), number of components, and regulatory specifications (De Toni et al., 1998; Hobday, 1998) require different knowledge types and actions (Henderson & Clark, 1990). Therefore, we argue that the success of firms' attempts to apply their resources and capabilities (Sonenshein, 2014) during the pandemic depended on the suitability of their resourcing approaches, the interpretative frames used to enact their resources, the characteristics of the product resourced (Weiss, Hoegl, & Gibbert, 2013), and, in some cases, relevant institutional support or governmental intervention (Hung, 2002). We start by proposing a theoretical framework that proposes successful resourcing is based on the relations among three dimensions: 1) objects- which are tangible and intangible assets that a firm owns or can access; 2) interpretative frames- which are constituted by firm's knowledge and provide the frames through which alternative uses of objects can be envisioned; and 3) product architecture- which refers to the characteristics of the product for which resourcing is directed. Based on this framework, we propose a typology of resourcing approaches that focuses on the relations among these dimensions. We also argue that when resource redeployment is too difficult for one firm because of a product's architecture, coordination among firms is necessary. We illustrate our framework by analysing case studies of recent resourcing initiatives for face shields, hand sanitiser, medical face masks, and medical ventilators. We selected these categories to reflect the implications of increasing architectural complexity. Our paper makes several theoretical and practical contributions. First, we contribute to the strategy literature by integrating the resourcefulness and resourcing theory with the literature on innovation types. Our framework proposes boundary conditions for the resourcefulness concept by suggesting how different resourcing approaches and actions are more appropriate in certain contexts. Second, our findings suggest that resourcefulness for architecturally complex product categories might require coordination by policy makers and collaborative innovation. Practically, our proposed framework can help managers who must assess a priori how feasible their innovation initiatives are. This framework can help prevent firms from pursuing unrealistic or unnecessary goals. For example, Dyson spent around £20 million to develop a ventilator that the UK government rejected. Our findings also offer useful advice for practitioners who must mobilise and manage industrial collaboration. Thus, our paper augments the current discourse in industrial marketing management on how resources can be re-deployed, shared, or combined (Chesbrough, 2020; Crick & Crick, 2020) and can thus reshape or open new market opportunities (Möller, Nenonen, & Storbacka, 2020). It also adds a perspective on managing through crisis (Pedersen et al., 2020). Finally, we see the paper as suitable for teaching strategic innovation management. In this paper, we first briefly review the resourcefulness and resourcing perspective and the innovation types literature, and we use these concepts to present our tripartite integrative framework. We then illustrate our theorizing through three case vignettes. Finally, we discuss our findings and develop theoretical and practical implications. 2 Resourcing theory: Differentiating between “objects” and “resources in use” The concept of resourcefulness is central in the entrepreneurship, innovation, and strategy literatures (Clough, Fang, Vissa, & Wu, 2019; Deken, Berends, Gemser, & Lauche, 2018; Feldman & Worline, 2011; Senyard et al., 2014; Wiedner, Barrett, & Oborn, 2017). Firms exhibit varying levels of resourcefulness- which we define as a firm's ability to bring, create, combine, and/or deploy existing or new resources to seize and respond to opportunities (Baker & Nelson, 2005; Korsgaard et al., 2016; Sonenshein, 2014). Resources have a “multitude of potential uses” (Korsgaard et al., 2016: 187) that are limited by managers' imaginations and their interpretative frames (Penrose, 1959). This concept applies both to resource-constrained firms that must make do with what they got through bricolage (Baker & Nelson, 2005; Welter et al., 2016) and to resource-rich firms that envision better alternative uses of their resources (Sonenshein, 2014). Unlike early accounts of the resource-based view (RBV), which focused on resources' innate qualities as determinants of firm's performance, the resourcing perspective shifts the attention to the process through which “resources gain their strategic value” (Deken et al., 2018: 1923). Early RBV perspectives (Barney, 1991; Barney, Ketchen Jr, & Wright, 2011) examined how the innate qualities of physical, human, and organisational assets (Eisenhardt & Martin, 2000) – or, as referred to in resourcing perspective ‘objects’ - can enable a firm to realise its strategy. This perspective does not, however, explain how assets become valuable (Schneider, Bullinger, & Brandl, 2020) or what the boundary conditions of an object being a valuable resource are. To explain this process, the resourcing perspective examines how practitioners enact assets in practice. It argues that assets become resources only when “organizational members take up and use assets as they pursue activities in line with what they wish to make happen in the world” (Feldman & Worline, 2011: 630). Resourcing theory distinguishes between an “object”, which can be a tangible or an intangible asset and a “resource”, which is an object that has already been acted on to offer value (Feldman & Worline, 2011). In this perspective, an object is defined broadly and can include various forms of knowledge and relational ties like a business ecosystem or supply chain relationships. Such knowledge or ties become resources only when they are deployed to create value, and their potential to become resources is shaped by actors' interpretative frames of how they can be used. That is, “the designation of resource is not just about the innate qualities of a material or nonmaterial asset, but about the nature of the relationship between the asset and what it helps to create” (Feldman & Worline, 2011: 631). As Feldman and Worline (2011) argue, breadcrumbs, as an object, becomes a resource when used to enact the meatball framework. In contrast, although pellets of metal could be used to enlarge meatballs, this would be inconsistent with the framework of meatballs as being edible. Similarly, resourceful enacting of meatballs with horse meat was deemed as beyond social and legal frameworks (Higgins & Castle, 2013). Thus, the limits to resourcefulness are dictated by shared interpretative frames, which reflect physical, legal, and social rules. Nonetheless, we still know little about the boundary conditions of successful resourcing. What roles are played by interpretative frames and the characteristics of the product that is being resourced for? For example, why could some firms successfully redeploy their resources in the effort to fight the COVID-19 pandemic, while others failed? 2.1 Interpretative frames Recently, strategy scholars have suggested that managerial cognition and mental models are the micro-foundations of dynamic capabilities (Eggers & Kaplan, 2013; Felin, Foss, & Ployhart, 2015; Maitland & Sammartino, 2015; Salvato & Vassolo, 2018). This shift recognizes that managers develop and deploy organisational resources and capabilities while relying on mental models, which are “simplified representations of the world in order to process information (Simon, 1955)” (Tripsas & Gavetti, 2000: 1148). Those interpretative frames (sometimes referred to as schema, schemata, or cognitive frames)4 therefore shape managers' cognition by i) focusing their attention on certain dimensions of the environment (Kaplan, 2008) and ii) providing them with assumptions about how the world works (Weick, 1995). While interpretive frames can facilitate decision-making by leveraging previous experience, they can also lead to competency traps, in which individuals become strongly committed to a certain frame that keeps them from considering alternative interpretations and leads to sensemaking failures (Tripsas & Gavetti, 2000; Weick, 1995). Overreliance on pre-existing frames can be problematic during unprecedented circumstances “that require inferential flexibility and alternative conceptualizations” (Cornelissen & Werner, 2014: 190). As Benner and Tripsas (2012) argue, such rigidity can occur when individuals analogically extend frames from their existing industry to an emerging one. Although analogical reasoning can be an effective way to transfer a solution across contexts, its success depends on how accurately actors conceptualise the differences between their base domain and the new target domain (Gentner, 1983). For similar contexts, actors with prior knowledge in these domains are more likely to use analogical reasoning successfully and effectively redeploy capabilities in new contexts (Gavetti, Levinthal, & Rivkin, 2005; Mastrogiorgio & Gilsing, 2016). Nonetheless, actors can overemphasize superficial similarities between contexts while ignoring critical differences (Cornelissen & Werner, 2014; Lovallo, Clarke, & Camerer, 2012), including regulatory or technical aspects of the product that are obscured by the apparent similarities between contexts. For instance, food companies that attempted to enter nutraceutical markets and pharmaceutical companies that attempted to enter food markets failed for this reason (Siedlok, Smart, & Gupta, 2010). As we argue below, the interpretation of a product is also affected by its architecture and the regulatory roles that govern its design specifications. 2.2 Product architecture: What is being resourced? A product architecture consists of the 1) the arrangement of its functional elements, which denote what the product does; 2) the mapping of functional elements to physical components, or which component accomplishes what function(s); and 3) the interfaces among the physical components (Ulrich, 1995). Ulrich (1995) distinguished between integral and modular product architectures, which exhibit differing levels of interdependence among components and interfaces. Modular architectures exhibit a one-to-one mapping between the product's physical components and its functional elements and a system of decoupled interfaces (Brusoni & Prencipe, 2001; Ulrich, 1995). Components in modular architectures can be easily changed and produced by different firms (Sanchez, 2008). This characteristic increases flexibility, makes it easier to upgrade components, and enables firms to offer a variety of products (Sanchez, 2008). In contrast, integral architectures involve a “complex mapping between physical components and functional elements and coupled interfaces between components” (Brusoni & Prencipe, 2001: 182). The high interdependence among components and the tightly coupled nature of interfaces mean that a change in one component has cascading effects on the product architecture (Burton, Nyuur, Amankwah-Amoah, Sarpong, & O'Regan, 2020). As the interdependence and interactions among a product's physical components increase (Mastrogiorgio & Gilsing, 2016), product complexity usually follows. Integral product architectures are a hallmark of high-end products (e.g., iPhone, Hard Disk Drives) and often rely on integrated supply chain architectures, with strong cross-company links that can create high entry barriers and limit the adaptive fit of a product or technology (Hung, 2002). This tight coupling limits the possibility of using alternative objects as subcomponents. As Dew, Sarasvathy, and Venkataraman (2004) argue, exaptation is more likely to take place in highly decomposable systems because the low interdependence among components allows actors to envision the use of different objects in the product design. Relatively high levels of decomposability usually lead to more expansive design options (Andriani & Carignani, 2014; Baldwin & Clark, 2000; Mastrogiorgio & Gilsing, 2016). This tendency is relevant to our study, where some components of medical products are in short supply because of the pandemic. Some non-specialised firms attempted to make up for this shortfall by attempting to use their objects as substitutes. While this strategy might work in simple products like face shields, it is more likely to fail for complex products with high interdependence among subparts. It is also less likely to work for medical equipment, which is highly regulated with detailed specifications to ensure product quality and patient safety. Because firms need to obtain regulatory approval for their products, product architectures stabilise and relatively strict design rules emerge (Baldwin & Clark, 2000). These rules may facilitate technical understanding among players in the industry, but they can impede the “ability to fundamentally (re-)define and develop architectural innovations, since the considered problem-definition and solving space will be constrained by the mere presence of design rules” (Hofman, Halman, & Van Looy, 2016: 1437). Resourcing therefore requires some fuzziness in rules to provide actors with the necessary level of guidance while also allowing them a wider space of possibilities.5 This fuzziness can be exploited by entrepreneurs who ignore established assumptions about the meaning of the technology or artefact, redeploying those in new contexts or new configurations (Gilbert-Saad, Siedlok, & McNaughton, 2018; Verganti & Öberg, 2013). Governments and regulatory agencies can be important in such instances because they can change design specifications to incentivise firms to resource for certain products. For example, to mobilise the private sector to produce ventilators, which can temporarily stabilise patients, the UK government introduced guidance in March 2020 on the minimal acceptable specifications for ventilators. To summarize, we argue that the success of firms' attempts to be resourceful by redeploying their objects in novel ways depends on the interrelations among three dimensions: 1) objects- which refer to the tangible and intangible assets that a company owns or can access; 2) interpretative frames- which provide a framework for how objects can be used differently; and 3) product architecture- which refers to the technical and regulatory characteristics of the product that is being resourced for (see Fig. 1 ).Fig. 1 Theoretical framework for resourcing strategy. Fig. 1 Firms might possess tangible assets such as equipment and production capacity or intangible assets such as specialised technical knowledge and well-established relationships within, or the knowledge required to coordinate, complex supply chains. Thus, we regard tacit knowledge as an object in this sense. These objects need to be enacted through interpretative frames, which guide actors about how to use these objects in new contexts. Successful transfer of objects to new contexts will therefore depend on the validity of the frames used to map the similarities between the base and target domain. The usefulness of these frames also depends on the technical and regulatory characteristics of the product that is being resourced for. When the product is simple and the target domain shares some similarities with the firm's base domain, managers might be able to analogically extend their frames and successfully redeploy their resources. However, analogical transfer in complex and uncertain situations is difficult (Gary, Wood, & Pillinger, 2012). In product categories with highly complex architecture, extending pre-existing frames to new contexts might be problematic; firms might overemphasize similarities and underestimate the complexity of the product (Schwenk, 1984) and hence fail to successfully redeploy their objects. In such situations, decision-makers might benefit from being more reflexive about the potential limits of their frames in new contexts (Gary et al., 2012; Hibbert, Siedlok, & Beech, 2016). Rather than going with “gut feelings”, convergent thinking and involvement from a wider range of stakeholders might be required (Gilbert-Saad et al., 2018) to augment knowledge about the new context. This, in turn, might require coordination or prior experience of working with partners across knowledge domains (Siedlok, Hibbert, & Sillince, 2015). As shown in Fig. 1, this resourcing process is embedded in an institutional context that shapes the socially accepted meaning of objects and the scope for extending interpretative frames across domains. Industry regulations and product specifications can also enable or restrict the scope for resourcing products. For example, by altering regulations or norms like the approval process and requirements for medical devices, changes in the institutional context can expand or narrow how an object is perceived and can be deployed. We argue that the success of firms' initiatives during the pandemic reflects the interrelations among objects, interpretative frames, product architecture, and the institutional context in which they are embedded. Differently configured initiatives may require different resourcing approaches. We next present our research methods and illustrate our theorizing through several vignettes of different product categories. 3 Methodology We aim to develop a theoretical explanation of successful resource deployment in new contexts through abductive reasoning, in which our theoretical framework is modified and refined by confronting it with the empirical world (Andersen & Kragh, 2010; Dubois & Gibbert, 2010). Case study approaches are particularly suitable for studying complex industrial marketing phenomena (Easton, 2010) such as resource redeployment in a naturalistic setting, where the boundaries between the context and phenomena are blurred (Dubois & Gibbert, 2010; Stake, 1995). As such, a multiple case study approach was deemed suitable due to its ability to build, extend, and refine theory (Eisenhardt, 1989; Graebner, Martin, & Roundy, 2012) and “to capture relevant features of a case through a particular framework” (Dubois & Gibbert, 2010: 131). The use of case studies therefore has an illustrative function (Graebner et al., 2012), which allows researchers to argue the validity of their theoretical propositions through real-life examples (see Finch & Geiger, 2011). Case studies also allow us to capture the similarities and differences in resourcing strategies across initiatives in different product categories (Elsahn, Callagher, Husted, Korber, & Siedlok, 2020) to assess how and why some organisations successfully deployed their resources in new contexts, while others failed (Eisenhardt, 1989; Lindgreen et al., 2010). 3.1 Case selection Our case selection was theoretical (Eisenhardt, 1989) and emergent. As Fletcher & Plakoyiannaki (2011: 173) argue, “the definition of the unit of analysis is the fundamental answer to the question ‘what to select’”. Our unit of analysis is the initiative undertaken by an organisation or a group of organisations to produce medical products. The case selection was iterative; we adjusted constantly among data collection, analysis, and case selection (Lingens, Miehé, & Gassmann, 2020). We began by focusing on specific firms, but later extended our focus to include other organisations and consortia. As our research progressed, we realised the characteristics of the product being resourced for helped shape the organisations' resourcing strategies. Thus, we decided to focus on initiatives across different product categories characterised by different levels of complexity and fuzziness. We initially considered a broad selection of product categories, including cloth face masks; track & trace systems, and other categories of PPE, but we decided to focus on initiatives across four product categories: face shields, hand sanitiser; medical face masks, and medical ventilators. These categories provide enough variance for the analysis. Fig. 2 illustrates the product categories that we sampled the initiatives from.Fig. 2 Case selection. Fig. 2 Within these categories, we sampled multiple initiatives that differed to detect variance across the initiatives. For example, we included initiatives that involved organisations working individually or collaboratively. Furthermore, we ensured that our sample included variation for both failed (e.g., abandonment, unacceptable quality or miniscule quantity, failing to secure necessary regulatory clearance) and successful (e.g., the end result was safe, clinically effective, and could be manufactured in high volumes6 ) initiatives to avoid success bias and to capture different patterns across the cases (Elsahn et al., 2020). Table 1 includes a list of the initiatives that we sampled in our study. We consider further what constituted success in the discussion section.Table 1 Overview of the data sources. Table 1Product category Sampled Initiatives Number and type of secondary sources Total Face Shields Apple initiative to manufacture face shields. Bauer, a sport equipment manufacturer, initiative to manufacture face shields. The makers community initiative to produce face shields through 3D printing. 11 articles from reputable news and magazines 2 company websites 1 industry association website 14 Hand sanitiser LVMH initiative to produce hand sanitisers. Craft distilleries and breweries initiative to produce hand sanitisers. 11 articles from reputable news and magazines 5 government/official information reports 1 university website 1 news bulletin video 18 Surgical Masks GM initiative to produce surgical masks. Taiwan's face mask team consortium initiative to produce surgical masks. 15 articles from reputable news and magazines 1 company website 1 industry association website 2 government / official information reports 3 news video bulletin/interviews 22 Ventilators Dyson's initiative to produce ventilators. Tesla's initiative to produce ventilators. NASA's initiative to produce ventilators. “VentilatorchallengeUK” consortium initiative to manufacture ventilators. “Vermontilator” initiative to produce ventilators. GM and GE healthcare initiative to produce ventilators. Ford initiative to produce ventilators. Taiwan ventilator team consortium initiative to produce ventilators. 55 articles from reputable news and magazines 7 company/consortia websites 1 industry association website 2 government / official information reports 65 Cross-case / general sources 15 articles from reputable news and magazines 15 Total sources used in initial analysis 134 Additional sources included during the review process 34 articles from reputable news and magazines 5 company/consortia websites 3 podcasts 3 reportage movies 45 Total sources 179 3.2 Data collection Our approach is similar to previous studies which relied on secondary data to develop an in-depth understanding of observed phenomena and to illustrate theorizing (e.g., Finch & Geiger, 2011; Hung, 2002; Ritala, Golnam, & Wegmann, 2014; Rusko, 2011). Several authors have argued that secondary data present an “unexploited and rich source of data that should be used when primary data is not available” (Ritala et al., 2014: 240; see Ambrosini, Bowman, & Collier, 2010; Cowton, 1998; Harris, 2001). Secondary data can be particularly useful for studying events such as the pandemic because they are heavily covered by the press and governmental agencies and offer an abundance of secondary data (Kummitha, 2020). In addition, especially in the cases of failed initiatives, secondary sources can be a better alternative to interviews that avoid access issues or retrospective rationalisation by managers (Cowton, 1998; Harris, 2001). To ensure the quality of our data, we relied on a variety of sources such as governmental reports and regulations on medical equipment, news articles by reputable media, company reports and press releases, and video interviews with managers and industry experts. We also focused on news articles that relied on interviews with company representatives and industry experts. In our search, we focused on the four product categories and the emerging approaches to resource redeployment within each product category. As Ritala et al., (2014) did, we provide illustrative quotes in our findings section to enhance the transparency of our analysis (Lindgreen, Di Benedetto, & Beverland, 2020) and to clearly connect data to our theorizing. Table 1 provides an overview of our data sources. 3.3 Data analysis In analysing our data, we adopted an abductive approach, which involved iteration between theory and data (Dubois & Gibbert, 2010) whereby our theoretical framework evolved “simultaneously and interactively with empirical observation” (Dubois & Gibbert, 2010: 131, italics in original). Specifically, we followed the “in vivo approach” (Andersen & Kragh, 2010), in which we took resourcing theory as a starting point to frame our inquiry, while continuously combining other theoretical perspectives and refining our theoretical framework in light of our engagement with the empirical material (Andersen & Kragh, 2010). This approach to theory building involved interpolation, which helped us “extend and/or combine received theory with empirical findings and other theoretical perspectives in order to build new theory” (Andersen & Kragh, 2010: 51). As argued by Dubois and Gibbert (2010) the in vivo approach is particularly suitable to multiple case study design as the phenomena of interest is kept constant across cases while the theoretical framework evolves to make sense of similarities and differences between cases. It is difficult to describe all the iteration between theory and data. Retrospectively, we can identify four main stages in our data analysis: developing an understanding of each case through within-case analysis, refining and modifying our theoretical framework, cross-case analysis (Lindgreen et al., 2020), and aggregating themes and developing our final theoretical framework. Our data analysis process is depicted in Fig. 3 .Fig. 3 Theorizing, data collection and data analysis: an iterative and abductive process. Fig. 3 Our inquiry started with the observation of firms attempting to deploy their existing objects in new contexts in the effort to fight the COVID-19 pandemic. By following several firms' initiatives, we noticed that many of these efforts were unsuccessful. To make sense of the variations in success, we turned to the resourcefulness and resourcing literature. This perspective provided an initial frame to make sense of the sampled cases through within-case analysis. We wrote a vignette for each initiative describing the resources (objects) used, the process, activities and overall deployment strategy, and the outcome. At this stage, we noticed that the characteristics of the product that was being resourced for and the actors' cognition and perception of the opportunity shaped the deployment strategy, and consequently its success. Therefore, we revisited our framework to incorporate insights from the cognition and product architecture literature to make sense of our observations. We developed our tripartite framework (Fig. 1), which is comprised of three dimensions: 1) objects- which are tangible and intangible assets that a firm own or can access; 2) interpretative frames- which are constituted by firm's knowledge and provide the frames through which alternative use of objects can be envisioned; 3) product architecture- which refers to the characteristics of the product to which resourcing is directed. We then revisited our cases and recoded them based on these dimensions. Subsequently, we engaged in cross-case analysis to identify the differences and similarities between initiatives to develop a theoretical explanation of the variations in success. We then identified five approaches to resourcing, as presented in Table 2 . We developed these inductively by analysing all our cases and data. While doing so, we discussed similarities and looked for emerging patterns to resourcing. We paid attention to clues that highlighted motivation, challenges, and how organisations interpreted the products in relation to their existing capabilities.Table 2 Main resourcing approaches. Table 2Resourcing approaches Characteristics of the core organisations It's in our brand High-profile organisations with access to complex supply chains, established clout (due to high visibility brand) and, potentially, leveraging their established brand in framing the resourced product. There seems to be a relation to the image of the company as being innovative (e.g. Tesla), design driven (e.g. Apple) and generally being proactive in bringing novel solutions or products on a regular basis, often with claims of helping consumers (cosmetic firms). The key factor underpinning the strategy being existing brand image. We are already making it! Kind of… Organisations that possess similar capabilities or already produce similar products, although sometimes operating in completely different markets (e.g. NASA, distilleries). The distance between home and target knowledge bases is generally small, though requiring analogical reasoning to make the connections between the existing and needed product or capability. Overall, these organisations, except for NASA, were often motivated by the opportunity to remain active and not needing to halt operations. Eager helpers Organisations or individuals that were intrinsically motivated, even if their resources and capabilities were not necessarily closely linked or fitting the requirements. There is a visible lack of assessment of the gap or consideration of other options of achieving the goal (e.g., partnering up), which often translated to reinventing the wheel or developing unnecessarily complex products or processes (e.g. 3D printing of face shields; focusing on developing new ventilator designs). We are all in it TOGETHER Organisations or individuals approaching the task in a more coordinated manner, leveraging different capabilities and resources across the partnership and recognising that collaboration is the only way to achieve the goals. Not so eager helpers Organisations or individuals that were in a position (e.g. existing mask producers), or deemed to be in a position (e.g. GM, Ford) to help by scaling up their efforts (which could be by partnering with others) or redeploying their resources (e.g. GM, Boeing), but lacked the same levels of intrinsic motivation to help. In those cases, governments utilised different mechanisms to either motivate them (e.g., payments tied to certain weekly production quota in Taiwan) or to compel them to act (e.g., GM). In most cases, these organisations were already involved in production efforts and fall into one of the other categories. Finally, we recorded new developments and news related to the four product categories and organisations that we focused on. Prompted by reviewers' comments, we reassessed our findings against new evidence and indicated the outcomes from a longer time perspective. This ongoing engagement allowed us to develop some additional insights related to the impact of these efforts after the initial goals were achieved. We highlight these in our discussion, along with limitations and future research. In the next section, we present our findings for the product categories that we studied, followed by a cross-case analysis in the discussion section to explicate our theoretical explanations (Piekkari, Plakoyiannaki, & Welch, 2010) and propose our typology of resource redeployment. 4 Redeployment of capabilities amid coronavirus pandemic In this section, we provide illustrative cases accompanied by brief analyses in which we assess each product category from the perspective of the theoretical framework we propose in Fig. 1. We analyse product architecture, interpretative frames, and the object of resourcing. We inductively derived those from the data and used the three success criteria (safety, efficacy, and volume) to assess whether the approach succeeded. We then analysed the approach, and we note its risks and challenges.7 4.1 Face shields: Apple, sport equipment manufacturers and the maker community Face shields are simple products that require little technical expertise and are not regulated in terms of design or manufacturing process. One manufacturer explained why so many firms attempted to produce shields: “shields need not be sterile, and “they're easiest to manufacture”.8 The interpretative frame of the product is generally agreed on: a piece of transparent material that protects the wearer's face from contamination, with relatively fuzzy design rules allowing for a range of design options or manufacturing approaches, without affecting performance. In Table 3 , we analyse Apple, which had never produced face shields, Bauer, a sport equipment manufacturer that already had a similar product, and the maker community that mobilised to manufacture a range of equipment.Table 3 Resourcing for Face Shields. Table 3Product architecture Face shields: (1) Very low product complexity. (2) Modular / simple component architecture. (3) No regulatory specifications for the product or the production process1. Manufacturing, expertise in safe machine use and design needed2. Fuzzy design rules allow for a range of combinations, without affecting product performance. Lack of regulations allows for many simultaneous designs. Example player(s) Apple:It's in our brand Bauer:We are already making it! Kind of… Maker communities:Eager helpers Was it successful? Safe:yes, easy to meet the criteria Effectiveness:yes, easy to meet the criteria Volume:yes, by leveraging supply chain management capability Safe:yes, easy to meet the criteria Effectiveness:yes, easy to meet the criteria Volume:yes, by leveraging existing manufacturing capability Safe:yes, easy to meet the criteria Effectiveness:yes, easy to meet the criteria Volume:no, small scale of production and often by adopting inefficient approaches fixated on existing technical capabilities Objects Access to production facilities and materials, both internal and supply chain Supply chain management capability Design capabilities Equipment and materials Knowledge of production process Spare capacity due to lockdown 3D printing equipment DIY capabilities Coordination and sharing of information via online platforms Interpretative frames (1) Familiarity with the component and architecture characteristics (2) Base domain of much higher sophistication in comparison to target base (3) No need for analogical reasoning due to the simplicity of the product and the existing supply chain capabilities. For the design team this would be a low-level challenge (1) Perfect alignment of component with minimal change to architecture (2) Base domain required some modifications and learning (production process) (3) Bauer displayed some levels of analogical reasoning to expand the frames of application for existing product. The process was aided by media coverage highlighting the demand and showing the design (1) Sufficient component and architecture knowledge of the product (2) Base domain needed some modification / development to apply existing knowledge (3) Analogical reasoning was needed to match the available materials with the designs and production methods. The process was aided by knowledge sharing via online platforms Deployment strategy Challenges: none Risks / inefficiencies: none Approach: Apple's position in the supply chain allowed it to muster the needed materials and production capabilities quickly and at scale. There was minimal challenge for the company as the components and the design are relatively simple. Additionally, Apple released detailed manufacturing instructions. Challenges: scaling up production with existing machinery Risks / inefficiencies: none Approach: Existing product capabilities had a direct application with no requirement for new knowledge development or changes to the product. Existing capabilities allowed for limited production. Management needed to recognise the analogy between two different markets (sport and medical grade PPE). Challenges: design, access to materials Risks / inefficiencies: fixation on 3D printing approaches when simpler approaches would have been more effective Approach: Sharing of knowledge through social media platforms ensured constant learning, adaptation, and access to needed components. Distributed work enables some scale. Illustrative quotes We've launched a company-wide effort, bringing together product designers, engineering, operations and packaging teams, and our suppliers to design, produce, and ship face shields for health workers.3 Kinnaly said one of his engineers approached him last month with the idea. A design was created, the machinery adjusted and soon after production was underway. The company began by making about 3000 units per week at each location and, as the work force grows more familiar with the process, Kinnaly hopes to ramp up production to 70,000 per week by the end of April.4 Facebook groups such as Open Source COVID19 Medical Supplies, which has more than 70,000 members, have become dispatch centres, through which hospital workers seek volunteers to print or make supplies, and volunteers trade tips on what materials to use and where to source them, and on sterilisation procedures. After bringing in an engineering design firm, the group decided to change tack. Instead of 3D printing, the frames and straps (…) are made from elastic and foam that can be purchased off-the-shelf in bulk form, and cut down either by machine or by hand. Darley says such components can be made in 20 s, compared with several hours through 3D printing.5 While 3D printing offers increasing promise for helping to solve the shortage of medical supplies during the pandemic, it's not so simple to crank up for mass production. “3 M's view is that 3D printing for PPE [personal protective equipment] does not provide the scale we need67 1 www.economist.com/united-states/2020/04/30/americas-makers-and-tinkerers-turn-their-hands-to-ppe 2 Apple statement: Manufacturing the face shields requires professional level expertise in manufacturing and design, and should only be done by professional engineers or machinists in a factory environment (https://support.apple.com/en-us/HT211142); https://www.washingtontimes.com/news/2020/apr/6/apple-make-and-ship-1-million-face-shields-each-we 3 https://www.cnbc.com/2020/04/05/apple-will-produce-1-million-face-shields-per-week-for-medical-workers.html 4 https:// www.nytimes.com/2020/04/07/sports/formula-one-bauer-coronavirus-ppe.html 5 https://www.nature.com/articles/d41586-020-01246-3 6 https://www.forbes.com/sites/amyfeldman/2020/03/24/ford-will-work-with-3m-and-ge-to-make-respirators-ventilators-and-n95-masks/#114e816e3dc2 7 https://media.ford.com/content/fordmedia/feu/ch/de/news/2020/04/30/ford-is-making-face-masks-and-face-shields-to-enable-employees-a.html These cases illustrate the ease of frame transfer and deployment of resources. For Apple, these included monetary resources, access to and the ability to orchestrate supply chains, and some design capabilities. For Bauer, the challenge was to scale production with its existing equipment. The drivers are also different: for Apple it was a mix of philanthropy and marketing strategy while for Bauer a lifeline to stay open during the lockdown. The community of makers was driven by eagerness to help. Although, in this case rigid frames caused individuals to deploy resources inefficiently, as shown by their fixation on 3D printing when manual cutting was more effective. Thus, whereas Apple and Bauer succeeded, some in the maker community produced only miniscule volumes and overengineered the production process. 4.2 Hand sanitiser: Perfume makers and distilleries Two main groups of companies tried to address the shortage of sanitiser: cosmetics / luxury brands such as LVMH and distilleries and breweries, ranging from multinational to craft producers. We analyse these two groups in Table 4 . While sanitiser is not a complex product (80% ethanol, distilled water, hydrogen peroxide, and glycerine) and the basic recipe is publicly available on the WHO's website, its production is often regulated and requires a range of health and safety certifications. For breweries, it also required additional resources and competencies.Table 4 Resourcing for Hand Sanitiser. Table 4Product architecture Hand sanitizer: (1) Low product complexity. (2) Modular architecture based on limited number of defined components. (3) Some regulatory specifications for the product. Regulatory specifications for the production process, with specific competencies of staff and certified facilities for handling flammable substances needed. In some jurisdictions, additional product specifications and certifications were needed. Some requirements have been temporarily eased1. (4) Limited fuzziness as components and their proportions are defined and often regulated.2 Example player(s) LVMH, cosmetics producers:It's in our brand / We are already making it! Kind of… Craft distilleries / breweries:We are already making it! Kind of… Was it successful? Safe:yes, easy to meet the criteria Effectiveness:yes, easy to meet the criteria Volume:yes, by leveraging existing in-house capabilities Safe:yes, easy to meet the criteria Effectiveness:yes, easy to meet the criteria Volume:partially, not as high volume as commercial production in dedicated facilities Objects Access to certified production facilities and raw materials Knowledge of production processes and certified workforce Supply chain access and coordination capability Spare capacity due to lockdown Access to certified production facilities (distilling) Access to some raw materials Distilling knowledge Interpretative frames (1) Perfect alignment of component with minimal change to architecture knowledge (2) Base domain closely aligned with target domain; no learning required. Minimal modification to production lines (3) Minimal levels of analogical reasoning were needed as both product characteristics and application are very similar (1) Partial alignment of component and architecture knowledge (alcohol vs gel based) (2) Base domain of one component closely aligned with target domain, but required additional learning related to the end product. Some (substantial) modification to production process (3) Straightforward analogical reasoning (alcohol as disinfectant) Deployment strategy Challenges: none Risks / inefficiencies: none Approach: Existing product capabilities had a direct application with no requirement for new knowledge development. Large-scale production capabilities and need for minimal process and equipment adjustments. Hand sanitiser is often within the same good category (personal care). Challenges: access to packaging, architectural knowledge of the slightly more complex product (gel), and certification. Risks / inefficiencies: some production-related hazards. In some jurisdictions switching requires stopping normal production (taxes), which can cause shortages for supply in the future.5 Approach: Existing component capabilities had a direct application but required additional knowledge development/acquisition. Production process needed some amendments. Supply chain access and capabilities were not always sufficient. Illustrative quotes Cosmetics manufacturing is actually a close cousin to pharmacy, and the factory equipment could be quickly repurposed. Sanitising gel requires three main ingredients — purified water, ethanol, hydrogen peroxide and glycerine — all of which LVMH already had on hand. In addition to perfumes, the Dior, Givenchy and Guerlain factories also make liquid soaps and moisturising creams for the brands. Those products are similar in viscosity to hand sanitising gel, so LVMH could use its usual filling machines, plastic bottles and pump dispensers. A tall metal tank at the Dior factory usually used to distill scent could be used to mix the ingredients, and a machine for filling up soap bottles drafted into packaging the gel.3 We can't make ventilators, and we can't make masks, but we can make something useful. Making hand sanitizer is deceptively simple but inherently dangerous. We're qualified to handle very flammable substances safely. Like all distilleries in New York, we have spark-resistant lighting, explosion-proof pumps, our electric is set at least five feet off the floor, and our staff is certified in fire protocols and spill response. Distillers needed to know how to make it safely, correctly and to make it effective – that's a lot of knowledge to transfer very quickly.4 The transition from alcohol production to hand sanitizer is fairly easy - we have the equipment and just needed a few supplies.5 1 https://www.gov.uk/guidance/producing-hand-sanitiser-and-gel-for-coronavirus-covid-19;https://www.ttb.gov/images/newsletters/archives/2020/ttb-newsletter03172020sp.html; 2 https://www.who.int/gpsc/5may/Guide_to_Local_Production.pdf;https://www.fdabasics.com/fda-requirements-for-hand-sanitizers/ 3 https://www.ft.com/content/e9c2bae4-6909-11ea-800d-da70cff6e4d3 4 https://news.cornell.edu/stories/2020/04/cornell-aids-distillers-making-hand-sanitizer 5 https://www.forbes.com/sites/fredminnick/2020/03/18/white-house-works-with-distillers-to-increase-hand-sanitizer-production/#4850eeb27fcd At LVMH, the production lines, skills and required materials for perfume production were closely aligned with producing hand sanitiser. The company could thus redeploy its capabilities and achieve large-scale production within days, at scale and without any issues. Repurposing for the luxury brands also enabled them to keep their operations running. Many distillers and brewers needed additional support to reconfigure and access new supply chains, implement new processes and policies, and change parts of the production (e.g., different packaging). In many jurisdictions, government rules also needed to be relaxed to allow for sanitiser produced by distilleries to be used in hospitals. Finally, some provisions in taxation rules and permits for alcohol production were implemented in some jurisdictions. Overall, though, the knowledge base of both groups was relatively close to the target knowledge base and only some adjustments were needed to succeed. 4.3 Medical face masks: GM, manufacturing sector and the Taiwan's face mask team For this category, we focused on surgical-grade face masks and N95 masks, both of which require certification and need to meet certain levels of protection.9 Surgical masks are made of three or four layers of fabric, with a non-woven and electrically charged middle layer that is ultrasonically welded, cut and assembled by specialised machinery. Masks also need to be produced in a sterile environment. While the non-woven fabric determines performance and is usually produced by a specialised manufacturer, the assembly machinery determines the needed scale of production. For N95 masks, fit is also important as it provides necessary level of safety for working in a hospital environment.10 While the complexity of the key components was somewhere between medium (protective layer) to low (rest of the components), we assume that the lack of knowledge about the production process posed a significant challenge to non-specialist firms, leading to low outputs, delays and a number of failed attempts.11 The usual time to set up a N95 manufacturing line is four to six months.12 Combined with the relative lack of knowledge sharing, the integral architecture of the product and the production process suggest why there were fewer examples of companies attempting to address this demand, relative to the greater number of attempts to produce non-medical masks.13 Two interesting cases in this category are GM and a handful of other manufacturers and Taiwan's Face Mask Team (TFMT): a consortium of government agencies, industry associations, tool and machinery manufacturers, face mask machinery manufacturers, fabric suppliers, and face mask manufacturers. There are some stark similarities and differences in the approaches presented in Table 5 . Both approaches relied on coordination of supply chains and development of production facilities. The key component, non-woven fabric, could be resourced relatively easily by existing actors in the supply chain13. The production line posed higher levels of complexity. Consequently, the scale of production differed significantly. GM relied on its existing facilities and, with some repurposing, created a relatively manual production line that could produce up to 1.5 million mask per month.14 TFMT took a different approach, with production increasing from fewer than 2 million mask per day at the beginning of the pandemic to 13 million by mid-March and over 20 million by the end of May.15 Table 5 Resourcing for Medical Face Masks. Table 5Product architecture Medical Face Masks: (1) Low to medium product complexity, with medium-high complexity of production process1. (2) Integral architecture based on limited number of components and highly dependent on production processes, based on tacit or protected by trade secrets knowledge. (3) Regulatory specifications for the product in terms of filtration and sterilisation, with more demanding specifications for the use in ICU context. Higher requirements for N95. (4) Relatively high fuzziness in terms of design / architecture and components. Low fuzziness in production process at large scale / efficiency2 Example player(s) GM, manufacturing sector:Eager helpers Taiwan's Face Mask Team:We are in it TOGETHER / Not so eager helpers Was it successful? Safe:yes, although need to meet medical (as opposed to industrial) safety criteria Effectiveness:yes, as long as at the lower end (medical but not N95) face masks Volume:no. High volume could not be achieved due to part-manual process Safe:yes, capability already within the consortium Effectiveness:yes, capability already within the consortium Volume:yes, capability already within the consortium for large scale production Objects Production facilities / equipment Access to supply chain Knowledge of manufacturing processes Expertise in equipment production Expertise in mask production Coordination of knowledge and production at a national scale Coordination of supply chain for technically complex equipment Interpretative frames (1) Relatively specialised components (non-woven microfibre fabric) and simple architecture (3 layers of material welded together; sealed design for N95). Large scale manufacturing requires specialist machinery (ultrasonic welding, sterilisation) (2) Distant base domain from target domain in both component and manufacturing process (3) Medium levels of analogical reasoning to identify suitable materials (from noise dumping to protective mask); straightforward analogical reasoning to identify the manufacturing capability fit (1) Perfect alignment of component knowledge but significant (initially) lack of architectural knowledge (2) Base domain was relatively close to the target domain (i.e. production of machinery), there was specialised tacit knowledge required for efficient production at large scale (3) Analogical reasoning demanded levels of reflexivity to focus on the needed expertise and addressing this gap through engaging with experts outside of the company Deployment strategy Challenges: understanding / meeting the regulatory requirements for higher grade masks; achieving scale requires specialised production lines. Risks / inefficiencies: mostly lowscale production; technical challenges, including quality requirements3, often limited to lower grade / unsuitable for hospital use. Approach: Mobilisation of suppliers to develop and deliver new, suitable material. Use of existing modified manufacturing processes and equipment for assembly. Relatively low scale production only as the existing production equipment is unsuitable for assembly at scale. Challenges: achieving high levels of efficiency (tacit knowledge dependence); IP issues; resolving uncertainty of future demand, coordination of multiple interests. Risks / inefficiencies: IP misappropriation; lack of involvement of key players (competence); overproduction and too much stock at the end of the crisis that can demotivate mask producers form investing in a scale up. Approach: Government-level coordination of knowledge sharing and additional levers for motivating engagement. Government purchase of the machinery and resolving potential IP issues. Active alignment of stakeholder interests (e.g. dynamically adjusting bonus systems to encourage mask makers to work overtime and weekends8) and securing future demand (change of public procurement policies). Long-term strategy to build national capabilities beyond the current crisis. Illustrative quotes GM worked with automotive suppliers to develop the three layers of fabric in the masks. These companies typically provide GM with sound-deadening insulation found in doors, headliners and trunks, but they quickly altered their production processes.4 Window-shade manufacturer uses the same nonwoven polyester material used in medical gear, and engineers there started prototyping surgical masks and gowns (…) The company (…) has been producing lower-grade surgical masks. (…) We're becoming quickly educated in an industry which was a bit foreign to us5 An inspection revealed that the FFP2 masks did not protect the face properly or had defective filter membranes.3 But when GM started making N95s, engineers with expertise in car interiors and air bags were charged with figuring out the process from scratch, the company said. Although they received advice from major mask makers, there were no groundbreaking corporate partnerships this time. The first N95s GM made were rejected by NIOSH. The second design didn't correctly fit most people. Other potential manufacturers went through the same challenges as GM, failing tests and making flat-fold N95s that experts worry do not offer a tight enough seal. “If there was some kind of intellectual sharing, they wouldn't be doing that,”.9 Rapidly increasing mask production will not be easy, as Taiwan's local mask industry is not very profitable and most manufacturers relocated elsewhere in the world more than 20 years ago. (…) Taiwan's remaining mask production equipment manufacturers are very small in size with limited staff and it will take four to six months to build 60 lines. Overall, it is extremely a challenge for mask production equipment manufacturers to fulfill the government goal. The machine tool manufacturers input its 30–40 years of production experience to help the mask production equipment manufacturers to shorten their production time. First, they dismantle the machine, classify the machine parts, and set the standard SOP of the mask equipment production line assemble work flow. They then developed modular production lines and assigned different machine tool companies to assist in a different operating cell. Everyone can therefore focus on their part and optimize their assemble efficiency.6 [machine manufacturers] understood that reverse engineering may not be able to achieve that critical 5% of secret [as] the specialty of machine tools is metal cutting, but the masks are different - the cloth is soft. The adjustment of the two when feeding is completely different expertise. Their machine can only achieve 95%, without the key 5% technology of the mask machine factory. The production efficiency of the machine may be the difference between 30,000 tablets per day and 100,000 tablets per day.7⁎ 1 https://www.thomasnet.com/articles/other/how-surgical-masks-are-made/#_Types_of_Masks;https://www.fda.gov/medical-devices/personal-protective-equipment-infection-control/n95-respirators-and-surgical-masks-face-masks#s2;https://www.youtube.com/watch?v=PG5bI8Z7ifc&feature=youtu.be;https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ 2 https://www.youtube.com/watch?v=ZvS3S8058Hk;https://www.youtube.com/watch?v=PG5bI8Z7ifc;https://www.gm.com/our-stories/commitment/face-masks-covid-production.html 3 https://www.dw.com/en/coronavirus-netherlands-recalls-defective-masks-bought-from-china/a-52949216 4 https://www.gm.com/our-stories/commitment/face-masks-covid-production.html 5 www.wsj.com/articles/new-york-manufacturers-mobilize-to-make-face-masks-medical-gowns-11585224003 6 https://www.taiwantrade.com/news/a-bravery-story-a-taiwan-national-machine-tool-team-for-surgical-mask-production-born-to-fight-against-covid-19-outbreak-1979557.html# 7 https://www.youtube.com/watch?v=PG5bI8Z7ifc⁎NOTE: translation from Youtube interview. Not a direct quote. 8 https://www.twreporter.org/a/covid-19-mask-national-team-taiwan-can-help 9 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ The Taiwanese government recognised that there was a need to (re)build lost capability at the national level by increasing the number of production lines. As the remaining few mask machine manufacturers initially had no capacity or willingness16 to address the need, the government called for industry to help. Taiwan Machine Tool & Accessory Builders Association (TMBA) mobilised around 30 machine and tool makers from across the supply chain, which were joined by three main government industrial research institutes.17 Nonetheless, the companies realised that none of them had prior experience in developing a mask making machine and that there are significant differences between the capabilities required to handle the (soft) material and their existing competencies. At first, this consortium considered reverse engineering existing machines, but it concluded that achieving the required efficiency and precision without access to tacit knowledge that the machine manufacturer possessed would be impossible. Additionally, the government introduced a range of levers (e.g., a dynamic payment bonus system) to motivate local manufacturers.18 It also specified that all future public purchases of masks would give preference to locally-made masks. The example illustrates high levels of reflexivity to recognise and address expertise gaps between base and target domains, rather than focusing on the similarities between them. As we illustrate in the next section, the lack of such reflexivity can be expensive. 4.4 Ventilators Ventilators are highly specialised, complex equipment that need to adhere to stringent manufacturing, testing and regulatory standards. They include highly specialised parts that might be difficult to replace, advanced sensors, and algorithms. Misadjusted flow, pressure, or pace can lead to irreversible lung damage. Consequently, they require well-trained specialists and must be able to operate in a busy hospital environment.19 Because of the close coupling between a patients' condition and the equipment's complexity, ventilators rely on integral architecture. Design fuzziness is limited. Simpler designs, such as AmbuBag, are often limited to emergency use or as a temporary solution until a fully functional ventilator is available. Despite the complex nature of ventilators, a vast mobilisation of enthusiasts, university teams, and a range of organisations joined the efforts to produce them. These organisations included FitBit, GM, Ford, F1 teams, Airbus, and NASA. Some tried to “do it alone,” and others partnered up or formed consortia. Some decided to work with existing, and often approved, designs and producers, while others attempted to design a ventilator from scratch. In the United States, some companies were compelled to speed up their efforts by President Trump, who invoked the Defense Production Act.20 We focus our analysis on four distinct approaches, as illustrated in Table 6 .Table 6 Resourcing for Ventilators. Table 6Product architecture Ventilators: (1) High product complexity1 (2) Integral architecture (high number of specialised components, software, testing, …) (3) Overall demanding regulatory specifications for the product (some variation based on geography or type of ventilator) and the production process, with licence attached to a specific manufacturer who bears responsibility for safety of the equipment2 (4) Very limited fuzziness in terms of architecture, some limited fuzziness in terms of components.3 Example player(s) Dyson, Tesla:Eager helpers / it's in our brand NASA:we are already making it! Kind of… / it's in our brand VentilatorChallengeUK; Vermontilator; GM*&GE Healthcare; Ford:we are in it TOGETHER / Not so eager helpers Taiwan Ventilator Team: we are in it TOGETHER Was it successful? Safe:no, failed to get an approval Effectiveness:no, most could be used in very limited capacity Volume:not clear as most did not enter production Safe:yes, approved Effectiveness:limited due to limited lifespan and only emergency use Volume:not clear, probably reasonable volume that could match regular production, but not as high volume as others could achieve Safe:yes, most got approved Effectiveness: mixed formlimited to relatively high, most could be used in limited capacity Volume:yes, most could achieve reasonably high volume, often higher than regular production Safe:yes, approved Effectiveness:yes, improving proven design Volume:not clear as most did not enter production. Based on the assessment of existing manufacturing capabilities and supply chain in place, can likely yes Objects Design and production capabilities in the mechanical engineering domains (broadly) Access to production lines and skilled staff Supply chain management capabilities Financial resources Design and production capabilities for highly complex equipment Established relationship with medical equipment community and with local regulatory agency (access to expert knowledge on medical devices) Prior experience in medical device development (mix of tacit knowledge and collaborative capability) Manufacturing and industrial engineering capabilities Access to and power to orchestrate specialised supply chain Medical device manufacturing and market expertise Specific component capabilities Existing ventilator design Approved design or experience in obtaining approval Integrated circuit design and manufacturing capabilities These capabilities were distributed among members Knowledge transfer and collaborative product development a Rapid prototyping a Medical research and testing a Medical equipment components manufacture b Manufacturing capabilities b Software development capabilities b Integrated circuit design and manufacturing capabilities b a, bindicates how these capabilities were distributed among:aITRI;bindustry members Interpretative frames (1) (Potential) familiarity with the component but lacking understanding of the architecture (2) Base domain significantly different from target domain, except for engineering principles (3) Analogical reasoning was clouded by focus on the mechanical design and the airflow aspect of a ventilator, at the expense of regulatory, safety, and continuous monitoring issues (1) Some familiarity with the component and architecture of the product and the process (2) Home and target base relatively distant, although overlaps exist. The gap is bridged by existing network relationships (3) Analogical reasoning demanded reflexivity to focus on the lack of expertise and engaging with experts outside of the company (1) Medical device partner possessing both the component and architectural knowledge, while partners often limited to some component knowledge only (2) The large gap between the domains is bridged through the network and knowledge sharing practices (e.g., augmented reality) (3) Analogical reasoning demanded high levels of reflexivity to focus on mapping of the complementarities and reaching to partners with needed expertise (1) Knowledge of the component but no knowledge of the architecture (2) Base domains distant from the target domain (e.g., no expertise in ventilator or similar medical device manufacturing), some overlap in medical components and R&D capabilities (e.g., prototyping, testing) (3) Analogical reasoning to bridge the missing expertise in ventilator development and certification from prior R&D experience of ITRI Deployment strategy Challenges: lack of medical devices manufacturing expertise; obtaining relevant certifications; understanding of the medical environment. Risks / inefficiencies: misinterpreting of the product as a “simple air moving machine” and disregarding the (stringent) regulations. Machines could often be used for only a short time as a bridge / emergency solution; no market after the pandemic34 Approach: Lacked realistic assessment of technology. Often opting for complete re-design of existing products. Most work was undertaken in-house and relied on existing capabilities, forcing to focus on the less complex types of ventilators (AmbuBag) which could gain necessary approvals for emergency use. Challenges: leveraging the existing network to meet the demands of the unknown market; engaging partners to build a potentially competitive product to their market. Risks / inefficiencies: the machine produced has limited lifespan. Approach: Leveraging the existing network. Reflexive assessment. Engaging and relying on medical device community and relationship with FDA. Design required relaxing of some of the rules. Potential risk of saturating the market mitigated by limited lifespan. Challenges: coordinating the network to achieve required quality and safety; sharing of knowledge, potential IP issues; access to necessary parts that are unavailable. Risks / inefficiencies: in some cases, only lesser functionality machines could be delivered; obtaining necessary approvals; safety issues; saturating the future market or creating own competitors; sharing of obsolete blueprints. Approach: Reflexive assessment and reaching out for missing expertise. In most cases the teams started with focusing on the task complexity (e.g. understanding of how lungs and ventilator work together) and needed expertise. Resourcing strategy based on complementarity capabilities. Partnerships spanned from cooperative to collaborative approaches. Challenges: lack of medical devices manufacture expertise; access to IP / device designs. Risks / inefficiencies: settling on subpar/obsolete design; obtaining international certifications; no local established producers to ‘take over’. Approach: R&D cost is covered by the government, with technology transferred to industry once mature; obtaining an IP with a plan to upgrade it in-house.11 Leveraging lack of local competition to mobilise the industry and seed a new industry (technological upgrading). Long-term strategy to build national capabilities beyond the current crisis. Illustrative quotes Firms with no prior experience were increasingly bullish that they could design and build a prototype within weeks. Without the independent regulatory teams, most of these projects would have gone nowhere. (…). It's easy to say you can just design a ventilator but the safety isn't just in the design, it's about how you make them, the quality management, servicing them. It's not an innovation programme, it was there to meet a clinical need.5 Musk responded saying the tech components produced at his Tesla and SpaceX factories were “sophisticated” and ventilators were “not difficult” in comparison. Tesla makes cars with sophisticated HVAC systems (…) Ventilators are not difficult, but cannot be produced instantly.6 I think the idea of automotive manufacturers or indeed any manufacturer that is not well-versed in the production of medical devices somehow quickly retooling and making an alternative product is very naïve.7 We specialize in spacecraft, not medical device manufacturing. But excellent engineering, rigorous testing and rapid prototyping are some of our specialties. Building a medical device is new. It goes against our culture to do something quickly in a domain where we're not experts.8 At first, the engineers began in the spirit of Apollo 13. (…) can we at J.P.L. design a ventilator that uses parts scrounged from a garage, or from a vacuum cleaner, or a Home Depot? That idea lasted about six hours. They next considered developing a reference design and open-sourcing it for do-it-yourselfers. A doctor who had come in to consult waved them off, explaining that his hospital would only use a device that had been F.D.A.-approved. “He dropped a lot of reality on everybody about the level of engineering we'd have to do”.9 The only group to have secured regulatory approval and supplied ventilators to the NHS in significant numbers is VentilatorChallengeUK, a consortium of manufacturers that focused on scaling up production of proven devices, rather than building new ones.10 A lung analogue was brought in from the hospital for testing; a regulatory expert began preparing an emergency report for the Food and Drug Administration, which had created a special approval process for stopgap ventilators; and several local contract manufacturers were lined up so that the device could be mass-produced. For G.M., Ventec has created a simplified version known as the V + Pro. G.M. flew six engineers to study the production process. “We took a lot of pictures and a lot of video,”. The VOCSN has around seven hundred parts; the V + Pro, around four hundred. By e-mailing lists of parts to around seventy of its “Tier 1” suppliers, G.M. was able to secure all of them by the following weekend. Suppliers had to adapt production lines to new specifications; they had to ask their own suppliers to do the same.9 It was tough for Ford and other big industrial companies to pivot into making medical equipment. Quite apart from the (obvious) challenge of sterilising previously filthy assembly lines, it was almost impossible to find basic manufacturing materials when cross-border supply chains collapsed during lockdown13 U.S. ventilator giant Medtronic shared the basic design specifications of its PB 560 portable ventilator, following which ITRI coordinated resources needed to produce a ventilator, including mechanisms, electronic controls, firmware, software, and data system integration, and it successfully sourced more than 500 key components, which demonstrates the outstanding flexibility and strengths of Taiwan's supply chain. ITRI has seized upon three factors to this end. The first key is software: The team successfully interpreted the software program and functions of the Medtronic prototype. The second key is system components: The team actively sought out components from the up-, mid-, and downstream industrial chain, including microprocessors, sensors, fan motors, blowers, and masks, and even is producing some items on its own via 3D printing. The third key is system validation: To domestically produce the key components of the ventilator is the first step. Then the prototype will need to pass software and hardware testing, calibration and validation.12 1 www.fortune.com/2020/03/25/coronavirus-ventilator-production-problems-shortage-national-strategic-stockpile/;www.wired.com/story/race-build-more-ventilators-coronavirus/;www.newyorker.com/magazine/2020/05/18/the-engineers-taking-on-the-ventilator-shortage 2 www.wired.com/story/race-build-more-ventilators-coronavirus/;www.theguardian.com/business/2020/may/04/the-inside-story-of-the-uks-nhs-coronavirus-ventilator-challenge 3 www.economist.com/international/2020/03/26/scientists-and-industry-are-dashing-to-make-more-ventilators; 4 www.theguardian.com/business/2020/apr/27/uk-to-halt-several-ventilator-projects-after-fall-in-demand 5 www.theguardian.com/business/2020/may/04/the-inside-story-of-the-uks-nhs-coronavirus-ventilator-challenge 6 www.businessinsider.com.au/elon-musk-says-tesla-factories-will-make-ventilators-coronavirus-shortage-2020-3?op=1&r=US&IR=T 7 www.wired.com/story/race-build-more-ventilators-coronavirus/ 8 https://edition.cnn.com/2020/05/01/health/nasa-ventilator-fda-approval-wellness-scn/index.html 9 https://www.newyorker.com/magazine/2020/05/18/the-engineers-taking-on-the-ventilator-shortage 10 www.theguardian.com/world/2020/apr/24/dyson-will-not-supply-ventilators-to-nhs-to-treat-covid-19 11 this strategy has been used in other industries and technologies by ITRI in the past. See, for example, Hung (2002) 12 markets.businessinsider.com/news/stocks/itri-unveils-taiwan-s-first-medical-grade-ventilator-prototype-1029181138?op=1 13 www.ft.com/content/049a36b7-a9f9-41b6-8571-134a6c2563d4 From Table 6, several observations can be made. First, the “Eager Helpers” approached the task from their interpretative frame; they saw a ventilator as a simple air-moving machine or a simple mechanical device. Companies in this group focused on their existing frames and the similarities to the product they were trying to resource. This led them to underestimate the complexity and the highly integral architecture and led them to attempt to develop a simpler ventilator. This approach led to designs that often could be deployed only for emergency use. It was also difficult to obtain approvals for new, unproven designs. Medical equipment manufacturers argued that “it's easy to say you can just design a ventilator but the safety isn't just in the design”.21 NASA, which also opted to develop a new design, approached the task differently. First, it relied on its prior experience of developing some medical devices and collaboration with the medical community. Second, it used reflexive analogical reasoning to focus on the expertise it lacked before it started on the project. It thus relied on established relationships with the medical engineering community and the FDA. Third, it had developed portable devices for medical use.22 Its VITAL design is tailored specifically for emergency use and has a limited life span, thus addressing the need only partially. The third distinct approach relied on a consortium of companies, often including a ventilator manufacturer or access to an approved design and licence. These consortia focused on leveraging the range of capabilities across the partnership. For example, Siare's partnership with Ferrari and Fiat Chrysler had the latter two focus on supplying one part. In other cases, established manufacturers such as GM were tasked with developing large-scale production systems, but relied heavily on the know-how of the experienced ventilator manufacturers. Some of these companies acknowledged the importance of tacit production knowledge and relied on augmented reality or sent their engineering teams to observe, film, and photograph the production processes.23 As one manufacturer pointed out, it is far more efficient to expand production when the know-how and approval are already available.24 Those partnerships relied on complementary expertise and deployed their objects where they could add the most value. In those partnerships, automakers played a contractor role to medical device manufacturers, which held the required licence (certificate) and were thus responsible for safety. Many consortia focused on understanding their complementary capabilities and on mapping and accessing missing competencies. As explained by a Ford executive, “our value-add was to apply high-volume manufacturing expertise that you see uniquely in the auto industry. We found quite a few places where you could change a process to improve cycle time, and move it around, so that the throughput of the whole assembly process got more things out of the back end”.25 The fourth approach also relied on a consortium of companies, but with governments coordinating effort and the lack of an established local medical equipment manufacturer. Based on the successful mobilisation of face mask manufacturing and prior experience of technology acquisition and dissemination (Hung, 2002), the Industrial Technology Research Institute (ITRI) in Taiwan coordinated efforts with the industrial community to redevelop and build ventilators based on an approved prototype that was released earlier under a special “permissive licence” by a prominent ventilator manufacturer (Medtronic). This approach relied on the realisation that the community possessed strong component-level expertise, but lacked architectural understanding of the product. The partnership relied on ITRI's R&D capabilities and its medical field research (e.g., biotechnology) to provide testing capabilities. It can be assumed that ITRI will pass the licence and know-how to a private company (c.f. Hung, 2002), with the goal of seeding a new industry.26 5 Discussion and implications When the pandemic began, many companies attempted to repurpose their resources and capabilities to provide needed products. These efforts suggest that resources can be used in various ways, reflecting how individuals enact them through interpretative frames (Feldman & Worline, 2011; Penrose, 1959; Sonenshein, 2014). The resourcefulness literature has proposed multiple strategies for using resources. These include bricolage as a way to make do with what you got (Baker & Nelson, 2005), creative use of resourcing through sensemaking (Ganz, 2000; Sarasvathy, 2001), envisioning different applications of objects (Sonenshein, 2014), and deploying and recombining resources through collaboration and open innovation (Deken et al., 2018). Furthermore, experimentation with different frames and practices can lead to new ways of conceptualising how objects can be used (Feldman & Worline, 2011; Kannan-Narasimhan & Lawrence, 2018). Yet we have little understanding of the boundary conditions for resourcefulness and the conditions under which different resourcing strategies can be effective. We contribute to this discussion by proposing that the success of different resourcing initiatives reflected the interrelations among the objects owned or accessed by firms, the interpretative frames used to deploy those objects in new contexts, and the architecture and institutional rules of the product resourced. As our findings indicate, the different resourcing approaches summarized in Table 2 can lead to different outcomes depending on the interrelations among the dimensions in our framework. Appreciating these distinctions can help managers understand the usefulness of each approach for different product categories. Indeed, failure often occurs when managers underestimate the complexities of product architecture and the differences between the base and target knowledge domains. When the target product is less complex and/or has a modular architecture, as face shields and hand sanitiser do, it is easier for firms that operate in relatively similar source domains or that possess relevant capabilities to redeploy their capabilities in the new context (Mastrogiorgio & Gilsing, 2016). Low product complexity means design specifications are fuzzy and thus allows a firm to experiment more and extend analogical interpretative frames across contexts (Sonenshein, 2014). As product complexity increases and the relations between subcomponents become more integral, however, product specifications become more defined and there is less room for experimentation (Sanchez, 2008; Ulrich, 1995). In such cases, the need for coordination increases because the firm attempting to enter the new domain may lack knowledge about certain components. These firms can contribute their manufacturing capacity or supply chain capabilities to help specialist firms ramp up their production. Our examples also suggest that production can be more challenging than the product design itself. Even when the product complexity is medium, as it is for medical face masks, success in achieving scale is affected by access to both the tacit dimension of assembly of production machinery and to industrial engineering capabilities. In our study, the most challenging context for resource redeployment involves highly complex products with integral architecture such as ventilators. The design and manufacturing of these products require tacit knowledge of the integral relations among components (Ulrich, 1995) and of strict regulatory specifications. The scope for resource transfer across contexts is thus limited. In these environments, non-specialist firms might fail to recognise the deep structural differences between their home and target domain while overemphasizing surface similarities in some components (Gary et al., 2012). We saw several examples of firms that analogically extended their frames without considering the differences between contexts. Their efforts resulted in ventilators unsuitable for ICU use or designs that failed to gain regulatory approval.27 Thus, successful resourcing in this context entails a coordinated approach through a consortium where specialist firms take the lead and non-specialist firms focus on helping to scale up production. Firms using this approach seemed to realise the applicability of their knowledge and frames in the new context was limited and adapted their interpretative frames by being reflexive (Hibbert et al., 2016). They may have focused less on potential similarities between the home and target knowledge domains than they did on the differences. In the case of the medical masks, cooperation with specialised producers of machinery and masks, and a consequent access to tacit knowledge, determined the success of scaling up the production of medical face masks: while coordination efforts helped TFMT, lack of such coordinated approach is being attributed to the chronic shortages of N95 in the US context.28 Noteworthy, the difficulties are related to the architecture of the production process rather than to the the specialised fabric (a key component) as this could be relatively easily resourced for27. Finally, our analysis highlights the role policy makers play in supporting resourcing strategies: our data suggest that such interventions can coordinate knowledge and interests across actors (Hung, 2002), thus mitigating market and IP risk and “stretching” design fuzziness by amending regulations. Such coordination can enhance the capabilities of existing firms through coopetition, which is cooperation between competing organisations in which resources and capabilities are shared with competitors to achieve shared goals (Crick & Crick, 2020) and potentially seed new industries. As explained by Taiwan's Deputy Minister of Economic Affairs, it was better to “use communication instead of prohibition, and “negotiate“ with manufacturers to get good results”. More recently, Taiwan's government allowed firms to export production machinery, opening new growth opportunities.29 Surprisingly, most governments did little to coordinate the efforts among organisations and sometimes seemed to lead companies into dead ends. For example, after providing misleading specifications, the UK government cancelled numerous orders.30 Similarly, an overestimate of demand in the United States led to contract terminations and uncertainty.31 There is also evidence that uncertainty about future demand will discourage companies from investing in more costly resourcing strategies.32 Using our Fig. 2, in which we plotted the different product categories on two dimensions, we can superimpose these dynamics, as illustrated in Fig. 4 .Fig. 4 Resourcing and product architecture: need for reflexivity and coordination. Fig. 4 Successful resourcing efforts thus depend on understanding product architecture and complexity. As this understanding increases, firms must become more reflexive to enable analogical thinking (Hibbert et al., 2016). At the same time, the need for coordination and support increases, particularly from government institutions. Such support can include amending the legal framing and the institutional context, aiding collaborative knowledge sharing and development, which might require provisions for protecting IP, and addressing the potential impact on existing markets that can affect current producers. 5.1 Theoretical and practical implications Our study contributes to a better understanding of which resourcing strategies are effective under different conditions. We begin to fill this gap by integrating insights from the resourcefulness literature with those from the product architecture literature. Our study indicates that when firms consider whether to redeploy their resources in new contexts, their managers need to be both reflexive and strategic. Accurate assessment of a product's architecture and firm's capabilities can save significant amount of money. Indeed, the case of ventilators illustrate how easy it is to focus on the component level and ignore a product's architectural complexity. As this complexity increases, a firm needs to consider working with partners that possess complementary skills. Managers' ability to reason analogically can be improved through tools and questions that induce reflection on the structural relations between different domains (Gary et al., 2012; Gentner, Loewenstein, & Thompson, 2003). As such, our framework and proposed typology provide a simple reflective tool that practitioners can use to decide the appropriate resourcing strategy. Our study also highlights the benefits that organisations can accrue by undertaking these resourcing initiatives. Although the efforts are ongoing and not all the initiatives or their impact can be ascertained, existing reports and our analysis allow us to outline the short and (potential) long-term effects of resourcing initiatives on relevant organisations, sectors, and economies. In the immediate and short term, in addition to helping in resourcing the critically needed medical products, the main benefits include remaining operationally active, thus avoiding job loses or (costly) production line closures. Furthermore, press coverage of the initiatives provided brand exposure, which can improve the firm's image, reinforce its brand strategies, or signal particular industrial capabilities to potential partners.33 Finally, organisations that undertook a collaborative approach managed to expand their networks, which can lead to enhancing their collaborative capability (Crick & Crick, 2020; Hibbert & Huxham, 2005). In the long term, resourcing initiatives can enhance organisations' innovation capability when firms work with new partners from different fields, thus broaden their interpretative frames34 (Chesbrough, 2020). Second, as a result of learning from these initiatives, firms may consider diversifying into new markets or product categories.35 While not all companies may be interested in entering those new markets,36 some already have begun to develop related products as part of their regular offering (e.g., face shields as fashion items37 ). Others have started to develop new segments of products (e.g. commemorative face masks for special occasions), entered into new distribution relationships or collaborative arrangements (e.g. a leading Taiwanese airline in collaboration with one of the members of the TFMT developed a new “Passenger Personal Protection Kit” containing specially designed mask and disinfectant for long haul flights).38 As the developments we report are ongoing, we can only assume other implications will become visible and offer interesting areas for future research. Finally, we found that governments facilitated effective resourcing. First, policy makers can adjust regulatory frameworks to increase fuzziness in product design. This effort can allow a broader range of actors to redeploy their resources. Second, policy makers can amend public procurement policies to ensure future demand as a lever to align the interests of the industry / key stakeholders, and thus be more willing to invest.39 Third, policy makers can assure firms that are wary of sharing their know-how by providing reassurances and addressing potential IP issues. They can also provide access to needed technology through enticing ground-breaking corporate partnerships and knowledge sharing.40 Moreover, policy makers can coordinate complex networks of partners to enable complementary resourcing. The examples of the ventilator challenge and the scaling up of mask production illustrate how policy makers can influence these outcomes. Finally, our analysis demonstrates the nested and multi-layered nature of business environments and the intrinsic interrelatedness that drive development of markets and business ecosystems (Möller et al., 2020). 6 Limitations As with any research, this study has limitations. First, we rely solely on secondary data, which can be incomplete and subject to our (mis)interpretation. However, considering the context, this reliance can be a strength. Second, the story of the pandemic is still unfolding. For example, we defined success as an initiative that delivers safe and effective products at volume. Yet an initiative that failed by this criterion might eventually help an organisation succeed. Learning from failure can lead to organisational transformation and changes in managerial interpretative frames (Madsen & Desai, 2010). Finally, our own frames of reference affect our interpretations of the cases. Again, we see pluralism and alternative explanations as a strength rather than a limitation (Elsahn et al., 2020). Going forward, our results open paths for further research focused on the different mechanisms to coordinate resourcing activities, the role of the government institutions and regulations, issues related to IP or, indeed, market dynamics. Conflict of interest None. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' biography: Ziad Elsahn is a Senior Lecturer in International Business at the Entrepreneurship, innovation, and Strategy department at Northumbria University, Newcastle Business School. His research interests lie at the intersection of International Business, Organisation theory, and Strategy. Specifically, his research focuses on internationalisation, knowledge coordination and integration within and between firms, and strategy process in multinational enterprises. Frank Siedlok is a Senior Lecturer in the Department of Management and International Business at the University of Auckland Business School. Frank's research is focused on collaborative practice development, the dynamics of interdisciplinary research collaborations, reflexivity and strategic management and decision-making. 1 In some cases, such as the United States or Taiwan, national governments also invoked various requisitioning acts, thus obliging some organisations to take part in the efforts. 2 We use the term medical face masks referring to masks that are approved for use in hospital setting and are made with specialist non-woven material and electrostatically charged. These include both the loose-fitting face masks (sometimes referred to as surgical masks) and close-fitting N95 respirators. 3 We define successful initiatives as those that led to products that were safe, effective for the intended use, and could be produced at scale. 4 We use the term ‘interpretative frames’ across the paper as a mean to simplify the argument and avoid lengthy deliberation on the related concepts of mental models, cognitive representations, mental schemata etc. (Eggers & Kaplan, 2013) 5 For simplicity, we refer to this set of rules as institutional fuzziness. 6 The definition based on these three characteristics is adopted from one such initiative - CoVent – and is based on how one of the managers on the project defined a successful development of a ventilator. See https://www.med-technews.com/features/working-round-the-clock-developing-a-ventilator-to-fight-cov/. 7 This is the part of analysis where we develop the five resourcing strategies proposed in Table 2. 8 www.economist.com/united-states/2020/04/30/americas-makers-and-tinkerers-turn-their-hands-to-ppe 9 www.fda.gov/medical-devices/personal-protective-equipment-infection-control/n95-respirators-and-surgical-masks-face-masks#s2;https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/883334/Essential_Technical_Specifications__5_.pdf 10 https://www.washingtonpost.com/lifestyle/2020/07/07/peter-tsai-n95-mask-covid/;https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/;https://www.economist.com/united-states/2020/04/30/americas-makers-and-tinkerers-turn-their-hands-to-ppe;https://smartairfilters.com/en/blog/comparison-mask-standards-rating-effectiveness/ 11 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ 12 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/;www.taiwantrade.com/news/a-bravery-story-a-taiwan-national-machine-tool-team-for-surgical-mask-production-born-to-fight-against-covid-19-outbreak-1979557.html# 13 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ 14 www.gm.com/our-stories/commitment/face-masks-covid-production.html 15 https://focustaiwan.tw/society/202005250014;https://focustaiwan.tw/society/202009030016 16 The chief reason is related to their prior experience of bearing the cost of scaling up production during SARS outbreak, only to be left with overcapacity and idle machines. Once the epidemic was over, due to the Government's ‘lowest price purchase guideline’ policy most mask purchased by hospital would be from China. Put differently, for firms investing in short-term capacity building seemed like a costly mistake to be repeated. This challenge was addressed by government reassurance to commit to ‘Made in Taiwan’ masks for government-run laboratories and hospitals after the pandemic. 17 Industrial Technology Research Institute (ITRI), Metal Industries Research Development Centre (MIRDC), Precision Machinery Research & Development Centre (PMC). 18 www.twreporter.org/a/covid-19-mask-national-team-taiwan-can-help 19 www.newyorker.com/magazine/2020/05/18/the-engineers-taking-on-the-ventilator-shortage;www.wired.com/story/race-build-more-ventilators-coronavirus/;www.economist.com/international/2020/03/26/scientists-and-industry-are-dashing-to-make-more-ventilators 20 https://www.theguardian.com/us-news/2020/mar/27/trump-defense-production-act-coronavirus-gm 21 www.theguardian.com/business/2020/may/04/the-inside-story-of-the-uks-nhs-coronavirus-ventilator-challenge; see also www.economist.com/international/2020/03/26/scientists-and-industry-are-dashing-to-make-more-ventilators;https://fortune.com/2020/03/25/coronavirus-ventilator-production-problems-shortage-national-strategic-stockpile/ 22 https://edition.cnn.com/2020/05/01/health/nasa-ventilator-fda-approval-wellness-scn/index.html 23 www.themanufacturer.com/articles/augmented-reality-is-playing-a-vital-role-in-supporting-ventilatorchallengeuk;https://www.wsj.com/articles/gm-hustles-to-pump-out-ventilators-to-fight-coronavirus-amid-trump-barbs-11585586925 24 https://fortune.com/2020/03/25/coronavirus-ventilator-production-problems-shortage-national-strategic-stockpile/ 25 https://chiefexecutive.net/better-ideas-fords-approach-got-pandemic-response-flowing/ 26 https://technews.tw/2020/05/26/tw-masks-8-million-0601/ 27 https://www.independent.co.uk/sport/motor-racing/formula1/coronavirus-f1-teams-ventilator-uk-order-cancelled-red-bull-renault-a9463711.html;https://uk.reuters.com/article/us-health-coronavirus-britain-ventilator-idUKKCN21U0UI 28 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/;www.twreporter.org/a/covid-19-mask-national-team-taiwan-can-help 29 https://www.ettoday.net/news/20200817/1786856.htm;https://www.knh.com.tw/homepage.html 30 https://www.independent.co.uk/sport/motor-racing/formula1/coronavirus-f1-teams-ventilator-uk-order-cancelled-red-bull-renault-a9463711.html;https://www.theguardian.com/business/2020/may/04/the-inside-story-of-the-uks-nhs-coronavirus-ventilator-challenge 31 https://www.nytimes.com/2020/03/26/us/politics/coronavirus-ventilators-trump.html;https://www.ksn.com/news/health/coronavirus/coronavirus-in-kansas/future-uncertain-for-spirit-aerosystems-employees-after-ventilator-orders-canceled/ 32 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ 33 https://www.voanews.com/covid-19-pandemic/unlikely-story-first-made-vietnam-ventilators-fight-covid-19;https://www.ettoday.net/news/20200817/1786856.htm 34 https://www.huffpost.com/entry/medtronics-leadership-amidst-pandemic-proves-the-power-of-innovation-and-inclusion_n_5e99d19dc5b6ea335d5ac367;https://chiefexecutive.net/better-ideas-fords-approach-got-pandemic-response-flowing/ 35 https://www.ettoday.net/news/20200817/1786856.htm 36 https://medicalxpress.com/news/2020-09-gm-ford-finish-ventilators.html 37 https://www.fastcompany.com/90550249/louis-vuittons-new-face-shield-doesnt-just-protect-you-from-covid-19 38 https://www.taiwannews.com.tw/ch/news/4001966;https://www.csd.com.tw/news 39 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/;www.twreporter.org/a/covid-19-mask-national-team-taiwan-can-help;https://www.youtube.com/watch?v=ZvS3S8058Hk&feature=youtu.be 40 https://www.washingtonpost.com/graphics/2020/local/news/n-95-shortage-covid/ ==== Refs References Ambrosini V. 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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Elsevier Inc. S0022-3999(21)00159-8 10.1016/j.jpsychores.2021.110514 110514 Article Effects of the COVID-19 pandemic on characteristics of functional (psychogenic) seizures Asadi-Pooya Ali A. ab⁎ Farazdaghi Mohsen a a Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran b Jefferson Comprehensive Epilepsy Center, Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA ⁎ Corresponding author at: Epilepsy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. 15 5 2021 8 2021 15 5 2021 147 110514110514 15 3 2021 12 5 2021 13 5 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective We investigated whether the COVID-19 pandemic has affected the clinical characteristics of patients with functional seizure (FS) (at the time of diagnosis). Methods In a retrospective study of a prospectively developed and maintained database, all patients diagnosed with FS before and during the COVID-19 pandemic were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from December 2008 until February 2021. Results Three hundred and eighty-eight patients were studied. Three hundred and sixty-four patients (94%) were diagnosed before and 24 persons (6%) during the pandemic. Patients diagnosed during the COVID-19 pandemic less frequently had generalized motor seizures [odds ratio (OR): 0.30, 95% confidence interval (CI): 0.12–0.77; p = 0.012] and had higher seizure frequencies (OR: 1.00, 95% CI: 1.00–1.01; p = 0.044). Functional seizures were inversely associated with the education level as a trend during the COVID-19 pandemic (OR: 0.36, 95% CI: 0.13–1.01; p = 0.052). Conclusion The COVID-19 pandemic has affected the characteristics of patients with FS (at the time of diagnosis). Larger and multi-center studies are needed to investigate the links and associations between the COVID-19 pandemic and characteristics of FS. Keywords COVID Coronavirus Dissociative PNES Seizure ==== Body pmc1 Introduction Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES) or dissociative seizures, are characterized by paroxysmal and self-limited changes in behavior, feelings, movements, and responsiveness. These seizures share semiological similarities with epileptic seizures, but without ictal epileptiform discharges in electroencephalography (EEG); they are often associated with psychological problems [1]. While the etiological underpinnings of FS are not well-recognized yet, some precipitating factors (e.g., injury, death of or separation from family members or friends, job loss, natural disasters, relationship difficulties, etc.) may occur over days to months before the onset of seizures [2]. Furthermore, semiology of FS may be associated with co-existing neuropsychiatric problems in these patients (e.g., patients with akinetic functional seizures may have fewer co-existing neuropsychiatric problems compared with those who have motor seizures) [[3], [4], [5], [6]]. Since late 2019, the world has been experiencing a catastrophic phenomenon, a deadly pandemic of a coronavirus disease (COVID-19) [7]. Iran reported its first confirmed patients of COVID-19 on 19 February 2020. As of 19 April 2021, there has been 66,732 COVID-19 deaths with 2,237,089 confirmed cases in Iran [8]. This virus has a high potential for person to person transmission. Implementation of social distancing measures has been advocated to control the outbreak around the world; as a result, this deadly outbreak has disrupted businesses and routine social activities. It has also caused hundreds of thousands of deaths, massive job losses, and increasing numbers of relationship difficulties (all of which are considered as the potential precipitating factors for FS) [2,9]. Therefore, it is plausible to consider this pandemic as a potentially significant factor that may affect the characteristics of FS. In the current study, we investigated whether the COVID-19 pandemic has affected the clinical characteristics of patients with FS. We hypothesized that there exist significant differences in the characteristics of FS (diagnosed) during the COVID-19 pandemic compared to that in the pre-COVID-19 era. It was a hypothesis drawn from personal observations in everyday clinical practice (with the impression that patients were more distressed and had more severe manifestations). 2 Methods 2.1 Participants This was a retrospective study of a prospectively developed and maintained database. We investigated all patients with FS, who were admitted at the epilepsy monitoring unit at Shiraz Comprehensive Epilepsy Center from December 2008 through February 2021. Patients had a confirmed diagnosis of FS, determined by clinical assessment and video-EEG monitoring with ictal recording of their seizures. There were no exclusion criteria. Written informed consent was obtained at the time of admission at the epilepsy monitoring unit from all patients. 2.2 Data collection We dichotomized the patients into two groups: 1. Those who were diagnosed before the COVID-19 pandemic started (From 1 December 2008 until 19 February 2020); and 2. Those who were diagnosed during the pandemic (From 1 March 2020 until 28 February 2021). We extracted all the relevant clinical and demographic data from our database. We studied and compared the characteristics of the two groups at the time of diagnosis: (i) the demographic characteristics: sex, age, age at onset, duration of illness, and education level (low: below 5 grades vs. others: 5 grades or higher); (ii) the seizure characteristics: FS frequency, loss of responsiveness (LOR) with FS, urinary incontinence with FS, generalized motor seizures, and ictal injury; and (iii) the associated risk factors: a family history of seizures, a history of physical abuse (i.e., corporal punishment or any physical injury resulted from aggressive behavior towards the patient), a history of sexual abuse (i.e., rape), and family dysfunction (i.e., divorce, significant family disputes, etc.). 2.3 Statistical analyses Values were presented as mean ± standard deviation (SD) for continuous variables and as number (percent) of subjects for categorical variables. Fisher's exact test, Kolmogorov-Smirnov normality test, Mann Whitney-U test, and binary logistic regression analysis model were used for statistical analyses. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. A p value (2-sided) less than 0.05 was considered as significant. We investigated the factors in association with the COVID-19 pandemic era in univariate analyses. Variables that had significant/near significant associations (p < 0.1) were analyzed in a binary logistic regression analysis model. 2.4 Standard protocol approvals, registrations, and patient consents The Shiraz University of Medical Sciences Institutional Review Board approved this study. 2.5 Data availability statement The data are confidential and will not be shared as per the regulations of Shiraz University of Medical Sciences. 3 Results 3.1 General characteristics of the patients Three hundred and eighty-eight patients with FS were studied. Three hundred and sixty-four patients (94%) were diagnosed before and 24 persons (6%) during the COVID-19 pandemic. The mean age of the patients was 29 years (SD: 10 years, range: 10 to 71 years, median 28 years, and interquartile range 15 years). They included 258 women (66%) and 130 men (34%). 3.2 Clinical characteristics of the patients Table 1 shows the clinical characteristics of patients diagnosed with FS before and during the COVID-19 pandemic in univariate analyses. Frequency of functional seizure per months and generalized motor semiology were significantly different between the two groups. During the pandemic 14 out of 24 patients (58%) had daily FS, but before the pandemic 127 out of 364 patients (35%) reported experiencing daily FS. Receiving psychiatric drugs and the education level showed a trend to be different between the two groups (more of the participants diagnosed before the pandemic received psychiatric drugs than the participants diagnosed during the pandemic (p = 0.096), and more of that participants diagnosed during the pandemic had a lower education level (p = 0.051).Table 1 Variables in association with the COVID-19 pandemic era in univariate analyses. Table 1Variables COVID-era, N = 24 Before COVID, N = 364 P value, df Age at onset, years (mean ± SD) 24.5 ± 13.1 23.9 ± 10.5 0.429, 388 Age at diagnosis, years (mean ± SD) 29.6 ± 11.3 29.1 ± 10.5 0.545, 388 Duration of disease, years (mean ± SD) 5.1 ± 7.4 5.1 ± 7.2 0.883, 388 Sex (Female) 18 (75%) 240 (66%) 0.504, 1 Frequency of seizure per months (mean ± SD) 106.4 ± 300.7 34.4 ± 67.7 0.017, 388 Loss of responsiveness with seizures 20 (83%) 308 (85%) 0.775, 1 Incontinence with seizures 4 (17%) 44 (12%) 0.523, 1 Generalized motor seizures 16 (67%) 315 (87%) 0.015, 1 Ictal injury 6 (25%) 113 (31%) 0.651, 1 A family history of seizure 10 (42%) 109 (30%) 0.255, 1 A history of physical abuse 4 (17%) 44 (12%) 0.515, 1 A history of sexual abuse 4 (17%) 31 (9%) 0.254, 1 A history of family dysfunction 7 (29%) 124 (34%) 0.822, 1 Receiving antiseizure medications 15 (63%) 231 (64%) >0.99, 1 Receiving psychiatry drugs 1 (4%) 69 (19%) 0.096, 1 Low education (below 5 grades) 6 (25%) 39 (11%) 0.051, 1 df: degree of freedom. Data were not available in 5 patients for incontinence, in 10 for physical abuse, in 12 for sexual abuse, in 10 for family dysfunction, and in 8 people for education in pre-COVID-19 era. We then included the variables with a p ≤ 0.1 (i.e., functional seizure frequency, generalized motor seizure semiology, taking psychiatric medications, and the level of education, as covariates) in a regression analysis model to clarify the role of each variable in association with the time of FS diagnosis (i.e., before or during the COVID-19 pandemic; the dependent variable). The results of the binary logistic regression analysis showed a significant model (p = 0.001). Patients diagnosed during the COVID-19 pandemic less frequently had generalized motor seizures (OR: 0.30, 95% CI: 0.12–0.77; p = 0.012) and had higher seizure frequencies (OR: 1.00, 95% CI: 1.00–1.01; p = 0.044). Functional seizures were inversely associated with the education level as a trend during the COVID-19 pandemic (OR: 0.36, 95% CI: 0.13–1.01; p = 0.052). Taking psychiatric medications lost its significance within the model (p = 0.103). 4 Discussion In the current study, we observed that functional seizure characteristics (at the time of diagnosis) were significantly different during the COVID-19 pandemic compared with that before the pandemic started. Those who were diagnosed during the pandemic less frequently had generalized motor seizures, but had higher seizure frequencies. In addition, the ratio of patients with lower education levels of below 5 grades increased from one in ten (before the pandemic started) to one in four people (during the pandemic). There is no comparable study in the literature, but below we try to provide some speculative explanations for these intriguing observations. Frequent attacks are important factors in patients with FS; they are disabling and may disrupt the normal life activities. Seizure frequency was often the primary outcome measure in clinical trials involving patients with FS; psychosocial and functioning measures were often secondary outcomes [10]. Hypothetically, frequent FS may be a marker of higher stress levels or more psychiatric issues. Mental health issues such as stress, anxiety, depression, frustration, and uncertainty have emerged progressively during the COVID-19 outbreak [11,12]. These mental health problems may have caused more frequent attacks in patients with FS. Interestingly, in one recent study of 54 patients with FS, 28% of the participants reported increased frequency of FS during the pandemic [13]. Patients with FS showing symptoms of anxiety and depression were at higher risk of seizure worsening [13]. Similarly, the seizure semiology in FS may have associations with the co-existing neuropsychiatric problems [[3], [4], [5], [6]]. The emerged mental health problems during the COVID-19 pandemic may have affected the semiology of FS, causing more patients to manifest non-motor seizures. We have to emphasize that any reliable semiological classification of FS should be done after ictal recording during video-EEG monitoring of the patient; seizure witnesses often provide unreliable accounts of the seizure semiology [14]. If access to video-EEG monitoring is limited, acquisition of home-video recordings of seizures may be helpful [14,15]. Finally, we observed that more patients had lower education levels during the pandemic compared with that before the pandemic started. Previous studies suggest that learning disability is a risk factor for the development of FS in children and adults [16,17]. People with learning disabilities and those with low educational backgrounds may have limitations in problem-solving and communication skills, or in their ability to verbalizing emotional distress and therefore, have increased risk of developing FS [16]. Speculatively, our observations could also be discussed in the light of high levels of alexithymia among patients with FS [18]. Functional seizures are part of a larger pattern of somatic symptoms responses to a wide range of negative events and emotions, including stress in adulthood (e.g., a pandemic) [[19], [20], [21]]. A study of 156 adult patients with FS showed elevated alexithymia and use of potentially more maladaptive emotion-focused coping strategies among patients with FS and comorbid post-traumatic stress disorder (PTSD) [22]. This line of discussion is worthy of further studies. 5 Limitations This was a clinic-based series and may not represent the full spectrum of patients with FS; the. mildest disease forms may not be referred to a busy university clinic and therefore, the possibility of selection bias exists. Furthermore, in functional neurological disorders, including FS, often some time passes between the onset and the diagnosis; in this study, we observed that for both groups averagely about 5 years had passed between these two time points. While we can talk about the differences of the characteristic of FS at the time of the diagnosis / at the time that patients sought medical attention for their condition, we cannot ascertain any differences at the onset of the disease and, most importantly, in general. In fact, we still do not know whether these characteristics will persist after the pandemic is over. 6 Conclusion The COVID-19 pandemic has affected the characteristics of patients with FS (at the time of diagnosis). Larger and multi-center studies are needed to investigate the nature and the causes of the links and associations between the COVID-19 pandemic and characteristics of FS (considering the above observations and speculations). Authors' contributions Ali A. Asadi-Pooya, M.D.: study design, data collection, statistical analyses, and manuscript preparation. Mohsen Farazdaghi: data collection and manuscript preparation. None of the authors listed on the manuscript are employed by a government agency. All are academicians. None of the authors are submitting this manuscript as an official representative or on behalf of the government. Funding This work was supported by 10.13039/501100004320 Shiraz University of Medical Sciences . The funding source had no involvement in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. Disclosure statement Ali A. Asadi-Pooya: Honoraria from Cobel Daruo, Tekaje, Sanofi, and RaymandRad; Royalty: Oxford University Press (Book publication); Grant from the National Institute for Medical Research Development. Mohsen Farazdaghi: no conflict of interest. Acknowledgment We thank 10.13039/501100004320 Shiraz University of Medical Sciences . ==== Refs References 1 Asadi-Pooya A.A. Brigo F. Mildon B. Nicholson T.R. Terminology for psychogenic nonepileptic seizures: Making the case for “functional seizures” Epilepsy Behav 104 Pt A 2020 106895 31986440 2 Reuber M. The etiology of psychogenic non-epileptic seizures: toward a biopsychosocial model Neurol. Clin. 27 2009 909 924 19853215 3 Griffith N.M. Szaflarski J.P. Schefft B.K. Isaradisaikul D. Meckler J.M. McNally K.A. Relationship between semiology of psychogenic nonepileptic seizures and Minnesota multiphasic personality inventory profile Epilepsy Behav. 11 2007 105 111 17602880 4 Selwa L.M. Geyer J. Nikakhtar N. Brown M.B. Schuh L.A. Drury I. Nonepileptic seizure outcome varies by type of spell and duration of illness Epilepsia 41 2000 1330 1334 11051130 5 Griffith N.M. Smith K.M. Schefft B.K. Szaflarski J.P. Privitera M.D. Optimism, pessimism, and neuropsychological performance across semiology-based subtypes of psychogenic nonepileptic seizures Epilepsy Behav. 13 2008 478 484 18602027 6 Asadi-Pooya A.A. Semiological classification of psychogenic nonepileptic seizures: a systematic review and a new proposal Epilepsy Behav 100 Pt A 2019 106412 31645005 7 Rothan H.A. Byrareddy S.N. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak J. Autoimmun. 109 2020 102433 32113704 8 https://www.worldometers.info/coronavirus/country/iran/ (accessed on 19 April 2021) 9 Jahangiri K. Sahebi A. Social consequences of COVID-19 pandemic in Iran Acta Medica Iranica 58 2020 662 663 10 LaFrance W.C. Jr. Baird G.L. Barry J.J. Blum A.S. Frank Webb A. Keitner G.I. Multicenter pilot treatment trial for psychogenic nonepileptic seizures: a randomized clinical trial JAMA Psychiatry 71 2014 997 1005 24989152 11 Luo M. Guo L. Yu M. Wang H. The psychological and mental impact of coronavirus disease 2019 (COVID-19) on medical staff and general public–a systematic review and meta-analysis Psychiatry Res. 291 2020 113190 32563745 12 Vahedian-Azimi A. Moayed M.S. Rahimibashar F. Shojaei S. Ashtari S. Pourhoseingholi M.A. Comparison of the severity of psychological distress among four groups of an Iranian population regarding COVID-19 pandemic BMC Psychiatry 20 2020 402 32770975 13 Valente K.D. Alessi R. Baroni G. Marin R. Dos Santos B. Palmini A. The COVID-19 outbreak and PNES: the impact of a ubiquitously felt stressor Epilepsy Behav. 117 2021 107852 33636526 14 Syed T.U. LaFrance W.C. Jr. Kahriman E.S. Hasan S.N. Rajasekaran V. Gulati D. Can semiology predict psychogenic nonepileptic seizures? A prospective study Ann. Neurol. 69 2011 997 1004 21437930 15 Erba G. Giussani G. Juersivich A. Magaudda A. Chiesa V. Laganà A. The semiology of psychogenic nonepileptic seizures revisited: can video alone predict the diagnosis? Preliminary data from a prospective feasibility study Epilepsia 57 2016 777 785 26949106 16 Reuber M. Qurishi A. Bauer J. Helmstaedter C. Fernandez G. Widman G. Elger C.E. Are there physical risk factors for psychogenic non-epileptic seizures in patients with epilepsy? Seizure 12 2003 561 567 14630494 17 Doss J. Caplan R. Siddarth P. Bursch B. Falcone T. Forgey M. Hinman K. LaFrance W.C. Jr. Laptook R. Shaw R. Weisbrot D. Willis M. Plioplys S. Risk factors for learning problems in youth with psychogenic non-epileptic seizures Epilepsy Behav. 70 2017 135 139 28427021 18 Sequeira A.S. Silva B. A comparison among the prevalence of alexithymia in patients with psychogenic nonepileptic seizures, epilepsy, and the healthy population: a systematic review of the literature Psychosomatics 60 2019 238 245 30876655 19 Bewley J. Murphy P.N. Mallows J. Baker G.A. Does alexithymia differentiate between patients with nonepileptic seizures, patients with epilepsy, and nonpatient controls? Epilepsy Behav. 7 2005 430 437 16095976 20 Tojek T.M. Lumley M. Barkley G. Mahr G. Thomas A. Stress and other psychosocial characteristics of patients with psychogenic nonepileptic seizures Psychosomatics 41 2000 221 226 10849454 21 Demartini B. Petrochilos P. Ricciardi L. Price G. Edwards M.J. Joyce E. The role of alexithymia in the development of functional motor symptoms (conversion disorder) J. Neurol. Neurosurg. Psychiatry 85 2014 1132 1137 24610939 22 Zeng R. Myers L. Lancman M. Post-traumatic stress and relationships to coping and alexithymia in patients with psychogenic non-epileptic seizures Seizure 57 2018 70 75 29573595

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==== Front Community Ment Health J Community Ment Health J Community Mental Health Journal 0010-3853 1573-2789 Springer US New York 1067 10.1007/s10597-022-01067-w Brief Report Program Considerations and Addressing At-Risk Populations in Active Minds Clubs: A Brief Report http://orcid.org/0000-0003-4043-4156 Stern Craig A. [email protected] 12 http://orcid.org/0000-0001-7834-3124 LaChappelle Melanie E. 1 1 grid.421818.6 0000 0000 9138 0897 School of Sociology and Human Services, San Juan College, Farmington, NM USA 2 Durango, USA 14 12 2022 18 17 7 2022 25 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Active Minds, a national non-profit, was created to combat suicide and mental health stigma among youth and young adults. Research has indicated that Active Minds has been effective in positively changing students’ attitudes and behaviors towards mental health. This study seeks to understand what else Active Minds can do to address mental health barriers and help-seeking within student populations and how Active Minds can better support at-risk populations in their wellness. This study consisted of four focus groups, and 13 participants completed a Brief Questionnaire and answered open-ended questions from a semi-structured interview guide. The narrative was coded and categorized, and thematic analysis was utilized. Eight themes were identified: (a) creating shared safe spaces (b) mental health stigma among Native American and Hispanic/Latinx students (c) the vulnerability of LGBTQ + student populations (d) addressing special populations in Active Minds programming (e) the role of gender and religion in mental health (f) cultural education and resources (g) normalizing mental health through education and family conversation, and (h) promotion of Active Minds and accessibility of counseling. Because the sample size only consisted of 13 participants the results cannot be generalized to students, but the results are transferable to student populations. The researchers recommend that Active Minds use a peer support model and develop curriculums that address mental health stigma and cultural education in diverse populations. Future research is needed to uncover those strategies that might engage males in mental health education. Keywords Active Minds Mental health Diverse populations Suicide Qualitative ==== Body pmcSuicide is a substantial problem in colleges and universities. It is the second leading cause of death among youth and young adults (Lamis et al., 2014). Stigma plays a key role on whether a college student will seek out mental health services. Roughly half of students do not use mental health services because of the stigma associated with it (The College Post, 2019). Additionally, students are less likely to use mental health treatment if it is stigmatized among their peers (Wolf, 2018). LGBTQ + , Native American, Hispanic/Latinx, and white males are vulnerable to mental health challenges and suicide. It is estimated that college students, who identify as LGBTQ + , are four times more likely to commit suicide (The Trevor Project, 2021). The suicide rate for Native Americans between the ages of 15–24 is the highest of any racial and/or ethnic group. Talking about mental health within the home or to a mental health professional can be considered taboo within the Hispanic/Latinx community (Almendrala, n.d. xxxx; National Alliance on Mental Illness, 2022). Furthermore, white males are almost four times more likely to die by suicide than white females (American Foundation for Suicide Prevention, 2022). Literature Review It has been documented that safe spaces for talking about mental health can be an effective tool for wellness among college students (Active Minds, 2019). Research has found that safe spaces, where LGBTQ + and Latina students could communicate without fear of being of silenced and judged, did provide a positive experience (Bedree et al., 2019; Revilla, 2004). Indigenous peoples have experienced historical trauma from being forced to assimilate to Western civilization (Brown-Rice, 2014). Because of the forced assimilation, tribal communities have adopted many norms of the white majority (Granbois, 2005). One of those norms is mental health stigma. According to Granbois (2005), this mental health stigma does exist within tribal communities. The Hispanic/Latinx culture is rooted in deep religious traditions and machismo attitudes (Mental Health America, 2022b). Jack (2022) states that Hispanic/Latinx families seek to keep personal challenges private and stereotypes towards persons with mental health challenges are prevalent within this culture. Research found that only 20% of the Hispanic/Latinx population talks about their mental health with a physician, and 10% visit a mental health provider (Consult QD, 1995–2022). Moreover, only 6% of mental health professionals can provide services in Spanish, and the language barrier can discourage the Hispanic/Latinx population from using a helping professional (Bailey & Hogan, 2019). As a result of stigma, the LGBTQ + population experiences discrimination, prejudice, violent acts, and rejection from their families (Pires & Ponte, 2019). Research shows that the lack of acceptance in society for the LGBTQ + population can lead to increased isolation, mental health challenges, and exacerbate suicidal risk (Mental Health America, 2022a). There are socially constructed norms for behavior that exist for females and males in the U.S. Men are expected to be strong and stoic without giving in to their emotions (Power, 2011). Research has found that 38% of men do not talk about their feelings, and roughly 30% of men have never cried or shown emotion in front of others (McCallister, 2022). Such attitudes can create distress, increasing susceptibility to a mental health challenge for males (Woolfe, 2020). A considerable proportion of people (38%) in the U.S. believe that mental health challenges can be cured through the bible and prayer (Holpuch, 2013). Bryant (2018) states while there is “nothing wrong with prayer”, people still have to be “proactive” by surrounding themselves with the proper supports and resources in the healing process of a mental health challenge. Prayer alone is not going to solve mental health challenges (Bryant, 2018). Many States do not educate their students about mental health (University of Wisconsin-Superior, 2021). Roy (2020) contends that early mental health education can prevent stigma, biases, isolation of youth, and encourage help-seeking behavior in student populations. Access to mental health services can be challenging for students. When asked where they could go for counseling services, 14% of college students report that they “don’t know where” to seek these services (Elflein, 2022). This problem is further complicated in rural communities. Research indicates that 65% of individuals lack access to a psychiatrist, and 47% do not have access to a psychologist in rural areas (Davis, 2019). Overview of Active Minds Active Minds, a 501(c)3 organization, is a leader in driving mental health conversations across colleges nationwide (Active Minds, 2022d). In 2000, Alison Malmon, the founder of Active Minds, lost her brother, Brian, to suicide (Active Minds, 2022d). Malmon’s brother was diagnosed with a mental health condition during his senior year in college and had there been an opportunity for him to discuss this challenge, Malmon believes that his passing could have been prevented (Active Minds, 2022d). As a result, Malmon worked to create UPenn’s first peer-to-peer group—Open Minds—which focused on destigmatizing mental health challenges and encouraging help-seeking behavior (Active Minds, 2022d). In 2013, a national office was established to provide support to the Open Minds chapters, and all were rebranded to align with the newly established Active Minds nonprofit organization (Active Minds, 2022d). Today, there are over 600 Active Minds clubs across the United States on college campuses today (Active Minds, 2022a). Additionally, Active Minds is aware of the mental health crisis in the primary and secondary school systems that have been exacerbated by racism, gun violence, homophobia, and the impact of the COVID-19 global pandemic (Active Minds, 2022b). Therefore, Active Minds is expanding its programs to a thousand high schools, middle schools, and elementary schools by 2025 (Active Minds, 2022b). Active Minds Programming Active Minds utilizes a peer-to-peer approach with the development and implementation of its mental health programming, which prepares student leaders to change mental health perceptions and create supportive campus environments (Active Minds, 2019). Active Minds’ student-led model incorporates a unique combination of contact-based education across small-group activities and large-scale campus programs alike (Active Minds, 2019). Theory Empowerment theory is aligned with the philosophy of Active Minds. Empowerment theory explains how people can reduce helplessness and barriers by using self-efficacy, strengths, and a collective consciousness (Virginia Commonwealth University School of Social Work, 2021). With the use of mutual support, the experts and those affected by the social problem inspire each other to believe that positive change is possible (Zimmerman, 2000). Due to the stigma associated with mental health, Active Minds encourages all parties to work together to make mental health awareness a reality on college campuses. Research on Active Minds In 2018, a longitudinal study with over 1000 students conducted by the RAND corporation learned of the benefits that Active Minds. The study revealed that Active Minds can encourage a student to reach out to a peer or friend with a mental health challenge; create a supportive campus environment; and positively transform student attitudes about mental health issues (Active Minds, 2022c; Sontag-Padilla et al., 2018). While Active Minds has proven to be effective on a variety of outcomes, current research has not addressed the following questions:How can Active Minds better support those at-risk students who are vulnerable to suicide and mental health challenges? What barriers exist for student populations when seeking support for their mental health? Methodology Location of the Study This study takes place at a rural community college in the Southwestern United States. In the spring of 2021, an Active Minds club was established at this college. Research Design This was a qualitative study and approved by Tulane University’s Institutional Review Board (IRB) and the community college’s IRB in the fall of 2021. The study included a Brief Questionnaire (See Appendix B), and four in-person focus groups using a semi-structured interview guide (See Appendix A). Any student at the community college, who was 18 years or older, could participate in the study. Sample A convenience sample of students at the college were asked to participate in the focus groups. Thirteen participants volunteered to participate in the study. Measures The Brief Questionnaire asked participants to identify specific personal and college demographics and their experiences with mental health and Active Minds (See Appendix A). The semi-structured interview guide asked participants about the role of mental health within their homes and their impressions of campus mental health services (See Appendix B). The researchers also sought input about the college’s Active Minds chapter and how Native American, Hispanic/Latinx, and LGBTQ + students can be supported when faced with a mental health challenge. Additional questions were asked to clarify participants’ responses. Procedures In the fall of 2021, all students at the community college were invited to participate in this study through email. Participants at the focus groups were given an informed consent to sign and completed the Brief Questionnaire before the focus group began. The focus groups lasted roughly an hour and 15 min and were audio-recorded. Data Analysis The data was transcribed by the two researchers in this study. Using thematic analysis, the two researchers for this study coded and categorized the data separately and wrote memos in the margin. The two researchers cross compared their coded and categorized data, and the researchers came to an agreement on the study’s themes and the narrative within each theme. The researchers used a constructivist paradigm, and a theoretical approach was used because the researchers were interested in data that aligned with the study’s research questions (Braun & Clarke, 2006). Moreover, a latent level of analysis was selected because the researchers were interested in interpreting the data and how the data related with previous theories (Braun & Clarke, 2006). Results There were 12 females and 1 male in this study. Over half of the students were in their 2nd year of college (53.8%). Most students who participated were nursing (23.1%) and psychology (23.1%) students. Many students identified as white (23.1%) and Hispanic/white (23.1%). A majority of the participants were members of the LGBTQ + community (53.8%). Most participants did not have a history of receiving mental health services (61.5%). While five participants had a family member with a mental health challenge (38.5%), five participants stated they were unsure if they had a family member with a mental health challenge (38.5%). Nearly all participants had a friend or peer with a mental health challenge (84.6%), and half of the participants had been previously involved with Active Minds. One participant did not complete the Brief Questionnaire. The following themes emerged from the analysis: (a) creating shared safe spaces (b) mental health stigma among Native American and Hispanic/Latinx Students (c) the vulnerability of LGBTQ + student populations (d) addressing special populations in Active Minds programming (e) the role of gender and religion in mental health (f) cultural education and resources (g) normalizing mental health through education and family conversation (h) and promotion of Active Minds and accessibility of counseling (i). Theme 1: Creating Shared Safe Spaces Participants noted it is essential to have a place where they can express and share their mental health challenges without fear of judgment.1 One participant noted, “I believe in the importance of having a space where you can talk about mental illness and wellness with peers who might also have concerns or problems or a situation going on.” [FG 4] Another participant stated, “I think it is important to encourage those who may not want to talk. It is important to include everyone in the conversation.” [FG 3]. Theme 2: Mental Health Stigma Among Native American and Hispanic/Latinx Students Participants, who identified as Native American in one of the focus groups, felt rejection and shame, for having discussed mental health issues with their families. One participant stated that their family member communicated that “It is all in your head. It is no big deal. Get over it.” and felt guilt for having addressed the topic within their family. [FG 1] Hispanic/Latinx participants commented, “You are perceived as weak if you go and get help” for your mental health, and it is “taboo to even touch” the topic, and “the knowledge is not there.” [FG 3, FG 4] One of the Hispanic/Latinx participants’ parents said, “I did not get counseling. It did not exist. You will do fine.” [FG 3]. Theme 3: The Vulnerability of the LGBTQ + Student Population Participants stated that identifying as LGBTQ + carries a “social stigma.” [FG 4] This social stigma takes a toll on a person’s mental health, and participants who identify as LGBTQ + expressed that they “…do not want someone to see them differently.” [FG 4] This “lack of acceptance and validation” can lead to suicidal ideation within the LGBTQ + population. [FG 4] Many youth from the LGBTQ + community, “do not know how to communicate how they feel. The younger they are, the harder it is.” [FG 4]. Theme 4: Addressing Special Populations in Active Minds Programming To open the mental health conversation among these special populations, it was suggested that Active Minds embrace a model that makes a special effort to welcome every “human” and “soul.” [FG 3, FG 4] As one participant described, having “people within your club that are diverse that are LGBTQ + , Latino, etc., It is more likely that I would join that club [that was] reaching out to those diverse people of color.” [FG 3] One participant discussed how they think it would be beneficial if Active Minds practices “…aligned with Indigenous people… that comes back again to collaborating on what is the best way to approach this.” [FG 1]. Participants also stated that Active Minds could have a cultural fair that addresses education, stereotypes, and facts of the Hispanic/Latinx, Native American, and LGBTQ + cultures with “food, pictures, and symbols” representative of each culture. [FG 4] Furthermore, “Having people from diverse backgrounds telling their story is great.” [FG 3] As one participant suggested, “… maybe Indigenous representatives from Active Minds [could] speak to students.” [FG 1]. Theme 5: The Role of Gender and Religion in Mental Health One participant discussed that males are taught at a young age to “repress feelings…You are supposed to be strong and stoic. You are supposed to be the rock of the house.” [FG 4] Additionally, there are strong “machismo” norms within the Hispanic/Latinx culture. [FG 3] One participant commented, “Men are told not to express their emotions, and if you do, you are weak.” [FG 3] One participant discussed how people believe mental health is something “you need to get fixed with G-d.” [FG 3, FG 4] Another participant discussed how, “All I really wanted was someone to talk to,” but instead received the message, “Did you read your bible and pray? I was told to find G-d’s perspective on it.” [FG 4] One participant commented how “throwing a bible at it does not solve the problem.” [FG 4]. Theme 6: Cultural Education and Resources Participants stated that students need to take responsibility for their own cultural awareness, as one participant states, “I think with the cultural sensitivity…it has to start within yourself. Become self-aware of your own culture and biases, so you can avoid biases with other cultures and become open-minded.” [FG 4] Creating a space for cultural awareness that would educate Active Minds’ members was important to participants. One participant suggested, educating “participants about LGBTQ + and how to be more inclusive of them and educate others on how to be more accepting towards other cultures. We are really lacking the knowledge of different cultures of the LGBTQ + communities.” [FG 4]. Exploring and bringing light to current issues faced by Native American populations was also noted as an important consideration when addressing cultural awareness and mental health challenges. As one focus group participant described, “We just need to start telling those real and true stories about what is going on with Indigenous people. Sometimes, it comes out but not enough. It does not get enough media attention.” [FG 1]. Identifying culturally relevant resources to support families in addressing mental health challenges was a point brought up several times throughout the focus groups. A few focus group participants stated, “Parents do not know where to go if their kids are having problems, especially if they only speak Spanish. More Spanish interpreters and advertisement are needed in the Latino community and more awareness is needed.” [FG 3]. Theme 7: Normalizing Mental Health Through Education and Family Conversation Multiple focus group participants shared consensus in the importance of normalizing mental health through education across all age groups, particularly among children and adolescents. As one focus group participant expressed, “…Active Minds, should be everywhere and in every school, of course, pertaining to their ages.” [FG 4] Additionally, mental health conversations can encourage younger people to discuss feelings, an idea that was agreed upon by many focus group participants. One participant noted, “It is definitely not normalized to talk about mental health, especially with smaller children. They also experience anxieties and trauma.” [FG 4]. Theme 8: Promotion of Active Minds and Accessibility of Counseling Participants felt that it was important that Active Minds work in cross-collaboration with centers and services, so that the “word” of the program is spread throughout the college campus. [FG 1] As one focus group participant suggested, “Involve the Native American Center…I know that the Hispanic club wants to work with Active Minds… work with [the] LGBTQ + club.” [FG 1] Promotion of the program could be in the form of virtual advertising accessible to students as well as “signs, posters, pamphlets, hotline numbers, etc.” [FG 1, FG 2, FG 3, FG 4] on campus. Many participants did not have a clear idea of how to utilize counseling services on campus; as one participant stated, “Everyone knows that there is counseling available, but we don’t where [to go] or who to contact.” [FG 3] A participant expressed, “Outreach would make a world difference.” [FG 3] One participant noted, “Our rural community in general does not have nearly enough mental health resources. We have a lack of providers,” which causes further distress for students. [FG 2]. Discussion The results of this study point to the importance of college students having a safe space to talk about their mental health challenges. Research supports this claim, and Active Minds advocates for it (Active Bedree et al., 2019; Craig, 2016; Minds, 2019; Revilla, 2004; Yee, 2019). It is also important to note that participants felt safe in these focus groups commenting that they were therapeutic and asked how these safe spaces for mental health discussions could be held on campus more frequently. Native American participants commented upon the mental health stigma that exists within their culture. Based on the narrative of the focus groups, it was evident that many Native American participants’ families have adopted the mental health stigma that exists in the general population (Grandbois, 2005). Hispanic/Latinx participants also elaborated on the mental health stigma that exists within their families and how these cultural norms can discourage help-seeking behaviors, and participants also discussed the stigma LGBTQ + students can experience from their family members, friends, and peers (Mental Health America, 2022a, 2022b; Pires & Ponte, 2019; Ramirez, 2017). Most of the participants stressed the importance of addressing the mental health challenges of diverse populations within Active Minds. Participants believed that the mental health education provided by Active Minds needs to be aligned with the students’ cultural practices. To accomplish this goal, participants recommended collaborating with centers and departments on campus that serve special populations (Active Minds, 2019). Participants believed that these centers and departments can provide Active Minds clubs with guidance on how to deliver programming that is sensitive to the cultural practices of diverse populations. Additionally, a few participants believed that Active Minds could address diverse populations by hosting or sponsoring cultural fairs. These cultural fairs could inform students about practices and traditions different from their own. Participants also recommended using guest speakers, who have lived experience with a mental health challenge and come from diverse backgrounds, to share their mental health stories with students, faculty, and staff. Although there was only one participant who identified as male in this study, this participant discussed the socially constructed norms that exist for men in society. This participant discussed how these norms can be a barrier to emotional wellness for men (Power, 2011). Moreover, Hispanic/Latinx participants elaborated on the traditional gender roles that exist within their culture (Mental Health America, 2022b). Most Hispanic/Latinx families are inegalitarian, and participants discussed how this family structure can affect whether a male decides to seek a mental health professional (Ramirez, 2017). Some participants commented that they were told to use prayer or the bible to heal their mental health challenge. A significant proportion of people in the U.S. believe that prayer or the bible can heal a mental health challenge (Holpuch, 2013). The participants who were given these messages expressed feeling isolated, and this exacerbated their current distress; they felt that healthy conversation, instead of the bible or prayer, would promote their wellness. Participants stated that it is essential to take individual steps to understand, reflect, and recognize the biases that may exist within us and make a concerted effort to understand persons from other backgrounds and how their mental health experiences might be different from the societal majority. Native American participants stressed that cultural awareness and knowledge of Indigenous peoples in the general population is lacking, and most people’s knowledge of Native peoples is stereotypical. Hispanic/Latinx participants also discussed how the white majority never has to worry about a language barrier when seeking services, and that it is important to advocate for employing mental health providers who are bilingual or multilingual (Bailey & Hogan, 2019). All participants stressed the importance of age-appropriate mental health education at all stages of development. Many commented that in their primary, secondary, and college education, mental health education did not exist. Participants felt that such education is essential to facing the developmental challenges during the lifespan (Roy, 2020). Many participants discussed how they are unsure about how to access mental health counseling on campus (Elflein, 2022). Participants felt that there needs to be more visibility and promotion of mental health services. They also discussed the lack of mental health services in rural communities, which also compounds the problem of accessibility (Davis, 2019). Limitations and Recommendations This study only consisted of 13 participants; therefore, the findings cannot be generalized to all college students and limit the study’s construct validity. While the participants within this study came from diverse backgrounds, only one male participated in the study. The small sample size and limited male participation may be result of stigma and the absence of mental health literacy of student populations at this college. Therefore, it is recommended that the Active Minds national organization consider the following programmatic initiatives. First, these focus groups provided a source of healing for participants; thus, Active Minds might want to consider using a peer support model on college campuses. A peer support model would provide students with the opportunity to share their mental health challenges with other students in a safe space, and research shows that peer support is an effective tool for mental health coping (Mental Health America, 2022c). Second, the Active Minds national organization may want to create curriculums that address mental health stigma and culturally relevant education within special populations. Third, it is also important to learn how Active Minds clubs can better engage white males in the importance of mental health literacy. Youth and young adults, who identify as male, are less likely to talk about their mental health than females and white males have the second highest suicide rate of any population group (Centers for Disease Control & Prevention, 2022; McCallister, 2022). Future researchers might want to create a questionnaire asking open-ended questions that address the mental health barriers for male college students or hold focus groups that are exclusively male. Conclusions This study revealed many programmatic considerations that could enhance the success of the Active Minds national organization and existing clubs. The researchers of this study suggest that Active Minds expand its programming through a peer support model. Curriculums that address cultural education and special populations could better assist Active Minds clubs in supporting diverse student populations in their wellness. Although Active Minds has been found to have a positive impact on students’ attitudes and responses towards mental health, there are student populations that are especially vulnerable to suicide. This study attempts to provide the Active Minds national organization and the clubs across the nation with some steps that could be taken to support students, especially students, who are high risk for suicide or a mental health challenge and come from diverse backgrounds. Appendix A Interview Questions: (1) Could you tell me about your experience with campus mental health services? (2) What do you feel are some of the challenges students face in receiving the mental health support they need? (3) What have been your experiences with mental health within your home/culture? (4) Research has shown that students who identify as Native American, Hispanic/Latinx, and LGBTQ + may not seek mental health services when they are having challenges. What is your understanding of why students from these backgrounds may not use mental health services? (5) What can Active Minds do to make sure all students are heard? (6) How can Active Minds help students access mental health services on campus? (7) What would you like to see from campus mental health services? What do you think will help students access mental health services on campus? Appendix B Brief Questionnaire (1) What is your gender? (2) What is your class standing? ☐ First Year Student ☐ Second Year Student.(3) What is your major? (4) What is your age group? ☐ 18—24 years. ☐ Older than 25 years.(5) Please describe your race and/or ethnicity: (6) Do you identify as someone who is part of the LGBTQ + community? ☐ Yes. ☐ No. (7) Do you have history of receiving mental health services? ☐ Yes. ☐ No. (8) Do you have a family member with a mental health condition? ☐ Yes. ☐ No. ☐ Not Sure. (9) Do you have a friend or peer with a mental health condition? ☐ Yes. ☐ No. ☐Not Sure. (10) Are you currently or have you been previously involved with Active Minds? ☐ Yes ☐ No Declarations Conflict of interest Authors declare that they have no known conflicts of interest to disclose. 1 FG denotes which of the four focus groups is being quoted. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Active Minds. (2019, August). Recommendations to shape a positive mental health climate on college campuses with and through peer-to-peer networks. Retrieved on 05/19/22 at https://www.activeminds.org/wp-content/uploads/2019/09/FINAL_Position-Statement_Student-Voice.pdf Active Minds. (2022a). Active Minds chapter network. Retrieved on 03/07/22 at https://www.activeminds.org/programs/chapter-network/ Active Minds. (2022b). K-12. Retrieved on 11/10/22 at https://www.activeminds.org/programs/k-12/https://www.activeminds.org/programs/k-12/ Active Minds. (2022c). Mission and impact. Retrieved on 03/03/22 at https://www.activeminds.org/about-us/mission-and-impact/ Active Minds (2022d). Our story. Retrieved on 03/06/22 at https://www.activeminds.org/about-us/our-story/ Almendrala, A. (n.d.). Native American youth suicide rates are at crisis levels. Retrieved on Mar 03,2022 at https://www.waterboards.ca.gov/waterrights/water_issues/programs/bay_delta/california_waterfix/exhibits/docs/PCFFA&IGFR/part2/pcffa_190.pdf American Foundation for Suicide Prevention. (2022). Suicide statistics. Retrieved on June 16, 2022 https://afsp.org/suicide-statistics/ Bailey, D. & Hogan, B. (2019, November 26). Hispanic American mental health care gap to reach almost $500 million by 2030. 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Retrieved on June 16, 2022 at http://www.personal.psu.edu/bfr3/blogs/applied_social_psychology/2011/10/the-social-construction-of-gender.html Ramirez, A. (2017, November 14). Latino childhood development research: Strategy-Family values. Retrieved on May 17, 2022 at https://salud-america.org/latino-childhood-development-research-strategy-family-values/#:~:text=Traditional%20gender%20roles%2C%20machismo%20and%20marianismo%2C%20are%20often,for%20raising%20the%20children%20and%20maintaining%20the%20house.141 RAND corporation. (2018, March 2). The relationship between mental health care access and suicide. Retrieved on Mar 05, 2022 at https://www.rand.org/research/gun-policy/analysis/essays/mental-health-access-and-suicide.html Revilla AT Raza Womyn engaged in love and revolution: Chicana/Latina student activists creating safe spaces within the university Cleveland State Law Review 2004 52 1–2 155 171 Roy, A. (2020, November 17). The importance of mental health education. Retrieved on May 17, 2022 at http://odyssey.antiochsb.edu/student-activism/the-importance-of-mental-health-education/ Sontag-Padilla L Dunbar MS Ye F Kase C Fein R Abelson S Seelam R Stein BD Strengthening college students' mental health knowledge, awareness, and helping behaviors: The impact of active minds, a peer mental health organization Journal of the American Academy of Child and Adolescent Psychiatry 2018 57 7 500 507 10.1016/j.jaac.2018.03.019 29960695 The College Post. (2019, October 29). Stigma stops college students from seeking mental health care. Retrieved on Mar 08, 2022 at https://thecollegepost.com/campus-mental-health-stigma/ The Trevor Project. (2021, December 15). Facts about LGBTQ suicide. Retrieved on https://www.thetrevorproject.org/resources/article/facts-about-lgbtq-youth-suicide/ University of Wisconsin: Superior. (2021, June 1). The importance of mental health in K-12 education. Retrieved on May 17, 2022 at https://online.uwsuper.edu/degrees/education/msed/school-counseling/mental-health-in-k12-education/ Virginia Commonwealth University School of Social Work. (2021, January 6). Empowerment theory in social work. Retrieved on Feb 21, 2022 at https://onlinesocialwork.vcu.edu/blog/empowerment-theory-in-social-work/#:~:text=Empowerment%20theory%20social%20work%20involves%20using%20intervention%20methods,focuses%20on%20how%20oppression%20contributes%20to%20this%20experience Wolf, J. (2018, January 23). Study shows stigma around mental health on campus correlates with students not seeking treatment. Retrieved on Mar 21, 2022 at https://newsroom.ucla.edu/releases/study-shows-stigma-around-mental-health-on-campus-correlates-with-students-not-seeking-treatment Woolfe, S. (2020, August 10). The role of masculine norms in the mental health crisis. Retrieved on June 19, 2022 at https://www.samwoolfe.com/2020/08/masculine-norms-male-mental-health-crisis.html#:~:text=The%20masculine%20norm%20of%20self-reliance%20is%20one%2C%20in,a%20sign%20of%20weakness.%20Wanting%20support%20is%20emasculating Yee, M. (2019, June 4). Why safe spaces are important for mental health-especially on college campuses. Retrieved on May 15, 2022 at https://www.healthline.com/health/mental-health/safe-spaces-college#1 Zimmerman MA Rappaport J Seidman E Empowerment theory Handbook of community psychology 2000 Springer

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==== Front Soc Indic Res Soc Indic Res Social Indicators Research 0303-8300 1573-0921 Springer Netherlands Dordrecht 3046 10.1007/s11205-022-03046-w Original Research Inclusive Education and Health Performance in Sub Saharan Africa Kouladoum Jean-Claude [email protected] Department of Economics, University of Moundou - Chad, Moundou, Chad 14 12 2022 122 29 11 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The study assesses the effect of inclusive education on health performance in 48 Sub Saharan African countries from 2000 to 2020. The study adopted the Driscoll/Kraay technique to address cross-sectional dependence and the GMM strategy to address potential endogeneity. The study employed three indicators of health performance which are the total life expectancy, the female life expectancy and the male life expectancy. Three gender parity index of educational enrolments are employed: primary education, secondary and the tertiary education as indicators of inclusive education. The findings of the study reveal that inclusive education enhances the health situation of individuals in Sub Saharan Africa. The findings further show that the health situation of both the male and the female are improved by inclusive education. The study recommends policymakers in this region to invest more in the education and the health sector so as to enhance the health performance of the citizens. Keywords Inclusive education Health performance Sub Saharan Africa ==== Body pmcIntroduction Education is considered an important factor which is known to influence health performance and inequalities in life expectancy (Asongu et al., 2021). Education in every society serves as a right for individuals and a principal component that determines socioeconomic status which tends to influence livelihood, especially in its dimension of health status. Its importance is confirmed in the sustainable development goal (SDG4) which is aimed at “ensuring inclusive and equitable quality education and promoting lifelong learning opportunities”. In many developing countries, life expectancy remains very low due to a lack of investments in human capital development where many children still have no access to education and health facilities (Bado and Susuman, 2016). The case in Sub-Saharan African countries is striking, with the rate of school dropped out still very high. In early 2018, UNESCO reported that 258.4 million children globally, youth and adolescents were out of school, representing one-sixth of the global population of the youth age group. The report further confirmed that one dropout is registered out of five children in sub-Saharan Africa in 2018. The links between life expectancy and education are well documented in literature both contemporary and non-contemporary literature (Sen, 1999; Saito, 2003; Hansen & Strulik, 2017; Olshansky et al., 2012; Hendi, 2017). According to Sen (1999), quality education is fundamental to maintaining a healthy lifestyle and well-being. Countries with well-established knowledge economies have higher standards of living and life expectancy relative to economies whose educational sectors are neglected (Asongu et al., 2020; Hansen & Strulik, 2017). Educated people pay for more nutritious food and quality medical care, which promotes a healthy lifestyle than individuals who do not monitor their health situation (Luy et al., 2019; Meara et al., 2008). Similarly, education influences health through the adoption of healthier lifestyles, better diets, and more effective management of chronic diseases (Olshansky et al., 2012). Higher education gained by citizens in developed countries enhances their socioeconomic status gradient and the amounts of their longevity. Health performance is greatly determined by the number of death in the first year of life, indicated by infant mortality and death in the first five years of life indicated by the under-five mortality rate. In Sub-Saharan African, about 4.8 million children die before the age of five annually, which translates to 9 deaths every minute and signifying poor health conditions (Anyamele et al., 2017). These rates have been very high in Sub-Saharan Africa which accounts for 45% of the world's child mortality (UN Inter-Agency Group 2012). The report of the World Bank shows that the number of under-five mortality has dropped drastically from 151 in 2000 to 73 in 2020 with the West and Central African countries recording the highest than the Eastern and Southern sub-Saharan African countries. However, the problem of gender disparities in adult literacy rates in Sub-Saharan Africa remains wide due to both cultural factors and lack of infrastructure, which greatly affect the health of individuals. These disparities are widely recorded between women but shreds of evidence indicate that some progress has been made at the regional and country levels to reduce health inequalities and the literacy level of women (Bad and Susuman, 2016). The Mortality rate of infants per 1,000 live births in Sub-Saharan Africa has decreased drastically over the years. In 2000, Sub-Saharan African countries recorded 91 deaths per 10,000 live births which reduce to 65 in 2010 and 50 in 2020. This decrease in infant mortality rate is attributed by the World Bank to an increase in investments in the health sector. According to Hoffman-Terry et al. (1992) and So et al. (2012), this falling mortality rate is due to increased immunizations and oral rehydration. Tremendous efforts have been made to improve the access to healthcare for children in Sub-Saharan African countries leading to a decrease in the death rate per 1000 of the population (Bado and Susuman, 2016). As a critic of the studies conducted in the literature investigating the effect of education on health performance in Sub-Saharan Africa, there is no consideration of the gender parity in inclusive education and the differences in educational levels. Similarly, the health performance of individuals with differences in educational levels has provided mixed results, though attributed by Hendi (2017) to results from differences in health performance indicators. In addition, these studies conducted on the effect of education on health performance have not considered the differences in female and male life expectancies which have been addressed in our study. The contribution of the study to the field of research is drawn from the limitations of existing studies. To the best of our knowledge, this is still the first study to be conducted on inclusive education and health performance in Sub-Saharan Africa. The study conducted in Africa that is closely related to our study is that of Guisan and Exposito (2016) who examined the link between education and life expectancy. In their study, they employed education as a composite indicator without accounting for different levels of educational enrolment. Among the fewer studies conducted on education and life expectancy in Africa, none has considered measuring education inclusively at a gender parity index. This study considered primary, secondary and tertiary education to account for differences at educational levels and also accounting for gender differences in the female and male life expectancies as indicators of health performance. Provided the role of education in livelihood sustainability, we conduct this study as one among many research works seeking to bring a solution to poor health problems in Africa, given the infectious diseases and epidemics with recent devastating health challenges of the cyclone in the southern African region, the Covid-19 unprecedented pandemic and the outbreak of the Ebola virus in the Congo Basins. The study employs the World Bank (2022) statistics to conduct this analysis while adopting the instrumental GMM strategy and the IV-2SLS strategy to address the problem of endogeneity. The findings of the study revealed that inclusive education is essential in improving health performance in Africa. The rest of the paper is structured as follows. Section 2 presents brief theoretical underpinnings and empirical findings on the education-health performance nexus. The data and empirical methodology are presented in Sect. 3. Section 4 presents and discusses the findings whereas, in Sect. 5, we conclude with policy recommendations and further research perspectives. Literature Review Several findings have reported conflicting conclusions about educational differences in life expectancy and their effect on the wellbeing of individuals. According to Hendi (2017), these differences are partly due to the use of unreliable data to compute life expectancy and educational enrolment measures. Some studies employed life expectancy at birth as a measure of health performance (Hansen & Strulik, 2017; Hendi, 2017; Olshansky et al., 2012) while others adopt infant and child mortality as an indicator of health performance (Anyamele et al., 2017; Shapiro & Tenikue, 2017; Bado and Susuman, 2016). Among studies that employed life expectancy as a health performance indicator is that of Hansen and Strulik (2017) who conducted a study in the United States of America and found that states with higher mortality rates from cardiovascular disease prior to the 1970s experienced increases in adult life expectancy and higher education enrollment. The findings of the study reveal that life expectancy increases with an increasing level of education from the primary to the tertiary education. Similarly, Olshansky et al. (2012) found out that in 2008, white men and women with more than 16 years of schooling had better life expectancies than citizens with fewer than 12 years of education. The results support the positive effect of education on health performance. Employing child survival indicator as a determinant of health performance, Mustafa and Odimegwu (2008) found out that the level of education and maternal awareness increase child survival in urban areas of Kenya, and is similarly to the findings of McTavish et al. (2010) who revealed that mothers in countries with higher literacy rates were more likely to use maternal health care and developed healthier habits which improve their health status than mothers with lower literacy rates. In the same line of studies, another strand of literature emerged with authors adopting infant and child’s mortality as an indicator of health performance. Among these works are those conducted in Africa such as those of Anyamele et al. (2017) who examined the role of mother’s educational attainment in explaining infant and child mortality in Sub-Saharan Africa, Shapiro and Tenikue (2017) who established a relationship between education and child mortality in both rural and urban areas in Sub Saharan Africa. These authors found that there is a small variability in the risk of infant and child mortality attributable to country differences in Sub Saharan Africa. Their findings further reveal a statistically negative significant difference in infant and child mortality with urban dwellers compared to rural dwellers, showing that mother’s education negatively correlated with infant and child mortality. In both works conducted in Sub Saharan Africa, they argued that increased women’s schooling reduces infant mortality. In the advent of the Covid-19, Aristovnik et al. (2020) in establishing the effect of education on health vulnerabilities, revealed in his empirical work that people who attended higher education controlled the Covid-19 pandemic and were not affected as much as those who attended only the basic education. Contrary to prior conclusions in the literature, Hendi (2017) conducted a study in the United States estimating life expectancy and lifespan variation by education using the American National Health Interview Survey and found out that life expectancy has either increased or decreased between 1990 and 2016 among all education-race groups except for non-Hispanic white women with less than a high school education who reside in the US. The author further reveals that there is an increase in life expectancy among white high school graduates and a smaller increase among black female high school graduates. Bado and Susuman (2016) conducted a study investigating the effect of education on the health performance of individuals in Sub Saharan Africa from 1990 to 2015. The authors found out that there is a significant decline in mortality among children of non-educated mothers compared to the decrease in mortality rates among children of educated mothers from 1990 to 2010 in Sub Saharan Africa. The findings also reveal that under-five mortality rates of children born to mothers without formal education are higher than the mortality rates of children of educated mothers. The authors concluded that the positive effect of education on health performance measured by under-five mortality as its inverse measure does not hold in Sub Saharan Africa between 1990 and 2015. Studies conducted in literature to investigate the health performance of individuals with differences in educational levels have provided mixed results, though attributed by Hendi (2017) to results from differences in the measure of health. Studies conducted in this regard did not consider inclusive education which accounts for both the male and the female educational enrolments. Similarly, these studies conducted on the effect of education on health performance have not considered the differences in the female and the male life expectancies which have been addressed in our study. Data and Methodology Data The study investigates the effect of inclusive education on health performance in148 Sub Saharan African countries from 2000 to 2020. The study employs secondary data obtained from the World Bank Development Indicators (2021). The sample size adopted, the number of countries selected and the time period of the study's investigation are constrained to limited data on inclusive education and the health performance indicators. Presentation of Variables The Dependent Variable The study’s dependent variable is health performance measured by life expectancy at birth. The total life expectancy summarizes the mortality pattern that prevails across both genders and all age groups in a given year (World Bank, 2022). Also, the study employs the male life expectancy and the female life expectancy as dependent variables in the robustness analysis. Life expectancy indicates the expected years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout his or her life. Life expectancy is adopted in literature as a measure of health performance (Chen et al., 2021; Hansen & Strulik, 2017). Life expectancy is always higher in countries that invest more in the health sector (proper health care systems) than in countries whose health sectors’ are neglected (poor health facilities). An increase in life expectancy of a country signifies that the health performance of its citizens has improved while a decrease indicates a deterioration in health performance. The trends of the three health performance indicators employed in the study are presented in Fig. 1. Figure 1 presents trends in health performance indicators. The total life expectancy increases from 52.52236 in 2000 to 63.28255 in 2020. The male life expectancy increases from 50.76683 in 2000 to 61.25102 in 2020 recording similar trends with the total life expectancy. The female life expectancy appears to be higher than the male life expectancy as it increases from 54.33012 in 2000 to 65.31148 in 2020. Figure 1 shows that the life expectancy in Sub Saharan African countries has increased over the years and according to the World Health Organization, this increase is due to increasing investments in the education and the health sectors. Independent Variable of Interest The main independent variable of interest is2inclusive education. Following the contemporary and the non-contemporary literature, education enrolment is said to be inclusive if measured by a gender parity index (Okkolin et al., 2010; Ali, 2015; Asongu et al., 2020, 2021; World Bank, 2022). The study employed the primary, secondary and the tertiary enrolments all at gender parity indexes. Inclusive education is expected to be enhancing the life expectancy of citizens in Sub Saharan Africa. As a determinant of health performance, education is widely employed in literature (Hansen & Strulik, 2017; Anyamele et al., 2017; Shapiro & Tenikue, 2017; Bado and Susuman, 2016; Hendi, 2017). Figure 2 presents the evolution of inclusive education between 2000 and 2020. Figure 2 presents trends in inclusive education within the period of the study. The figure shows that the rate of educational enrolments increases between 2000 and 2020. The enrolments rate of primary education increases from 0.8569636 in 2000 to 0.9962364 in 2020. The rate of secondary enrolments increases from 0.7735246 in 2000 to 1.006456 in 2020. The rate of tertiary enrolment has increased by a rate 0.36505 between 2000 (0.5832992) and 2020 (0.948253). Figure 3 presents scatter plots with an established correlation between inclusive education and life expectancy in Sub Saharan Africa. Figure 3 presents the correlation analysis between inclusive education and health performance in Sub Saharan Africa. Figure 3 presents a strong positive correlation between life expectancy and inclusive primary, secondary and tertiary enrolments. The first row of the figure presents the correlation of total life expectancy and educational enrolments, the second row presents the correlation between the female life expectancy and inclusive growth, and the third row presents the correlation between male life expectancy and inclusive growth. All the scatter plots indicate a positive correlation between inclusive education and life expectancy with the different genders taken into account. Figure 3 represents a preliminary analysis which presents the nature of the correlation between inclusive education and life expectancy. The correlation analysis’s results will be confirmed by a robust GMM results. Control Variables To account for other determinants that could influence the health performance of citizens in Sub Saharan African countries, we chose trade in services, employment in services, economic growth, domestic credit, gross savings and health expenditures as control variables of the study. Trade in services is expected to have a significant influence on health performance in Sub Saharan Africa. The inspiration to employ trade as a determinant of health performance is inspired by the works of Tahir (2020), Braine et al. (2020). The level of employment in services as a determinant of health performance is inspired from the works Assari (2018). Employment is expected to have positive effect on life expectancy in Sub Saharan Africa if the salaries of employees are being paid but expected to be insignificant if there is loss of paid employment. The level of economic performance indicated by gross domestic product is expected to have a positive influence on the level of health performance if the growth in economic sector is transmitted to more investments in the health sector (Chen et al., 2021 and Kunze, 2014; He & Li, 2020). Domestic credit employed as an indicator of financial development and employed as a determinant of life expectancy is inspired by the works of Alam et al. (2021). Financial development is expected to have a significant influence on health performance. Savings can have both positive and negative influence on life expectancy depending on the fluctuations of the prices of accessing health services (Lien et al., 2021). Health expenditures as a determinant of life expectancy is expected to have a positive significant influence on health performance. Expenditure as a determinant of life expectancy is inspired from the works of Shahraki (2019) and Jakovljevic et al. (2016). The descriptive characteristics of all the variables in terms of their mean, standard deviation, their maximum and the minimum values are presented in Table 1 of descriptive statistics. Fig. 1 The evolution of life expectancy in Sub Saharan African countries Fig. 2 The evolution of inclusive education in Sub Saharan African countries Fig. 3 The correlation analysis between inclusive education and health performance in Sub Saharan Africa Table 1 Descriptive Statistics Variable Obs Mean Std. Dev Min Max Life expectancy 1008 57.883 7.009 39.441 77.237 Life expectancy male 1008 59.707 7.406 40.005 82 Life expectancy male 1008 56.071 6.672 38.861 72.7 Primary 757 .925 .099 .553 1.151 Secondary 554 .865 .211 .287 1.388 Tertiary 478 .703 .344 .064 1.711 Trade in services 811 18.424 17.188 2.855 143.98 Employment in services 940 35.145 15.553 5.98 72.41 Gross savings 709 18.214 11.535 −19.903 57.85 Health expenditure 913 44.062 60.469 .92 364.834 Domestic credit 933 17.82 16.655 0 106.26 Internet 952 10.53 14.312 .006 79 GDP 968 3.989 5.716 −46.082 63.38 Methodology The system GMM is adopted as a strategy to estimate the influence of inclusive education on health performance proxied by life expectancy at birth. The GMM addresses the potential problem of endogeneity through an instrumentation technique. This problem is addressed by accounting for the concern of reverse causality and time-invariant omitted variables (Nchofoung, 2022; Nchofoung & Asongu, 2022). The GMM technique is a robust technique that accounts for the error term related problems. The adoption of the system generalized method of moment technique in this study is based on the following justifications. (1) The number of cross-sections (48 countries) in the study should exceed the time series (21 years). The GMM technique produces unbiased results when the number of individuals is greater than the time duration. This is considered the main argument for the adoption of the GMM technique. (2) The study adopted a data set structured in a panel form for 48 African countries between 2000 and 2020. The GMM is a widely used strategy for panel analysis which takes care of cross-country variation since they are inherent in panel analyses (Baum et al., 2003; Kouladoum et al., 2022; Nchofoung & Asongu, 2022). (3) Thirdly, there is a high correlation between health performance variables and their first lags. The correlation between the total life expectancy and its first lag is 0.999, male life expectancy and its first lag is 0.992, and that of female life expectancy at birth and its first lag stands at 0.999. These higher correlations between life expectancy variables and their first lags which are all greater than the threshold of 0.800 considered an established rule of thumb for assessing the variable's persistence justified the adoption of the GMM technique (Asongu and Odhiambo, 2019). The estimated equation with all the control variables is summarized as follows1 HPit=β0+β1IEit+β2Tradeit+β3Empit+β4Savingsit+β5HEit+β2Intit+β2GDPit+εit. where i and t represent individual cross sections and time respectively, HP signifies health performance, IE stands for inclusive education measured by captured by the gender parity school enrolments. Trade represents trade in services, emp signifies employment in services, savings represents domestic savings, HE stands for health expenditure, GDP and Intrepresent gross domestic product and the number of individuals using the internet with the the error term represented by ε. The system GMM technique adopted in the study can be summarised with the equation in levels as follows2 HPit=β0+β1HPit-τ+β2IEit∑h=1kδhWhit-τ+ηi+γt+εit…… The model can be summarised with the equation in first difference as follows3 HPit-HPit-τ=β1(HPit-τ-HPit-2τ)+β2(IEit-β2IEit-τ+∑h=1kδh(Whit-τ+Whit-2τ)+(γt-γt-τ)+εit-εit-τ…… where W is a vector of control variables. ηi is the country specific effect, γt is the time-specific constant, τ is the lagging coefficient and εit is the error term. The problems usually associated with the GMM framework is the problem of weak identification, exclusion and simultaneity restrictions. To solve these problems, all explanatory variables are treated as exogenous with the time fixed effect used as instruments in the underlying regression (Stock and Wright, 2000; Kouladoum et al., 2022). In the context of the Two-step system GMM, the validity of over identifying restrictions are commonly tested via the J statistic of Hansen (1982). The J statics of Hansen represents the value of the GMM objective function that determines whether the GMM instrumental process is valid (Baum et al., 2003; Hill-Burns et al., 2017; Meghir et al., 2018) This is done under the following hypotheses:4 H0:EzJu=0The model is correct. 5 H1:EzJu≠0 where H0 represents the null hypothesis, E(zju) represents the Hansen J statistics under which instruments have to be validated for the model to be efficient. Following Baum et al. (2003) the Hansen j statistics has to be greater than 10% (Has to be insignificant) for the model validity. Investigating the effect of inclusive education on the health performance suffices to derivate the health equation in 3 with respect to inclusive education to obtain the estimated effect of IE on HP. The derivative equation of elasticity is given as6 ∂HPit∂IEit=β1 The coefficient β1 represents the value at which health performance will vary if there is a 1% change in inclusive education. This value of the coefficient is determined in the GMM results presented in Tables 3, 4 and 5. Results and Discussions This section presents and discusses the findings of the study. The section begins with the presentation of the baseline results obtained by adopting the Driscoll/Kraay model. The Driscoll/Kraay strategy addresses the problem of cross-sectional dependence (Asongu & Nchofoung, 2021; Verma et al., 2022). The baseline results are presented in Table 2. The baseline results of the Driscoll/Kraay estimates do not accounts for the potential problem of endogeneity, heterogeneity and time variant effect. The highlighted estimation problems are addressed by the Generalized Method of Moment (GMM) technique in our study.The results of the GMM technique is presented in Tables 3, 4 and 5. The baseline results of the Driscoll/Kraay estimates indicate a positive effect of primary education on health performance. The secondary and the tertiary enrolment enhance life expectancy in Sub Saharan Africa. The results of the baseline analysis will be further confirmed by a more robust GMM technique.Table 2 The effect of inclusive education on health performance in Sub Saharan Africa (Baseline results) Variables (1) (2) (3) Total life expectancy Total life expectancy Total life expectancy Primary 0.414*** (0.0410) Secondary 0.201*** (0.0250) Tertiary 0.0982*** (0.0225) Trade in services −0.00182*** −0.00251*** −0.000175 (0.000348) (0.000464) (0.000505) Employment in services 0.000864 2.63e−05 −0.000170 (0.000588) (0.000983) (0.00118) Gross savings 0.000175 0.000409 0.000437 (0.000554) (0.000405) (0.000739) Health expenditure −0.000219 −0.000360*** −0.000488*** (0.000162) (7.84e−05) (0.000137) Domestic credit 0.00190*** 0.00179*** 0.00366*** (0.000459) (0.000360) (0.000676) internet 0.00282*** 0.00277*** 0.00253*** (0.000465) (0.000394) (0.000575) GDP −0.000934 0.000306 −0.000493 (0.000848) (0.00131) (0.000723) Constant 4.023*** 4.060*** 4.028*** (0.0317) (0.0381) (0.0332) Observations 512 365 355 Number of groups 38 35 37 df_m 8 8 8 df_r 19 19 19 F 414.9 504.0 136.1 R2-squared 0.612 0.599 0.555 lag 2 2 2 Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1 Table 3 The effect of inclusive education on health performance in Sub Saharan Africa (Two step GMM strategy) (1) (2) (3) Variables Total life expectancy Total life expectancy Total life expectancy L.Total life expectancy 0.961*** 0.962*** 0.964*** (0.00560) (0.00360) (0.00339) Primary 0.0102*** (0.00347) Secondary 0.00538*** (0.00193) Tertiary 0.00193* (0.00108) Trade in services −0.000139** −0.000312*** −7.09e−05** (5.58e−05) (6.01e−05) (2.58e−05) Employment in services −0.000176*** −0.000193*** −0.000182*** (2.89e−05) (2.90e−05) (1.91e−05) Gross savings 0.000113*** 7.77e−05** −1.29e−06 (2.82e−05) (3.23e−05) (2.32e−05) Health expenditure 1.81e−05* −3.36e−05** −4.69e−06 (1.03e−05) (1.25e−05) (5.53e−06) Domestic credit 6.18e−05 0.000259*** 0.000116*** (5.14e−05) (4.60e−05) (2.35e−05) internet 0.000116*** 0.000122** 5.71e−05*** (2.91e−05) (5.09e−05) (1.68e−05) GDP 4.33e−05 −9.95e−05** 0.000144*** (2.93e−05) (3.76e−05) (3.24e−05) Constant 0.172*** 0.169*** 0.159*** (0.0231) (0.0147) (0.0142) Observations 347 224 196 Number of individuals 35 30 31 Prop > AR1 0.00963 0.0182 0.161 Prop > AR2 0.00421 0.0108 0.112 Number of Instruments 28 28 28 Prop > hansen 0.109 0.475 0.325 Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1 Table 4 The effect of inclusive education on the male and the female life expectancies in Sub Saharan Africa (Two step GMM strategy) Variables (1) (2) (3) (4) (5) (6) Male life expectancy Male life expectancy Male life expectancy Female life expectancy Female life expectancy Female life expectancy L.Malelife expectancy 0.957*** 0.965*** 0.960*** (0.00467) (0.00335) (0.00384) Primary 0.0115*** 0.00783* (0.00265) (0.00413) Secondary 0.00357** 0.00138 (0.00140) (0.00229) Tertiary 0.00155** 0.00199* (0.000706) (0.00103) Trade −0.000130*** −0.000317*** −4.01e−05** −0.000109 −0.000404*** −8.74e−05*** (4.64e−05) (5.51e−05) (1.78e−05) (7.76e−05) (5.22e−05) (2.16e−05) Employment −0.000175*** −0.000170*** −0.000171*** −0.000189*** −0.000197*** −0.000200*** (2.75e−05) (3.50e−05) (2.69e−05) (2.42e−05) (2.95e−05) (2.05e−05) Savings 0.000102*** 0.000106*** 3.35e−06 0.000135*** 6.55e−05* 4.89e−06 (2.66e−05) (2.11e−05) (2.43e−05) (2.61e−05) (3.49e−05) (2.15e−05) Health expenditure 9.79e−06 −3.54e−05*** −5.95e−06 2.10e−05* −3.76e−05*** −8.19e−06* (9.07e−06) (1.23e−05) (4.80e−06) (1.08e−05) (1.16e−05) (4.54e−06) Domestic credit 8.01e−05* 0.000248*** 0.000100*** 4.26e−05 0.000254*** 0.000131*** (4.54e−05) (4.64e−05) (2.18e−05) (4.98e−05) (4.40e−05) (1.66e−05) Internet 0.000109*** 0.000114** 6.15e−05*** 9.96e−05*** 0.000226*** 6.64e−05*** (2.47e−05) (5.23e−05) (1.35e−05) (2.39e−05) (3.95e−05) (1.48e−05) GDP 7.83e−05** −7.05e−05** 0.000122** −8.24e−05** −0.000127*** 0.000127*** (3.03e−05) (3.08e−05) (5.01e−05) (3.39e−05) (3.54e−05) (3.15e−05) L.Femalelife expectancy 0.969*** 0.970*** 0.969*** (0.00664) (0.00415) (0.00343) Constant 0.188*** 0.158*** 0.174*** 0.140*** 0.139*** 0.143*** (0.0191) (0.0135) (0.0158) (0.0271) (0.0174) (0.0138) Observations 347 224 207 377 239 207 Number of id 35 30 31 35 31 31 Prop > AR1 0.00612 0.0159 0.105 0.0154 0.0186 0.203 Prop > AR2 0.00275 0.00720 0.101 0.00495 0.0135 0.0863 Instruments 28 28 28 28 28 28 Prop > hansen 0.215 0.360 0.368 0.230 0.259 0.256 Standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1 Table 5 The effect of inclusive education on life expectancies in Sub Saharan Africa (IV-2SLS strategy) Variables (1) (2) (3) Life expectancy Life expectancy Life expectancy Primary 0.438** (0.201) Secondary 0.646** (0.316) Tertiary 0.487*** (0.0612) Trade in services −0.00386** −0.00413 −0.000305 (0.00164) (0.00264) (0.000587) Employment in services −0.000710** −0.00309** −0.000644*** (0.000337) (0.00150) (0.000166) Gross savings 0.00299** −0.00591 0.00324*** (0.00125) (0.00664) (0.000837) Health expenditure −0.00374*** −0.000510 −0.00279*** (0.00114) (0.00156) (0.000572) Domestic credit 0.00392*** 0.00887** 0.00383*** (0.00134) (0.00437) (0.000697) internet 0.00422*** −0.00250 0.00509*** (0.00103) (0.00347) (0.000602) GDP 0.00141 0.00861 −0.000434 (0.00314) (0.00654) (0.00186) Constant 0.612*** 0.932*** 0.488*** (0.0873) (0.271) (0.0204) Observations 300 281 386 R-squared 0.326 0.3011 0.563 Kleibergen-Paap Lm 131.140 17.565 12.202 Kleibergen-P-value 0.0000*** 0.0035** 0.022* Hansen p-value 0.105 0.475 0.124 Robust standard errors in parentheses ***p < 0.01, **p < 0.05, *p < 0.1 The conditions for the adoption of the GMM strategy hold in the study since it is a panel data analysis and the number of individual cross-Sects. (48 countries) is greater than the time series (21 years). Also, the condition of the role of thumb for the consistency of the dependent variables has been verified since the correlation between the dependent variables and their first lags are all greater than 0.800. The GMM findings presented in Table 3 are well estimated given that the Hansen probability values are all greater than 10%, prompting the rejection of weak identification of variables in Eq. 4. The nature of the Hansen probability validate the instruments used in the study. The results indicate that primary, secondary and tertiary enrolment enhances health expectancy in Sub Saharan Africa. The primary and the secondary enrolment enhances life expectancy at a 1% significance level while the tertiary education enhances life expectancy at a 10% statistical significance level. The results indicate that inclusive education measured by the gender parity index enhances health performance in Sub Saharan Africa. The results indicate that Africans live for long when they are educated with a better life expectancy. Inclusive education for all is considered one of the objectives in the African continent where the rate of school dropped out is very high due to early marriages for the male population and poverty in some rural areas. The findings on the positive effect of education on health performance are supported by those of Anyamele et al. (2017), Shapiro and Tenikue (2017), Bado and Susuman (2016) and Hendi (2017). Controlling for other determinants of health, we employed trade in services, employment in services, GDP, internet, health expenditures and domestic savings as determinants of health performance. Trade-in services appeared to have a negative influence on the total life expectancy. Employment in service also has a negative impact on life expectancy within the period of the study. Gross savings have a positive effect on life expectancy in Sub Saharan Africa. This signifies that the domestic savings enhance the health performance of citizens in these countries and is supported by the findings of Lien et al. (2021). The effect of health expenditure is not clear since the signs in different equations differ. As indicated in the first equation, health expenditure is likely to enhance health performance measured by life expectancy. This is so because healthcare expenditures are associated with an increase in health performance and a reduction in the number of infant and neonatal deaths (Jaba et al., 2014). Domestic credit has a significant positive effect on life expectancy which conforms with the findings of Alam et al. (2021). It signifies that the level of health performance is enhanced by increasing domestic credit to households in Sub Saharan African countries. Domestic credit helps to access household items to boost their wellbeing. ICT measured by the number of individuals using the internet shows that it enhances life expectancy in Sub Saharan Africa which corroborates the findings of Lee et al. (2019). GDP has both a positive and a negative effect on life expectancy at birth. GDP can have a positive effect on health performance as in equation if the growth in the economic sector is transmitted also to the health sector. The level of economic development remains insignificant to the health sector if the growth is not transformed in the health sector to ameliorate the health performance and the life expectancy of the citizens. Robustness Checks Accounting for Life Expectancies Across Different Genders To test the consistency of our findings, the study employed life expectancy of the male and the female population as measures of health performance to determine whether the effect of inclusive education on health performance in Sub Saharan African countries varies across the male and the female genders. The robustness analysis is done by adopting a two-step system GMM. The results of the robustness checks are presented in Table 4 on the effect of inclusive education on health performance employing the male life expectancy and the female life expectancies.as indicators of health performance. The findings presented in Table 4 indicate that the male life expectancy is enhanced by inclusive education both at the primary, secondary and at the tertiary level. The results also show that the primary and the secondary education have a more statistically significant influence on health performance than on the tertiary. The results also indicate a positive statistically significant effect of inclusive education on the female health performance. The primary and the tertiary enrolment both have positive significant effect on the female life expectancy in Sub Saharan Africa. The secondary enrolment still maintains a positive relationship with life expectancy, but appears to be insignificant. The robustness checks confirm the consistency of the study’s findings from the preliminary findings of the correlation analysis presented in Fig. 3 and the GMM analysis (Table3) when the total life expectancy is being employed as an indicator of health performance. Robustness Checks with an Instrumental Two-Stage Least Square (IV-2SLS) This section of the study employs the IV-2SLS strategy to test whether the GMM instrumental results are consistent. The 2SLS strategy addresses the feedback loops in the model to improve its efficiency. The IV-2SLS instrumentation process is validated by the Hansen and the Kleibergen tests (Baum et al., 2003 and Biørn, 2003). In our instrumentation process, we employ the control variables as endogenous and instrumental variables following the works of Hill-Burns et al. (2017). The Hansen test of over-identifying restrictions has to be insignificant for the rejection of the joint null hypothesis revealing valid instruments with uncorrelated residuals. Also, the Kleibergen P-values have to be significant to prompt the rejection of the null hypothesis (weakly identified) that can cause the results to be poorly estimated when instruments are weak. The Hansen probability values are greater than 0.10 (insignificant) in all equations with normal distribution. The null hypothesis of equations exactly identified are not rejected and hence, we conclude that the 2SLS estimates are efficient. Similarly, all the Kleibergen P-values in all equations are significant, prompting the rejection of the null hypothesis of weak identification, validating the IV-2SLS strategy. The findings presented in Table 5 confirm the role of inclusive education in the improvement of health performance given that all the inclusive education indicators have positive statistically significant effects on health performance in Africa. Conclusion and Policy Recommendations The study investigated the impact of inclusive education on health performance in 48 Sub Saharan African countries from 2000 to 2020. The study adopted the Driscoll/Kraay estimator for its baseline analysis and the system GMM technique for its conclusive remarks. Inclusive education is measured in gender parity which encompasses both the male and the female genders. Inclusive education is measured at the primary, secondary and tertiary levels. The study employed the total life expectancy as an indicator of health performance. The results of the study reveal that inclusive education enhances life expectancy in Sub Saharan African countries. The robustness checks have been done by employing the male and the female life expectancy as indicators of health performance whose results are presented in Tables 4. The findings of the robustness checks reveal that inclusive education enhances both the male and the female life expectancy in these 48 Sub Saharan African countries. Also, the IV-2SLS strategy is adopted for further robustness checks to ensure that the GMM strategy provides consistent results. From the findings of the study, we can therefore conclude that inclusive education enhances health performance in Sub Saharan Africa. The findings of the study are supported by those of Bado and Susuman (2016), Anyamele et al. (2017) and Hendi (2017). The study suggests policy orientate at enhancing the level of education in Sub Saharan Africa since it is the region with the highest illiteracy rate in the World. Policymakers are also called upon to invest more in the health sectors to make available health facilities that can help enhance the level of health performance in the region. As reported by the World Bank, there are a total of more than 30 million children in sub-Saharan Africa who do not attend school and also have a lower secondary completion rate. These recommendations are very important since African countries present higher infant mortality rates and lower life expectancy which all indicate poor health performance. The study does not provide the cases of individual Sub Saharan African countries and how education in these countries affects their life expectancy since it has been conducted in the whole region. For further perspectives, scientific works should be conducted to determine the effect of inclusive education on health performance by adopting other health indicators such as infant mortality rate in the case of individual African countries. Appendix 1 See Table Table 6 Matrix of correlations Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (1) life expectancy 1.000 (2) L.life expactancy 0.999 1.000 (3) life expectancy female 0.994 0.993 1.000 (4) L.life expectancy female 0.999 1.000 0.993 1.000 (5) life expectancy male 0.993 0.991 0.974 0.991 1.000 (6) L.life expectancy male 0.992 0.994 0.999 0.994 0.972 1.000 (7) Primary 0.492 0.495 0.499 0.495 0.479 0.502 1.000 (8) Secondary 0.407 0.409 0.461 0.409 0.343 0.464 0.638 1.000 (9) Tertiary 0.406 0.413 0.477 0.413 0.326 0.484 0.317 0.786 1.000 (10) trade in service 0.647 0.655 0.686 0.655 0.593 0.695 0.274 0.466 0.558 1.000 (11) employment 0.379 0.394 0.425 0.394 0.328 0.440 0.379 0.535 0.633 0.453 1.000 (12) Savings 0.137 0.148 0.164 0.148 0.106 0.176 0.151 0.350 0.343 0.303 0.451 1.000 (13) health expenditure 0.374 0.379 0.420 0.379 0.325 0.425 0.251 0.362 0.568 0.353 0.695 0.105 1.000 (14) Domestic credit 0.638 0.644 0.692 0.644 0.574 0.698 0.358 0.519 0.642 0.715 0.695 0.193 0.810 1.000 (15) internet 0.570 0.570 0.603 0.570 0.529 0.603 0.348 0.533 0.611 0.467 0.685 0.169 0.642 0.675 1.000 (16)GDP −0.054 −0.060 −0.075 −0.060 −0.033 −0.080 −0.012 −0.150 −0.155 −0.114 −0.155 0.014 −0.176 −0.184 −0.170 1.000 6. Appendix 2 See Table Table 7 Data, source and description of variables Variables Definition Source Health performance The total life expectancy at gender parity is measured according to the World Bank as “life expectancy at birth which indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life” The male life expectancy: Is computed as the male life expectancy at birth which indicates the number of years a male newborn infant would live if prevailing patterns of mortality are maintained through his life -The female life expectancy: It measures the female life expectancy at birth which estimates the number of years a male newborn infant would live if prevailing patterns of mortality are maintained through his life WDI (2021) Inclusive education Primary enrolments: “Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools” Secondary enrolments: Measured as a gender parity index for gross enrollment ratio in secondary education. “It is the ratio of girls to boys enrolled at secondary level in public and private schools” Tertiary enrolments: It is measures at a gender parity index for gross enrollment ratio in tertiary education. It is the ratio of women to men enrolled at tertiary level in public and private schools WDI (2021) Internet The World Bank defines this indicator as “Individuals who have used the Internet in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV.” WDI (2021) Trade in services Computed as the sum of service exports and imports divided by the value of GDP Employment in services “Employment in services is computed in services which consists of wholesale and retail trade, restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services” WDI (2021) GDP growth GDP growth is measured as the growth in money value of goods and services in a country for a period of one year WDI (2021) Health expenditures Health expenditures are private expenditures on health per capita whose sources include funds from households, corporations and non-profit organizations WDI (2021) Gross domestic savings Gross domestic savings are determined as the part of GDP that does not belong to the final consumption expenditure WDI (2021) Domestic credit It refers to financial resources provided to the private sector by other depository corporations such as commercial banks and deposit taking corporations except central banks WDI (2021) WDI: World Development Indicators of the World Bank (2021) 7. 1 Angola, Benin, Botswana, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Dem. 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K., & Debata, B. (2022). Does ICT diffusion make human development sustainable in the era of globalization? An empirical analysis from SAARC economies. World Bank World development indicators 2021 2021 The World Bank World Bank Glossary. (2022). https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SP.DYN.LE00.IN Zahid MN Perna S Continent-wide analysis of COVID 19: Total cases, deaths, tests, socio-economic, and morbidity factors associated to the mortality rate, and forecasting analysis in 2020–2021 International Journal of Environmental Research and Public Health 2021 18 10 5350 10.3390/ijerph18105350 34069764

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==== Front Arch Dermatol Res Arch Dermatol Res Archives of Dermatological Research 0340-3696 1432-069X Springer Berlin Heidelberg Berlin/Heidelberg 2506 10.1007/s00403-022-02506-0 Review Clinical characteristics and treatment outcomes of Pityrosporum folliculitis in immunocompetent patients Green Maxwell [email protected] 1 Feschuk Aileen M. 2 Kashetsky Nadia 2 Maibach Howard I. 3 1 grid.265219.b 0000 0001 2217 8588 Tulane University School of Medicine, 131 S Robertson Ave, 15th Floor, New Orleans, LA 70112 USA 2 grid.25055.37 0000 0000 9130 6822 Faculty of Medicine, Memorial University of Newfoundland, St John’s, Newfoundland & Labrador Canada 3 grid.266102.1 0000 0001 2297 6811 Department of Dermatology, University of California San Francisco, San Francisco, CA USA 14 12 2022 113 24 7 2022 10 11 2022 4 12 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Pityrosporum folliculitis (PF) is a fungal acneiform disease of the hair follicles that often presents with pruritic papules and pustules on the upper body and face. This condition is commonly mistaken for acne vulgaris and can be distinguished from bacterial acne by the presence of fungal spores in the follicular lumen. Although studies have been performed to describe PF in cohorts, little work has been done to aggregate these data. Thus, the goal of this review is to describe the clinical characteristics and treatment outcomes of PF in immunocompetent patients. PubMed, Web of Science, and Embase were searched using the terms “Pityrosporum folliculitis” or “Malassezia folliculitis.” All cohorts reporting PF characteristics in patients classified as immunocompetent were reviewed. A total of 15 studies were included. Majority of patients were male (64%) with the average age of presentation of 24.26 years. The most common locations of lesions were the chest (70%) and back/shoulders (69.2%). Pruritus was reported by the majority of patients (71.7%). Additionally, 40.5% of patients reported a history of unsuccessful treatment regimens. Treatment was most successful with an oral antifungal (92%), followed by a topical antifungal (81.6%). In conclusion, majority of patients with PF were younger males. Many patients were primarily treated incorrectly, suggesting the importance of proper diagnosis. PF may be distinguishable from acne vulgaris by the presence of pruritus or suggested when a new acneiform eruption develops following antibiotic therapy or immunosuppression. When properly diagnosed, majority of cases of PF achieve complete response with oral or topical antifungals. Keywords Pityrosporum folliculitis Malassezia folliculitis Acne vulgaris Antifungal Acneiform eruption ==== Body pmcIntroduction Pityrosporum folliculitis (PF) is fungal acneiform disease of the hair follicles that presents with papules and pustules often associated with pruritus [1]. PF lesions most commonly present on the chest, shoulders, back or face most often appear during the second and third decades of life [1]. Although other types of fungal folliculitis exist, over 90% have been associated with Malassezia spp. [2]. The pathogenesis of PF likely involves a primary occlusion event in the hair follicle, followed by an overgrowth of Malassezia spp. leading to an inflammatory cascade causing PF [3]. PF and acne vulgaris have very similar presentations, and PF is often misdiagnosed as acne vulgaris (AV) [1]. Malassezia spp. fungi require a lipid source for survival and reproduction which leads these species to overgrow in sebum-rich areas of the body, where AV often occurs [5]. Both PF and AV can present with papules and pustules and occur in similar locations across the body: face, chest, and back. New acneiform eruptions caused by PF have been associated with immunocompromised states such as post-transplant, HIV/AIDS, and malignancies [6]. This is presumed to be because these immunocompromised states allow the overgrowth of Malassezia spp. Most recently, PF has also been associated with COVID-19 in patients presenting to the hospital [7]. This may be an additional sign that may alert a practitioner to consider a diagnosis of PF rather than AV. In cases where clinicians struggle to distinguish PF and AV, a potassium hydroxide (KOH) smear can be used on lesional scrapings to reveal spores. This test has been shown to be both sensitive and specific with values as high as 84.6% and 100%, respectively [8]. Patients with PF are often incorrectly managed with traditional AV treatments, such as antibiotics, with little to no improvement [5]. Thus, in cases of AV resistant to traditional management strategies, the diagnosis of PF should be considered [4]. Given that PF is unresponsive to AV treatment, it is imperative for clinicians to properly distinguish PF and AV in order to correctly manage these two separate conditions. Although small cohort studies have been performed on patients with PF, little work has been done to aggregate these data to describe how PF in immunocompetent individuals differs in clinical presentation and treatment outcomes when compared to AV. Thus, the goal of this review is to summarize clinical characteristics and treatment response in immunocompetent individuals with PF. Methods The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) were used to guide methodology and reporting (Fig. 1) [9]. A comprehensive literature search was performed using the databases PubMed, Web of Science and Embase in March of 2022 using the terms “Pityrosporum folliculitis” or “Malassezia folliculitis.” Studies were included if they described clinical characteristics or treatment outcomes in immunocompetent human patients with PF. Patients recently started on oral steroids were considered immunocompetent for the purpose of this review. Studies describing patients with an immunocompromised state were excluded from this review. No geographic or language restrictions were used.Fig. 1 Flow diagram of the literature search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Adapted from http://prisma-statement.org An initial title and abstract screening was performed on articles collected by the initial search strategy by two researchers (M.G. and A.F.). Then, those articles which passed the initial screening underwent full text review by two researchers (M.G. and A.F.). The data were collected by one researcher (M.G.) and confirmed by two additional researchers (N.K., A.F.). Any discrepancies in process for study selection were settled by a fourth independent researcher (H.M.). Results Ultimately, a total of 15 studies met inclusion criteria. Aggregate clinical presentation data and responses to treatment are described in Table 1. Overall, the average age of patients upon presentation was 24.3 years with the majority of patients being male (64.0%). PF lesions occurred most commonly on the chest (70.0%) and shoulders (69.2%) and less commonly on the face (30.9%). These lesions demonstrated pruritus in the majority of cases described across studies (71.7%). Additionally, a large portion of patients across studies had a history of unsuccessful treatment regimen seemingly directed toward another dermatologic diagnosis (40.5%). Finally, treatments using oral antifungals (92.0%), topical antifungals (81.6%), and combination oral and topical antifungals (77.3%) were highly successful.Table 1 Presentation and Treatment Response of Pityrosporum Folliculitis in 1238 Patients Variable Result Sample size Number of studies Age Mean = 24.26yrs N = 403 8 Sex 64.0%, male N = 970 12 Location Chest 70.0% N = 720 5 Back/shoulders 69.2% N = 409 5 Forehead 30.9% N = 459 4 Presence of pruritus 71.7% N = 540 7 History of unsuccessful treatment regimen (Antibiotics, steroids, etc.) 40.5% N = 802 10 Treatment Oral antifungal 92.0% improvement N = 547 8 Topical antifungal 81.6% improvement N = 310 7 Combination oral + topical 77.3% improvement N = 78 4 Prindaville et al. (2018) found that 60/60 (100.0%) of their patients improved with topical antifungals and that 45/60 (75.0%) had been treated with an oral antibiotic previously [10]. Levy et al. (2007) also found that almost 65% of the 26 patients in their study had previously been diagnosed and treated with AV [11]. Purnak et al. (2018) used a prospective cohort design of 217 patients that PF was significantly more common in summer versus winter months [12]. The relationship of steroid acne to PF was reviewed Yu et al. (1998) which found that 26/34 (76.5%) patients with suspected steroid acne showed Malessezia fungi present on biopsy [13]. Ran et al. (1988) found that AV commonly presented alongside PF in 4/7 (57.1%) patients and often had to be managed simultaneously [14]. Ayers, Sweeney and Wiss (2005) also looked at concurrent AV and PF in a cohort of six females with all patients (100.0%) exhibiting pruritus and the majority (83.3%) receiving past treatment with oral antibiotics [15]. Tsai et al. (2018) found atypical presentations of PF in a cohort of 22/94 patients in a retrospective cohort; atypical presentation consisted of less papules, atypical macules/patches in 6/22 (27.3%), or plaques in 4/22 patients (18.2%) [16]. Tsai et al. (2019) found differences in pediatric versus adult PF, with pediatric patients showing significantly more PF on the face (n = 17/79, 21.5% pediatric; n = 28/242, 11.6% adults, p = 0.027); both age groups showed PF more commonly in males and occurring during summer months [17]. Danby (2016) found that treatment with pulsed ketoconazole treatment over 8 weeks was effective in reducing PF lesions in the vast majority (92.1%) of patients in a 151 patient cohort [18]. Suzuki et al. (2016) also researched the effectiveness of treatment and found that 37/37 (100.0%) patients treated with 2% topical ketoconazole for 27 days and 7/7 (100.0%) patients treated with oral itraconazole for 14 days all showed improvement in PF lesions [19]. Parsad, Saini, and Negi (1998) also researched the effectiveness of oral itraconazole versus placebo in a double-blind study and found the majority of those receiving itraconazole showed complete resolution (n = 9/13, 69.2%) with only one of thirteen (7.7%) showing no change in lesions. The majority of patients receiving placebo (n = 8/12, 66.7%) showed no change or worsening of their PF [20]. Lim, Giam and Tan (1987) found in a retrospective cohort of 70 patients that the majority (n = 43/48, 89.6%) were treated with antibiotic therapy for their PF with no improvement in symptoms; additionally, 34/48 (70.8%) patients also cited heat and/or sweating as an aggravating factor for their PF [21]. Yong, Tan and Tan (2021) conducted an additional cohort review on 214 patients in Singapore and found those treated with oral antifungals showed a slightly higher treatment success rate than those receiving topicals (n = 118/128, 92.2% versus n = 66/82, 80.5%, respectively) [22]. Back, Faergemann, and Hornqvist (1985) found that 20/51(39.2%) patients in a cohort previously received an incorrect diagnosis and were therefore unsuccessful on previous therapies [23]. Finally, Abdel-Razek et al. (1995) found in a clinical trial model that oral ketoconazole 200 mg once daily with application of topical 2% ketoconazole (group 1, n = 20) showed clearance of PF lesions in 100.0% of patients, while oral ketoconazole 200 mg once daily (group 2, n = 20) showed clearance in 75.0% of patients [24]. Descriptions of study design and key findings for each of the 15 studies included are summarized in Table 2.Table 2 Detailed description of PF studies (n = 15) Study characteristics Patient characteristics PF information Treatment and outcomes Other variables Author, year Study type Sample size Age, mean Sex (majority) PF diagnosis method Location Symptoms Concomitant AV Prior failed treatments Treatment Treatment outcome Other variables noted Prindaville et al. (2018) Retrospective cohort 110 15 F: n = 76/110, 69% KOH test (n = 110/110) Forehead (n = 81/110, 74%), upper back (n = 80/110, 73%) Pruritus (n = 72/110, 65%) NR Previously treated for AV with antibiotics (approximately 75%) Oral antifungals (n = 26/110); topical antifungals (n = 60/110) Oral antifungals: improvement or complete response (n = 25/26, 96%); topical antifungals: improvement or complete response (n = 60/60, 100%) NR Levy et al. (2007) Retrospective cohort 26 46 M: n = 22/26, 85% Microscopy or histology (n = 26/26) NR NR NR Previously treated for AV (approximately 65%) Oral ketoconazole; combination oral and topical ketoconazole (n = NR) Complete response in approximately 75% of cases NR Purnak et al. (2018) Prospective cohort 55 23 F: n = 42/55, 76% Tzanck smear (n = 55/55) Most commonly forehead, cheeks, and trunk Pruritus (n = 39/55, 71%) NR Previously treated for AV (n = 28/38, 74%) Oral itraconazole and ketoconazole cream (n = 38/55) 50% or more reduction in lesions (n = 26/38, 68%) Season: PF was significantly more common in the summer compared to the winter (n = 34/55 vs. n = 21/55, p = 0.001) Yu et al. (1998) Retrospective cohort 75 NR NR Twenty-six of 34 (76.5%) patients diagnosed with steroid acne tested positive for Pityrosporum ovale spores in the hair follicles; 19/21 (90.5%) patients diagnosed with PF tested positive for spores, and 3/20 (15%) patients diagnosed with AV tested positive for spores NR NR NR NR Oral itraconazole (n = 29/75); oral minocycline (n = 8/75), and isoconazole nitrate or resorcinol/salicylic acid solution (n = 11/75) Improvement in n = 27/29 (93.1%) with oral itraconazole, n = 4/8 (50%) with oral minocycline, n = 5/11 (46%) with topical isoconazole nitrate or resorcinol/salicylic acid solution NR Ran et al. (1988) NR 7 27 M: n = 6/7, 86% Histopathology (n = 7/7) Back (n = 7/7, 100%), chest (n = 6/7, 85.7%) Pruritus (n = 7/7, 100%) AV at the time of presentation (n = 4/7, 57%) NR NR NR No history of steroid or antibiotic use (n = 7/7) Ayers, Sweeney and Wiss (2005) NR 6 NR F: n = 6/6, 100% KOH test (n = 6/6) Most commonly face, forehead and cheeks (n = 5/6, 83.3%) or shoulders and back (n = 4/6, 66.7%) Pruritus (n = 6/6, 100%) Concurrent AV (n = 6/6, 100%) Previously treated for AV with oral antibiotics (n = 5/6, 83.3%) Oral and topical antifungal combination regimens Improvement (n = 6/6, 100%) NR Tsai et al. (2018) Retrospective cohort 94 NR NR Histological and periodic acid-Schiff stain (n = 94/94) Most commonly face and scalp (n = 11/22, 50%), legs (n = 4/22, 18.2%) NR NR NR NR NR Morphology: typical (n = 72/94, 77%) with characteristic papules and pustules, or atypical (n = 22/94, 23%) with significantly less papules, and having atypical macules/patches, and plaques Tsai et al. (2019) Retrospective cohort 321 Pediatric n = 79/321, 25% Adult: n = 242/321, 75% M: n = 57/79, 72.2% Microscopy or biopsy (n = 321/321) Most commonly chest (n = 59/79, 74.7% pediatrics; n = 177/242, 73% adults), face (n = 17/79, 22% pediatric; n = 28/242, 12% adults, p = 0.027) Pruritus (n = 24/79, 30.4% pediatrics; n = 93/242, 38% adults) NR History of antibiotic use (n = 17/79, 21.5% pediatrics; n = 60/242, 24.8% adults) Pediatric: oral antifungals (n = 60/79, 76%); topical antifungals (n = 19/79, 24%) Adult: oral antifungals (n = 157/242, 65%); topical antifungals (n = 85/242, 35%) Pediatric: improvement in n = 41/47 (87%) with oral antifungals; n = 9/9, 100%) with topical antifungals Improvement in n = 119/126 (94%) with oral antifungals and in n = 34/42 (81%) with topical antifungals Season: most commonly during summer months (n = 34/79, 43% pediatrics, n = 98/242, 40.5% adults) Effectiveness of direct microscopy with KOH versus histology for PF diagnosis: direct microscopy increased the annual diagnosis of PF Danby (2016) NR 151 NR NR Clinically (n = 151/151) NR NR NR Oral antibiotics or steroids prior to PF presentation (51%) Oral ketoconazole Only 12 patients showed no change in the number of lesions after the eight-week treatment, with greater than half showing complete resolution or significant improvement NR Suzuki et al. (2016) NR 44 36 M: n = 35/44, 80% Methylene blue stain (n = 36/36) Chest in approximately 60% of patients Pruritus in approximately 80% of patients NR No antibiotic use was reported in any patients, but 8/44 (18.2%) patients reported recent topical steroid use 2% ketoconazole cream (n = 37/44, 84%), oral itraconazole (n = 7/44, 16%) Improvement in n = 37/37 (100%) with 2% ketoconazole cream, and in n = 7/7 (100%) with oral itraconazole NR Parsad, Saini, and Negi (1998) Double-blind study 26 NR M: n = 16/26, 62% KOH test (n = 26/26) NR NR NR NR Itraconazole (n = 13/26), placebo 9n = 12/26) Complete response in n = 9/13 (69%) and no response in n = 1/13 (8%) with itracanazole. No response or worsening in n = 8/12 (67%) or marked response in n = 1/12 (8%) with placebo NR Lim, Giam and Tan (1987) Retrospective cohort 48 22 M: n = 44/48, 92% Histology or gram-stain (n = 48/48) NR Pruritus (n = 37/48, 77%) Concurrent AV (n = 9/48, 19%) Antibiotic therapy (n = 43/48, 89.6%) Additionally, some patients PF was preceded by antibiotic or corticosteroid use (n = 8/48, 16/7%, n = 1/48, 2.1%) NR NR Aggravating factors for PF: n = 34/48 (71%) cited heat and sweating as an aggravating factor Common concomitant diagnoses: seborrhea (n = 48/48, 100%), seborrheic dermatitis (n = 18/48, 38%) Yong, Tan and Tan (2021) Cohort 214 NR M: n = 162/214, 76% Gram-stain (n = 214/214) Most commonly back (n = 137/214, 64%), chest and trunk (n = 123/214, 57.5%) NR NR NR Oral antifungals (n = 128/214, 60%); or topical antifungals (n = 82/214, 38%) Complete response in n = 118/128 (92%) with oral antifungals and (n = 66/82, 81%) with topical antifungals NR Back, Faergemann, and Hornqvist (1985) NR 51 30 (median) F: n = 39/51, 77% Cellophane stripping stained with methylene blue or KOH (n = 51/51) NR NR Concurrent AV (n = 3/51, 6%) Misdiagnosed and unsuccessfully treated (n = 20/ 51, 39%) Selenium sulfide shampoo (n = 22/25, 88%), propylene glycol 50% in water (n = 12/12, 100%), or econazole cream (n = 8/10, 80%) NR Common concomitant diagnoses: seborrheic dermatitis (n = 9/51, 18%), pityriasis versicolor (n = 8/51, 16%) Abdel-Razek et al. (1995) Clinical trial 62 22 F: n = 40/62, 64% NR Most commonly trunk (n = 59/62, 95.2%) Pruritus (n = 59/62, 95%) Concurrent AV (n = 8/62, 13%), NR Oral ketoconazole and topical 2% ketoconazole (n = 20/62), oral ketoconazole (n = 20/62), econazole nitrate 1% solution (n = 12/62), and miconazole nitrate 2% cream (n = 10/62) Complete response in 100% (n = 20/20) with oral ketoconazole and topical 2% ketoconazole; 75% (n = 15/20) with oral ketoconazole, 0% with econazole nitrate 1% solution and miconazole nitrate 2% cream Common concomitant diagnoses: tinea versicolor (n = 6/62, 10%) and seborrhea (n = 5/62, 8%) Discussion This study summarizes clinical characteristics and treatment outcomes of 1238 immunocompetent patients with PF leading to several important clinical conclusions. First, across studies, PF most commonly presented in male patients (64.0%) with an average age of 24.3 years. PF lesions were most commonly located on the chest (70.0%) and back (69.2%), both of which are common presentation sites for AV. These demographics and lesion sites are likely explained by the common increase in sebum production in this group of patients (young adult males) and at these anatomical sites. Sebum production is typically increased in males compared to females and tends to increase during adolescence and young adulthood, leveling off with age [25]. Additionally, pruritus was commonly described in the clinical presentation for PF in this review (71.7%). This is of relevance given that pruritus is an important distinguishing factor when present from AV [1]. The exact pathophysiology of the pruritus in PF is unknown, but it likely stems from the overgrowth of Malassezia spp. in the hair follicle leading to keratinocyte induction of an inflammatory cascade [1]. Another important finding across studies was that patients presenting with PF had commonly failed a previous treatment regimen, most commonly an antibiotic, for a previously misdiagnosed dermatologic condition (40.5%). It is thought that the use of antibiotics may predispose individuals to PF by disturbing the skin microbiome, thus allowing the overgrowth of Malassezia spp. First line antibiotic therapy for AV typically consists of either a macrolide, clindamycin or a tetracycline targeting P. acnes; in doing so, the commensal balance between bacterial and fungal species can be disrupted to cause overgrowth, thus leading to conditions such as PF [26]. However, 59.5% of patients in this study had no described history of antibiotic use or previously failed treatment regimen. With Malassezia spp. being the most common component in the normal skin flora of healthy individuals, much work remains to be done in understanding how PF develops in fully immunocompetent groups. Additionally, the studies by Purnak et al. (2018) and Tsai et al. (2019) found that PF outbreaks were most common in summer months. This is likely due to increased sweating and subsequent sebum production providing lipids for Malassezia spp. Growth [27]. In addition, studies by Lim, Giam and Tan (1987) and Abdel-Razek et al. (1995) found the most common co-diagnoses with PF to be AV, seborrheic dermatitis and pityriasis versicolor. Malassezia spp. have been associated with AV development, especially the non-inflammatory subtype, and are the fungi responsible for pityriasis versicolor [28]. Although the exact pathogenesis of seborrheic dermatitis is still under investigation, it is known that Malassezia yeast and inflammatory responses play important roles in the disease progression [29]. Thus, patients with these previous dermatologic diagnoses may be of higher suspicion for PF. There are currently no national guidelines in place for treating Malassezia folliculitis; however, majority of individuals across studies showed improvement in response to either oral antifungals (92.0%), topical antifungals (81.6%), or combination regimens of topical and oral antifungals (77.3%). Oral antifungals have been shown to be slightly more effective than topicals against Malassezia spp. skin conditions [30]. Given the successful response to topical antifungals in PF patients, though, topical antifungals may be recommended as first-line therapy to minimize side effects, such as hepatotoxicity, from oral antifungal therapy [30]. Given these antifungals must be prescribed off label, patients may struggle receiving reimbursement from insurance plans. This is an important health care accessibility barrier that must be considered in dermatology, as the use of off-label medications is becoming commonplace in the standard of care for a wide-range of dermatologic conditions, including PF [31]. There are a number of limitations to this review. First and most importantly, PF is likely highly underreported in the literature due to its common misdiagnosis as AV and ability to self-resolve. Additionally, the severity of disease could not be objectively compared across studies, and the definition of improvement with treatment was subjectively defined in each study. Additionally, in majority of reviewed studies, there was no control group to properly gauge treatment efficacy. Finally, in many studies, approximate percentages were used, and therefore, the exact number of patients with a given clinical history or treatment outcome had to be estimated. Conclusion PF is a condition commonly misdiagnosed as AV in the clinical setting. Both conditions consist of papules and pustules commonly occurring on the face, back, and chest most frequently in the second and third decades of life. The correct diagnosis of PF is essential in effectively treating patients, as many are incorrectly managed with oral antibiotics for suspected AV. With correct clinical diagnosis, PF can successfully be managed with topical and/or oral antifungals. The diagnosis of PF should be suspected over AV in patients presenting with pruritus, a history of lesions unresponsive to traditional acne vulgaris treatment, or a new onset acneiform eruption following an immunocompromised state. When in doubt, a KOH mount is highly sensitive and specific for identifying fungal Malassezia spp. in biopsied lesions. Acknowledgements None. Author contributions MG and Dr. HM worked on study concept and design. MG, AF, and NK worked on data collection, data analysis, and drafting of the manuscript. MG and Dr. HM made final edits to the manuscript. Funding sources None. Data availability Not applicable. Declarations Conflicts of interest No authors have any conflict of interests to report in regards to this manuscript. Additionally, there were no sources of funding for this review. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Abdel-Razek M Fadaly G Abdel-Raheim M Al-Morsy F Pityrosporum (Malassezia) folliculitis in Saudi Arabia–diagnosis and therapeutic trials Clin Exp Dermatol. 1995 20 5 406 409 10.1111/j.1365-2230.1995.tb01358.x 8593718 2. Ayers K Sweeney SM Wiss K Pityrosporum folliculitis: diagnosis and management in 6 female adolescents with acne vulgaris Arch Pediatr Adolesc Med 2005 159 1 64 67 10.1001/archpedi.159.1.64 15630060 3. Bäck O Faergemann J Hörnqvist R Pityrosporum folliculitis: a common disease of the young and middle-aged J Am Acad Dermatol 1985 12 1 Pt 1 56 61 10.1016/S0190-9622(85)70009-6 3980804 4. Corzo-León DE MacCallum DM Munro CA Host responses in an Front Cell Infect Microbiol 2020 10 561382 10.3389/fcimb.2020.561382 33552997 5. Danby FW (2016) Malassezia infections-Management with pulsed oral ketoconazole. J Am Acad Dermatol 74(5): AB159 6. Del Rosso JQ Silverberg N Zeichner JA When acne is not acne Dermatol Clin 2016 34 2 225 228 10.1016/j.det.2015.12.002 27015783 7. Durdu M Güran M Kandemir H Ilkit M Seyedmousavi S Clinical and laboratory features of six cases of candida and dermatophyte folliculitis and a review of published studies Mycopathologia 2016 181 1–2 97 105 10.1007/s11046-015-9939-5 26337525 8. Gupta AK Lyons DC The rise and fall of oral ketoconazole J Cutan Med Surg. 2015 19 4 352 357 10.1177/1203475415574970 25775613 9. Hald M Arendrup MC Svejgaard EL Lindskov R Foged EK Danish society of dermatology. Evidence-based danish guidelines for the treatment of malassezia-related skin diseases Acta Derm Venereol 2015 95 1 12 19 10.2340/00015555-1825 24556907 10. Hill MK Goodfield MJ Rodgers FG Crowley JL Saihan EM Skin surface electron microscopy in Pityrosporum folliculitis. The role of follicular occlusion in disease and the response to oral ketoconazole Arch Dermatol 1990 126 8 1071 1074 10.1001/archderm.1990.01670320095018 2143368 11. Kim S Park JW Yeon Y Han JY Kim E Influence of exposure to summer environments on skin properties J Eur Acad Dermatol Venereol 2019 33 11 2192 2196 10.1111/jdv.15745 31199529 12. Lim KB Giam YC Tan T The epidemiology of Malassezia (Pityrosporon) folliculitis in Singapore Int J Dermatol 1987 26 7 438 441 10.1111/j.1365-4362.1987.tb00586.x 3498698 13. Lévy A Feuilhade de Chauvin M Dubertret L Morel P Flageul B Folliculites à Malassezia: Caractéristiques et réponses thérapeutiques chez 26 malades [Malassezia folliculitis: characteristics and therapeutic response in 26 patients] Ann Dermatol Venereol 2007 134 11 823 828 10.1016/S0151-9638(07)92824-0 18033060 14. Luebberding S Krueger N Kerscher M Skin physiology in men and women: in vivo evaluation of 300 people including TEWL, SC hydration, sebum content and skin surface pH Int J Cosmet Sci 2013 35 5 477 483 10.1111/ics.12068 23713991 15. Page MJ McKenzie JE Bossuyt PM The PRISMA 2020 statement: an updated guideline for reporting systematic reviews Rev Esp Cardiol (Engl Ed) 2021 74 9 790 799 10.1016/j.recesp.2021.06.016 34446261 16. Parsad D Saini R Negi KS Short-term treatment of pityrosporum folliculitis: a double blind placebo-controlled study J Eur Acad Dermatol Venereol 1998 11 2 188 190 10.1111/j.1468-3083.1998.tb00781.x 9784054 17. Peres FLX Bonamigo RR Bottega GB Staub FL Cartell AS Bakos RM Pityrosporum folliculitis in critically ill COVID-19 patients J Eur Acad Dermatol Venereol 2022 36 3 e186 e188 10.1111/jdv.17842 34839545 18. Piquero-Casals J Hexsel D Mir-Bonafé JF Rozas-Muñoz E Topical non-pharmacological treatment for facial seborrheic dermatitis Dermatol Ther (Heidelb) 2019 9 3 469 477 10.1007/s13555-019-00319-0 31396944 19. Prindaville B Belazarian L Levin NA Wiss K Pityrosporum folliculitis: a retrospective review of 110 cases J Am Acad Dermatol. 2018 78 3 511 514 10.1016/j.jaad.2017.11.022 29138059 20. Pürnak S Durdu M Tekindal MA Güleç AT Seçkin D The prevalence of Skinmed. 2018 16 2 99 104 29911526 21. Ran YP Zhou GP Pityrosporum folliculitis. Clinical and pathologic report of seven cases Chin Med J (Engl). 1988 101 10 748 749 3150708 22. Rubenstein RM Malerich SA Malassezia (pityrosporum) folliculitis J Clin Aesthet Dermatol 2014 7 3 37 41 23. Suzuki C Hase M Shimoyama H Sei Y Treatment outcomes for malassezia folliculitis in the dermatology department of a University Hospital in Japan Med Mycol J 2016 57 3 E63 66 10.3314/mmj.16-00003 27581777 24. Tsai YC, Wang JY, Wu YH, Wang YJ (2018) Atypical clinical presentations of Malassezia folliculitis: a retrospective analysis of 94 biopsy-proven cases. Int J Dermatol. 57(3):e19–e20 25. Tsai YC Wang JY Wu YH Wang YJ Clinical differences in pediatric and adult Malassezia folliculitis: Retrospective analysis of 321 cases over 9 years J Am Acad Dermatol. 2019 81 1 278 280 10.1016/j.jaad.2019.03.014 30885754 26. Tu WT Chin SY Chou CL Utility of Gram staining for diagnosis of Malassezia folliculitis J Dermatol 2018 45 2 228 231 10.1111/1346-8138.14120 29131371 27. Vest BE, Krauland K (2022) Malassezia Furfur. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing 28. Xu H Li H Acne, the skin microbiome, and antibiotic treatment Am J Clin Dermatol 2019 20 3 335 344 10.1007/s40257-018-00417-3 30632097 29. Xu X Ran X Tang J Skin microbiota in non-inflammatory and inflammatory lesions of acne vulgaris: the underlying changes within the Pilosebaceous unit Mycopathologia 2021 186 6 863 869 10.1007/s11046-021-00586-6 34498139 30. Yong AM Tan SY Tan CL An update on pityrosporum folliculitis in Singapore from a single tertiary care dermatological centre Singapore Med J. 2021 62 10 526 528 10.11622/smedj.2020068 32349197 31. Yu HJ Lee SK Son SJ Kim YS Yang HY Kim JH Steroid acne vs. Pityrosporum folliculitis: the incidence of Pityrosporum ovale and the effect of antifungal drugs in steroid acne Int J Dermatol. 1998 37 10 772 777 10.1046/j.1365-4362.1998.00229.x 9802688

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==== Front Neurocrit Care Neurocrit Care Neurocritical Care 1541-6933 1556-0961 Springer US New York 1658 10.1007/s12028-022-01658-1 Viewpoint Substandard and Falsified Medications: A Barrier to Global Health Equity Exemplified in Ecuador Yakhkind Aleksandra 1 http://orcid.org/0000-0002-4347-6514 Lang Adam Edward [email protected] 23 Brophy Gretchen 4 Tesoro Eljim 5 Levasseur-Franklin Kimberly E. 6 Maldonado Nelson 7 1 grid.67033.31 0000 0000 8934 4045 Department of Neurology, Tufts University School of Medicine, Boston, MA USA 2 grid.475621.3 Department of Primary Care, McDonald Army Health Center, Fort Eustis, VA USA 3 grid.224260.0 0000 0004 0458 8737 Department of Family Medicine and Population Health, Virginia Commonwealth University School of Medicine, Richmond, VA USA 4 grid.224260.0 0000 0004 0458 8737 Department of Pharmacotherapy and Outcomes Science, School of Pharmacy, Virginia Commonwealth University, Richmond, VA USA 5 grid.185648.6 0000 0001 2175 0319 Department of Pharmacy Practice, University of Illinois at Chicago, Chicago, IL USA 6 grid.67033.31 0000 0000 8934 4045 Department of Pharmacy, Tufts University Medical Center, Boston, MA USA 7 grid.412251.1 0000 0000 9008 4711 Department of Neurology, Universidad de San Francisco, Quito, Ecuador 14 12 2022 16 17 6 2022 15 11 2022 © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Medicines have been developed and have become globalized at a pace faster than traditional medical education can keep up. Physicians, pharmacists, nurses, and advanced practice providers learn the names and functions of these medications, but not how they are made and how they get to the bedside. The often economically driven intricacies behind these processes have a dramatic effect on patient care and outcomes. A staggering proportion of medications worldwide are reported to be substandard or falsified. This article explores one country’s story of how medication gets to the bedside, describes how this process can go wrong, and outlines what providers can do to work toward the goal of equitable access to quality medications for all. Keywords Substandard medications Falsified medications Global health ==== Body pmcIntroduction One in ten medications in low-income and middle-income countries are substandard or falsified (SF). SF medications do not do what they are labeled to do or cause unexpected adverse effects. Substandard medications are ineffective or harmful due to poor manufacturing causing contamination or mislabeling or due to degradation of the active or inactive ingredients. Falsified medications are advertently mislabeled and may contain no active ingredients at all [1]. In 2017, it was estimated that only one third of National Medicines Regulatory Authorities (NMRAs), such as the Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in the European Union, had the capacity to conduct key regulatory functions that would prevent these SF medications from harming patients. Ecuador is an example of a country with ongoing reports of SF medications and is used here as a case study to shed light on the global burden of this problem. A Primer on Regulation of Medication Safety How has the supply of medications in the United States become so seamless that we rarely question their origin or integrity? In brief, the U.S. government and European authorities have played a large role in standardizing medications from the days of snake oil to the clinical trials of today. As early as the year 1000 BCE, there is documentation of the regulation of apothecaries. Ingredients and manufacturing processes, however, were not systematically scrutinized until the London Pharmacopoeia began publication in 1618 [2]. It took 200 years of subsequent editions to gain consensus among experts and exclude extraneously perilous ingredients [3]. The first American national pharmacopeia was published in 1820 in efforts to “rid the country of the evil and uncertainty in the preparation of medicine” [4]. Nevertheless, medication producers could say anything they wanted about their product regardless of the truth. It wasn’t until 1902 when the United States mandated labeling on food and drug products, and 4 years later legislation was passed outlawing misbranded or adulterated drugs. Despite this legal proclamation, the government was not equipped to evaluate claims and persecute medication producers until more than 30 years later [5]. In 1938, more than 100 people died of poisoning from a sulfanilamide elixir containing diethylene glycol. New regulations mandated that the recently formed FDA must approve medications prior to them being allowed on the market. Approval was based on the review of ingredients and manufacturing practices but did not require evidence of efficacy and safety in humans. In 1961, thalidomide, marketed to treat morning sickness, was found to cause serious birth defects in children in Europe. In response to this, the FDA tightened its regulation and mandated that batch records and safety information be disclosed. The FDA started unannounced visits to production plants to enforce regulation and prohibited companies from manufacturing if they did not comply. The 1970s growth of biochemical technology and increased regulatory scrutiny contributed to a rise in medication prices. In an effort to decrease costs, new 1984 regulations allowed companies to produce bioequivalent medications after patent expiration of the branded product through an expedited FDA approval process [5]. Europe followed suit with similar policies driven by the EMA. Pitfalls in Quality Control Despite the pillar of accountability that the FDA became, the rate of drug discovery and quantity of companies that produce them have made it more difficult for the agency to prevent serious drug-related events. In 2012, a batch of Avastin® (bevacizumab) was found to have no active ingredient after 76 physicians had used it in hundreds of patients across the United States. There have been reports of patients finding glass and hair in their pills [6]. In 2013, 45 people died as a result of fungal meningitis linked to contaminated methylprednisolone injections compounded in Massachusetts [7]. The task of inspecting every drug manufacturing plant around the globe is labor-intensive and sometimes competes with economic interests. Oversight often becomes a game of catch-up, but the FDA and EMA’s reputation of rigor keeps the quality of medications in the United States and Europe relatively high, respectively [4]. In 1977, the World Health Organization (WHO) mandated the establishment of NMRAs in any country that wanted to import or produce medications [8]. Adoption of the mandate was slow at best, and as generic drug manufacturing and distribution grew so did reports of SF medicines. It was clear that direct enforcement of these regulations was needed, and in 2013 the WHO began to formally monitor drug quality through the Global Surveillance and Monitoring System for SF medical products [1, 2]. Ecuador as a Case Study Regulation of medication safety in Ecuador has only recently taken shape and continues to evolve. Before the formation of a national public procurement service known as SERCOP in 2011, the government allotted money to each health center to buy medications, which led to inefficiencies and excess costs. Since then, SERCOP has centralized the process and now uses a reverse auction, through which pharmaceutical companies provide competitive pricing proposals and medications are then distributed by the government [9]. Applicants initially had to submit a chemical analysis of their product, but not proof of bioequivalence. Enrique Teran, a physician and pharmacology researcher in Quito, recounted that applicants could have scammed the system by submitting a brand name pill for analysis. Contracts were signed but subsequent inspections of selected manufacturing facilities found that many were not up to par and others were nonexistent. Some companies vanished while others were persecuted [10]. For patients, there was a delay in medication access, a public uproar, and far more government expenditures than planned. Reputable international drug manufacturers moved to other South American countries with more welcoming economies as a result of mistrust and suspicion of corruption [11, 12]. In 2016, an amended auction process was repeated, in which SERCOP preemptively reviewed products in more detail [9]. Despite increased scrutiny, stockpiles of falsified medications were found in warehouses as recently as 2019 [13]. Ecuador’s equivalent to the FDA, the Agencia Nacional de Regulación, Control, y Vigilancia Sanitaria, works with law enforcement to crack down on such violations, but corruption and bribery have been blamed for ongoing transgressions [10, 11, 14]. Unfortunately, some of these low-quality medicines have gotten to patients before being intercepted. Dr. Nelson Maldonado, a neurointensivist in Quito, found many inconsistencies in the expected effects of medications compared with those he encountered during his training in the United States. Heparin did not lead to an appropriate elevation of activated partial thromboplastin time. Propofol and midazolam did not produce expected sedative effects in patients at up to three times the typical dose, and responses fluctuated by the vial. This led to difficulties treating status epilepticus and increased observed morbidity and mortality. Dr. Maldonado eventually left the public hospital because he felt he could no longer work in a system that did not have the infrastructure to allow him to properly treat his patients [15]. After further investigation and interviewing local health care providers, it was apparent that Dr. Maldonado was not the only one who noticed these inadequacies [15–17]. In April 2019, patients with chronic myelogenous leukemia noticed that when their medication nilotinib, a tyrosine kinase inhibitor, was switched from brand name Tasigna® to a local generic, they had more side effects, including dizziness, nausea, insomnia, muscle aches, and fatigue. An oncologist at a public hospital in Quito found that her patients who were provided generic nilotinib sustained remission for only 2 years compared with 5 years on Tasigna® [16]. Patients protested and in response, the contract with the generic manufacturer was terminated and patients were provided with Tasigna® [17]. Dr. Patricio Correa, a neuroimmunologist who trained in Spain, had ongoing challenges treating patients with multiple sclerosis (MS). In 2016, the government switched from brand name fingolimod (Gilenya®) to a generic called Lebrina®. Over the coming months, Dr. Correa noticed a pattern: patients who transitioned from Gilenya® to Lebrina® had increased hospitalizations and progression of disease. In 28 documented cases, there was a doubling in the Expanded Disability Status Scale from two to four and an increase in T2 fluid-attenuated inversion recovery lesions on magnetic resonance imaging from a total of 29 to 41 over the course of a year. In Correa’s cohort, 8% of patients on Gilenya® had been hospitalized compared with 22% of those who had switched to Lebrina® [18]. Maricruz Izurieta is one of approximately 270 patients in Ecuador with MS and was on Gilenya® [19, 20, 25]. She and a group of patients with MS went to court and to the media. Their outcry was that bioequivalence testing, as is normally conducted for generics before they go on the U.S. market, was never published for Lebrina® [21]. In Ecuador, this is not a unique phenomenon, and generic medications without bioequivalence testing are called “copy drugs” [21, 22]. Lebrina® had undergone some testing by a third party [23], but data on bioequivalence were not provided on request from the manufacturer. The courts decided that if patients show evidence of treatment failure with Lebrina®, they can be provided Gilenya®. However, as of January 2022, Gilenya® is still not available to patients in Quito [21, 24]. There are reports of poor-quality medications that do not make the news. Dr. Grace Salazar, a hematologist in Quito, noted that a formulation of oral hydroxyurea did not increase levels of fetal hemoglobin as it should in patients with sickle cell disease. She, along with other neurologists and a hospital pharmacist, reported that warfarin led to swings in international normalized ratio without changes in dose out of proportion to the norm [16]. This same pharmacist noted that patients taking oral furosemide did not have improvements in their fluid overload symptoms with increasing doses [26]. Neurologists at one of Quito’s public hospitals reported the Ecuadorian formulation of carbidopa/levodopa had a longer onset of action than another forms [27]. A generic of lamotrigine required higher doses, and generic carbamazepine was less efficacious and caused more side effects than their respective brand name equivalents. Dr. Daniella Di Cupua, an epileptologist trained in Spain, noted that she had found certain formulations of levetiracetam to be less effective than the brand name drug Keppra®. She reported good outcomes with an on-patent anticonvulsant drug lacosamide (Vimpat® made by GSK), but GSK left Ecuador because of difficulties in negotiating contracts with SERCOP [27]. Global Context Ecuador is an example of a country whose NMRA is young compared with the United States and Europe. Most countries in the world are similar to Ecuador. Reports of SF medications hail from all over the world, but primarily Africa and Asia. Many of these are for SF antibiotics, antiseizure medications, and sedatives, which are commonly used in the critical care setting [28, 29]. Each of these classes of medications, when inadequately dosed or tampered with, can lead to adverse events. Uncontrolled seizures and infections can result in increased hospitalizations and mortality. Propofol is particularly dangerous because irregular fluctuations in effective dose can lead to hemodynamic instability [30]. Although the FDA’s reputation and scrutiny keeps many SF medications out the U.S. market, the consequence of such strict oversight are medication shortages. Critical care medications, particularly during the coronavirus disease 2019 pandemic, have been particularly taxed. Heparin had to be substituted with low molecular weight heparin in many institutions due to illness in pigs in China [31]. Normal saline was in low supply for months due to a hurricane in Puerto Rico [32]. The FDA publishes an online database of medications with shortages, which, at the time of writing this article, is about 100 medications long [33]. Medication shortages can occur due to national disasters and epidemics but are more often due to failed factory inspections or production company consolidation for economic reasons. Different sides of the same coin, medication shortages and SF products are reminders to not take access to medications for granted. Global Response Our team sent reports of discrepancies to the WHO Substandard and Falsified Medical Products working group. Anecdotal reports are just the start and need to be validated by chemical and functional equivalence testing. The Pan American Health Association (PAHO), a regional office of the WHO, works with member countries including Ecuador to intersect, report, and inform the public of incidents of SF medications [34]. In other parts of the world, the United Nations works with Interpol to criminally persecute manufacturers and distributers who have knowingly participated in the spread of SF medications [35]. They also published a legislative guide to combating falsified medications for governments [36]. The World Bank and the International Council on Harmonization, made up of member pharmaceutical companies, work on a systems level to implement standards for medication regionally [37, 38]. Nongovernmental donor agencies, such as the U.S. Agency for International Development, The Global Fund, and The Bill and Melinda Gates Foundation, have funded efforts to vet the quality of certain medications for areas in need [38]. The U.S. Pharmacopeia is involved in at least two campaigns to promote quality in medications worldwide [39]. These efforts are a drop in the bucket compared with the work that needs to be done. For example, the UK identified 34,000 unregistered and unauthorized products advertised for sale as treatments for coronavirus just as recently as March 2020 [40]. PAHO found 596 reported instances of medications between 2017 and 2018 in Latin America. Their reporting mechanism is still new, and PAHO estimates that this is just a fraction of true cases, as evidenced by our experience in Ecuador [34]. SF medications continue to affect millions of patients. Some of these patients speak up, but most remain unaware that the medications that they take do not work. This results in more adverse effects, progression of disease despite perceived treatment, hospitalizations, and death. Quality medications that can save lives exist, and access to them depends on ongoing advocacy and systemic changes on the level of policy. Conclusions On the wall of the pharmacy waiting room inside of the public Hospital de Especialidades Eugenio Espejo in Quito, there is a poster entitled “Farmacovigilancia,” which translates to pharmacovigilance. It states that anyone can report adverse drug effects and therapeutic failures by filling out a “tarjeta amarilla”—a yellow card. Dr. Maldonado and many others at the hospital have filled these out time and time again. Despite the public hospital’s efforts, the yellow cards have yielded limited change. Many skilled providers have left for the private sector, in which patients self-pay for medications, because of this barrier to adequate patient care. Consequently, this leaves the most vulnerable population at the highest risk. The plight for safe medications in Ecuador represents that of so many countries across the world and in our own communities. We all work diligently to prevent medication errors on an individual scale, but large-scale errors can fall through invisible cracks in the system. The timing of this issue is even more crucial while the world grapples with the aftershocks of a global pandemic, during which drug shortages in already overwhelmed critical care settings have had devastating consequences [41]. Although the FDA proactively collaborates with manufacturers to reduce the risk of critical drug shortages, many countries may not have the bandwidth to coordinate such an effort. These countries are not served by the large-scale trackable supply chains, leaving patients at an increased risk of obtaining SF medications, especially when supply chains are taxed during or after a pandemic [42, 43]. Luckily, a web of organizations has evolved since the turn of the century geared to address the very issue that physicians, pharmacists, and activists across the world have brought to light. Patients are speaking out and using local courts to get access to quality medications that work. They cannot succeed unless governments and industry step up to effectively serve their communities and customers. Equitable access to quality medications globally requires a multidimensional approach. As health care providers, we have a responsibility to educate ourselves and a duty to advocate for not just individual patients, but for the safety of medical systems on a larger scale. The only things that separate us from the people of Ecuador are distance and the happenstance of history. Ecuador’s story could look more like ours were the FDA to become overwhelmed with a graver pandemic or more severe interruptions in the supply chain. More research is desperately needed to quantify the extent of SF medications around the globe, especially in Latin America. If you are interested in studying SF medications in your country or on an international level, please check out the resources in Table 1 and contact the authors to connect you to a network of experts who dedicate their lives to ensure that patients have access to safe and effective medications.Table 1 Resources for further reading and to get involved Resource name Link to resource World Health Organization https://www.who.int/teams/regulation-prequalification/incidents-and-SF/background Bottle of Lies by Katherine Eban, 2019 https://www.harpercollins.com/products/bottle-of-lies-katherine-eban Countering the Problem of Falsified and Substandard Drugs by The National Academy of Sciences, 2013 http://www.nap.edu/catalog.php?record_id=18272 International Conference on Harmonization https://www.ich.org/ United States Pharmacopeia https://www.usp.org/our-impact/medicines-we-can-trust#:~:text=Medicines%20We%20Can%20Trust%20is,and%20quality%20medicines%20for%20everyone%2C Source of Support No funding was associated with this article. Disclaimer The views expressed in this publication are those of the authors and do not necessarily reflect the official policy of the Department of Defense, Department of the Army, U.S. Army Medical Department, Defense Health Agency, or the U.S. Government. Conflict of interest The authors have no conflict of interest. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. WHO global surveillance and monitoring system for substandard and falsified medical products. 2017. https://www.who.int/publications/i/item/9789241513425 2. Rago L. Drug regulation: history, present and future. drug benefits and risks: international textbook of clinical pharmacology, revised 2nd edition. IOS Press and Uppsala Monitoring Centre; 2008:65–77. 3. Holmes EM Chisholm H Pharmacopoeia Encyclopædia Britannica 1911 11 Cambridge University Press 353 355 4. Eban K. Bottle of lies: the inside story of the generic drug boom. HarperCollins Publishers; 2019. 5. Administration FaD. Milestones in U.S. Food and Drug law. https://www.fda.gov/about-fda/fdas-evolving-regulatory-powers/milestones-us-food-and-drug-law-history Accessed 22 February 2020. 6. Johnston A Holt DW Substandard drugs: a potential crisis for public health Br J Clin Pharmacol 2014 78 2 218 243 10.1111/bcp.12298 24286459 7. Graedon Joe GT. The people's pharmacy: the people's perspective on medicine. LoudNoises, LLC. https://www.peoplespharmacy.com/about Accessed 20 February 2020. 8. Ndomondo-Sigonda M Miot J Naidoo S Dodoo A Kaale E Medicines regulation in africa: current state and opportunities Pharmaceut Med 2017 31 6 383 397 10.1007/s40290-017-0210-x 29200865 9. Ortiz-Prado E. Ecuadorian pharmaceutical market. Quito University of the Americas; 2018. 10. Villagomez S. Interview with Ecuadorian businessmen II. In: Yakhkind A, editor. 2019. 11. Moretti G. 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Abad P Pérez M Castro E Alarcón T Santibáñez R Díaz F Prevalence of multiple sclerosis in Ecuador Neurologia 2010 25 5 309 313 10.1016/j.nrl.2009.12.005 20643041 20. Correa EP. Interview with an MS specialist at Hospital Carlos Andrade Marin. In: Yakhkind A, editor. 2019. 21. Reporter S. Patients with multiple sclerosis claim the lack and quality of medicines. 2018. http://www.teleamazonas.com/2018/09/pacientes-con-esclerosis-multiple-reclaman-la-falta-y-la-calidad-de-las-medicinas-video/ Accessed January 22, 2020. 22. Maldonado NE. Evidence-based practice gaps worldwide in neurocritical care pharmacotherapy. 2017 23. Hung NA Costa FG Hung CT Rosenberg ME Bioequivalence study of 2 capsule formulations of fingolimod 0.5 mg assessing both parent drug and active metabolite in New Zealand healthy subjects (Truncated Design) Clin Pharmacol Drug Dev. 2020 9 5 610 620 10.1002/cpdd.813 32468719 24. Correa EP. Report of adverse medication reactions. Neurology HCAM. 2018:6. 25. Ripalda XaL, Trajano. Interview with MS patient. In: Yakhkind A, editor. 2020. 26. Morales M. Interview with hospital pharmacist. In: Yakhkind A, editor. 2019. 27. Capua DD. Interview with Ecuadorian neurologist. In: Yakhkind A, editor. 2019. 28. Mumphansha H Nickerson JW Attaran A An analysis of substandard propofol detected in use in Zambian Anesthesia Anesth Analg. 2017 125 2 616 619 10.1213/ANE.0000000000002226 28682949 29. Institute of Medicine (U.S.). Committee on understanding the global public health implications of substandard falsified and counterfeit medical products, Buckley GJ, Gostin LO. Countering the problem of falsified and substandard drugs. National Academies Press; 2013:351. 30. Otte WM van Diessen E van Eijsden P Counterfeit antiepileptic drugs threaten community services in Guinea-Bissau and Nigeria Lancet Neurol 2015 14 11 1075 1076 10.1016/S1474-4422(15)00255-0 31. Edney A. Mass pig deaths in China cause short supply of U.S. blood thinner. Bloomberg. https://www.bloomberg.com/news/articles/2019-08-30/mass-chinese-pig-deaths-cause-short-supply-of-u-s-blood-thinner 32. Mazer-Amirshahi M Fox ER Saline shortages - many causes, no simple solution N Engl J Med 2018 378 16 1472 1474 10.1056/NEJMp1800347 29561694 33. Administration FaD. Current and resolved drug shortages and discontinuations reported to FDA. https://www.accessdata.fda.gov/scripts/drugshortages/default.cfm 34. Rojas-Cortés R Substandard, falsified and unregistered medicines in Latin America, 2017–2018 Rev Panam Salud Publica 2020 44 e125 10.26633/RPSP.2020.125 33033498 35. Crime UNOfDa. Trafficking in falsified medical products. United Nations. https://www.unodc.org/unodc/en/fraudulentmedicines/introduction.html Accessed 11 March 2020. 36. Organized Crime Branch DfTA, United Nations Office on Drugs and Crime. Combating falsified medical product-related crime a guide to good legislative practices. Online guide. United Nations. Updated May 2019. https://www.unodc.org/documents/treaties/publications/19-00741_Guide_Falsified_Medical_Products_ebook.pdf Accessed 11 March 2020. 37. Harmonisation ICo. Mission. https://www.ich.org//page/mission Accessed 11 March 2020. 38. Nayyar GML Breman JG Mackey TK Falsified and substandard drugs: stopping the pandemic Am J Trop Med Hyg 2019 100 5 1058 65 10.4269/ajtmh.18-0981 30860016 39. Trust MWC. Our mission. https://medswecantrust.org/the-campaign/ Accessed 12 March 2020. 40. Rees V. Global operation identifies 34,000 falsifed coronavirus medicines. European Pharmaceutical Review. 2020. 41. Choe JCM, Greene J et al. Pandemic and the supply chain: addressing gaps in pharmaceutical production and distribution. 2020;20. https://www.jhsph.edu/research/affiliated-programs/johns-hopkins-drug-access-and-affordability-initiative/publications/Pandemic_Supply_Chain.pdf 42. Shiferie F Kassa E The scourge of substandard and falsified medical products gets worse with COVID-19 pandemic Pan Afr Med J 2020 37 344 10.11604/pamj.2020.37.344.26322 33738032 43. Borse NC, Chase J, Gaur CG, Koduri R, Korai-Kun CK, Kwan JF, Lee DC, Moore R, Raghavendran JC, Takara V, Zeine LS. Responding to the surge of substandard and falsified health products triggered by the Covid-19 pandemic. USP. https://www.usp.org/sites/default/files/usp/document/our-impact/covid-19/surge-of-substandard-and-falsified-health-products.pdf

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==== Front Soc Indic Res Soc Indic Res Social Indicators Research 0303-8300 1573-0921 Springer Netherlands Dordrecht 3043 10.1007/s11205-022-03043-z Original Research COVID-19 and Social Capital Loss: The Results of a Campus Outbreak http://orcid.org/0000-0001-6016-5382 Fulkerson Gregory [email protected] 1 Thomas Alexander 1 Ho Jing-Mao 2 Zians James 1 Seale Elizabeth 1 McCarthy Michael 2 Han Sallie 1 1 grid.264272.7 0000 0001 2160 918X SUNY Oneonta, 108 Ravine Pkwy, Oneonta, NY 13820 USA 2 Utica University, 1600 Burrstone Rd., Utica, NY USA 14 12 2022 112 11 11 2021 21 6 2022 18 11 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This study examines the effects of a COVID-19 outbreak on levels of social capital on a college campus, drawing on survey data collected from students at two colleges—one that experienced an outbreak and one that did not. Social capital is examined as an individual level resource and as a campus level normative tool used to fight collective action problems. We test the hypothesis that the outbreak, as a “shock” to the campus, diminished social capital. We also test hypotheses on gender, race, and ethnicity and social capital, informed by prior research. Our findings suggest that the outbreak did reduce social capital at both the individual and campus levels, though individual social capital had a mitigating effect that increased campus social capital. We find also that gender was significantly linked to campus social capital, while race was predictive of individual level social capital. Keywords Covid-19 social capital community shock college campus ==== Body pmcIntroduction The COVID-19 pandemic poses a global collective action problem, and the fight against it is playing out differentially across communities. The onset of epidemic disease has frequently been a turning point in history, weakening some societies while creating opportunities for others (Oldstone, 2010). As early as the Black Death, public health authorities have been arguing for quarantine measures that created conflict with local religious figures insistent on gathering for worship (Cipolla, 1979). Similarly, during the Spanish Flu, there were conflicts over mask-wearing at World War I victory parades (Barry, 2005). Though the role of social factors in controlling the spread of diseases such as cancer is reasonably well understood, important questions remain (Anand et al., 2008). Resiliency in the face of disease spread hinges on the successful resolution of collective action problems (McNeill, 1998), and the ability to respond collectively depends on achieving high levels of social capital (Brehm & Rahn, 1997). However, what happens when the collective action problem itself—the pandemic—undermines social capital and the ability to respond effectively? In this study, we examine how a COVID-19 outbreak on a college campus led to diminished levels of social capital, thus weakening trust in peers. Further, we consider how the critical effects of gender and race exacerbate inequalities of social capital and collective action. Literature Review Fulkerson and Thompson (2008) identify a critical difference in how social capital is defined and used in social science research. Perhaps most common is the “normative” definition, in which social capital refers to collective networks and norms of trust and reciprocity (Coleman, 1988, 1990; Putnam et al., 1993; Putnam, 2000). Brehm and Rahn (1997) offer a slight variation that defines it as the cooperative relationships that can resolve collective action problems. Building on these definitions, we view this type of social capital as the normative assets of collectivities that can enable them to overcome collective action problems. Engaging in protective behaviors during a pandemic indicates that community members are contributing to the collective good in the form of public health. Alternatively, the “resource” social capital definition defines it as a personal or individual asset that emerges from supportive and rewarding social relationships (Granovetter, 1973; Fernandez, Castilla, and Moore, 2000; Lin, 2001; Portes, 1998). Knowing someone who can provide valuable information or access to employment are forms of this second kind of social capital. For our purposes, the ability to receive support to help manage distress during the pandemic lockdown indicates this type of social capital (Fulkerson et al., 2022). Providing further nuance, Nahapiet and Ghoshal (1998) distinguish between structural, cognitive, and relational dimensions of social capital. In their framework, structural refers to the network, while relational refers to norms, values, and beliefs, and cognitive corresponds to subjective understandings of the structural and relational (Claridge, 2018). While these dimensions may be dissected analytically, they are highly intertwined (Uphoff and Wijayaratna, 2000), and may be found at the collective and individual levels. Brehm and Rahn (1997) suggest that individual (resource-focused) and collective (normative-focused) levels of social capital are linked—with individual social capital acting as a precursor to collective social capital. Jicha et al. (2011) find support for the hypothesis that individual social capital was predictive of participation in collective action—and thus normative social capital—in the wake of a natural disaster. Social Capital and COVID-19 Research focusing on the relationship between social capital and the COVID-19 pandemic is emerging and often adopts the normative conception of social capital. For example, a study by Wu (2021) found that social capital led to a more effective response to the pandemic, defined as collective action and norms of trust and reciprocity. Bian et al. (2020) suggest that the connectedness of Chinese citizens through the WeChat app helped create “virus-combat social capital” that was especially effective at creating a public response. Lau (2020) finds that prior experience combatting the SARS virus gave Hong Kong higher levels of social capital that could be activated to mobilize during the COVID-19 pandemic. The underlying theme of these studies is that normative social capital enabled a better response to the pandemic, providing resiliency through a collective commitment to protective practices such as masking and social distancing. A related body of research examines the mitigating qualities of social capital on the consequences of the COVID-19 pandemic. These studies tend to adopt an individual resource conception. Borgonovi and Andrieu (2020) found that higher levels of social capital at the county-level prompted a quicker response time when the nation (Italy) went into lockdown, thereby preventing outcomes such as death or severe illness requiring a ventilator. Caballero-Dominguez (2021) examined the relationship between social capital and psychological distress in Colombia under the COVID-19 lockdown, finding evidence that social capital could help mitigate distress levels. Finally, Ohta and Yata (2021) found that social connectedness was necessary for the well-being of the rural elderly in Japan. In this case, the authors suggest social capital was an outgrowth of the tradition of “osekkai,” or the willingness to volunteer help and provide social support to the elderly. However, finding mixed or even contrary results, Elgar et al.’s (2020) cross-national analysis of the pandemic found that countries with higher levels of social trust and belonging were experiencing higher death rates, though nations with higher confidence in state institutions and civic engagement experienced lower death rates. Lindström (2020) cautions that these unexpected results may stem from a methodological flaw—the World Values Survey data are collected at different times for different countries on a rolling basis. Perhaps more socially isolated individuals are less likely to interact and thus become infected. Additional research is needed to resolve these questions. Social Capital, Race, and Gender on College Campuses Though not explicitly focused on COVID-19, some research on the effects of social capital, race, and gender on college campuses has been conducted. Schwartz et al. (2018) found that having more individual social capital, measured as on-campus connections, was associated with improved relationships with instructors, higher GPAs, and improved overall attitudes and behaviors of first-year college students. Weitzman and Kawachi (2000) found that higher levels of social capital, measured as volunteer time, were associated with a 26% lower risk for binge drinking. Buettner & Debies-Carl (2012) examined the role of alcohol parties on student social capital, finding that they were more of a supplement to existing social capital than a source of new social capital for students—they could strengthen existing social ties but did not often create new connections. Other studies explore social capital on campus by gender and race, usually as an individual resource. Clopton (2012) found that social capital varied for student-athletes, with females and white students reporting better social outcomes than their male and non-white peers. Hypolite (2020) found that campus Black Cultural Centers (BCCs) were effective at fostering social capital for black students, underscoring their value to campus diversity efforts. Park and Bowman (2015) examine the role of religious organizations in promoting campus bridging social capital as it relates to cross racial interaction (CRI), finding a positive effect. Harper’s (2008) study suggests that social networks could provide a powerful tool for high-achieving African American male undergraduate students. However, Twitty (2013) found that the post-graduation wage gap between white and black students was only reduced when students were networked with highly influential career centers at prestigious universities, which is a function of high elite socioeconomic status. In a study of an elite selective private college, Martin (2013) found that individual-level social capital reproduced class advantages, as increased connections led to better academic performance and post-graduation outcomes. Going beyond studies of college campus, research on race, gender, and social capital has found that individuals who are white and male are more likely to experience the rewards of individual social capital. For instance, McDonald and Day (2010) find that unsolicited job leads are more likely to benefit white males than non-white minorities and women. This is further supported by Song & Chen (2014), who found that unsolicited job leads could also lead to psychological distress for those with less socioeconomic status (SES). Schafer and Vargas (2016) find that while inequalities in social support reflect broader patterns of social inequality, higher SES can preserve social capital over time, thus magnifying gender and racial disparities as they interact with SES. Finally, McDonald (2011) found that work experience created more social capital for men than women, thus explaining part of the gender wage gap. Disaster, Shocks, and Social Capital Much of the research reviewed to this point conceives of social capital as an independent cause or a mitigating factor with various outcomes. However, less research posits social capital as a dependent outcome. One way to approach social capital as a consequence comes from the “community shock” framework (Besser, Recker, & Agnitsch, 2008). A community shock may be considered a “sudden event that significantly challenges the status quo of a community” (Besser, Recker, & Agnitsch, 2008, p. 580). This would be the case following a hurricane but may also be “slow-moving” such as a gradual loss of employment in a community over several years. The COVID-19 lockdown was a sudden event that changed the status quo for most communities, including campus communities. The effect of community shocks on social capital spurns much debate in the literature. One school of thought suggests a “consensus crisis” will ensue (Couch and Kroll-Smith, 1994; Drabek, 1999), whereby people are united in solidarity following a crisis. Alternatively, the “corrosive community” hypothesis (Freudenburg and Jones, 1991) suggests that crisis will erode community social capital, leading people to become distrustful and unable to solve collective action problems. Negative shocks may lead to the breakdown of social norm compliance, resulting in deviations such as stealing and cheating (Bogliacino, et al., 2021), and may diminish cognitive functioning while promoting risky behaviors, fear, and negative reciprocity in the form of a desire to punish others (Bogliacino, Codagnon, Montealegre, et al., 2021; Codagnone, Bogliacino, & Gómez, 2021). Recent evidence has emerged for the idea that negative shocks diminish collective levels of trust only when shock leads to greater inequality—when inequality is absent, pro-social behaviors may follow, leading to positive outcomes (Bejarano, Gillet, and Rodriguez-Lara, 2018, 2021). Identifying and understanding the effects of inequality may help resolve the debate in the community shock literature. Besser, Recker & Agnitsch (2008) and Recker (2013) observe that not all community shocks are negative. They find that diminished social capital levels could be remedied by introducing positive shocks. These positive shocks could be large and sudden, or small and gradual, building over time. This observation offer hope for developing policy solutions. Research Questions and Hypotheses Existing studies suggest that having robust norms of trust and reciprocity will allow collectivities, such as college campuses, to respond with greater agility and prevent disease spread through protective behaviors. However, campus social capital has not yet been considered in light of a COVID-19 outbreak. Nor has the literature consistently evoked the distinction between collective normative and individual resource social capital, leaving some important gaps in understanding. We wish to examine the mitigating effect of individual on campus levels of social capital. In approaching this question, we evaluate the two competing hypotheses from the community shock literature reviewed above: consensus crisis vs. corrosive community. The former would predict higher social capital as a result of the outbreak, while the latter would predict lower levels of social capital. Further, we consider the role of inequality in shaping the response, based on the effects of race and gender. Though social capital research on college and university campuses has not focused on COVID-19 directly, related research on drinking behaviors suggests that individual social capital can help protect students from risks to their health and safety. Most of this literature has focused on how inequalities translate into unequal levels of individual-level resource social capital. This research has demonstrated that social capital has led to beneficial effects for students who are predominantly white and female (based on the study of athletics) and from a higher social class. These benefits include better grades, better relations with professors, and greater success post-graduation. The broader literature (looking beyond the context of college) would suggest that males are more likely to benefit than females. Researchers are also examining whether strategies for improving diversity, such as creating Black Cultural Centers or promoting campus religious participation, can improve social capital. Based on the studies we reviewed, we expect that individual social capital levels will be higher for white and female students in the context of a college campus. Methods and Measurement We conducted survey research on the impacts of COVID-19 in the fall of 2020 through the spring of 2021 at two colleges—one small private college and one medium-sized public college. This survey was part of a larger research project conducted by the ICIC research team (Intermountain COVID-19 Impact Consortium). Other projects include surveys of employees on campus, p-12 teachers, childcare workers, municipal government workers, and small businesses. Over the study period, we collected six different waves of cross-sectional survey data. The analysis presented here uses the fourth wave, administered in late January 2021, as it contained the most relevant measures for the research questions we wish to explore. This wave included a combined sample of 1,710 student respondents, of which 478 were at the smaller college, while 1,232 were at the larger college. The response rate was 19.2%. Our descriptive statistics and correlation matrix are provided for our analysis variables in Table 1 for the combined samples. Table 1 Descriptive Statics and Correlations Statistics V1 V2 V3 V4 V5 V6 V7 V1 Outbreak (Experienced) 72.05% --- V2 Gender (Male) 28.55% − 0.086** --- V3 Race (White) 79.37% 0.010 − 0.066* --- V4 Individual SC: Relationships are Supportive/ Rewarding x̄ =5.67, SD = 1.30 − .048ϯ − 0.020 0.087** --- V5 Campus SC1: Peers’ Skills/Abilities x̄ =4.55 SD = 1.78 − 0.241*** 0.140*** − 0.033 0.187*** --- V6 Campus SC2: Peers are Willing x̄ =4.85 SD = 0.05 − 0.171*** 0.110*** − 0.031 0.208*** 0.750*** --- V7 Campus SC3: Peers’ Time/Effort x̄ =4.51 SD = 0.05 − 0.168*** 0.074** − .049ϯ 0.188*** 0.665*** 0.757*** --- Alpha= ϯ.10, *0.05, **0.01, ***0.001 Outbreak The larger college began the year with a severe outbreak on campus, while the smaller college did not experience an outbreak through the academic year. This allows us to assess outbreak’s impact through a quasi-experimental design comparing the two campuses. We created an outbreak binary dummy variable. Within the sample, 72.05% experienced a campus outbreak of COVID-19, while the remainder did not. Individual Social Capital We conceptualize individual social capital as individuals who possess social relationships or ties that are valuable, supportive, and rewarding. Our measure approximates this conception and is based on a survey question that asks for agreement with the statement, “My social relationships are supportive and rewarding.” Response options ranged from 1 to 7, with 7 indicating a high level of agreement with the statement. The average student reported a relatively high level of agreement with this statement (5.67/7). Campus Social Capital We conceptualize campus (collective) social capital as high levels of trust and reciprocity that lead to enhanced collective action. Our measures of collective campus trust include three different variables that relate to the extent to which respondents trust their peers to engage in protective behaviors (wearing masks and social distancing). Each item asks for agreement with a statement, in which the answers range from 1 to 7, with 7 indicating a high level of confidence in peers. The first item asks for agreement with the statement, “I have confidence in the skills and abilities of my peers at the college to develop and sustain successful COVID-19 protective behaviors.” The second item asks for agreement with the statement, “My peers at the college are willing to engage in the necessary efforts to develop and sustain successful COVID-19 protective behaviors.” The third item states, “I believe that my peers intend to prioritize their time and effort on COVID-19 preventive behaviors.” The averages (4.55, 4.85, and 4.51) indicate similar, slightly higher than neutral levels of agreement. Social Inequalities In order to examine research questions about social inequalities, we used questions that measured student gender and race. Due to sample size limitations, we decided to recode the original responses into binary variables for students as White/Non-White and Male/Non-Male. The sample includes 79.37% white students and 28.55% male students. Analysis and Findings Our analysis relied on IBM SPSS Statistics to organize and manage the analysis variables and complete the analysis. We then turned to SEM analysis within IBM AMOS to test our hypotheses. This was necessary to examine the mitigating role of individual social capital on campus social capital, which could not be tested with an additive model in OLS regression. As the correlation matrix in Table 1 shows, the outbreak variable had a small but significant negative relationship with gender (-0.086), meaning that the school going through the outbreak had fewer male students. There was no difference between the schools in terms of racial composition. The outbreak was correlated with a lower level of individual social capital (-0.048) and a larger negative and significant relationship with the three indicators of campus social capital (-0.241, − 0.171, and − 0.168, respectively). Therefore, on a bivariate level, it appears the outbreak had a negative relationship with both individual and campus levels of social capital. Turning to questions regarding race and gender, apart from the relationships to the outbreak noted above, we find that gender (being male) was related to slightly lower levels of individual social capital (-0.048) and moderately higher levels of trust in peers or campus social capital (0.140, 0.110, and 0.074, respectively). White students reported slightly higher levels of individual social capital (0.087) and weak to no relationship to trust in peers, with only one indicator—peers’ time/effort in protective behaviors—marginally significant and negative (-0.049). Next, we find that individual social capital has a moderate positive relationship with each indicator of campus social capital (0.187, 0.208, and 0.188, respectively). The three indicators of campus social capital/peer trust correlate highly with an alpha (average correlation) of 0.724. This high intercorrelation suggests they are measuring a latent construct, which we label “Trust in Peers” in the next stage of our analysis. SEM Analysis Next, we tested our research questions and hypotheses using Structural Equation Modeling (SEM). This allowed us to examine both direct and indirect effects, which was essential for testing the moderating effects of individual social capital on campus social capital. Figure 1 presents the results of the analysis, showing standardized coefficients. Initially, we created a saturated model in which all possible paths were included between the variables. We then trimmed from the model the non-significant paths. The resulting model, therefore, only includes statistically significant paths. Overall, the model fit statistics are deemed acceptable, as the chi-square to degrees of freedom ratio is below 5 (4.015), the various fit indices each approach the limit of 1, and the RMSEA is below 0.08 (0.042). The squared multiple correlations in the model suggest that the independent variables account for about 11% of the variation in explaining trust in peers while the respective indicators show explained variation as 67% (peers’ skills/abilities), 84% (peers are willing), and 67% (peers’ time/effort). Fig. 1 Path Model with Standardized Coefficients. Model Fit: χ2 = 40.152, df = 10, ratio = 4.015; NFI = 0.985, RFI = 0.958, IFI = 0.989, TLI = 0.968, CFI = 0.989, RMSEA = 0.042 The standardized effects shown in the model paths inform us of the strength and direction of the relationships between the variables. SEM models allow us to examine latent variables, which we have utilized by creating the “Trust in Peers” construct. The three indicators of this construct—peers’ skills/abilities, peers are willing, and peers’ time/effort—have large path coefficients, ranging from 0.82 to 0.92. These are higher than the intercorrelations between the variables noted earlier. With respect to the effect of the outbreak, the model shows results similar to the bivariate correlations—a modest negative relationship with individual social capital and a modest (-0.19) negative relationship with trust in peers. Also consistent with the bivariate correlation analysis is the positive effect of individual social capital on the latent variable, “Trust in Peers.” Table 2 reports the total effects, which take both direct and indirect effects into account. The table shows that the total effect of the outbreak on trust in peers (campus social capital) is − 0.205. The total effect accounts for the moderating effect of the outbreak on individual social capital. Since the outbreak led to a slight reduction in individual social capital, its positive effects on trust in peers were reduced, resulting in a larger net negative effect of the outbreak on campus social capital (trust in peers). Table 2 Total Effects (Standardized) Race (White) Outbreak (Experienced) Gender (Male) Supportive/Rewarding Relationship (Individual SC) Trust Peers Supportive/Rewarding Relationship (Individual SC) 0.087 − 0.050 0.000 0.000 0.000 Trust Peers (Latent Variable) 0.019 − 0.205 0.111 0.220 0.000 Peers’ Time/Effort 0.016 − 0.168 0.091 0.180 0.821 Peers are Willing 0.017 − 0.188 0.102 0.202 0.918 Peers Skills/Abilities 0.016 − 0.168 0.091 0.180 0.817 Next, we turn to our questions about social inequalities. We find that in the model, the direct effect of gender (being male) led to a higher level of trust in peers. Despite the significant bivariate correlation, the effect of gender on individual social capital, net of the other variables in the model, was not significant. It was therefore trimmed out of the model. Regarding the effects of race, being white led to a higher reported level of individual social capital, as indicated by having more supportive and rewarding relationships. Consistent with the bivariate correlations, net of the other variables in the model, race did not have a significant direct relationship with trust in peers (campus social capital). However, there is an indirect relationship since being white leads to greater levels of individual social capital, which in turn leads to higher levels of trust in peers (campus social capital). The total effect of being white on trust in peers remains modest (0.019), as shown in Table 2. Discussion and Conclusions The analysis above provides us with preliminary conclusions for our research questions and hypotheses. First, regarding the outbreak, it appears that the “corrosive community” hypothesis was supported over the “crisis consensus” hypothesis. Rather than the outbreak bringing the campus together, the effect was a damaging blow to campus social capital, resulting in degraded trust in peers to engage in collective protective behaviors. This was exacerbated by a slight reduction in individual social capital, owing to its mitigating effects. Students with more supportive and rewarding relationships were more likely to trust their peers, but since the outbreak damaged these supportive relationships, there was less mitigation of campus social capital. In terms of our analysis of social inequalities, we tested the effects of race and gender on social capital. While we expected higher levels of individual social capital for white and female students, our model provided partial support. Gender had no direct effect on individual social capital. It did, however, impact campus social capital, though in the direction of favoring males. There was a direct effect between race and individual social capital with white students experiencing higher levels, as anticipated. Because of the mitigating quality of individual on campus social capital, being white led to a minor indirect boost in trust in peers. Prior studies have only modeled the direct effects of race and gender and have not distinguished individual from campus levels. Hence, our findings provide novel insights into these dynamics of social inequality and social capital. Limitations, Implications, and Future Research While this analysis provides valuable insights and contributions to our understanding of campus social capital dynamics amid the COVID-19 pandemic, we caution against overgeneralization. The colleges in our analysis were predominantly female, for instance, and may not be representative of colleges with different gender proportions. The racial composition of the respondents was close to national averages but slightly overrepresents white students. Next, the two colleges in the study are different in size and this is reflected in the size of their respective samples. This may have an effect on statistical power and the ability to make inferences. Finally, the study’s cross-sectional design limits our ability to fully establish causality, and we caution against interpreting correlations and path coefficients as causal relationships without further longitudinal testing. We hope this study will encourage future investigations of college campuses from diverse communities, deepening our understanding of campus social capital. It would be worthwhile to understand the differences between the effects of a pandemic disaster and, for instance, a natural disaster such as a hurricane or flood. In addition, gender, race, and other categorical differences in how individuals experience and respond to disasters might be implicated in individual and community-level social capital. Future research should continue to investigate how social inequalities shape access to social capital. From a policy perspective, our study implies that college campuses can prepare for threats such as pandemics by investing in students’ personal relationships. Discrepancies in social capital by race and gender are concerning, and efforts to close gaps should be pursued, such as those identified earlier in the literature review. Importantly, solving collective action problems requires trust and cooperation from community members on campus. Colleges and universities should explore strategies for enhancing the value and supportiveness of personal relationships for the student body. It benefits the individual, helping them to limit distress, while also bolstering the campus community’s resilience against external threats, including the COVID-19 pandemic. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 7696 10.1007/s00500-022-07696-3 Application of Soft Computing Evaluation of quality of online shopping services in times of COVID-19 based on E-S-QUAL model and Fuzzy TOPSIS method http://orcid.org/0000-0002-0550-5177 de Melo Fagner José Coutinho [email protected] 1 http://orcid.org/0000-0002-1955-2484 Xavier Larissa de Arruda [email protected] 2 http://orcid.org/0000-0002-2764-0732 de Albuquerque André Philippi Gonzaga [email protected] 2 http://orcid.org/0000-0001-7927-9021 de Medeiros Denise Dumke [email protected] 2 1 grid.26141.30 0000 0000 9011 5442 Departamento de Administração, Universidade de Pernambuco, Av. Academico Helio Ramos, S/N, CDU, Recife, Pernambuco 50.740-530 Brazil 2 grid.411227.3 0000 0001 0670 7996 Departamento de Engenharia de Produção, Universidade Federal de Pernambuco, Av. Academico Helio Ramos, S/N, CDU, Recife, Pernambuco 50.740-530 Brazil 14 12 2022 115 18 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This paper aims to propose an approach to evaluate the quality of online shopping services in times of pandemic COVID-19, from the ordering of quality attributes taking into account customers' perception. The proposed approach was developed from a structured questionnaire containing 25 quality attributes adapted from the E-S-QUAL model and applied to consumers of online shopping services. Fuzzy set theory was used in the approach to simplify the subjectivity of human judgment, along with the extension of Technique for Order Performance by Similarity to Ideal Solution (TOPSIS). Therefore, this research was classified as applied, exploratory, quantitative and survey. To achieve the research objective, 819 questionnaires were collected. Among the main findings, it is highlighted that the attributes “product availability”, “products with excellent quality”, “confidence in online shopping processes” and “ease of buying online” were the ones that presented the best perceptions of quality by the respondents. At the other end, the attributes "opinion sharing on social networks", "buying online is a good option when you have little time", "distraction in online shopping searches" and "shopping online is a pleasure" showed the highest level of dissatisfaction with the service. Thus, this article highlights the importance of online shopping services in times of the pandemic caused by COVID-19, and its main contribution and originality is the development of an approach that aims to support the decision-making process, establishing strategic actions for the continuous improvement of online shopping services with the reduction of subjectivity in customer perception and with successive refinements. Keywords Evaluation of quality Online shopping E-S-QUAL TOPSIS Fuzzy set ==== Body pmcIntroduction Coronavirus (CID10) was discovered in the 1960s, and its nomenclature is characterized by its profile like a crown. In December 2019, a new agent of this virus was identified in China, which was named COVID-19 (Zhu et al. 2019; Han et al. 2020; Perlman 2020; Huang et al. 2020). According to the World Health Organization (WHO), the form of contagion happens from one sick person to another or through close contact through the touch of the handshake or saliva droplets. Some laws were created to face the public health emergency with preventive measures resulting from this 2019 outbreak (WHO 2020). Moreover, behavioural changes have been affecting society, and some preventive attitudes have been put into practice, due to the COVID-19 pandemic situation. Social isolation was the most effective measure adopted by government agencies for flattening the contagion curve (Wu et al. 2020; Chen 2020; Huang et al. 2020; Perlman 2020). Anguish and fear of contamination cause those who have this privilege to reduce the number of times that they leave their homes to shop, choosing new alternatives to make their purchases. As a result, social isolation becomes an ally for a significant increase in online shopping, while in physical stores client turn out may be slowing, although some factors need further observation. Alharthey (2020) checked the impact of online shopping confidence on online shopping intentions in the Kingdom of Saudi Arabia. The study included 452 people, and the author concluded that online trust positively impacts online buying attitudes. Rubin et al. (2020) examined the role of 103 consumers' mindset on online shopping cart abandonment. Their conclusion was that abstract consumer mindset tended to rank products inserted into shopping carts as a purchase priority (most important) and that such an attitude reduced shopping abandonment. Duong and Liaw (2021) developed a statistical model to identify the factors that determine online shopping addiction among Vietnamese university students. There was a strong correlation between daily online shopping frequency and daily internet shopping use, and internet experience had a significant negative effect on online shopping addiction. Moon et al. (2021) analysed the common characteristics between 251 consumers who used offline and online shopping channels during the pandemic in Korea. The researchers concluded that, in times of pandemic like that of COVID-19, consumers are likely to decrease consumption through offline distribution channels (purchase in person), and increase consumption through online distribution channels. The authors further noted that male consumers between the ages of 20 and 30 tend to use more offline distribution channels, and this audience decreased purchases from these distribution channels due to COVID-19. Al-Hattami (2021) developed a model to assess the intention to continue using online shopping during COVID-19 by integrating the expectancy confirmation model, the task technology adjustment model, and the trust factor. The model was applied to 222 individuals. The results indicated that the factors “satisfaction”, “perceived usefulness” and “trust” have positive impacts on consumers' intention. Chang and Meyerhoefer (2021) checked how the COVID-19 pandemic impacted online food shopping services in Taiwan. Online shopping grew around 5.7%, in the sector of grains, fresh fruits and vegetables, with frozen foods growing the most. The study also showed that the media influenced online food shopping in the COVID-19 pandemic. It is clear that, with the arrival of the COVID-19 pandemic, the theme related to online shopping has been widely studied, but few are the studies that sought to evaluate the quality of online shopping services taking into account the particularities of the COVID-19 pandemic. Thus, this paper seeks to answer the following research problem: Which attributes related to online shopping services in times of the pandemic of COVID-19 are considered of quality by the customer? To answer the research problem, this paper aims to propose an approach to evaluate the quality of online shopping services in times of pandemic COVID-19, from the ordering of quality attributes taking into account customers' perception. The proposed approach was developed from a structured questionnaire containing 25 quality attributes adapted from the E-S-QUAL model and applied to consumers of online shopping services. Fuzzy set theory was used in the approach to simplify the subjectivity of human judgment, along with the extension of Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) proposed by Chen (2000), adapted by Wang and Lee (2007) and used by Aquino et al. (2019), Melo et al. (2022a) and Melo et al. (2022b) in the quality assessment scenario to rank the investigated attributes. In these works, the technique was used to order the attributes based on the perception of the service user, this being the only item for the managerial decision-making process related to the continuous improvement of the services provided. Although the traditional TOPSIS method takes into consideration several alternatives and criteria, being classified in this way as a multi-criteria decision method, the present research uses the extension of the TOPSIS technique proposed by Chen (2000), adapted by Wang and Lee (2007) and used by Aquino et al. (2019), Melo et al. (2022a) and Melo et al. (2022b) to order the quality attributes from the perception of the service user in online shopping. Thereby, the TOPSIS extension used in this paper cannot be classified as a multi-criteria decision method, since the definition assumes that there are at least two action alternatives that can be chosen, being motivated by the desire to achieve multiple goals, often conflicting with each other, associated with the consequences of the choice for the alternative to be followed (Almeida, 2007, Frej et al., 2017 and Roselli et al., 2020). It is worth mentioning that there are many ways to evaluate the quality of the service provided like the following examples. Guimarães Jr et al. (2020) sought to explore the relationship between the five dimensions of quality and perceived quality in the logistical service of internet purchases in Brazil through structural equation modelling, that is, using the statistical model. In the study developed by Chen et al. (2014) to assess the quality of home delivery of products in Taiwan, the quality function deployment (QFD) was used to propose a model of a home delivery service model. The analytic hierarchy process (AHP) was used in the work proposed by Li et al. (2015) to develop a three-tier hierarchical model for online purchasing product quality control in China. Jakhar et al. (2020) used the fuzzy AHP to develop a model to assess the quality of online clothing stores. Kuo and Liang (2011) used the Fuzzy VIKOR integrated with the GRA to propose an approach to assess the quality of service at international airports in Northeast Asia. Despite the diversity of the highlighted methods, the Fuzzy TOPSIS method was used in this article at the expense of other methods such as statistical models, QFD, AHP, Fuzzy VIKOR, among others, due to the method's own purpose and suitability for the purpose of this article, in addition to the method has proved to be an easy-to-apply tool in the multi-criteria decision-making process and allows a comparison with the perfect alternative through the screening process, thus reducing subjectivity in the client's perception. The originality of the research is based on the proposal to evaluate quality taking into account attributes perceived by customers in the services offered; on the absence, verified by the literature review, of studies that present the integration of the E-S-QUAL scale with the Fuzzy TOPSIS method; and on the gap of studies that perform the application of these methods in the provision of online shopping services in times of pandemic COVID-19. From this application, actions can be developed and implemented for organizations to unfold their strategies for improvements in quality services. This article is structured in seven sections. This section contains the introduction of the paper, and the context of the problem to be discussed, as well as the purpose of the study. In the second section, there is a theoretical background that will base the research dealing with subjects such as quality in service, the E-S-QUAL model and the TOPSIS technique. The third section presents the methodology adopted with the characterization of the research and the structure of the proposed approach for assessing quality in services. The fourth section presents the profile of the sample, as well as the application of the proposal. Section five is the discussion, and the sixth section describes the Managerial Implications of the research. Finally, the conclusion of this paper is presented in the seventh section. Theoretical background In this section, the main topics that will guide this research are described as: quality in service, the E-S-QUAL model and the TOPSIS technique. Quality in services The concept of quality of service is defined as a long-term cognitive judgment regarding an organization's “excellence or superiority” (Ma and Zhao 2012; Wahab et al. 2017). The term “quality of service” is used to assess service through customer satisfaction (Jeeradist et al. 2016). Service quality is a measure of how well the service level has delivered customer expectations (Yang et al. 2012). According to Suresh and Mohan (2016), the quality of services is determined by several dimensions that customers expect from the service they want. Improving quality does not mean that customers will be completely satisfied with the service, so the objective is to find the factors that will help determine the quality assurance in the service. The search for satisfying personal needs and desires, past experiences that the customer has had with the service, communication between customers, external communication, and the price of the service are factors that influence the formation of customer expectations. As the service is an experience, the quality of the service is strictly linked to customer satisfaction (Bezerra and Gomes 2015; Silva et al. 2017). Several relevant models for assessing quality in service have already been created, such as the Grönroos (1984) technical and functional quality model, the GAP Analysis model (SERVQUAL) by Parasuraman et al. (1985; 1988), the performance-based model (SERVPERF) by Cronin and Taylor (1992), the Evaluated Performance / Normed Quality model by Teas (1993), the E-S-QUAL model by Parasuraman et al. (2005), the HEDPERF model by Abdullah (2006), among other models. With the evolution of commercial transactions, accelerated by the COVID-19 pandemic, services migrated and expanded their activities to e-commerce, the physical suddenly became digital, what was only a trend has become the current and growing business model. In this not so new scenario, it is fundamental for organizations to continue investing in the quality of their products and services. This investment has customer satisfaction as its main purpose (Anderson et al. 2020; Bhatti et al. 2020). According to Lopes et al. (2019), the studies that measure quality evaluation in electronic services are still being developed both theoretically and empirically. Recent research on the quality of electronic services is concentrated on e-banking as described in the articles by Mujinga (2020); Li et al. (2021) and Egala et al. (2021). However, with the increase in food delivery services in the pandemic, this niche has also become of academic interest, as reported by Belanche et al. (2021); Uzir et al. (2021) and Hernando and Gunawan (2021). With the intensification of virtual retail, the demand for online customers also grows in the same proportion. In addition, the online customer also takes into account the information, contents, opinions and experiences of other users and digital influencers, which has led some companies to raise the quality of their services. And with the popularization of the use of electronic services, consumers do not tolerate the poor quality of the service provided (Lopes et al. 2019). Therefore, understanding and measuring the quality of e-shopping services is essential for maintaining competitiveness and success in this sector, because first, it is necessary to understand how the consumer assesses quality to only then provide a service of superior quality (Akinci et al. 2010; Li et al. 2021). To achieve the objective of this work, the E-S-QUAL model was adapted to assess the quality of the online shopping purchase service in times of the COVID-19 pandemic by ordering the attributes using the Fuzzy TOPSIS. Quality of online shopping service Online retailing is among the fastest-growing areas today. Understanding the factors that contribute to both the success and the survival of companies is essential to improving the quality of the services provided (Shankar et al. 2020). The search to understand the issue of quality of online shopping services is not new today, but we have gone through a global transformation that radically changed consumer behaviour both in their physical purchases, but especially in their online purchases (Gezer et al. 2022). Consumers now look at the products and brands from a new perspective, characterized by being more sensitive and considering several factors to only then acquire a product or service in their online shopping (Wang et al. 2022). With the acceleration of online shopping caused by the pandemic, new sales platforms have also emerged, increasing competition and leading companies to compete for more markets and customer loyalty (Mamakou and Roumeliotou 2022). Within this strand, studying and understanding customer satisfaction issues has become a vital concern for organizations that need to diversify from the competition and enhance their customers' consumption experience (Juwaini et al. 2022). What is already known about online shoppers, is that they seek online shops that are secure with their personal and banking information, in addition to offering ease of site navigation, search functions, price comparison, description devices, the payment system and the information available, as well as customer service ranging from phone, email and chat support, and not the least product delivery within the stipulated time (Akıl and Ungan 2022; García-Salirrosas et al. 2022; Rusdiana 2022). Given the boom of online shopping worldwide and searching for answers to unravel what now impacts customer satisfaction and the quality of online shopping services, many kinds of research have emerged recently, to name a few. Yunus et al. (2022) studied the effect of online shopping service quality on customer satisfaction and their repurchase intention through an online consumer rating tool as an intervening variable in the marketplace of a large online shopping platform. The researchers, Goutam et al. (2022), analysed the impact of technology readiness on e-service quality and their effect on purchase intention and behavioural loyalty in the context of online shopping. However, García-Salirrosas et al. (2022) aimed to understand what is the influence of customer satisfaction trust on perceived value and purchase intention of users of small business online shops in countries such as Mexico, Peru and Colombia during the economic crisis caused by the COVID-19 pandemic. Juwaini et al. (2022) determined various effects of service quality on customer satisfaction and loyalty, online service trust on customer satisfaction and loyalty and the direct effect of online shop customer satisfaction and loyalty. Some research was more sector-specific; for example, Saricam (2022) evaluated service quality and its relationship with customer satisfaction and loyalty in the sportswear retail market. According to Yang et al. (2022), the quality of the online shopping service is a crucial factor for the success of companies. Soon, e-commerce companies need to provide the best quality of e-services because according to Juwaini et al. (2022), the quality of e-service can affect consumer satisfaction and trust. That can be potential loyal customers. E-S-QUAL The E-S-QUAL model was developed by Parasuraman et al. (2005) to measure customer perceptions about the quality of services provided in online purchases. The authors identified a set of 113 items representative of 11 dimensions, which are: access, ease of navigation, efficiency, customization, security, responsiveness, guarantee, knowledge of price, aesthetics, reliability, and flexibility. After identifying the items, the authors used an exploratory factor analysis test, where the dimensions were reduced to 4 (efficiency, system availability, service, and privacy), linked to 22 questions. Thus, with the development of the E-S-QUAL model, several studies were carried out, such as Akinci et al. (2010), Rafiq et al. (2012), Yaya et al. (2017), Handayani et al. (2018), Ghosh (2018), Lopes et al. (2019); Ketema and Selassie (2020); Raza et al. (2020); Mujinga (2020); Belanche et al. (2021); Egala et al. (2021). Considering the general characteristics of the E-S-QUAL model proposed by Parasuraman et al. (2005), and the attributes considered important in the literature on quality of online shopping services, the dimensions and attributes used in this study are summarized in Table 1.Table 1 Dimensions and attributes of quality in online shopping in COVID-19 times Dimension Attribute Description References Ease of use 1 Ease of Buying Online Finn (2011), Zavareh et al. (2012), Vos et al. (2014), Bressolles et al. (2014), Tandon et al. (2017), Ghosh (2018), Lopes et al. (2019), Al-dweeri et al. (2019), Ketema and Selassie (2020), Egala et al. (2021), Hernando and Gunawan (2021) 2 Distraction in online shopping searches Aesthetics 3 Shopping online is stimulating Finn (2011), Zavareh et al. (2012), Fan et al. (2013), Bressolles et al. (2014), Khan et al. (2019), Al-dweeri et al. (2019) 4 Shopping online is a pleasure Access 5 Ease of viewing online shopping reviews on social media Rafiq et al. (2012), Kurt and Atrek (2012), Zehir and Narcıkara (2016), Tsao et al. (2016), Ayo et al. (2016), Jaiyeoba et al. (2018), Lopes et al. (2019), Widodo et al. (2019) 6 Ease of viewing online shopping reviews on the website 7 Searching for information about online stores on websites Security 8 Viewing Publications on Social Networks about Products Rafiq et al. (2012), Kurt and Atrek (2012), Bressolles et al. (2014), Zhang et al. (2015), Ayo et al. (2016), Malik et al. (2016), Tandon et al. (2017), Shahid et al. (2018), Khan et al. (2019), Lopes et al. (2019), Ketema and Selassie (2020), Egala et al. (2021) 9 Influence of expert opinions on the purchase decision 10 Living with other people influences online shopping decisions 11 Social media sharing, positive posts about online shopping products 12 Opinion sharing on social networks Customization 13 Buying online is a good option when you have little time Fan et al. (2013), Vos et al. (2014), Tandon et al. (2017), Sundaram et al. (2017), Shahid et al. (2018), Wijayanti et al. (2018), Lopes et al. (2019) Efficiency 14 Convenience regarding distance from home vs. product purchase Elsharnouby and Mahrous (2015), Zehir and Narcıkara (2016), Tsao et al. (2016), Yaya et al. (2017), Jaiyeoba et al. (2018), Javed et al. (2018), Hartwig and Billert (2018), Lopes et al. (2019), Ketema and Selassie (2020), Mujinga (2020), Raza et al. (2020) Belanche et al. (2021), Egala et al. (2021) 15 Product availability Flexibility 16 Flexibility due to opening hours Yaya et al. (2017), Ghosh (2018), Widodo et al. (2019), Khan et al. (2019), Lopes et al. (2019), Al-dweeri et al. (2019), Hernando and Gunawan (2021) Reliability 17 Products with excellent quality Finn (2011), Hussien and El Aziz (2013), Wen et al. (2014), Palese and Usai (2018), Javed et al. (2018), Al-dweeri et al. (2019), Ketema and Selassie (2020), Raza et al. (2020) knowledge of price 18 Advantage in the relationship between price and quality Wijayanti et al. (2018), Ghosh (2018), Khan et al. (2019), Lopes et al. (2019) Assurance 19 Trust in online shopping stores Finn (2011), Hussien and El Aziz (2013), Wen et al. (2014), Shahid et al. (2018), Palese and Usai (2018), Javed et al. (2018); Widodo et al. (2019) 20 Confidence in online shopping processes Delivery 21 Deadline Yaya et al. (2017), Handayani et al. (2018), Wijayanti et al. (2018), Ghosh (2018), Aquino et al. (2019), Widodo et al. (2019) 22 Products are delivered without defect 23 The delivery of the products respects the conditions informed by the company 24 Similarity between purchased and chosen product 25 Contact the online shopping delivery person TOPSIS HWANG and Yoon (1981) were the precursors to the TOPSIS technique (Technique for Order Preference by Similarity to Ideal Solution). According to Kin et al. (1997), the main advantage of this technique is understanding the individual's rationality in the decision-making process. It provides a value considered negative and an ideal positive for the alternative, it is simple in the use of technology, for it handles data in electronic spreadsheets, and the technique can be applied to several factors at the same time. This technique consists of establishing an order for the attributes following norms already established. Therefore, it seeks to evaluate the attributes according to their distance, the ideal positive and negative solutions simultaneously. The positive ideal solution or solution of the ideal point (A +) is the one desired by the professionals who are evaluating the quality, as it is considered the best alternative in the given quality attribute to be evaluated. Hence, the ideal point will most likely not be present in the feasible set, corresponding to the alternative that would have the combination of the best possible implications for all estimated quality attributes (Choudhury 2015). In contrast, the ideal negative solution, also known as Nadir (A-), is the least desired since it is the worst possible assessment for the quality attribute. Hence, the ideal negative solution is equivalent to the alternative that would bring the worst possible consequences for all quality attributes considered (Aquino et al. 2019). Methodology The nature of this scientific research was classified as applied research, for its practical character. As for the objective, it was classified as exploratory because it provides an approximate view of a problem with a view to making it explicit. It uses a quantitative survey approach for measuring the study variables and, as it sought to know the respondent's behaviour, the research method was classified as a survey type (Forza 2002). In this section will be presented the structure of the proposed approach, which aims to propose an approach to evaluate the quality of online shopping services in times of pandemic COVID-19, from the ordering of quality attributes taking into account customers' perception. Figure 1 shows the steps of the proposed approach.Fig. 1 proposed approach to assessing the quality of online shopping services Figure 1 illustrates the steps of the proposed approach for assessing the quality of online shopping services. The proposed approach presents the six steps and their respective refinements for the achievement of continuous improvement of the quality management system, with the purpose of satisfying consumers. It is noteworthy that the proposed approach is justified as it aims to support the decision-making process, making it an essential tool for organizations to establish strategic actions for the continuous improvement of online shopping services (Fofan et al. 2019). For Liou and Chen (2006), the creation of approaches to achieve the desired quality, with the reduction of subjectivity in the customer's perception and with successive refinements, results in the process of continuous organizational improvement. The steps of the proposed approach to evaluate the quality of online shopping services during COVID-19 times are described below. Step 1: Identify quality attributes—quality attributes are identified through a literature review based on the E-S-QUAL model. After identification, the data collection instrument was developed to capture the perception of quality of the online shopping service in the days of COVID-19 from the customer, based on the E-S-QUAL model. The questionnaire was prepared considering ten dimensions proposed by the E-S-QUAL model (access, ease of use, efficiency, customization, security, assurance, knowledge of price, aesthetics, reliability, and flexibility), together with the dimension “delivery”, necessary at this time of pandemic. The questionnaire was designed in two parts: the first part consists of collecting basic information about customers and the second part of the questionnaire seeks to assess customer perception by relating customer satisfaction in the service received from the scale of five points by Likert (1932), using linguistic terms: 1. total disagreement, 2. partial disagreement, 3. neither agree nor disagree, 4. partial agreement, and 5. total agreement. Step 2: Collect data—In this step, data will be collected from customers of the online purchase service. The questionnaires should be applied to consumers buying online through social networks and messaging applications. After data collection, it is recommended to calculate Cronbach's alpha coefficient to analyse the reliability of the scale of the collection instrument. The Cronbach's alpha coefficient was calculated using Jasp Statistical Software. The collected data were treated and analysed in Microsoft Excel 2010 software, where the fuzzification of the data was calculated and the overall quality evaluation was measured. Step 3: Fuzzification of the Likert (1932) scale—After data collection, a triangular fuzzy number must be proposed for each point on the scale of Likert (1932), since it is desired to measure the quality level of online purchasing services in times of crisis. This transformation begins with the identification of the points on the scale "Total disagreement", "Partially disagreement", "Neither agree nor disagree", "Partially agreement" and "Total agreement", followed by their transformation into a fuzzy number. To reduce the subjectivity of the evaluation in the data analysis, the points on the scale of Likert (1932) must go through the fuzzification process; that is, for each point a fuzzy triangular number represented by (a, b, c) will be associated as shown in Table 2.Table 2 Linguistic terms in the fuzzy environment Linguistic term Scale Fuzzy number Total disagreement 1 (0; 1; 3) Partially disagreement 2 (1; 3; 5) Neither agree nor disagree 3 (3; 5; 7) Partially agreement 4 (5; 7; 9) Total agreement 5 (7; 9; 10) The triangular numbers used are evenly distributed between zero and ten. The use of the triangular fuzzy number is given by the best representation of the client's preferences in each linguistic term and degree of relevance in the range of [0.1]. Step 4: Fuzzification of collected data—Once the service is studied through its variables, it will be evaluated by several customers. Thus, it is necessary to create a single fuzzy triangular number for each variable. Then, the fuzzified evaluations of each user were aggregated, according to Chen (2000) and Aquino et al. (2019), according to Eq. 1, where M is the global value of each fuzzy evaluation of the “n” clients of the service studied for the variable z. Through this operator, the individual assessments for each analysed variable are aggregated into global assessments.1 M=a1,b1,c1=1n⊕M1⊕M2⊕⋯⊕Mn∑i=1na1(i),∑i=1nb1(i),∑i=1nc1(i)n Step 5: Overall quality evaluation—As previously described, the triangular fuzzy number is represented by a,b,c these being the values of the linguistic terms, and “n” the number of individuals of each linguistic variable by category of attribute. To assess quality from the numbers obtained by fuzzification, the evaluation scale was proposed by Aquino et al. (2019), where if parameter “b” is between 0 and 2.0, the quality assessment is terrible, the customer is dissatisfied with the service provided; between 2.0 and 4.0, the quality assessment is rated as bad; between 4.0 and 6.0, the evaluation is considered regular; between 6.0 and 8.0, the quality assessment is considered good; between 8.0 and 10.0, the quality assessment is considered excellent. Step 6: Ordering of quality attributes—To order the attributes using the TOPSIS Method proposed by Wang and Lee (2007) and Chen (2000), it is necessary to determine the ideal solutions, determine the Euclidean distance of the fuzzy assessments, calculate the sums of the Euclidean distances, and calculate the ordering itself. Initially, the ideal positive (A+) and negative ideal (A-) solutions must be determined. The ideal positive solution is represented by the maximum consumer rating and is described by the triangular number (A+=7,9,10). On the other hand, the ideal negative solution is represented by the minimum consumer rating and is described by the triangular number (A-=0,1,3) (Chen 2000; Wang and Lee 2007; Massami et al. 2016; Aquino et al. 2019). After determining the ideal solutions, it is necessary to determine the Euclidean distance of the fuzzy evaluations using Eqs. (2) and (3). The Euclidean distance shows how far the evaluation of the attribute is from the positive and negative ideal solution (Chen 2000; Wang and Lee 2007; Aquino et al. 2019).2 d+Vij,Aj+=a1+-a12+a2+-b22+a3+-c3231/2 3 d-Vij,Aj-=a1--a12+a2--b22+a3--c3231/2 To calculate two attributes, it is necessary to calculate the Euclidean differences between the Eqs. 4 and (5) and then calculate the order itself (Eq. 6).4 Si+=∑j=1nd+Vij,Aj+withi=1,…,m; 5 Si-=∑j=1nd-Vij,Aj-withi=1,…,m; 6 Ci=Si-Si++Si-withi=1,…,meCi∈0,1 Results The questionnaire based on the E-S-QUAL model used in this research was designed considering the dimensions and attributes identified in Table 1. A total of 819 questionnaires were collected. When asked whether they were shopping online, 279 people said they were shopping online, while 540 people said they were not shopping online. Of the individuals who were shopping online, 31.7% were female and 68.3% male, with an average age of 32 years (minimum 18 and maximum 75 years). Regarding the fear of COVID-19, it was asked how the participants felt, and 91.8% say they are afraid of COVID-19. Initially, it was possible to obtain the aggregation of the fuzzified evaluations of the respondents, according to Eq. 1. Table 3 presents the fuzzy numbers of the studied attributes.Table 3 Fuzzy numbers of the studied attributes Attributes Fuzzy triangular number Overall quality evaluation a b c Attribute 1 5.99312715 7.98969072 9.35051546 Good Attribute 2 3.72508591 5.55670103 7.29553265 Regular Attribute 3 4.65292096 6.61512027 8.27147766 Good Attribute 4 4.1443299 6.07216495 7.75601375 Good Attribute 5 5.3024055 7.26116838 8.73883162 Good Attribute 6 5.35738832 7.32989691 8.81099656 Good Attribute 7 4.94158076 6.88316151 8.43986254 Good Attribute 8 5.51546392 7.50171821 8.97594502 Good Attribute 9 4.54295533 6.47079038 8.09965636 Good Attribute 10 4.75945017 6.71821306 8.35051546 Good Attribute 11 4.80068729 6.76632302 8.39862543 Good Attribute 12 2.4467354 4.15463918 6.02061856 Regular Attribute 13 3.14089347 4.93814433 6.73539519 Regular Attribute 14 5.73195876 7.70790378 9.11340206 Good Attribute 15 6.36082474 8.35395189 9.59106529 Excellent Attribute 16 5.80756014 7.7766323 9.13402062 Good Attribute 17 6.27491409 8.27147766 9.53264605 Excellent Attribute 18 4.62886598 6.61512027 8.33333333 Good Attribute 19 4.83505155 6.81443299 8.4742268 Good Attribute 20 6.24742268 8.2371134 9.48109966 Excellent Attribute 21 4.24054983 6.18900344 7.93814433 Good Attribute 22 5.29209622 7.28865979 8.85223368 Good Attribute 23 5.39175258 7.38487973 8.94158076 Good Attribute 24 5.62886598 7.6185567 9.07560137 Good Attribute 25 5.29209622 7.27491409 8.79725086 Good From Table 3, it can be seen that attribute 15 (Product availability), corresponding to the Reliability dimension (6.36082474; 8.35395189; 9.59106529), attribute 17 (Products with excellent quality), corresponding to the Efficiency dimension (6.27491409; 8.27147766; 9.53264605) and attribute 20 (Confidence in online shopping processes), corresponding to the Assurance dimension (6.24742268; 8.2371134; 9.48109966), were the attributes that presented the greatest modal value of the fuzzy number and were evaluated as excellent. While attribute 12 (Opinion sharing on social networks), corresponding to the “Security” dimension (2.4467354; 4.15463918; 6.0206185), attribute 13 (Buying online is a good option when you have little time), corresponding to the dimension “Customization” (3,14,089,347; 4,93,814,433; 6,73,539,519) and attribute 2 (Distraction in online shopping searches), corresponding to the dimension “Ease of use” (3,72,508,591; 5,55,670,103; 7,29,553,265), were the attributes that presented the lowest modal value of the fuzzy number and were evaluated as regular. As seen in Table 3, no dimension presented an assessment considered to be terrible or bad, demonstrating that the online shopping service was not considered unsatisfactory by customers. Seeking an evaluation of the service referenced by its worst and best evaluation, the results of the application of an extension of the Fuzzy TOPSIS method are presented. From the ideal positive solution (A+ = {7,9,10}) and the ideal negative solution (A-=0,1,3), according to the fuzzy scale used and the respondents' evaluation, the Euclidean distances for each variable were calculated according to Eqs. (2) and (3) (Table 4). Then, using Eqs. (4), (5) and (6) it was possible to order the evaluations of the researched variables according to the view of the respondents.Table 4 Euclidean distances and ordering of attributes Attributes d+Vij,Aj+ d-Vij,Aj- Ci Ranking Attribute 1 0.90486609 6.45761556 0.87709768 4 Attribute 2 3.15676595 4.20679569 0.57129904 23 Attribute 3 2.17441339 5.19511713 0.70494547 19 Attribute 4 2.69335875 4.6733997 0.63439024 22 Attribute 5 1.58070745 5.78077048 0.78527308 13 Attribute 6 1.5167011 5.8462776 0.79400985 10 Attribute 7 1.92804943 5.43516066 0.73815097 14 Attribute 8 1.35368439 6.0112284 0.81619818 8 Attribute 9 2.31266627 5.05221026 0.6859871 20 Attribute 10 2.07745152 5.29082042 0.71805445 17 Attribute 11 2.03229671 5.33673546 0.72421118 16 Attribute 12 4.47382077 2.8903517 0.39248832 25 Attribute 13 3.74384024 3.62065233 0.49163636 24 Attribute 14 1.16382947 6.19744596 0.84189839 6 Attribute 15 0.57536967 6.78190832 0.92179585 1 Attribute 16 1.10581217 6.25237846 0.84971684 6 Attribute 17 0.65189828 6.70632534 0.91140548 2 Attribute 18 2.16700297 5.20898008 0.70620825 18 Attribute 19 1.98256474 5.38988292 0.7310846 15 Attribute 20 0.68741694 6.66860188 0.90655041 3 Attribute 21 2.56693824 4.80601321 0.65184387 21 Attribute 22 1.54520637 5.82529437 0.79035259 11 Attribute 23 1.4508991 5.9200244 0.80315912 9 Attribute 24 1.24404005 6.12106628 0.83109001 7 Attribute 25 1.56412512 5.80198089 0.78765916 12 As a reflection of the aggregated fuzzy numbers in Table 4, attribute 15 (Product availability), attribute 17 (Products with excellent quality), attribute 20 (Confidence in online shopping processes) and attribute 1 (Ease of Buying Online) were the ones that presented greater weight in the service evaluation, and were close to the ideal positive solution. Thus, they had the better perception of quality by the respondents and therefore considered as attributes with low priority for management actions. In contrast, the attributes 12 (Opinion sharing on social networks), 13 (Buying online is a good option when you have little time), 2 (Distraction in online shopping searches) and 4 (Shopping online is a pleasure) had greater proximity to the ideal negative solution. Thus, they presented with the greater level of dissatisfaction with the service. Discussion In this study, we explored an approach to measure the quality of online shopping services during COVID-19, that minimizes the subjectivity of customers' perceptions, and thus more precisely provide what attributes influence the quality of online shopping in times of pandemic. This research was instigated by the atypical and fast scenario that the pandemic caused in commercial transactions, and the diversity of factors that could affect the quality of online shopping services offered by companies in this period. Online shopping has grown astronomically and has become substantial in the pandemic. Even with the increase and popularization of online purchases, the survey identified that 66% of all participants did not conduct this type of commercial transaction. According to Daroch et al. (2021), despite the advantages, some customers may find online shopping risky and unreliable. In addition to not providing sensory sensations, the fear of fraud, damaged products and delivery problems are some factors that lead many consumers not to adopt this type of shopping (Al-Debei et al. 2015; Brands and van Wilsem 2021). García-Salirrosas et al. (2022) state that in developing countries consumers are more traditional and distrustful of technology, and thus, factors such as shipping costs, poor customer service, problems of lack of stock, delivery delays are factors that lead to low consumption of products and services in online shops by the population of these countries. Another fact reported by the participants of this study was about the feeling caused by the pandemic, and more than 90% reported fear of COVID-19. According to Chi et al. (2021), fear in times of pandemic is natural, because it is a life-threatening event, where contamination passes from person to person. However, this perception of fear can be enhanced by fake news and future uncertainties during and after the pandemic. The degree of fear reported by the participants corroborated with those presented in the studies by Ahorsu et al. (2020); Sakib et al. (2021) and Iversen et al. (2021). Based on the methodology of fuzzification, which treats inaccuracies, being an attempt to approximate the required precision, the study found that the attributes of service quality perceived by consumers were classified as excellent, good and regular. Of the attributes where the perception of the quality of the service was evaluated as excellent were: attribute 15 (Product availability), corresponding to the “Reliability” dimension. This result is consistent with the studies by Mendoza et al. (2020); Raza et al. (2020); Belanche et al. (2021) and Egala et al. (2021), who used the structural equation to confirm this dimension as the one that most influenced the perception of quality of online customer services, be they banking or food delivery services. The attribute 17 (Products with excellent quality), which had the perception of quality evaluated as excellent, corresponds to the dimension “Efficiency”. This dimension had a positive effect on the satisfaction of perceived quality in online services in the studies by Ketema and Selassie (2020) and Goutam et al. (2021). The characteristics of this dimension greatly impact the expectation of the online service, because they can control the quality of the system, thus influencing the expectation of consumers, which, if not met, strongly influence the perception of quality (Ketema and Selassie 2020). And the attribute 20 (Trust in online shopping processes), corresponding to the “Guarantee” dimension, which also had the perception of quality assessed as excellent, was the dimension of the greatest importance for customers in the study by Widodo et al. (2019), which used multiple regressions to reach this conclusion. Among the attributes in which the perception of the quality of the online service was evaluated as regular, was attribute 12 (Sharing of opinion on social networks), corresponding to the dimension “Security”. This attribute is related to Social Commerce, described by Linda (2010), as the use of social media in the context of e-commerce, to assist in the purchase and sale of products and services online. Through social media, companies can market their products and services, influence consumers, and impact a large number of people around the world. On the other hand, electronic word-of-mouth convinces consumers to share their experiences and knowledge about products and services with other consumers on social networks, and thus influence them in buying decisions (Goraya et al. 2019; Adam and Alhassan 2021). No study addressed this perspective of social networks in the context of E-S-QUAL, and only in the studies of Ketema and Selassie (2020) does this dimension have a high impact on customer satisfaction. The study by Yunus et al. (2022) does not address E-S-QUAL, but it does suggest that e-service quality has a positive effect on customer evaluations within a shopping platform, and these evaluations have a positive effect on customer repurchase intention. Attribute 13 (Buy online is a good option when you have little time), corresponding to the dimension “Customization”, had the perception of quality evaluated as regular. According to Adnan (2014), the advantages of online shopping when one has little time is in the availability of purchases 24 h a day, on all seven days of the week, variety of products and services, ease of price comparison, a greater number of payment options, exclusive products and lower costs. On the other hand, the author reports that the lack of time to analyse the purchases may cause the consumer to hesitate to make online purchases, since they are exposed to financial risks, risks of non-delivery and risks of receiving defective products, for example. It is worth noting that no study has approached this attribute from the perspective raised in this research. The “Customization” dimension is one of the dimensions that significantly impact consumer satisfaction in the studies by Tandon et al. (2017), which addresses the scenario of perceived quality in websites. Regarding attribute 2 (Distraction in online shopping searches), also classified as regular, corresponding to the dimension “Ease of use”, Huang et al. (2021) claims the distraction in online shopping affects the consumer’s attention and can negatively influence the accuracy of the product’s judgment. The “Ease of use” dimension was highly significant in the judgment of the quality of online services by consumers in the studies by Ketema and Selassie (2020) and Hernando and Gunawan (2021), and it is considered one of the main dimensions to be investigated in online quality of service surveys (Shankar et al. 2020). It may impact online shopping service quality, purchase intention and behavioural loyalty (Goutam et al. 2022). In agreement with Shankar et al. (2020), the quality perceived by consumers is the foundation for success in digital businesses. And the quality ratio in online services influences the increase in customer satisfaction, their retention, and intention to repurchase, especially in pandemic scenarios such as COVID-19, where consumers are more sensitive to the services provided (Al-Ghraibah 2020). Mamakou and Roumeliotou (2022) reiterate that the quality of service of e-shops makes customers satisfied and may lead them to be more loyal and online sales may increase in atypical cases as it was in the isolation of COVID-19, resulting not only in the survival of the business, but also in a greater and better positioning of the shop in the market. This study demonstrated that the E-S-QUAL scale developed by Parasuraman et al. (2005) was valid to measure the quality of the service perceived in the scenario of online purchases in times of pandemic. The methodology Fuzzy TOPSIS helped to reduce the subjectivity in the measurement of the perception of the quality of services, to present an evaluation closer to the reality. Thus, decision-making in the points of improvement and maintenance of the quality of online services was improved. Management implications From a practical perspective, the findings in this article can provide some useful clarifications for marketing professionals, enabling the development of analyses that help in understanding the characteristics of consumer behaviour in the face of the global COVID-19 pandemic crisis. The results obtained in this study suggest, first, that companies devote more attention to interaction with their users on social media since digital communication is already an essential aspect of modern consumption (Kim et al. 1997). Companies must build a reputation on social networks to be closer to the thinking of their customers, respond promptly to user comments and provide alternative communication channels for users, to positively impact the consumer and make him visit the site again (Wu et al. 2015). Secondly, this study suggests the customization of the presentation of products according to the needs of customers, optimally, both to promote the flow necessary to manage consumers' willingness to buy, and the perception of the risks associated with Internet shopping (Smith and Sivakumar 2004). This customization also permits not to disperse and distract the consumer, and thus not negatively impact the decision to buy, since, according to Huang et al. (2021), in this highly competitive scenario of online commerce, for companies to earn more they must have the ability to hold the consumer’s attention until the purchase takes place. In addition, one should consider the perception of customers regarding the service provided, so that organizations can invest in the implementation of improvements through new practices in the quality of their services, to meet the needs of their customers and achieve their satisfaction, which is the main objective of an organization. Finally, managers and entrepreneurs can use the data and results presented in this survey to understand how consumers behave when confronted with an existing product and/or service, to launch new products and services in the online shopping market, to promote a positive experience for your audience target. Conclusion With the development of this study, it may be possible to build hypotheses about the topic, since it is something unprecedented in society and in the academic literature. It is worth mentioning that the results found in this research were a consequence of the characteristics of the sample. The behavioural changes in society, resulting from the COVID-19 pandemic, allow the development of new studies to explore factors that lead to consumer satisfaction. Based on the literature review and analysis of the collected data, this paper presented an approach to evaluate the quality of online shopping services in times of pandemic COVID-19, from the ordering of quality attributes taking into account customers' perception, using an integration of extension TOPSIS technique and fuzzy set theory. It was interesting to notice with the results that even with social isolation as the most effective preventive measure to combat the rapid contagion of the virus, the study pointed out that the vast majority of respondents (91.8%) were afraid of the Coronavirus. Of this percentage, 33% said they felt a lot of fear and 58.8% said they felt little fear, but 65.94% still continued doing their shopping in person, especially in supermarkets. For the sample of consumers who obeyed the preventive measure and continued shopping online, it was possible to notice that the characteristics linked to 'Efficiency', 'Reliability' and 'Assurance' were the best evaluated attributes, considered as excellent and that led to customer satisfaction. Such characteristics were associated with consumer satisfaction by Parasuraman et al. (1985). The TOPSIS technique allowed us to establish a ranking for ordering the attributes according to the distance from the positive and negative ideal solution. Through the analysis of the positive ideal solution (A +), it was interesting to realize that among the characteristics that had the highest importance perceived by consumers, the most important was 'Reliability' (attribute 15—Product availability), followed by 'Effciency' (attribute 17—Products with excellent quality) and 'Assurance' (attribute 20—Confidence in online shopping processes), respectively. In other words, reliability is the assurance of excellence of the functionality in a proper way of the service bringing the perception of trust to the customer, leading to their satisfaction (Liu et al. 2018; Jenelius 2018; Amai 2020). The main limitation of this research was the scarcity of studies already published on this topic. As the studies with this objective were few, it is common not to obtain published content to compare with our results. In addition, it would be interesting to expand the sample, in order to obtain other perceptions about the services offered. Therefore, this research contributed to a discussion of the academic literature, enabling the development of future research that helps to understand the behaviour of consumers in relation to their online purchases, in and out of crisis. With this, organizations can invest in the qualification of their services to meet the needs of their customers. Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 and the Fundação de Amparo a Ciência e Tecnologia de Pernambuco. Funding Not applicable. Data availability All data generated or analysed during this study are included in this published article. Declarations Conflict of interest The authors declare no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. 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==== Front Eur Econ Rev Eur Econ Rev European Economic Review 0014-2921 0014-2921 Elsevier B.V. S0014-2921(21)00160-4 10.1016/j.euroecorev.2021.103818 103818 Article Covid-19 crisis and hostility against foreigners Bartoš Vojtěch b Bauer Michal cd Cahlíková Jana a⁎ Chytilová Julie cd a Max Planck Institute for Tax Law and Public Finance, Marstallplatz 1, 80539 Munich, Germany b Department of Economics, University of Munich, Geschwister-Scholl-Platz 1, D-80539 Munich, Germany c CERGE-EI (a joint workplace of Charles University and the Economics Institute of the Czech Academy of Sciences), Politických vězňů 7, 111 21 Prague, Czech Republic d Institute of Economic Studies, Faculty of Social Sciences, Charles University, Opletalova 26, 110 00 Prague, Czech Republic ⁎ Corresponding author. 30 6 2021 8 2021 30 6 2021 137 103818103818 11 12 2020 7 5 2021 18 6 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Harmful behavior against out-group members often rises during periods of economic hardship and health pandemics. Here, we test the widespread concern that the Covid-19 crisis may fuel hostility against people from other nations. Using a controlled money-burning task, we elicited hostile behavior among a nationally representative sample (n = 2,186) in the Czech Republic during the first wave of the pandemic. We provide evidence that exogenously elevating the salience of the Covid-19 crisis increases hostility against foreigners from the EU, USA and Asia. This behavioral response is similar across various demographic sub-groups. Further, we observe zero to small negative effects for both domestic out-groups and in-groups, suggesting that the salience of Covid-19 might negatively affect behavior not only towards foreigners but to other people more generally, though these findings are not conclusive. The results underscore the importance of not inflaming anti-foreigner sentiments and suggest the need to monitor impacts of the crisis on behavior in the social domain. Keywords COVID-19 pandemic Hostility Inter-group conflict Experiment ==== Body pmc1 Introduction Intergroup conflicts are among the most pressing problems facing human society (Bowles, 2009; Fiske, 2002; Blattman and Miguel, 2010). Social scientists have long argued that difficult life conditions imposed upon individuals by external forces that threaten physical wellbeing and safety, such as economic and political upheavals or widespread disease, may create a fertile environment for xenophobia and out-group hostility. Evidence suggests that hostile behaviors and conflicts increase during periods of economic problems (Anderson et al., 2017; Grosfeld et al., 2020; Miguel et al., 2004). In the context of a crisis caused by a contagious disease, a particularly plausible mechanism is that people may form hostile attitudes to members of the groups that are associated with transmission of the disease (Murray and Schaller, 2016; O'Shea et al., 2020). In light of this reasoning, the Covid-19 crisis, arguably the most severe health and economic shock since WWII (Baldwin and Weder di Mauro, 2020; New York Times, 2020), has created an unfortunate but suitable testing ground for exploring whether an important, naturally-occurring shock in the health and economic domains spills over to the social domain and magnifies inter-group animosity. Since Covid-19 originally surfaced in China and spreads across borders via interactions with people from other countries, contemporary commentators have suggested that it may foster prejudice against foreigners, particularly against people from Asia (CNN 2020). For example, Fernand de Varennes, the UN Special Rapporteur, warns that “COVID-19 is not just a health issue; it can also be a virus that exacerbates xenophobia, hate and exclusion.” (United Nations, 2020). Rigorously identifying the causal effects of Covid-19 on inter-national and domestic group divisions is fundamental for understanding the current and future social and political landscape. Such divisions may reduce support for global initiatives to tackle the pandemic, create barriers to re-establishing international trade, strengthen support for extreme right-wing political parties and increase the risk of conflicts. Despite the importance of this issue, causal evidence on how fears associated with major health and economic shocks shape hostility against particular groups is lacking. This is not surprising because of several empirical challenges. First, hostility denotes aggressive harmful behavior motivated by positive utility from reduced welfare of certain individuals or groups, in contrast to harmful behavior motivated by personal material gain. Using naturally occurring data to uncover hostility, such as the prevalence of robbery or violence, is problematic because hardship often goes hand in hand with greater financial needs. Similarly, avoidance of out-group members or support for border closures can be a rational protective strategy. Thus, using these measures does not allow us to separate selfish motivations, based on a rational calculus of potential (material) benefits to ones’ self, from hostility. Second, a clean measurement requires an exogenous variation in the identity of the victim of the hostile behavior, in order to distinguish whether hardship fuels hostility towards particular groups, rather than towards people in general. The third challenge is identification of causal impacts. For understanding impacts of a shock that hits the whole country at a similar point in time, a key issue is finding a ceteris paribus variation in fears that is not correlated with time trends or unobserved confounders between individuals. Simply comparing individuals from localities with lower versus higher prevalence of disease during a health pandemic can be misleading. More pro-social and tolerant individuals can self-select into residing in localities that have a greater capacity to cope with the crisis. Moreover, individuals vary along many unobserved dimensions. For example, out-group hostility can be related to economic vulnerability and personal characteristics that affect people's ability to cope with economic or health shocks. Further, individuals who are more strongly inclined to socialize and hence are more likely to contract the virus may show greater tolerance of out-group members, possibly due to more frequent inter-group contact. Here we provide evidence that a health pandemic accompanied by a severe economic shock increases prevalence of harmful behavior towards people living in other countries. Our evidence is based on a large-scale experiment implemented in midst of the Covid-19 crisis. We elicited hostile behavior among a nationally representative sample (n = 2,186) in the Czech Republic, a medium-sized country in Central Europe, while the pandemic was on the rise during its first wave, and the entire population lived under lockdown and border closure; see Supplementary Information (SI) for more details about the background. Several features of our experimental design help us to overcome the empirical challenges described above. First, we directly elicit willingness to cause financial harm in a controlled money-allocation task. Subjects make anonymous, one-shot allocation decisions, in which they can decide to decrease a monetary reward for another person. Since reducing the reward does not result in pecuniary benefits for the decision-maker (or for anyone else), the choice reveals individual willingness to engage in hostile behavior. Second, we exogenously manipulate information about the identity of the recipient of the reward, in order to identify discrimination against foreigners. Third, we randomly assign the participants either to a treatment condition that increased the salience of Covid-related problems and fears, or to the control condition in which Covid-related challenges were not made salient. Random allocation ensures that participants in the treatment and control conditions are comparable in terms of observable and unobservable characteristics, helping to overcome selection issues and concerns about spurious correlation. Finally, an attractive feature of our empirical approach is that it can be easily employed on large representative samples in virtually any country with well-developed data collection infrastructure. The paper is related to several literatures. In terms of measuring discrimination, we build on economic experiments designed to uncover biases in social preferences towards people with specific real-life group attributes (e.g., ethnicity), using incentivized allocation tasks (e.g., Bernhard et al., 2006; Fershtman and Gneezy 2001; Angerer et al., 2016; Berge et al., 2020).1 A noteworthy aspect of our work is the focus on multiple dimensions of group identity, since most of the earlier work studies only a single group attribute, for an exception see Kranton et al., (2020). The paper also adds to an emerging empirical literature which tests the role of environmental factors that may influence behavior to out-group members. The focus has been mostly on the effects of inter-group contacts (Rao, 2019; Mousa, 2020), social environment (Bauer et al., 2018), exposure to violent elections (Hjort, 2014) or violent inter-group conflict (Shayo and Zussman, 2011; Bauer et al., 2014). Finally, the paper is related to work on “Behavioral immune system” in social psychology which has documented a correlation between greater exposure to (real or perceived) health threats and measures of group biases in explicit and implicit attitudes. For example, in US states with higher rates of infectious diseases, people exhibited greater racial prejudice (O'Shea et al., 2020). A representative survey from the US shows that citizens who felt more vulnerable to contracting Ebola displayed greater prejudice against immigrants in survey questions (Kim et al., 2016). Moving beyond correlations, showing a disease-related picture primes increased prejudice among subjects in the lab (Duncan and Schaller, 2009) and among a sample of M-Turk workers (O'Shea et al., 2020). We contribute by providing causal evidence of the impacts of a naturally occurring health pandemic on incentivized behavior among a representative sample. 2 Experimental design 2.1 Sample We collected experimental data on a large, nationally-representative sample, using an approach inspired by Almas et al. (2020) and Falk et al. (2018) and took advantage of the online infrastructure of a leading data-collection agency in the Czech Republic (NMS Market Research and PAQ Research). The data were collected via the agency from a sample of 2,186 adults from March 30 to April 1, 2020. The sample is nationally representative in terms of age, sex, education, employment status before the Covid-19 pandemic, municipality size, and regional distribution, with a higher share of people living in large cities (Table S.1). 2.2 Measuring hostility We developed a detailed experimental module, designed to uncover the shape of hostile preferences towards people with different group attributes. We administered a series of decisions in an allocation task that we label a Help-or-Harm task (HHT), which combines features of the well-established Dictator game and the Joy of Destruction game (Abbink and Sadrieh, 2009). The participants were asked to increase or decrease rewards to a set of people with different characteristics. The default allocation was CZK 100 (USD 4). Participants could allocate any amount between CZK 0 and CZK 200 (USD 0–8), using a slider located in the middle of the 0–200 scale (see Fig. S.1). The participants had to make an active choice - even if they decided to keep the reward at the default allocation, they had to click on the slider. The advantage of implementing a salient reference point is that we can identify (i) changes in basic pro-social behavior and (ii) changes in the prevalence of hostile behavior. We denote behavior as pro-social when subjects choose to increase rewards above CZK 100, revealing that a participant cares positively about the recipient. Next, we refer to behavior as being hostile when subjects allocate less than CZK 100 to the recipient, since in order to do so they have to actively cause financial harm with no pecuniary benefit to themselves. Thus, such behavior cannot be explained by selfish motivations. In the analysis, we also consider the most extreme manifestations of such behavior, when subjects destroy all recipient's earnings, by allocating CZK 0. There were no pecuniary costs for the decision-maker when choosing to engage in pro-social or hostile behavior (there were costs only in terms of effort). Note that although it is common in economic experiments to impose monetary costs on a decision-maker, the advantage of this design is that behavior should not be affected by pure income effects.2 This is useful because our goal is to isolate the psychological effects of economic and health shocks on preferences and decision heuristics from the mechanical income effects caused by the crisis. Because the increased salience of Covid-related problems in the treatment condition may also trigger individual financial concerns, introducing monetary costs might lead to a biased estimate of the psychological effects. Further, because it was costless to behave in a hostile way, the task identifies individuals with even a relatively weak preference to harm. It is possible that introducing monetary costs would result in a lower prevalence of hostile (or generous) behavior, as in experiments documenting that individuals respond to manipulations in the costs of giving (Andreoni and Miller, 2002). Note that since the previous literature documents that a non-negligible fraction of people tend to act in hostile ways even towards in-group members (Bauer et al., 2018; Abbink and Sadrieh, 2009), hostile behavior towards out-group members does not necessarily reflect anti-outgroup bias. A clear measurement of such bias requires a comparison of the prevalence of hostility towards in-group members and towards out-group members. 2.3 Manipulating identity of a recipient Each participant made seventeen decisions in the HHT, where each choice affected a recipient with different pre-specified characteristics. In order to measure nationality-based divisions and hostile behavior towards foreigners, the participants made five decisions on whether to increase or decrease money sent to a person living in each of the following regions: the Czech Republic, the EU, the USA, Asia, and Africa. We chose not to mention specific countries, such as Italy or China (the countries most saliently linked to the Covid-19 pandemic during our data collection period), in order to avoid inducing an experimenter demand effect. In the analysis, we focus on average behavior towards a foreigner (from the EU, the USA, Asia or Africa), and compare it to behavior towards a person from the Czech Republic. This choice aims to uncover “generalized” social behavior: no information was provided about the recipient except that the person lives in the respondent's home country (the Czech Republic). Further, in order to measure domestic divisions and hostility to out-group members from one's own country, in the second set of decisions participants allocated money to people who all live in the Czech Republic but who either share a group attribute with them (in-group) or not (out-group). We focused on the following dimensions: region of residence (3 decisions), political orientation (2 decisions), ethnicity (3 decisions), and religion (4 decisions). In the analysis, we study average behavior towards domestic in-group members and towards domestic out-group members. Supplementary Information section 1.2 presents the specific wording of all decisions. The choices were consequential—the subjects knew that thirty participants would be randomly selected and one of their choices would be implemented. The instructions made it clear that the decision makers could not also be receivers, in order to avoid the potential role of indirect reciprocity. After the experiment was completed, “a person living in your region” was selected as the payoff-relevant category of recipients. Thirty participants were randomly selected and their decisions for this category were implemented. The money was allocated to individuals from the database of the survey company who lived in corresponding regions and who did not participate in our experiment. 2.4 Manipulating salience of Covid-19 In order to exogenously manipulate the intensity of Covid-19-related concerns when subjects made decisions, we used a priming technique. Each participant was randomly allocated either to the COVID-19 (n = 1,142) or to the CONTROL condition (n = 1,044). In the COVID-19 condition, before making decisions in the Help-or-Harm tasks, the subjects answered a series of survey questions focusing on the coronavirus crisis, specifically on their preventive health behavior, social distancing, economic situation, and psychological wellbeing. The prime is designed to activate or intensify a complex set of thoughts and concerns that characterize people's lives during the coronavirus crisis. The median time the respondents spent answering this set of questions was 13 minutes. In the CONTROL condition, the participants made the decisions in the Help-or-Harm tasks at the beginning of the survey, and answered the coronavirus-related questions only later. This is a relatively unobtrusive way of introducing priming into an online experiment, since given that the data were collected during the first wave of the pandemic, it was natural to ask questions about this issue. Thus, we believe experimenter demand effects are unlikely to drive the observed differences in behavior across the COVID-19 and CONTROL conditions. Table S.1 shows that randomization was successful, since participants do not exhibit systematic differences across conditions in terms of observable characteristics. See Supplementary Information for more details about the sample, experimental design, definition of variables, and complete experimental protocol. The priming technique allows us to measure purely psychological impacts of a greater intensity of Covid-related concerns on hostility. Priming is a well-established technique in social science (Bargh and Chartrand, 2000; Cohn and Maréchal, 2016) and has been successfully used to shed light on a range of other important issues (Cohn et al., 2014; Mani et al., 2013; Cohn et al., 2015). Also note that this technique identifies impacts of greater intensity of Covid-related thoughts, rather than the overall effects of Covid-19. Thus, to the extent that people in the CONTROL condition also have Covid-19 concerns very much at top of mind, this technique may underestimate the actual effects of the pandemic. 3 Results 3.1 Behavior towards foreigners We find that, on average, participants allocate less money to foreigners (CZK 92) than to a person from their own country (CZK 133; Table 1 ; Somer's D test, P < 0.001). In order to test whether thinking about Covid-19 magnifies such nation-based discrimination by increasing hostility towards foreigners, we compare choices in the COVID-19 condition with choices in the CONTROL condition.Table 1 Mean allocations in the Help-or-Harm task by the identity of the recipient, across CONTROL and COVID-19 conditions. Table 1 Mean allocations [95% confidence intervals] Effect [z-statistic, p-value] N All Control COVID-19 (1) (2) (3) (4) (5) Panel A: Indexes Domestic (Czech) 132.8 133.5 132.2 -1 [p=0.390] 2,186 Foreign 91.6 94.1 89.3 -5 [p=0.013] 8,743 (2,186 clusters) (vs. Domestic) -41 [p<0.001] -39 [p<0.001] -43 [p<0.001] Domestic in-group 125.1 127.3 123.0 -4 [p=0.026] 9,297 (2,186 clusters) Domestic out-group 95.0 96.5 93.5 -3 [p=0.043] 16,935 (2,186 clusters) (vs. in-group) -30 [p<0.001] -31 [p<0.001] -30 [p<0.001] Panel B: Foreign Asian 89.0 91.4 86.9 -4 [p=0.044] 2,186 (vs. Domestic) -44 [p<0.001] -42 [p<0.001] -45 [p<0.001] European Union 103.4 107.1 100.0 -7 [p=0.003] 2,186 (vs. Domestic) -29 [p<0.001] -26 [p<0.001] -32 [p<0.001] US 76.2 78.9 73.8 -5 [p=0.013] 2,186 (vs. Domestic) -57 [p<0.001] -55 [p<0.001] -58 [p<0.001] African 97.8 99.2 96.6 -3 [p=0.296] 2,185 (vs. Domestic) -35 [p<0.001] -34 [p<0.001] -36 [p<0.001] Panel C: Domestic in-group/out-group Region in-group 129.7 133.0 126.7 -6 [p=0.015] 2,783 (2,186 clusters) Region out-group 111.0 112.6 109.6 -3 [p=0.053] 3,775 (2,186 clusters) (vs. in-group) -19 [p<0.001] -20 [p<0.001] -17 [p<0.001] Political in-group 119.5 120.5 118.6 -2 [p=0.207] 2,186 Political out-group 92.3 94.3 90.5 -4 [p=0.080] 2,186 (vs. in-group) -27 [p<0.001] -26 [p<0.001] -28 [p<0.001] Majority in-group 123.4 125.6 121.4 -4 [p=0.045] 2,186 Roma ethnicity out-group 74.6 76.4 73.0 -3 [p=0.058] 2,186 (vs. Majority in-group) -49 [p<0.001] -49 [p<0.001] -48 [p<0.001] Immigrant out-group 94.6 95.5 93.8 -2 [p=0.550] 2,186 (vs. Majority in-group) -29 [p<0.001] -30 [p<0.001] -28 [p<0.001] Religion in-group 126.4 128.4 124.5 -4 [p=0.142] 2,142 (2,142 clusters) Religion out-group 93.5 95.1 92.0 -3 [p=0.070] 6,602 (2,186 clusters) (vs. in-group) -33 [p<0.001] -33 [p<0.001] -32 [p<0.001] Notes: Mean allocations in the Help-or-Harm task. "In-group" indicates that the respondent and the recipient share the group attribute. Differences reported in column 4 and on respective rows indicate a comparison group (e.g., vs. Domestic). We report p-values from the Wilcoxon rank-sum equality test whenever the number of observations is the same as the number of clusters, and Somer's D p-values clustered at individual level whenever we have more observations than clusters. The number of observations equals the number of individual decisions considered for each group of recipients (See Supplementary Information 1.2 for detailed descriptions of recipient group construction) Thinking about Covid-19 has negative impacts on behavior towards foreigners (Fig. 1 a and Table 2 ). While in the CONTROL condition, participants on average allocated CZK 94 to foreigners, in the COVID-19 condition they allocated CZK 89 (OLS, P = 0.022). In contrast, the effect on behavior towards a domestic recipient is small in magnitude and not statistically significant (P = 0.753). In a regression analysis, we find a negative interaction effect between COVID-19 and an indicator variable for ‘foreigner’ (as compared to a domestic person), but it does not reach statistical significance at conventional levels (Table S.2, P = 0.140).Fig. 1 Effect of the COVID-19 condition on allocations in the Help-or-Harm task, by the identity of the recipients. Notes: Coefficient plots. Bars represent 95 percent confidence intervals. In a, the dependent variable is the amount allocated. In b, the dependent variable is a binary variable indicating hostile behavior, equal to 1 if allocation is strictly lower than the default allocation (100 CZK). Both panels present estimated coefficients of the COVID-19 condition relative to the CONTROL condition (corresponding regression models including numbers of observations appear in Panel A of Table 2 and Panel A of Table S.4). Data for all 2,186 participants used. Fig 1 Table 2 Effect of the COVID-19 condition on the amount allocated in the Help-or-Harm task, by the identity of the recipient (domestic vs. foreign). Table 2 (1) (2) (3) (4) (5) (6) Identity of the recipient: Domestic Foreign Asian European Union US African Panel A: Baseline controls COVID-19 -0.698 -4.882 -4.332 -7.933 -4.625 -2.628 p-value [0.753] [0.022] [0.094] [0.001] [0.063] [0.364] p-value (MHT; 2 hypotheses) [0.045] p-value (MHT; 17 hypotheses) [0.755] [0.202] [0.459] [0.008] [0.398] [0.797] Panel B: No controls COVID-19 -1.277 -4.842 -4.478 -7.144 -5.110 -2.628 p-value [0.558] [0.025] [0.076] [0.002] [0.039] [0.349] p-value (MHT; 2 hypotheses) [0.044] p-value (MHT; 17 hypotheses) [0.559] [0.178] [0.369] [0.025] [0.249] [0.775] Panel C: Additional controls COVID-19 -0.284 -5.473 -4.969 -8.094 -5.440 -3.386 p-value [0.898] [0.011] [0.059] [0.001] [0.031] [0.247] p-value (MHT; 2 hypotheses) [0.024] p-value (MHT; 17 hypotheses) [0.889] [0.118] [0.348] [0.004] [0.217] [0.623] Panel D: Probability weights COVID-19 -2.740 -5.726 -6.131 -6.127 -8.298 -2.344 p-value [0.337] [0.049] [0.071] [0.039] [0.011] [0.536] CONTROL mean 133.5 94.1 91.4 107.1 78.9 99.2 # Clusters 2,186 Observations 2,186 8,743 2,186 2,186 2,186 2,185 Notes: OLS. P-values reported in square brackets (robust standard errors clustered at an individual level in column 2 where multiple observations are used per individual). The dependent variable is the amount allocated in the Help-or-Harm task. In Panel A, each regression controls for gender, age category (6 categories), household size, number of children, region (14 regions), town size (7 categories), education (4 categories), economic status (7 categories), household income (11 categories) and task order. Panel B reports results from regressions without control variables. In Panel C, each regression controls for baseline controls (as in Panel A) and further controls for the variables approximating economic situation, mental health, Covid-19 symptoms and activities during the lockdown (see Supplementary Information 1.4 for the list and definition of variables). Panel D reports results of weighted OLS regressions with no controls, using probability weights to correct for the oversampling of respondents from large municipalities. We also report multiple hypothesis testing corrected p-values using a method developed by Barsbai et al. (2020). See Supplementary Information 1.6 for details on the procedure and the hypotheses tested. Next, we take a more granular approach and explore the effects on behavior towards foreigners from different parts of the world separately. We find a negative impact of COVID-19 on behavior towards people from the EU, the USA and Asia, but not from Africa (Fig. 1a and Table 2). The effect is largest for behavior towards people living in the EU—as compared to CONTROL, participants in COVID-19 allocated on average CZK 8 less to them (OLS, P = 0.001). The drop in COVID-19 as compared to CONTROL is CZK 5 for a person from the USA (P = 0.063) and CZK 4 for a person from Asia (P = 0.094). Although the negative effects on behavior towards people from the EU, the USA and Asia are statistically significant, the magnitude of the effects is not large. This is not surprising for two reasons. First, the prime employed in the COVID-19 condition was quite subtle (answering a set of survey questions related to Covid-19 before making choices in the Help-or-Harm task). Second, the priming technique we used allows us to identify a marginal effect of greater salience of Covid-19 related thoughts, rather than the overall effects of Covid-19. As a baseline specification, we report unweighted results for all 2,186 participants (Fig. 1a and Panel A of Table 2, Fig. 3 and Panel A of Table S.3). Baseline models control for gender, age, household size, number of children, region, town size, education, economic status, household income and task order. Precise definitions of all variables are provided in the Supplementary Information. As a robustness check, we report results of 1) OLS models with no controls, 2) OLS models with additional controls for the variables approximating economic situation, mental health, Covid-19 symptoms and activities during the lockdown, and 3) weighted OLS regressions, using probability weights to correct for the oversampling of respondents from large municipalities (Panels B-D of Table 2 and Table S.3). The results are robust. Further, we show that the COVID-19 condition reduces money allocations to foreigners not only due to reduced pro-social behavior, but primarily due to increased prevalence of hostile behavior (Fig. 1b and Table S.4). We define an indicator variable equal to one if the participant actively destroyed the money allocated to the other person, i.e. reduced the reward to an amount below 100. The effect on prevalence of hostility is again largest for behavior towards a person living in the EU. In CONTROL, 20% decided to act in a hostile way towards a person from the EU, while in COVID-19 the prevalence of this behavior increased by 6 percentage points (OLS, P = 0.002). The difference is 5 percentage points (P = 0.035) and 4 percentage points (P = 0.049) for recipients living in the USA and Asia, respectively. Interestingly, the observed increase in the prevalence of hostility towards these groups of foreigners is driven by the extreme manifestation of hostility, namely the prevalence of decisions to reduce the rewards to CZK 0 (Panel B of Table S.4). Again, the magnitude of these effects is largest for the recipient from the EU— while in the CONTROL condition, 6.2% of participants destroyed all the earning of a recipient from the EU, the proportion increases to 9.3% in the COVID-19 condition (OLS, P = 0.007). The prevalence of extreme hostility is very low towards domestic recipients, 2.3% in CONTROL and 2.5% in COVID-19 (P = 0.797). Our conclusion that the COVID-19 condition magnifies the extreme form of hostility towards foreigners is further supported by a difference-in-differences estimation comparing the foreign recipients to the domestic recipient (Panel A of Table S.5). We provide further support for these conclusions in Fig. S.2, which shows full distributions of choices across both COVID-19 and CONTROL conditions. As expected, we also observe that COVID-19 reduces the prevalence of basic pro-sociality, defined as a willingness to increase rewards above the default allocation (Panel C of Table S.4), but the effects are relatively small and mostly not significant statistically. The size and diversity of our sample allows us to explore whether the observed effects of COVID-19 on hostility against foreigners is a broad response spanning across demographics, or behavior that characterizes certain demographic sub-groups of the population. Fig. 2 and Table S.6 displays the effect of the COVID-19 condition on the mean amount of money allocated (i) to all foreigners on average, and (ii) to recipients from the EU, for whom we observe the largest effects, across age groups, gender, education level, income level, and size of municipality. Overall, the results are similar across demographics.Fig. 2 Sub-group analysis of the effect of the COVID-19 condition on the amount allocated in the Help-or-Harm task, by the identity of the recipients. Notes: Coefficient plots. Bars represent 95 percent confidence intervals. The dependent variable is the amount allocated. The Fig. presents estimated coefficients of the COVID-19 condition relative to the CONTROL condition (corresponding regression models including numbers of observations are in Table S.6). Age and net monthly household income are divided by the median (50 years and CZK 35,000). Municipalities are divided into cities with more than 100,000 inhabitants, and smaller villages and towns. Data for all 2,186 participants used. Fig 2 3.2 Behavior towards domestic in-group and out-group members Next, we explore how thinking about Covid-19 affects behavior towards domestic in-group and out-group members. To study this, we measure the average amount allocated to (i) domestic in-group members who share selected group attributes with the decision-maker (region of residence, ethnicity, political party preference, or religious beliefs) and (ii) domestic out-group members who do not share a given group attribute. To start, we study mean allocations to each type of domestic in-group and out-group in the CONTROL condition (Table 1 and the right-hand side of Fig. 3 ). We observe an intuitive pattern. On average, the participants allocated larger rewards to in-group than to out-group recipients (CZK 127.3 vs. CZK 96.5, P < 0.001). This pattern holds for different domains, based on which in-group and out-group are defined. Recipients from a respondent's region, with similar political preferences, of the same ethnicity, or with the same religious affiliation as the respondent were allocated higher amounts than recipients with different characteristics. Consistent with previous research, which documented high levels of discrimination against the Roma ethnic minority in the Czech Republic (Bartoš et al., 2016), the difference is largest for ethnic divisions (a member of Roma ethnicity received, on average, CZK 76.4, while a member of the majority ethnic group received, on average, CZK 125.6, P < 0.001).Fig. 3 Effect of the COVID-19 condition on allocations in the Help-or-Harm task, by the group identity of domestic recipients. Notes: Coefficient plots. Estimated coefficients of the COVID-19 condition relative to the CONTROL condition, the dependent variable is the amount allocated. Bars represent 95 percent confidence intervals. Corresponding regression models including numbers of observations appear in Panel A of Table S.3. Data for all 2,186 participants used. Fig 3 Interestingly, the random person living in the Czech Republic received a larger allocation not only compared to domestic out-group members (by CZK 37.0, P < 0.001), but also compared to domestic in-group members (by CZK 6.2, P < 0.001). This result indicates that participants consider the random person living in the Czech Republic a member of their in-group. At the same time, the result that a random Czech person received higher allocations than Czech recipients who also shared another group attribute with the respondent is somewhat surprising. Since we use behavior towards a random Czech person as a neutral benchmark category to assess whether the effects of Covid-19 salience on behavior are specific for foreigners, we conduct a series of robustness tests presented in Section 4, in which we use a set of alternative benchmark groups. When analyzing the effects of the COVID-19 condition, we find an indication of negative effects on behavior towards both in-group and out-group members living in the same country as the respondent (Fig. 3 and Table S.3). However, most of the effects do not reach statistical significance and they are generally smaller in magnitude than the effects on behavior towards foreigners. Further, the effects of the COVID-19 condition on hostile behavior are not stronger for domestic out-groups than for domestic in-groups in a difference-in-difference estimation (Tables S.7 and S.8). Together, the results about the effects on behavior towards domestic recipients need to be interpreted with caution. On one hand, we find virtually no effects on behavior towards a random person from the Czech Republic (Fig. 1). At the same time, the estimated coefficients on behavior towards specific groups within the Czech Republic are negative, although most of them are not statistically significant. While it remains an open question whether Covid-19 has null or mildly negative effects on behavior towards people from own country, in any case, our results do not support the optimistic view that the Covid-19 crisis may create stronger social bonds, as we do not see more favorable behavior towards in-group members in COVID-19 compared to CONTROL. 4 Additional results and robustness checks In order to study in greater detail whether the COVID-19 condition increased hostility more towards foreigners than towards members of other groups, in Tables S.5 and S.9-S.10 we present the results of additional difference-in-difference estimations. This is useful for two reasons. First, because subjects allocated relatively large amounts to a random person living in the Czech Republic (as compared to what they allocated to more narrowly defined in-group members), we use an alternative benchmark group to proxy behavior towards a person living in the Czech Republic. Because the Czech Republic is a relatively homogenous country in terms of ethnicity, we use a person of the majority ethnicity living in the Czech Republic as such a proxy (Panel B). Second, we are interested in whether the effects on foreigners are larger than those on domestic out-group members. To study this, we use two benchmark groups: (i) allocations to members of any type of domestic out-group (Panel C), which should capture overall behavior towards a broad spectrum of out-group members, and (ii) a person living in a different region of the Czech Republic than the respondent (Panel D), which allows us to study differences in effects on behavior to recipients who all live in a different location than the respondent, in one case in a different country and in the other case in a different region within the same country. The increase in hostility in the COVID-19 condition is stronger against foreigners from the European Union relative to most comparison groups, independently of whether we examine the amounts allocated in the HHT (Table S.9), the prevalence of hostile behavior (Table S.10), or the prevalence of extreme manifestations of hostility—reducing the reward to zero (Table S.5). As for foreigners overall, the estimated effects of the COVID-19 condition are always stronger than the effects on domestic comparison groups, but the differences do not reach statistical significance except when we focus on the prevalence of extreme hostility (Table S.5). Based on this, we conclude that the results suggest that negative effects on behavior are greater for foreign recipients, as compared to other groups, but we acknowledge that this finding is not robust in all specifications. Next, we address the potential concern of false discovery of significant effects in one of our seventeen outcomes of interest. In Table 2 and Table S.3 we present three types of p-values. The first is standard “per comparison” p-values. These are appropriate for researchers with an a priori interest in a specific outcome. For instance, researchers interested in the impact of Covid-19 on behavior specifically towards foreigners, or specifically towards people living in Asia, should focus on these p-values. In addition, the analysis also presents additional p-values that account for multiple hypothesis testing, using the method developed by Barsbai et al., (2020), since a potential concern might be that our results are susceptible to false discovery of significant results that arise simply by chance when testing the impacts on multiple outcome variables. Since the paper is motivated by concerns about Covid-19 fostering out-group hostility, we consider the two main outcome variables capturing behavior towards out-group members: (i) behavior towards foreigners and (ii) behavior towards domestic out-group members. Thus, researchers with a priori interest in behavior towards out-group members should focus on these p-values. The effect on foreigners remains statistically significant (P = 0.045). Finally, we take the most conservative approach, relevant for the question whether Covid-19 affects social behavior in general, towards any type of recipient. Consequently, we adjust for the seventeen hypotheses corresponding to all dependent variables for which we estimate the effects. Even under this approach, the effect on the recipients from the EU is statistically significant (P = 0.008). Nevertheless, the effects on behavior towards foreigners, people living in the USA and in Asia do not reach statistical significance after this correction. The use of “within-subject” design, as compared to “between-subject” design, when eliciting behavior towards individuals with different group attributes has the advantage of providing a rich picture of individual discriminatory preferences, but can potentially affect the size of the estimated discrimination if subjects realized the purpose of the study. In principle, social desirability biases could reduce the estimated levels of discrimination if subjects choose to hide their true preferences, while experimenter demand effects may induce subjects towards greater differentiation in behavior towards individuals with different group attributes. We took several steps to address this issue. First, the seventeen HHT decisions were organized in six blocks, which were presented in a randomized order (96 different orderings), and we control for order effects in the regression analysis. Second, our main focus is on estimating the effects of greater Covid-19 salience on behavior. Thus, even if subjects were to some extent induced to differentiate their behavior towards individuals with different group attributes by the “within-subject” design, this is a concern only to the extent to which this confound interacts with the COVID-19 condition. Our results suggest this is unlikely. We show that the main effects hold when we focus on decisions made early in the experimental module, and thus mimic a “between-subject” design (Table S.11). Specifically, when we restrict the sample to choices made in the first block, the estimated negative effects of the COVID-19 condition on behavior towards foreigners are at least as strong as in the baseline specification where we use all decisions. We arrive at similar conclusions when we restrict the sample even further and consider only the very first decision made by each participant.3 Another potential concern is that thinking and answering questions in the COVID-19 condition may have caused fatigue and led to less attention to allocation decisions, and thus may have affected choices without activating Covid-related concerns and fears. This explanation is not supported by our data. Subjects in COVID-19 are neither more prone to stick to the default allocation, nor less likely to correctly answer attention check questions (Table S.13). Both of these patterns would be expected if subjects were less attentive. In fact, the effects of COVID-19 on behavior towards foreigners is caused by reduced likelihood of sticking to the default allocation, and an increased tendency to actively reduce recipients’ income (Fig. S.2). Subjects’ response time is somewhat lower in COVID-19, but all results are robust to controlling for response time (Table S.14). 5 Discussion and open questions The evidence presented in this paper illuminates that Covid-19 can cause damage in the social domain, but it should be seen as only an initial step. Below we discuss several limitations and directions for future research. First, although the effects of the prime on behavior towards various groups of foreigners are statistically significant, the magnitude of these effects is not large. This is not surprising given that we identify marginal effects of greater salience of Covid-19 concerns, and not their overall effects. While the priming technique is a useful tool to uncover qualitative effects, it is less informative about the magnitude of the impact of real-life experiences. The second open question concerns the underlying mechanisms. Overall, the results are consistent with the idea that people form more negative attitudes towards people with group attributes associated with the transmission of the disease. First, at the time of the data collection, infection rates in the Czech Republic were relatively low, as compared to most of the EU countries, such as Italy, Germany, and Spain. The first reported cases of Covid-19 in the Czech Republic got infected in Italy. The Czech government quickly responded, by closing borders, in addition to other measures. These aspects may have fostered the perception of the coronavirus pandemic as an external threat, spreading mainly from other EU countries. Further, at the time of the data collection, Africa had been much less associated with the virus compared to EU, the US and Asia, thanks to relatively low infection rates, helping to explain why we observe negligible effects on behavior towards people from Africa. In this context, it may seem surprising that we do not see greater effect on behavior towards individuals from Asia, as compared to behavior towards people from the EU or the US. While China was mentioned regularly by Czech public officials and the media, it mainly concerned positive remarks about medical supplies delivery.4 Further, the Covid-19 crisis has entered people's lives in complex ways. It has created fears about people's own health, and that of friends and family members. To many, it has imposed economic hardships and uncertainty about future material well-being. It has also forced people to isolate themselves socially. The prime used in this paper may have activated all these concerns, and we cannot separate their roles in triggering the observed increase in hostility towards foreigners. A fruitful avenue for future research would be to try to more sharply disentangle these aspects, perhaps by designing a set of Covid-related primes, each aiming to activate a different dimension of concerns. Further, the mechanisms above consider direct effects of the pandemic on individual preferences. Another possibility is that the observed increase in anti-foreigner sentiments may have been created by the behavior of politicians who may have incentives to blame foreigners for spreading the virus, in order to redirect attention from their own internal problems fighting the pandemic. Specifically, the increase in hostility towards recipients from the European Union could potentially be driven by Czech politicians blaming the EU for slow responses at the beginning of the pandemic. Eurobarometer 93 reports worsening attitudes towards the EU between Fall 2019 and Summer 2020—the share of Czech people trusting the EU dropped by 4 p.p. (Eurobarometer, 2020), while the share of Czechs with a negative view of the EU increased by 5 p.p., although other surveys provide more mixed evidence5 . We provide the following test of this explanation. If these indirect effects were driving the increase in anti-foreigner sentiments, we would expect the effects to be more pronounced among people who were more exposed to social media or media in general. The results do not provide support for this explanation, since we do not observe such heterogeneous effects (Table S.15). Nevertheless, we cannot fully rule out this mechanism and future work could study this further by, for example, comparing the effects of Covid-19 in countries in which politicians did and did not incite anti-foreigner sentiments or did and did not try to place blame on the European Union. Finally, this paper focused on the immediate effects of the first coronavirus wave, but it will be important to study how the attitudes towards out-groups develop during the later stages of the pandemic and post-pandemic recovery. Also, the evidence comes from a single country and more research is needed to explore how generalizable the effects are across settings. 6 Conclusions This paper provides initial evidence documenting how concerns triggered by a global health pandemic, Covid-19, shape hostility towards people with different group attributes. The main result is that thinking about Covid-19 increases anti-foreigner sentiments, making people more prone to financially harm people from the EU, the USA and Asia. We show that this is a relatively general response, present across various demographic groups. Some of our results also indicate that the salience of Covid-19 negatively affects behavior towards other people more generally, since we find zero to small negative effects for domestic out-groups as well as in-groups. Thus, the negative effects may not be limited to behavior towards foreigners, but this evidence should be viewed only as suggestive. Our results underscore the importance of making sure political and other opinion-leaders avoid associating or blaming foreigners and other countries for the crisis. Placing blame as a political strategy can either create or tap into elevated anti-foreigner sentiments, and consequently increase the risk that the health and economic crises will become compounded by unravelling of international collaborations. Appendix Supplementary materials Image, application 1 Image, application 2 We thank PAQ Research and NMS Market Research for implementing the data collection. Bauer and Chytilová thank ERC-CZ/AV-B (ERC300851901) for support and funding of the data collection. Bartoš thanks the German Science Foundation for their support through CRC TRR 190. The research was approved by the Commission for Ethics in Research of the Faculty of Social Sciences of Charles University. Participation was voluntary and all respondents provided consent with participation in the survey. The datasets and do-file replication files are available in the Harvard Dataverse repository (doi: 10.7910/DVN/XD8OOL). Declarations of interest: none. Note that the dataset which we used in the earlier version of this paper (version: May 2020) contained a coding error, which impacted some of the estimates (please see the Supplementary Information for more details). This version presents the results after the correction. The main finding of the paper – that greater salience of the Covid-19 crisis increases hostility to foreigners – still holds. 1 In-group favoritism and out-group discrimination have also been measured using a minimal-group experimental paradigm, by creating artificial group boundaries in the laboratory (based on, for example, having T-shirts of the same color or sharing preferences for art). The most prominent examples are Tajfel (1981), Chen and Li (2009), and Charness, Rigotti, and Rustichini (2007). 2 Earlier research has used costless redistribution tasks to measure fairness preferences and found that similar fairness views are observed for monetarily uninterested individuals (“spectators”) and for individuals whose payoffs were affected by own decisions (Cappelen et al. 2013). Such spectator designs have been successfully employed in recent large-scale experiments, see, for example (Cappelen et al. 2020; Almas, Cappelen, and Tungodden 2020). 3 In addition, Table S.12 presents the results from the first block of choices for domestic in-groups and out-groups, where we find a negative effect of the COVID-19 condition on behavior towards domestic in-groups (-5.9 CZK, p=0.047) and smaller and insignificant effect towards domestic out-groups (-3.2 CZK, p=0.232), in line with the patterns observed with full data. 4 We performed a text analysis of all governmental Covid-19 related press conferences held before the end of our data collection, and the twitter feeds of the governmental officials most involved in the management of the Covid-19 crisis in the Czech Republic. We did not find a single case of labeling Covid-19 as “Chinese virus” or similar. A sole critical tweet pointed to the dependency of the EU on Chinese medical production. 5 The survey agency CVVM did not find many changes in attitudes towards the EU between April 2019 and July 2020 (CVVM 2020). The survey agency STEM finds a drop in satisfaction with the EU membership between Fall 2019 and May 2020, but the opinions are volatile, with May 2020 being very similar to Summer 2019 (STEM 2020). Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.euroecorev.2021.103818. ==== Refs References Abbink Klaus Sadrieh Abdolkarim The Pleasure of Being Nasty Economics Letters 105 3 2009 306 308 Almas Ingvild Cappelen Alexander Tungodden Bertil Cutthroat capitalism versus cuddly socialism : are americans more meritocratic and efficiency-seeking than scandinavians? J. Polit. Econ. 128 5 2020 1753 1788 Anderson Robert Warren Johnson Noel D. 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O'Shea Brian A Watson Derrick G Brown Gordon D A Fincher Corey L Infectious disease prevalence, not race exposure, predicts both implicit and explicit racial prejudice across the United States Social Psychol. Personal. Sci. 11 3 2020 345 355 Rao Gautam. Familiarity does not breed contempt : generosity, discrimination, and diversity in delhi schools Am. Econ. Rev. 109 3 2019 774 809 Shayo Moses Zussman Asaf Judicial ingroup bias in the shadow of terrorism Q. J. Econ. 126 3 2011 1447 1484 STEM. 2020. “Klíčové Slovo: Evropská Unie.” Https://Www.Stem.Cz/Tag/Evropska-Unie/. Tajfel Henry. Human Groups and Social Categories: Studies in Social Psychology 1981 Cambridge University Press Cambridge United Nations. 2020. “COVID-19 Stoking Xenophobia, Hate and Exclusion, Minority Rights Expert Warns.” Https://News.Un.Org/En/Story/2020/03/1060602.

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==== Front Meteorol Atmos Phys Meteorology and Atmospheric Physics 0177-7971 1436-5065 Springer Vienna Vienna 948 10.1007/s00703-022-00948-9 Original Paper Linkages between Madden–Julian oscillation and drought events over Kenya http://orcid.org/0000-0002-9989-4141 Ochieng Phillip Okello [email protected] 12 Nyandega Isaiah 1 Wambua Boniface 1 http://orcid.org/0000-0002-5110-2870 Ongoma Victor 3 1 grid.10604.33 0000 0001 2019 0495 Department of Earth and Climate Sciences, University of Nairobi, P.O. Box 30197-00100, Nairobi, Kenya 2 Kenya Meteorological Services, P.O. Box 30259-00100, Nairobi, Kenya 3 Mohammed VI Polytechnic University, Lot 660, Hay Moulay Rachid, 43150 Ben Guerir, Morocco Responsible Editor: Clemens Simmer, Ph.D. 14 12 2022 2023 135 1 923 12 2021 6 12 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Increased frequencies and intensities of extreme weather events have negatively impacted climate-sensitive socio-economic sectors in Kenya and larger Equatorial East Africa (EEA). Madden–Julian oscillation (MJO) influence intra-seasonal weather variability over Kenya although less attention has been given to its effect on extreme weather events such as droughts and floods, which have increased in frequency and intensity. Outgoing Longwave Radiation (OLR) was used in this work as proxy data for rainfall to study the geographical distribution and circulation anomalies associated with MJOs and their impacts on extreme weather events. Extreme weather events are identified using the self-calibrating Palmer Drought Severity Index (sc-PDSI), based on sc-PDSI, 2013/2014 and 2017/2018 as the drought and flood years, respectively. The background power spectral analysis reveals that MJOs are more dominant during the March–May (MAM) season than other seasons. The variance analysis depicted that the maximum power of MJO-filtered OLR is cantered within the tropical Indian Ocean, maritime continent and the tropical Pacific Ocean. Upper tropospheric (200 hPa) wind signatures give a clear Matsuno-Gill-type circulation compared to the lower tropospheric wind flows. Thus, the signatures can be used to develop a dynamic MJO index for prediction purposes. There exists a weak direct relationship between MJO and sc-PDSI; however, the influence may result from its modulation of atmospheric circulation as illustrated by the wind and velocity potential patterns before and after the passage of the convective MJO phase. Supplementary Information The online version contains supplementary material available at 10.1007/s00703-022-00948-9. Kenya Climate Smart Agricultural ProjectKCSAP- WORLD BANK/ IDA Credit P154784. Ochieng Phillip Okello issue-copyright-statement© Springer-Verlag GmbH Austria, part of Springer Nature 2023 ==== Body pmcIntroduction Extreme weather events such as droughts and floods have far-reaching consequences on the economy and the environment at large (Seneviratne et al. 2012; Lyon 2014; Basu et al. 2016). Although the occurrence of droughts is inevitable, a lot can be done to minimize the associated negative impacts. The measures may include but are not limited to an early warning system that enhances preparedness for sustainable adaptation and mitigation actions (Kilavi et al. 2018). Drought forecasting forms a fundamental component of the drought early warning system by adopting better forecasting methods. Arid and Semi-Arid Lands (ASALs) in Kenya are the most prone and vulnerable to droughts. Unlike other meteorological hazards experienced in these regions (e.g., flash floods, landslides), drought events are more devastating since they exacerbate the water scarcity condition. The over-dependency of the local community on pastoralism which relies on rain-fed pastures as the main economic activity further aggravates this situation. The 2008–2010 drought, for example, affected the entire East Africa region, with approximately 13 million people directly impacted (Muller 2014). Pastoralists lost over 60 percent of their livestock (Huho and Kosonei 2014); an estimated 3.2 million people in ASALs of Kenya were left in need of emergency help. The drought situation that used to occur after every 5 years has become more frequent and intense, and its management has been complicated by other pandemics such as COVID-19 (Funk 2020). The rainfall pattern in Kenya is characterized by two maxima experienced during October to November (OND) and March to May (MAM), locally referred to as ‘short’ and ‘long’ rains, respectively (Okello et al. 2021). At numerous temporal and geographical dimensions, many factors govern the start, length, and rebound from climate extremes, especially floods and droughts (Frei et al. 2006; Dai 2013; Sun et al. 2016). In particular, teleconnections such El Nino–Southern Oscillation (ENSO), Madden Julian Oscillations (MJO), the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), and the Pacific Decadal Oscillation (PDO) have a substantial impact on climatic extremes (Weisheimer et al. 2017; Zhang et al. 2010). For instance, the positive and negative phases of ENSO are associated with above and below-normal rainfall, respectively, over East Africa OND (Omondi et al. 2014; Indeje et al. 2000b; Kalisa et al. 2020; Mpelasoka et al. 2018). The IOD is the primary influencer of rainfall variability over Kenya throughout the MAM season (Mpelasoka et al. 2018; Ongoma et al. 2015; Owiti et al. 2008). Warming (cooling) in the western Indian Ocean is connected with a positive (negative) phase of the IOD. The positive phase of IOD is associated with enhanced rainfall in Equatorial East Africa (EEA). Madden–Julian Oscillation (MJO, a 30- to 60-day oscillation) is one of the most important features that influence climate variability within the tropics (Zhang 2005). It is known to cause intra-seasonal to seasonal rainfall variability in Kenya (Hogan et al. 2015; Omeny et al. 2008; Pohl and Camberlin 2006). The Indian Ocean, the Caribbean, the Pacific Ocean and Africa are all affected by this belt of deep convective clouds moving eastward at a speed of about 5 miles per hour (8.0 km/h). According to Madden and Julian (1994), the MJO is strongest in the winter and weakest in the summer, even though it is present year-round. Although its strength varies seasonally, it impacts climate and weather events all year round in the tropics and extra-tropics. MJO is also known to have a considerable impact on the atmospheric circulation in the global tropics, as well as causing fluctuations in the weather and temperature in non-tropical places around the world (Bond and Vecchi 2003; Zhang 2005). Zhang (2005) provided a comprehensive study of the MJO's properties and dynamics. Similar studies have been done by Li (2014), and Demott et al. (2015). A convective core that is made up of a large number of small-scale deep convective structures is what distinguishes the “active phase” of the MJO from other phases. Near the surface, zonal winds blow in the same direction as the core, but higher up, they are blowing in opposite directions. Active phase convergence is seamlessly connected to a “suppressed phase” of low convection and surface divergence that flows along with the active phase. This results in the production of a convective dipole that can traverse significant areas of the world’s tropical regions. Large-scale tropospheric heating that is linked with the active phase of the MJO is what typically leads to eastward-propagating dry Equatorial Kelvin waves and westward-propagating Equatorial Rossby Waves. In Kenya, MJO influence on extreme weather events, which could be vital information for the prediction of inter-seasonal rainfall in East Africa (Kimani et al. 2020; Kilavi et al. 2018; Omeny et al. 2008). There is a correlation between the MJO’s influence on precipitation and its modulation of large-scale tropospheric circulation, oscillating between favourable and unfavourable circumstances for upward vertical movement and convection (Schreck et al. 2013). Mutai and Ward (2000) observed that a form of intra-seasonal rainfall variability in EEA short rains corresponds to MJO timeframes. They determined that intensified MJO convection in the Indian Ocean lags positive rainfall anomalies by about 5 days. As this link is time-lagged, Mutai and Ward (2000) indicate that EEA convection may lead to the generation of MJO episodes in the Indian Ocean. However, they did not study the processes by which the MJO influences EEA precipitation, but they did underline the vital implications of scale interlinks between intra-seasonal oscillations, including the MJO, and inter-annual climatic variability impacting EEA precipitation. Omeny et al. (2008) quantified the relationships between MJO and precipitation over Kenya. Similar to Pohl and Camberlin (2006), they reported a substantial association between highland rainfall, in this instance rainfall in western Kenya, and MJO when the MJO convective core is in the Indian Ocean. When the MJO advances into the Western Pacific, rainfall in western Kenya decreases. The outcome is the same for both short and long rains. The study highlights that this link might be used to inform 10-day-ahead rainfall estimates, but it advocates for integrating MJO information with other diagnostics to account for a larger proportion of variability. Omeny et al. (2008) observed insignificant relationships between the MJO and precipitation in eastern Kenya. Berhane and Zaitchik (2014) extend the research of Pohl and Camberlin (2006) and Omeny et al. (2008) by analysing sub-seasonal variation in the MJO’s contribution on EEA during both the short and long rainy seasons. They found that MJO convection in the Indian Ocean is related to increased EEA highland rainfall throughout the long rains, but that the association is strongest near the conclusion of the MAM season. During the short rains, substantial connections with highland precipitation are weaker in October than in November and December. In October, however, there is a strong negative relationship between Maritime Continent MJO convection and coastal EEA precipitation that is absent later in the season. Extreme drought research has gained a lot of research interest on the international scene. Understanding the dynamic dynamics behind extreme drought occurrences has not yet been established. The catastrophic droughts have affected Kenya in the recent past and are projected to persist into the future over most parts of East Africa (Haile et al. 2020). There is a great deal of scientific value in investigating and uncovering the reasons behind these incidences. This study’s findings may help improve present monitoring and prediction technologies for catastrophic climate occurrences like Kenya’s droughts. As mentioned earlier, larger-scale atmospheric and oceanic variability effects and modulates all processes. All of this prior research has focused on the effects of the MJO on either rainfall over the study region. However, the effects of MJO droughts on floods have been rarely investigated over the study area. This research aims at finding the effects of the tropical MJO on this bimodal rainfall pattern and examine the physical factors for the occurrence of drought events in terms of the persistent intra-seasonal atmospheric circulation anomalies in the MJO, as well as to provide scientific evidence for the monitoring and prediction of extreme droughts and floods through establishing the nexus between MJO and drought. Data and methodology Study area Kenya lies between longitude 34° E and 42° E and latitude 5° S and 5° N (Fig. 1). The country is surrounded by Uganda, Ethiopia, Tanzania, Southern Sudan and Somalia. The country’s economy and most households are largely dependent on rain-fed agriculture (Eichsteller et al. 2022). The central highlands have the highest elevation, whereas the climate and natural systems of the low-lying eastern, northwest, and north-eastern parts are mainly ASAL. The average annual precipitation ranges from less than 250 mm in the ASALs to more than 2000 mm in regions with high potential (Ochieng et al. 2022). The Indian Ocean to the south regulates the local climate pattern of the adjacent coastal zones, but the enormous water basin of Lake Victoria on the western flanks of the research area drives land-lake breezes with modifying the climate of its basin (Okoola 1999).Fig. 1 Digital elevation map and the location of the study area a Kenya b Africa and c Eastern Africa. Data represents the Shuttle Radar Topography Mission (SRTM) 30 m image for Kenya. These SRTM was created through mosaicking tiles and clipping to the extent of the country. The digital elevation and major lakes were mapped using the shapefiles obtained from the World Resources Institute (WRI) (National Space Agency et al. 2000) Owiti et al. (2008) conducted extensive research on Kenya’s annual rainfall cycle, while most studies (e.g., Indeje et al. 2000b; Ogwang et al. 2015; Okello et al. 2021) focused on rainfall over the larger East Africa region. The studies found that the first and second maxima of precipitation occur in MAM and OND, respectively, in these geographical areas. The minimum rainfall is recorded during December–February (DJF) and June–August (JJA). The meridional and zonal propagation of the ITCZ (convective rainfall belt) is mainly responsible for this rainfall seasonality (Camberlin et al. 2001; Ongoma et al. 2015; Nicholson 2018). Droughts across Kenya and the wider EEA region are complex phenomena involving multiple teleconnection mechanisms. There is evidence that the ENSO is responsible for some of the extreme weather in East Africa (Indeje et al. 2000a; Kalisa et al. 2020; Lyon 2014; Masih et al. 2014). Data This study utilized a monthly gridded temperature dataset for the computation and modification of the projected Thornthwaite Evapotranspiration. The data has a spatial resolution of 0.5° × 0.5°, sourced from the Climatic Research Unit, CRUTS4.03 (Harris et al. 2014). CRU temperature dataset has successfully been applied by Polong et al. (2019) in the computation of potential evapotranspiration (PET) over the Tana River basin, Kenya. Similarly, Ayugi et al. (2020) successfully utilized the data to calculate PET in their evaluation of drought over Kenya based on SPEI. Climate Hazard Group Infrared Precipitation with Station monthly precipitation datasets (CHIRPS v2; Funk et al. 2015) was used in the computation of sc-PDSI. The spatial resolution of the CHIRPS data package is 0.05 (~ 5.3 km). This dataset is a blend of satellite and ground observation and has the highest correlation with the real observations (see the supplementary file). The accuracy of CHIRPS data to delineate rainfall characteristics has also extensively been studied (e.g., Ayugi et al. 2019; Kimani et al. 2017; Ngoma et al. 2021). These studies pointed out that CHIRPS dataset reproduces the observed rainfall over East Africa wells. All the aforementioned datasets are analysed for the period of 1980–2018. The climatological Soil Available Water Holding Capacity (AWHC), also referred as AWC or Root Zone Water Holding capacity was obtained from Oak Ridge National Laboratory Distributed Active Archive Centre (ORNL DAAC) for biochemical dynamics (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=548). The data has a spatial resolution of 1° × 1°. This dataset has extensively been applied (e.g., Dai 2011; Trenberth et al. 2014) in computing sc-PDSI. Convection is inferred using the daily interpolated National Ocean and Atmospheric Administration (NOAA) OLR data collection (Liebmann and Smith 1996). It is a 2.5° × 2.5° global gridded data set with global coverage. Data running from 1980 to 2018 is used in this study. Daily datasets from the National Centres for Environmental Prediction–National Centre for Atmospheric Research (Kanamitsu et al. 2002) are utilized to study the circulation anomalies associated with MJO. This dataset comprises meteorological variables (wind, temperature, geo-potential height, humidity on pressure levels, surface variables, and flux variables like precipitation rate). It has a spatial resolution of 2.5° × 2.5° observed 4 times a day at 0000, 0600, 1200 and 1800 UTC, with 17 pressure levels from 1000 to 10 hPa. This study used daily values from January 1, 1980, and December 31, 2018. Methodology Computation and self-calibration of palmer drought severity index Among the most commonly adopted drought index for assessing the duration and severity of droughts is the PDSI (Aiguo et al. 2004; Wellet al. 2004; Hua et al. 2011; Palmer 1965). The PDSI is determined using an elaborate water balance procedure that integrates past data on temperature, precipitation, AWHC, and possible evapotranspiration. The complete PDSI calculation technique includes various quantitative variables that are assessed depending on the hydrothermal conditions of the study area. As a result, the PDSI's shortcomings in regional hydrological studies are apparent. As a consequence, since it uses a significant number of climate variables as data, this index gives a detailed instrument for assessing global warming impact drought (Dai 2013; Liu et al. 2012). Wells et al. (2004) presented the sc-PDSI as an enhanced form of the “classical” PDSI. For the purposes of clarity in the self-calibrating PDSI calculation technique, we commenced with Palmer (1965) PDSI computation. The symbol without a subscript indicates the initial variable for the PDSI calculation. Using a water balance model, the water deficit, d, can be calculated. Step 1: Computation of water deficiency, d The determination of the PDSI value for a particular month in a particular year begins with the calculation of water balance model elements depending on precipitation, temperature, and AWC. Based on the recorded precipitation and the precipitation under the Climatically Appropriate for Existing Condition (CAFEC), the water deficit, d, may be calculated. The water balance model uses ET (evapotranspiration), R (recharge of the actual world soil moisture), RO (runoff), L (loss of the real world soil moisture), and PE (potential evaporation), PR (potential soil moisture recharge), PRO (potential runoff), and PL (potential soil moisture loss) to calculate d. The PET was calculated using the Thorthwaite algorithm (Thornthwaite 1948). On the basis of actual precipitation and prospective evapotranspiration, the Double-Layer Soil Model (DLSM) was used to determine the remaining water balance elements. The DLSM analysis separated the soil layer into two distinct portions (Eq. (1)). As a result, the AWC consisted of two distinct components: the AWC of the surface soil layer (AWCs), which measured approximately 1 inch (or 25.4 mm), and the AWC of the underlying soil layer (AWCu), which measured approximately 9 inches (or 228.6 mm):1 AWC=AWCS+AWCU During the first few days of the month, we assumed that the initial moisture content of the surface soil and the layer underneath is, Ss and Su respectively (Eq. 2). During the first month, the moisture content of the surface soil equals the AWCS, whereas the moisture content of the soil beneath the surface equals the AWCU. The Ss and Su values for subsequent months can be calculated using the observed values of soil moisture in the real world for the months that came before them. The difference between the effective soil moisture and the real-world observed soil moisture is the highest potential water retention (PR) that the soil volume can hold (Eq. (2)):2 PR=AWC-Ss+Su The PRO is the total soil moisture, which is determined using Eq. (3):3 PRO=Ss+Su According to DLSM, when rainfall is insufficient to fulfil ET, the soil moisture of the surface soil layer can supplement ET’s water inadequacy, while the soil moisture of the subsurface soil layer can partially satisfy ET. In this instance, the PL of the DLSM was calculated using Eqs. 4, 5, 6:4 PL=PLs+PLu 5 PLs=minPE,Ss 6 PLu=PE-PLs.SuAWCPLu≤Su The ET, R, RO and L can be computed based on P and PET: given PET ≤ P, ET = PET, L = 0, R = 0, RO = P − ET − PR; given PET > P, ET = P + L, RO = 0, L = Ls + Lu, R = min (PE − P, PR). Where the elements of the DLSM are derived using (Eqs. 7 and 8).7 Ls=minPE-P,Ss 8 Lu=PE-P-Ls.SuAWCPLu≤Su On the basis of the previously discussed water balance elements, the remaining water balance elements can be derived by (Eqs. 9, 10, 11, 12):9 ET^=αiPE 10 R^=βiPR 11 RO^=γiPRO 12 L^=δiPL where αi=ETi¯PEi¯;βi=Ri¯PRi¯;γi=ROi¯PROi¯;δi=Li¯PLi¯ i denotes months of a given year and ranges from 1,2,… to 12. αi, βi, γi, and δi denote coefficients of the water balance components related to ith month. The letter with a straight line cap signifies the average value for a particular month, and ET^, R^, RO^, and L^ denote the ET, recharge of soil moisture, runoff, and loss of soil moisture under CAFEC. Then, we determined the water deficit based on the actual rainfall quantity observed in the real world (Eq. 13) and the rainfall quantity under the CAFEC for a particular month (Eq. 14):13 d=P-P^ 14 P^=ET^+R^+RO^-L^ where d represents the water deficit, P represents actual precipitation, and P^ represents precipitation under the CAFEC. Step 2: Determination of Z values The water inadequacy, d, is employed to quantify the variation between the actual precipitation total for the current month and P^ in inches or millimeters. Nevertheless, the P^ value varies from place to place and month to month. Thus, the same d value may indicate varying humidity situations depending on the place and month, e.g., identical water deficit may indicate different drought intensities in arid and humid regions or during the rainy and dry seasons, correspondingly. In this instance, modification factors, K, were implemented to measure water demand and supply relationships in a particular location as in Eq. (15). We estimate the water requirements with PE¯+RO¯+R¯ and the water supply with P¯+L¯. We adjusted the water inadequacy, d, to the water deficit index, Z, to accurately represent the variations in moisture and dryness as in Eq. (16).15 K=Water DemandWater Supply=PE¯+R¯+RO¯P¯+L¯ 16 Z=dK Z index is the departure of actual wetness/dryness from the long-term yearly mean water availability in a specific location for a certain month. In arid climates, water requirement exceeds water supply; thus, drought conditions are more dependent on scarcity or water supply in arid areas compared to other places. Hence, K > 1 and K operate as a booster and underscores the importance of water supply, which is not advantageous for PDSI-based drought monitoring over a wider area. Considering drought monitoring in a particular region with distinct water availability and other particular geographic characteristics, K value should be modified periodically. The K factor was further enhanced, as demonstrated by Eq. 17.17 Di¯=∑all yearsdiLeghth of Years The parameter with a small line above it represents the long-term yearly mean of this parameter for a specific month, i.e., Di represents the long-term yearly average of the absolute value of the water deficiency, di, for a particular month, i.e., Ki′ is, therefore, the calibrated K value for a particular location during a particular month. Various ∑Di¯Ki′ were determined through evaluations of different locations within the study location; the mean of these values, 17.67, was used as the base case, while the ∑Dj¯Kj′ for a particular region was used as the denominator. Once more, the K value was modified (Eq. 18). The succeeding K value can only be employed for drought monitoring in the regions studied by Palmer; it is not applicable for drought monitoring in other geographic areas. A simplified version of K values for a specific month is shown in Eq. (19).18 Ki′=1.5log10PEi¯+ROi¯+Ri¯Pi¯+Li¯Di¯ 19 Ki=17.67∑j=112Dj¯Kj′Ki′ where K is the moisture anomaly index. Without taking into account the patterns of the latest rainfall, the Z index can be applied to provide an indication of the level of dryness or wetness experienced during a given month. You can also use it to approximate the value of the PDSI for a particular month by using Eq. 20, which is as follows:20 Xi=0.897Xi-1+13Zi The only distinction between the PDSI and the sc-PDSI is that the empirical constants (K) and the duration factors (0.897 and 1/3) are replaced with values that are automatically created based on the research site’s historical climate data This gives the sc-PDSI geographical comparability and calibrates the index’s performance at any region (Wang et al. 2015). Sequentially the 98th and 2nd percentile values of the PDSI then finally sc-PDSI is computed as shown in Eq. 21.21 Xt=1-mm+bXi-1+CZtm+b Thus, considering any category of drought, specified as C, the computed index is calibrated as long as m and b can be computed, where m is the line slope and b is the y intercept. Table 1 shows the sc-PDSI values as well as drought categories. The theory of run (Le et al. 2019; Yevjevich 1969) is then applied to the sc-PDSI time series to isolate the most severe droughts and their facets.Table 1 PDSI classification (Palmer 1965) PDSI category Weather PDSI category Weather  > 4.00 Extremely wet  < − 4.00 Extreme drought 3.00–3.99 Very wet − 3.00 to − 3.99 Severe drought 2.00–2.99 Moderately wet − 2.00 to − 2.99 Moderate drought 1.00–1.99 Slightly wet − 1.00 to− 1.99 Mild drought 0.50–0.99 Incipient wet spell − 0.50 to − 0.99 Incipient drought 0.49 – − 0.49 Near normal Power spectral analysis and wavenumber-frequency filtering MJO modes characterized by zonal dispersion are investigated using zonal space–time spectral analysis and filtering of OLR data in the wavenumber–frequency dimension. The spectral analysis reveals the power dispersion in the wavenumber-frequency spectrum related to the moving phases. By applying a filter, we can get a rough approximation of the longitude-time pattern typical of various locations in the wavenumber-frequency dimension. Studies (e.g., Wheeler and Kiladis 1999; Roundy and Janiga 2012; Roundy 2012) applied a similar approach to figure out the spectral characteristics of Intra-Seasonal Oscillations' spectral characteristics. To initiate the spectral analysis, the yearly periodicity was determined at every grid point by standardizing a sequence of daily means. This annual loop was eliminated to create a collection of anomalies. The anomalies at every latitude were then grouped into sixty globally zonal 97-day temporal pieces that spanned 65 days and intersected one another. The periodic average, regular and parabolic tendencies were eliminated from each component to exclude pulses with frequencies less than or equal to 1/97 cycles per day. The zonal space–time spectral calculations for every section were computed by decaying the portions into sophisticated wavenumber and frequency aspects for eastward and westward-moving perturbations (Roundy and Janiga 2012) using a discrete Fourier transform in space preceded by one in time, and then calculating the product of this decay and its sophisticated conjugate. Based on the specified spectral structure, the generated power was then averaged across all relevant portions and all appropriate latitudes or latitude permutations. In spectrum analysis, only bands reflecting data from 15° N to 15° S were considered is in Wheeler and Kiladis (1999). The statistical significance of spectral power is determined using the red-noise spectrum and the Student’s t test. Presented in Eq. 22 is the precise formulation of the red noise spectra prn.22 prnk,w=σz2k1+α1k2-2α1kcos2πω, where k is zonal wavenumber, ω is frequency, σz2 is the variance of driving white noise, and 1 is lag-1 autocorrelation coefficient computed from the observed time series whose longitudinal component is transformed into wavenumber space. In Eq. 1, σz2 is chosen so that it is equivalent to the amplitude of the original spectrum when integrated over frequency at each wavenumber. Transforming the time-domain wavelet transform of OLR anomalies down the long axis yields the space–time wavelet transform Eq. (23). Morlet’s wavelet,23 φs=1√πBexpiσsexp-s2B is applied, where s represents x or t for the spatial or temporal transforms, respectively, and s represents angular frequency, v or wavenumber, k. The bandwidth parameter B was assigned a value of 4v/2π-3/2 for the temporal transform and 1.5k/2π-3/2 for the spatial transform. The above procedure can be summarized step by step as:Longitude–time arrays of OLR anomalies are used to organize the data. To create symmetric filtering, both north and south latitudes are added or subtracted from each other and then divided by two in the following phase (for asymmetric filtering). These several 96-day segments that overlap each other by 48 days are the product of symmetry filtering. After that, the segments’ linear temporal trends are removed and they are progressively tapered in time using a sine function called cosine bells (to reduce spectral leakage). As a consequence of the tapering, some wavelet transform may have been removed. Afterwards, the Fourier transform is done throughout the longitude spectrum. The coefficients obtained as a result are converted in time. The spectral power of the resulting complex arrays is obtained by multiplying them by the complex conjugates of the producing complex arrays. The power of each segment is averaged throughout the entire collection of segments to get a mean spectrum. Once the symmetric and antisymmetric spectra have been calculated, they are smoothed 30 times with a 1-2-1 filter to obtain an approximation of the background spectrum for each spectrum. The normalized spectrum is calculated by dividing the original spectrum by the smoothed background. Longitude-time arrays of anomalous OLR aggregated between 30° N and 30° S are translated in longitude and time (without regular segmentation) to extract MJO features with unique phase speeds from the OLR. The wave-number-frequency field’s related factors are decreased to zero, focusing on eastward propagation beyond the required phase speeds band. Using the spatial–temporal inverse Fourier transform, it is possible to acquire filtered (Kiladis et al. 2005; Ventrice et al. 2013). Variance analysis The following procedure was used to determine the proportion of variance attributable to the MJO and each of the equatorial wave types. To begin, the original data was subtracted from the 39-year mean and the first three harmonics of the seasonal cycle. Second, each grid point’s standard deviation was used to partition the daily anomalies. The wavenumbers and frequencies associated with each wave type were then filtered from the standardized anomalies. Each wave type’s share of the total variance is represented by the variance of these filtered values. Lagged regression composite analysis To study the MJO-filtered OLR bands’ spatial structure and evolution, we used a variety of methods. To begin, the OLR filtered by the MJO was gathered at a starting location (39° E, 1° N). Relying on a reference grid point over Kenya (1° N, 39° E), a time series, henceforth known as the MJO index, was generated. The MJO index consists of all days where the minimum negative MJO-filtered OLR was less than − 1.5 standard deviations during the active/negative phase and days where the maximum positive MJO-filtered OLR was greater than + 1.5 standard deviations during the suppressed/positive phase during the 1980–2018 time frame. In the remaining time series, we determined the dates of all peaks and minima that were more than two standard deviations apart ±2σ. To create lagged composites for a given field, the data were averaged over these dates so that the highest amplitude occurred on day zero. Linear regression has previously been used to construct symmetric composites between positive and negative phases (e.g., Klotzbach 2010; Schreck 2021; Ventrice et al. 2013). It was possible to create time-varying composites by merely averaging data points from extremes of the same sign, as in Roundy (2008). Composite anomalies were tested for statistical significance using a bootstrap test similar to Wheeler and Kiladis (1999) method. Null examples were detected in this test utilizing the same months and days as the original composite but from the other 37 years of the period in question. For example, on January 13, 1981, the maximum filtered value occurred, and every January 13 from 1980 to 2018 and from 1982 to 2018 would be considered null cases. The baseline composite was contrasted against randomly selected null dates from the null cases to create the null composites. These composites were generated 1000 times by repeating this procedure. A two-tailed test indicates that an anomaly at a specified grid point in the original composite was 95% significant if it occurred in 975 of the 1000 null composites. This statistical test is valid since it considers sample size and seasonal fluctuation. The daily datasets are initially filtered using Lanczos (1950) technique to reduce the noise inherent in the data. Characterisation of drought Probability Density Function (PDF) plot is used to characterise droughts in this study. Previous studies by Kalisa et al. (2020) and Ongoma et al. (2018) successfully applied PDF to examine the drought and precipitation characteristics over East Africa (EA), respectively. The initial step of plotting PDF in this study begins with the determination of the frequencies. In this study, the frequencies are expressed as counts of individual droughts events that falls within a given drought category as described in Table 1. The drought frequencey, F, determined as in Eq. 24. Equation 24 Determination of drought frequency24 Fj=∑i=1mMN×100% In Eq. 24, N is the length of the time series (from 1980 to 2018 = 39 years) and m is the number of drought events in a given Palmer category/class; M is the drought category. To compute the cumulative frequency: the first category has the cumulative probability of itself. The next category is the sum of the previous category and itself and all were expressed in percentage as obtained from Eq. 24. The cumulative frequency tells you the probability of drought being below a given category. The next step is to compute the probability of exceedance. The probability of exceedance signifies the probability of drought being above the given category. It is simply the maximum probability minus the probability of the drought being below the category. To compute it: subtract the cumulative probability of a given category from the maximum probability and is similarly expressed as a percentage. Results and discussions Historical dry events and their characteristics The extreme events are identified using the sc-PDSI as discussed in Sect. 2.3.1. The most severe dry spell was experienced between March 1983 to March 1988 lasting 61 months followed by the Oct 1998 to Oct 2001 dry spell which lasted for 37 months. The severe droughts peaks captured by the sc-PDSI runs were February 1981, August 1984, April 1992, June 1994, February 1997, June 2001, July 2011, June 2014. The period in which the dry events were experienced during the study period is shown in Table 2. These results are in concurrence with Balint et al. (2013) which identified 1983–1984, 1992–1993, 1999–2000, and 2009–2011 as some of the drought years though using different approaches to study drought. It is evident that there has been a decline in the severity of drought over the last decade. However, it is not clear what might be contributing to this shift of events given that recent studies (Dai 2013; Ongoma et al. 2018) have presented results indicating an increase in the frequencies and severity of the drought events over the larger East African domain. The relationship between the sc-PDSI time series and MJO is later examined in Sect. 3.6.Table 2 The duration, severity, intensity and inter-arrival of occurrence of some of the major historical (1980–2018) dry events (scPDSI ≤ − 1) in Isiolo County, Kenya Period of occurrence Duration (months) Peak Severity Intensity Inter-arrival (months) Jan 1980–Feb 1981 12 Feb 1981 33.294 2.774500 20 Nov 1981–Mar 1982 5 Mar 1982 10.759 2.151800 16 Mar 1983–Mar 1988 61 Jun 1984 126.412 2.072328 97 Apr 1991–Dec 1992 21 Apr 1992 45.837 2.182714 23 Mar 1993–Sep 1994 19 Jun 1994 37.510 1.974211 35 Feb 1996–Mar 1997 14 Feb 1997 29.174 2.083857 32 Oct 1998–Oct 2001 37 Jun 2001 117.092 3.164649 53 Mar 2003 1 Mar 2003 1.402 1.402000 25 Apr 2005–Feb 2006 11 Feb 2006 17.582 1.598364 33 Jan 2008 1 Jan 2008 1.007 1.007000 4 May 2008–Jul 2009 15 Jun 2008 17.895 1.193000 29 Oct 2010–Aug 2012 23 Apr 2012 38.609 1.678652 36 Oct 2013–Feb 2015 17 Jun 2014 24.718 1.454000 36 Oct 2016 1 Oct 2016 1.081 1.081000 7 May 2017–Jul 2017 3 Jun 2017 3.511 1.170333 20 The annual distribution of the sc-PDSI values based on Table 1 from 1980 to 2018 is presented in Fig. 2. Mild droughts and extreme droughts account for the highest percentage of the drought categories with 15.38 and 12.82%, respectively. While moderate and severe droughts accounted for 5.13 and 2.56%, respectively. The probability of exceeding the extreme drought category is very low at 0% while the probability of exceedance of the mild, moderate and severe drought categories are 20.5, 15.4 and 12.8%, respectively. Generally, more wet periods were observed compared to the dry periods as depicted by the high frequencies. The frequent occurrence of these extreme weather events can be alluded to be driven by climate change and other anthropogenic causes (IPCC AR6 2022). This trend of continuous wet and hot scenario is projected to remain the same in the future (Ochieng et al. 2022). Therefore, there is a need to have resilience mechanisms adopted to minimize these uncertain climate hazards which have a high potential of causing both environmental and socio-economic destructions.Fig. 2 Probability Density Function (PDF) of the annual drought categories histograms represents the frequency (%), while the red and green lines represent the cumulative frequencies (%) and the probability of exceedance (%) Time evolution and process analysis for Kenya’s 2013–2015 drought The failure of the 2013 short rains resulted in a devastating drought that lasted through the 2014 long rains and short rains long rains. A long-term lack of precipitation directly caused the 2014–2015 drought. Figure 3 illustrates the climatological distribution of the seasonal rainfall over Kenya. Higher rainfall intensity is observed during the MAM and OND seasons when Kenya receives long and short rains. During these seasons, the western zones and the central highlands record higher rainfall amounts than other locations. Rainfall is suppressed during the DJF (Fig. 2d), with the climatologically rainfall received being less than 40.mm/month. The seasonality of rainfall is influenced by the ITCZ with the two latter-mentioned rainfall maxima being experienced when the ITCZ is over Kenya during MAM and OND (Ongoma et al. 2015). The rainfall displays great spatial heterogeneity due to the varied geography, including steep mountains and valleys, as well as huge water basins like Lake Victoria along the Kenya-Uganda-Tanzania border and the adjacent Indian Ocean (Hession and Moore 2011). The fluctuations in global Sea Surface Temperatures (SSTs) have the most significant effect on the inter-annual precipitation patterns. ENSO (Indeje et al. 2000b) and the Indian Ocean Dipole (IOD) (Owiti et al. 2008; Owiti and Ogalo 2014) are some of the outcomes of SST anomalies in the Pacific and Indian Oceans. The complexity of droughts’ spatial–temporal characteristics is determined by atmospheric tele-connection patterns and Intra-Seasonal Oscillations such as MJO (Kalisa et al. 2020; Le et al. 2019; Vicente-Serrano 2005). The linkages between drought conditions and MJO can be observed at a seasonal scale (Dai 2011; Trenberth et al. 2014).Fig. 3 Distribution of mean seasonal rainfall over Kenya (mm/month) a MAM, b JJA, c OND, d DJF Figure 4 illustrates the spatial distribution of the percentage departure of the seasonal rainfall determined based on Yang and Wu (2010) for 2013/2014. The failure of the short rains of 2013 forms the drought genesis. It is evident that during the latter season, the central and eastern locations of the country are adversely affected by rainfall deficiency, with below more than 40% rainfall being observed. This was followed by poor rainfall performance during MAM 2014 season (Fig. 4b); it is inherent that most western parts of the country received less rainfall than the long-term mean values, with these regions recording 50% fewer rainfall amounts. Even though 2014 JJA (Fig. 4c) appears to have positive percentage departures, the spatial distribution is poor. Since drought is aggravated by the persistence of water deficit caused by an imbalance in the water system, failure of the subsequent OND 2014 (Fig. 4d) rains shown by the negative percentage departures over the central, eastern and north-eastern zones led to a humanitarian crisis of 2015.Fig. 4 Distribution of rainfall percentage departures from normal over Kenya in 2013/2014 a 2013 OND, b MAM 2014, c JJA 2014 and d OND 2014 Seasonal dependancy of MJO To identify ISO-related variability in raw OLR, we employ wavenumber-frequency spectrum analysis. Approaches comparable to Wheeler and Kiladis (1999) are used in this study. OLR outgoing long-wave radiation (OLR) data show power spectrum maxima that are equivalent to Kelvin waves, equatorial Rossby waves, mixed Rossby gravity (MRG) waves, and inertia gravity (IG) waves’ dispersion features (Roundy 2018; Roundy et al. 2009; Roundy and Frank 2004; Wheeler and Kiladis 1999) For deep tropical convective activity, the spectral distribution of the daily raw OLR can be utilized as a proxy (Fig. 5). As a further inspection of the figures shows, a constant frequency of around 0.25 Cpd and zonal wavenumbers of zero to nine symmetrically around longitude 0°, which is not along any of the theoretical equatorially limited wave dispersion curves, is shown to reflect the MJO. Findings from these studies are consistent with those of Roundy et al. (2009) and Kiladis et al. (2005). There is a low frequency of MJO occurring in these eastward propagating atmospheric disturbances with a periodic cycle of 20–100 days, which is analogous to the results found by Okello et al. (2021) for the convectively coupled equatorial Kelvin waves (CCEKWs) with a periodic cycle of 2.5–18 days. To illustrate the seasonal differences in MJO activity, the intensity of the red background colour is used. While the OND and DJF seasons have the least amount of activity, the seasonal distribution of the eastward propagating ISOs suggests that MJO activities are more prominent during the MAM season. It has been found that MJO activity accounts for 20% of intraseasonal rainfall variability during the MAM and OND seasons over equatorial East Africa’s western areas, which include Kenya and Uganda. Berhane and Zaitchik (2014) found out that the regional and magnitude of MJO influence over East Africa varies from month to month and thus is seasonal as in Figs. 4 and 5. They concluded that the regions that MJO significantly impacts are located within the proximity of the meridional arm of the ITCZ. Enhanced rainfall is experienced when the MJO convective centre is between 20° E and 140° E and dry anomalies prevail when the MJO is located in the region from 140° E to 10° W.Fig. 5 Wavenumber–frequency power spectrum a MAM, b JJA, c OND, d DJF for15° S to 15° N, omitting the equator, divided by a red background. The base-10 logarithm of the power has been plotted. Filter bands used in this study are indicated by the cyan boxes. Black lines denote shallow-water dispersion curves for MRG, Kelvin, ER, and inertio-gravity waves with equivalent depths of 8 and 90 m Geographic distribution of MJO-filtered OLR variance Figure 6 depicts the seasonal distribution of the fraction of daily total OLR variance falling inside the MJO filter band in Kenya over the four main seasons from 1980 to 2018 (as averaged). These maps, which were generated following the procedures outlined in Sect. 2.3.3, show where MJO activity can be found. During the JJA and OND seasons Fig. 6b, c, respectively, the most significant OLR power is observed over the Arabian Gulf, India, and the Bay of Bengal. In contrast, the peak values shift eastward during the monsoon season (MAM) as in Fig. 6a, with the western equatorial Pacific region being the focal point. During DJF, the MJO’s epicentre is dispersed, with the highest OLR values reported over the Australian continent. MJO’s OLR signal (Fig. 6a–d) is cantered in the Indian Ocean and the northern coast of the Australia subcontinent; this is coherent with previous findings (Klotzbach 2014; Masunaga 2007; Roundy 2014; Roundy and Frank 2004; Wilson et al. 2013).Fig. 6 Geographical distribution of Seasonal MJO band of daily filtered OLR (shading;Wm-2) For much of DJF, tropical intra-seasonal convection tends to move east; but, during JJA, it tends to go northeast (Schreck et al. 2013). The boreal summer intra-seasonal oscillation is another name for this phenomenon (BSISO: Kikuchi et al. 2012). The BSISO controls the monsoon activity, which is where the OLR signals are still focused in the Indian Ocean. Tropical cyclone activity in the eastern North Pacific and the Gulf of Mexico is influenced by a secondary signal that appears over the eastern North Pacific (Aiyyer and Molinari 2008; Kossin et al. 2010; Schreck et al. 2013). MJO dispersion in equatorial waves during dry and wet weather events Individual events’ time–longitude sections show the westward group velocity of the MJO without the requirement for compositing. Using the (Wheeler and Kiladis 1999) and (Kiladis et al. 2005) techniques, OLR anomalies of the 20–100-day filtered and MJO-filtered OLR anomalies are shown in Fig. 7. There are three strongest occurrences in each column, and they are located in three different continents and oceans: the Indian Ocean (5° N–5° S, 60°–100° E), the Maritime Continent (5° N–5° S, 100°–120° E), (5° N–5° S, 30°–120° W), eastern Pacific and the western Pacific (15° N–15° S, 140° E–180°). Waves in several of these sequences are grouped in westward-migrating packages. Beginning with the first wave at or east of the Maritime Continent, between 120° E and the dateline, subsequent waves move westward. Mid-latitude Rossby waves and dispersive equatorially trapped waves like the mixed Rossby–inertial and inertial-gravity modes (Figs. 12, 16, and 20) exhibit similar eastward dispersion. The most conspicuous difference in the features observed in Fig. 6a, b is the Rossby wave signatures over the Atlantic and the Indian Ocean basin is more pronounced during the dry year compared to the wet year. While the MJO and Kelvin waves propagates eastwards the dominant Rossby waves propagates westwards. Figure 6 further suggests that Kelvin waves play a more significant role in rainfall variability compared to MJO during both dry and wet year.Fig. 7 Time-Longitude section of equatorial waves averaged from 5° N to 5° S during a Dry year 2014 and b Wet year 2018. The black contours are regions with 95% significant MJO activities, Magenta-Equatorial Rossby and green-Kelvin waves, with the contours are drawn at an interval of 4Wm-2. The negative contours are dashed while the positive contours are continuous Physical mechanisms associated with MJO and associated atmospheric circulation anomalies By applying composites, Berhane and Zaitchik (2014) confirm that there is a relationship between precipitation and MJO indices and that the association is widespread in November–December, March, and May, covering large portions of Equatorial East Africa. The relationship is weaker in October and April. Therefore, this study examined the influence of MJO on convective activities during the rainy MAM season and compared it with the dry DJF season. 850hpa wind anomalies and regressed OLR (shading) and geopotential height (contours) during the lead time of 0 days during MAM are shown in Fig. 8. There are wind vectors when the difference between the values of u/v exceeds the 95 per cent confidence level. It appears that deep convection or enhanced convection is taking place over Kenya’s designated base point (39° E, 1° N). The negative 850 hpa geopotential anomalies in the southern Indian Ocean depicts a low-pressure system over the Mascarene region and the Mozambique Channel; this condition is favourable for the meridional movements of the ITCZ due to the resultant east–west pressure gradient created. The latter also leads to moisture flux from the Atlantic Ocean and the moist Congo Air Mass (CAM), enhancing rainfall over Kenya. The difference in OLR power for positive and negative MJO phases are clearly shown in Fig. 8a, b. Negative/positive OLR anomalies are observed during negative and positive events, respectively. A westerly flow may be visible across the western Congo. In the eastern Congo, westerlies are also likely to form. According to positive 850 hPa velocity potential anomalies, 850 hPa flow convergence occurs in the eastern Congo Mountains and the mountains east of the Lake Victoria basin. During DJF (not shown), similar patterns are observed except for negative 850 hPa geopotential anomalies observed during mean adverse events.Fig. 8 a Lag-0 regression patterns as compared with the mean of MJO band filtered OLR anomalies (shading;Wm-2), 850hpa wind vectors referenced at point (39° E, 1° N-Green asterisk) for winds ≤1m-2s. The contours of 850 hpa geopotential height anomalies m are plotted every 1 m with the contours beginning from − 10 to 10 m. The zero contour is omitted. b and c represent the composite means of the positive and negative events, respectively Velocity potential was used as a signature for lower (850 hPa) and upper (200 hPa) tropospheric convergence and divergence motions. At upper levels, divergence is usually associated with updrafts from towering cumulus clouds within the proximity of the tropopause as a result of condensational heating. During MAM, positive velocity potential anomalies are observed over east Africa at lag-5 days with easterly lower tropospheric (850 hPa) winds (see Fig. 9c), at lag + 5 days and lag + 10 days positive velocity potential anomalies are observed to have shifted eastwards to the tropical central Indian ocean and the eastern Indian Ocean, respectively. These regions are characterized by low-level convergence. Comparatively, at 200 hPa during the MAM season, upper-level divergence is observed over the central pacific region at lag0 days (Fig. 10d). However, the zones of upper tropospheric divergence are spread over the South American continent, Atlantic at lag + 5 days (Fig. 10e); this is elongated to the entirety of the African continent at lag + 10 days (Fig. 10f). The upper-level winds depict a clear Matsuno-Gill type wave signature with easterly flows on the convectively active zones' western sections and westerly flows on the eastern sections.Fig. 9 Lagged regressed MJO filtered OLR anomalies (shading;Wm-2), patterns of mean fields of 850hpa winds vectors referenced at point (39° E, 1 °N), and 850 hpa velocity potential m2s-1 contours during MAM drawn from − 10 to 10 Fig. 10 Lagged regressed Lancoz filtered MJO-OLR anomalies (shading;Wm-2), patterns of mean fields of 200hpa winds vectors referenced at point (39° E, 1° N), and 200hpa velocity potential (m2s-1) contours during MAM drawn from − 10 to 10 Compared with the MAM season, during the DJF season at lag-5 days (Fig. 11c), positive velocity potential anomalies (convergence) are observed over the Gulf of Mexico and East Africa region. Similar observations are also made at lag0 days (Fig. 11d) over the central tropical Indian Ocean during DJF instead of MAM season. The 200 hPa velocity potential at lag + 15 days (Fig. 12g) during DJF shows a different pattern than MAM (Fig. 11g).Fig. 11 Lagged regressed MJO filtered OLR anomalies (shading;Wm-2), patterns of mean fields of 850hpa winds vectors referenced at point (39° E, 1° N), and 850hpa velocity potential m2s-1 contours during DJF drawn from − 10 to 10 Fig. 12 Lagged regressed Lancoz filtered MJO-OLR anomalies (shading;Wm-2), patterns of mean fields of 200hpa winds vectors referenced at point (39° E, 1° N), and 200hpa velocity potential m2s-1 contours during DJF drawn from − 10 to 10 All over the Atlantic and African continents, there are areas of convergence at the higher level. Omeny et al. (2008) study statistical relationships between the MJO and Kenyan rainfall from an operational approach. According to the researchers, a strong association between highland rainfall and the MJO occurs when the MJO centre of convection is located in the Indian Ocean. When the MJO advances into the Western Pacific, rainfall in western Kenya decreases. For both short and long rains, the results are the same. For long-range forecasts of 10 days or more, they advocate combining MJO information with other diagnostics to explain a larger portion of the variability in the weather. Omeny et al. (2008) found no significant links between MJO and rainfall in eastern Kenya. Several possible processes of MJO influence are supported by the above-mentioned high precipitation, OLR, and low-level wind anomalies. For example, increased moisture transport to the region, stronger low-level convergence, and diminished stability in the lower troposphere could increase EA precipitation. MJO over the tropical Atlantic and Indian Oceans has a vertical wind structure with upper-tropospheric winds (200 hPa) in opposition to lower tropospheric winds (850 hPa) (recall Figs. 9d and 10d). As a result, the vertical wind shear patterns over Kenya and the greater East African region are predicted to be affected by the MJO. Figures 13, 14 show the lagged regression patterns of the MAM and DJF vertical wind shear pattern over the tropical East Pacific, sections of the South American continent, tropical Atlantic, Africa, and the Indian Ocean. This variation in shear direction represents an angle between 850 and 200 hPa. This picture illustrates that the tropical Atlantic is characterized by two different background vertical wind shear states, which agrees with Aiyyer and Thorncroft (2011). Subtropical westerly jets in the western tropical Atlantic are responsible for westerly vertical wind shear, while tropical easterly jets are responsible for the easterly vertical wind shear in the eastern tropical Atlantic.Fig. 13 Lagged regression patterns of the mean field of 850-200hpa wind shear vectors ms-1 and wind shear vector magnitudes anomalies (shading;ms-1) during MAM Fig. 14 Lagged regression patterns of the mean field of 850-200hpa wind shear vectors ms-1 and wind shear vector magnitudes anomalies (shading;ms-1) during DJF At positive lags during MAM season, easterly shear vector anomalies occur within and to the west of the active phase of the composited MJO over the west tropical (Fig. 13d–g), while the westerly wind shear vector anomalies appear to the east of tropical East Africa during the negative lags (Fig. 13a–c). The westerly shear vector anomalies over the tropical Indian Ocean migrate eastward with time as the MJO transitions between the leading convectively suppressed and convectively active phases (Fig. 13a–c). Figure 13f, g depict the development of an anomalous anticyclonic shear signature within the convectively active phase of the MJO at lags of 10 and 15 days, respectively. This anomalous anticyclonic shear signature highlights the atmospheric response to adiabatic heating associated with deep convection (e.g., Dias and Pauluis 2009; Ferguson et al. 2009; Ventrice et al. 2013). In terms of wind structure, this anomalous anticyclonic signature is similar to the wind structure of the MJO across the Atlantic and parts of South America at lag 5 (Fig. 13c). During the MAM season, easterly shear vector anomalies occur inside and to the west of the active phase of the composited MJO over the tropics (Fig.13d–g), while westerly wind shear vector anomalies occur east of tropical East Africa during the negative lags (Fig.13e–h) (Fig. 13a–c). Westerly shear vector anomalies move eastward over the tropical Indian Ocean with time, from MJO's leading convectively inhibited phase to its active convective phase. Figures 11f, g show the atmospheric response to adiabatic heating associated with deep convection at lags of 10 and 15 days, respectively, in MJO convectively active phase (e.g., Dias and Pauluis 2009; Ferguson et al. 2009; Ventrice et al. 2013). Wind patterns over the Atlantic and South America at lag-5 of the MJO are similar to this aberrant anticyclonic characteristic (Fig. 13c). At lag0 during MAM (Fig. 13d) and DJF (Fig. 14d), the anomalous shear signature directions are predominantly westerly to the west and easterly to the east of the active convective MJO zone (see Figs. 9, 10, 11, 12), is coherent with the Gill–Matsuno-type model response to deep convection (Gill 1980). The notable difference between the wind shear patterns is that during DJF, the shear vectors are generally inclined to NW–SE orientation over the Indian Ocean basin (Fig. 14e–g), while during MAM the orientation is mainly Easterly (Fig. 13e–g). Over the east pacific region during MAM westerly shear vectors were observed from lag 10–15 days (Fig. 13f, g), during DJF 10–15 days after the passage of convective MJO band (Fig. 14f, g), over the eastern Pacific and South American regions are characterized by cyclonic flows. Anticyclone shear signatures are also observed over the southern Indian Ocean 5–15 days after the passage of the MJO during MAM (Fig. 13e–g). For this paper, it is unlikely that tropical cyclone intensity and structure will significantly impact our understanding of climate change. When cyclones are prevalent in the southwest Indian Ocean, Ethiopia can experience drought conditions. Shanko and Camberlin (1998) demonstrated this previously. EEA convection occurs when the MJO low-pressure centre is in the western Indian Ocean due to westerly wind intrusions are drawn into the region (Pohl and Camberlin 2006). In the mountains and coastal plains of the EEA, the MJO and rainfall have a special relationship, according to the authors of the paper. The MJO has a substantial link with coastal rainfall anomalies, but this correlation is out of phase with respect to the highlands, with enhanced coastal rainfall occurring before the MJO arrives in the EEA region. As a result of MJO-associated low pressure in the Atlantic sector, more significant easterly trade winds may bring moist air from the Indian Ocean into the coastal EEA, resulting in increased coastal rainfall. According to Pohl and Camberlin (2006a), this anomaly in coastal rainfall is strati form in type and does not reflect strong OLR anomalies associated with deep convection. The temporal all-year relationship of sc-PDSI, MJO index and rainfall extracted at base point 39° E, 1° N is presented in Fig. 15. The most intriguing observation is that the MJO and rainfall are either in phase or out of phase. It is observed that MJO is in phase mainly during El-Nino years, with 1997 being the most notable. A weak correlation exists between MJO and rainfall/sc-PDSI of 0.27 and − 0.29, respectively. Previous studies have found that the influence of MJO over the East coast of Africa is not direct but indirect through the modulation of atmospheric circulation anomalies. For instance, Okello et al. (2021) found a Walker circulation-type air-sea interaction pattern associated with the active phase of convectively coupled Kelvin waves (CCKWs). Since CCKWs are embedded within the MJO structure, this may also be similar to MJOs. There may be a halt in the MJO’s effect in the middle of the long rainy season, based on the low correlation between MJO indices and Kenyan precipitation. Even if this is the case, our data reveals that the MJO's features change from month to month and decade to decade and that the apparent hiatus may be a product of this variability (Suhas and Goswami 2010).Fig. 15 Time series plot of yearly averaged (1980–2018) values of sc-PDSI (black solid line), OLR-MJO filtered band (red solid line) and the annual total rainfall (mm/year) Research by JunMei et al. 2012 revealed that MJO varies the atmospheric circulation by creating a situation for descending motions that is not favourable for convective activities. MJO phase over the global Ocean basins can alter the Sea Surface Temperatures (SSTs) gradients, consequently changing the teleconnection patterns like the ENSO and IOD (Shimizu and Ambizzi 2016; Shimizu et al. 2017). Conclusion Droughts and floods have a significant impact on the lives of millions of people in East Africa, delaying progress toward the United Nations Millennium Development Goals. Developing weather and short-term climate forecasting systems to limit the effects of deviations from average weather in the region requires knowledge of variability on intra-seasonal periods. Our results show generally that the influence of MJO on drought variability is seasonally dependent. This influence is visible in the atmospheric circulation patterns and shown by the regression analysis. Thus, the modulation of drought patterns associated with the MJO might somehow regulate the propagation and structure of the atmospheric phenomena responsible for convention such as ENSO, Walker circulation, ITCZ among others. Even while the impact of MJO on rainfall variability in eastern tropical Africa has long been recognized, no in-depth studies have been conducted on the effects of MJO on abnormalities in atmospheric circulation and extreme weather events. This study addresses this void and establishes the groundwork for a complete and systematic evaluation of the impact on rainfall in Kenya. We used the sc-PDSI index to classify drought events and the convective MJO index filtered from the OLR climate data record to examine how MJOs affect rainfall intensity in Kenya. Both of these indexes were found to be helpful in this study. Regression analysis (Figs. 8, 9, 10, 11, 12, 13) shows that some eastward migrating signals with wavenumbers 3–8 and periods less than 20 or 25 days also exhibit spatial patterns compatible with the MJO, even though the MJO is generally associated with planetary sizes and periods of 30–60 days (shown in Fig. 4). During MAM and DJF, the MJO contributes a more significant percentage of the variance than during JJA and OND (Fig. 5). There is a strong correlation between the presence of these cyclones and anticyclones in wind shear structures inside the MJO and their arrival at that place by similar mechanisms (Aiyyer and Molinari 2008; Klotzbach 2014; Schreck 2015). This study has demonstrated that regions of positive shear magnitudes are conducive for the formation of subtropical westerly jet streams, while the negative shear magnitude is associated with easterly jet streams in the tropics. The subtropical westerly jet may be responsible for moisture flux through the advection of the Conga air mass to the east, consequently leading to enhanced rainfall. Likewise, a stronger subtropical easterly Jetstream is conducive to forming strong easterly winds, which leads to an advection of the dry Indian Ocean air mass, causing extreme dry conditions (Finney et al. 2020). The increase of dry extreme events is a matter of grave concern due to the effects on the population and for the survival of the entire ecosystem. MJO influence on rainfall over a given location is phase dependent (Schreck 2015), this was not factored in this study and therefore the drought. These results of this study can be used to improve drought early warning system by strengthening drought prediction. Further progressive research is needed to understand the linkages between the MJO and drought, and other convective disturbances such as convective Kelvin waves and Rossby waves are, therefore, certainly required based on robust statistical comparisons of their kinematic and thermodynamic structures along with theoretical and simple modelling over the study region. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 260 KB) Acknowledgements The World Bank provided initial funding for this project as part of the Kenya Climate Smart Agricultural Project (KCSAP). To complete this project, Carl Schreck III's input was essential. Free OLR and reanalysis data from NOAA/ESRL are much appreciated. The first author thanks the Kenya Meteorological Department and the University of Nairobi for all the supported accorded to him. Author contributions The authors confirm their contribution to this paper as follows: study conception and design: OP, NI; data collection: OP; analysis and interpretation of results: OP, OV; draft manuscript preparation: OP, OV. All authors reviewed the results and approved the final version of the manuscript. Funding This work was funded by the Kenya Climate Smart Agricultural Project (KCSAP) through the World Bank grant reference KCSAP- WORLD BANK/ IDA Credit P154784. Data availability The data that support the findings of this study is available from the corresponding author upon reasonable request. Code availability Codes are available on request. Declarations Conflict of interest The authors unilaterally declare no competing interests. Ethical approval The manuscript was prepared by adhering to the ethical standards of the Meteorology and Atmospheric Physics journal. Consent to participate Not applicable. Consent for publication The authors confirm that this research is scientifically founded and consent for it to be published. 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Retrieved October 16, 2021, from https://datasets.wri.org/dataset/kenya-digital-elevation-model-90m-resolution Ngoma H Wen W Ojara M Ayugi B Assessing current and future spatiotemporal precipitation variability and trends over Uganda, East Africa, based on CHIRPS and regional climate model datasets Meteorol Atmos Phys 2021 133 3 823 843 10.1007/s00703-021-00784-3 Nicholson SE The ITCZ and the seasonal cycle over equatorial Africa Bull Amer Meteorol Soc 2018 99 2 337 348 10.1175/BAMS-D-16-0287.1 Ochieng P Nyandega I Wambua B Spatial-temporal analysis of historical and projected drought events over Isiolo County Kenya Theor Appl Climatol 2022 148 1–2 531 550 10.1007/s00704-022-03953-5 Ogwang BA Chen H Tan G Ongoma V Ntwali D Diagnosis of East African climate and the circulation mechanisms associated with extreme wet and dry events: a study based on RegCM4 Arabian J Geosci 2015 8 12 10255 10265 10.1007/s12517-015-1949-6 Okello P Guirong T Ongoma V Nyandega IA Influence of convectively coupled equatorial kelvin waves on march-may precipitation over East Africa Geogr Pannonica 2021 25 1 24 34 10.5937/gp25-31132 Okoola RE A diagnostic study of the eastern Africa monsoon circulation during the Northern Hemisphere spring season Int J Climatol 1999 19 2 143 168 10.1002/(SICI)1097-0088(199902)19:2<143::AID-JOC342>3.0.CO;2-U Omeny PA Okoola R Hendon H Wheeler M East African rainfall variability associated with the Madden-Julian oscillation J Kenya Meteorol Soc 2008 2 2 105 114 Omondi PA Awange JL Forootan E Ogallo A Girmaw B Fesseha I Kululetera V Mbati M Kilavi M King M Adek P Njogu A Badr M Musa A Muchiri P Changes in temperature and precipitation extremes over the Greater Horn of Africa region from 1961 to 2010 Int J Climatol 2014 34 1262 1277 10.1002/joc.3763 Ongoma V Chen H Temporal and spatial variability of temperature and precipitation over East Africa from 1951 to 2010 Meteorol Atmos Phys 2017 129 131 144 10.1007/s00703-016-0462-0 Ongoma V Guirong T Ogwang B Ngarukiyimana J Diagnosis of seasonal rainfall variability over east africa: a case study of 2010–2011 drought over Kenya Pakistan J Meteorol 2015 11 22 13 21 Ongoma V Chen H Gao C Nyongesa AM Polong F Future changes in climate extremes over Equatorial East Africa based on CMIP5 multimodel ensemble Nat Hazards 2018 90 2 901 920 10.1007/s11069-017-3079-9 Owiti Z, Ogalo L (2014) Linkages between the Indian Ocean Dipole and East African Rainfall Anomalies Linkages between the Indian Ocean Dipole and East African Seasonal Rainfall Anomalies. 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Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change, pp 109–230. 10.1017/CBO9781139177245.006 Shanko D Camberlin P The effects of the southwest Indian ocean tropical cyclones on Ethiopian drought Int J Climatol 1998 18 12 1373 1388 10.1002/(SICI)1097-0088(1998100)18:12<1373::AID-JOC313>3.0.CO;2-K Shimizu MH Ambrizzi T MJO influence on ENSO effects in precipitation and temperature over South America Theoretical and Applied Climatology 2016 124 1–2 291 301 10.1007/s00704-015-1421-2 Shimizu MH Ambrizzi T Liebmann B Extreme precipitation events and their relationship with ENSO and MJO phases over northern South America Int J Climatol 2017 37 6 2977 2989 10.1002/joc.4893 Suhas E Goswami BN Loss of significance and multidecadal variability of the Madden-Julian oscillation J Clim 2010 23 13 3739 3751 10.1175/2010JCLI3180.1 Sun Q Miao C AghaKouchak A Duan Q Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans Geophys Res Lett 2016 43 6528 6537 10.1002/2016GL069628 Trenberth K Dai A van der Schrier G Global warming and changes in drought Nature Clim Change 2014 4 17 22 10.1038/nclimate2067 Ventrice MJ Wheeler MC Hendon HH Schreck CJ Thorncroft CD Kiladis GN A modified multivariate Madden-Julian oscillation index using velocity potential Mon Weather Rev 2013 141 12 4197 4210 10.1175/MWR-D-12-00327.1 Vicente-Serrano SM El Niño and La Niña influence on droughts at different timescales in the Iberian Peninsula Water Resour Res 2005 41 W12415 10.1029/2004WR003908 Wang L Chen W Zhou W Huang G Teleconnected influence of tropical Northwest Pacific Sea surface temperature on interannual variability of autumn precipitation in Southwest China Clim Dyn 2015 45 9–10 2527 2539 10.1007/s00382-015-2490-8 Weisheimer A Schaller N O'Reilly C MacLeod DA Palmer T Atmospheric seasonal forecasts of the twentieth century: multi-decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution Q J R Meteorol Soc 2017 143 917 926 10.1002/qj.2976 31413423 Wells N Goddard S Hayes MJ A self-calibrating palmer drought severity index J Clim 2004 17 12 2335 2351 10.1175/1520-0442(2004)017 Wheeler M Kiladis GN Convectively coupled equatorial waves: analysis of clouds and temperature in the wavenumber-frequency domain J Atmos Sci 1999 56 3 374 399 10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2 Wilson EA Gordon AL Kim D Observations of the madden julian oscillation during Indian ocean dipole events J Geophys Res Atmos 2013 118 6 2588 2599 10.1002/jgrd.50241 Yang SE, Wu BF (2010) Calculation of monthly precipitation anomaly percentage using web-serviced remote sensing data. In: Proceedings - 2nd IEEE International Conference on Advanced Computer Control, ICACC 2010, vol 5, pp 621–625. 10.1109/ICACC.2010.5486796 Yevjevich V An objective approach to definitions and investigations of continental hydrologic droughts J Hydrol 1969 7 3 353 10.1016/0022-1694(69)90110-3 Zhang C Madden-Julian oscillation Rev Geophys 2005 10.1029/2004RG000158 Zhang X Wang J Zwiers FW Groisman PY The influence of large-scale climate variability on winter maximum daily precipitation over North America J Clim 2010 23 2902 2915 10.1175/2010JCLI3249.1

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==== Front Eur Econ Rev Eur Econ Rev European Economic Review 0014-2921 0014-2921 Elsevier B.V. S0014-2921(21)00155-0 10.1016/j.euroecorev.2021.103810 103810 Article Inequality, fiscal policy and COVID19 restrictions in a demand-determined economy Auerbach Alan J. a Gorodnichenko Yuriy a⁎ Murphy Daniel b a UC Berkeley and NBER, Berkeley, CA, United States b Darden School of Business, University of Virginia, VA, United States ⁎ Corresponding author: 530 Evans Hall #3880, Berkeley, CA 94720-3880, United States. 21 6 2021 8 2021 21 6 2021 137 103810103810 22 2 2021 4 6 2021 16 6 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. We evaluate the effects of inequality, fiscal policy, and COVID19 restrictions in a model of economic slack with potentially rigid capital operating costs. Rich households satiate their demand for goods/services (and consume an endowment on the margin), whereas poor households’ spending on goods/services is limited by their income (which in turn depends on spending by the rich and on fiscal transfers). The model implies that inequality has large negative effects on output, while also diminishing the effects of demand-side fiscal stimulus. COVID restrictions can reduce current-period GDP by more than is directly associated with the restrictions themselves when rigid capital costs induce firm exit. Higher inequality is associated with larger restriction multipliers. The effectiveness of fiscal policies depends on inequality and the joint distribution of capital operating costs and firm revenues. Furthermore, COVID19 restrictions can cause future inflation, as households tilt their expenditure toward the future. Keywords COVID19 Fiscal policy Firm exit Spending multipliers Inequality ==== Body pmc “Low-income households have experienced, by far, the sharpest drop in employment, while job losses of African-Americans, Hispanics and women have been greater than that of other groups. If not contained and reversed, the downturn could further widen gaps in economic well-being that the long expansion had made some progress in closing” –Jerome Powell, testimony to Senate Banking Committee, June 16th, 2020. 1 Introduction Rising inequality and COVID-related reductions in consumer spending are among the most prominent features of the current economic environment. Yet the macroeconomic implications of inequality and COVID-19 restrictions are far from clear. With respect to inequality, Summers (2015) and Krugman (2016) have posited that inequality can substantially dampen aggregate demand, while more formal models have not found support for this hypothesis (Auclert and Rognlie, 2020). More generally, the key macroeconomic transmission mechanisms during a pandemic remains an open question. For example, firms are faced with falling revenues and potentially rigid (pre-committed) payments (e.g., debt payments and rent). Which fiscal policies are the most effective in such an environment? Understanding the transmission channels of fiscal policy is as pressing now as ever, with trillions of dollars on the line. In this paper we offer new insights on the roles of inequality and COVID19-related restrictions, separately and in conjunction in transmitting macroeconomic shocks, and we evaluate the effectiveness of various fiscal stimuli. In particular, we extend a model of economic slack (Murphy, 2017) to an environment in which inequality, capital costs, and multiproduct firms play a central role.1 The mechanism through which inequality affects output is a formalization of the relationship conjectured by Krugman and Summers that declines in the share of income accruing to the majority of the population pull down aggregate demand. Households in our model have non-homothetic preferences over goods/services that are produced with negligible marginal costs (NMC) and an endowed numeraire good. The rich satiate their demand for goods/services in the NMC sector, whereas poor households’ spending on NMC goods/services is limited by their income (which in turn depends on spending by the rich and on fiscal transfers). Therefore, a lower income share for the poor (and hence higher inequality) is associated with lower spending by poor households and hence lower aggregate income and output. Inequality has potentially strong effects even though all households can access credit. Inequality not only pulls down aggregate output, but it also dampens the multiplier effects of shocks to aggregate demand. Government transfers, for example, have weaker effects the lower is the income share of the poor, as each round of spending leads to a lower increase in income for the poor. This result stands in stark contrast to the predictions of standard models with heterogeneous agents, in which higher inequality is often associated with more credit-constrained households and hence stronger fiscal multipliers. Recent empirical evidence (e.g., Miranda-Pinto et al., 2020a; Yang, 2017) has documented an inverse relationship between inequality and fiscal effects, thus pointing to the potential relevance of the mechanism in our theory. In addition to offering general insights on the effects of inequality, the model also provides a lens through which to analyze the pandemic. We model the economic restrictions associated with COVID19 as a temporary decrease in the share of varieties of goods/services that can be sold by multi-product firms.2 The effect of COVID19 restrictions depends on the steady-state level of transfers to poor households, as well as the extent to which firms face fixed capital operating costs. If there are no steady-state transfers and capital operating costs are flexible (such that the price of capital adjusts to prevent firm exit), then the output loss is proportional to the fraction of varieties that are directly subject to COVID19 restrictions – that is, the restriction multiplier is unity, and consumption of unrestricted products remains the same. This is because the direct reduction in household spending on restricted products equals the reduction of household income from selling those products (and hence no additional adjustments are necessary). Positive steady-state transfers to the poor lead to a smaller restriction multiplier. Households allocate their expenditure across varieties and across time. When fewer varieties are available in the current period, households reallocate their transfer income (if any) toward the remaining existing products during the pandemic and toward products available in the future. This higher per-product spending leads to higher output per product in the current period, which mitigates the aggregate output effects of the restrictions. One of the most pressing policy questions to emerge during the pandemic is how consumer spending will react once the pandemic subsides. Our model predicts that COVID-19 restrictions cause future nominal spending to increase beyond what it would have been in the absence of restrictions (due to the expenditure reallocation across time). This effect is magnified by government transfers. If prices are flexible in the future, the restrictions cause future inflation. Restriction multipliers are potentially much larger when firms’ fixed operating costs are rigid. Restrictions on a subset of firms’ products pulls down firm revenue, which causes firms for which fixed operating costs are high relative to steady-state revenues to exit and therefore cease production of other unrestricted goods and services. For example, restaurants are restricted from serving customers in the establishment but are able to provide carry-out and delivery services, and airlines shut down some routes (and/or passenger seats) but maintain others. If restrictions cause some restaurants’ revenues to decline below their fixed costs, then these restaurants will cease producing carry-out services. Likewise, airlines with revenues below fixed costs will cease flying entirely. This firm exit channel leads to large indirect (multiplier) effects of economic restrictions and provides a strong rationale for policies aimed at mitigating fixed operating costs. In the absence of these multiplier effects or significant re-entry costs, it might be optimal to allow firms to temporarily exit and then re-enter once restrictions are lifted. But the large multipliers imply that such exit can be very costly. We examine the effect of various fiscal transfers in this environment: direct transfers to households, direct transfers to firms, and various targeted transfers. The preferred policy depends on inequality, the joint distribution of firm revenues and fixed operating costs, and the extent to which the government can target particular groups of households and firms. Targeted transfers to low-income households can increase spending on unrestricted items, thus supporting income during the restrictions. However, the transfers have smaller effects during the pandemic period since there are fewer products on which to spend, and they can lead to an inflationary spending boom in the post-pandemic period. Furthermore, the output effect of transfers is falling in inequality, as spending multipliers are increasing in the income share of the poor. The strongest effect of fiscal stimulus is through targeted transfers to multiproduct firms for which the restrictions push their revenues below their fixed operating costs. Such targeted transfers prevent firm exits that lead to large secondary (multiplier) output declines. In practice it may be difficult to identify and target such firms, although the model offers some guidance. The firms most at risk of exit are those with relatively low profitability and for which fixed operating costs are the largest or most rigid. As documented by Gilje et al. (2020), rigid capital contracts can arise from asymmetric information regarding firms’ ability to cover capital costs. In our context, the asymmetric information friction is perhaps the most severe for smaller businesses that are not subject to the same reporting requirements as public firms. Direct loans and transfers to small private businesses may therefore target the firms on the margin of exit and have large benefits per dollar spent. While targeted transfers to firms have the largest potential benefit, untargeted transfers to firms have among the least benefit. Not only are some resources spent on firms that are not in danger of exit, but a large share of the transfers received by firms ultimately accrues to high-income households for whom spending is less sensitive to transfers. This paper is broadly related to emerging work evaluating the indirect economic effects of COVID19, with different papers focusing on different transmission channels. For example, Baqaee and Farhi (2020) focus on the production network, Fornaro and Wolf (2020) focus on productivity growth, and Caballero and Simsek (2020) examine the role of asset prices. Using a large calibrated HANK model, Auclert and Rognlie (2020) find that, for standard business cycles, inequality has relatively mild effects on output, while we find that the effect of inequality can be quite large in the NMC setting.3 Most closely related is Guerrieri et al. (2020), who model COVID19 as a restriction on labor supplied to a subset of firms. They argue that COVID19 restrictions can cause a further fall in output in the presence of strong complementarities between restricted goods and other goods (low elasticity of substitution across products, EOS), a large intertemporal elasticity of substitution (IES), and large shares of credit-constrained households–parameters for which there is strong disagreement in the literature. If these conditions are sufficiently strong, the economy can exhibit a multiplier whereby output falls by more than the size of the direct supply restrictions. Our approach to modeling the COVID19 restrictions is similar in that a subset of firms cannot sell output to consumers but the transmission mechanisms in our model are quite different (e.g., our model does not rely on credit constraints, high IES, or low EOS).4 In particular, the transmission mechanisms in our model stem from a subset of households for which current and future spending are limited by their income (which is determined by autonomous government and rich-household spending). The link between spending and income for these households is based not on credit constraints but rather on interactions between preferences and permanent income heterogeneity. Furthermore, our tractable household demand system along with negligible marginal costs permits an analytical assessment of the inflationary effects of macroeconomic shocks, as firms’ prices depend directly on household income rather than only indirectly through marginal costs.5 We also contribute to the rapidly growing literature emphasizing the effects of micro-level heterogeneity for macroeconomic outcomes. This line of work typically focuses on differences in liquidity constraints and idiosyncratic income risk. For example, Bilbiie (2020) provides a particularly illuminating analysis of how a New Keynesian cross can arise in a two-agent economy (liquidity-constrained and unconstrainted households) and more generally in a New Keynesian economy with heterogenous agents. This literature also examines how inequality can amplify or attenuate business cycles. Similar to this earlier work, we also stress micro-level heterogeneity but at the same time we abstract from liquidity constraints, treat inequality as exogenous, and focus on differences in preferences and permanent income. Finally, the role of firm exit in amplifying shocks shares features of the mechanism in Bilbiie and Melitz (2020), whereby a negative supply shock induces exit among firms that cannot fully adjust prices in response to the increase in marginal costs. In our model, the driving force for exit is a reduction in revenues due to restrictions on a subset of products (rather than broad-based changes in marginal costs). Both models imply amplification of shocks through the firm exit channel in the presence of nominal rigidities. At attractive feature of our model environment is that it yields an analytic analysis of the interactions among COVID-19 restrictions, inequality, multiproduct firms, fixed operating costs, and fiscal policy. As a result, the model delivers new insights, including (as discussed above) the potentially large effects of inequality on GDP, post-pandemic nominal spending booms, the importance of mitigating fixed operating costs during the pandemic, and the cost effectiveness of fiscal transfers targeted to firms on the margin of exit. 2 Baseline model 2.1 Motivation and overview Our model is motivated both by a long-term trend of rising inequality as well as the recent pandemic crisis of 2020 (and the corresponding fiscal policy response). Early in the pandemic many local governments imposed restrictions on travel and other contact-intensive services. Meanwhile, many rich households voluntarily cut back on purchases of contact-intensive services even in the absence of government restrictions (e.g., Alexander and Karger, 2020, Chetty et al., 2020, Goolsbee and Syverson, 2020). These restrictions and cutbacks led to a massive recession, including a rapid decline in the number of small businesses (Fairlie, 2020) and sharp declines in household wages and income, especially for households with low income (Cajner et al., 2020). In response to the recession, the U.S. government passed the CARES Act, which authorized over $2 trillion in fiscal transfers including rebate payments to households of up to $1200 and transfers to businesses that maintained their payroll (the Paycheck Protection Program). Given the unprecedented scale of the recession and fiscal stimulus, understanding which policy levers are the most effective is imperative. One view is that fiscal stimulus was unnecessary, since the recession was an efficient response to a global health crisis. Another view is that spending plummeted by more than would be implied by private spending cuts directly associated with health concerns, justifying government intervention to stimulate aggregate demand (and raising the question of which interventions are most effective). 2.2 Model To formally study these issues, we develop the heterogeneous-household version of the negligible-marginal-cost (NMC) model in Murphy (2017). This version of the model features rich and poor households, denoted by h∈{R,P}, each of which receives different shares of income from the NMC sector and consumes services from the NMC sector. We further extend the model to incorporate government transfers, multiproduct firms, and features that map into the pandemic crisis. The model also features an endowment good that is owned and consumed by the rich. The endowment represents land or other factors of production that are used to produce goods consumed primarily by the rich (e.g., beach homes and other luxury items). One should interpret rich households as those who are sufficiently wealthy that, on the margin, they spend additional income on items (e.g., high-end properties) that are not consumed by the non-wealthy. For example, a millionaire likely directs additional income toward purchasing a beachfront home rather than spending more on restaurant meals. Note that, by construction, the endowment good is in fixed supply, and hence additional spending on it does not contribute directly to real GDP. As discussed below, changes in the demand for the endowment good may affect nominal GDP through the impact on the endowment's market-clearing price. A key aspect of this modeling assumption is that the wealthy will have a negligible marginal propensity to consume out of increases in income on NMC-sector commodities, which is consistent with the evidence that very wealthy households have marginal propensities to spend additional income on typical goods and services of effectively zero (Ganong et al., 2020). Poor households in the model should be interpreted as including middle-income households and more generally any household that has not satiated its demand for standard goods and services (e.g., haircuts, restaurant meals, etc.). For simplicity, and without loss of generality, we aggregate across all non-rich households and refer to them as poor. The endowment pins down the interest rate and the consumption path of the rich household (and hence delivers a unique equilibrium). Agents trade bonds to satisfy their desired time paths of consumption, subject to a no-Ponzi constraint that the present value of their asset position must be weakly greater than zero. We assume that time can be split into two periods: t=0, which captures the crisis, and t=1, which, without loss of generality, corresponds to all post-crisis time. We evaluate policy responses to a one-time, exogenous restriction in spending at date t=0. 2.3 Households There is a unit mass of homogenous varieties in the NMC sector. Households inelastically supply labor to the NMC sector, and there are zero marginal costs of labor associated with increasing output.6 In this sense there is firm-level slack. In the initial period, a share 1−ξ of the varieties is restricted from being sold. We interpret a reduction in ξ as either direct restrictions imposed by the government or choices by households to avoid certain services due to health concerns.7 Household type h maximizes(1) Uh=∑t=01βt(yth+∫0ψt∫0ξt(θthqjkth−γ2(qjkth)2)dkdj), subject to the budget constraints(2) ∫0ψ0∫0ξ0pjk0qjk0hdkdj+y0h+QB=Π0h+e0h+T0h, (3) ∫0ψ1∫0ξ1pjk1qjk1hdkdj+y1h=Π1h+e1h+T1h+B, where qjkth is type h’s consumption of variety k∈[0,1] from firm j∈[0,1] from the NMC sector in period t. The household's preferences are over each producer-commodity (jk) element. ξt is the fraction of goods/services that can be sold without restriction and ψt≤1 is the endogenously determined number of firms in the economy. We will assume that ξt=1 and ψt=1 in the absence of COVID-related restrictions, and that the restrictions imply ξ0≡ξ<1,ξ1=1 (and potentially ψt<1).8 For simplicity we assume that reductions in ξ are associated with equal restrictions for poor and rich households, although there is some evidence that rich households may have been more likely to avoid spending due to health concerns (Chetty et al., 2020).9 Πth is agent h’s income from the NMC sector of the economy, ethand yth are h’s endowment and consumption of the numeraire, where etP=0. Tth is net transfers from the government. Q is the price of a bond B that pays a unit of the numeraire in period 1. Since agents can smooth consumption (and hence the effect per unit of the present value of future net transfers is the same as that of present-period net transfers), we will write the present value of total net transfers as Th≡T0h+QT1h. A convenient feature of the quasilinear utility function is that agents consume only the good from the NMC sector when their income is sufficiently low (depending on θ and γ).10 This feature, along with the assumption that poor agents are not endowed with the numeraire, etP=0∀t, greatly simplifies the analysis and maintains the focus on demand-determined output in the NMC sector. We assume parameter values such that the rich household consumes the NMC-sector goods and the numeraire endowment good, while the poor household consumes only the NMC-sector goods. One implication of this assumption is that, similar to the Lucas-tree model, variation in endowments e pins down Q to the discount factor β of the rich, that is Q=β. This assumption is a reduced-form attempt to model the economy when interest rates are fixed at some level (for example, the effective lower bound). 2.4 Firms Output in the NMC sector is produced by firms who hire workers as fixed costs and pay a fixed capital operating cost fjt. Firm j faces demand for product jk from household type h (4) qjkth=1γ(θth−λhtpjkth), where λht is household h’s budget multiplier at time t. We assume that prices for NMC goods/services in period t=0 are fixed at the respective levels for the poor and the rich, p¯jk0P and p¯jk0R, that we would observe if firms set their prices on their expectation that ξ=1. These prices remain fixed in the presence of shocks in period t=0. We write p¯jk0P and p¯jk0R with an overbar to emphasize that these prices are rigid.11 Prices in the post-crisis period t=1 are fully flexible.12 For analytic convenience, we assume that firms can discriminate between the rich and the poor when setting prices. The profit-maximizing price charged to household type h is(5) pjkth=θth2λhtS, where due to rigid initial-period prices λh0S is the household h’s period-0 budget multiplier in the state of the world in which there are no shocks (ξ=1) and λh1Sis the household's period-1 budget multiplier adjusted for the realization of shocks. The rich household's budget multiplier is pinned down by the marginal utility of the numeraire, λRt=1, so prices charged to the rich are invariant to all shocks other than the rich household's preference for NMC-sector goods/services θR. Given prices in Eq. (5) and imposing λRt=1 we can write quantities demanded as(6) qjk0h=1γ(θh−λh0p¯jk0h),qjk1h=θh2γ and expenditure by each household type h on each good jk as(7) cjkth≡pjkthqjkth. Let θ¯R be the firms’ expected rich-household demand parameter prior to the realization of any shocks. Then we can write cjk0R=θ¯R2γ(θ0R−θ¯R2) and cjk1R=(θ1R)2/4γ. Rich-household expenditure on any given firm-commodity is a function only of exogenous parameters and we therefore treat cjktRas exogenous for the remainder of the analysis. This invariance of rich-household expenditure to other macroeconomic conditions greatly simplifies the analytic derivation of results. One can interpret cjktR as “autonomous” spending in the economy. A firm j’s revenues are equal to expenditures across households and products: Rjt=∫0ξt(cjktR+cjktP)dk. By symmetry of varieties (all firm-commodity combinations that continue to be produced in equilibrium have the same revenue), we can write Rjt=ξt(cjktR+cjktP). Firm j pays a fixed capital operating cost fjt in period t.13 We assume that households own capital in the same proportion to their share of firm profits and so we roll capital income into profits (i.e., Π includes profits and fjt). A firm exits for period t if Rjt<fjt. We assume that the distribution of fixed costs is such that the unit mass of firms all produce if ξ=1 and that only a share ψ0(ξ)<1 continue to produce in the initial period if ξ<1. If there are additional costs to re-entry once restrictions are lifted, then ψ1<1. In the absence of such costs to re-entry, ψ1=1. The poor household receives a share κ of the revenues from the NMC sector in each period, while the rich household receives the remaining 1−κ share.14 The poor household also owns a share κ of the capital stock (and therefore earns a share κ of the payments from firms for fixed capital operating costs). It can be shown that there exists a threshold value κ¯ such that ∀0<κ<κ¯, the poor consume output only from the NMC sector. κ¯ depends on model parameters and fiscal policy. We assume parameter values such that κ<κ¯. Output (real GDP) Yt is defined as the product of quantities consumed per product and total mass of available products:(8) Y0=ξψ0(qjk0P+qjk0R),Y1=ψ1(qjk1P+qjk1R). 2.5 Equilibrium Equilibrium consists of prices and quantities such that households maximize utility (1) subject to budget constraints (2) and (3), firms’ prices are given by Eq. (5), and ψt is determined by the number of firms for which revenues exceed fixed capital operating costs (specified below).15 The interesting aspects of the equilibrium are based on the expenditure of poor households (since the rich household's real expenditure is effectively exogenous). Total expenditure by household h in period t is the sum of expenditure on the varieties. Given the assumptions about ξt, we can write(9) c0h=∫0ψ0∫0ξ0cjk0hdkdj=ψ0ξcjk0h,c1h=∫0ψ1∫0ξ1cjk1hdkdj=ψ1cjk1h. Let CP be the present value of the poor household's total lifetime expenditure. Then substituting (9) into (2) and (3) and simplifying implies that the present value of the poor household's total lifetime expenditure is(10) CP=c0P+Qc1P=ψ0ξcjk0P+Qψ1cjk1P. To be clear, cjkth represents the equilibrium level of spending, which is the same for any jk produced in equilibrium. To save notation, from now on, jk denotes spending on any variety. The poor household's lifetime income IP is(11) IP=κψ0ξRj0+Qκψ1Rj0+TP=κψ0ξ(cjk0P+cjk0R)+Q(κψ1(cjk1P+cjk1R))+TP, which reflects the fact that the poor household earns a share κ of total expenditure. Since households own capital in the same proportion to their share of firm profits and households (as firm owners) are both liable for firms’ capital operating costs and receive income from payments to capital, capital costs and income are netted out of household income. Setting lifetime expenditure CP equal to lifetime income IP and collecting terms yields(12) cjk0Pψ0ξ(1−κ)+cjk1Pψ1(Q−Qκ)=cjk0Rψ0ξκ+cjk1RQψ1κ+TP. Inspection of Eq. (12) implies that increases in transfers and rich-household spending will increase poor-household spending (and hence increase GDP in periods in which prices are rigid). Indeed, many of the results derived below follow directly from Eq. (12). We can represent the equilibrium condition graphically by writing CP as a function of IP using Eqs. (10) and (11):(13) CP=[IPκ−(ψ0ξcjk0R+Qψ1cjk1R+TPκ)]. This expression, along with the equilibrium condition that lifetime expenditure equals lifetime income (represented by Eq. (12)), pins down the equilibrium in a quasi-Keynesian cross, as depicted in Fig. 1 . It is immediately apparent that increases in κ (declines in inequality) flatten the consumption line, thus increasing equilibrium lifetime income. Increases in transfers, rich-household consumption, and the number of available products lead to increases in equilibrium income, as they reduce the y-intercept and shift out the consumption line. Since the slope of the consumption line is greater than unity, these shifts lead to even larger income effects in equilibrium. An increase in transfers, for example, leads to an increase in desired consumption, which increases income (and hence consumption), leading to large multiplier effects. Propositions 1 and 6 below follow from this graphical depiction of the equilibrium.Fig. 1 Equilibrium determination. Fig. 1 The quasi-Keynesian cross depicted in Fig. 1 implies larger multipliers than a traditional Keynesian cross, in which the consumption function is flatter than the equilibrium (income=expenditure) line. In this sense our cross is similar to the Keynesian cross implied by the two-agent model in Bilbiie (2008) and Bilbiie (2020).16 While both our model and Bilbiie's model imply large multipliers, the mechanisms are quite different: in Bilbiie's model, large multipliers are driven by credit-constrained households whose income shares rise with aggregate output. Here, large multipliers are driven by increases in permanent income that cause higher desired spending (and hence higher income and so on) among households who can smooth consumption. In this sense, the model is most similar to the Keynesian cross implied by Murphy (2015), in which autonomous (government) spending raises expectations of permanent income due to imperfect information, which induces additional private spending and income.17 One of the distinguishing features of our model is that large consumption multipliers occur even in the presence of perfect information and the absence of credit constraints. 2.6 NK vs. NMC frameworks To draw contrast between the NMC framework and a New Keynesian (NK) approach, note that a simple way of capturing the mechanics of a New Keynesian model is to assume(14) Y0NK=C0,Y1NK=Y¯, where the superscript indicates the New Keynesian representation of the model. Here, future output Y1 is determined by the endowment Y¯, reflecting the supply-side dominance of New Keynesian models at horizons after which price rigidities have dissipated. To solve the model, one must simply determine C0, which in general will be based on consumption smoothing and an intertemporal budget constraint. A simple version of consumption smoothing can be written as(15) C0=C1, and the budget constraint can be written (assuming β=1) as(16) C0+C1=Y0NK+Y1NK. Substituting the equilibrium conditions from (14) and solving for C0 yields(17) C0=Y¯⇒Y0=Y¯. Therefore, in the presence of consumption smoothing (the absence of credit constraints), output in the demand-determined period depends on the future supply side of the economy. In short, in the absence of credit constraints, the supply side dominates. As a result, credit constraints (and associated high MPCs) and the strength of intertemporal substitution are key considerations for policymakers in thinking about the macroeconomic effect of the restrictions (e.g., Guerrieri et al., 2020). If one is persuaded by recent evidence that many middle-to-low-income households are not credit-constrained but rather have low MPCs (see, e.g., Miranda-Pinto et al. 2020b for a survey), or by evidence that the elasticity of intertemporal substitution is well below unity (e.g., Cashin and Unayama, 2016; Schmidt and Toda, 2019), then one may conclude that output effects of the restrictions are not a reason for policy intervention. Now consider a situation in which future output is demand-determined.18 In this case, the equilibrium conditions can be written as(18) C0=12(Y0+Y1), where Y0=C0 and Y1=C0(by consumption smoothing). Here, any level of desired consumption is a potential equilibrium. This is similar to the indeterminacy of equilibria in some NK models featuring liquidity traps (e.g., Benhabib et al., 2002). Our NMC model avoids this indeterminacy problem because a share of spending (in particular, that of rich households on the NMC sector) is determined by exogenous parameters and is independent of income. As discussed above in relation to Fig. 1, the poor-household income share parameter in our model, κ, affects GDP. As we discuss below, it also affects fiscal multipliers. In this sense κ plays a similar role to credit constraints in New Keynesian models with heterogeneous agents. However, the economic interpretation is quite different. Here, higher inequality is associated with lower GDP and lower multipliers. In standard New Keynesian models, higher inequality is associated with higher shares of credit-constrained households and hence higher fiscal multipliers. Our model also differs from New Keynesian models in that our model abstracts from monetary policy, a clear limitation of our analysis. Specifically, the real interest rate in our model is pinned down by the rich household's linear utility over the numeraire (which is traded at a flexible price). Given that the real rate is fixed, one can interpret our setting as an economy in a liquidity trap. A useful extension of our model (especially for quantitative analyses) would be to incorporate a role for monetary policy, which would require introducing curvature in utility of the numeraire and either a) introducing sticky prices in the numeraire sector or b) interpreting the numeraire as money, which can be created by a central bank. These extensions would come at the cost of analytic convenience, which we perceive to be a useful feature of our current setup. In particular, these extensions would imply that rich-household spending would adjust in response to macroeconomic shocks and interest rate changes set by monetary policy. We conjecture that such an extension, while adding significant complexity and nonlinearity to the model, would not substantially alter the qualitative predictions that we derive from our more analytically tractable setting.19 3 Demand shocks and fiscal policy in the NMC model To study properties of the model described in the previous section, we linearize the model around the steady state with no shocks (i.e., ξ=1, ψ0=ψ1=1) and no transfers to the poor (i.e., TP=0). For some exercises, it will be instructive to consider cases where TP>0 in the steady state. As a first step, we explore how structural parameters such as the share of income going to the poor (which also controls the level of inequality in the economy), spending by the rich (“autonomous” spending), and transfers to the poor affect key endogenous variables in the model. Then we introduce the COVID19 shock to the model and investigate how this shock propagates in the economy. Finally, we study how various fiscal policies can counter the COVID19 shock. The effect of different transfers depends on how they are financed. It is clear that taxing low-income households (which decreases TP) will reduce GDP (all else equal). An alternative source of funding is to tax the rich exclusively. As long as the rich maintain enough post-tax resources to satiate their demand for NMC commodities and spend on the numeraire at the margin, there will be no effect of this taxation on GDP in the NMC sector for either period. There is also the possibility that the transfers could be financed through money creation by the central bank (e.g., Galí, 2020). While this process is not specified in our model, one could, for example, adopt an alternative interpretation of the numeraire as money, consistent with models of monetary non-neutrality driven by money in the utility function. In our model money-financed transfers would have the same effect as taxing the rich (via an inflation tax). For the remainder of the analysis we assume that transfers are financed through taxing the rich, and we will examine the relative effectiveness of different types of spending.20 3.1 Inequality, rich-household spending, and government transfers Inspection of Eq. (12) implies that poor-household expenditure (and hence aggregate expenditure on NMC goods and hence GDP) is falling in inequality and increasing in spending by rich households and in transfer income. In general, any factor that increases the income of the poor generates an increase in spending and aggregate output, as output is limited only by poor households’ spending (which is limited by their income): Proposition 1 GDP and poor-household expenditure are increasing in the income share of the poor (falling in inequality), spending by the rich, and transfers. The effects of rich-household spending and transfers are increasing in the income share of the poor. In particular, in the absence of steady-state transfers TP : (19) dcjk0Pdκ|TP=0=cjk0P+cjk0R1−κ⇒dqjk0Pdκ|TP=0=1p¯jk0Pcjk0P+cjk0R(1−κ)dcjk0Pdcjk0R|TP=0=κ1−κ⇒dqjk0Pdcjk0R|TP=0=1p¯jk0Pκ1−κdcjk0PdTP|TP=0=1(1+Q)(1−κ)⇒dqjk0PdTP|TP=0=1p¯jk0P1(1+Q)(1−κ). Proof: Appendix. See Fig. 2 for a graphical proof.Fig. 2 Graphical proof of Proposition 1. Fig. 2 As the income share of the poor κ increases (inequality falls), poor households spend more in both periods. Due to rigid initial-period prices, this additional spending translates into higher initial-period consumption by the poor (and hence higher real GDP). The effect of inequality is quantitatively large. One can show that dlogcjk0Pdκ|TP=0=1κ(1−κ), which is bounded below by 10.5(1−0.5)=4. Rich-household spending and transfers from the government also increase income for the poor, which in turn induces higher spending by the poor and higher GDP. These relationships are consistent with recent evidence from Chetty et al. (2020) and Coibion et al. (2020), documenting that fiscal transfers associated with the CARES Act increased spending by low-income households. Furthermore, spending cuts during the pandemic were largest among low-income households working in areas that were most exposed to the decline in rich-household spending. The effect of transfers on GDP is higher the larger is the income share of the poor (the lower is inequality). The relationship between the fiscal transfer multiplier and inequality reflects the fact that the general-equilibrium effects implied by the model are much larger than would be implied by examining the partial equilibrium response of poor-household spending alone, since in general equilibrium the initial spending causes additional poor-household income, which generates additional spending, and so on. For example, the partial-equilibrium effect of transfers on poor-household income is ∂IP∂TP=1. But the general-equilibrium effect – accounting for the effect of poor-household spending on their own income – is dIPdTP=1+κdCPdTP, which is rising in the income share of the poor. We discuss this relationship in more detail below when we compare alternative fiscal policies. 3.2 COVID19 shock The social distancing restrictions associated with COVID19 can be modeled as a decrease in ξ from an initial value of 1, reflecting the restrictions on the exchange of services such as restaurant meals, movie theaters, and sporting events. The size of the restriction multiplier – the net effect of restrictions (dY0/dξ) relative to the direct effect (∂Y0/∂ξ) – depends on the extent to which restrictions cause firms to exit. In the absence of firm exit (e.g., due to flexible capital costs), the restriction multiplier equals unity. Proposition 2 In the absence of a firm exit margin, the decline in output is bounded by the share of products that are restricted (the restriction multiplier is unity). Firm exit causes a larger fall in output – a restriction multiplier greater than unity: (20) dY0dξ|TP=0=Y0(1+dψ0dξ),∂Y0∂ξ=Y0. Proof: Appendix. In the absence of transfers, restrictions decrease poor-household aggregate spending to a degree that perfectly balances the decrease in poor-household aggregate income. Given product symmetry, there is no adjustment within product categories.21 This also implies that in the absence of a firm-entry margin, there are no multiplier effects from product-level restrictions. In other words, GDP falls by an amount proportional to the share of products that are restricted, ∂Y0∂ξ=Y0. If restrictions force firms to exit (dψ0dξ>0), the restriction multiplier is dY0dξ/∂Y0∂ξ|TP=0=1+dψ0dξ>1. Chetty et al. (2020) document higher rates of small business closure in places in which spending was cut the most, which suggests that dψ0dξ>0. Workers in these locations experienced larger income declines and cut their spending by more, consistent with model's prediction of the adverse effect of firm exit on consumer income and spending.22 The multiplier effects of COVID restrictions are smaller in the presence of positive steady-state transfers. This is because, in the presence of positive transfers, the poor household responds to the restrictions by spreading its transfer wealth over the fewer available initial-period products and the products available in the future. This per-product spending increase is associated with higher output per product in the initial period (when prices are fixed) and higher prices in the future period (when prices are flexible). We formalize this point in the following proposition. Proposition 3 (Economic Restrictions and Future Inflation). In the presence of positive steady-state transfers, spending restrictions cause future inflation, as households reallocate spending across the remaining set of goods/services available in the current and future periods: (21) dcjk0Pdξ=dcjk1Pdξ=−TP(1+dψ0dξ)(1+Q)2(1−κ),dpjk1Pdξ=2θPdcjk0Pdξ. Proof: Appendix. Intuitively, with positive transfers, the poor households’ reduction in income is proportionally smaller than the reduction in the number of commodities they buy, so it increases their demand for all remaining commodities. When households must forgo spending on a subset of products, they reallocate their wealth (transfers) across the remaining available products in the initial period and in the future. The increase in per-product expenditure causes higher per-product output in the current period (when prices are rigid) and higher prices in the future (when prices are fully flexible). If we were to expand the model to include an intermediate period t={0→1} in which prices are only partially flexible, then the initial-period restrictions would be followed by a boom in output, as households would consume more per product and (if dψ{0→1}=0) would have the full set of products available to purchase. This prediction of the model is consistent with the behavior of the U.S. economy following World War II. Inflation surged to nearly 20 percent within a year-and-a-half of the end of the war, consistent with households transferring spending power from during the war (when spending was restricted) to after the war. The inflationary effects of the pandemic restrictions and fiscal stimulus are driven by higher future consumer spending (which induces less-elastic product-level demand curves). Stepping outside of the model, one could imagine forces that exacerbate or mitigate inflation. Inflation could be amplified by firms that run into capacity constraints (positive marginal costs), which we have abstracted from for simplicity. Inflation could be attenuated in a setting with an active role for monetary policy. The net effect of these forces depends on specific assumptions and parameter values. Note that Eq. (21) implies that restrictions increase per-product spending (initial-period output and future-period prices) by more the greater the income share of the poor κ (lower inequality), holding fixed the number of firms in the economy. This is because the poor household recycles its purchasing power more the higher is its income share. COVID restrictions increase per-product spending on unrestricted products (e.g., streaming video services, computer games), and this increased spending is multiplied more the larger is the income share of the poor. Therefore, economic restrictions reduce aggregate output by less (more) in the presence of higher (lower) poor-household income shares, which correspond to lower (higher) inequality. Even accounting for the endogenous response of firm entry, the output response to COVID restrictions can be shown to be increasing in inequality (see Corollary 1 in Appendix). 3.3 The response of firm exit The discussion above makes clear that COVID restrictions have large multipliers (>1) in our model only if firms exit. To study this margin, we must specify fj, the fixed cost of operating firm j. Let the PDF of the distribution of f be v and the CDF be V. Thenψ0=∫0Rj0v(f)df=V(Rj0) anddψ0=v(Rj0)dRj0, where dRj0=(cjk0P+cjk0R)dξ+ξdcjk0P. The COVID shock thus affects the number of firms directly: COVID restrictions (ξ<1) reduce the number of products that firms can sell and thus push some firms into the red, forcing them to exit. There is also an indirect channel: the COVID shock increases the spending of the poor household on remaining products, which helps to mitigate firm exit. In the absence of steady-state transfers to poor households (TP=0), the indirect channel is not operational and firm exit is entirely determined by the distribution of firms’ fixed costs:(22) dψ0dξ|TP=0=v(Rj0)(cjk0P+cjk0R). As discussed in Section 3.2 (after Proposition 2), in the absence of transfers, restrictions decrease poor-household aggregate spending to a degree that perfectly balances the decrease in poor-household aggregate income. Given product symmetry, there is no adjustment within product categories. If transfers TP are positive in the steady state, the increase in per-product household spending induced by the restrictions can help mitigate firm exit in response to COVID restrictions. The poor households spread their transfer wealth over the fewer remaining products, which helps to support firm revenues and mitigate firm exit. If we interpret fixed costs as the rental costs of the existing capital stock, then fj0 is the rental price of capital and firm exit is associated with a reduction in demand for the existing capital stock. If prices fj0 are rigid, there will be excess supply of capital. However, if prices fj0 are flexible, then they will adjust downward to mitigate the effect of falling revenues on firm profits. Given the inelastic supply of capital and the symmetry of firms, the capital market clears once the mass of operating firms is at its steady-state level (ψ=1). We collect these results in the following proposition. Proposition 4 If fixed costs are rigid, COVID restrictions induce firm exit. If fixed costs are flexible, there is no exit (and hence the restriction multiplier equals unity). Proof: Appendix. Clearly, if costs of operation can be adjusted in response to the COVID shock (e.g., set v(Rj0)as low as zero), firm exit can be avoided entirely and thus the adverse effects of the COVID shock minimized. However, there are plenty of reasons to expect that capital costs may not be flexible, at least in the short run. Asymmetric information between capital owners and the firms that rent the capital is among the reasons for rigid capital prices. If capital is imperfectly substitutable such that owners have pricing power, then capital owners may be reluctant to adjust if they cannot identify which firms can pay and which cannot. Indeed, recent empirical evidence documents a strong role for asymmetric information in preventing renegotiations between capital owners and firms even when such renegotiations would otherwise benefit both (Gilje et al., 2020). The experience of the pandemic to date is consistent with rigid costs. The number of small business owners plummeted at the fastest rate on record between February and April 2020 (Fairlie, 2020). The adverse experiences of small businesses led to a sharp fall in household wages and income, especially for households with low income (Cajner et al., 2020). Household evictions also accelerated, according to data from the Eviction Lab, indicative of rigid housing rental prices.23 Note that the disproportionate fall in income for poor households documented by Canjer et al. (2020) is consistent with the model. Rich households receive income from their ownership of the numeraire and from the NMC sector, whereas poor households receive income only from the NMC sector. Therefore, adverse shocks to the NMC sector disproportionately reduce the income of poor households.24 3.4 Inequality and firm exit The effect of restrictions on firm exit is stronger the higher is the income share of the poor, unless the distribution of fixed costs is strongly decreasing at Rj0 – that is, unless the elasticity of the density function Rj0v′(Rj0)v(Rj0) is less than −1:d2ψ0dξdκ|TP=0=(1+Rj0v′(Rj0)v(Rj0))v(Rj0)dcjk0Pdκ. Higher income of the poor is associated with higher spending on each product and hence a greater revenue loss for each product that is restricted. If the higher spending does not sufficiently reduce the number of firms on the margin of exit, then in the face of high poor-household-income-shares, restrictions pull down revenues more and induce more firm exit. Therefore, higher inequality can mitigate the adverse effect of restrictions on firm exit. 3.5 Fiscal policy Government transfers to households and/or firms can mitigate the adverse effects of the restrictions. 3.5.1 Transfers to households Consider first transfers to low-income households. One can show that the effect on real GDP is(23) dY0dTP=Y0v(Rj0)ξdcjk0PdTP+dqjk0PdTP>0. Transfers to low-income households of sufficient size can in principle fully offset secondary economic effects of the COVID-related restrictions. Transfers stimulate output through two channels. First, they increase spending on products sold by existing firms. Second, they induce firm entry, and this entry causes additional private-sector spending on the products of the entering firms. This firm entry margin is consistent with recent empirical evidence of the effects of fiscal stimulus (Auerbach et al., 2020b) and more generally with the effect of aggregate demand shocks (Campbell and Lapham, 2004). Inspection of Eq. (19) implies that the effects of demand shocks on poor-household spending and consumption are falling in inequality. This, along with Eq. (23), implies that the effect of transfers on GDP is falling in inequality: the smaller is the income share of low-income households, the less spending circulates back as income to low-income households (and hence the less they can spend). Proposition 5 (Fiscal multipliers and inequality): Transfers induce firm entry, amplifying the fiscal multiplier. Furthermore, the fiscal transfer multiplier is falling in inequality (rising in the income share of the poorκ):d2Y0dTPdκ>0. Proof: See Appendix. Intuitively, lower inequality increases the multiplier because the larger is the income share of low-income households, the more spending circulates back as income to low-income households (and hence the more they can spend). This can be seen graphically through the quasi-Keynesian cross (Fig. 3 ). A higher income share of the poor κ implies a flatter consumption line, which yields a stronger equilibrium response of consumption (and hence output). This is a rather surprising result, given that inequality has often been associated with large shares of credit-constrained households (and hence potentially large fiscal multipliers, as in Brinca et al., 2016 and Lee, 2020). However, recent empirical evidence documents an inverse relationship between fiscal effects and inequality (Miranda-Pinto et al., 2020a; Yang, 2017). Our theory and this evidence imply that household-level transfers are less effective during the recent episode of rising inequality.Fig. 3 Higher Income Share of the Poor Implies higher Fiscal Multipliers. Fig. 3 3.5.2 Transfers to firms An alternative to household-level transfers is to provide transfers TF to firms. If the government cannot target the firm transfers but instead must allocate across all firms, then firm-level transfers are unambiguously less effective than household-level transfers: for a firm-level transfer, low-income households (which drive spending multipliers) only end up with a share of the transfer κ. More formally,dY0dTF:All=κdY0dTP<dY0dTP, where TF:All is untargeted transfers across firms. Such transfers stimulate spending and firm entry, but their effect is diminished because a share 1−κ of the transfer ends up with the rich households, who do not contribute to spending multipliers. While untargeted firm-level transfers are not an effective form of stimulus, targeted firm-level transfers are potentially very effective. In particular, transfers that are targeted to firms on the margin of exit (those for which fixed costs are a large share of their revenues, Rj0≈fj0) prevent firm exit, which as discussed above is the mechanism though with COVID restrictions cause large multiplier effects. In particular, for each dollar targeted to marginal firms, the government would create dψ0=1v(Rj0) firms. Equivalently, if the mass of marginal firms is v(Rj0), the government must spend that amount to keep them alive. So dψ0dTF:Target=1v(Rj0). If the government can target such firms, the extra multiplier from targeted transfers TF:Target (relative to untargeted firm-level transfers TF:All) is (see the Appendix for derivations)(24) dY0dTF:Target−dY0dTF:All=(κdY0dTP+∂Y0∂ψ01v(Rj0))−(κdY0dTP)=∂Y0∂ψ01v(Rj0), where(25) ∂Y0∂ψ0|TF=0=Y0. For example, if fixed costs f are uniformly distributed on [0,U], the marginal targeted tax dollar creates Y0/U additional units of GDP compared to the marginal untargeted tax dollar. In the absence of large changes in the distribution of fixed costs as revenues change, lower inequality (higher κ) is associated with larger relative benefits of targeted transfers. Each firm is associated with higher GDP the larger is the income share (and hence spending) of poor households. Therefore, saving these marginal firms is associated with larger net output gains. The relative benefit (in terms of GDP per dollar spent) of targeted transfers to firms versus transfers to low-income households depends on how many firms are kept afloat with each dollar spent:(26) dY0dTF:Target−dY0dTP=(κdY0dTP+∂Y0∂ψ01v(Rj0))−(dY0dTP)=∂Y0∂ψ01v(Rj0)−(1−κ)dY0dTP. In this sense, the benefits of targeted transfers to firms (relative to transfers to the poor) are proportional to the indirect costs of the COVID19 restrictions (i.e., endogenous firm exit). If there are large restriction multipliers (1+dψ0dξ) (based on the joint distribution of fixed capital costs and firm revenues), then the relative benefits of targeted transfers are large and these benefits could be even larger if there are costs of reentry. Furthermore, lower inequality (higher κ) is associated with larger relative benefits of targeted firm-level transfers because as discussed above Y0 is increasing in κ. Poor households also receive a higher initial (direct) share of the firm-level transfer, although this effect is offset by the fact that the effect of household transfers on output is increasing in κ. Proposition 6 (The optimal composition of transfers): Targeted transfers to firms can be the most cost-effective method of using transfers to mitigate a restriction multiplier above unity. The relative benefit of targeted transfers depends on the joint distribution of firm revenues and capital operating costs. The relative benefit is higher the greater is the income share of the poor (lower inequality) as long as(v′(Rj0)v(Rj0))is not too large. Proof: Appendix. In practice it may be difficult to identify and target marginal firms, although the model offers some guidance. The firms most at risk of exit are those with relatively low profitability and for which capital operating costs are the largest or most rigid. Because of low profit margins, small businesses are likely to be particularly prone to exit (consistent with the evidence in Fairlie, 2020), therefore implying an important role of targeted transfers to firms. If the government attempts to target firms but can do so only imperfectly, the multiplier will be in the range [dY0dTF:All,dY0dTF:Target], with a larger effect the more of the stimulus goes to marginal firms. An alternative policy to firm-level transfers is government loans to firms. But firms still need to cover their future-period fixed costs. Firms for which the present value of revenues in both periods falls below the present value of fixed costs will not be helped by loans (specifically, ψ0 and ψ1 can fall below 1 even if the government offers loans). Loans are only effective for the firms that cannot cover their fixed costs in the initial period but nonetheless earn profits in present value. Chetty et al. (2020) document a limited impact of loans to small businesses on firm employment and suggest that liquidity injections are insufficient for restoring employment at small businesses. Their evidence, interpreted through the lens of our model, is consistent with a decline in the present value of revenues sufficient to push firms to exit. 4 Conclusion The COVID crisis has both raised immediate policy questions and highlighted key structural relationships in the economy. Inequality has risen, rich households have cut back spending on services, and firms have been pushed to the brink of exit in the face of rigid capital operating costs. We develop a model capable of addressing the roles of inequality and other key features of the pandemic economy. Our results have general implications for the macroeconomic effects of the interactions of inequality and fiscal policy, while also providing guidance on the relative merits of alternative fiscal policies in the face of COVID restrictions on economic activity. Our framework implies that rising inequality will drag down GDP, as will any additional reallocation of spending by rich households away from service sectors in which low-income households work. In the absence of these developments, the strongest macroeconomic threat associated with COVID19 is firm exit resulting from restrictions on the exchange of services and rigid capital costs, a pattern clearly observed in the data. Our model suggests that the adverse effects may be offset by transfers to households and firms. Furthermore, we show that transfers to firms on the margin of exit are particularly effective in mitigating economic contraction. Our framework indicates a number of measures that will be useful to monitor as the COVID19 crisis evolves. In the absence of rising inequality or reductions in spending by high-income households, nominal GDP will rebound to a level at or potentially beyond what it would have been in the absence of COVID. Rising inequality or reductions in spending by high-income households can mitigate this boom or cause a prolonged slump. Fiscal stimulus will be especially useful in the event of a slump, although its effect per dollar spent is decreasing in inequality. Another important measure is the rental prices of firms’ operating capital, especially for firms that have large fixed operating costs relative to revenues and for multiproduct firms. Downward adjustment of capital prices can mitigate large restriction multipliers. Appendix The following relationships are referenced throughout the proofs. First, in equilibrium the bond price Q equals the discount factor β. This follows from the rich household's first-order-condition with respect to the bond and the fact that λRt equals the marginal utility of the numeraire. In particular, the first-order condition for either household is Qλ0h=βλ1h. Since λRt=1, it follows that Q=β. Furthermore, it follows that λP0=λP1. Second, in the steady-state (in the absence of shocks) households smooth their expenditure: cjk1h=cjk0h. Plugging firms’ prices (Eq. (5)) into the household's demand (Eq. (4)) implies that equilibrium steady-state quantities are qjkth=θh2γ and equilibrium expenditure is(27) cjkth=(θh)24γλht. Expenditure smoothing (in steady-state) follows from λh0=λh1. Third, the responses to shocks of product-level poor-household consumption at time t = 0, prices at t = 1, and poor-household expenditure in either period all move in the same direction. This is because the response of each can be captured by the response of the budget multiplier:(28) dcjk1P=dcjk0P=(θP)24γλP02dλP0 (29) dqjk0P=−1γpjk0dλP0 (30) dpjk1P=−θP2λP02dλP0 The results follow from dcjk0P=pjk0Pdqjk0P due to fixed initial-period prices, qjk0P=−1γpjk0dλP0, and dcjktP=d(θ24γλP1)=d(θ24γλP0)=−θ24γλP02dλP0. These relationships imply that we can infer the direction of output per product in the initial period and the direction of prices in the future from the direction of spending in either period (or equivalently, from the response of the budget multiplier). Proof ofProposition 1: Total differentiation of (12), after imposing the steady-state values ξ,ψ0,ψ1=1 and dcjk1P=dcjk0P, yields(31) TP1+Qdξ+TP1+Qdψ0+(cjk1P(1−Qκ)−Qcjk0Rκ)dψ1+(1+Q)(1−κ)dcjk0P=(1+Q)κdcjk0R+dTP+(1+Q)[cjk0P+cjk0R]dκ. It follows that around a steady-state in which TP=0 (and for simplicity setting dψ1=0):(32) dcjk0Pdκ=cjk0P+cjk0R1−κ⇒dqjk0Pdκ=1p¯jk0Pcjk0P+cjk0R(1−κ)dcjk0Pdcjk0R=κ1−κ⇒dqjk0Pdcjk0R=1p¯jk0Pκ1−κdcjk0PdTP=1(1+Q)(1−κ)⇒dqjk0PdTP=1p¯jk0P1(1+Q)(1−κ). ■ Proof ofProposition 2: The total derivative of initial-period GDP (from (8)) is(33) dY0=Y0(dξ+dψ0)+dqjk0P+dqjk0R. It follows that (imposing dqjk0Pdξ|TP=0=0 from (31) and the steady-state relationship c0P(1−κ)−cjk0Rκ=TP(1+Q), as well as imposing dqjk0Rdξ=0 from (6))dY0dξ|TP=0=Y0(1+dψ0dξ). ■ Proof ofProposition 3:dcjk0P and dpjk1P are related by substituting dλP0 out of (28) and (30):dpjk1P=2θPdcjk0P. From (31), we can write(34) dcjk0Pdξ=−TP(1+dψ0dξ)(1+Q)2(1−κ). Therefore, dpjk1Pdξ=−2θPTP(1+dψ0dξ)(1+Q)2(1−κ). When ξ falls, future prices increase. ■ Corollary 1 In the presence of steady-state transfers, inequality is associated with stronger output effects of COVID restrictions.d2qjk0Pdξdκ=−TPpjk0P[(1−κ)d2ψ0dξdκ+(1+dψ0dξ)(1+Q)2(1−κ)2], which is strictly negative as (1−κ)d2ψ0dξdκ+(1+dψ0dξ)>0. Proof ofCorollary 1: Note that by (34) and dcjk0P=pjk0Pdqjk0P we can writedqjk0Pdξ=−1pjk0PTP(1+dψ0dξ)(1+Q)2(1−κ). Taking the derivative with respect to κ yieldsd2qjk0Pdξdκ=−TPpjk0P[(1−κ)d2ψ0dξdκ+(1+dψ0dξ)(1+Q)2(1−κ)2], which is positive if (1−κ)d2ψ0dξdκ+(1+dψ0dξ)>0. First, we must derive a closed-form expression fordψ0dξ=v(Rj0)[(cjk0P+cjk0R)+dcjk0Pdξ], Substituting in dcjk0Pdξ=−TP(1+dψ0dξ)(1+Q)2(1−κ) from Eq. (31) and rearranging yieldsdψ0dξ=v(Rj0)[(cjk0P+cjk0R)−TP(1+dψ0dξ)(1+Q)2(1−κ)] dψ0dξ=v(Rj0)[1+ξTP(1+Q)2(1−κ)][(cjk0P+cjk0R)−TP(1+Q)2(1−κ)] Using the steady-state relationship cjk0P(1−κ)−cjk0Rκ=TP(1+Q), this expression can be rewritten as(35) dψ0dξ=v(Rj0)[1+ξTP(1+Q)2(1−κ)][TPQ(1+Q)2(1−κ)+cjk0R(1−κ)]. which is strictly positive. The next step is to derive d2ψ0dξdκ. Taking the derivative of (35) with respect to κ yields(d2ψ0dξdκ)=v(Rj0)[1+TP(1+Q)2(1−κ)][TPQ(1+Q)2(1−κ)2+cjk0R(1−κ)2]+[TPQ(1+Q)2(1−κ)+cjk0R(1−κ)][1+TP(1+Q)2(1−κ)]2((1+TP(1+Q)2(1−κ))v′(Rj0)dRj0dκ−v(Rj0)TP(1+Q)2(1−κ)2). It can be shown that this reduces to(d2ψ0dξdκ)=11−κdψ0dξ{1+1[1+TP(1+Q)2(1−κ)]((1+TP(1+Q)2)v′(Rj0)v(Rj0)dRj0dκ−TP(1+Q)2(1−κ))}. Since dRj0dκ>0, this expression is strictly greater than Ψ≡11−κdψ0dξ{1−A[1+A]}, where A≡TP(1+Q)2(1−κ). Therefore, it is sufficient to prove that (1−κ)Ψ+(1+dψ0dξ)>0. Indeed, we havedψ0dξ{1−A[1+A]}+(1+dψ0dξ)=dψ0dξ(2−A[1+A])+1>0. ■ Proof ofProposition 4: The effect of restrictions in the presence of rigid capital costs follows from (22). When capital costs are flexible, the price of capital will adjust to clear the capital market: Let r be the rate at which capital is rented out to firms, and let γj be the fixed capital requirement of firm j (so that fj=rγj). If the capital market is flexible, then the rate will adjust so that the rental rate equals the revenues of the marginal firm. Without loss of generality, we can assume that the rental rate in the absence of COVID restrictions is such that there is a unit mass of firms: ∫01γjdj=K. Covid restrictions shifts in the demand for capital (as firms’ revenues fall). Given the inelastic supply of capital, a flexible capital market implies that r adjusts so that in equilibrium there remains a unit mass of firms, with the price determined by the firm on the margin of exit. ■ Proof ofProposition 5: Substitute for dcjk0PdTP and dqjk0PdTP from (19) into the expression for dY0dTP in (23):dY0dTP=Y0v(Rj0)ξ1(1+Q)(1−κ)+1pjk0P1(1+Q)(1−κ) Taking the total derivative yieldsd2Y0dTP=Y0v(Rj0)ξ1(1+Q)(1−κ)[dY0+v′(Rj0)dRj0]+1(1+Q)(Y0v(Rj0)ξ+1pjk0P)1(1−κ)2dκ d2Y0dTPdκ>0follows from dRj0dκ,dY0dκ>0. ■ Proof ofProposition 6(Effect of Targeted Firm-Level Transfers): For each dollar targeted to marginal firms, the government would create dψ0=1v(Rjt) firms. Equivalently, if the mass of marginal firms is v(Rjt), the government must spend that amount to keep them alive. So dψ0dTTarget=1v(Rjt). κdTF:Target would also be transferred to households (as they own a share κ of capital). ThereforedY0dTF:Target=∂Y0∂TPdTPdTF:Target+∂Y0∂ψ0dψ0dTF:Target dY0dTF:Target=κdY0dTP+∂Y0∂ψ01v(Rj0) dY0dTF:Target=κdY0dTP+∂Y0∂ψ01v(Rj0) If the government could not target firms – but rather spent across all firms, it would create onlydY0dTF:Firms=∂Y0∂TPdTPdTF:Firms=κdY0dTP. Targeted firm transfers have an additional multiplier effect given bydY0dTF:Target−dY0dTF:Firms=∂Y0∂ψ01v(Rj0), where∂Y0∂ψ0|TP=0=Y0. The relationship between the net benefit of targeted transfers and inequality isddκ(Y01v(Rj0))=1v(Rj0)dY0dκ−Y0v(Rj0)v2(Rj0)dRj0dκ=1v(Rj0)1p¯jk0Pcjk0P+cjk0R(1−κ)−Y0v′(Rj0)v2(Rj0)dcjk0Pdκ. Substituting in the steady-state relationship cjk0P|TP=0κ(1−κ)cjk0R, as well as expressions for p¯jk0P and dcjk0Pdκ, this relationship can be written as (see Lemma 1 below for details)ddκ(Y01v(Rj0))=cjk0Rv(Rj0)(1−κ)2[κ(1−κ)(θR)2(θP)2−(θP+θR)2γv′(Rj0)v(Rj0)]. As long as the percent change of the distribution v′/v is not too high (specifically, as long as v′v<κ(1−κ)(θR)2(θP)22γθP+θR), a higher income share of the poor (lower inequality) is associated with a higher net benefit of targeted transfers. Note that the same result applies to the net benefit of target firm-level transfers relative to transfers to poor households,dY0dTF:Target−dY0dTP=Y01v(Rj0)−(1−κ)dY0dTP, since it is straightforward to show that ddκ(−(1−κ)dY0dTP)=0.■ Lemma 1 ddκ(Y01v(Rj0))=1v(Rj0)dY0dκ−Y0v(Rj0)v2(Rj0)dRj0dκ=1v(Rj0)1p¯jk0Pcjk0P+cjk0R(1−κ)−Y0v′(Rj0)v2(Rj0)dcjk0Pdκ Subincjk0P|TP=0κ(1−κ)cjk0Randdcjk0Pdκ|TP=0=cjk0P+cjk0R1−κ ddκ(Y01v(Rj0))=cjk0Rv(Rj0)(1−κ)2[1p¯jk0P−Y0v′(Rj0)v(Rj0)] Substitute in p¯jk0P=θP2λPandY0=θP2γ+θR2γ. Note we can solve for λ from cjk0P|TP=0κ(1−κ)cjk0R, where cjk0P=θ24γλ. Specifically, λP=θ24γcjk0R1−κκ, where cjk0R=(θR)24γ. Thenddκ(Y01v(Rj0))=cjk0Rv(Rj0)(1−κ)2[κ(1−κ)(θR)2(θP)2−(θP+θR)2γv′(Rj0)v(Rj0)] ■ Acknowledgement We thank the editor (Florin Bilbiie), anonymous referees and Adrien Auclert for their constructive comments. 1 Theories of economic slack posit that workers and capital experience periods of idleness that represent wasted resources (e.g., Michaillat and Saez, 2015, Murphy, 2017). For empirical evidence of the relevance of models of slack, see e.g. Auerbach and Gorodnichenko, 2012, Auerbach and Gorodnichenko, 2013, Auerbach et al. (2020a, 2020b), Demyanyk et al. (2019), Egger et al., 2019, and Boehm and Pandalai-Nayar, 2020. 2 We also examine voluntary spending cuts by rich households, a prominent feature of the pandemic (Chetty et al., 2020). Cuts in rich-household spending are associated with large Keynesian multiplier effects, with the size of the effect increasing in the income share of the poor. The larger is the income share of the poor (the lower is inequality), the more a spending shock circulates back to the poor as additional income (and hence additional spending). Therefore, the model implies that while inequality has had a direct effect of reducing output, it has weakened the (nonetheless strong) effects of rich-household spending cuts. 3 While Auclert and Rognlie (2020) focus primarily on the role of income risk in a New Keynesian framework, we abstract from income risk and focus on permanent earnings differences between rich and poor households. 4 The firm exit margin is the driving force behind large effects of COVID restrictions in our model. In Guerrieri et al. (2020), firm exit amplifies the effects of restrictions operating through credit constraints, a low EOS, and a high EIS. In other words, in their model the consumer spending channel is necessary for the firm exit margin to matter, whereas in our model the firm exit margin is driven by multiproduct firms and does not rely on consumer spending multipliers. 5 We conjecture that our model can provide insights into other important macroeconomic phenomena that are outside the scope of our current analysis, including the well-documented correlation between real exchange rates and country-level income per capita (e.g., Simonovska, 2015, Murphy, 2019). In our setting prices depend directly on consumer income rather than only indirectly through parameters of the demand system. Our model framework may also address the often-observed recessionary effects of nominal exchange rate devaluations, since higher import costs reduce real incomes for local residents, which would (according to our model) reduce their purchases of locally produced goods and hence reduce local GDP. We thank Adrien Auclert for suggesting this connection. 6 See Auerbach et al. (2020b) for an overview of the empirical relevance of negligible marginal labor costs. 7 See Alexander and Karber (2020), Chetty et al. (2020), and Goolsbee and Syverson (2020) for evidence that households voluntarily avoided purchases of services perceived to be of high health risk. 8 Given the separability of preferences, shutting down access to any jkt element has symmetric effects on all other jkt elements and hence there are no changes in the composition of remaining commodities (and hence no direct demand spillover effects on unaffected producer-commodities). 9 As discussed below, spending adjustments by the rich only map into reductions in θR (and hence cjk0R). 10 While the rich households’ marginal propensity to consume (MPC) on NMC goods/services is zero, their MPC that includes spending on the endowment e is equal to the poor households’ MPC on NMC goods/services (the poor do not spend anything on endowment good e). Hence, the “total” MPC is the same for the poor and the rich. 11 The degree to which prices in period t = 0 are rigid determines the extent to which shocks affect real GDP. With rigid prices, real GDP is more responsive to shocks. If prices are fully flexible, macroeconomic shocks generally do not affect real GDP. 12 Perfectly rigid prices are not necessary for the qualitative predictions of our model, but they are useful for analytical tractability. In particular, a notable feature of the demand function in Eq. (4) is that under flexible prices equilibrium output is independent of consumer income. This result would not hold in general under the Melitz and Ottaviano (2008) utility function in which utility from any product variety is falling in consumption of other varieties. In that more complicated setup, equilibrium output would vary with consumer income even if prices could flexibly adjust to macroeconomic shocks. 13 We assume that cost fjt is fixed in nominal terms in period t=0 but it is free to adjust in period t=1 so that the mass of firms cannot be greater than 1. 14 We do not specify whether income is in the form of wages or dividends from firm profits. Therefore, the labor share of income is not determined by the model. 15 Note that the market for the endowment good clears even with taxes and transfers and no change in its price. For example, when the government taxes the endowment of the rich, the taxed portion eventually ends back in the hands of the rich as poor households spend the transfer on the NMC sector. If a poor household is given a dollar in transfers, it will spend the dollar on NMC goods/services. 1−κ share of the dollar will become income of the rich (who will spend it on the endowment good) while κ share will become income of the poor. This “second-round” income of the poor will be spent on the NMC goods/services again so that (1−κ)κ will become income of the rich and κ2 will become income of the poor. These rounds of spending will continue and, in the end, the rich will get their $1dollar in taxes back in income (1−κ)+(1−κ)κ+(1−κ)κ2+…=1 which they spend on the endowment good. 16 The working paper version of Bilbiie (2008) features a Keynesian cross with a steep consumption function, as in Fig. 1. 17 The large multipliers in our model are driven by the assumption that autonomous spending does not push firms against capacity constraints. In other words, the quasi-Keynesian cross in Fig. 1 is valid in a region of zero marginal costs. A capacity constraint would bind when marginal costs become positive. Since wages in our model are unspecified, our model shares the feature of that in d'Aspremont et al. (1990), where there could be no positive wage level that would bring output up to its capacity level. 18 In the NMC model, households smooth over changes in expenditure (to a first-order approximation) and future nominal expenditure is demand-determined (since in equilibrium future output is pinned down by demand parameters). 19 For an example of monetary policy in the NMC framework, see Murphy and Young (2021). However, their model does not feature heterogeneous agents or an endowment sector, which are key elements of our model. 20 It might seem that an alternative policy is for the government to lend to poor households. However, since the households in this environment are already able to smooth their consumption, the lending has no effect. Therefore, one can think of our model as an environment in which monetary policy has extended credit to households to an extent that is sufficient for them to smooth consumption. The benefits of fiscal transfers are evaluated above and beyond the credit-enhancing benefits of monetary policy. 21 There would be within-product adjustments if ξ varied by household. For example, Chetty et al. (2020) document that rich households were more likely to avoid purchasing goods/services with a high risk of infection. In our model, such spending adjustments by the rich only map into reductions in θR (and hence cjk0R). 22 The dependence of the firm exit margin on rigid capital operating costs is similar to the “entry-exit multiplier” in Bilbiie and Melitz (2020). 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Young. 2020a. Saving constraints, debt, and the credit market response to fiscal stimulus. Mimeo. Miranda-Pinto, J., D. Murphy, K. Walsh, and E. Young. 2020b. A model of expenditure shocks. Mimeo. Murphy D.P. How can government spending stimulate consumption? Rev. Econ. Dyn. 18 2015 551 574 Murphy D.P. Excess capacity in a fixed-cost economy Eur. Econ. Rev. 91 2017 245 260 Murphy D.P. Demand complementarities and cross-country price differences Can. J. Econ. 52 1 2019 253 279 Murphy, D.P. and E.R. Young. 2021. Government debt limits and stabilization policy. Mimeo. Simonovska I. Income differences and prices of tradables: insights from an online retailer Rev. Econ. Stud. 82 4 2015 1612 1656 Schmidt, L.D.W., and A.A. Toda. 2019. Bad news and robust comparative statics for the elasticity of intertemporal substitution. Mimeo. Summers L.H. Demand side secular stagnation Am. Econ. Rev. 105 5 2015 60 65 Yang, C. 2017. Income inequality and government spending multipliers. Mimeo.

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==== Front Eur Econ Rev Eur Econ Rev European Economic Review 0014-2921 0014-2921 Elsevier B.V. S0014-2921(21)00153-7 10.1016/j.euroecorev.2021.103809 103809 Article COVID-induced sovereign risk in the euro area: When did the ECB stop the spread?☆ Ortmans Aymeric a Tripier Fabien ab⁎ a Université Paris-Saclay, Univ Evry, EPEE, 91025, Evry-Courcouronnes, France b CEPII, France ⁎ Corresponding author at: Université Paris-Saclay, Univ Evry, EPEE, 91025, Evry-Courcouronnes, France. 24 6 2021 8 2021 24 6 2021 137 103809103809 17 9 2020 7 6 2021 9 6 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This paper studies how the announcement of the ECB’s monetary policies stopped the spread of the COVID-19 pandemic to the European sovereign debt market. We show that up to March 9, the occurrence of new cases in euro area countries had a sizeable and persistent effect on 10-year sovereign bond spreads relative to Germany: 10 new confirmed cases per million people were accompanied by an immediate spread increase of 0.03 percentage points (ppt) that lasted 5 days, for a total increase of 0.35 ppt. For periods afterwards, the effect falls to near zero and is not significant. We interpret this change as an indicator of the success of the ECB’s March 12 press conference, despite the “we are not here to close spreads” controversy. Our results hold for the stock market, providing further evidence of the effectiveness of the ECB’s March 12 announcements in stopping the financial turmoil. A counterfactual analysis shows that without the shift in the sensitivity of sovereign bond markets to COVID-19, spreads would have surged to 4.2% in France, 12.5% in Spain, and 19.5% in Italy by March 18, when the ECB’s Pandemic Emergency Purchase Programme was finally announced. Keywords COVID-19 European Central Bank Sovereign debt Monetary policy Local projections ==== Body pmc “I can assure you on that page that first of all we will make use of all the flexibilities that are embedded in the framework of the asset purchase programme, (...) but we are not here to close spreads”. Christine Lagarde, president of the ECB, press conference, 12 March 2020. “The ECB will ensure that all sectors of the economy can benefit from supportive financing conditions that enable them to absorb this shock. This applies equally to families, firms, banks and governments”. ECB Governing Council press release, 18 March 2020. 1 Introduction The COVID-19 virus pandemic started on December 31, 2019, in China and reached Europe almost one month later, according to the World Health Organization (WHO).1 As a serious threat to the economy, the rapid spread of the virus led to sizeable financial turmoil in Europe. The downturn was particularly strong in Italy, the most affected country in Europe, where the interest rate spread vis-à-vis Germany rose sharply from 1.4% to 2.5% and the stock market fell by 40% between February 19 and March 12 (Fig. 1). On March 12, the European Central Bank (ECB) announced a set of monetary policy measures to support the economy in the face of the pandemic. The announcement of these measures gave rise to controversy over ECB president Christine Lagarde’s announcement that the ECB would certainly use “all the flexibilities that are embedded in the framework of the asset purchase programme” but also that the central bank was “not here to close spreads”. This last sentence has been widely cited as a communication failure, contrasting with the famous “whatever it takes” of her predecessor Mario Draghi.2 After a crash on March 12, the stock index plateaued, while the interest rate spread kept soaring to reach more than 2.8% on March 17. On March 18, the ECB conducted an exceptional longer-term refinancing operation (LTRO) to provide liquidity and announced the launch of a massive intervention program known as the Pandemic Emergency Purchase Programme (PEPP), which led to a turnaround in sovereign rates and a reboot in stock prices (Fig. 1). While the COVID-19 pandemic continued to spread in Europe, its transmission to financial markets stopped in Italy and the rest of the euro area. What was the role of these successive ECB interventions in stopping the spread of the pandemic to financial markets? What would have happened without these interventions? To answer these questions, we measure the reaction of sovereign spreads to new COVID-19 cases and examine how it evolved around the time of the ECB interventions. Using local projection methods developed by Jordà (2005), we measure the reaction at the time of impact, that is, on the day of the occurrence of COVID-19 cases, and in dynamics, that is, up to 5 days after the release of data on new confirmed COVID cases. We provide state-dependent estimates of the sovereign spread reaction to COVID-19 by splitting our full sample (from January 2, 2020, to May 29, 2020) into two subsamples divided at a reference date falling between March 5 and March 25. We include national stock markets and both country and time fixed effects to capture an unbiased measure of the time-varying impact of COVID-19 severity on euro area sovereign risk.Fig. 1 COVID-19 pandemic outbreak, government bond spread and stock market in Italy. Note: Vertical lines correspond to ECB announcement dates: March 12, 2020 (dashed), and March 18, 2020 (dot-dashed). LHS: Total COVID-19 confirmed cases are reported as the number of cases per million people. RHS: 10-year government spread (in %) is computed relative to the yield on 10-year German bonds; the stock market index is the FTSE MIB index. We show that despite the controversy generated by the “we are not here to close spreads” declaration of Christine Lagarde (March 12),3 the ECB actually stopped the spread of the pandemic-sparked crisis to the euro area sovereign debt markets on March 12, before the announcement of the PEPP and the conduct of market operations that occurred on March 18, leading to the reversal of sovereign spreads (Fig. 1). Unfortunately, it should be stressed that the methodology and the data used in this paper do not allow us to dissociate the effects of ECB monetary policy announcements from those of Christine Lagarde’s statements at the press conference. Indeed, these two events took place simultaneously on March 12, and it is quite possible that Christine Lagarde’s statement substantially canceled out the effects of the ECB announcements.4 Nevertheless, our study allows us to identify the effectiveness of ECB communication since the announcements on March 12 were not accompanied by any major market operations.5 In fact, the ECB’s balance sheet expansion in reaction to the COVID-19 pandemic outbreak started the week after, on March 18, through substantial LTROs of €109.1305 billion, while the PEPP actually began on March 24. At the start of the pandemic outbreak, the sovereign spread reaction to COVID-19 was increasing in the time horizon: the occurrence of 10 new cases per million people was accompanied by an immediate spread increase of 0.03 percentage points (ppt), which lasted 5 days for a total increase of almost 0.35 ppt. This explosive pattern is a hallmark of financial market turmoil in times of sovereign debt crises. Thus, we support the view that the ECB’s unprecedented monetary policy responses to the COVID-19 pandemic were very effective in disrupting the explosive path of sovereign default risk within eurozone countries.6 Indeed, our estimates indicate that without these interventions, sovereign debt rates would have risen to 4.2% in France, 12.5% in Spain, and 19.5% in Italy by March 18, which would have undoubtedly raised the question of debt sustainability in these countries and potentially led to a sovereign debt crisis. Our study provides empirical evidence for the theoretical framework developed in Arellano et al. (2020) that clarifies the link between the ongoing COVID-19 pandemic and the increasing probability of sovereign debt default in emerging economies. Introducing a standard epidemiological methodology into a sovereign default model, the authors argue that lockdowns imposed by governments in reaction to the pandemic-induced health crisis save lives but are costly in terms of output and unemployment. They show how fiscal transfers engaged by governments to smooth consumption are constrained by borrowing capacity and default risk, which, in turn, increases the cost of lockdown. Hence, according to their model, the more severe the pandemic, the higher the risk of default on sovereign debt. This argument holds for the euro area as well. Indeed, ECB (2020a) indicates that the outbreak of the crisis led to an immediate increase in direct costs, mainly to address the public health consequences, but that from a macroeconomic perspective, much of the impact relates to the containment measures, which place a severe economic burden on firms, workers and households, and the packages of fiscal measures implemented in all euro area countries. As a result, the general government budget deficit in the euro area was projected to increase significantly in 2020 to 8% of GDP, compared with 0.6% in 2019. The risk of transmission to the banking sector through a worsening of bank balance sheets was emphasized early by Schularick and Steffen (2020) and analyzed in Couppey-Soubeyran et al. (2020), among others. In a recent publication, ECB (2020b) warns that banks in some countries have indeed increased their domestic sovereign debt holdings, triggering concerns that the sovereign-bank nexus could re-emerge in the euro area. Our paper also supplements recent empirical works on the drivers of euro area sovereign risk during the COVID-19 crisis. Among them, Delatte and Guillaume (2020) highlight the heterogeneous effects of European policies on sovereign spreads: while the announcement of the PEPP reduced spreads in the euro area, the contrary was true for the financial assistance announced by the European Council. In regard to the direct impact of the COVID-19 crisis, they report a nonlinear relationship between spreads and the logarithm of the number of deaths per 100,000 people but do not consider the variation in the number of cases and deaths, as we do. Augustin et al. (2020) and Klose and Tillmann (2021) are closer to our setup since they consider the daily percentage change in COVID-19 cases. Augustin et al. (2020) use a large international panel of developed countries (including European countries) and also report results for a set of U.S. states. They show that countries’ sovereign risk reacts positively and significantly to the pandemic outbreak and that the strength of this reaction is conditional on initial fiscal conditions. Klose and Tillmann (2021) consider both sovereign and equity markets in Europe and conclude that monetary policy has been more effective in closing spreads. Finally, Andries et al. (2020) measure the intensity of the pandemic as the day when the number of cases and deaths reaches a threshold and do not consider the daily change, as we do. They study how the intensity of the pandemic and policy measures explain the cumulative abnormal returns of sovereign Credit Default Swap (CDS) spreads. Our contribution with respect to these references is as follows. First, we go further by dealing with the dynamic response of sovereign bond spreads to the COVID-19 pandemic outbreak in the euro area. Our results demonstrate that these dynamics are a key feature of COVID-induced sovereign risk, which is cumulative over days. Focusing on the sensitivity of spreads to COVID-19 news at the time of impact leads to a sharp underestimation of the severity of the issue. Second, by running a split sample analysis, we can identify when this sensitivity was broken and interpret the results as being in line with the calendar of policy announcements. Third, we apply our empirical procedure to the stock market to provide additional evidence on the evolution of the nexus between the ongoing pandemic and financial markets. Fourth, we assess possible spillovers from the spread of the pandemic in Italy that may have been at work during the COVID crisis. Fifth, we provide a counterfactual analysis by simulating the path of sovereign bond spreads that would have occurred without this change in the sensitivity of bond spreads to the COVID-19 crisis. Related literature. This paper is part of the burgeoning literature on the macroeconomic effects of the COVID-19 crisis and policy responses to the pandemic outbreak, as studied in Guerrieri et al. (2020), for instance. Atkeson (2020) and Eichenbaum et al. (2020) investigate the economic impact of the spread of the pandemic using a simple SIR model.7 In the latter, the severity of the pandemic is measured by the number of new deaths. This proxy has been found to strongly affect macroeconomic aggregates such as GDP or consumption and rates of return on stocks and government bills (Barro et al., 2020; Jordà et al., 2020). This paper also contributes to the extensive strand of literature using panel regression to estimate the determinants of long-term government yields and sovereign bond spreads in European Monetary Union (EMU) countries, including Manganelli and Wolswijk (2009), Favero and Missale (2012), Aizenman et al. (2013), Georgoutsos and Migiakis (2013), Costantini et al. (2014), and Afonso et al. (2015b). Furthermore, Delatte et al. (2017) use a panel smooth threshold regression model and show that EMU sovereign risk pricing is state dependent. Other papers assess a time-varying relationship between EMU sovereign spreads and their fundamental determinants such as liquidity or risk factors, as in Afonso et al., 2015a, Afonso et al., 2018 or Afonso and Jalles (2019). The latter papers also highlight the role of ECB monetary policies as an important driver of sovereign bond spreads.8 The methodology used in this paper is based on the growing literature employing local projection methods developed by Jordà (2005). Local projection methods have been employed to conduct inference on dynamic impulse responses to address several issues in applied macroeconomics.9 For instance, Ramey and Zubairy (2018), Auerbach and Gorodnichenko (2013), Born et al. (2019) and Cloyne et al. (2020) use state-dependent local projections to examine fiscal issues. Meanwhile, state-dependent aspects of monetary policy transmission are also studied in Tenreyro and Thwaites (2016).10 Structure of the paper. The rest of the paper is organized as follows. Section 2 presents the data and the chronology of events related to COVID-induced sovereign risk in the euro area. Section 3 explains the methodology used in this paper. Section 4 is devoted to the results. Section 5 is dedicated to several robustness checks. Section 6 proposes an extension of our baseline model, including an application to the stock market in the euro area, a cross-country analysis, and a counterfactual exercise. Section 7 concludes. 2 Data sources and chronology This section presents the sources of data and summarizes the main events of the COVID-19 outbreak in Europe. The data are given at a business daily frequency (5 days per week) and run from January 2, 2020, to May 29, 2020. They come from different sources. European sovereign debt and stock markets. Long-term interest rates and stock indices are from Datastream via Thomson Reuters Eikon.11 Sovereign bond spreads are constructed as the yield differentials between bonds issued by each euro area government and German bonds at a given maturity. The 10-year spread is our benchmark, and we consider the 2-year spread for robustness analysis. We restrict the sample to 15 euro area countries for which 10-year spreads and stock market indices are available on a daily basis for this period: Austria, Belgium, Cyprus, Finland, France, Greece, Ireland, Italy, Lithuania, Malta, Netherlands, Portugal, Slovakia, Slovenia, and Spain. Spreads are plotted for each country in Online Appendix B. The pattern highlighted above for Italy in Fig. 1 is representative of most European countries, which experienced a sharp increase in their government spreads when the pandemic spread to Europe. Stock market indices are also plotted in Online Appendix B. The figures show that all the countries in our sample experienced an enormous drop in their main national stock market index. This crash occurred at the same time that euro area government spreads started to skyrocket, stressing how severe financial markets in the euro area interpreted the economic impact of the pandemic to be. Health statistics on the COVID-19 pandemic in Europe. COVID-19 data are extracted from the European Centre for Disease Prevention and Control (ECDC), an agency of the European Union aimed at strengthening defenses against infectious diseases.12 Since the beginning of the pandemic, the ECDC has been collecting the number of COVID-19 cases and deaths on a daily basis based on reports from health authorities worldwide. To be consistent with our financial series database, we discard observations for weekends to obtain a business week database of COVID-19 cases and deaths. The main implication of this transformation is that (business) daily variations in the number of cases and deaths on Monday are computed with respect to the previous Friday and not to Saturday or Sunday, when financial markets are closed. Total cases and deaths are plotted for each country of our sample in Online Appendix B. Our database starts just after the report by the Wuhan Municipal Health Commission in Wuhan City of a cluster of 27 pneumonia cases (December 31).13 The pandemic then spread to Europe. The first European case was reported in France on January 24, but Italy was the most heavily affected country in Europe. The Italian authorities reported clusters in Lombardy on February 22 and implemented lockdown measures on March 8 at the regional level, which were rapidly extended to the national level on March 11. The Director General of the WHO declared COVID-19 a global pandemic on March 11 and said that Europe had become the epicenter of the pandemic on March 13. All countries of the European Union were affected by March 25, according to the ECDC. ECB interventions. Central banks’ response to the COVID-19 crisis was quick and massive, as documented by Cavallino et al. (2020) and Delatte and Guillaume (2020). Major central banks across advanced economies launched new asset purchases and lending operations to face the pandemic outbreak. Among them, the ECB reacted strongly to the COVID-induced economic downturn by making substantial decisions during March 2020.14 On March 12, the Governing Council decided on a package of policy measures providing (i) additional longer-term refinancing operations (LTROs) to provide liquidity for the euro area financial system until June 2020, (ii) more favorable terms for the third series of targeted longer-term refinancing operations (TLTRO III) from June 2020 to June 2021 to support bank lending to small and medium-sized enterprises affected by the spread of the virus, and (iii) a temporary envelope of additional net asset purchases of €120 billion until the end of 2020 to support financing conditions under the existing Asset Purchase Programme (APP). On March 18, the ECB announced the launch of a new temporary asset purchase program called the Pandemic Emergency Purchase Programme (PEPP) consisting of assets purchases of €750 billion, including assets eligible for the APP, until the end of 2020.15 Fig. 2 shows the growth rate of ECB total assets (in percentage, at weekly frequency) and the respective contribution of the two main open market operations: “LTROs” and “Securities held for monetary policy purposes”. The category “Others” includes all other assets on the ECB balance sheet. Unfortunately, these series are not available on a daily basis and cannot be decomposed into national shares.16 However, they provide several helpful insights for interpreting our results. ECB interventions can be classified as only communication on March 12 and as a mix of communication and market operations on March 18. Indeed, the ECB balance sheet expansion started the week that ended on March 20 and not on March 13. Thus, the ECB intervention on March 12 can be considered a communication policy only, without significant market operations. This is not the case for the ECB press conference on March 18. On this date, the ECB provided exceptional LTROs of €109.1305 billion for 98 days,17 which implies an enormous increase of 17.73% for LTROs in comparison with their level in the previous week. Considering the full balance sheet, this increase explains half of the 4.74% increase in total assets on March 20–with increases of 2.31% from LTROs, 0.36% from securities held for monetary policy purposes, and 2.05% from other assets.18 Actually, the new PEPP was announced on March 18 by Christine Lagarde but was only effective from March 24.19 As shown in Fig. 2, the rise in debt securities held for monetary policy purposes (which include the PEPP) was gradual and became predominant in the expansion of the ECB balance sheet only from April 2020. Thus, the ECB intervention on March 18 was a mix of communication (mainly on the PEPP) and market operations (through LTROs). Additionally, it is important to mention that all European institutions were involved in managing the crisis.20 On March 10, the members of the European Council and heads of European institutions, including the ECB’s Christine Lagarde, held a video conference on COVID-19. They discussed how to coordinate European Union efforts to respond to the pandemic outbreak.21 We focus on the ECB interventions, which came earlier and were more commented on in terms of their effects on sovereign debt markets. For example, the activation of the general escape clause of the Stability and Growth Pact was proposed by the European Commission on March 20 and agreed upon by the ministers of finance of the member states of the EU on March 23, after the main ECB interventions.Fig. 2 Growth of ECB balance sheet. Note: The dashed line represents the growth of total assets/liabilities in percent. The bar chart depicts the contribution of “LTROs” (green bars), “Securities held for monetary policy purposes” (red bars) and “Others” (blue bars) to the growth of total assets in ppt. The category “Others” is computed as Others=Totalassets−(LTROs+Securitiesheldformonetarypolicypurposes). The gray shaded area covers the period from March 16, 2020, onward.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) ECB. The March 5–25 window. Based on the data and on the abovementioned events, we focus on the March 5–25 period to identify when and how the sovereign interest rate response to the spread of the COVID-19 pandemic changed. This choice is motivated by two considerations. First, March 5 fell one business week before the first ECB intervention (March 12), and March 25 fell one week after the ECB decision of March 18. Thus, the window is large enough to ensure that we do not miss any monetary policy effects in our analysis. Second, by March 5, only a few European countries had reported deaths (France, Italy, and Spain), while by March 25, only Latvia, Malta and Slovakia had not reported deaths from COVID-19. Thus, the window corresponds to the period of the generalization of the pandemic in Europe. In the remainder of this paper, we take as our benchmark the series of COVID-19 cases and not that of deaths. Since the number of confirmed cases leads the number of reported deaths, the series of COVID-19 cases provides more data for the estimation at the beginning of the sample–by March 5, only six countries had not reported cases, against fourteen that had not reported deaths. 3 Methodology Our primary interest is in the dynamic response of government spreads to the outbreak of the COVID-19 pandemic. To obtain an estimate of the response, we rely on the local projection method following Jordà (2005). Considering the whole sample period, we estimate: (1) Δsi,t+h=αi,h+ηt,h+βhΔxi,t+Γh(L)si,t−1+Θh(L)zi,t+εi,t+h for country i and horizon h=0,1,…,H as of time t, where εi,t+h is the error term. Δsi,t+h=si,t+h−si,t−1 is the variation in the 10-year government bond spread at horizon h. Δxi,t=xi,t−xi,t−1 is the daily change in the number of total COVID-19 cases in country i as of time t. We consider the change in the number of cases per 100,000 people. The main motivation for this choice is that the attention of observers has been focused on the number of daily new cases by population since the beginning of the pandemic, sometimes in absolute terms but never as a percentage of the number of total cases already reported, as illustrated by the very popular figures published and massively distributed by the Financial Times. However, we check the robustness of our results by considering the daily change in the number of deaths per million people due to COVID-19, the 3-day rolling average of new cases, new cases in absolute terms, the lagged values of new cases, and the growth rate of total cases as the independent variable. Additionally, other robustness checks involve separately adding the growth rate of total cases, the logarithm of the total number of cases, the first difference of new cases, or the lagged values of new cases as control variables in the baseline specification. Tables and figures containing the results are given in Online Appendices D and E. The coefficient of interest βh measures the variation in government spreads h days after the release of data on new COVID-19 cases. A series of regressions are estimated for each horizon h. Since the model is estimated on a business daily basis, we assume that a one-week horizon is sufficiently long to capture the path of the response coefficients βh. Then, we set H=5. To obtain an accurate estimate of these coefficients, we use a two-way fixed effects framework and add a set of control variables as recommended by Herbst and Johannsen (2020).22 First, country fixed effects αi,h take into account the structural differences between countries. Second, time fixed effects ηt,h absorb features that are common across all countries but change over time, including the global evolution of the COVID-19 pandemic. Third, the current value and the first four lags of the log of the stock index zi,t control for the state of the economy and the effects of other news that could have an impact on government spreads. Θh(L) is a polynomial in the lag operator associated with the domestic stock markets, with Θh(L)=∑n=0Nθh,nLn, where N stands for the number of lags. Finally, it also includes the first four lags of the dependent variable to control for any serial correlation in the error term through the polynomial in the lag operator Γh(L), defined by Γh(L)=∑n=0N−1γh,n+1Ln. We set N=4 as the number of lags. The linear local projection method described above can be transformed into a state-dependent model. State-dependent local projection methods have been mainly applied to fiscal policy issues by Auerbach and Gorodnichenko (2013) and Ramey and Zubairy (2018). For the linear model, we estimate a series of regressions at each horizon h: (2) Δsi,t+h=αi,h+ηt,h+Dt,t¯[βa,hΔxi,t+Γa,h(L)si,t−1+Θa,h(L)zi,t] +(1−Dt,t¯)[βb,hΔxi,t+Γb,h(L)si,t−1+Θb,h(L)zi,t]+εi,t+h where Dt,t¯ is a dummy variable that takes 0 before a given date t¯, that is, when t<t¯, and 1 thereafter, when t≥t¯. Equation (2) captures the dynamic response of government bond spreads to new COVID-19 cases conditional on the ECB intervention through the coefficients βa,h and βb,h. It is worth emphasizing that this response is different from the direct effect of a policy intervention on sovereign rates, which is gauged by the time fixed effect ηt,h. Since we are mostly interested in the βa,h and βb,h coefficients, responses in period t+h to new information on the severity of the COVID-19 situation at time t, conditional on the state of the economy, are computed as in Born et al. (2019) by the following expression: (3) ∂Δsi,t+h∂Δxi,t|Dt,t¯=Dt,t¯×βa,h+(1−Dt,t¯)×βb,h which is a linear combination of impulse response coefficients. As our aim is to investigate possible nonlinearities in the response coefficient βh according to the state of the economy during the March 5–25 window (see Section 2), event dummies are constructed according to t¯∈3∕5,…,3∕25. 4 Results This section presents our main results to identify when the COVID-induced rise in sovereign spreads was halted. Results for the full sample. Let us start with equation (1) for the full sample of observations. Fig. 3 shows the path of the estimated coefficient βh and the 95% confidence interval, and Table C.1 ( Online Appendix) contains the estimation results. The response coefficient is slightly negative at all horizons. However, the magnitude of the effect is very small: the change in the interest rate spread is very close to zero at all horizons and reaches −0.002 ppt at horizon h=3 for 10 new cases per million people. As shown by the confidence interval, the impact of new cases is not significantly different from zero when we consider the full sample. As explained above, this does not mean that policy interventions have no direct effects on sovereign interest rates23 but that these rates do not react significantly to the occurrence of new COVID-19 cases. What happens, however, when the sample is split? In particular, we draw attention to the period before ECB interventions. The difference between the beginning and the end of the March 5–25 window. Fig. 4 compares the response coefficients βb,h and βa,h of 10-year government spreads to new COVID-19 cases before and after March 5 (t¯=3∕5, the first date of our window). For the period before March 5, without the ECB intervention, the response coefficient βb,h follows an explosive path. Spreads on 10-year government bonds increase by more than 0.021 ppt for 10 new cases per million people on impact. This rise significantly accelerates to reach 0.240 ppt up to 5 business days. This explosive path severely threatened debt sustainability in the euro area as the pandemic spread. On March 12, Italy reported 38.256 new cases per million residents and Spain 24.66 and France 7.614 new cases. This βb,5 estimate considering only this date would imply a cumulative increase in the spread over 5 days of 0.92 ppt in Italy, 0.59 ppt in Spain and 0.18 ppt in France for 10 new cases per million people. After March 5, the estimates for this sample including the ECB interventions show a response coefficient βa,h that is very close to zero and not significant.Fig. 3 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Impulse responses represent the βh coefficient from equation (1), and the gray shaded area represents the 95% confidence interval. Fig. 5 also compares the response coefficients βb,h and βa,h of bond spreads to new COVID confirmed cases before and after March 25 (t¯=3∕25, the last date of our window). The response coefficient βb,h on impact (h=0) is smaller (0.001 ppt against 0.021 for t¯=3∕5) and still not significantly different from zero. However, in this case, the coefficient no longer follows an explosive path: the response of the interest rate spreads to new cases is even below zero at a 3-day horizon and becomes slightly positive up to a 5-day horizon (reaching 0.002 instead of 0.240 for t¯=3∕5). Note that the βb,h coefficient is not significant at any horizon. Similarly, the response coefficient βa,h is muted when we consider the subsample after March 25. In the latter case, government bond spreads do not react to new cases at all. These results indicate that a major change took place in the euro area sovereign debt market between March 5 and March 25. To identify when it occurred, we now consider various split dates t¯ falling within this time interval.Fig. 4 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Impulse responses are computed following equation (2). The left panel shows the coefficient βb,h (before the split date), whereas the right panel shows the coefficient βa,h (after the split date). The gray shaded area represents the 95% confidence interval. Fig. 5 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Impulse responses are computed following equation (2). The left panel shows the coefficient βb,h (before the split date), whereas the right panel shows the coefficient βa,h (after the split date). The gray shaded area represents the 95% confidence interval. Time-varying split dates for the March 5–25 window. Fig. 6 depicts estimated values of the coefficient βb,h at each horizon h based on various split dates t¯∈3∕5,…,3∕25. At horizon h=0, the coefficient is positive and significantly different from zero up to March 9. After this date, βb,0 is not significantly different from zero when the estimation sample includes the announcement of the ECB on March 12 and then decreases continuously with t¯. This pattern is even more striking at horizons between h=2 and h=5, with βb,h first sharply falling around t¯=3∕9 and remaining positive afterwards but not significantly different from zero and falling again around t¯=3∕18, after which the coefficients are very close to zero. Fig. 7 summarizes the three regimes of the response coefficients: highly significant and explosive (in red, for t¯=3∕5,…,3∕9), low and not strongly significant (in blue, for t¯=3∕10,…,3∕16), and close to zero and not significant at all (in green, for t¯=3∕17,…,3∕25). When we look at the calendar of (monetary) policy interventions in March 2020 in the euro area, these coefficient regimes are identified according to break dates that coincide with dates around the first ECB announcements. Moreover, it seems that the ECB intervention on March 12 (prior to March 18) strongly contributed to lower COVID-induced sovereign risk in EMU countries and broke the sovereign risk-pandemic outbreak dynamics within the euro area. To identify more precisely when the ECB closed spreads, we implement a statistical test for structural breaks. Testing for structural breaks in response coefficients. Table 1 shows the results of a Chow test (Chow, 1960) to confirm the existence of structural breaks in the estimated response coefficients. It presents p-values from the Chow test at horizons ranging from h=0 to h=5 and for break dates t¯ between March 5 and March 25. We select March 9 as the structural break date on which the βa,h and βb,h coefficients are no longer statistically equal at each horizon simultaneously. Indeed, the p-value of the test implemented on the coefficients βa,h and βb,h is lower than 5% at all horizons on March 9 only, which is not the case for any other dates. In other words, the results suggest rejecting the null hypothesis that the βb,h and βa,h coefficients are equivalent at the 5% level of significance after March 9. Fig. 8 shows the path of the response coefficients associated with this reference date, and Table C.2 ( Online Appendix) contains the estimation results.Fig. 6 Evolution of impulse response coefficients by horizon. Note: Impulse responses are computed following equation (2). Each panel shows the impulse response coefficients βb,h estimated before split dates t¯∈3∕5,…,3∕25 at different horizons. The gray shaded area represents the 95% confidence interval. Fig. 7 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Impulse responses are computed following equation (2). The impulse response coefficients βb,h are estimated before the following split dates: t¯∈3∕5,…,3∕9 in red, t¯∈3∕10,…,3∕16 in blue, and t¯∈3∕17,…,3∕25 in green. The shaded area represents the 95% confidence interval for each coefficient.(For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Table 1 Chow test (p-values). Horizon h=0 h=1 h=2 h=3 h=4 h=5 March 5 0.17 0.50 0.74 0.00 0.00 0.00 March 6 0.01 0.79 0.00 0.00 0.00 0.00 March 9 0.03 0.01 0.00 0.00 0.00 0.00 March 10 0.10 0.11 0.42 0.03 0.01 0.01 March 11 0.20 0.50 0.30 0.06 0.03 0.07 March 12 0.37 0.49 0.40 0.21 0.34 0.49 March 13 0.28 0.51 0.68 0.68 0.90 0.87 March 16 0.27 0.56 0.84 0.94 0.78 0.94 March 17 0.61 0.97 0.65 0.79 0.31 0.19 March 18 0.77 0.73 0.51 0.29 0.09 0.09 March 19 0.99 0.90 0.45 0.18 0.08 0.08 March 20 0.50 0.75 0.36 0.15 0.08 0.09 March 23 0.75 0.48 0.26 0.10 0.04 0.06 March 24 0.62 0.28 0.22 0.06 0.10 0.72 March 25 0.22 0.53 0.30 0.29 0.41 0.44 Note: The table displays p-values of Chow statistics from the test. The Chow test results confirm the existence of a highly significant break in the dynamic response of sovereign risk to the COVID-19 outbreak around the date of the first ECB intervention on March 12. To link this date with the timeline of political events, it should be emphasized that an impact response at a 3-day horizon on March 9 measures the effect of new cases reported on March 9 on spreads 3 days later (i.e., March 12). Moreover, the March 10 video conference between the members of the European Council and heads of European institutions, including the ECB (see Section 2), may have been perceived by financial markets as a positive signal of future ECB decisions scheduled on March 12. Hence, it could explain our key finding of a break date on March 9 through market expectations.24 Overall, our results indicate that the decision made by the ECB on March 12 was decisive in closing the spread of the COVID-19-induced financial crisis to euro area sovereign bonds. Although we believe that ECB interventions–particularly those on March 12–were effective in controlling the COVID-induced sovereign risk in the euro area, we are fully aware that the break around March 12 may be the consequence of the generalization of the pandemic and not of ECB announcements. From this point of view, the strong relationship identified between COVID-19 cases and sovereign spreads may have been relevant only at the beginning of the pandemic, allowing financial markets to integrate the risk associated with the occurrence of a pandemic before losing their sensitivity to the severity of the health crisis. Thus, it is crucial to check the robustness of our results to the modeling of the pandemic outbreak. Fig. 8 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Impulse responses are computed following equation (2). The left panel shows the coefficient βb,h (before the split date), whereas the right panel shows the coefficient βa,h (after the split date). The gray shaded area represents the 95% confidence interval. 5 Robustness This section is dedicated to alternative specifications of our model to test the robustness of our results. First, alternative measures of pandemic dynamics are introduced as controls in the specification to differently capture the evolution of the pandemic. Then, the sample countries are divided into two subgroups according to their debt-to-GDP level to assess the role of initial fiscal conditions in COVID-induced sovereign risk in the euro area. Additional robustness tests are provided in Online Appendix D, where our baseline model is specified with alternative dependent and independent variables. 5.1 Controlling for the shape of the pandemic Our baseline regression (2) is extended to include additional controls. Using the reference date, we investigate whether these controls may alter our estimate of βa,h and βb,h for the reference date t¯. The specification now takes the following form: (4) Δsi,t+h=αi,h+ηt,h+Dt,t¯[βa,hΔxi,t+Ψa,h(L)Xi,t+Γa,h(L)si,t−1+Θa,h(L)zi,t] +(1−Dt,t¯)[βb,hΔxi,t+Ψb,h(L)Xi,t+Γb,h(L)si,t−1+Θb,h(L)zi,t]+εi,t+h where Ψ•,h(L) is a polynomial in the lag operator associated with the control variable Xi,t defined hereafter. The results are reported in regression tables in Online Appendix E.25 The symbol • indicates both before (b) and after (a) for estimated coefficients. Growth rate of total cases. First, we control for the growth rate of the number of total cases. The growth rate of total cases is measured as the first difference (daily change) of the logarithm of the number of total cases. Hence, Xi,t=Δlogxi,t. Moreover, since no lagged value of controls is included in the estimate, we set Ψ•,h(L)=∑n=0Nψ•,h,nLn, with N=0. Table E.1 ( Online Appendix) shows that including the growth rate of the number of total cases in the model does not alter our baseline results. The ψb,h,0 coefficient is close to zero and not significant at all over the horizon. Logarithm of total cases. We also control for the logarithm of the number of total cases to take into account the state of the ongoing pandemic in its effect on sovereign bond spreads. Hence, Xi,t=logxi,t. As in the previous case, we set Ψ•,h(L)=∑n=0Nψ•,h,nLn, with N=0. Table E.2 ( Online Appendix) shows that including the log of total cases by population in the model does not alter our baseline results, even if the ψb,h,0 coefficient is significantly positive up to horizon h=4. Lagged values of new cases. Next, we control for lagged values of new COVID cases, and we estimate equation (4) setting Ψ•,h(L)=∑n=0N−1ψ•,h,n+1Ln, with N=2. The control variable is expressed as Xi,t=Δxi,t−1, where Δxi,t−1=xi,t−1−xi,t−2. The lagged values of new cases are measured as the first and second lags of new cases per 100,000 people. Table E.3 ( Online Appendix) reports the results. The βb,h coefficient is not as strong and statistically significant as in our baseline estimates. Note that both coefficients on the first and second lagged values of new cases, ψb,h,1 and ψb,h,2, respectively, are often significant over the horizon. This is especially true for the ψb,h,2 coefficient. Thus, we capture the persistent effect of new confirmed COVID cases on government bond spreads. First difference of new cases. Finally, we use the “variation of the variation” of new COVID-19 cases to account for the stretched S-shaped dynamics of the pandemic. In this case, Xi,t=Δxi,t−Δxi,t−1, which is positive in the first phase of the pandemic outbreak and negative at the end. The new cases variable (in its first difference) is now measured as the daily change in the number of new cases per 100,000 people. Also, we set Ψ•,h(L)=∑n=0Nψ•,h,nLn, with N=0. We then estimate equation (4). The results in Table E.4 ( Online Appendix) show that including the first difference of new cases as a control variable does not change our baseline results much. Note, however, that the ψb,h,0 coefficient is significantly positive on impact and turns out to be negative over the horizon but is always lower than the estimated βb,h. 5.2 Public debt-to-GDP Delatte and Guillaume (2020) and Augustin et al. (2020), among others, highlight the key role of initial fiscal conditions in the sovereign debt market reaction to the pandemic outbreak. To investigate the role of country fiscal conditions, we run the regressions defined by equation (2) for two subsamples of countries. The first subsample refers to high debt-to-GDP countries and consists of states for which the debt-to-GDP ratio is above the median calculated for the full sample at the end of 2019: Belgium, Cyprus, Spain, France, Greece, Italy, and Portugal. The second subsample refers to low debt-to-GDP states and includes countries with a ratio below the median: Austria, Finland, Ireland, Lithuania, Malta, the Netherlands, Slovenia, and Slovakia. Estimation results are shown in Online Appendix F. Figure F.1 reports the results for our benchmark split date, that is, March 9. Like Delatte and Guillaume (2020) and Augustin et al. (2020), we observe substantial heterogeneity in the response of bond spreads to new COVID-19 cases, which are positive and significant in the high debt-to-GDP subsample but not significantly different from zero in the low debt-to-GDP subsample of countries. We then investigate whether this heterogeneity alters our narrative of the crisis. To do so, we conduct a Chow test to identify structural breaks between the coefficients βa,h and βb,h in high debt-to-GDP countries only. The results are reported in Table F.1. The test results indicate that the null hypothesis is now rejected at the 10% level of significance for the period after March 9. For the full sample, March 9 turns out to be the key reference date after which the sovereign debt markets no longer reacted to the development of the pandemic. 6 Extensions This section extends the analysis to three issues. First, we assess whether the ECB stopped the euro area stock market crash. Second, we assess the existence of spillovers from the Italian pandemic outbreak to other European sovereign markets. Third, and finally, we investigate what would have happened without the structural break identified in the sovereign market reaction to the occurrence of new COVID cases. 6.1 Did the ECB stop the euro area stock market crash? In this section, we extend our empirical strategy to assess the dynamic effect of the COVID-19 outbreak on stock markets in the euro area. Thus far, we have included equity market data as a control variable in our regressions for sovereign spreads to measure their reaction to the occurrence of new COVID-19 cases given all the information already anticipated by the markets.26  Cox et al. (2020) find evidence that Federal Reserve announcements were decisive in the reversal of the U.S. equity markets in March and April after the market crash in February. At that time, only a tiny fraction of the credit announced had been distributed, leading the authors to conclude that market movements were the outcome of a shift in investors’ risk aversion. To investigate the response of the stock market to new cases in the euro area, the model defined by equation (1) now takes the form: (5) Δzi,t+h=αi,h+ηt,h+βhΔxi,t+Γh(L)zi,t−1+Θh(L)si,t+εi,t+h with the notation described in Section 3. The dependent variable is written Δzi,t+h=zi,t+h−zi,t−1 and is the variation of the log of the stock index (i.e., the cumulative logarithmic return) at horizon h. The coefficient of interest βh is the response of the national stock index to the pandemic outbreak. The model is still specified with country and time fixed effects and a set of control variables including the first four lags of the dependent variable and the current and four past values of 10-year sovereign bond spreads. The horizon is still 5 days, H=5. In the spirit of equation (2), the state-dependent local projection framework is now expressed as follows: (6) Δzi,t+h=αi,h+ηt,h+Dt,t¯[βa,hΔxi,t+Γa,h(L)zi,t−1+Θa,h(L)si,t] +(1−Dt,t¯)[βb,hΔxi,t+Γb,h(L)zi,t−1+Θb,h(L)si,t]+εi,t+h where Dt,t¯ is a dummy variable that takes 0 before a given date t¯, that is, when t<t¯, and 1 thereafter, that is, when t≥t¯. Here, again, these event dummies are constructed according to t¯∈3∕5,…,3∕25. We employ exactly the same procedure as that developed in Section 4: comparing the impulse response coefficients βa,h and βb,h with the split dates set on March 5 (t¯=3∕5) and March 25 (t¯=3∕25), focusing on the path of βb,h when the model runs over various split dates t¯∈3∕5,…,3∕25, and testing for structural changes in the response coefficients over time.Fig. 9 Impulse responses of stock market indices (in logs) to new COVID-19 cases in the euro area. Note: Impulse responses represent the βb,h coefficient from equation (6). The impulse response coefficients βb,h are estimated before split dates: t¯∈3∕5,…,3∕9 in red, t¯∈3∕10,…,3∕16 in blue, and t¯∈3∕17,…,3∕25 in green. The shaded area represents the 95% confidence interval for each coefficient. The results are presented in Online Appendix G and summarized in Fig. 9, which replicates Fig. 7 for the cumulated stock market return instead of the sovereign spread. For the period up to March 9, the stock market response to new COVID-19 cases is explosive, with a cumulative fall of 11% in the stock market index 5 days after the occurrence of new cases.27 The response is no longer explosive thereafter (blue lines) and is completely muted when the last dates of the window are considered (green lines). Hence, ECB interventions not only closed spreads in the euro area but also prevented an even more dramatic stock market crash. Given the timing of balance sheet expansion, these results also support the existence of the communication channel linking the ECB intervention to stock markets–as reported in Cox et al. (2020) for the U.S. economy–since there was no significant balance sheet expansion before March 18. 6.2 Are there spillovers from the Italian pandemic outbreak? As recalled in Section 2, Italy was the first country in Europe to be severely affected by the COVID-19 pandemic. It is interesting to assess the extent to which both sovereign debt and stock markets in other European countries reacted to the health crisis in Italy, which may indicate how the markets anticipated the spread of the pandemic and the economic crisis in the rest of Europe. In this regard, we investigate potential spillovers from the Italian pandemic outbreak on financial markets across the euro area. For the sake of clarity, it is noteworthy that the notion of spillovers that is used hereafter refers to the effect of new COVID cases reported in Italy on sovereign spreads and stock indices in the other countries of our sample. This definition differs from the one employed in the literature on financial markets’ interdependence following Diebold and Yilmaz (2009), which measures spillovers from one financial market to others.28 To examine this issue, we adapt our empirical framework as follows. Instead of using panel data regressions defined by equation (2), we estimate country by country29 the following series of regressions at each horizon h: (7) Δsi,t+h=αi,h+Dt,t¯[βa,h,iΔxi,t+βa,h,iITΔxIT,t+Γa,h,i(L)si,t−1+Θa,h,i(L)zi,t] +(1−Dt,t¯)[βb,h,iΔxi,t+βb,h,iITΔxIT,t+Γb,h,i(L)si,t−1+Θb,h,i(L)zi,t]+εi,t+h with the notation described in Section 3. There are a couple of differences with respect to equation (2). First, we consider the occurrence of new COVID cases per 100,000 people as explanatory variables both in country i (Δxi,t) and in Italy (ΔxIT,t) simultaneously, β•,h,iIT being the response of sovereign spreads in country i to new COVID cases in Italy. Second, all other estimated coefficients β•,h,i, Γ•,h,i, and Θ•,h,i are also now specific to country i. Third, there are no longer time fixed effects, and αi,h denotes an intercept. Fourth, we drop Italy from the sample of countries. The aim of this estimation is to compare the distribution of βb,h,i, that is, the sensitivity of sovereign spreads to domestic COVID cases, with βb,h,iIT, that is, the sensitivity of sovereign spreads to COVID cases in Italy, before the reference date t¯. A high value of βb,h,iIT would suggest strong spillovers from the pandemic outbreak in Italy to other European countries. Fig. 10 shows the distribution of the estimated coefficients (the median and the interquartile range) before t¯ using March 9 as the reference date. Our main results are as follows. First, it can be seen that the median of the coefficients βb,h,i estimated using the country-by-country regressions defined by equation (7) is not too far from the average estimate βb,h using panel regressions. Interestingly, even if the interquartile range is quite large, it does not include the zero value, which reinforces the robustness of our main results described in Section 4. Second, the median of the coefficients βb,h,iIT is much lower than βb,h,i at all horizons h, and the interquartile range of βb,h,iIT includes the zero value at horizon h≤2. We thus conclude that national sovereign spreads are much more sensitive to the COVID cases that occur domestically than to those in Italy. Considering that the health crisis in Italy preceded those in other European countries, we conclude that the spillover effects of the Italian crisis were fairly weak and did not lead to significant anticipation in other European sovereign debt markets.Fig. 10 Impulse responses of 10-year government bond spreads to new COVID-19 cases in the euro area. Note: Distribution of βb,h,i and βb,h,iIT for COVID cases in Italy (IT). Impulse responses are computed following equation (7) before the split date (March 9). We replicate this country-by-country analysis for the stock markets by estimating the following regressions: (8) Δzi,t+h=αi,h+Dt,t¯[βa,h,iΔxi,t+βa,h,iITΔxIT,t+Γa,h,i(L)si,t−1+Θa,h,i(L)zi,t] +(1−Dt,t¯)[βb,h,iΔxi,t++βb,h,iITΔxIT,t+Γb,h,i(L)si,t−1+Θb,h,i(L)zi,t]+εi,t+h where the dependent variable Δzi,t+h is the variation of the log of the stock index (i.e., the cumulative logarithmic return) at horizon h and the notation used is that described in Sections 3, 6.1. Fig. 11 reports the results. They confirm the robustness of our conclusions based on panel data regressions and show weak spillover effects from new COVID cases reported in Italy to other European stock markets. Fig. 11 Impulse responses of the stock market (in logs) to new COVID-19 cases in the euro area. Note: Distribution of βb,h,i and βb,h,iIT for COVID cases in Italy (IT). Impulse responses are computed following equation (8) before the split date (March 9). 6.3 What would have happened without the structural breaks? This section proposes a counterfactual analysis. We simulate the path of the spread between March 9 and March 18 given the number of cases reported during this period using the estimated coefficient βb,h for t¯=3∕9 depicted in Fig. 8.30 We interpret this path as the spread induced by the COVID crisis that would have occurred without the break in the relationship between the pandemic outbreak and sovereign risk that we attribute to policy interventions during this period. New cases Δxi,t in country i at time t induce a spread variation for the h period ahead denoted Δsi,t+hx that is defined as follows: (9) Δsi,t+hx=βb,hΔxi,t for h=0,1,.,H. The COVID-induced spread deviation as of time t is then the sum of the values of new cases reported H periods before weighted by the coefficient βb,h: (10) Δsi,tx=∑h=0Hβb,hΔxi,t−h By definition, the spread at K periods ahead is equal to the initial value of the spread plus the cumulative sum of spread variations. Then, the spread induced by the COVID crisis is given by: (11) si,t+Kx=si,t−1+∑h=0H∑k=hKβb,hΔxi,t+k−h where K=0 on March 9. Also, we assume that new cases reported up to March 9 have no impact on the predicted spreads series. Fig. 12 shows the evolution of si,t+Kx between March 9 and March 18 for Italy, Spain, and France. On March 6, the Italian government bond spread, denoted by si,t−1 in equation (11), was at 1.807%, and the number of total confirmed cases per million people rose from 121.978 to 521.089 between March 9 and March 18 in Italy. Given the value of βb,h estimated before March 9, the spread induced by the COVID crisis in Italy would have surged during this week to reach 19.5% on March 18. We can then conclude that without any change in the effect of new COVID cases on sovereign yields in Italy, a sovereign debt crisis may have occurred in the middle of March. The pattern for Spain and France would have been less dramatic but still dangerous with spreads of approximately 13% and 4%, respectively. Hence, this counterfactual analysis shows that the earlier policy intervention of the ECB on March 12 seriously restrained the spread of pandemic-induced crisis to sovereign debt markets.Fig. 12 Counterfactual sovereign spreads. Note: Counterfactual sovereign bond spreads (in %) are computed following equation (11). The historical series are presented in Online Appendix B. The red shaded area represents the 95% confidence interval. 7 Conclusion The COVID-19 health crisis has revived fears of a sovereign debt crisis in Europe. The results presented in this paper indicate that the first confirmed COVID-19 cases were at the origin of an explosive increase in interest rate spreads on sovereign debt. The results also show that this explosive dynamic broke around the time of the ECB’s intervention on March 12 and that otherwise, there could have been a sudden surge in rates in the countries most affected by COVID-19 (Italy, Spain, and France), reaching spread values close to those observed during the 2010–2012 sovereign debt crisis in Europe within just a few days. This conclusion rests on the study of sovereign debt markets during the first few months of the sanitary crisis and is corroborated by the extension of our analysis to stock markets. The duration of this health crisis is still uncertain given the state of medical knowledge. However, its economic consequences for public finances will certainly be longer lasting and raise additional challenges for public decision-makers in Europe and around the world in managing the public debt induced by the COVID crisis. Appendix A Supplementary data The following is the Supplementary material related to this article. MMC S1 . ☆ We thank the editor Florin Bilbiie and two anonymous referees for helpful advices, as well as participants at CRIEF, EPEE, LEDa, LEM, RITM seminars, ADRES 2021, and GSE Barcelona Forum for helpful comments. All remaining errors are ours. This research received financial support from the 10.13039/501100001665 French National Research Agency , under the grant DEMUR (ANR-20-CE26-0013), and is part of the E3 Project of the University Paris-Saclay. 1 The WHO offers regular rolling updates on the coronavirus disease. See also its daily situation reports. 2 The Bloomberg article “Christine Lagarde Does Whatever It Doesn’t Take” illustrates the reaction in the press and social media to Christine Lagarde’s press conference. 3 Christine Lagarde walked back this spreads comment by stating in a CNBC interview after the press conference, “I am fully committed to avoid any fragmentation in a difficult moment for the euro area. High spreads due to the coronavirus impair the transmission of monetary policy. We will use the flexibility embedded in the asset purchase programme, including within the public sector purchase programme. The package approved today can be used flexibly to avoid dislocations in bond markets, and we are ready to use the necessary determination and strength”. 4 Our daily data do not allow us to identify the specific effects of each event, and our conclusions should be interpreted as the global effect of all March 12 announcements. Further work should be carried out in the future using intradaily data to dissociate the effects of the different announcements on the markets, taking into account the television interview of Ms. Lagarde on CNBC. 5 We are conscious of the above-described dramatic consequences of the March 12 statement on that day, in particular for Italian financial markets. We consider that despite this crash, this ECB intervention could have stopped the transmission of the pandemic outbreak to sovereign spreads and stock indices. 6 The ECB was not the only European institution involved in the management of the crisis. However, as explained in Section 2, its interventions were earlier than those of other bodies such as the European Commission and the European Council. 7 SIR models are widely used in epidemiology and consist of studying the transmission of infectious diseases through a population (SIR stands for three population categories: S = number of susceptible, I = number of infectious and R = number of recovered–or deceased–individuals). 8 Asset purchase and especially bond-buying programs have directly contributed to lowering bond spreads within the euro area, as discussed by Falagiarda and Reitz (2015), Kilponen et al. (2015), Szczerbowicz (2015), Eser and Schwaab (2016), Fratzscher et al. (2016), Gibson et al. (2016), Ghysels et al. (2017), Jäger and Grigoriadis (2017), De Pooter et al. (2018), Krishnamurthy et al. (2018), and Pacicco et al. (2019). Casiraghi et al. (2016) focus on the impact of the ECB’s unconventional monetary policy on Italian government bond yields, Trebesch and Zettelmeyer (2018) emphasize the Greek case, and Lhuissier and Nguyen (2021) uses an external instrument to estimate the impact of ECB’s APP on intra-euro area sovereign spreads. 9 See the series of papers using local projections to assess the impact of credit expansion on business cycle fluctuations (Jordà et al., 2013), equity and housing price bubbles on financial crisis risks (Jordà et al., 2015; Jordà et al., 2016), austerity on macroeconomic performance (Jordà and Taylor, 2016), and monetary interventions on exchange rates and capital flows (Jordà et al., 2020). Recently, local projections have been introduced for micro data as an alternative to vector autoregressive (VAR) models to avoid any distortion in impulse responses in nonlinear frameworks (see Favara and Imbs, 2015; Crouzet and Mehrotra, 2020 and Cezar et al., 2020). 10 Similarly, local projection methods have been applied in other monetary analyses to investigate the yield impact of unconventional monetary policy (Swanson, 2021) or uncertainty (Castelnuovo, 2019; Tillmann, 2020). 11 The Reuters identification codes (RICs) used to construct the dataset are listed in Online Appendix A. 12 The complete COVID-19 dataset is updated daily by “Our World in Data” and is available in a CSV file on the OWID webpage. We downloaded the dataset on May 30, 2020, and do not consider updated versions since we are interested in the market reaction to the numbers of cases and deaths publicly available in real time during the pandemic outbreak and not in the revised data reported afterwards. We have checked with the ECDC Epidemic Intelligence team and the Head of Data of OWID that no major data retro-correction has been recorded from January to May 2020 to make sure that our results are not affected by any COVID data revision. 13 For additional information, see the ECDC timeline and WHO timeline. 14 https://www.ecb.europa.eu/press/pr/html/index.en.html. 15 Simultaneously, the ECB stated on March 18, “ The Governing Council was unanimous in its analysis that in addition to the measures it decided on 12 March 2020, the ECB will continue to monitor closely the consequences for the economy of the spreading coronavirus and that the ECB stands ready to adjust all of its measures, as appropriate, should this be needed to safeguard liquidity conditions in the banking system and to ensure the smooth transmission of its monetary policy in all jurisdictions”. 16 The ECB publishes a bimonthly breakdown of public sector securities under the PEPP. 17 There was also an MRO of €1.4699 billion for 7 days this day; see the calendar of open market operations. 18 This was mainly due to the change in the net position of the Eurosystem in foreign currency, as explained by the ECB ( link). 19 See the Q&A on PEPP. March 24 is the date of the publication of the ECB decision. In June 2020, monthly net purchases under the PEPP reached a maximum with an amount of €120,321 million, in comparison with €15,444 million in March 2020. 20 See the ‘‘Timeline of EU action’’. 21 Four priorities were identified at the end of the meeting: limiting the spread of the virus, providing medical equipment, promoting research (including vaccine research), and dealing with the socioeconomic consequences. For more details, see the dedicated meeting webpage. 22 Moreover, Herbst and Johannsen (2020) also suggest using large sample sizes to avoid bias in impulse responses estimated by local projections. Our setup is in line with this recommendation since the size of our subsample exceeds 500 observations. 23 Indeed, as indicated in Figure C.1 ( Online Appendix), the time fixed effects ηt,h are significantly negative around key ECB intervention dates, namely, on March 12 and March 18. 24 This point is discussed in detail in Section 6.1. Note also that the meeting held on March 12 was scheduled, which was not the case for the meeting of March 18 (see ECB’s March 2020 calendar). 25 March 9 is chosen as the break date in all the regression tables to allow for comparison with our baseline results. Figures depicting the impulse response functions and Chow test tables are available upon request. 26 Davis et al. (2021) show that the stock market foreshadows workplace mobility. 27 Lucca and Moench (2015) and Cieslak et al. (2019) show that asset prices could be affected by central banks outside of the public communication events. Interestingly, and as a possible explanation of our main results, the former paper documents high stock excess returns in anticipation of monetary policy decisions made at scheduled meetings of the Federal Open Market Committee (FOMC) in the U.S. 28 Bostanci and Yilmaz (2020) recently applied this methodology to sovereign debt markets. 29 Canova (2020) discusses the reliability of cross-sectional estimates and shows how their results could be biased due to heterogeneity. 30 The regression results are reported in Online Appendix C. Appendix A Supplementary material related to this article can be found online at https://doi.org/10.1016/j.euroecorev.2021.103809. ==== Refs References Afonso A. Arghyrou M.G. Bagdatoglou G. Kontonikas A. On the time-varying relationship between EMU sovereign spreads and their determinants Econ. Model. 44 2015 363 371 Afonso A. Arghyrou M.G. Gadea M.D. Kontonikas A. “Whatever it takes” to resolve the European sovereign debt crisis? Bond pricing regime switches and monetary policy effects J. Int. Money Finance 86 2018 1 30 Afonso A. Arghyrou M.G. Kontonikas A. The Determinants of Sovereign Bond Yield Spreads in the EMU: European Central Bank Working Paper Series No 1781 2015 European Central Bank Afonso A. Jalles J.T. Quantitative easing and sovereign yield spreads: Euro-area time-varying evidence J. Int. Financ. Mark. Inst. Money 58 2019 208 224 Aizenman J. Hutchison M. Jinjarak Y. What is the risk of European sovereign debt defaults? Fiscal space, CDS spreads and market pricing of risk J. Int. Money Finance 34 2013 37 59 Andries A.M. Ongena S. Sprincean N. The COVID-19 Pandemic and Sovereign Bond Risk: Swiss Finance Institute Research Paper 20–42 2020 Arellano C. Bai Y. Mihalache G.P. Deadly Debt Crises: COVID-19 in Emerging Markets: Working Paper Series 27275 2020 National Bureau of Economic Research Atkeson A. What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios: Working Paper Series 26867 2020 National Bureau of Economic Research Auerbach A.J. Gorodnichenko Y. Fiscal multipliers in recession and expansion Fiscal Policy After the Financial Crisis 2013 University of Chicago Press 63 Augustin P. Sokolovski V. Subrahmanyam M.G. Tomio D. In sickness and in debt: The COVID-19 impact on sovereign credit risk 2020 Available At SSRN 3613432 Barro R.J. Ursúa J.F. Weng J. The Coronavirus and the Great Influenza Pandemic: Lessons from the “Spanish Flu” for the Coronavirus’s Potential Effects on Mortality and Economic Activity Working Paper Series 26866 2020 National Bureau of Economic Research Born B. Müller G.J. Pfeifer J. Does austerity pay off? Rev. Econ. Stat. 2019 1 45 Bostanci G. Yilmaz K. How connected is the global sovereign credit risk network? J. Bank. Financ. 113 2020 105761 Canova F. Should We Trust Cross Sectional Multiplier Estimates?: CEPR Working Paper 15330 2020 Casiraghi M. Gaiotti E. Rodano L. Secchi A. ECB unconventional monetary policy and the Italian economy during the sovereign debt crisis Int. J. Central Bank. 12 2 2016 269 315 Castelnuovo E. Yield curve and financial uncertainty: Evidence based on US data Aust. Econ. Rev. 52 3 2019 323 335 Cavallino P. De Fiore F. Central banks’ response to Covid-19 in advanced economies BIS Bull. 21 2020 Cezar R. Gigout T. Tripier F. Cross-border investments and uncertainty: Firm-level evidence J. Int. Money Finance 2020 102159 Chow G.C. Tests of equality between sets of coefficients in two linear regressions Econometrica 1960 591 605 Cieslak A. Morse A. Vissing-Jorgensen A. Stock returns over the FOMC cycle J. Finance 74 5 2019 2201 2248 Cloyne J.S. Jordà Ò. Taylor A.M. Decomposing the Fiscal Multiplier: Working Paper Series 26939 2020 National Bureau of Economic Research Costantini M. Fragetta M. Melina G. Determinants of sovereign bond yield spreads in the EMU: An optimal currency area perspective Eur. Econ. Rev. 70 2014 337 349 Couppey-Soubeyran J. Perego E. Tripier F. European Banks and the Covid-19 Crash Test: Technical Report 2020 CEPII Policy Brief 32 Cox J. Greenwald D.L. Ludvigson S.C. What Explains the COVID-19 Stock Market?: Working Paper Series 27784 2020 National Bureau of Economic Research Crouzet N. Mehrotra N.R. Small and large firms over the business cycle Amer. Econ. Rev. 110 11 2020 3549 3601 Davis S.J. Liu D. Sheng X.S. Stock Prices, Lockdowns, and Economic Activity in the Time of Coronavirus: Working Paper Series 28320 2021 National Bureau of Economic Research De Pooter M. Martin R.F. Pruitt S. The liquidity effects of official bond market intervention J. Financ. Quant. Anal. 53 1 2018 243 268 Delatte A.-L. Fouquau J. Portes R. Regime-dependent sovereign risk pricing during the euro crisis Rev. Finance 21 1 2017 363 385 Delatte A.-L. Guillaume A. Covid 19: A New Challenge for the EMU: CEPR Discussion Paper DP14848 2020 Diebold F.X. Yilmaz K. Measuring financial asset return and volatility spillovers, with application to global equity markets Econ. J. 119 534 2009 158 171 ECB F.X. The COVID-19 crisis and its implications for fiscal policies 2020 ECB Monthly Bulletin June ECB F.X. Overview 2020 ECB Financial Stability Review, November Eichenbaum M.S. Rebelo S. Trabandt M. The Macroeconomics of Epidemics: Working Paper Series 26882 2020 National Bureau of Economic Research Eser F. Schwaab B. Evaluating the impact of unconventional monetary policy measures: Empirical evidence from the ECB’s Securities Markets Programme J. Financ. Econ. 119 1 2016 147 167 Falagiarda M. Reitz S. Announcements of ECB unconventional programs: Implications for the sovereign spreads of stressed euro area countries J. Int. Money Finance 53 2015 276 295 Favara G. Imbs J. Credit supply and the price of housing Amer. Econ. Rev. 105 3 2015 958 992 Favero C. Missale A. Sovereign spreads in the eurozone: which prospects for a Eurobond? Econ. Policy 27 70 2012 231 273 Fratzscher M. Duca M.L. Straub R. ECB unconventional monetary policy: Market impact and international spillovers IMF Econ. Rev. 64 1 2016 36 74 Georgoutsos D.A. Migiakis P.M. Heterogeneity of the determinants of euro-area sovereign bond spreads; what does it tell us about financial stability? J. Bank. Financ. 37 11 2013 4650 4664 Ghysels E. Idier J. Manganelli S. Vergote O. A high-frequency assessment of the ECB Securities Markets Programme J. Eur. Econom. Assoc. 15 1 2017 218 243 Gibson H.D. Hall S.G. Tavlas G.S. The effectiveness of the ECB’s asset purchase programs of 2009 to 2012 J. Macroeconomics 47 2016 45 57 Guerrieri V. Lorenzoni G. Straub L. Werning I. Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?: Working Paper Series 2020 National Bureau of Economic Research Herbst E.P. Johannsen B.K. Bias in local projections 2020 Finance and Economics Discussion Series 2020–010 Jäger J. Grigoriadis T. The effectiveness of the ECB’s unconventional monetary policy: Comparative evidence from crisis and non-crisis Euro-area countries J. Int. Money Finance 78 2017 21 43 Jordà Ò. Estimation and inference of impulse responses by local projections Amer. Econ. Rev. 95 1 2005 161 182 Jordà Ò. Schularick M. Taylor A.M. When credit bites back J. Money Credit Bank. 45 s2 2013 3 28 Jordà Ò. Schularick M. Taylor A.M. Leveraged bubbles J. Monetary Econ. 76 2015 S1 S20 Jordà Ò. Schularick M. Taylor A.M. The great mortgaging: housing finance, crises and business cycles Econ. Policy 31 85 2016 107 152 Jordà Ò. Schularick M. Taylor A.M. The effects of quasi-random monetary experiments J. Monetary Econ. 112 2020 22 40 Jordà Ò. Singh S.R. Taylor A.M. Longer-run economic consequences of pandemics Rev. Econ. Stat. 2020 1 29 Jordà Ò. Taylor A.M. The time for austerity: estimating the average treatment effect of fiscal policy Econ. J. 126 590 2016 219 255 Kilponen J. Laakkonen H. Vilmunen J. Sovereign risk, European crisis resolution policies and bond yields Int. J. Central Bank. 11 2 2015 285 323 Klose J. Tillmann P. Covid-19 and financial markets: A panel analysis for european countries Jahrb. Natl. Stat. 241 3 2021 297 347 Krishnamurthy A. Nagel S. Vissing-Jorgensen A. ECB policies involving government bond purchases: Impact and channels Rev. Finance 22 1 2018 1 44 Lhuissier S. Nguyen B. The Dynamic Effects of the ECB’s Asset Purchases: a Survey-Based Identification: Technical Report 2021 Banque de France WP 806 Lucca D.O. Moench E. The pre-FOMC announcement drift J. Finance 70 1 2015 329 371 Manganelli S. Wolswijk G. What drives spreads in the euro area government bond market? Econ. Policy 24 58 2009 191 240 Pacicco F. Vena L. Venegoni A. Market reactions to ECB policy innovations: A cross-country analysis J. Int. Money Finance 91 2019 126 137 Ramey V.A. Zubairy S. Government spending multipliers in good times and in bad: evidence from US historical data J. Polit. Econ. 126 2 2018 850 901 Schularick M. Steffen S. A Protective Shield for Europe’s Banks: Technical Report 2020 mimeo Swanson E.T. Measuring the effects of federal reserve forward guidance and asset purchases on financial markets J. Monetary Econ. 118 2021 32 53 Szczerbowicz U. The ECB unconventional monetary policies: have they lowered market borrowing costs for banks and governments? Int. J. Central Bank. 11 4 2015 91 127 Tenreyro S. Thwaites G. Pushing on a string: US monetary policy is less powerful in recessions Am. Econ. J.: Macroecon. 8 4 2016 43 74 Tillmann P. Monetary policy uncertainty and the response of the yield curve to policy shocks J. Money Credit Bank. 52 4 2020 803 833 Trebesch C. Zettelmeyer J. ECB interventions in distressed sovereign debt markets: The case of Greek bonds IMF Econ. Rev. 66 2 2018 287 332

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==== Front Lancet Reg Health Am Lancet Reg Health Am Lancet Regional Health. Americas 2667-193X The Authors. Published by Elsevier Ltd. S2667-193X(22)00226-5 10.1016/j.lana.2022.100409 100409 Articles Impact of healthcare capacity disparities on the COVID-19 vaccination coverage in the United States: A cross-sectional study Cuadros Diego F. ab∗ Gutierrez Juan D. c Moreno Claudia M. d Escobar Santiago ab Miller F. DeWolfe e Musuka Godfrey f Omori Ryosuke g Coule Phillip h MacKinnon Neil J. i a Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA b Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH, USA c Universidad de Santander, Facultad de Ingeniería, Grupo Ambiental de Investigación Aplicada-GAIA, Bucaramanga, Colombia d Department of Physiology and Biophysics, University of Washington School of Medicine, Seattle, WA, USA e Department of Tropical Medicine and Medical Microbiology and Pharmacology, University of Hawaii, Honolulu, HI, USA f International Initiative for Impact Evaluation, Harare, Zimbabwe g Division of Bioinformatics, International Institute for Zoonosis Control, Hokkaido University, Sapporo, Japan h Department of Emergency Medicine, Medical College of Georgia, Augusta University, Augusta, GA, USA i Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, USA ∗ Corresponding author. Digital Epidemiology Laboratory, University of Cincinnati, Cincinnati, OH 45221, USA. 14 12 2022 2 2023 14 12 2022 18 100409100409 24 6 2022 16 11 2022 21 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The impact of the COVID-19 vaccination campaign in the US has been hampered by a substantial geographical heterogeneity of the vaccination coverage. Several studies have proposed vaccination hesitancy as a key driver of the vaccination uptake disparities. However, the impact of other important structural determinants such as local disparities in healthcare capacity is virtually unknown. Methods In this cross-sectional study, we conducted causal inference and geospatial analyses to assess the impact of healthcare capacity on the vaccination coverage disparity in the US. We evaluated the causal relationship between the healthcare system capacity of 2417 US counties and their COVID-19 vaccination rate. We also conducted geospatial analyses using spatial scan statistics to identify areas with low vaccination rates. Findings We found a causal effect of the constraints in the healthcare capacity of a county and its low-vaccination uptake. Counties with higher constraints in their healthcare capacity were more probable to have COVID-19 vaccination rates ≤50, with 35% higher constraints in low-vaccinated areas (vaccination rates ≤ 50) compared to high-vaccinated areas (vaccination rates > 50). We also found that COVID-19 vaccination in the US exhibits a distinct spatial structure with defined “vaccination coldspots”. Interpretation We found that the healthcare capacity of a county is an important determinant of low vaccine uptake. Our study highlights that even in high-income nations, internal disparities in healthcare capacity play an important role in the health outcomes of the nation. Therefore, strengthening the funding and infrastructure of the healthcare system, particularly in rural underserved areas, should be intensified to help vulnerable communities. Funding None. Keywords COVID-19 Vaccine uptake Healthcare capacity Healthcare underserved communities United States ==== Body pmc Research in context Evidence before this study We searched PubMed and Web of Science for publications on COVID-19 vaccination in the US, published between May 1, 2021, and March 1, 2022. We used the keywords “COVID-19”, “determinants of COVID-19 vaccine uptake”, “COVID-19 vaccination campaign in the US”, and “COVID-19 vaccine disparities” and searched for articles in English. We found that most studies focused on assessing the impact of vaccine hesitancy and other social and behavioral determinants of vaccine uptake in the US. However, little is known about the impact of structural determinants such as the local healthcare capacity in the COVID-19 vaccination coverage in the country. Added value of this study To our knowledge, this is the first study that examines the impact of the healthcare capacity as a determinant of the COVID-19 vaccination coverage disparities in the US. Using COVID-19 vaccine data from 2417 US counties, we assessed the association between healthcare capacity and the vaccination coverage at the county level using causal inference and geospatial analyses. Implications of all the available evidence Although vaccination hesitancy has played an important role in driving the disparities in vaccination uptake in the US, our results suggest that healthcare system capacity plays an important and overlooked role as well. We found a positive association between the deficient healthcare capacity and the low vaccination uptake at the US county level. We also found that COVID-19 vaccination in the US exhibits a distinct spatial structure with defined clustered areas of population with a low percentage of vaccination. The COVID-19 pandemic has uncovered the impact of healthcare disparities in the country, and it has exposed the weakness in rural healthcare. In high-income nations like the US, disparities in access to healthcare and healthcare capacity are internal determinants that play a key role in the health outcomes of the whole country. Therefore, it is imperative that federal, state, and county decision-makers consider the importance of strengthening the healthcare structure in these vulnerable low-vaccinated areas to increase vaccination uptake and relieve the burden that the pandemic has brought to these vulnerable communities. Introduction After more than 2 years into the pandemic, as of September 12, 2022, COVID-19 has caused 6,515,039 deaths worldwide, and the US has reported 1,050,426 of these deaths.1 Among high-income nations, the US has one of the highest COVID-19 mortality rates. One of the potential reasons for this is that the US has failed to achieve vaccination levels similar to those in other developed countries. As of September 2022, only 68% of the US population has been fully vaccinated against COVID-19, and this value is low compared to several high-income nations.1 Although this percentage is very close to the 70% goal that the US government established, if vaccination coverage is examined at the state level, the differences are striking. Vaccination coverage in the US is geographically heterogeneous. While some areas of the US have achieved full vaccination in more than 80% of their population, other regions still lag behind with rates below 50%.2 A successful long-term management of the pandemic can only be achieved if vaccination uptake is substantially increased to diminish this spatial heterogeneity. However, it is necessary first to understand the factors driving the disparities in vaccination coverage and uptake in the country. Vaccination hesitancy has been broadly discussed as a key driver of the low vaccination uptake, especially in the US.3 However, COVID-19 hesitancy in the country has been estimated to be around 20%, a percentage far below the actual percentage of unvaccinated people,4 suggesting that additional unidentified key factors are behind the low vaccination rates observed in some areas of the US. It is known that the pandemic has disproportionally affected Americans living in socially vulnerable areas.5 , 6 In fact, areas with low vaccination in the US experienced the highest mortality rates during the recent Delta and Omicron waves.7 Social vulnerability arises from a combination of socio-economic factors that include limited healthcare resources and barriers to accessing these resources.8 The impact of poor healthcare capacity on the vaccination coverage is a factor that has been proposed to play a major role in low-income countries, but not in high-income ones as the US.9 However, the COVID-19 pandemic has shown that the scenario is much more complex. High-income countries with better healthcare resources have had, in fact, a higher burden of COVID-19 cases, related hospitalisations and deaths than low-income countries with fewer healthcare resources.10 Although the US, as a nation, ranks number one in the Global Health Security Index that measures the capacity of a country to prepare for epidemics and pandemics (https://www.ghsindex.org/country/united-states/),11 , 12 at the local level the landscape is different. The healthcare system in the US is characterised by substantial variation in local infrastructure and capacity, with many underserved communities lacking adequate access to healthcare.5 , 13 , 14 These disparities include the number of healthcare workers and number of hospitals per capita, health insurance coverage, and healthcare funding, which have influenced the spatial structure of several health problems in the US, including chronic and mental health diseases.14 , 15 However, how much these healthcare capacity disparities have affected the management of the COVID-19 pandemic is unknown. In this study, we conducted causal inference and geospatial analyses to assess the impact of the local healthcare capacity on vaccination coverage disparities in the US at the county level. Understanding the impact of healthcare capacity on vaccination coverage disparities will help refine local strategies to increase vaccination coverage in areas with the highest health needs. Methods Variables and data sources Institutional review board approval and informed consent were not necessary for this cross-sectional study because all data were deidentified and publicly available (Common Rule 45 CFR §46). This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. We used a causal inference analysis to evaluate the causal relationship between a treatment and an outcome, with the treatment defined as the healthcare system capacity at the county level measured by the Resource-Constrained Health System (RCHS) index, and the outcome defined as a COVID-19 vaccination rate less than or equal to 50% of the county's population. The RCHS index is a measure that integrates indicators of low healthcare system capacity with indicators of healthcare system weakness at the county level. These indicators include healthcare workforce per capita, healthcare infrastructure per capita, healthcare spending per capita, and care quality indicators (Table 1 ). A high RCHS index value indicates a weak healthcare system capacity, whereas a low value indicates a strong healthcare system of the county. The RCHS index is one of the five measures comprising the recently released Surgo COVID Vaccine Uptake Index (CVAC; https://vaccine.precisionforcovid.org).16 Vaccine coverage was measured as the proportion of the fully vaccinated population per US county, defined as the percentage of people who have received two doses of the mRNA Pfizer-BioNTech or Moderna vaccines, or a single dose of the Janssen/Johnson & Johnson vaccine. Data for cumulative full vaccination rates in the total population at a county level were obtained from the Centers for Disease Control and Prevention (CDC) COVID data tracker for the contiguous US (https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&data-type=CommunityLevels).17 We excluded the states of Colorado, Georgia, Texas, Virginia, and West Virginia due to incomplete or unreliable vaccination data. As a result, data from 2417 counties (77% out of the 3143 in the continental US) were included in the analysis. Counties were classified as rural or urban based on the 2013 National Center for Health Statistics.18 , 19 Cumulative vaccination rates were estimated as of March 31, 2022. For the causal inference analysis, counties were aggregated into low (vaccination rate county median ≤ 50%) and high (>50%) vaccination coverage groups. Based on the literature review and on the US COVID-19 Vaccine Coverage Index, we incorporated the Social Vulnerability Index (SVI; https://www.atsdr.cdc.gov/placeandhealth/svi/index.htm)20 and the Healthcare Access Barriers Index (HABI; https://vaccine.precisionforcovid.org/)16 as common causes that influence the treatment and outcome in the causal analysis. Vaccine hesitancy data from the CDC at the county level (https://data.cdc.gov/stories/s/Vaccine-Hesitancy-for-COVID-19/cnd2-a6zw/).21 was included as a modifier of the outcome. A detailed description of the indexes and data sources used for this study is presented in Table 1. Vaccination level area comparisons relative to healthcare capacity were conducted using the average number of medical doctors per 1000 people (https://www.ruralhealthinfo.org/data-explorer?id=197),22 the average number of intensive care unit (ICU) beds per 1000 people (https://www.kaggle.com/datasets/jaimeblasco/icu-beds-by-county-in-the-us),23 and the other health-related indices including RCHS, SVI, and HABI. Statistical comparisons between the two vaccination level groups for these variables were conducted using generalized linear mixed-effects models with the state as a random variable to adjust for the variability merging at the state level generated by factors such as healthcare policies and spending.Table 1 Summary of the variables included in the analysis. Variable Description Source Vaccination rate Cumulative full vaccination rates in the total population at a county level CDC COVID data tracker for the contiguous US https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&data-type=CommunityLevels Resource-Constrained Health System Index (RCHS) This index is composed by two subthemes using the following indicators:1) Low Healthcare System Capacity- Provider workforce per capita (Total active federal and non-federal Medical Doctors, Doctors of Osteopathy, Advanced Practice Registered Nurses, Physician Assistants, and Pharmacists) - Infrastructure for vaccine administration per capita (Hospitals, Urgent Care Facilities, Veterans Health Administration Medical Facilities, Federally Qualified Health Centers and look-alike, Pharmacies) 2) Weak Healthcare System- AHRQ Prevention quality indicator - Health spending per capita - Total healthcare funding (CDC COVID Funding, Public Health Emergency Preparedness (PHEP) funding, CDC grant funding for Immunization and respiratory Diseases and Vaccines for children, State Public Health Funding) Surgo Ventures - The US COVID-19 vaccine coverage index Original source for each indicator can be found at https://cvi-data-output.s3.amazonaws.com/assets/CVAC_Methodology_Feb2021.pdf Healthcare Accessibility Barriers Index (HABI) This index is composed by two subthemes using the following indicators:1) Barriers due to Cost- Proportion of individuals without health insurance coverage - Proportion of adults who reported that there was a time in the past 12 months when they needed to see a doctor but could not because of cost. 2) Barriers due to Transportation- Households without a vehicle - Transit Connectivity Index Social Vulnerability Index (SVI) SVI indicates the relative vulnerability of every county ranking 15 social factors, including high poverty, unemployment, education, crowded housing, minority status, and disability https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2018Documentation_01192022_1.pdf Vaccine Hesitancy Percentage of the population in each county that may be vaccine hesitant or unsure about vaccination Assistant Secretary for Planning and Evaluation (ASPE), U.S. Census Bureau's Household Pulse Survey (HPS) https://data.cdc.gov/stories/s/Vaccine-Hesitancy-for-COVID-19/cnd2-a6zw/ Causal inference analysis Randomised controlled trials offer the most plausible unbiased estimates of the effect of a given treatment on a specific health outcome.24 However, epidemiological experiments of that type are not possible due to ethical, feasibility, or time limitations.25 Causal inference approaches using epidemiological observational data are perhaps the best alternative to estimate the causal association between a treatment and a health outcome in an epidemiological context.26 , 27 In this study, we implemented a causal inference approach to assess, in an unbiased manner, the average effect of the constraints in the healthcare system capacity of a US county, measured by the RCHS index, over the county's COVID-19 vaccination rate. We designed a Directed Acyclic Graph (DAG), as illustrated in Fig. 1 . This non-parametric graphical model visualization represents the assumed causal relationships between the established variables of interest. We included in the DAG the SVI and the HABI as two important confounders that could have a causal effect on both, the treatment, the RCHS index, and the outcome, low vaccination rate (≤50%) estimated as of March 31, 2022. Another variable included in the DAG was vaccination hesitancy, which was classified as a modifier affected by the RCHS index and affecting the vaccination outcome. An unobserved variable, U, was also included in the causal analysis to account for the unmeasured variables that can be potential confounders. We implemented a correlation analysis to test the conditional independences assumed in the DAG in the dataset, and also assuming a linear relationship between the treatment and the outcome since the treatment is a continuous variable and the outcome is a binary variable in our analysis. We conducted the analysis using the package DAGitty of R version 0.3-1.28 No conditional independences were identified in the DAG.Fig. 1 Directed Acyclic Graph (DAG) used to estimate the causal effect of the resource-constrained health system index (RCHSI) on the low-vaccination coverage ≤50% of a county. The orange arrow represents the causal association of interest. Variables considered as common causes that affect the treatment and the outcome simultaneously are shown in blue (healthcare accessibility barriers index (HABI) and social vulnerability index (SVI)). Vaccine Hesitancy was classified as a modifier affected by the RCHS index and affecting the vaccination outcome. The U variable represents unmeasured variables that can be potential confounders. Inset boxes detail the indicators used for calculating each of the indexes. The statistical estimand of the causal analysis, defined as the numerical value of the effect of the RCHS index over the occurrence of a county in the low-vaccination group (vaccination rate ≤ 50%) was tested in a set of four variations of the double machine learning algorithm29 using the Python modules DoWhy version 0.630 and EconML version 0.13.31 We assessed the effect of the RCHS index on the low vaccination coverage (≤50%) at the county level using an Average Treatment Effect (ATE). Additionally, we estimated the effect of the RCHS index, conditioned by Hesitancy, using the Conditional Average Treatment Effect (CATE). A detailed description of the calculations and testing of the estimand can be found in the Supplementary Material. We implemented five sensitivity tests to validate the causal association between the RCHS index and low vaccination rate (≤50%), including the addition of a random common cause, the addition of an unobserved common cause, the replacement of a random subset, running the estimate on a random sample of the data containing measurement error in the confounders (Bootstrap refutation), and adding a placebo treatment. The dataset and the Python and R scripts used for this study are available at: https://github.com/juandavidgutier/healthcare_capacity_disparities-. Geospatial analysis Spatial analyses were conducted to identify and map the geographical locations of areas with low COVID-19 vaccination coverage in the US. The spatial structure of vaccination uptake was analysed using a spatial scan statistical analysis of cumulative vaccination at the county level as of March 31, 2022, implemented in the SaTScan software.32 , 33 This methodology has become the most widely used test for clustering detection in epidemiology,32 and its efficiency and accuracy are well documented.34 , 35 We used scan statistics to identify geographical locations where the number of fully vaccinated individuals was lower than expected under the null hypothesis of a random spatial distribution of the vaccinated individuals across the country.33 Then, we evaluated their statistical significance by gradually scanning a circular window that spans the study region. We analysed vaccination uptake using the SaTScan Poisson model with the size of the population at risk by location (county) included as an offset. Briefly, the identification of coldspots (areas with low vaccination rates) using the Poisson model implemented in SaTScan is achieved by testing each potential cluster against the null hypothesis that the distribution of cases (fully vaccinated individuals was proportional to the population size [no clustering] using likelihood ratio and t-tests).32 An associated p-value of the statistics was then determined through Monte Carlo simulations and used to evaluate whether fully vaccinated individuals are randomly distributed in space. A coldspot was identified if the p-value was less than 0.05. After a cluster was identified, the strength of the clustering was estimated using the relative risk (RR) within the cluster versus outside the cluster. Furthermore, temporal trends of vaccination rates were analysed by aggregating the counties within vaccination coldspots and counties outside the coldspots. Retrospective temporal vaccination rates within and outside the coldspots were estimated for each month from April 2021 to March 2022. All geographic information system (GIS) analyses and cartographic displays were performed with ArcGIS Pro version 2.936 software. Plots were built using GraphPad Prism 9. Role of the funding source No funding to declare. Results As of March 31, 2022, 166,239,504 (63.1%) of 263,365,882 residents living in the counties included in the analyses were fully vaccinated. We estimated that 1160 (48.0%) out of the 2417 counties included in the study had a vaccination rate equal to, or lower than 50%, with 36,074,972 individuals residing in these low-vaccination counties. The average RCHS index was 0.50 (95% confidence interval [CI] 0.48–0.52) in lower-vaccinated and 0.37 (95% CI 0.35–0.38) in higher-vaccinated counties (Table 2 ). Likewise, SVI was 0.53 (95% CI 0.25–0.56) in low-vaccinated and 0.44 (95% CI 0.42–0.46) in high-vaccinated counties. Moreover, the average number of medical doctors per 1000 in low-vaccinated counties was 0.19 (95% CI 0.18–0.20) compared to 0.81 (0.76–0.85) in high-vaccinated ones. Similarly, the average number of ICU beds per 1000 in low-vaccinated counties was 0.12 (0.11–0.14) compared to 0.18 (95% CI 0.17–0.19) ICU beds in high-vaccinated ones.Table 2 Healthcare capacity comparisons between low-vaccination (vaccination rate county median ≤ 50%) and high-vaccination (>50%) areas. Index Low vaccination coverage (95% confidence interval) High vaccination coverage (95% confidence interval) p value Resource-Constrained Health System 0.50 (0.48–0.52) 0.37 (0.35–0.38) <0.001 Healthcare Access Barriers 0.55 (0.53–0.57) 0.39 (0.38–0.41) <0.001 Social Vulnerability 0.53 (0.52–0.56) 0.44 (0.42–0.46) <0.001 Vaccine hesitancy 0.22 (0.21–0.23) 0.17 (0.16–0.18) <0.001 Medical doctors per 1000 people 0.19 (0.18–0.20) 0.81 (0.76–0.85) <0.001 Intensive care unit beds per 1000 people 0.12 (0.11–0.14) 0.18 (0.17–0.19) <0.001 Casual inference analysis We found nonconditional independences between the variables used in the DAG implemented to estimate the effect of the RCHS index on the low vaccination coverage of a county (vaccination rate county median ≤ 50%). The machine learning algorithm with the largest RScorer was double machine learning (RScorer = −0.0025). The average ATE of the RCHS index on having a vaccination rate (≤50%) was 0.37 (95% CI: 0.23–0.50). The RCHS index is a continuous treatment variable ranging from 0 to 1, where 0 indicates no resource-constrained healthcare system. Taking this into account, the ATE needs to be interpreted as a linear effect on the risk scale. Thus, our results indicate that an increase of 0.01 in the RCHS index increases by 0.37% the probability of a county to be included in the low vaccination coverage group (vaccination rate county median ≤ 50%). The estimation of the CATE of the RCHS index on the low vaccination coverage conditioned by Vaccine Hesitancy showed no change in magnitude for different values of hesitancy rate (Supplementary Fig. S1 in Supplementary Materials), indicating that Vaccine Hesitancy does not modify the effect of the RCHS index on the low vaccination of a county. Further results from the causal inference analysis are summarized in Supplementary Materials. Geospatial analysis SatScan identified 38 clusters with low vaccination rates (vaccination coldspots) with an RR ranging from 0.66 to 0.98. These coldspots were distributed across the entire country, comprising 1300 out of the 2417 counties included in the study, with 930 (71.5%) of these counties being rural, compared to 612 (54.8%) of rural counties located outside the vaccination coldspots. As of March 31, 2022, the vaccination rate within the coldspots was 52.1% compared to 68.1% outside these areas. Coldspots with a RR between 0.66 and 0.73 (the lowest RR range), were located in the states of Nevada, Montana, North and South Dakota, and Nebraska, with most of them grouped in the Rocky Mountain region. Vaccination coldspots with an RR between 0.74 and 0.78 were located in Idaho and in several states located in the Gulf Coast and Lower Atlantic regions, including Oklahoma, Arkansas, Mississippi, Alabama, and New Mexico. Coldspots with an RR between 0.79 and 0.83 were in the Midwest and South regions, in the states of Kansas, Indiana, Ohio, Kentucky, Tennessee, and Louisiana (map in Fig. 2 ).Fig. 2 Spatial structure of COVID-19 vaccine coverage in the US. The map on the left illustrates the geospatial location of the COVID-19 vaccine coldspots. The strength of the coldspot (relative risk of being vaccinated) is illustrated in a blue colors scale, with the lowest relative risk for being vaccinated illustrated in dark blue, and the highest relative risk for being vaccinated among the coldspots illustrated in light blue. The plot in the upper right corner illustrates the vaccination time trend within the clusters (blue) and outside the clusters (light grey). The plot in the lower right corner illustrates the monthly increase in the percentage of the vaccinated population within the clusters (blue bars) and outside the clusters (light grey bars) from April 2021 to March 2022. The vaccination rate was 24.9% within the low vaccination clusters, compared to 34.5% outside the coldspots at the early stage of the vaccination rollout campaign in April 2021 (area plot in Fig. 2). A slower rise in the vaccination rates within the coldspots was observed during the months of May and June 2021, with 6.4% and 3.7% increments, compared to 11.5% and 6.0% increments during the same period outside the vaccination coldspots. The percentage of the vaccinated population surpassed 50% in July 2021 in counties outside the vaccination coldspots, while the same rate was reached 6 months later (January 2022) in counties within the coldspots (bar chart in Fig. 2). Discussion Being one of the wealthiest nations in the world, it could be assumed that the capacity of the US healthcare system is not a limiting factor in shaping the national heterogeneous vaccination COVID-19 uptake observed in the US. In this ecological study, we found that that is not the case. Our causal and geographical analyses unveiled a striking association between the disparities in the healthcare system capacity and the disparities in COVID-19 vaccination coverage. After controlling for other factors including vaccine hesitancy, health access barriers, and social vulnerability, we estimated that an increase of 0.01 (1%) in the Resource-Constrained Health System (RCHS) index increases the probability of a county to be in the group of low vaccinated counties (≤50% vaccination rate) by 0.37%. Likewise, low-vaccination areas had an average county RCHS index of 0.5, 35% higher compared to high-vaccination areas (RCHS = 0.37). In other words, our analysis showed that low-vaccination areas in the US were characterised by having a smaller health provider workforce per capita, a smaller healthcare infrastructure per capita, lower preventive care, and lower healthcare funding. Likewise, these low-vaccination areas were also characterized by having a higher average Social Vulnerability Index, and a higher average Healthcare Access Barrier Index, lower numbers of medical doctors and ICU beds per 1000 people compared to high-vaccination ones. Regarding the spatial structure of COVID-19 vaccination, we found that the US exhibits defined clustered low-vaccination areas (coldspots) distributed mainly among 17 states (NV, MT, WY, ND, SD, NE, NM, OK, MS, AL, AR, LA, TN, KY, KS, IN, and OH). Interestingly, all of these 17 states are at the bottom of the ranking for healthcare access, healthcare quality, and public health in the US.37 In addition, 12 of these states fall below the US average poverty rates with 12%–20% of their population living in poverty.38 Adding all these factors, it is not surprising that many of these states have been at the epicenter of the different epidemic waves in the country.5 , 7 At the county level, we found that more than 71% of the counties inside coldspots were rural counties. These counties were mainly located inside geographically distinct regions including the Rocky Mountains, the Gulf Coast, the lower Atlantic region, and the Midwest. Challenges imposed by the local geography of these regions could be an important limiting factor for the deployment of vaccines and for the access of residents to rural clinics.5 Collectively, these findings show the economic and health vulnerability of primarily rural communities residing in the low-vaccinated areas in the US. Rural communities within the states identified in this study may be facing challenges that exacerbate the lower rates of COVID-19 vaccination. These challenges include but might not be limited to, restricted access to testing, vaccine and treatment supplies, and number of healthcare workers.39 With COVID-19 incidence and mortality increasing throughout 2020, the beginning of the immunisation campaign faced unprecedented challenges that went beyond those of standard vaccination programs. Our analysis shows a clear difference between the vaccination rates in those counties that would become vaccination coldspots one year later by the end of 2021. Strikingly, the rate at which vaccination uptake increased within these coldspots was much slower than the rate in the counties outside of these low-vaccinated areas, particularly at the early stage of the vaccination rollout. Whereas the percentage of the vaccinated population outside the vaccination coldspots increased from 31.5% in April 2021 to 43.0% in May and reached more than 50% of the vaccination rate by July 2021, the vaccinated population within the coldspots was only 25% in April 2021, increased to 31.4% in May, and reached more than 50% vaccination rate only by January 2022. The slower vaccination uptake inside the coldspots was evident during the first 3 months of the period analysed. It was relatively similar both outside of, and within the coldspots after July 2021. This suggests that pre-existent barriers in these coldspots counties played, from the beginning, an essential role in limiting the number of people who were vaccinated. Our results showed that counties inside the coldspots face a more resource-constrained health system, suggesting that critical healthcare capacity and infrastructure, and barriers to access to adequate healthcare were essential determinants of vaccination uptake. The influence of these determinants was strongly relevant during the early stages of the vaccination campaigns, a period in which vaccination availability, distribution, and prioritization needed a strong healthcare structure to aversively deliver the maximum number of doses in the shortest time. However, further studies need to be conducted to completely understand these COVID-19 vaccination disparities during the early stage of the vaccination rollout in the US. Our study had limitations worth noting. An ecological study like the one presented here is an approach for examining the association between factors and diseases, performing population analyses in specific areas, and they do not correspond to individual risk and associations. It is difficult to adjust for all potential confounding factors due to the lack of individual data in ecological studies, and thus our results need to be interpreted with caution. Moreover, we recognize that the assumption about the linearity in the relationship between the treatment and the outcome is another limitation of our study. Further research on this topic could implement causal frames of the type of dose–response curve to analyse the treatment and outcome as continuous variables.40 Implementing new developments based on a Gaussian process to estimate the causal effects of a continuous exposure could help to assess the effect of the linearity assumptions. Furthermore, several factors that could play an important role in the vaccination uptake disparities such as religious beliefs and political preferences were not included in our analysis, and while these factors might be measured by vaccine hesitancy, a variable included in our analysis, further analyses might focus on estimating the actual impact of these variables in the vaccination uptake in the country. Additionally, vaccination coverage was estimated using the definition of fully vaccinated individuals, and we did not include data for boosted vaccination. Lastly, data from five states were not included due to incomplete or unreliable vaccination data, and thus our results might not represent the current health structure and vaccination scenario in these states. However, we analysed data from more than 70% of the counties from the entire continental US that provide reliable results to depict the national–level associations discussed in our study. Now that SARS-CoV-2 is projected to become endemic, the control of the surge of potentially dangerous new variants and seasonal epidemic outbreaks depends on the design of effective long-term immunisation programs. COVID-19 vaccines have proven to be the most effective intervention to reduce SARS-CoV-2 transmission, severity, and death. It is key that federal, state, and county decision-makers consider the importance of strengthening the healthcare structure in these vulnerable low-vaccinated areas to increase vaccination uptake and relieve the burden that the pandemic has brought to these vulnerable communities. Healthcare disparities and differential vaccination coverage may continue to influence the pandemic trajectory and delay efforts for epidemic control. In addition, the consequences of long-term COVID-19 will become a new challenge for the local healthcare capacity, increasing the probability of long-term health disparities in these areas. Contributors Concept and design: All authors. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: D.F.C., C.M.M. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: D.F.C., J.G. Access to data and verified the data: D.F.C., J.G. Data sharing statement All data are available in public repositories: https://vaccine.precisionforcovid.org/; https://covid.cdc.gov/covid-data-tracker/#county-view?list_select_state=all_states&data-type=CommunityLevels; https://www.ruralhealthinfo.org/data-explorer?id=197; https://www.kaggle.com/datasets/jaimeblasco/icu-beds-by-county-in-the-us; https://www.atsdr.cdc.gov/placeandhealth/svi/index.html. Editor note The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Declaration of interests The authors have no conflicts of interest to declare. Appendix A Supplementary data Supplementary Material Caption for Supplementary Material Appendix A Supplementary data related to this article can be found at https://doi.org/10.1016/j.lana.2022.100409. ==== Refs References 1 University J.H. 2019 Novel coronavirus COVID-19 (2019-nCoV) data repository by Johns Hopkins CSSE https://github.com/CSSEGISandData/COVID-19 2 Murthy B.P. Sterrett N. Weller D. Disparities in COVID-19 vaccination coverage between urban and rural counties—United States, December 14, 2020–April 10, 2021 MMWR Morb Mortal Wkly Rep 70 20 2021 759 34014911 3 Olivera Mesa D. Hogan A.B. Watson O.J. Modelling the impact of vaccine hesitancy in prolonging the need for non-pharmaceutical interventions to control the COVID-19 pandemic Commun Med 2 1 2022 14 35603311 4 Khubchandani J. Sharma S. Price J.H. Wiblishauser M.J. Sharma M. Webb F.J. COVID-19 vaccination hesitancy in the United States: a rapid national assessment J Community Health 46 2 2021 270 277 33389421 5 Cuadros D.F. Branscum A.J. Mukandavire Z. Miller F.D. MacKinnon N. Dynamics of the COVID-19 epidemic in urban and rural areas in the United States Ann Epidemiol 59 2021 16 20 33894385 6 Karaye I.M. Horney J.A. The impact of social vulnerability on COVID-19 in the US: an analysis of spatially varying relationships Am J Prev Med 59 3 2020 317 325 32703701 7 Cuadros D.F. Miller F.D. Awad S. Coule P. MacKinnon N.J. Analysis of vaccination rates and new COVID-19 infections by US county, July-August 2021 JAMA Netw Open 5 2 2022 e2147915 35142835 8 Tolbert J. Orgera K. Garfield R. Kates J. Artiga S. Vaccination is local: COVID-19 vaccination rates vary by county and key characteristics. KFF 2021 9 Lawal L. Aminu Bello M. Murwira T. Low coverage of COVID-19 vaccines in Africa: current evidence and the way forward Hum Vaccines Immunother 18 1 2022 2034457 10 Demombynes G. COVID-19 age-mortality curves are flatter in developing countries 2020 11 Cuadros D.F. Moreno C.M. Musuka G. Miller F.D. Coule P. MacKinnon N.J. Association between vaccination coverage disparity and the dynamics of the COVID-19 Delta and Omicron waves in the US Front Med (Lausanne) 9 2022 898101 35775002 12 2021 GHS index country profile for United States https://www.ghsindex.org/country/united-states/ 13 Miller I.F. Becker A.D. Grenfell B.T. Metcalf C.J.E. Disease and healthcare burden of COVID-19 in the United States Nat Med 26 8 2020 1212 1217 32546823 14 Vanderbilt A.A. Isringhausen K.T. VanderWielen L.M. Wright M.S. Slashcheva L.D. Madden M.A. Health disparities among highly vulnerable populations in the United States: a call to action for medical and oral health care Med Educ Online 18 1 2013 20644 15 Cook B.L. Hou S.S.-Y. Lee-Tauler S.Y. Progovac A.M. Samson F. Sanchez M.J. A review of mental health and mental health care disparities research: 2011-2014 Med Care Res Rev 76 6 2019 683 710 29877136 16 Ventures S. The U.S. COVID-19 vaccine CoverageIndex: leaving no community behind in the COVID-19 vaccine rollout https://surgoventures.org/resource-library/the-us-covid-19-vaccine-coverage-index-leaving-no-community-behind-in-the-covid-19-vaccine-rollout 2021 17 CDC COVID data tracker https://covid.cdc.gov/covid-data-tracker/#datatracker-home 2021 18 Ingram D.D. Franco S.J. 2013 NCHS urban-rural classification scheme for counties: US Department of Health and Human Services, Centers for Disease Control and … 2014 19 ESRI 2019 USA population density 2020 20 Flanagan B.E. Hallisey E.J. Adams E. Lavery A. Measuring community vulnerability to natural and anthropogenic hazards: the Centers for Disease Control and Prevention's Social Vulnerability Index J Environ Health 80 10 2018 34 21 Prevention CfDCa Estimates of vaccine hesitancu for COVID-19 https://data.cdc.gov/stories/s/Vaccine-Hesitancy-for-COVID-19/cnd2-a6zw/ 2021 22 RHIhub Rural Health Information Hub https://www.ruralhealthinfo.org/data-explorer?id=197 2022 23 ICU beds by county in the US https://www.kaggle.com/datasets/jaimeblasco/icu-beds-by-county-in-the-us 2020 24 Deaton A. Cartwright N. Understanding and misunderstanding randomized controlled trials Soc Sci Med 210 2018 2 21 29331519 25 Porzsolt F. Kliemt H. Ethical and empirical limitations of randomized controlled trials Med Klin (Munich) 103 12 2008 836 842 19099213 26 Nichols A. Causal inference with observational data STATA J 7 4 2007 507 541 27 Craig P. Dieppe P. Macintyre S. Michie S. Nazareth I. Petticrew M. Developing and evaluating complex interventions an introduction to the new Medical Research Council guidance Evidence–Based Public Health: Effectiveness and Efficiency 2010 1:185–203 28 Textor J. van der Zander B. Gilthorpe M.S. Liśkiewicz M. Ellison G.T. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’ Int J Epidemiol 45 6 2016 1887 1894 28089956 29 Chernozhukov V. Chetverikov D. Demirer M. Double/debiased machine learning for treatment and structural parameters 2018 Oxford University Press Oxford, UK 30 Sharma A. Kiciman E. DoWhy: an end-to-end library for causal inference arXiv 2020 preprint arXiv:201104216 31 Battocchi K. Dillon E. Hei M. EconML: a Python package for ML-based heterogeneous treatment effects estimation 2019 32 Kulldorff M. Nagarwalla N. Spatial disease clusters: detection and inference Stat Med 14 1995 799 810 7644860 33 Kulldorff M. A spatial scan statistic Commun Stat 26 6 1997 1481 1496 34 Aamodt G. Samuelsen S. Skrondal A. A simulation study of three methods for detecting disease clusters Int J Health Geogr 5 2006 15 16608532 35 Kulldorf M. Zhang Z. Hartman J. Heffernan R. Huang L. Mostashari F. Benchmark data and power calculations for evaluating disease outbreak detection methods MMWR Suppl 53 2004 144 151 15714644 36 ESRI ArcGIS Pro.x 2020 ESRI Redlands, CA, USA 37 U.S. News Health care rankings https://www.usnews.com/news/best-states/rankings/health-care 2022 38 Review WP Poverty rate by state 2022 https://worldpopulationreview.com/state-rankings/poverty-rate-by-state 2022 39 Rothbart M.F. Karáth K. Ndhlovu L. How Covid-19 has exposed the weaknesses in rural healthcare BMJ 2022 376 40 Ren B. Wu X. Braun D. Pillai N. Dominici F. Bayesian modeling for exposure response curve via Gaussian processes: causal effects of exposure to air pollution on health outcomes arXiv 2021 preprint arXiv:210503454

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==== Front J Mol Struct J Mol Struct Journal of Molecular Structure 0022-2860 1872-8014 Elsevier B.V. S0022-2860(21)00285-4 10.1016/j.molstruc.2021.130154 130154 Article Bromhexine and its fumarate salt: Crystal structures, Hirshfeld surfaces and dissolution study Zhang Ya-an a⁎ Yan Cui-Min b Sun Bai-Wang b⁎ Wang Lin-Xuan a a School of Pharmaceutical and Chemical Engineering, Chengxian College, Southeast University, Nanjing 210088, PR China b School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, PR China ⁎ Corresponding authors. 24 2 2021 5 6 2021 24 2 2021 1233 130154130154 14 9 2020 3 2 2021 15 2 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Bromhexine is an expectorant drug repurposing as a TMPRSS2 inhibitor, which has also been proposed for potential treatment in COVID-19 infection. Multicomponent crystal strategy has been applied in bromhexine to improve its poor solubility, which limits its bioavailability and efficacy. A new bromhexine crystal and its fumarate salt crystal have been successfully obtained by slow evaporation technique. Both compounds have been characterized by X-ray single-crystal diffraction, TGA and FT-IR spectroscopy. Hirshfeld surface analysis has been carried out to further quantify the patterns of intermolecular interactions. Compared with bromhexine, the multicomponent crystal with pharmaceutically acceptable conformer of fumaric acid shows improved thermal stability and solubility in water. Keywords Bromhexine Crystal structure Multicomponent crystal Salt Hirshfeld surface Solubility ==== Body pmc1 Introduction In the recent years, supramolecular chemistry together with crystal engineering has become one of the research hotspots in the field of pharmaceutical chemistry [[1], [2], [3]–4]. Preparation of multicomponent crystals including co-crystallization and salt formation promotes the design and synthesis of new solid type of Active Pharmaceutical Ingredient (API) with the desired physicochemical properties by exploring the advantages of supramolecular interactions [5–6]. Considerable researches have shown that both cocrystals and salts obtained by co-crystallization of the API and the GRAS (Generally Recognized as Safe) coformers have improved solid state properties of API such as better stability, solubility, bioavailability, and dissolution without altering the pharmacological activity of the API [[7], [8], [9], [10], [11], [12], [13], [14]–15]. Bromhexin is an effective and safe expectorant drug, which is mainly used in patients with chronic bronchitis, asthma [16]. It has shown that bromhexine is also a potent inhibitor of TMPRSS2, a key protease in the transmission of SARS-CoV-2 [17–18]. Therefore, several clinical trials on the prevention or treatment of Novel Coronavirus Infectious Disease (COVID -19) with bromhexin are underway in the world to evaluate its prophylactic potential against SARS-CoV-2 [[19], [20], [21], [22]–23]. However, the poor solubility of bromhexine gives rise to difficulties in pharmaceutical formulation for oral use, which may lead to variation in bioavailability [24]. To overcome these difficulties, salt or co-crystal strategy has been applied in bromhexine to develop novel salt or co-crystal with good solubility and stability bioavailability. Thus, screening experiments were performed for bromhexin co-crystallization with a series of carboxylic acid coformers by slow evaporation method. Finally, a new bromhexine crystal and its fumarate salt crystal have been obtained, which have not been found in the literature or Cambridge Crystallographic Data Centre (CCDC) so far [[25], [26], [27]–28]. The two crystals have been characterized by single-crystal and powder X-ray diffraction, IR spectroscopy, thermal analysis (TGA). Hirshfeld surfaces analysis has been carried out in order to analyze the intermolecular contacts. The equilibrium solubility and powder dissolution of the two crystals have been also evaluated. The fumarate of bromhexine exhibits better thermal stability and water solubility, compared with the free bromhexine base. 2 Experiment section 2.1 Materials and physical measurements Bromhexin (> 99%) was gifted from Chia Tai Qingjiang Pharmaceutical Co., Ltd, Jiangsu, China. All the coformers and solvents were analytically pure purchased from Sinopharm Chemical Reagent and used as received without further purification. With the samples prepared as KBr pellets, infrared spectra were performed using a Bruker-TENSOR27 FT-IR spectrometer in the range 4000-400 cm−1. Thermo gravimetric analysis (TGA) were performed with a thermal analyzer TG209 F3 at a heating rate of 10˚C/ /min under an atmosphere of N2. 2.2 Crystals growth Single crystal of bromhexine (Compound 1): 60 mg (0.160 mmol) bromhexine and 23 mg (0.160mmol) L-glutamine were dissolved in 10 ml ethanol and 2 ml distilled water by ultrasonic treatment for 20 min, then left for slow evaporation at room temperature after filtrating. The colorless plate-like crystals were obtained after about 2 weeks with the total yield of 50.2%. Fumarate of bromhexine (Compound 2): 100 mg (0.266 mmol) bromhexine and 31 mg (0.266 mmol) fumaric acid were dissolved in 20 ml acetonitrile and 2 ml methanol. The mixture was stirred at room temperature for 30min followed by filtrating. The colorless block-like crystals were obtained after about 2 weeks for slow evaporation with the total yield of 45.0%. 2.3 X-Ray crystallography Crystal structures of compounds 1–2 were determined by single-crystal X-ray diffraction, the date of compound 1 were collected at 293 K with Cu Kα radiation (λ = 1.54184 Å), while the date of compound 2 were collected at 193 K with Ga Kα radiation (λ = 1.34138 Å), using ω-scan method. The structures were solved by SHELXT 2015 program [29] in direct method and refined by SHELXL-2015 program [30], respectively. A summary of the crystallographic data for compounds 1–2 was provided in Table 1 . Molecular graphics were prepared using DIAMOND [31] and Mercury program [32]. CCDC reference numbers 2025907 and 2025908 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge via http://www.ccdc.cam.ac.uk/conts/retrieving.html.Table 1 Crystal data and structure refinements for Compounds 1-2. Table 1Compound 1 2 Formula C14 H20 Br2 N2 C18 H24 Br2 N2 O4 Formula weight 376.14 492.21 Crystal system monoclinic monoclinic Space group P 21/n P 21/n Z 4 4 a/ Å 6.5271(1) 8.1069(12) b/ Å 23.5874(2) 26.277(4) c/ Å 9.9592(1) 9.4104(13) α/° 90 90 β/° 94.190(1) 103.994(4) γ/° 90 90 V, Å3 1529.19(3) 1945.1(5) T/K 293(2) 193 μ (mm−1) 6.596 3.790 D calc (Mg m−3) 1.634 1.681 Cryst dimensions(mm) 0.09 × 0.13 × 0.22 0.10 × 0.15 × 0.20 No. of reflns collected 3110 3554 No. of unique reflns 2725 3502 No. of params 173 251 Goodness of fit on F2 1.095 1.207 R1,wR2 ((I>2σ(I)) 0.0418, 0.1162 0.0621, 0.1472 R1,wR2 (all data) 0.0478, 0.1261 0.0628, 0.1477 CCDC No. 2025907 2025908 2.4 Powder X-ray diffraction (PXRD) Room temperature PXRD analyses were performed using a Bruker D8 Discover diffractometer (Bruker, AXS) with Cu Kα radiation (λ = 1.5406 Å) at 40 kV and 40 mA. The data were collected over an angular range from 5°to 50°(2θ) in continuous scan mode with a step size of 0.02°(2θ) and a step time of 0.15 s. Mercury CSD 3.5.1 program was used to calculate PXRD patterns with the data of single crystal structures [33]. 2.5 Hirshfeld surfaces analysis Molecular Hirshfeld Analysis for both compounds was performed using the Crystal Explorer 3.1 software to investigate the nature of intermolecular interactions and their relative contributions in the crystals [34]. The distances from the Hirshfeld surface to the closest nucleus outside and inside the surface are defined as de and di, respectively. The normalized contact distance dnorm is based on di,de and the van der Waals radii of the atoms. The dnorm values can be mapped onto the Hirshfeld Surface, which facilitates easy comparison of intermolecular contacts relative to van der Waals radii by a simple red-white-blue color scheme (contacts shorter or longer than van der Waals contacts and equal to the sum of the van der Waals radii, are visualized as red, blue and white colours in the Hirshfeld surfaces respectively). [35]. In this study, a standard (high) surface resolution was used to generate the Hirshfeld surfaces of bromhexine molecules in compounds 1–2. The 3-D dnorm surfaces were mapped with a fixed color scale of 0.76 Å (red) to 2.4 Å (blue). Using standard 0.6 Å–2.6 Å view, the 2-D fingerprint plots were displayed with the de and di distance scale. 2.6 Solubility and dissolution experiments To compare the equilibrium solubility values, an excess amount of bromhexine and its fumarate was added in 10 ml distilled water and the supersaturated solutions were shaken at 37°C. After 72 h, the suspensions were filtered and the concentrations of the drugs were analyzed by high-performance liquid chromatography (HPLC). For each dissolution experiment, powdered sample containing 480 mg of bromhexine or its equivalent in fumarate was added in 100 ml distilled water, and the resulting suspension stirred at 25°C and 500 rpm. At a predetermined time interval, each aliquot was taken out filtered through a 0.45 μm membrane filter, and then the concentration of the drug was quantified by HPLC. The concentration of bromhexine was determined by Agilent 1260 HPLC equipped with a variable UV detector (set at 284 nm). A C18 column (100 mm × 4.6 mm, 5 μm) was used and the flow rate was set to 1.0 mL/min with the column temperature at 30°C. The mobile phase consisted of 0.1% phosphoric acid buffer (adjusted to pH 3.5 with trimethylamine): acetonitrile (10:90 v/v). Solubility and dissolution experiments for each sample were conducted three times to calculate the standard deviations. 3 Results and discussion 3.1 Crystal structural analysis Compound 1 crystallizes in monoclinic space group P21/n with Z = 4 and the thermal ellipsoid plot at 50% probability is shown in Fig. 1 a. In the molecular structure, the C–N–C bond angle is 111.07(2) °and the cyclohexane ring adopts the most stable chair configuration. Br1, Br2, N1 are almost coplanar with benzene ring, with slight deviations of 0.071(2) Å, 0.004(1) Å, 0.086(1) Å, respectively. In addition, the molecule is stabilized by intramolecular hydrogen bonds N1–H1B···N2 [N1···N2 distance of 2.855(4) Å, H1B···N2 distance of 2.115(3)Å, N1–H1B···N2 angle of 142.2(4) °] (Table 2 ). A weak N1–H1A···Br1 interaction has been detected with the N1···Br1 distance of 3.098 Å significantly shorter than sum of the van der Waals radii (3.40 Å) [36], formed intramolecular five-membered hydrogen bonds which have also been reported in the crystal structures of bromhexine hydrochloride [25] and other bromo-substituted aromatic amides [37]. As shown in Fig. 1b, the bromhexine molecules are connected in a head-to-tail fashion, resulting in the formation of a one dimensional zigzag chain through C-H···Br [H···Br distance of 2.966(1) Å] contacts. The adjacent chains are further linked by C-Br···Br halogen bonding contacts [Br···Br distance of 3.6670(7) Å] shown in Fig. 1c, belonged to type-I halogen-halogen interactions [38], with both C-Br···Br angles of 148.04(9)°.Fig. 1 Molecular structure of compound 1(a); One-dimensional chain structure of compound 1(b); Two-dimensional network of compound 1 (c). Fig 1 Table 2 Hydrogen bond parameters in compounds 1-2. Table 2D-H···A D-H (Å) H···A (Å) D···A (Å) ∠D-H···A (deg) Symmetry operation compound 1 N1-H1A··· Br1 0.87(3) 2.65(3) 3.098(3) 114(3) x, y, z N1-H1B···N2 0.87(3) 2.12(3) 2.855(4) 142(4) x, y, z compound 2 N1-H1A···Br1 0.87(7) 2.71(8) 3.050(5) 105(6) x, y, z N1-H1A···O2 0.87(7) 2.14(7) 2.854(6) 139(7) x,y,-1+z N1-H1B···O3 0.90(7) 2.17(7) 3.011(6) 155(6) -1+x,y,-1+z N2+-H2···O1− 0.90(6) 1.84(6) 2.692(5) 159(5) x, y, z O4-H4···O2 0.840 1.710 2.534(5) 167 1+x,y,z C7-H7B···N1 0.990 2.560 2.926(7) 102 x, y, z C9-H9···O4 0.95(5) 2.46(5) 3.267(6) 142(4) -1+x,y,z Compound 2 also crystallizes in monoclinic space group P21/n with Z = 4 and the basic structure unit consists of one protonated bromhexine cation and one deprotonated fumaric acid anion, where one proton transfers from carboxylate of fumaric acid to N-methyl amino group of bromhexine(Fig. 2 a). The C–N–C bond angle is 111.6(4) ° and the cyclohexane ring also adopts the most stable chair configuration, similar to those in compound 2. Bromhexine cations and fumaric acid anions are held together via N2+–H2···O1− [N2+···O1− distance of 2.692(5) Å, H2···O1 distance of 1.836(6) Å, N2–H2···O1 angle of 158.7(5)°] along with N1–H1···O2 [N1···O2 distance of 2.854(6) Å, H1···O2 distance of 2.144(7))Å, N1–H1···O2 angle of 138.7(7) °] hydrogen bond interactions to give infinite 1D chains (Fig. 2b). The 1D chains are further interacting by R33(9) and R32(8) supramolecular heterosynthons through C9–H9···O4 [C9···O4 distance of 3.267(6) Å, H9···O4 distance of 2.462(5) Å, C9–H9···O4 angle of 141.9(4)°], O4–H4···O2 [O2···O4 distance of 2.534(5) Å, H4···O2 distance of 1.710 Å, O4–H4···O2angle of 166.6°] and N1–H1B···O3 [N1···O3 distance of 3.011(6) Å, H1B···O3 distance of 2.167(7) Å, N1–H1B···O3 angle of 155.2(6)°] hydrogen bonds thereby form a 2D layer along the (010) plane(Fig. 2c, Table 2). In addition, the weak π–π stacking interactions are detected with distance of 3.580 (3) Å, which also involved in stabilizing the crystal structure (Fig. 2d).Fig. 2 Molecular structure of compound 2(a); One-dimensional chain structure of compound 2(b); Two-dimensional network of compound 2(c); crystal packing stabilized by π–π stacking interactions are detected with distance of 3.580 Å viewed from c-axis. Fig 2 3.2 Fourier-transform infrared spectra (FT-IR) analysis FT-IR spectroscopy is an effective tool used to differentiate distinct chemical structures and environments. In FT-IR spectra of compounds 1-2, obvious distinctions have been exhibited in the N-H stretching, N-H bending and carbonyl stretching vibrations frequencies (Supplementary Fig.S1 and Table S1). Compound 1 shows IR absorption frequencies at 3412 cm−1 (asymmetric N-H stretching) and 3134 cm−1 (symmetric N-H stretching) and at 1601 cm−1 (N-H bending) without carbonyl stretching. Compound 1 shows N-H stretching at 3412 cm−1, 3134 cm−1 and N-H bending at 1601 cm−1 attributed to aromatic amino groups, while no carbonyl stretching has been observed. On the other hand, compound 2 displays blue-shift of N-H stretching frequencies at 3444 cm−1 and 3333 cm−1, with N-H bending at 1633 cm−1 due to changes in molecular conformations and hydrogen bonding. The N+-H stretching vibration of the protonated aminomethyl group in compound 2 appears at 3232 cm−1. The carbonyl stretching vibration for compound 2 appears at 1688cm−1, confirming the presence of carboxyl groups associated with the fumaric acid coformer. From comparison the spectrum with respect to fumaric acid, an increase in carbonyl acid stretching frequency has been observed for compound 2, also indicated the formation of salt. 3.3 Thermal analysis The thermal behaviors of compounds 1–2 have been investigated and the TGA curves are shown in Fig.S2. Compound 1 undergoes one step of mass loss from 125°C, with a continued weight loss completed by 300°C. Compound 2 undergoes three steps of mass loss, a first loss of 10% at approximately 152°C; the second step is a loss of 23% at approximately 172°C, while the third step is a loss of 44% at approximately 266°C. In addition, the melting points of compounds 1–2 have been tested with X-5 precise micro melting point tester. Compound 1 melted at about 60°C, while compound 2 melted at about 148°C. The results of thermal analysis confirm that compound 2 as a novel solid-state form improved the physical stabilities through higher decomposition and melting point. 3.4 Hirshfeld surface analysis In order to provide further insight into the intermolecular interactions, Hirshfeld surfaces of bromhexine molecules in compounds 1–2 associated finger print plots have been calculated. The Hirshfeld surface mapped with dnorm of bromhexine molecule in compound 1 is illustrated in Fig. 3 a. Small red areas on the dnorm Hirshfeld surfaces of compound 1 represent the C–H···Br interactions, while slight red area is in accordance with weaker Br···Br halogen bonding interactions. It's noticeable that there are more deep red spots in the 3D-dnorm surfaces of bromhexine molecule in compound 2 compared to that in compound 1, mainly due to close-contact N−H···O interactions from fumaric acid(Fig. 3b).Fig. 3 Hirshfeld surfaces mapped with dnorm of bromhexin molecules in compound 1 (a) and 2 (b), respectively. Fig 3 On the shape index surface of bromhexine molecule in compound 2, adjacent red and blue triangles have been observed, indicating the presence of aromatic stacking (Fig.S3). However, the π–π stacking interactions are absent in compound 1, because there is no evidence of adjacent bow-tie patterns on the shape index surfaces [39]. On the coincident curvedness surfaces of bromhexine molecule in compound 2, the relatively large and green flat regions, marked with ovals in Fig.S3, also provide proof for the existence of aromatic stacking interactions. The 2D finger print plots for the main intermolecular contacts of the two compounds are shown in Fig. 4 . For compound 1, the H···H interactions exhibit the most significant contribution (52.3%) to the total Hirshfeld surfaces. The H···Br intermolecular interactions are seen as sharp spikes in the 2D fingerprint plots, accounting for 28.6 % of the total Hirshfeld surfaces. Whereas in compound 2, the proportion of H···H interactions is decreased after salification, account for 41.2% of the total Hirshfeld surfaces. However, close-contact O···H intermolecular interactions appear as a distinct spike in the 2D fingerprint plot, obviously increasing up to 16.3% of the total Hirshfeld surfaces. The other observed interactions are summarized in Table 3 .Fig. 4 2D fingerprint plots of bromhexin molecules in compounds 1-2. Fig 4 Table 3 Relative percentage contributions (in %) of various intermolecular contacts to the Hirshfeld surface area for bromhexin molecules in compounds 1-2. Table 3 Compound l Compound 2 H···H 53.2 41.2 H···Br 28.6 20.3 C···H 11.5 8.6 O···H 0 16.3 C···C 0 3.4 Br···Br 2.6 4.3 C···Br 1.8 2.5 others 3.2 3.4 In summary, the results of Hirshfeld surface analysis indicate that there are stronger intermolecular forces in compound 2. It is found that forming the salt multicomponent crystal changed the bonding mode and intermolecular force of the two compounds. 3.5 Solubility and dissolution study It has been reported that the solubility of bromhexine hydrochloride is increased by 5 fold at 15 mmol/L of methylated β-cyclodextrin [40], while the stability and safety of inclusion complexes need to be further studied [41]. It is also found that amino acid prodrugs can increase the solubility of bromhexine hydrochloride by about 3-5 times [42], but with complicated chemical synthesis and difficult purification. Therefore, development of new stable cocrystals/salts with simple processing and lower cost is still necessary. In this study, the equilibrium solubility value of compound 2 at 37°C in water (4.9 ± 0.20 mg/ml) is more than 10-fold higher than that of compound 1, and slightly higher than that of bromhexine hydrochloride used in clinical (4.4± 0.25 mg/ml reported in the literature) [42].The powder dissolution profiles of compound 1-2 in water are depicted in Fig. 5. Compound 1 is a practically insoluble in water, and the salt formation results in a significantly improvement in the case of compound 2. A close examination of the remaining powder after dissolution experiments showed that no phase transition occurs for both compounds (Supplementary Fig.S4). In addition, fumaric acid is a GRAS molecule according to US Food and Drug Administration and is easy to obtain by industrial preparation with lower cost. Thus, the present study offers a new perspective and possibility to improve the solubility of bromhexine by forming the salt multicomponent crystal.Fig. 5 Powder dissolution profiles of compounds 1-2 in water. Fig 5 4 Conclusions Bromhexine is a powerful expectorant and has been repurposed as a TMPRSS2 inhibitor also proposing for COVID-19 therapy. In this study, a new bromhexine crystal and its fumarate salt crystal have been obtained and characterized by single crystal X-ray diffraction, TGA and FT-IR spectroscopy. It is found that both compounds feature different crystal structures and proton transfer occurred from fumaric acid to bromhexine molecule in compound 2. The results of Hirshfeld surface analysis further confirm that there are stronger intermolecular forces in compound 2. Moreover, compound 2 exhibits higher thermostability and also shows an enhanced solubility in water and a better dissolution profile. By forming the salt multicomponent crystal, thermostability and solubility of compound 2 has been improved from the parent drug. Therefore, with a pharmaceutically acceptable coformer of fumaric acid, compound 2 is a promising candidate worthy of further research. CRediT authorship contribution statement Ya-an Zhang: Data curtion, Formal analysis, Writing – original draft, Visualization. Cui-Min Yan: Methodology, Validation. Bai-Wang Sun: Conceptualization, Supervision, Funding acquisition. Lin-Xuan Wang: Investigation. Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents aconflict of interest in connection with the work submitted. Appendix Supplementary materials Image, application 1 Acknowledgments This work was supported by the Research and development Fund for young teachers of Southeast University Chengxian College (No. Z0022). We also give our sincere thanks to Dr. Yang-hui Luo and Dr. Yu-Heng Ma, who offered generous assistance throughout our whole work. Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.molstruc.2021.130154. ==== Refs References 1 Aakeröy C.B. Champness N.R Janiak C. Recent advances in crystal engineering CrystEngComm 12 2009 22 43 2 Mohana M. Muthiah P.T. McMillen C.D. Supramolecular hydrogen-bonding patterns in 1:1 cocrystals of 5-fluorouracil with 4-methylbenzoic acid and 3-nitrobenzoic acid Acta Crystallogr. C 73 2017 259 263 3 Yousef M.A.E. Vangala V.R. Pharmaceutical cocrystals: molecules, crystals, formulations, medicines, cryst Growth Des. 19 2019 7420 7438 4 Luo Y.H. Sun B.W. Pharmaceutical Co-crystals of pyrazinecarboxamide (PZA) with various carboxylic acids: crystallography, Hirshfeld surfaces, and dissolution study Crystal Growth Des. 13 2013 2098 2106 5 Berry D.J. Steed J.W. Pharmaceutical cocrystals, salts and multicomponent systems; intermolecular interactions and property based design Adv. Drug Deliv. Rev. 117 2017 3 24 28344021 6 Ainurofiq A. Mauludin R. Mudhakir D. Umed D. Soewandhi S.N. Putra O.D. Yonemochi E. Improving mechanical properties of desloratadine via multicomponent crystal formation Eur. J. Pharm. Sci. 111 2018 65 72 28958892 7 Ma Y.H. Ge S.W. Wang W. Zheng Q. Zuo Y.W. Zhong C.J. Sun B.W. Novel perchlorate and phosphate salts of vinpocetine: characterization, relative solid-state stability evaluation and Hirshfeld surface analysis J. Mol. Struct. 1105 2016 1 10 8 Ma Y.H. Ge S.W. S, Synthesis, Characterization B.W. and theoretical studies of a novel salt (dexmedetomidine: perchloric acid = 1:1) and the investigation of its stability Chin. J. Struct. Chem. 34 2015 1179 1186 9 Zhu Z.Y. Zhou Y.M. Yao Q.Z. Sun B.W. Wang M.L. Zhong X. Wang B.B. Xue Y. Chen X.C. Two polymorphs and a sulfate of buprofezin: crystal structure and Hirshfeld surface analysis Polyhedron. 155 2018 85 93 10 Maryam K.J. Luis P. Walker G.M. Croker D. Creating cocrystals: a review of pharmaceutical cocrystal preparation routes and applications Cryst. Growth Des. 18 2018 6370 6387 11 Duggirala N.K. Perry M.L. Almarsson Ö. Zaworotko M.J. Pharmaceutical cocrystals: along the path to improved medicines Chem. Commun. 52 2016 640 655 12 Yan Y. Dai X.L. Jia J.L. Zhao X.H. Li Z.W. Lu T.B. Chen J.M. Crystal structures, stability, and solubility evaluation of two polymorphs of a 2:1 melatonin−piperazine cocrystal Cryst. Growth Des. 20 2020 1079 1087 13 Samie A. Desiraju G.R. Banik M. Salts and cocrystals of antidiabetic drugs, gliclazide, tolbutamide and glipizide: solubility enhancements through drugcoformer interactions Cryst. Growth Des. 17 2017 2406 2417 14 Aitipamula S. Caddena J. Chow P.S. Cocrystals of zonisamide: physicochemical characterization and sustained release solid forms CrystEngComm 20 2018 2923 2931 15 Nechipadappu S.K. Ramachandran J. Shivalingegowda N. Lokanath N.K. Trivedi D.R. Cocrystal/salt synthesis of flucytosine drug: structural and stability aspects New J. Chem. 42 2018 5433 5446 16 Zanasi A. Mazzolini M. Kantar A. A reappraisal of the mucoactive activity and clinical efficacy of bromhexine Multidiscip. Respir. Med. 12 2017 7 28331610 17 Habtemariam S. Nabavi S.F. Ghavami S. Possible use of the mucolytic drug, bromhexine hydrochloride, as a prophylactic agent against SARS-CoV-2 infection based on its action on the Transmembrane Serine Protease 2 Pharmacol. Res. 157 2020 104853 18 Gil C. Ginex T. Maestro I. Nozal V. Barrado-Gil L. Cuesta-Geijo M.Á. Urquiza J. Ramírez D. Alonso C. Campillo N.E. Martinez A. COVID-19: drug targets and potential treatments J. Med. Chem. 2020 10.1021/acs.jmedchem.0c00606 19 National Library of Medicine U.S. https://clinicaltrials.gov/ct2/show/NCT04355026, 2020 (accessed 18 August 2020). 20 National Library of Medicine U.S. https://clinicaltrials.gov/ct2/show/NCT04340349, 2020 (accessed 18 August 2020). 21 National Library of Medicine U.S. https://clinicaltrials.gov/ct2/show/NCT04424134, 2020 (accessed 18 August 2020). 22 National Library of Medicine U.S. https://clinicaltrials.gov/show/NCT04273763, 2020 (accessed 18 August 2020). 23 National Library of Medicine U.S. National Library of Medicine U.S. https://clinicalt-rials.gov/show/NCT04405999, 2020 (accessed 18 August 2020). 24 Schubert M.A. MüllerGoymann C.C. Solubilization of bromhexine hydrochloride in aqueous lecithin dispersions. Physicochemical characterization of interactions between drug and carrier Pharmazie Die 56 2001 734 737 25 Kong C.H. Jung Y.J. Lee S.W. The crystal and molecular structure of bromhexine·HCl Arch. Pharm. Res. 7 1984 115 120 26 Shimizu N. Nishigaki S. Structure of 2-amino-3,5-dibromo-N-cyclohexyl-N-methylbenze-nemethanamine–1,2-benzisothiazol-3(2H)-one 1,1-dioxide (1:1), C14H20Br2N2.C7H5NO3S Acta Cryst. C 39 1983 502 504 27 Shimizu N. Nishigaki S. Nakai Y. Osaki K. Structures of 2-amino-3,5-dibromo-N-cyclohexyl-N-methylbenzenemethanamine–salicylic acid (1:1)(ABCMBMA–SALA), C14H20Br2N2.C7H6O3 Acta Cryst. C 39 1983 891 893 28 Maste M.M. Mahapatra S. Ramachandran K.K. Venugopalab K.N. Bhat A.R. N-(2-Amino-3,5-dibromobenzyl)-N-methylcyclohexan-1-aminium -p-toluenesulfonate Acta Cryst. E 67 2011 o2032 29 Sheldrick G.M. SHELXT-integrated space-group and crystal-structure determination Acta Cryst. A 71 2015 3 8 30 Sheldrick G.M. Crystal structure refinement with SHELXL Acta Cryst. 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Growth Des. 8 2008 1294 1300 38 Wzgarda-Raj K. Rybarczyk-Pirek A.J. Wojtulewski S. Palusiak M. N-Oxide–N-oxide interactions and Cl...Cl halogen bonds in pentachloropyridine N-oxide: the many-body approach to interactions in the crystal state Acta Cryst. C 74 2018 113 119 39 Gumus I. Solmaz U. Binzet G. Keskin E. Arslan B. Arslan H. Supramolecular self-assembly of new thiourea derivatives directed by intermolecular hydrogen bonds and weak interactions: crystal structures and Hirshfeld surface analysis Res. Chem. Intermed. 45 2019 169 198 40 Kaewnopparat N. Chuchom T. Sunthornpit A. Jangwang A. Kaewnopparat S. Enhanced bromhexine hydrochloride solubility and dissolution by inclusion complexation with methylated β-cyclodextrin Isan. J. Pharm. Sci. 5 2009 114 122 41 Crumling M.A. King K.A. Duncan R.K. Cyclodextrins and iatrogenic hearing loss: new drugs with significant risk Front. Cell. Neurosci. 11 2017 355 10.3389/fncel.2017.00355 29163061 42 Aggarwal A.K. Gupta M. Solubility and solution stability studies of different amino acid prodrugs of bromhexine Drug Dev. Ind. Pharm. 38 2012 1319 1327 22283553

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==== Front Int J Nurs Stud Int J Nurs Stud International Journal of Nursing Studies 0020-7489 1873-491X Elsevier Ltd. S0020-7489(22)00044-X 10.1016/j.ijnurstu.2022.104215 104215 Article Author's response to ‘Comment on Wendt et al (2022) “Exploring infection prevention practices in home-based nursing care: A qualitative observational study”’ Wendt Benjamin Radboudumc IQ healthcare, Nijmegen, Gelderland, the Netherland 27 2 2022 5 2022 27 2 2022 129 104215104215 17 2 2022 23 2 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcDear Editor, We would like to thank professor Hallberg (2022) for her interest and thoughtful comments on our paper on exploring infection prevention practices in home-based nursing care (Wendt et al., 2022) and thank the editor for the opportunity to respond to these comments. We would like to take advantage of this opportunity in the form of a short reply. In our paper we concluded that “the daily practice of infection prevention in home-based nursing care appears to be suboptimal” (Wendt et al., 2022). Professor Hallberg summarised the findings as an illustration of “poor practice, both at the level of the nurses and at the level of the organisations[…], through a “violation of knowledge, regulations and laws” and “harsh criticism” in the discussion would have been – in her view - appropriate. While we do feel professor Hallberg raises a fair and valid point; it is nevertheless complex and deserves further exploration. One point that was raised is what could be considered the concept of ‘patient safety culture’; defined as “the product of individual and group values, attitudes, perceptions, competencies and patterns of behaviour that determine the commitment to and the style and proficiency of, an organization's health and safety management” (Health and Safety Commission, 1993). The presence of a ‘patient safety culture’ is related to patient and healthcare worker safety (Hessels and Larson, 2016); however, a quick literature search suggests that it might not get the attention it deserves in the context of home-based nursing care (Gallego et al., 2012; Olsen and Bjerkan, 2017). Being an understudied topic may – in part – explain why it has been underexposed in the discussion and indicates that this is an important issue for further research. A second point concerns the role or perspective of the researcher. If the goal of research is to generate or develop ‘knowledge’; for example, provide a description of practices and experiences, one can argue that the researcher should play the role of ‘intermediary’ between the practices and experiences of the participants and the reader, and therefore the researcher needs to have a certain ‘neutrality’ and leave the ‘judgment’ up to the reader. Another perspective is that the researcher is morally obliged take a stand or even be the ‘patients’ advocate’ when he or she is witness of a poor practice or harm. This question is further complicated if the researcher is also a healthcare provider and research principles and the well-being of clients need to be balanced, or else it might lead to a role conflict (Orb et al., 2001). We think that there are no easy answers to these ethical concerns as they originate from different ontological and epistemological backgrounds. Therefore we warmly invite the international nursing community to further explore the topic and participate in the ‘debate’. On behalf of the research team, Sincerely, Declaration of Competing Interest There is no conflict of interest related to the submitted response to commentary. ==== Refs References Gallego B. Westbrook M.T. Dunn A.G. Braithwaite J. Investigating patient safety culture across a health system: multilevel modelling of differences associated with service types and staff demographics Int. J. Qual. Health Care 24 4 2012 311 320 22687703 Hallberg I.R. Comment on Wendt et al (2022) “Exploring infection prevention practices in home-based nursing care: a qualitative observational study.” (Wendt et al. 2022) Int. J. Nurs. Stud. 2022 10.1016/j.ijnurstu.2022.104190104190 Health & Safety Commission ACSNI Human Factors Study group: Third report. Organising for Safety 1993 HMSO London Hessels A. Larson E. Relationship between patient safety climate and standard precaution adherence: a systematic review of the literature J. Hosp. Infect. 92 4 2016 349 362 26549480 Olsen R.M. Bjerkan J. Patient safety culture in Norwegian home health nursing: a cross-sectional study of healthcare provider's perceptions of the teamwork and safety climates Saf. Health 3 1 2017 1 8 Orb A. Eisenhauer L. Wynaden D. Ethics in qualitative research J. Nurs. Scholarsh. 33 1 2001 93 96 11253591 Wendt B. Huisman-de Waal G. Bakker-Jacobs A. Hautvast J.L. Huis A. Exploring infection prevention practices in home-based nursing care: a qualitative observational study Int. J. Nurs. Stud. 125 2022 104130

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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Elsevier Inc. S0022-4804(21)00388-7 10.1016/j.jss.2021.06.019 Society of Asian Academic Surgeons Reinventing Yourself Virtually: Fifth Annual Society of Asian Academic Surgeons Virtual Conference Somasundar Tharun a1 Dimick Justin B. a2 Wong Sandra L. b2 Gosain Ankush c2 Zheng Feibi d2 Lee W.P. Andrew e2 Yip Linwah f2 Brahmbhatt Tejal S. g⁎ a Department of Surgery, Boston University School of Medicine, Boston, Massachusetts, USA b Department of Surgery, The Geisel School of Medicine at Dartmouth, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA c Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center, Children's Foundation Research Institute, Le Bonheur Children's Hospital, Memphis, Tennessee, USA d Department of Surgery, Houston Methodist Hospital, Weill Cornell Medicine, Houston, Texas, USA e Office of the Provost and Dean, University of Texas Southwestern Medical Center, Dallas, Texas, USA f Department of Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA g Department of Surgery, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts, USA ⁎ Corresponding author. Department of Surgery, Boston University School of Medicine, 840 Harrison Ave, Dowling 2 South, Suite 2509, Boston, MA 02118, 617-414-5683 (telephone), 617-414-7398 (fax). 1 TS and TSB compiled a transcript of the session and critically revised the document. 2 TSB, JBD, SLW, AG, FZ, WPAL, LY all contributed by participating in the panel as outlined in the manuscript and its critical revision. 14 7 2021 11 2021 14 7 2021 267 612618 29 3 2021 10 6 2021 10 6 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Virtual forms of communication have been integrated into academic surgery now more than ever. The COVID-19 pandemic accelerated its implementation in an effort to support social-distancing. Academic surgery is now learning valuable lessons from early experiences to optimally integrate this communication mode. The Society of Asian Academic Surgeons convened an expert panel during the society's fifth annual meeting that explores these lessons. Realms of virtual communication including meetings, networking, surgery department administration, social media, application processes, and advice for early or mid-career academic surgeons are explored. Virtual conferences pose a new challenge by removing the in-person component that is evident to be integral to networking, collaboration, and all aspects of academic socialization. Strategies such as creating virtual chat rooms, mentor-mentee virtual introductions, and deliberate interactions can enhance the experience. Virtual administrative meetings require special attention to preparation and strategies to insure engagement. Social media can be a valuable tool to integrate into academic careers but special attention needs to be made to utilize it deliberately and not to shy away from our individuality. The interview process can be enhanced when made virtual to give opportunities to those typically disadvantaged in the usual, in-person process. Keywords Society of asian academic surgeons Academic surgery Virtual conference Virtual meeting Virtual networking Social media ==== Body pmcDue to the ongoing COVID-19 pandemic, the fifth annual Society of Asian Academic Surgeons Conference took place in a virtual format in late September 2020. Of this year's four sessions organized by our Program Committee, led by the Program Committee Chair, Dr. Jennifer Kuo, the second session entitled, “Reinventing Yourself Virtually,” was a panel discussion which focused on how medical professionals can adapt and make the most of the increasing presence of the virtual world in their medical practice and academic career. The moderators of the session were Dr. Tejal Brahmbhatt, Assistant Professor of Surgery, Boston University School of Medicine and Dr. Linwah Yip, Associate Professor of Surgery at the University of Pittsburgh School of Medicine. The panel for this session represented a diverse group of surgical organizations. Dr. Sandra Wong serves as Professor and Chair of the Department of Surgery at the Geisel School of Medicine, Dartmouth. Dr. Justin Dimick is Professor and Chair of the Department of Surgery at the University of Michigan. Dr. Ankush Gosain is Associate Professor of Surgery and Pediatrics at the University of Tennessee, Health Science Center, and Dr. Feibi Zheng is an Assistant Professor of Surgery at the Weill Cornell Medical College and an endocrine surgeon and Assistant Clinical Director of Surgical Quality and Population Health at Houston Methodist Hospital. Dr. W. P. Andrew Lee is Executive Vice President for Academic Affairs, Provost, and Dean at University of Texas Southwestern Medical School. With the rise of virtual forms of communication that have been acutely and forcibly amplified by the pandemic, professionals now have no choice but to learn how to navigate through this new world of communication. While the pandemic made these virtual modes the sole form of communication out of necessity, this evolution will not be going away even as the rest of the world starts to resume its new normalcy. Networking, finding mentors, and collaborating academically and clinically, to name a few, have virtual analogs to the traditional in-person setting. Our panelists use their experiences piloting through these changes in their respective environments to discuss many important aspects of being a medical professional in this growing virtual professional world. The edited transcript of the session is as below. Dr. Brahmbhatt Whether or not we like it, the world is becoming very small, and the evolution of human socialization has noticeably reached a pivot point. As we've gone through the turn of the century, partially exacerbated by events like the pandemic, lives and interactions are becoming increasingly more metaphysical or “virtual.” This virtual world is here to stay and is becoming an ever noticeably more important aspect of our lives. Our panel has been convened for us to explore this new world, in the context of reinventing ourselves virtually. How has the virtual world evolved from your vantage point to become something that needs to be addressed and needs to be consciously looked at? Leadership and Running a Surgery Department in the Virtual World Dr. Wong There's nothing like a good crisis to accelerate things which were already happening, and in truth, most of us were already getting much more facile with using virtual platforms. But, there was a point where we just couldn't have in person meetings anymore and that really hastened the need to get to the point where we had to be much more virtual. Dr. Dimick As we entered the pandemic, I thought about “What have we lost?” due to the social disruption and, as a leader, “What are we doing to fill the gaps?” We have certainly not found all the answers; but we have engineered solutions in two categories: 1) local social connections and 2) national professional networks. These networks serve as important hubs for social and scientific collaboration. We have been working to create new virtual forums to fill those gaps. As a leader, I think about how I can facilitate those. In particular, I strive to look out for those people who are the most vulnerable for slipping through the cracks in the transition. Dr. Wong That's a good framework. We are all learning “on the fly.” There was a critical point at which we just had too many people in a conference room and there was no feasible way to have these meetings safely. In a fell swoop we converted to virtual. Locally, groups really had to adapt pretty quickly to smaller in person meetings or convert to virtual bigger group meetings or conferences. We all had to learn how to run better meetings in both local and national situations. We all know our pet peeves about virtual meetings and there were a couple that really drove us to think about different ways to run meetings. The first one is when people are clearly not listening. For example, during a discussion you call on somebody and they say ‘oh I'm sorry, could you repeat that last point?’ and it was just clear that they were on their smartphone. On the other hand, saying we need everybody to pay attention doesn't necessarily feel good either. These conflicting approaches drove a lot of us to start thinking about how to run a better meeting. One mistake I was making at the very beginning was just treating meetings like regular in-person meetings but on a virtual platform. Zoom time is compressed time so having the same length meeting may not work. Meetings can be shorter but have really high-end content. This keeps everybody engaged and energized, and people can always continue the conversation offline. In addition, there is always this fussing around with your audio because you're on mute and then you're trying not to mute. I learned to pause for an appropriate amount of time so that people can unmute themselves and actually get their point across without being run over by other people's conversations. And depending on the size of the group I think video is appropriate sometimes but when the group is too big, video is actually tough to do. You really have to think about the use of the chat box or the hand raised function, and you have to know your group. A lot of it is prepping for the meeting and making sure that there's intentional content so people aren't just online to be online. Going virtual created an impetus for me to really prep and be prepared for meetings especially on a local level. National meetings or bigger meetings have changed dramatically. Converting to virtual interviews for jobs and residency positions created a rapid need to learn how manage camera presence. Dr. Dimick Locally, we are working to re-establish the connections that we lost such as the ad hoc connections and the social sense of cohesion among members of our department. We focused on reimagining those virtually and keeping an eye on vulnerable members of our community. For local networking, a solution from a leadership perspective was to intentionally create those communities, such as virtual M&Ms, journal clubs, or other ritual gatherings. In addition, we encouraged our faculty to create virtual hubs of research activity. In our department, we have a Center for Healthcare Outcomes and Policy, a Center for Basic and Translational Sciences, Center for Surgical Training and Research, and the Center for Global Surgery. Each one has created virtual touch points with their members to make sure that they're fostering connections. My job as a leader is to make sure that these connections are happening in this new, more virtual world. We have now just started to do this more intentionally. When we go into a virtual world, existing networks strengthen because people fall back on these; and it makes it harder for new people to access and enter. For example, a medical student at Michigan very early in the pandemic, had switched to surgery late. You can imagine that this created an incredible stress, especially during a pandemic. She and I discussed this challenge, how first-generation students and those from underrepresented minority backgrounds who may not have existing networks are perhaps most vulnerable. Other vulnerable people include research residents who have completely had their national opportunities for networking wiped out. Also, junior faculty who are working to develop a national reputation, who can no longer present at national meetings. As a leader I think a lot about how to help these individuals recreate these opportunities. If you are one of the individuals, you need to be intentional and proactive about creating networks. I think it's completely acceptable in the virtual world to do what you would normally do in the hallway in a national meeting, but by email and set up a zoom meeting. If you ask a leader to set up a zoom meeting and want to talk about your grant or whatever it is, feel free to be proactive about it and create your own network. If you have a national group of people that are in your specialty area or research area, set up periodic monthly meetings and create your own community. I see a lot of this happening. For example, one of the fun things about being virtual is that junior faculty from other institutions attend our health services research workshop in a virtual forum. In a virtual world, we do not have geographic boundaries. This is an opportunity. Look to find these communities and we should collectively use this as an opportunity to further breakdown barriers. If you don't have access to a community, build one. I guarantee you other junior faculty from other places have like-minded ideas and would be interested in participating. Dr. Yip Dr. Lee do you have thoughts about what things you've done at UT Southwestern in terms of maintaining cohesion, building communities, and maintaining communication? Running a Virtual Meeting Dr. Lee I believe that for any meeting, whether virtual or in person, it's most productive when everyone participating in the meeting is fully engaged. First of all, it's very difficult to have a meeting that is more than 10 people, and still keep everyone engaged. Secondly, it's bad enough during the in-person days for distractions to occur. We all have difficulty resisting the temptations of looking at our phone, getting a text message, or answering an email. Lack of engagement can be even worse in a virtual meeting because when people go off the video they may be either checking the emails, eating breakfast, speaking on the phone, or doing whatever else. Therefore, for the meeting that I run, I ask people to keep their video on and to be fully engaged. Furthermore, when you have a group of people, there are often some who are vocal and some who are generally quiet. Thus, I pay particular attention to the quiet ones and would specifically call on them because, from experience, they can have very valuable input, but just have maybe a greater threshold for speaking up in the group. Those are the things that I pay attention to particularly in a virtual meeting. I want to emphasize a point that Dr. Wong made about preparation for a meeting. I'm a firm believer that, whether it's a virtual or in-person meeting, the productivity is directly proportional to the amount of preparation that one puts into the meeting ahead of time. This can include talking with some participants ahead of time, getting the number and sequence of agenda items just right, or anticipating people's reaction including resistance to some ideas. The more planning and forethoughts one puts into it, the more productive and efficient the meeting will be. When it comes to a virtual meeting, the somewhat paradoxical aspect is that I found it to be easier to schedule meetings because during the pandemic people are not traveling. One caveat though: I've been through some days where I have one virtual meeting after another, which I think is bad for one's physical and mental health, not to mention productivity. So I have made my meetings no more than 45 minutes long, so I can have at least 15 minute breaks to look in my inbox, to do other things, to get up and walk around, or to prepare for the next meeting. I find that to be helpful for me. Networking Virtually Dr. Yip Several have brought up valuable points about one who is trying to move up in organizations and is trying to build research collaborations. How do we do that now that we are all virtual? What are things that people can do to try to make new connections? Dr. Gosain I think Drs. Wong, Dimick and Lee have said many of the things that I was going to say, in a much more erudite fashion than I'm capable of saying them, but I'll try and reinforce some of those points. I think that one of the things that has not changed from the pre pandemic era to now is being intentional in preparing for national meetings. Even before the pandemic, I would tell my trainees and my research mentees that you want to be very intentional about what your plan is during a national meeting. You're not going to the meeting just to present your research and give your talk, but you're also mapping out the talks that you want to attend and the senior mentors that you want to track down and speak to. You should have a plan for potential networking connections before you go to the meeting, and make sure that you accomplish everything that you have laid out. This has obviously changed a little bit with the new format, but I think you can still be intentional about it with some advanced planning. In fact now this is a little bit easier because before you might have had three different abstract presentations that you want to see that are in different parts of the conference, and they're all happening at the same time. Now you can see all of them because they're often prerecorded and you're not missing content. You may miss out on some of the chance interactions that happened in the hallways, but if you're going to a meeting and your plan was to track down Dr. Dimick and have a sidebar conversation with him about your future career, your research, methodology, etc., you can and should still reach out in advance to schedule these conversations. I think that most people in academic spheres that I've met are going to be pretty receptive to that. They will carve out that time to meet with you and that's true whether you're a medical student or a mid-career faculty and trying to move up along the tenure and promotion pathway. One other critical aspect that was touched upon is making sure that voices are heard. This is something that I learned from Dr. Carla Pugh when I was a junior faculty member and was on a committee that she was running. She was always very intentional about going through the list of people on phone calls and making sure that every voice was heard and had an opportunity to comment before moving on to the next agenda item. I think that has become somewhat easier in the era of video conferencing because you have the visual cue. You can go around the screen and say okay, of the 12 people on screen, I want to hear from Dr. Brahmbhatt, I want to hear from Dr. Yip, I want to hear from Dr. Dimick, systematically. Using Social Media in a Professional Setting Dr. Brahmbhatt One very useful aspect of the virtual world is that across large physical distances, people use different modes of social media to communicate. Using Twitter as an example, we will poll our audience to see how many, for example, use Twitter within the context of their professional career. Dr. Zheng I use Twitter predominantly to keep up with my fellow endocrine surgery colleagues. It has been actually very useful for me in terms of keeping up with interesting cases that they might post or new surveys that are coming up from the AAES that they want some surgeon participation in. Another medium that I found to be really helpful for me, something that existed before the pandemic, is one of our specialty specific Facebook groups called Surgeon Moms Group, or what I refer to as my personal old boys’ club. Through that group, I've had multiple opportunities to promote other women surgeons as well as be promoted myself. For example, earlier last year one of the surgeons was trying to find a guest editor for an issue of Surgical Clinics of North America. I volunteered, and she put me in contact with the consulting editor. This year I was responsible for putting together a panel at the American College of Surgeons on perioperative pain management, and three of the four surgeons that I recruited for that panel were from the Surgeon Moms Group. Dr. Brahmbhatt In looking at the poll results, about two thirds of the respondents are using Twitter within a professional context. Since we don't know the demographics of our poll takers, this leads me to wonder whether the comfort with platforms like this has a generational aspect where many that did not grow up with social media simply have more difficulty accepting a transition to using social media altogether. Dr. Gosain So I'll just comment a little bit. We are focusing in on Twitter, but I think more broadly speaking, one of the things that the pandemic has encouraged people to do is to really embrace all digital tools and go beyond social media. There are other ways of organizing your life as far as to do lists or task managers, electronic lab notebooks, ways of communicating, Zoom, Teams, etc. You can try and use 10 different platforms and you're unlikely to be productive, or you can identify how one or two of these platforms can be adapted to your professional needs and then be phenomenally productive. My research group for example uses Slack as a messaging platform, both for clinical and basic science research. We're able to share files with collaborators all around the world, and that's extremely helpful. I have my specific use cases for Twitter that are probably very different from each of the panelists here. Again, you should identify your professional needs that are not being met or not being optimized with the tools you have in front of you and how can you intentionally fill that gap. Dr. Wong It has been a great opportunity for people who were previously not as engaged on those platforms to become engaged. For people who aren't currently active on social media, it's never too late to take this opportunity to start. People who are engaged are always happy to get other people engaged. Dr. Brahmbhatt One of the things that is lost and that many miss, is the physical connection that we sometimes have with people. That physical connection, to some degree, is a way for people to promote themselves in their careers. What do you think we could do with the virtual world in this aspect?  What kind of mechanisms do you think we could employ in the virtual world to help promote our careers? Dr. Dimick As an individual, I struggle with the notion of using social media platforms as promotion per se. I tend to approach it with the idea that it's about dissemination of work, discussion, and interaction around ideas, and networking to promote collaboration. But building a brand for yourself and self-promotion only gets you so far. There should be substance behind the style. As a department chair, this year the only way outside applicants are going to interact with us is through our website and through social media platforms. We have really doubled down on what we're doing and pushing out through our social media channels. We have a communications team that is constantly producing content. For individuals, I think of Twitter as probably the best professional interacting social media site. Think of it as like a national meeting in that if you're not involved you're missing out. It probably is at least as big and as influential as going to the American College of Surgeons. And if you're involved in the community, you know what I'm talking about. If you're not, you wouldn't know that it is an incredibly rich interactive source whether it's for your scientific community or learning. I'd have to say my primary use of social media right now is listening; and I get a ton of information. I follow a lot of different people, and especially in the current climate, social media helps me understand what's going on in the world outside of my own little corner. Virtual Presence of Applicants Dr. Yip One of the questions from the audience asks “How much do your programs look at other people's social media accounts?” For example, applicants who are coming in for residency applications or medical student applications. In the future, do you think that programs are going to be looking at this to get a better sense for who the applicants are? Dr. Zheng Even pre-pandemic, after we made our rank list, we would just do a quick sweep through the social media accounts of the top list, just to make sure there weren't any red flags. Obviously, we do recognize that these are people's personal lives and might not totally be representative of who they are, but we're just looking for things that we, we as a group have decided, are potential red flags that may not fit with the culture of our institution. Dr. Gosain I completely agree with Dr. Zheng that, even before the pandemic, we would Google applicants in advance of interviewing them, not so much looking for red flags but looking for other things that I might learn about them that might be interesting points of discussion. I think that I would turn it around and say that from an applicant's standpoint this is an opportunity to define your brand. Knowing that people are going to be searching for information about you, it's helpful to know what's out there about yourself. I myself have Google alerts for myself set up so I know about any web search changes. But, you can be proactive about it and know what's out there on Twitter, on Facebook, or create your own website. You now have this opportunity to cultivate your brand that is going to last well beyond COVID. Medical School Application Process Dr. Brahmbhatt Dr. Lee, at the University of Texas Southwestern, what kinds of things is the medical school looking at from your point of view that might be relevant to applicants? Dr. Lee The rules are completely changed for this season because people can apply to as many programs as they want and not worry about traveling expenses. Furthermore, because everything is in the virtual format, we have invested significant energy and resources in presenting the campus via digital media. We have a campus that we love to show off in person, but short of that we have made high-quality interactive videos that applicants can access from anywhere. We have also talked with department chairs and program directors extensively about preparing for the virtual interviews. We're also preparing our medical students for virtual interviews. Thus it's a different application season with a different set of rules this year, and those institutions that are best prepared will come out ahead at the end. Translating Personal Interactions to the Virtual World Dr. Yip Another comment from our audience suggests that one of the most important parts of networking is creating personal interaction beyond just talking about work issues. These are the watercooler conversations or the things that you discuss when you run into people in the middle of the hallway and get ideas. How do you do that without the normal face to face social interactions? Dr. Gosain I think that's a very prescient question. We touched on this topic a little bit in that you can be intentional yourself, as much as you would have been pre-pandemic and reach out proactively to people that you want to meet and want to talk to. And as I said before, I think the vast majority of those you reach out to will embrace that and take the time. A shameless plug for the SAAS happy hour after the keynote this afternoon where we're trying to recreate some of that spontaneity. The other thing I would say is that, as faculty, try and be intentional about bringing your trainees into your office when you are having video conferences with other PI's, other faculty around the country, and have them be part of those conversations. This would be similar to when your mentees might walk up to you when you're talking with one of those other faculty at a meeting, and then you introduce them and everybody starts getting to know each other on a deeper level. My research mentees are often in my office when I'm having a video chat with a collaborator somewhere else. You start by creating an opportunity for your mentees to be present, hear the conversation and then hopefully participate or lead the conversation and get something deeper out of the connection. Dr. Zheng As somebody who is early career, I found that during the pandemic you fall back upon some of your existing relationships. Most of us have actually been to at least a couple of meetings, and we have a list of people that we've met but then actually never followed up with. This is actually a good time for that backburner research idea. If some of your other research has slowed down because primary data collection is really hard to do right now, you can start re-exploring some of those old ideas. You can go back and reach out to those people that you've met who you never deepened your connection with. Dr. Wong I'll amplify that. I actually think that's such a key point, because at meetings, we meet a ton of people standing in the hallway. Now if there's a reach out it's totally intentional and it's a little bit more one-on-one. So while we all miss that personal interaction I think we can still make personal connections. From a professional perspective, the networking we do now can be more intentional and I try to look at the positive in that. It's really important for us to remember that we need to continue to promote our trainees and our younger faculty. Dr. Brahmbhatt One question from the audience that I believe is difficult to answer is “What are the red flags for an institution for residency and fellowship, besides the obvious things like sexism and racism, which has recently garnered a lot of national attention on Twitter?” Recently there has been a lot of chatter in social media about how an institution defines behavior that could be reflective of things not consistent with the institution's mission, and how do you handle that? Where do you start trying to tackle this particular aspect of encountering something that gives you pause? How do we interpret social media identities and not compromise promoting diversity and inclusion? Diversity and Inclusion Dr. Lee We're very proud of our effort in diversity, equity, and inclusion, even though there is always more we can do. Our underrepresented minority students represent 27% of our entering class this year. Incidentally, since this is the SAAS meeting, I also want to mention the percentage of our Asian medical students is 41%, as compared to white students at 32%. We have invested a lot of resources and personnel in actively recruiting URM students and making them feel they belong at UT Southwestern. We have a great student inclusion program, not just for the medical school but also for the graduate school and Health Professions School as well. In the last few months, we have held several listening sessions that I participated in, along with our Associate Deans where the graduate students and the medical students could speak up and voice their concerns. We invited the campus police chief to these sessions so that he could address some of the concerns that were raised directly. And we have taken a lot of the feedback from the students, acted upon them, and maintained the communication so our efforts are sustained. In the meantime, we continue to enhance our infrastructure for diversity, we've added two new leadership positions in the Dean's office for diversity and inclusion, because I firmly believe the way to address diversity and inclusion is not just to recruit more diverse individuals when they have already become candidates, but to enhance the pipeline of applicants. One of the things of which we took note is the stagnation of the number of African American men entering medicine. When you look at the surgical specialties such as mine (plastic and reconstructive surgery), there are very few underrepresented minority members. So it's not just recruiting diverse candidates, but we need to encourage our residents, our medical students, and even students at an earlier level to consider medicine, and in our case, consider surgery to enter our ranks. One of our faculty members began a program called Black Men in White Coats for African American male high school students. Our URM faculty attend special functions as role models to impress the students that medicine is very much a reachable goal for them, that they can get a lot of fulfillment from a career in medicine, and that there are resources to help them to enter the career. So I believe it takes a multifaceted and sustained effort for us to make progress in this area. Advice for Early or Mid-career Academic Surgeons Dr. Yip I think those are really important points. It likely means everybody has a lot of work to do, and it is nice to hear some of the things that are happening. Do you have any tips or advice for somebody who is perhaps an early or mid-career faculty person for what can be done now to set yourself up for the future in today's virtual world? Managing Your Time/pace Dr. Zheng I think one of the things that's been most challenging for me, as a mother of young children, is this fear of missing out during this virtual age because literally you could be on some sort of Zoom meeting or some sort of Grand Rounds every hour of every day. Every once in a while, I have to tell myself that I need to spend time with my kids, and it's okay to disengage and focus on my family. I would say that for the people out there who feel like they're falling behind, everybody is struggling with the same issues during this time, especially those of us who are parents. It is okay that you're doing things at your pace and not someone else's pace. Dr. Dimick For my closing comment, I'll agree with those words that you have to give yourself a break, and you have to pick and choose intentionally what you think is going to be the most high yield, and then just get rid of the F.O.M.O. for everything else. I think the two most valuable things you can do as a junior faculty is to create a community, whether it's locally, nationally, or internationally and then bring in expertise; and be creative about how you do that. Figure out how to use Twitter productively. But don't let social media rule you. Finally, I just wanted to touch on the point that we talked about what's unprofessional. I think I'll sum it up by saying: don't be afraid to be your whole self. We want you to be your whole self and to come into our programs and enrich our programs with what is unique about you. Most surgical chairs in most programs will want that. There are important causes to advocate for and we want people who are change agents in our residency programs. So do not be afraid to engage in social justice in your social media. I can't speak for every chair, but I speak for all of those that I'm close with and I know that we want you to bring your whole self to American surgery. Dr. Gosain I agree with everything that has been said. I think that there is an opportunity to really embrace the pandemic, and the barriers that it's gotten rid of, as far as broadening your horizons. So, Dr. Dimick talked about how he spends a lot of time listening and consuming information. I think that for our trainees or faculty at any level, previous huge barriers have been time and money to travel and go to meetings and broaden your horizons, nationally or internationally, and I think some of those barriers are now gone. Because meetings are virtual there's a lot of content that's being put out by societies like ours and many others, in webinar formats that are free of charge and asynchronous. This is an opportunity to really broaden your horizons and identify what other societies are doing, including meetings you might not have gone to otherwise. Embrace this opportunity to really educate yourself, which will potentially change your perspective, both in your clinical practice and in your research program. Dr. Wong These are all great points, and I guess the only thing I would add is that I would embrace this as opportunity, especially if we're thinking about creating opportunities. It is a lot easier to attend virtual meetings, but it's also a lot easier to have virtual meetings. Dr. Dimick gave an example about doing research meetings with people from outside of their own institution and we should be able to do that for seminars and grand rounds. It really opens up an opportunity to increase the speaker pool which is a really great thing for people who may be at the point in their career where giving Grand Rounds would really make a difference for them professionally. Most of us are craving some content that's a little bit different and hearing from people who we don't normally hear from and meeting new people. While many of us were very optimistic that we'd be back to in person meetings, clearly for the foreseeable future we're going to be virtual. I know a lot of our medical students are thinking about doing their own conferences because they hunger for it too, and it is a great opportunity for them to plan meetings and to actually engage themselves and others as lectures and speakers. Institutional Adaptation to COVID Dr. Yip Many are worried about meeting academic targets and feel that the pandemic is putting them behind. Dr. Lee, from aDean's perspective, could you provide examples of what those in leadership positions are doing to try to make any COVID-related allowances. Dr. Lee Like other institutions, we had to make rapid adjustments at the beginning of the pandemic including the closure of our research laboratory, which we now have ramped back up, as well as the closure of many non-therapeutic clinical trials. So we recognized very early on that some faculty members’ academic productivity progression may be slowed because of circumstances beyond their control. We instituted supplemental guidelines to our promotion criteria where faculty on the tenure track can apply for a one-year extension to their “tenure clock.” All such applications have been approved so rather than eight years, faculty who need an extra year will now get nine years to achieve tenure. Invited lectures to other institutions, and panel/paper presentation at national meetings that are cancelled due to the pandemic are still taken into account by our Promotion Committee. However, the criteria for promotion didn't change, and our promotion committee has continued to meet virtually through the pandemic season. Dr. Brahmbhatt One theme that has dominated our conversation today, is that the virtual world is clearly here to stay. There are definitely domains within every aspect of our academic lives that this could touch upon. Being mindful and deliberate about it clearly sounds like something that is of priority as this is ever evolving. The virtual world is something that will continue to interdigitate every aspect of our academic lives from the classroom, to the operating room, to the clinic, and to the conference room. Table Summary points. Running a virtual meeting: Be prepared Encourage input from all participants High-end content Networking in a virtual world: Create and engage in local and national communities Be intentional and reach out to mentors and collaborators Include those who may be vulnerable such as students, residents, junior faculty Facilitates breaking down geographic barriers and may expand opportunities Using social media: Engagement is important and an opportunity to fill in professional gaps

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10.1016/j.jss.2021.06.019

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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)00509-0 10.1016/j.envres.2021.111215 111215 Article The possible role of the surface active substances (SAS) in the airborne transmission of SARS-CoV-2 Ciglenečki Irena a∗ Orlović-Leko Palma a Vidović Kristijan ab Tasić Viša c a Ruđer Bošković Institute, Laboratory fot Physical Oceanography and Chemistry od Aquatic Systems, Division for Marine and Environmental Research, Zagreb, Croatia b National Institute of Chemistry, Hajdrihova 19, SI, 1000, Ljubljana, Slovenia c Mining and Metallurgy Institute, Bor, Serbia ∗ Corresponding author. 30 4 2021 7 2021 30 4 2021 198 111215111215 10 12 2020 18 3 2021 20 4 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Surface active substances (SAS) have the potential to form films at different interfaces, consequently influencing the interfacial properties of atmospheric particulate matter (PM). They can be derived from both human activities and natural processes and can be found in an indoor and outdoor environment. This paper's fundamental question is the possible role of the SAS in stabilizing respiratory aerosols in the closed space. In that context, we discuss results of preliminary measurements of the SAS and dissolved organic carbon (DOC) concentrations in the water-soluble fractions of PM2.5 and PM10 that were sampled simultaneously in primary school inside and outside of the building. The concentrations of SAS were determined using highly sensitive electrochemical measurements. It was observed that SAS and DOC concentrations have been enhanced indoor in both PM fractions. Consistent with these results, a discussion arises on the possibility that SAS could play a crucial role in respiratory droplet dispersion as stabilizers, especially in a closed space. At the same time, we assume that they could prolong the lifetime of respiratory aerosols and as well viability of some (possible SARS-CoV-2) virus inside of the droplets. Graphical abstract Image 1 Keywords SARS-CoV-2 Atmospheric surfactants Indoor atmosphere Bioaerosols Particulate matter (PM2.5, PM10) AC voltammetry ==== Body pmc1 Introduction The review of the literature shows that there is still unknown on the conditions that facilitate the airborne transmission of the new virus- SARS-CoV-2 (Alsved et al., 2020; Asbach et al., 2020; Brlek et al., 2020; Jayaweera et al., 2020; Li et al., 2020; Morawska et al., 2020; Morgenstern, 2020; Tang et al., 2020; WHO, 2020). However, particulate matter especially fine particles (PM2.5) have been discussed as a potential SARS-CoV-2 carrier (Jayaweera et al., 2020; Morgenstern, 2020; Nor et al., 2020 and references therein). In the study by Nor et al. (2020) SARS-CoV-2 RNA was confirmed within fine (PM2.5) indoor ambient particles sampled in hospital wards with different infected clusters. The term "airborne transmission" assumes the infections spreading through exposure to the fine solid particles or deliquescent particles containing an infectious virus, at greater distances or over longer times. These particles are originated from respiratory droplets produced by an infectious; after water is evaporated, solid particles are formed by condensation of low volatile compounds (Morawska, 2006; Lindsley et al., 2010, 2013 bib_Lindsley_et_al_2010). A previous study has reported that the oral cavity is the major source of expiratory droplets of which the vast majority during speech and coughing is less than 2 μm (Johnson and Morawska, 2009). Atmospheric particles in the Aitken (smaller than 100 nm) and accumulation mode (100–1000 nm) (Seinfeld and Pandis, 2006) have a shorter relaxation time and stopping distance, and consequently can remain suspended for several days, resulting in a long-range transport (Willeke and Whitby, 1975; Hinds, 1999; Moris et al., 2015; Leonardi et al., 2020 and references therein). On the other hand, aerosols, as well as other colloidal dispersions (particles ranging from 1 nm to 1 μm), are thermodynamically unstable systems because they tend to minimize surface energy by coagulation of particles and finally by their sedimentation (Goodarzi and Zendehboudi, 2019, and references therein). The heterogeneous systems with phase boundaries could be stabilized by surface active substances (SAS) defined by amphiphilic nature (hydrophobic and hydrophilic segments). Therefore, they tend to adsorb at the phase boundaries such as liquid/liquid, liquid/solid, liquid/gas, or solid/gas (Attwood and Florence, 1983; Myers, 1999). By adsorption and solubilization, SAS may influence physico-chemical properties and structure of natural interfaces, and in that way moderate transfer processes between different phases. Depending on the adsorbate and nature of the interface, adsorption of SAS can be influenced by hydrophilic, electrostatic hydration, and hydrophobic interactions (Conway, 1976; Westall, 1987). Surface active compounds are found in a different part of the environment as a product of natural processes and human activities (Olkowska et al., 2014; Renard et al., 2016; Orlović-Leko et al., 2016; Ciglenečki et al., 2020). The latter is very often, especially in the case of detergents, less abundant in mass while on the contrary, they may represent the most surface-active organic material in the different environmental compartments (Olkowska et al., 2014). SAS were confirmed in various atmospheric samples (cloud water, rainwater, snow, aerosol particles) (Facchini et al., 1999; Orlović-Leko et al., 2004, 2009, 2010, 2020; Renard et al., 2016; Kroflič et al., 2018; Gérard et al., 2019; Cvitešić Kušan et al., 2019). In microscopic systems with relatively large surface areas, such as atmospheric droplets and deliquescent particles, adsorption of SAS at interface solutions/air is highly important (Morris et al., 2015; Malila and Prisle, 2018; Lin et al., 2020; Bzdek et al., 2020). The presence of SAS can cause significantly reducing the surface tension of droplets. That is relevant for increasing the population of droplets of smaller size which are more easily transported from one place to another (Bzdek et al., 2020). Thereby, the surface tension of aqueous particles is discussed as a function of the relative humidity (RH) in the atmosphere (Morris et al., 2015; Bzdek et al., 2020); smaller particles need a higher ambient RH to maintain equilibrium than larger ones. The SAS have been detected in the indoor air as well (Morawska et al., 2003, 2006; Wolkoff et al., 1998; Ahmad et al., 2009). The surfactants derived from cleaning agents can irritate the human respiratory system leading to allergies and asthma as well as to dried eyes by reducing the surface tension of the tear film (Olkowska et al., 2014 and references therein). Once inhaled, atmospheric surfactants may interact with a pulmonary surfactant, i.e., those that cover lung alveolar surface and include polar phospholipids and hydrophobic specific low-molecular proteins (Exerowa et al., 2014). The pulmonary SAS, which is crucial for the normal breathing function, could also contribute to the dissolution of aerosol particles (Brimblecombe and Sukhapan, 2002). As stated, respiratory aerosols are created when air passes over a layer of fluid (Fiegel, 2006; Morawska, 2006, 2009). The surfactant increase in overall droplet formation has been already discussed as a cause for smaller droplets. The small particles (Aitken and accumulation mode, 10–1000 nm) least likely impact and settle on surfaces and can float on the air and spread much further following the airflow stream especially after being dried (Lindsley et al., 2013). Consistent with these considerations, in this paper, we discuss the possibility that the organic SAS could play a significant role in stabilizing the "cloud of these respiratory particles". We assume that SAS's presence could prolong bioaerosols' lifetime, protecting particles from the deposition process. At the same time, SAS that are adsorbed on the respiratory droplets bearing virus can keep it for a longer time viable. According to the above discussion, it could be expected that SAS can act in respiratory droplets dispersion as a stabilizer, which can result in their persistence in the air for more extended periods, especially in the closed environment. As support to our assumption, we present results of total SAS and DOC concentrations in the water-soluble organic carbon (WSOC) fraction of PM2,5 and PM10 collected parallel in the primary school's outdoor and indoor environment. These measurements confirmed up to two times higher concentrations of the SAS in the indoor samples. 2 Methodology 2.1 Sampling and preparing analyte solution The PM2.5 and PM10 were sampled on the Quartz fiber filters (Whatman QMA, 47 mm diameter) at the primary school located in the city center, from 8 a.m. to 8 p.m. (teaching hours) and from 8 p.m. to 8 a.m. (no teaching hours) (Kovačević et al., 2015). The indoor and outdoor measurements were carried out at the same time using low volume (2.3 m3/h) samplers, LVS3 (Sven/Leckel LVS3) with size-selective inlets for PM10 and PM2.5 fractions. PM concentrations were obtained from gravimetric analysis of sampled filters. Indoor samples were taken in the hallway with a floor space of 60 m2 and a volume of 200 m3. The hallway was occupied with an average of 80 pupils during the breaks between classes, while ambient air was sampled on the balcony about 10 m above the ground. There was no additional ventilation system in the building. Pre-conditioning and post-conditioning of filters were undertaken in accordance with the general requirements of EN 12341:2014. Approximately 15% of all gravimetric samples were collected as field blanks. After preconditioning in a clean room, filters were weighing using the Mettler Toledo semi-micro balance (with min. 10 μg mass resolution). PM concentrations were calculated using the average (each filter is measured three times) weight of filters. The water-soluble organic components (WSOC) of atmospheric indoor particles were extracted by placing half of the filters in 25 mL of MilliQ water (Millipore Corp.) for 24 h and filtered through 0.7 μm GF/F filters (Whatman, 47 mm diameter). In this analyte solution, the SAS was quantified by highly sensitive electrochemical measurements (EM) of the adsorption effect at the mercury electrode. 2.2 Electrochemical measurements The study was performed by highly sensitive electrochemical in-house methodology (Ćosović and Vojvodić, 1989) developed for determination of SAS concentration in different water samples (Orlović-Leko et al., 2004, 2004, 2009, 2010, 2016, 2020; Ćosović et al., 2007; Ciglenečki et al., 2020 and references therein) by the phase sensitive alternating current (AC) voltammetry, PSACV (out-of-phase signal, frequency 77 Hz, amplitude 10 mV). Electrochemical analyser μAutolab-type (Eco Chemie B.V., The Netherlands) equipped with GPES 4.6 software (Eco Chemie B.V., The Netherlands) was used. Adsorption effect of SAS was measured at the hanging mercury drop electrode (HMDE, Metrohm, Switzerland) of the surface area A = 0.022 cm2. The measured potentials are reported with respect to the Ag/AgCl (3 M KCl) electrode. The base electrolyte was 0.55 M NaCl. The concentration of SAS was expressed as equivalent (eqv.) in mg L−1 to the nonionic surfactant, polyoxy ethylene-t-octylphenol (Triton-X-100, Rohm and Hass, Milano, Italy) based on external calibration by using Triton-X-100 calibration curve (conc. range between 0.01 and 1 mg L−1) in 0.55 M NaCl (Ćosović et al., 2007; Orlović-Leko et al., 2020). Triton X-100 is likely to represent many atmospheric SAS, as its critical micelle concentration (CMC and γCMC) is consistent with those reported in atmospheric PM (Leonardi et al., 2020). The limit of detection (LOD) for voltammetric SAS determination was 0.01 mg L−1 eqv. of T-X-100, with limit of quantification (LOQ) of 0.03 mg L−1. In addition, WSOC content, i.e. DOC was determined by the high-temperature catalytic oxidation (HTCO) method at a TOC-VCPH instrument (Shimadzu, Japan) (Ćosović et al., 2007; Cvitešić Kušan et al., 2019). The WSOC sample aliquot (15 mL) was acidified with 2 M HCl to pH ~ 3 in order to eliminate the inorganic carbonates. The concentration of each sample was calculated as an average of three to five replicates. The LOQ was 0.228 M for dissolved organic C with reproducibility of 5%. 3 Results and discussion Results of the voltametric measurement of SAS in the WSOC fraction of atmospheric PM2.5 and PM10 samples collected simultaneously in the indoor and outdoor air of the school environments were presented in Fig. 1 and Table 1 . Additionally, in Table 1, the DOC concentrations and relevant data about PM samples were listed (Kovačević et al., 2015).Fig. 1 Examples of AC (out-of-phase) voltamograms recorded in solutions of 0.55 mol dm−3 NaCl (electrolyte) and electrolyte with analyte containing aerosol particles: A) PM2.5 sampled from 8 a.m. to 8 p.m. and B) PM10 sampled from 8 p.m. to 8 a.m. (starting potential, EA = −0.6 V vs. Ag|AgCl reference electrode, accumulation times at the stating potential of 30 s). The decrease in the capacitive current (i/nA) value is a direct measure of the SAS presence. Fig. 1 Table 1 Sampling information, mass concentrations of PM (Kovačević et al., 2015), DOC and the results of electrochemical measurement (EM) of SAS in the WSOC fractions of aerosols samples (PM2.5 and PM10). Table 1No. Fraction PM Indoor/outdoor Time of sampling Conc. PM [μg m−3] EMa Δi [nA] SASb [mg L−1 eqv. Triton-X-100] DOC [mg L−1] 1 PM 2.5 Out day 20.0 100.8 0.15 0.82 2 PM 2.5 In day 26.6 156.0 0.24 1.30 3 PM10 out night 47.4 88.9 0.12 0.90 4 PM10 in night 40.0 160.0 0.25 1.17 a EM-electrochemical measurement: Δi is a difference between the capacity current obtained in the solutions of electrolyte and electrolyte with analyte; it is a direct measure for SAS adsorption at the Hg electrode surface (Ćosović et al., 2007; Orlović-Leko et al., 2020). b SAS concentration in WSOC analyte solution. As can be seen from Table 1, indoor concentrations of PM2.5 collected during school teaching hours were higher (26.6 μg m−3) than outdoor concentrations (20.0 μg m−3) while the PM10 concentrations found in classrooms, during no teaching hours, were slightly lower (40.0 μg m−3) than in outdoor air (47.4 μg m−3). Kovačević et al. (2015) concluded that the high outdoor PM concentrations and resuspension of particles could be the main possible reasons for the elevated indoor PM concentrations. Generally, in a closed space without strong particle sources, the indoor PM would be expected to be the same as, or lower than, outdoor levels. In addition, indoor PM levels have the potential to exceed PM levels in the ambient air. This observation could be coupled to the particles' generation by specific sources and/or personal activities of occupants. However, indoor PM can also be of biological origin (Cox et al., 2020). In this work, in the analyte solutions prepared from the same aerosol samples (Table 1), quantification of SAS was done by using calibration curve of the Triton -X-100 (external calibration) (Ćosović et al., 2007) and measured values of Δi [nA] (Fig. 1) from the recorded AC voltammograms. The decrease in the capacitive current value in the analyte samples, concerning the current of the supporting electrolyte (Δi), is a direct measure of the surface-active organic material adsorbed at the working (in our case Hg) electrode. It is evident that the surface activity of the WSOC fraction of PM indoor (PM2.5 = 0.24 and PM10 = 0.25 mg L−1 eqv. Triton-X-100) was up to two times higher than that of the PM outdoor samples (PM2.5 = 0.15 and PM10 = 0.12 mg L−1 eqv. Triton-X-100). Accordingly, higher DOC concentrations were measured in PM WSOC fractions of the indoor samples (Table 1), indicating that the indoor air contained 40% more DOC. However, in this preliminary set of measurements, the difference in the indoor SAS concentrations during teaching and not teaching hours was not expressed. Considering that the school is located in a street with heavy traffic, it could be expected that indoor SAS level would be affected by traffic emissions, i.e., by compounds that migrate from the ambient air. However, our results point to the importance of the indoor generated SAS fraction of DOC. The cleaning agents with surface-active components are likely to be crucial in explaining this observation, especially after teaching hours, when cleaning of classrooms is usually performed. It is known that household cleaning activities and evaporation processes of semi-volatile compounds (anionic and nonionic surfactants) from different surfaces can be a significant source of SAS in the closed environment (Ahmad et al., 2009; Olkowska et al., 2014). In addition, several other studies suggest that in the indoor environment, the SAS derived from cleaning agents could be argued as agents promoting sick-building syndrome (Wolkoff et al., 1998; Sukhapan and Brimblecombe, 2002). Disinfectants are especially highlighted as the most dangerous group of cleaning agents which significantly contribute to the SAS pool (Wolkoff et al., 1998). This study's general idea is that indoor generated SAS could stabilize bio-aerosols and influence its microphysics processes. Due to their dual nature, SAS with molecules that contain hydrophobic and hydrophilic parts tend to adsorb at different phase boundaries. Adsorption of SAS at the solutions/air interface is significant for small atmospheric droplets due to their relatively large surface areas (Prisle et al., 2010; Malila and Prisle, 2018; Lin et al., 2020; Bzdek et al., 2020). The lower the droplet's radius or, the higher the surface to volume ratio, the higher the WSOC concentration in organic PM was measured (Ervens and Volkamer, 2010). The specific properties of SAS could cause a reduction of surface tension of droplets (an essential factor for their vapor pressure) and, in that way, increase and stabilize the population of droplets of the smaller sizes. Such SAS coated droplets or deliquescent particle could remain suspended longer in the air (Tessum et al., 2014; Bzdek et al., 2020). Additionally, it is essential to know that SAS can either enhance or slow down the transfer of water across the surface according to the hydrophilic or hydrophobic nature of the aerosol organic coating. These organic coats are common on aerosol particles and might retard the evaporation of molecules present in the water phase, reduce gas transfer, influence chemical reactions, and alter the absorption or reflection properties of aerosols (Renard et al., 2016; Bzdek et al., 2020). This kind of thinking certainly goes in favor of observations that the SARS-CoV-2 virus has spread much faster in the indoor environment. 4 Conclusion The preliminary results of highly sensitive AC voltammetry out-of-phase measurements of the indoor and outdoor SAS concentrations in the WSOC fraction of the atmospheric PM2.5 and PM10, sampled inside and outside of the primary school building, during and after school time, have been shown that indoor SAS concentration was almost two times higher than those found in the outdoor air. Such results indicate that indoor SAS could have different sources than outdoor, as already reported (Ahmad et al., 2009). We have assumed that SAS derived from cleaning agents can be the main reason for the observed higher indoor SAS levels, especially after teaching hours. However, biosurfactants from bioaerosols (Cox et al., 2020) that are expected to be enhanced during teaching hours by breathing, speaking, singing, coughing, sneezing when children are in school, could also be a critical source of dissolved organic carbon with surface active properties (SAS). These surface-active organic carbon molecules, present in a sufficient concentration, can potentially act as a stabilizer of the cloud of respiratory droplets in the indoor air/environments. SAS that are adsorbed on the respiratory droplets bearing virus (potentially SARS-CoV-2) can prolong their lifetime in the indoor air, and in that way may significantly contribute to airborne transmission of COVID-19. Further research to prove posted assumptions in this paper is planned, including analyses of fine (PM2.5 and smaller) indoor aerosols from different community locations (schools, gyms, first aid stations) on possible SARS-Cov-2 presence. Author contribution Irena Ciglenečki and Palma Orlović-Leko: conceptualization, writing – original draft, writing – review & editing. Irena Ciglenečki: idea, resources, funding acquisition. Kristijan Vidović: discussion, writing – review & editing. Viša Tasić: sampling, manuscript editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This work arose from discussions on organic matter characterization in natural samples including marine aerosols, during the implementation of the Croatian Science Foundation project: IP-2018-01-1717, MARRES. The authors thank A. Cvitešić-Kušan for part of the SAS measurements; T. Lovrinčević for help in designing of graphical abstract, and the anonymous reviewers for their valuable comments that improved the manuscript. ==== Refs References Ahmad A. Othman M.R. Latif M.T. 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PMC9750166

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2022-12-16 23:24:14

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Environ Res. 2021 Jul 30; 198:111215

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Environ Res

10.1016/j.envres.2021.111215

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==== Front J Clin Epidemiol J Clin Epidemiol Journal of Clinical Epidemiology 0895-4356 1878-5921 Published by Elsevier Inc. S0895-4356(22)00301-8 10.1016/j.jclinepi.2022.11.013 Editor's Choice November 2022 Editor's choice Tugwell Peter Tovey David 14 12 2022 11 2022 14 12 2022 151 A1A2 © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcCOVID-19 has provided the stimulus for evidence-focused collaboration globally on many fronts, with over 20 articles here in the Journal of Clinical Epidemiology (JCE). A commentary by McCaul et al. in this issue is one of several reports in the JCE [1,2] that have emerged as a result of the COVID-19 Evidence Network to support Decision-making initiative and the Global Commission on Evidence. This is a global initiative that brought together more than 50 of the world's leading evidence synthesis groups, with the objective to support and promote better co-ordination, leading to improved prioritization, fewer decision critical gaps, reduced waste, and consistently reliable quality across the evidence ecosystem. This was achieved partly through the work of several working groups each tasked with finding solutions in key areas. The commentary reports on the work of two of these groups, the synthesizing and recommending groups. In addition to the substantive outputs produced by the network, members of the collaborative have supported various methodological advances in the evidence ecosystem such as speed, living systematic reviews, and recommendation mapping. One important issue is addressing the major needs of large- and middle-income countries as research resources are so inequitably distributed that it is rare to find trained local systematic review teams that can take existing high-quality systematic reviews and guidelines from high-income counties and then adapt these to their systems so that they are contextualized to their settings. In this commentary, McCaul et al. provide an example from South Africa of what can be achieved, which is described as a part of the network's work in support of evidence and guidelines producers. Forty-two rapid systematic reviews have been completed and contextualized, as commissioned by the National Essential Medicine List Committee to inform their guidelines on how health-care workers should treat people with COVID-19. No doubt stimulated by the COVID-19 pandemic as described above, acceptance and adoption of rapid systematic reviews completed in a few weeks is one of most striking changes adopted by many organizations such as Cochrane. However, as the Cochrane Rapid Review Methods group point out, “while rapid review producers must answer the time-sensitive needs of the health decision-makers they serve, they must simultaneously ensure that the scientific imperative of methodological rigor is satisfied.” In order to adequately address this inherent tension, methodological research and standard development are needed [https://methods. cochrane.org/rapidreviews/welcome2022]. In this issue, Beecher et al. report on using the James Lind Alliance methodology with a panel of multiple stakeholders (including patients and the public and those who conduct these reviews) to answer the top 10 unanswered research questions about rapid review methodology. The top 10 prioritized questions are wide-ranging; they include establishing the research question, which stakeholders to involve, including from underserved groups, comparison of findings from rapid reviews with traditional full systematic reviews, and which methods of a full review can be omitted. This list provides a parsimonious but critical agenda for future research to directly improve the robustness of rapid reviews. The authors call for funders to incorporate these priorities into research agendas. One of the ways that the field of clinical epidemiology is evolving is through the increasing interest in moving from efficacy trials, which assess “can it work?”, to effectiveness (or pragmatic) trials, “does it work in practice?” [3]. Pragmatic trials thus focus on providing a realistic estimate of benefit and harm that can be generalized to real-world practice. However, as Taljaard et al. report, despite this concept being taught as a basic principle in all critical appraisal and graduate courses, only few studies report sufficient design characteristics to justify the term pragmatic. They emphasize that the characteristics are multidimensional and argue that they should address each of the following elements included in the PRECIS tools: eligibility, recruitment, setting, organization, delivery flexibility, adherence, follow-up, relevant outcomes, and inclusion of all data in the analysis (https://www.jclinepi.com/article/S0895-4356(16)30410-3/fulltext). They reviewed 415 primary trial reports from ClinicalTrials.gov that used terms or phrases known to be associated with pragmatic approaches to trial design. A third failed to provide any justification, and most of the remainder failed to describe more than 1 or two of the 9 characteristics. Indeed, many studies included design elements more consistent with efficacy/explanatory trials so should not have been classified as pragmatic trials. Because even efficacy randomized controlled trials, let alone pragmatic trials, are not available for many interventions, clinical epidemiology is broadening its focus to endorsing well-planned observational designs [4]. One model that provides real-world evidence and is growing in frequency is the “target trial” design [5]. The target trial design provides an explicit framework modeled on randomized clinical trials, for comparative effectiveness research using big data. JCE is receiving increasing numbers of examples. One such example in this issue addresses a thromboprophylaxis clinical challenge in COVID-19 patients. The authors have emulated the standards of a controlled trial in comparing the observational data results of 1200 patients to assess the risks of bleeding and coagulopathy with and without an increased baseline risk. Although no randomized controlled trials were available for this question, we encourage more evidence comparing this target trial methodology to traditional controlled trials of the same question paper. ==== Refs References 1 Dewidar O, Kawala B., Antequera A et al. Methodological guidance for incorporating equity when informing rapid policy and guidelines development. J Clin Epidemiol, 150, p142-p153 2 Stewart R. Boutron I. Akl E.A. The Global Evidence Commission’s Report provided a wake-up call for the evidence community J Clin Epidemiol 2022 10.1016/j.jclinepi.2022.10.002 3 Haynes B. Can it work? Does it work? Is it worth it? The testing of healthcare interventions is evolving BMJ 319 1999 652 653 10480802 4 Deeks J.J. Dinnes J. D'Amico R. Evaluating non-randomised intervention studies Health Technol Assess 7 2003 1 173 5 Hernán M.A. Sauer B.C. Hernández-Díaz S. Platt R. Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses J Clin Epidemiol 79 2016 70 75 27237061

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PMC9750167

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2022-12-16 23:24:14

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J Clin Epidemiol. 2022 Nov 14; 151:A1-A2

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J Clin Epidemiol

10.1016/j.jclinepi.2022.11.013

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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)00530-2 10.1016/j.envres.2021.111236 111236 Article Impact of COVID-19 lockdown on NO2 and PM2.5 exposure inequalities in London, UK Kazakos Vasilis a Taylor Jonathon b Luo Zhiwen a∗ a School of the Built Environment, University of Reading, Reading, UK b Faculty of Built Environment, Tampere University, Tampere, Finland ∗ Corresponding author. School of the Built Environment, University of Reading, United Kingdom. 4 5 2021 7 2021 4 5 2021 198 111236111236 16 12 2020 19 4 2021 23 4 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Amid the COVID-19 pandemic, a nationwide lockdown was imposed in the United Kingdom (UK) on March 23, 2020. These sudden control measures led to radical changes in human activities in the Greater London Area (GLA). During this lockdown, transportation use was significantly reduced and non-key workers were required to work from home. This study aims to understand how population exposure to PM2.5 and NO2 changed spatially and temporally across London, in different microenvironments, following the lockdown period relative to the previous three-year average in the same calendar period. Our research shows that population exposure to NO2 declined significantly (52.3% ± 6.1%), while population exposure to PM2.5 showed a smaller relative reduction (15.7% ± 4.1%). Changes in population activity had the strongest relative influence on exposure levels during morning rush hours, when prior to the lockdown a large percentage of people would normally commute or be at the workplace. In particular, a very high exposure decrease was observed for both pollutants (approximately 66% for NO2 and 19% for PM2.5) at 08:00am, consistent with the radical changes in population commuting. The infiltration of outdoor air pollution into housing modifies the degree of exposure change both temporally and spatially. Moreover, this study shows that the impacts on air pollution exposure vary across groups with different socioeconomic status (SES), with a disproportionate positive effect on the areas of the city home to more economically deprived communities. Keywords COVID-19 Lockdown measures Population activity Population-weighted exposure change Concentration change Socioeconomic inequalities ==== Body pmc1 Introduction Ambient air pollution levels are strongly associated with human activities, such as transportation, and can have significant population health impacts; in the UK, for example, air pollution is thought to contribute 28,000 to 36,000 excess deaths a year (PHE, 2019). On March 23, 2020, the UK government imposed a nationwide lockdown due to increasing transmission of coronavirus, which subsequently led to radical changes in human activities including the transportation and time-activity behaviours of the population. The COVID-19 lockdown offers a unique natural experiment to evaluate and quantify the impact of rapid changes in people's activity patterns and emissions on air pollution and subsequent population exposure. Numerous studies have already evaluated the impact of COVID-19 lockdowns on outdoor air quality worldwide (Muhammad et al., 2020). The vast majority of these studies show that radical shutdown measures in big cities led to lower and less variable outdoor concentrations of urban air pollutants (Mahato et al., 2020, Mbandi, 2020, Sharma et al., 2020, Tanzer-Gruener et al., 2020, Zhao et al., 2020). However, most of these studies focus solely on the reduction of outdoor concentrations, and a relative few studies have assessed the impact of lockdowns on population exposure to urban air pollution (Williams, 2020; Zhu et al., 2020). This is important, as exposure to outdoor air pollution also occurs in non-outdoor microenvironments (MEs) due to the infiltration of polluted air; for example, housing is thought to significantly modify population exposures (Taylor et al., 2014). Exposure is also dependent on the time-activity profiles of the population. In cities under lockdown, much of the population radically changed their daily activities, including working from home instead of their usual workplace and by avoiding all unnecessary travel. For example, the lockdown led to a greater than 70% decrease in public and private transportation in London, likely reducing exposure to outdoor generated air pollution (Williams, 2020). Therefore, to assess spatial and temporal changes in exposure during the lockdown, key factors such as changes in population activity patterns and concentrations in different microenvironments, where people spent their daily time (for example at home, workplaces, in transit, and outdoors) need to be considered. In addition, there may be important differences in exposure between population groups. Socioeconomic inequalities in concentration and exposure to outdoor pollution are well established (Tonne et al., 2018; Shiels et al., 2017; Stringhini et al., 2017; Rivas et al., 2017; Rotko et al, 2000, 2001) and there is emerging evidence of similar disparities indoors (Ferguson et al., 2020). Several studies have shown a strong connection between communities of either lower or higher socioeconomic position and increased concentrations and exposure to urban air pollution (Hajat et al., 2015). In US, several studies have shown that deprived areas experience higher levels of outdoor pollution exposures (Su et al., 2012; Gray et al., 2013; Hajat et al., 2015). In London and other big cities, it has been suggested socioeconomic inequalities in outdoor levels of traffic-related air pollution are driven by differences in road traffic volume, which affects the amount of emissions (Brook and King, 2017, Padilla et al., 2014, Tonne et al., 2018) Therefore, changes in road traffic following the lockdown provide a unique natural experiment opportunity to investigate exposure disparities across socioeconomic groups, with potential changes in outdoor generated air pollution resulting in differences in exposure changes across different such groups. In this study, we seek to 1) understand how the COVID-19 lockdown changed population-level outdoor air pollution exposures, and 2) evaluate whether changes in exposures varied across socioeconomic groups and explore the role of traffic-related pollution on exposure inequalities. To achieve this, we aim to quantify and illustrate the spatio-temporal change in population exposure to outdoor-generated air pollution in London during the lockdown period relative to previous years for the same period. By accounting for the spatial and temporal variability of outdoor air pollution, dwelling Indoor-Outdoor (I/O) ratios (the proportion of outdoor air pollution that infiltrates indoors), and changes in diurnal population activity patterns, we assess the impact of the lockdown on the population exposure levels. Moreover, we also evaluate socioeconomic differences in exposure reduction. Understanding the spatial and temporal distribution of air pollution across different MEs, and subsequent exposure inequalities, is important to develop policies to reduce inequalities and improve sustainable development. 2 Material and methods 2.1 Study period and air quality data Lockdown measures were applied to the Greater London Area (GLA), UK, on March 23rd, 2020. To examine the impact on short-term air quality, a one-month period (23 March to 22 April) in 2020 was compared against the same calendar period, averaged from 2017 to 2019. The hourly monitoring data for two major traffic-related air pollutants (NO2 and PM2.5) were obtained from the London Air Quality Network (LAQN) (King’s College London,). For NO2, 98 monitoring sites were included, whereas for PM2.5 only 21 monitoring sites were available for the study period. Average hourly concentrations for each hour of the study period were calculated for each monitoring site. The Voronoi Neighbor Averaging (VNA) tool in QGIS was used to spatially interpolate hourly data between monitoring sites, estimating hourly outdoor concentrations pre and post-lockdown at Lower-Super Output Area (LSOA) level (a census unit with an average of 1500 residents). 2.2 Microenvironments and infiltration of outdoor pollutants Four different MEs have been considered in this work:(i) The home; (ii) Work, assuming that all individuals work inside buildings; (iii) Transport, including public or private transportation (i.e., bus, private car/taxi and train) to travel; and (iv) Outdoors, including people who are walking or cycling. We only consider exposure to outdoor-generated pollution. Estimates of indoor pollution from indoor sources are highly uncertain and have not been considered in this study due to a lack of data. The infiltration of outdoor NO2 and PM2.5 into the home ME was considered using previously derived hourly I/O ratios across GLA for the same calendar period (Taylor et al., 2014). This data includes the hourly average I/O ratios of 1.5 million London dwelling (covering approximately 46% of London dwellings), and accounts for seasonal wind pressures and summertime window opening; here we use hourly average dwelling I/O ratios for April to represent the lockdown period, averaged by LSOA (Fig. S1). For both pollutants, central London shows the lowest I/O ratios, likely due to the newer building stock and the large number of flats in multi-dwelling buildings, where the available surface for infiltration is considerably smaller. The average I/O ratio in the GLA ranges from 0.40 to 0.63 for PM2.5 and 0.15–0.40 for NO2. The I/O ratio is likely to significantly modify population exposure to outdoor air pollution due to the extended amount of time that people spent at home during the lockdown period. The spatially and temporally resolved I/O ratios provided by Taylor et al. (2014) have been derived only for domestic buildings and are not representative of commercial areas and workplaces. Thus, for the workplace, we have selected representative values according to the available literature. For PM2.5, we selected an average value of 0.60 (Singh et al., 2020; Soares et al., 2014; Hänninen et al., 2011) and for NO2, we chose to use an average value of 0.68 (Hu and Zhao, 2020; Kornartit et al., 2010). For outdoor air pollution exposure in the transportation ME, we calculated the in-vehicle concentration using a mass balance equation (Smith et al., 2016). The same input values as Smith et al. (2016) were used except for the outdoor concentrations which were updated. As in Smith et al. (2016), the surface area of each commuter was derived as per Song et al. (2009). 2.3 London population data and activity The spatial distribution of the London population was derived from 2011 census data from the Office of National Statistics (ONS, 2011), representing 95% of households, and was assumed to be the same in both the baseline and study periods. The spatial distribution of the population was considered during daytime (defined as the period from 7:00am to 19:00) population (Fig. S2a in SI) and night-time (defined as the period from 20:00 to 06:00am) population (Fig. S2b in SI). The Census usual resident population was used for the night-time period and the workday population for the daytime period. As expected, under normal circumstances the daytime population density is much higher in Inner London due to much of the population commuting into the city centre, whereas the night-time distribution is much more uniform across the GLA. 2.3.1 Pre-COVID For the pre-COVID-19 period, we analyzed the amount of people at home, at work, in transportation and outdoors using Census and London Travel Demand Survey (LTDS, 2011) data. We used the Census workplace population (the number of people in each LSOA that were in their workplace during a usual weekday) to calculate the percentage of people normally at work. From the LTDS, the total number of trips per hour of a weekday, and the number of average trips per person were used to calculate the number of people that use public transportation each hour. As there was no data on the movements of populations in each LSOA, the temporal variation of the percentage of people in each ME was estimated using the LTDS data and the daytime and night-time population distributions. LTDS also provides data on the number of people commuting at each hour, defined as travelling between the home and workplace. Thus, at each hour the respective number of commuters was subtracted from the workplace population. The diurnal variation of the population activity in the four MEs is presented in Fig. 1 A. During the morning and afternoon rush hours, the percentage of people in the transportation ME peaks. During daytime, more than 30% of people are either at work or in transportation, while at the night after 22:00 more than 90% of the population are at home. In this study, children were included in the home population.Fig. 1 Diurnal variation of the percentage of people in each ME: a) during the pre-COVID-19 period and b) during the COVID-19 period (first lockdown). Fig. 1 2.3.2 COVID lockdown For the COVID-19 period, changes in population daily movements between MEs were obtained from App Maps (Apple,) and Google statistics (Google statistics,). Google statistics used the median value of each day of the week in January 2020 (i.e., 5-week period from 3 January until 5 February) as baseline, while App Maps used January 13, 2020. Both datasets show significant changes in population travel and working behavior after March 23rd, with transportation reduced by more than 70%, and more than 75% of the working population remaining at home. The remaining population at work during the lockdown period likely consists largely of key workers, who continued going to their workplace. The data also shows that less than 1% of the total population are outside at most hours of the day. This data was used alongside spatial distribution of the usual resident (night-time) population in order to estimate the variation of the percentage of the population in each ME during the COVID-19 period (Fig. 1B). 2.4 Population-weighted exposure The population-weighted mean exposure (PE) is estimated from the concentration level in each ME and the amount of people that spent time in those MEs. The PE was calculated as:(1) PE=∑i=1nCi,t,j×Pi,t,jPT where PE is the population weighted mean exposure for a population, n is the number of the populated geographical units (here LSOAs); C and P are the mean concentration of the pollutant and the number of people, respectively, for LSOA i, microenvironment j and hour t of the day; and P T is the respective total population. 2.5 Socioeconomic analysis To compare concentration and exposure across socioeconomic status (SES), we used LSOA – level deprivation data from the 2019 Index of Multiple Deprivation (IMD). The IMD is an overall relative measure of deprivation constructed by combining seven domains of social and economic deprivation (i.e.,’ Income Deprivation’, ‘Employment Deprivation’, ‘Education, Skills and Training Deprivation’, ‘Health Deprivation’, ‘Crime’, ‘Barriers to Housing and Services' and ‘Living Environment Deprivation’). The IMD was linked to population exposure in each LSOA based on the usual resident population distribution. We then examined the statistical relationship between the IMD and the average change of concentration and exposure to PM2.5 and NO2 at LSOA-level using Spearman's correlation. Our goal was to show the strength of association between the time-averaged air pollution reductions and SES. Spearman's correlation was chosen for the statistical analysis because it is considered as a suitable technique to correlate ordinal variables, such as the ranked IMD data, and has been previously used to correlate UK IMD data with different environmental exposures (Tonne et al., 2018). 3 Results 3.1 Spatial and temporal change in air pollution concentration and exposure 3.1.1 Spatial distribution of concentrations and exposure reduction Hourly average outdoor concentrations changed significantly following the COVID-19 lockdown. Before the lockdown, the three-year London average (2017–2019) NO2 and PM2.5 concentrations from 23 March to 23 April were 45.1 μg/m3 and 18.2 μg/m3, respectively. After implementation of lockdown measures, the average outdoor concentrations of NO2 and PM2.5 during the same period were 26.7 μg/m3 and 15.7 μg/m3 (Table 1 ), respectively, representing a decrease of 40.9% ± 6% for NO2 and 13.9% ± 4% for PM2.5.Table 1 Total exposure and concentrations before (2017–19) and during the lockdown period (2020). Table 1MEs 2017–19 2020 NO2 (μg/m3) PM2.5 (μg/m3) NO2 (μg/m3) PM2.5 (μg/m3) Outdoor 45.1 18.2 26.7 15.7 Transportation 37.4 15.1 22.1 13.1 Work 28.9 10.9 17.1 9.4 Home 11.9 9.9 7 8.6 Total Exposure concentration 16.2 10.3 7.7 8.7 As changes in outdoor concentrations of NO2 and PM2.5 due to COVID-19 shutdown have been presented and analyzed by several studies, we focus here on changes in population-weighted exposure across different environments. We estimate that transportation was the most highly polluted ME during the lockdown with an average exposure of 22.1 μg/m3 for NO2 and 13.1 μg/m3 for PM2.5, while the average workplace concentration was 17.1 μg/m3 for NO2 and 9.4 μg/m3 for PM2.5. The home ME had the lowest NO2 and PM2.5 concentrations with 7 μg/m3 and 8.6 μg/m3, respectively. Population and time-weighted exposure is impacted by population activity patterns, I/O ratios and outdoor concentration. The indoor levels of outdoor air pollution are directly affected by the I/O ratios of dwellings, thus modifying exposures to outdoor air pollution. Here, we found that the average population-weighted mean exposure decreased following lockdown from 16.2 μg/m3 to 7.7 μg/m3 (a 52.3% reduction) for NO2, and from 10.3 μg/m3 to 8.7 μg/m3 (a 15.7% reduction) for PM2.5. The fact that a much higher percentage of people were spending their daytime inside their homes (an increase from 50% to 90%), has led to a greater reduction in exposure during the lockdown due to the protective role of housing on outdoor air pollution exposures (Smith et al., 2016). Fig. 2, Fig. 3a show the concentration and exposure change across London. For NO2, the greatest exposure reductions (55–71%) were observed in Inner London (Fig. 2a). PM2.5 showed the greatest reductions (28%–32%) in East and West areas of Inner London (Fig. 3a). Relatively few areas in West London showed only minor reductions in exposure (<2%). The spatial variation of exposure reduction is also in-part due to changes in the distribution of the population across London and the I/O ratios of the dwellings where they spend their time. The large decrease in exposure in Central London was due to various factors, particularly the more uniform distribution of the population during the lockdown, when the population was not concentrated in central London during working hours (Fig. S2). Additionally, the lower average I/O ratios of dwellings (Fig. S1) and the greater reduction in outdoor concentrations (Fig. 2, Fig. 3) also contributed to reduced exposure. In contrast, some areas in western London, which showed higher I/O ratios (particularly PM2.5) and low reductions in outdoor pollution show comparatively low decreases in overall exposure levels.Fig. 2 Maps a) and b) illustrate the spatial distribution of average NO2 concentration and exposure reduction (%) during the lockdown period across London. Maps c) and d) illustrate the spatial distribution of average NO2 concentration and exposure reduction (%) at 08:00am. Fig. 2 Fig. 3 Maps a) and b) illustrate the spatial distribution of average PM2.5 concentration and exposure reduction (%) during the lockdown period across London. Maps c) and d) illustrate the spatial distribution of average PM2.5 concentration and exposure reduction (%) at 08:00am. Fig. 3 3.1.2 Temporal change in air pollution concentration and exposure Fig. 4 describes the average hourly reduction in concentration and population exposure to NO2 and PM2.5 during the lockdown. As expected, there is little difference between the concentration (Fig. 4a) and exposure (Fig. 4b) reduction during most hours of the day for both pollutants. However, during morning during rush hours the percent reduction fluctuates differently for both pollutants, which reveals the strong impact of the change in population activity on exposure. Both pollutants show the greatest exposure decrease during morning and evening peak rush hours. During those two time periods, the lowest percentage of people are inside the home relative to other hours of the day (Fig. 1) pre-COVID-19, and thus we expect to observe the most significant changes after lockdown measures at these times. In particular, there was the greatest reduction in population exposure for NO2 (66.1% ± 5.1%) and PM2.5 (19.2% ± 3.9%) at 08:00am.Fig. 4 Average diurnal a) concentration reduction (%) and b) exposure reduction (%) during the lockdown period (yellow represents NO2 and blue PM2.5). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Fig. 4 The spatial distribution of the concentration and exposure reduction at the time of the greatest hourly decrease (i.e., 08:00am) is illustrated on Fig. 2, Fig. 3b. NO2 exposures show the highest percent reduction (>65%) in Inner and Northwest London, while PM2.5 exposure is reduced more in the Northeast, South, and parts of Inner London. Because NO2 is strongly related to traffic, the most traffic congested areas of London, such as central London, show the highest exposure change. PM2.5 shows a slightly different and more uniform distribution of exposure reduction, due to factors discussed in section 3.1.1. 3.2 Socioeconomic status Air pollution concentration and exposure data are summarized to illustrate the differences between IMD classifications. Fig. 5, Fig. 6 present PM2.5 and NO2 concentrations and exposure differences between the two examined periods across each deprivation decile. For PM2.5, the concentration and exposure differences in the most deprived LSOAs (deciles 1, 2, and 3) demonstrate the lowest variability, while the LSOAs with moderate deprivation (i.e., deciles 4,5,6) show the largest variability. For NO2, LSOAs in IMD decile 2 show the highest average and the greatest variability for both concentration and exposure difference, while the least deprived LSOAs (i.e., decile 10) show the lowest variability and slightly lower average difference (8.6 μg/m3) compared to the most deprived (8.9 μg/m3). The magnitude of the variability in each IMD decile is likely influenced by the corresponding spatial variation of I/O ratios and outdoor concentrations among the LSOAs of each decile. The smaller variability across the deprivation deciles observed for PM2.5 reductions relative to NO2 may be explained by the less variable particle concentrations across London (Williams, 2020). Moreover, the reductions in concentration also indicate that highly deprived populations in London are disproportionately impacted by air pollution from traffic sources. For both pollutants, the results demonstrate a negative relationship between deprivation deciles and the average exposure and concentration difference during the study period (Table 2 ). Therefore, disadvantaged areas were associated with higher reduction of concentration and exposure to PM2.5 and NO2. Only a very weak association was found for NO2 with correlations of −0.11 and −0.05 for concentration and exposure, whereas the PM2.5 concentration and exposure difference were more strongly correlated with IMD. All correlations are statistically significant (p-value <0.05). This study provides evidence of weak associations, but in the direction of the predictions of several previous studies that suggest a great concentrations or exposure in the most deprived areas (Brook and King, 2017, Padilla et al., 2014, Tonne et al., 2018)Fig. 5 a) Variation of PM2.5 concentration difference and the total population of all LSOAs in each decile, b) Variation of PM2.5 exposure difference and the total population of all LSOAs in each decile. Fig. 5 Fig. 6 a) Variation of NO2 concentration difference and the number of people in each decile; b) Variation of NO2 exposure and the number of people in each decile. Fig. 6 Table 2 Spearman's correlation coefficient between deprivation index (IMD) and air pollution concentration (exposure) difference. Table 2 Concentration Exposure IMD NO2 PM2.5 NO2 PM2.5 −0.11* −0.25* −0.05** −0.26* *p-value <0.001, **p-value<0.05. 4 Discussion Lockdown measures in different parts of the world due to the COVID-19 outbreak have provided an opportunity to evaluate the human impact on the urban environment. In this work, we evaluate the relationship between population exposure and time-activity patterns, including the time spent indoors. We found a high average percent reduction in NO2 exposure (52.3% ± 6.1%) with the greatest decrease in Inner London, while PM2.5 exposure showed a considerably lower average percent reduction (15.7% ± 4.1%). The very high reductions in exposure to both pollutants during the morning rush hours show the strong influence of changes in population commuting. By linking population SES and exposure change, we demonstrate variation in air pollution exposure reduction following lockdown across IMD deciles, and provide evidence supporting the conclusion that deprived communities in London are disproportionately affected by road transport pollution. Numerous prior research studies have investigated and evaluated the influence of coronavirus on air quality globally, and several approaches can be broadly identified. According to recent literature, reductions in NO2 and PM2.5 concentrations during the lockdown ranged from 10% to greater than 50% worldwide (Fonseca, 2020, Tanzer-Gruener et al., 2020, Williams, 2020, Wu et al., 2021, Zhao et al., 2020) with the highest emission reductions observed during morning rush hours. Here, we estimate an average reduction of approximately 50% and 16% for NO2 and PM2.5, respectively. The radical changes in population activity and the significant change in the spatial distribution of the population are likely to have significantly contributed to this reduction in emissions. As with other studies, we estimated the greatest exposure reductions during morning rush hours and during the evening peak hours, particularly at 08:00am when there was the greatest reduction in population exposure for NO2 (66.1% ± 5.1%) and PM2.5 (19.2% ± 3.9%). The steep decrease in air pollution exposure levels during rush hours reflects the importance of the temporal variation of population activity and spatio-temporal variation of the domestic I/O ratios. Conversely, during night hours and early morning hours, the reduction in exposure was much lower. As the number of night workers is much lower than the number of day or evening workers and over 90% of the population was at home during night or early morning, only minor changes were observed to the population activity patterns at these times. Many large cities around the world demonstrated lower outdoor concentrations of air pollution during the quarantine measures, improving air quality (Arregocés et al., 2021; Kumar et al., 2020). However, it is worth noting that some studies show higher PM2.5 concentrations in several locations (Daniella Rodriguez-Urrego and Leonardo Rodriguez-Urrego, 2020) relative to the pre-covid period, and the effect of the lockdown on some pollutants might be still questionable. A direct comparison between studies is frustrated by the different periods and sites considered, and the methodologies used to quantify the changes. In the UK, a selection of studies have investigated the impact of the shutdown on the concentration of urban pollutants (Williams, 2020; Fonseca et al., 2020). However, there is little research on how changes in population exposure are distributed across urban areas, accounting for the spatial and temporal variability of the exposures in different MEs. Our novel approach includes hourly average I/O ratios of more than 1.5 million dwellings - averaged by LSOA - and estimates an average population exposure reduction of 66% and 19% for NO2 and PM2.5. For NO2, the highest reduction was observed in Central, Northwest and Southeast London and for PM2.5 in the West and East of Inner London. For both concentration and exposure, NO2 show notably higher reductions than PM2.5 post lockdown. This is likely due to a significant decrease in traffic-rated emissions in London, meaning pollutants that are strongly related to traffic emissions, such as NO2, are more significantly affected. On the other hand, for outdoor PM2.5, the contribution of local transport emissions is smaller than for NO2 (Reis et al., 2018) and particulate pollution may be influenced by other factors (for example, local meteorology, transboundary transport, resuspension and the use of fireplaces). Health studies have suggested that lower SES populations are more likely to suffer premature mortality from air pollution exposure than higher SES populations (Krewski et al., 2009). Multiple studies have been conducted in large cities and metropolitan areas around the world associating the SES with the air pollution concentration and exposure. Most of them demonstrate high associations between the most deprived areas and high outdoor (Sarmadi et al., 2020; Cakmak et al., 2016; Pinault et al., 2016; Padilla et al., 2014; Gray et al., 2013) and indoor concentrations (Ferguson et al., 2020). Here, we provide new information about the impact of lockdown measures on people across different IMD groups. Results indicate negative associations between the reductions of concentration and exposure during the lockdown period and the area-level deprivation status, where PM2.5 is more strongly correlated than NO2. Several studies conducted in large urban areas have presented similar outcomes (Padilla et al., 2014). In London, (Brook and King, 2017) predicted that reductions in exposure to NO2 would be higher for areas that fall within IMD decile 1 (most deprived) after the implementation of air pollution reduction measures. Furthermore, Tonne et al. (2018) analyzed the relationship between SES and outdoor air pollution, finding an exposure different of 0–1.9 μg/m3 between the highest and lowest household income groups, and greater reductions in air pollution in the least advantaged areas after the activation of the Congestion Charging Zone in London. The main strengths of our study are the large dataset, including population information at LSOA-level, travel behavior from a representative sample of the London population and the large spatio-temporal variability of the I/O ratios for dwellings. The indoor environment is protective of exposure to outdoor air pollutants and that is usually reflected in much lower exposures when Home MEs have been taken into account. Amid the pandemic lockdown measures, when more than 90% of the population had to stay at their home during the daytime, the incorporation of the spatial and temporal distribution of domestic I/O ratios when estimating the population-weighted exposure significantly modifies the magnitude and distribution of the exposure change. This study contains several limitations. The limitations is the quality of the derived air pollution data and the absence of meteorological effects. Because our study is based on recent measurements, most of the available concentrations for 2020 have not yet been fully ratified by the LAQN. However, in order to reduce the uncertainty and improve the quality of our data, we did not include any negative or unusually extreme hourly values to our analysis. A few monitoring sites did not provide 100% of the data for the whole study period and some hourly readings were missing (or not included). No sites provided less than 70% of the data (Lang et al., 2019; King's College London, 2015). Temporal and spatial variability of air pollution concentrations are subject to changes in emissions and meteorology, which may impact the exposure levels (Bujin et al., 2020). NO2 levels can be directly linked to the reduction of transport emissions due to its strong relation to traffic (He et al., 2020a, 2020b). However, transboundary transport of PM and precursors from mainland European sources and the associated meteorology play an important role in PM concentrations in London. Thus, post-COVID-19 concentrations might be different than pre-COVID-19 due to reasons that are not directly related to lockdown. The wind conditions during 2020 have been exceptional in many ways across the UK (Carslaw, 2020). Moreover, the lockdown period also coincides with the period of the year where there is an increased frequency of PM2.5 episodes in Europe (Air Quality Expert Group, 2020). Therefore, the lack of accounting for weather conditions in our assessment is likely to have affected our results and some reductions may have been over-estimated. However, our approach of averaging the same calendar period of the previous years might have the benefit of reducing meteorological variability. Another limitation is that exposure to other urban air pollutants was not considered, mostly due to data in availability. In this study we focused on the two most important major air pollutants for London's air quality (https://www.london.gov.uk/). Many air pollutants have common sources, and air pollution reduction strategies that take advantage of these common sources may achieve economies of scale that control strategies that target one pollutant at a time cannot. Moreover, pollutants can also be connected by similar precursors or chemical reactions once in the atmosphere. Thus, control strategies that target one pollutant may affect others, perhaps in unintended ways. A much denser network of monitoring stations was available for the NO2 compared to PM2.5. As the concentration of air pollution can change across small distances, the denser network can lead to higher prediction accuracy. In this work, roadside and urban background sites were included, with roadside sites mostly located within Inner London. The denser NO2 monitoring network and the smaller distances between the sites were able to provide adequate coverage of background sites for non-traffic locations. However, the interpolation of roadside measurements, especially for the less dense PM2.5 network, may have led to an overestimation of the impacts of reduced traffic by interpolating to non-traffic areas. The surrounding urban environment can significantly influence pollutant transport and concentration, and to account for this, high-skilled urban modelling accounting for complex urban morphology is required. However, this kind of advanced modelling was not feasible for this study, but could be incorporated to future studies. Moreover, schools and commercial buildings were assumed to have same values as home microenvironment and children were included in the home population. Finally, the average workplace I/O ratio used in this study was assumed from several European cities (Soares et al., 2014; Hänninen et al., 2004, 2011). Data on I/O ratios in commercial buildings, and for different type of workplaces are scarce. Therefore, it was assumed that the values demonstrated in Europe were also representative for London. This work utilized Google Statistics and App Maps to determine differences in travel patterns. Both App Maps and Google statistics are based on data sent from users' devices and users that opt-in to location history for their account, respectively. Consequently, those data sources contain limitations in terms of their representativeness of the overall population. Apple Maps has no demographic information about its users, making it difficult determine data representativeness. In the calculations, Google statistics includes only data from users that use their Google account and have opted-in to Location History. Those data also have to meet Google's privacy threshold. Consequently, this location data may not represent the exact behavior of the wider population. As described in methodology section, the IMD is based on seven main domains. The ‘Living Environment’ domain contributes approximately 9% to the production of the overall index and measures the quality of the local environment and the indicators fall into two sub-domains. The ‘indoors’ and the ‘outdoors’, which consists of two elements: air quality and road accidents. However, the already included ‘air quality’ element is not likely to have affected our calculations, because here we examined the associations between the IMD and the reduction of concentration and exposure. Other studies have already used IMD to investigate SES inequalities in air pollution (Brook and King, 2017, Sheridan et al., 2019, Tonne et al., 2018) Some segments of the working population – so-called essential or key workers - had to continue to travel to work in their original workplace during the lockdown period. When estimating the population-weighted exposure, we assumed that all SES groups are equally likely to stay at home during lockdown, however many essential workers are likely to be low SES individuals. Their total exposure to air pollution may still decrease due to the reduction in outdoor concentration, however the change in their exposure to air pollution would be different from other working groups because their daily activity during the lockdown would be the same as the pre-COVID-19 period. Due to the unavailability of data, essential workers could not be linked with the IMD analysis to investigate how this may impact exposure differences between IMD groups. By using the workplace population for the work ME, and by applying the mean percent reduction for the population that continued going to workplaces during the shutdown, we assume that the percentage of population in work ME during post-COVID-19 period (28%) represents essential workers. This percentage is consistent with the ONS estimate that essential workers are approximately 29.5% of London's workforce (ONS, 2020). While further work is required to understand uncertainties in travel and work patterns of low-SES essential workers, these results allow us to conclude that the lockdown provided significant exposure reductions to low-income communities in London. 5 Conclusions The implementation of stay-at-home measures due to the global outbreak of COVID-19 has offered a unique opportunity to assess the effect of the rapid changes in population activity patterns on air pollution concentration and population exposure. This study quantified and analyzed spatial and temporal changes in population-weighted mean exposure to air pollution of outdoor origin between the COVID-19 lockdown period and previous 3-year average during the same calendar period. Subsequently, we evaluated socioeconomic variation across the distribution of exposure change. We demonstrate that changes in diurnal population activity and outdoor concentrations have reduced exposure to air pollution, predominately during the morning rush hours. The average exposure to NO2 showed a greater than 50% reduction, which was consistent with the remarkable decrease in traffic levels, a major source of NO2. For PM2.5, the 16% decrease in average exposure could not be linked directly to the reduction in urban traffic, because other factors, such as meteorological conditions, may have affected the magnitude of the change in the outdoor concentrations. While there were not large inequalities in how the exposure change was distributed among people with different SES, our results provided useful evidence about the strength of association between the concentration and exposure reduction, and the impact on the most and the least deprived areas. By quantifying exposure reduction, and accounting for the significance of the time spent indoors and the spatio-temporal variability of average dwelling I/O ratios, this study offers insight into the effectiveness of extreme traffic-control measures on reducing the outdoor pollution and the exposure. Although these measures are extreme and highly unlikely to be adopted under normal conditions, this natural experiment offers the opportunity to assess the influence of some key elements (e.g., population activity, important indoor MEs) on population exposure, using largely real-world data. The estimated exposure reductions may provide best-case estimates of the degree to which more realistic control strategies for stationary and mobile urban sources, such technological (e.g., new-source certifications, retrofits of existing vehicles, etc.) or non-technological (e.g., management of transportation, etc.) may reduce exposures. The analysis of the SES inequalities across the distribution of the exposure reduction also demonstrates the importance of developing strategies that can reduce existing exposure inequalities. Credit author assessment Vasilis Kazakos: Data curation, Data analysis, Writing- Original draft preparation. Zhiwen Luo: Conceptualization, Methodology, Supervision. Jonathon Taylor: Data acquisition, Methodology. All: Writing- Reviewing and Editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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S0013-9351(21)00480-1 10.1016/j.envres.2021.111186 111186 Article Persistent high PM2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China Sulaymon Ishaq Dimeji a Zhang Yuanxun ab∗ Hopke Philip K. cd Hu Jianlin e Zhang Yang a Li Lin e Mei Xiaodong a Gong Kangjia e Shi Zhihao e Zhao Bin f Zhao Fangxin a a College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China b CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, 361021, China c Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, 13699, USA d Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA e Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China f Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, USA ∗ Corresponding author. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China. 27 4 2021 7 2021 27 4 2021 198 111186111186 19 11 2020 9 4 2021 12 4 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Lockdown measures to curtail the COVID-19 pandemic in China halted most non-essential activities on January 23, 2020. Despite significant reductions in anthropogenic emissions, the Beijing-Tianjin-Hebei (BTH) region still experienced high air pollution concentrations. Employing two emissions reduction scenarios, the Community Multiscale Air Quality (CMAQ) model was used to investigate the PM2.5 concentrations change in this region. The model using the scenario (C3) with greater traffic reductions performed better compared to the observed PM2.5. Compared with the no reductions base-case (scenario C1), PM2.5 reductions with scenario C3 were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. During high-pollution days in scenario C3, the percentage reductions in PM2.5 concentrations in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 6.10, 3.61, and 3.67%, respectively. However, significant increases caused by unfavorable meteorological conditions counteracted the emissions reduction effects resulting in high air pollution in BTH region during the lockdown period. This study shows that effective air pollution control strategies incorporating these results are urgently required in BTH to avoid severe pollution. Keywords COVID-19 Reduced anthropogenic emission Prolonged heavy haze Meteorology WRF-CMAQ Beijing-Tianjin-Hebei region ==== Body pmc1 Introduction Towards the end of December 2019, an infectious disease that was later linked to the family of coronaviruses broke out in Wuhan, the capital city of Hubei province, China (Sulaymon et al., 2021). A cluster of coronavirus 2019 (COVID-19) cases was confirmed in Wuhan by the Chinese authority in January 2020. However, within a short time, it spread to the neighboring cities in Hubei province and beyond. To limit the spread of the pandemic, a nationwide lockdown was announced by the Chinese government on January 23, 2020. The lockdown measures were implemented primarily to reduce large gatherings and thereby control the spread of the virus (China State Council, 2020; Wang et al., 2020a). During the lockdown period, control measures included shutting down of all public transportation systems, schools, business centers, parks, non-essential industries, restaurants, and entertainment houses. There have been many studies from around the world on the impacts of COVID-19 lockdowns on air quality (Chen et al., 2020a; Li et al., 2020; Liu et al., 2020; Mahato et al., 2020; Muhammad et al., 2020; Sharma et al., 2020; Sulaymon et al., 2021; Wang et al., 2020b). Globally, about 1,216,357 deaths had been linked with COVID-19 as of November 6, 2020 (WHO, 2020). On the impacts of air pollution on human existence, several epidemiological studies have established strong correlations between high PM2.5 concentrations and severe human health risks (Croft et al., 2018; Hopke et al., 2019; Jimoda et al., 2018; Sulaymon et al., 2018, 2021). Also, high concentrations of PM2.5 pose adverse health effects on human health (Global Burden of Disease GBD, 2020; U.S. Environmental Protection Agency USEPA, 2019). Annually, about 4.2 million people die prematurely due to exposure to air pollution (Ambient air pollution, 2021). The important roles being played by the meteorological variables (wind speed, temperature, relative humidity, and planetary boundary layer height) in the formation, transportation, diffusion, and deposition of air pollutants cannot be overemphasized (Hu et al., 2016; Shi et al., 2020; Sulaymon et al., 2020, 2021; Wang et al., 2020b) as unfavorable meteorological conditions exacerbate high pollution. Such high pollution conditions are more frequent in winter even with limited reductions in anthropogenic emissions (Chen et al., 2020b; Fu et al., 2020; Liu et al., 2020; Shi et al., 2020; Wang et al., 2020b; Yang et al., 2019). In terms of its economy, industrialization, urbanization, and population growth, the Beijing-Tianjin-Hebei (BTH) region is one of the most developed regions in China. In recent decades, persistent high air pollution has been reported in the region (Chang et al., 2018, 2019; Zhao et al., 2019) especially during the winter period due to unfavorable meteorological conditions. During international events such as Beijing Olympic Games 2008, APEC Summit 2014, and the Military Parade 2015, several air pollution control policies were enacted by the Chinese authorities as a way of improving the air quality in the BTH region. Previous studies have documented the effectiveness of the emission control policies during the events in BTH region (Cheng et al., 2016; Wang et al., 2016; Wu et al., 2016; Xu et al., 2017; Yang et al., 2016; Zhao et al., 2016; Zhou et al., 2016a, 2016b). However, the duration of these control periods was short and the measures were not strict compared to this year's prolonged COVID-19 lockdown with very strict measures in BTH region and China as a country. To the best of our knowledge, this is the first study to evaluate the contributions of emissions reductions and meteorological conditions to PM2.5 concentrations before and during the COVID-19 pandemic lockdown periods in the 10 major cities of BTH region using chemical transport model. Hence, this study provide an assessment of these measures across multiple cities and can provide information to guide future control planning. This study investigated and quantified the contributions of anthropogenic emission reductions due to COVID-19 lockdown along with the impacts of meteorological conditions on air quality in the BTH region. Three different emission scenarios were formulated and simulated using the Community Multi-Scale Air Quality (CMAQ) model to investigate why the region was still characterized with several high-pollution days despite the lockdown being in place. 2 Materials and methods The Community Multiscale Air Quality (CMAQ V5.2) model was used to simulate air quality in the Beijing-Tianjin-Hebei (BTH) region. The model was configured with SAPRC07tic photochemical mechanism and the AERO6i aerosol module (Fu et al., 2020; Liu et al., 2020). A one-way, triple nested domain was used. The first domain (36 km horizontal resolution) covers China mainland and part of East and Southeast Asia; the second domain (12 km horizontal resolution) covers eastern China, while the innermost domain (4 km horizontal resolution) covers the study area (the BTH region). Default profiles provided by the CMAQ model were used as the initial and boundary conditions of the first domain while the results of the outer domains served as the initial and boundary conditions for the subsequent inner domains. The simulation began on December 27, 2019 and ended on February 29, 2020. In order to minimize the impact of initial conditions, the results of the first five days (spin-up) were not included in the analysis. Two simulation periods were defined: pre-lockdown (January 1st to January 22nd) and lockdown (January 23rd to February 29th). The meteorological fields were simulated with the Weather Research and Forecasting (WRF v4.0) model. Detailed configurations of WRF model adopted in this study have been described in previous related studies (Hu et al., 2015a, 2016; Zhang et al., 2012). To provide the anthropogenic emissions from China, the Multi-resolution Emission Inventory for China (MEIC) of year 2016 with resolution of 0.25° × 0.25° (http://www.meicmodel.org) was used. For other countries in the domain, emissions from the gridded Regional Emission inventory in Asia, version 2 (REAS2) with resolution of 0.25° × 0.25° were used (Kurokawa et al., 2013). Biogenic emissions were generated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1). For biomass burning emissions, the Fire INventory from NCAR (FINN) (Wiedinmyer et al., 2011) was used. During the CMAQ simulations, sea salt and windblown emissions were generated inline. Detailed descriptions of the emission processing are provided in Hu et al. (2016) and Qiao et al. (2015). To quantify the impacts of the reduction in anthropogenic emission on ambient air quality, three scenarios were simulated for comparison (Table 1 ). For the base case scenario (C1), the original anthropogenic emission inventory (MEIC16) was used throughout the whole simulation period. In scenario 2 (C2), a reduction in industry scaled with a factor of 0.80 and transportation with a scaling factor of 0.80 was implemented while the emissions from residential sources was adjusted by scaling factor of 1.10 since people were required to be at home. Emissions from agriculture were set to be the same as C1. For the third simulation scenario (C3), transportation emissions were scaled by factor of 0.20 while the remaining emission sources were those in C2. The emissions from power plants were held constant in the 3 scenarios since there was little change in the demand for electricity. Even though there was slight reduction in industrial activities during the lockdown period, the stay-at-home orders that were in place led to possibly higher demand and consumption of power for home heating and lighting since the lockdown period was in winter. In the absence of official emission inventory during the lockdown, the emission scaling factors used in this study followed the suggestions by the Chinese Research Academy of Environmental Sciences (CRAES, 2020) regarding the status of emission inventory during the lockdown and were also consistent with those of Wang et al. (2020a).Table 1 Emission scaling factors and the configuration of simulation scenarios. Table 1Scenario ID Residential Transportation Power Industry Agriculture C1 1.00 1.00 1.00 1.00 1.00 C2 1.10 0.60 1.00 0.80 1.00 C3 1.00 0.20 1.00 0.80 1.00 To investigate the effects caused by the changes in anthropogenic emissions, the difference between the concentrations in C3 and C1 was designated as the impact of the emissions reductions attributed to the COVID-19 lockdown since the same meteorology was used for the two simulations. The difference in pollutant concentrations between high-pollution days and low-pollution days in C3 during the lockdown was considered as the effects of changes in meteorology. 3 Results and discussion 3.1 Model validation 3.1.1 WRF model performance The significant role played by the meteorological variables in the formation, transportation, diffusion, and deposition of air pollutants has been documented in previous studies (Hu et al., 2015a, 2016). Measured data were downloaded from the National Climate Data Center (NCDC). The data were used for the validation of WRF performance, including relative humidity (RH) and temperature (T2) at 2 m above surface, and wind speed (WS) and wind direction (WD) at 10 m above the ground level. The statistical results of the model performance are shown in Table 2, Table 3 and include mean observation (OBS), mean prediction (PRE), mean bias (MB), gross error (GE), as well as the root mean square error (RMSE). The benchmarks used in this study were suggested by Emery et al. (2001). The WRF model slightly over-predicted WS (Fig. S1) with positive MB value of 1.2, which is beyond the benchmark. However, the GE and RMSE values of WS are within the benchmarks. With the MB value of −0.7, WD (Fig. S2) meets the benchmark of ≤±10° and the result is acceptable. GE value of WD was above the benchmark by 56%. T2 (Fig. S3) had an MB value of 3.1, indicating a slight over-estimation compared to the observations. The GE value of T2 was higher than the benchmark by 90%. RH (Fig. S4) was under-estimated with MB value of −4.9. Generally, air pollutants’ concentrations are associated with meteorological parameters especially in highly polluted areas in China (Shi et al., 2020; Wang et al., 2014). Also, bias in simulated meteorological variables largely contributes to bias in the predicted PM2.5 concentrations (Hu et al., 2015b; Shi et al., 2020). The performance of WRF model in this study is comparable to other previous studies in BTH region (Chang et al., 2018; Zhang et al., 2018, 2019) and China as a whole (Fu et al., 2020; Hu et al., 2016; Qiao et al., 2018, 2019; Wang et al., 2020b; Zhang et al., 2012).Table 2 Meteorology performance during January 01 to February 29, 2020 (OBS means observation; PRE means prediction; MB means mean bias; GE means gross error; RMSE is root mean square error). The values that do not meet the criteria are highlighted in bold. Table 2Parameters Indices Jan 01-Feb 29, 2020 Benchmarka T2(K) OBS PRE MB GE RMSE 272.6 275.8 3.1 3.8 4.8 ≤±0.5 ≤2.0 WS10(ms/s) OBS PRE MB GE RMSE 2.0 3.3 1.2 1.4 1.8 ≤±0.5 ≤2.0 ≤2.0 WD10(°) OBS PRE MB GE RMSE 178.1 177.3 −0.7 46.8 64.8 ≤±10 ≤±30 RH2(%) OBS PRE MB GE RMSE 64.0 59.1 −4.9 12.7 16.1 a The benchmarks used were suggested by Emery et al. (2001). 3.1.2 PM2.5 model performance Ten major cities (Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai) in BTH region were selected for analyses. The hourly PM2.5 observation data from the air quality monitoring stations in the cities were downloaded from the China's National Environmental Monitoring Center (http://www.cnemc.cn). Validation and necessary quality checks of the data followed the approach of Sulaymon et al. (2021). To identify the model performance in different cities of BTH region, model validations were performed separately for each city. PM2.5 model performance in different cities and periods are shown in Table 3 (C3) while those of C1 and C2 are in the Supplementary Material. The mean observations (OBS), mean predictions (PRE), mean fractional bias (MFB), mean fractional error (MFE), mean normalized bias (MNB), and mean normalized error (MNE) were estimated. The predicted and observed daily PM2.5 in C1, C2, and C3 in the ten major cities of BTH are illustrated in the Supplementary Material, while the predicted daily PM2.5 major components with observed daily PM2.5 in the cities in C1, C2, and C3 are shown in the Supplementary Material. In C1, PM2.5 was well predicted in all cities before lockdown except Hengshui with an MFB (0.63) above the criterion suggested by EPA (2007) while the model performance statistics of MFB fell within the EPA criterion for all cities during the lockdown period. MFE values for all cities during the two periods were within the EPA criterion value of ≤0.75. In addition, prior to lockdown period, negative MFB values were obtained for all cities except Beijing, Cangzhou, and Hengshui, indicating the model under-predicted the PM2.5 concentrations. A similar situation was obtained during the lockdown in all cities except Hengshui.Table 3 Model performance of PM2.5 in C3 before and during the lockdown (OBS is mean observation; PRE is mean prediction; MFB is mean fractional bias; MFE is mean fractional error; MNB is mean normalized bias; MNE is mean normalized error). The performance criteria for PM2.5 were suggested by EPA (2007). The values that do not meet the criteria are highlighted in bold. Table 3Before Beijing Tianjin Shijiazhuang Baoding Cangzhou Chengde Handan Hengshui Tangshan Xingtai Criteria PM2.5 (μg/m3) OBS 41.30 91.23 144.47 106.91 61.44 108.39 140.16 28.71 80.53 138.22 PRE 57.28 51.25 68.24 62.95 62.60 54.92 66.26 69.08 55.81 65.16 MFB 0.31 −0.21 −0.41 −0.30 0.13 −0.38 −0.42 0.57 −0.22 −0.43 ≤±0.6 MFE 0.48 0.43 0.46 0.33 0.37 0.38 0.44 0.65 0.32 0.45 ≤0.75 MNB 0.86 −0.14 −0.47 −0.36 0.41 −0.46 −0.48 1.61 −0.23 −0.49 MNE 1.07 0.62 0.55 0.41 0.71 0.46 0.52 1.71 0.42 0.53 During Beijing Tianjin Shijiazhuang Baoding Cangzhou Chengde Handan Hengshui Tangshan Xingtai Criteria PM2.5 (μg/m3) OBS 72.75 78.13 100.28 106.89 82.21 70.58 86.62 39.76 82.64 85.51 PRE 49.28 47.56 55.44 56.33 56.02 48.10 55.16 56.39 49.14 54.10 MFB −0.05 −0.16 −0.28 −0.25 −0.08 −0.19 −0.22 0.24 −0.24 −0.23 ≤±0.6 MFE 0.44 0.32 0.36 0.32 0.34 0.26 0.32 0.38 0.30 0.34 ≤0.75 MNB 0.25 −0.10 −0.27 −0.23 0.09 −0.18 −0.20 0.82 −0.24 −0.20 MNE 0.83 0.48 0.49 0.45 0.58 0.36 0.45 1.00 0.41 0.49 Generally, the predicted PM2.5 concentrations agreed well with observations, with the model performance statistics meeting the suggested criteria in all the cities, scenarios, and periods except Hengshui in C1 and C2 before the lockdown period. However, relatively large bias in model predicted concentrations were found in some cities especially before lockdown period. Model bias is mainly attributed to uncertainties associated with meteorological fields, emission inventory, model treatment, and configurations. Further studies are still needed to continue improving the model capability in accurately predicting air quality in China. In comparison with the predicted PM2.5 concentrations in C1 and C2, the simulated results in C3 were better since significant reductions in PM2.5 concentrations in all the cities before and during the lockdown periods were captured. Due to the shutting down of major sectors (public transportation systems, schools, business centers, parks, non-essential industries, restaurants, and entertainment houses) during the lockdown period, which led to reductions in anthropogenic emissions (Sulaymon et al., 2021), the results of scenario C3 had better predictions and were used in further discussion. 3.2 Impacts of emission reductions on PM2.5 in different cities The average predicted concentrations of PM2.5 major components and observed PM2.5 concentrations in the selected ten major cities under the three scenarios during the lockdown period are illustrated in Fig. 1 . The reduction in the anthropogenic emissions in C3 did not cause significant reduction in PM2.5 major components' concentrations across the cities. The highest reduction was recorded in Cangzhou (−3.00 μg/m3) while the lowest was found in Tianjin (−2.35 μg/m3). The reductions in PM2.5 major components’ concentrations in C3 when compared with C1 were −2.50, −2.66, −2.73, −2.74, −2.60, −2.73, −2.51, and −2.57 μg/m3 in Beijing, Shijiazhuang, Baoding, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Fig. 2 shows the spatial variations of the changes of simulated PM2.5 between the base case (C1) and the two emission reduction scenarios (C2 and C3) during the lockdown period. When compared to C1, PM2.5 increased by up to 10 μg/m3 in C2. This rise could be attributed to the increase in residential combustion sources (10%) in C2 as people were mandated to stay at home during the lockdown period. The spatial changes of PM2.5 concentrations, which increased by up to 20 μg/m3 in C2 before the lockdown period are illustrated in Fig. S15 in the Supplementary Material.Fig. 1 Average predicted daily PM2.5 with major components and observed PM2.5 in the 10 major cities under the three scenarios during the lockdown. Units are in μg/m3. Fig. 1 Fig. 2 Spatial distributions of predicted PM2.5 and the changes between the base case (C1) and the two emission reduction scenarios (C2 and C3) during the lockdown period. Units are in μg/m3. Fig. 2 The averaged predicted daily PM2.5 with its major components and observed PM2.5 concentrations in the ten major cities under study during the high-pollution and clean (low-pollution) days are displayed in Fig. 3 . According to the second level of Chinese NAAQS, high-pollution days are defined by daily PM2.5 concentrations above 75 μg/m3. During the lockdown period, the reductions in PM2.5 concentrations during high-pollution days in C3 when compared with C1 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.72, 4.27, 3.47, 3.92, 4.40, 3.98, 3.76, 9.03, 4.50, and 3.74 μg/m3, respectively. Overall, the changes were below 10%. Hengshui had the highest reduction of 6.10%. For other cities, the percentage reductions were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 3.61, and 3.67% in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Tangshan, and Xingtai, respectively. The percentage of high polluted days was highest in Shijiazhuang (64.9%) followed by Xingtai (56.8%) and the lowest was traced to Hengshui (10.8%).Fig. 3 Average predicted daily PM2.5 with major components and observed PM2.5 in the 10 major cities under the three scenarios on high-pollution days and low-pollution days during the lockdown. The number of days in high (H) and low (L) pollution days are indicated after the city names. Units are in μg/m3. Fig. 3 Alternatively, during low-pollution days, the reductions were 1.80, 0.72, 1.20, 1.53, 1.84, 1.91, 1.41, 1.97, 0.66, and 1.10 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. The reductions were less than 5% across the cities and the highest was found in Chengde (2.91%) while the lowest was observed in Tangshan (1.35%). For the low-pollution days, Hengshui had the highest percentage of clean days with 89.2%, followed by Beijing (62.2%) and the lowest was recorded in Shijiazhuang (35.1%). The benefits of emission reductions were counteracted by unfavorable meteorological conditions resulting in high PM2.5 pollution. Thus, this study showed that substantive reductions in transportation emissions and slight reductions in industrial emissions could not guarantee improved air quality in BTH region when unfavorable weather conditions occur. This study has also showed that the relationships between emissions and concentrations are not usually linear (Xing et al., 2020). Understanding and including the roles of both chemistry and meteorology in the formation of air pollutants is important as illustrated in this study. It is also important to use multi-pollutant nonlinear response models when designing effective emission control strategies as suggested by Xing et al. (2020). 3.3 Relative changes due to emission reductions Fig. 4 shows the relative changes of major PM2.5 components between C3 and C1 across the ten cities. Significant reductions were found in nitrate (NO3 −), elemental carbon (EC), and ammonium (NH4 +) while the changes in sulfate (SO4 2−) and primary organic aerosols (POA) were below 10% in all the cities. For instance, in Chengde where the highest reductions were observed, the concentrations of NO3 −, EC, NH4 +, SO4 2−, and POA decreased by 24.2, 22.6, 15.0, 9.0, and 4.3%, respectively. Other cities followed the same trends. Alternatively, the concentrations of secondary organic aerosols (SOA) and the group of compounds labelled as OTHER increased (rather than decreases) even though these increases were <3% across the cities.Fig. 4 Relative changes in concentrations of PM2.5 major components between C3 and C1 in the 10 major cities during the lockdown. Units are in %. Fig. 4 Fig. S16 presents the changes in PM2.5 major components concentrations before and during the lockdown in C3. In Tianjin, Baoding, Cangzhou, and Chengde. All the chemical species except SO4 2− and SOA decreased during the lockdown compared to their concentrations before the lockdown. There were reductions in the concentrations of all the species in the remaining six cities. Comparing the two periods (before and during the lockdown), significant reductions were found in NO3 −, POA, EC, and NH4 + while the changes in SO4 2− and SOA were less than 10% in all the cities. The highest reduction of NO3 − was observed in Chengde (−40.6%) while Shijiazhuang had the highest reductions of POA (−34.79%), EC (−31.69%), and NH4 + (−24.17%). The substantial reductions in NO3 − during the lockdown period could be attributed to reduction in its precursors (NOx) due to drastic reduction in vehicular movement and suspension of public transport across the country. The reduction in NH4 + could also be due to reduction in light-duty vehicles with catalytic converters (Heeb et al., 2006; Zhou et al., 2019). 3.4 Impacts on PM2.5 of emissions from festival activities During the spring festival, intensive fireworks are usually displayed beginning on the eve of the Chinese New year. The Chinese New Year began on January 25, 2020, while the Lantern festival was celebrated on February 8, 2020. In addition to this, there is an annual traditional event in the northern part of China called “Sheng Wang Huo” that is usually celebrated during the spring festival. As a way of celebrating Sheng Wang Huo annual event, a large pillar of wood and coal is usually ignited on New Year's Eve and burnt for about five days. Also, during the Lantern festival and other major cultural events, burning of wood and coal is a norm for the Chinese people. Although, the Chinese authorities at local government levels had banned this event as a way of reducing its impact on ambient air quality, a recent study by Dai et al. (2020) revealed that the people in the rural areas of provinces such as Hebei, Shanxi, and Inner Mongolia still observed Sheng Wang Huo during this year's Spring and Lantern Festivals. The atmospheric residence time of particulate matter from fireworks is around four days (Dai et al., 2020). The significant contributions due to fireworks and coal/wood burning were observed as higher PM2.5 pollution occurred starting from January 24 and lasted till January 31 in the cities except Hengshui where no record of high pollution was observed during the spring festival. For instance, the ranges of PM2.5 concentrations between January 24 to January 31 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Tangshan, and Xingtai were 76.29–169.67, 85.08–237.00, 129.38–206.58, 92.29–377.38, 80.75–247.29, 80.71–184.29, 100.25–157.25, 78.08–243.00, and 95.38–151.63 μg/m3, respectively. Also, during the Lantern festival, the contributions attributed to fireworks and coal/wood burning were significant and lasted until February 13 across the cities. Between February 7 and February 13, the ranges of PM2.5 concentrations were 119.83–205.67, 97.42–169.79, 103.13–213.17, 105.17–199.38, 82.46–194.82, 76.54–127.50, 77.50–167.63, 84.83–95.67, 75.75–190.71, and 90.13–170.5 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. The continuous high-pollution days recorded during the two festival periods could be attributed to the emissions from fireworks, coal/wood burning, and residential burning and were driven by poor meteorological conditions. 3.5 Impacts of meteorological conditions As a result of reduction in anthropogenic emissions in C3, reduction in the concentrations of PM2.5 were noticed across the ten cities of BTH region, though very low. As discussed earlier and shown in Fig. 3, high pollution persisted in all the cities except Hengshui despite reductions in anthropogenic activities during the lockdown period. It can be inferred that meteorological conditions such as low wind speed, high relative humidity, high temperatures, and low planetary boundary layer heights favored stagnation rather than ventilation, and enhanced the higher pollution formation especially during the festival periods as discussed in section 3.4. During the simulation period, there were high temperatures (Fig. S3), high relative humidity (Fig. S4), low wind speeds (Fig. S1), and low PBLHs (Fig. S5) across the cities in BTH region. The formation of secondary particulate matter was enhanced by these higher temperatures and RH (Li et al., 2019; Wang et al., 2019, 2020c, 2020c; Wu et al., 2019; Zhang et al., 2020) because reaction rates are higher without the temperatures reaching values that favor dissociation of the ammonium nitrate given that the mean temperature for the study period was ~0 °C. Alternatively, the dispersion of atmospheric pollutants was hindered by low wind speed and lower planetary boundary layer height (Li et al., 2019; Liu et al., 2017; Wang et al., 2020b). The efficiency of atmospheric dispersion was evaluated using the ventilation coefficient (VC). VC is the product of the wind speed times the mixed layer height and is reported in m2 s−1. Details of the VC can be found in Dai et al. (2020) and Tiwari et al. (2019). Due to poor dispersion, low VC values are seen for higher pollutant concentrations at a fixed emission rate. Fig. 5 shows the time series of PM2.5 concentrations and VC for Beijing, Tianjin, and Shijiazhuang while Figs S17 and S18 in the Supplementary Material show the time series for other cities. Lower values of VC led to high PM2.5 concentrations across the cities and subsequently resulted to several high-pollution days especially during the festival periods and up to six days after the festivals. Days with very high VC values had correspondingly low pollutant concentrations.Fig. 5 Time series of PM2.5 concentrations and VC for Beijing, Tianjin, and Shijiazhuang. Fig. 5 The changes due to meteorological conditions and reduction of anthropogenic emissions were calculated and illustrated in Fig. 6 . Reduction in emissions caused reductions in PM2.5 in all the cities. The changes were very small and less than 5 μg/m3 across the cities. Cangzhou (−3.30 μg/m3) had the highest and the lowest was observed in Tianjin (−2.53 μg/m3). The changes in other cities were −2.70, −2.90, −2.98, −2.81, −2.82, −2.98, −2.68, and −2.83 μg/m3 in Beijing, Shijiazhuang, Baoding, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Alternatively, substantive but opposite changes were observed for PM2.5 due to unfavorable meteorological conditions during the COVID-19 lockdown period. This situation led to increments in PM2.5 concentration across the cities. Tangshan had the highest contribution of 72 μg/m3, followed by Tianjin (66 μg/m3) while the lowest was found in Xingtai (19 μg/m3). Clearly, in all the ten cities, the significant increment caused by unfavorable meteorological conditions had counteracted the very low positive effects linked to emission reductions. Thus, severe high pollution occurred in BTH region during the COVID-19 lockdown period.Fig. 6 Contributions of emission reduction and meteorological changes to averaged PM2.5 concentrations in 10 major cities during the lockdown period. Units are in μg/m3. Fig. 6 4 Conclusions This study investigated the impacts of emission reductions and changes in meteorological conditions on PM2.5 concentrations during the COVID-19 lockdown in BTH region. The reductions in anthropogenic emissions led to decreased PM2.5 concentrations across the cities. The reduction in predicted PM2.5 concentrations were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Across the cities, the levels of PM2.5 showed the opposite trend due to unfavorable meteorological conditions during the lockdown period. Tangshan had the highest contribution of 72 μg/m3, followed by Tianjin (66 μg/m3) while the lowest was found in Xingtai (19 μg/m3). During the lockdown, the percentage of days with high-pollution was 64.9% in Shijiazhuang, followed by Xingtai (56.8%) and the lowest was observed in Hengshui (10.8%). Obviously, in all the ten cities, the significant increment caused by unfavorable meteorological conditions had counteracted the very low positive effects attributed to emission reductions. Thus, high air pollution occurred in BTH region during the lockdown period. In designing effective emission control strategies to reduce PM2.5 level in BTH region, it is pertinent to understand and take into consideration the significant roles that both chemistry and meteorology play in the formation of air pollutants as highlighted in this study. Credit author contribution statement Ishaq Dimeji Sulaymon: Conceptualization, Methodology, Formal analysis, Data curation, Visualization, Writing - original draft, Writing - review & editing. Yuanxun Zhang: Conceptualization, Supervision, Funding acquisition, Writing - review & editing. Philip K. Hopke: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Jianlin Hu: Conceptualization, Writing - review & editing. Yang Zhang: Writing - review & editing. Lin Li: Data curation, Formal analysis. Xiaodong Mei: Data curation, Formal analysis. Kangjia Gong: Data curation, Formal analysis. Zhihao Shi: Data curation, Formal analysis. Bin Zhao: Writing - review & editing. Fangxin Zhao: Data curation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Acknowledgements This study was made possible with the support of CAS-TWAS President's Postgraduate Fellowship Program, the 10.13039/501100001809 National Natural Science Foundation of China (NSFC, No. 41877310), and partly by the 10.13039/501100012166 National Key Research and Development Program of China (No. 2016YFC0503600). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2021.111186. ==== Refs References Ambient air pollution https://www.who.int/airpollution/ambient/health-impacts/en 2021 Chang X. Wang S. Zhao B. Xing J. Liu X. Wei L. Song Y. Wu W. Cai S. Zheng H. 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S0013-9351(21)00480-1 10.1016/j.envres.2021.111186 111186 Article Persistent high PM2.5 pollution driven by unfavorable meteorological conditions during the COVID-19 lockdown period in the Beijing-Tianjin-Hebei region, China Sulaymon Ishaq Dimeji a Zhang Yuanxun ab∗ Hopke Philip K. cd Hu Jianlin e Zhang Yang a Li Lin e Mei Xiaodong a Gong Kangjia e Shi Zhihao e Zhao Bin f Zhao Fangxin a a College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China b CAS Center for Excellence in Regional Atmospheric Environment, Chinese Academy of Sciences, Xiamen, 361021, China c Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, 13699, USA d Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA e Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing, 210044, China f Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, USA ∗ Corresponding author. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China. 27 4 2021 7 2021 27 4 2021 198 111186111186 19 11 2020 9 4 2021 12 4 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Lockdown measures to curtail the COVID-19 pandemic in China halted most non-essential activities on January 23, 2020. Despite significant reductions in anthropogenic emissions, the Beijing-Tianjin-Hebei (BTH) region still experienced high air pollution concentrations. Employing two emissions reduction scenarios, the Community Multiscale Air Quality (CMAQ) model was used to investigate the PM2.5 concentrations change in this region. The model using the scenario (C3) with greater traffic reductions performed better compared to the observed PM2.5. Compared with the no reductions base-case (scenario C1), PM2.5 reductions with scenario C3 were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. During high-pollution days in scenario C3, the percentage reductions in PM2.5 concentrations in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 6.10, 3.61, and 3.67%, respectively. However, significant increases caused by unfavorable meteorological conditions counteracted the emissions reduction effects resulting in high air pollution in BTH region during the lockdown period. This study shows that effective air pollution control strategies incorporating these results are urgently required in BTH to avoid severe pollution. Keywords COVID-19 Reduced anthropogenic emission Prolonged heavy haze Meteorology WRF-CMAQ Beijing-Tianjin-Hebei region ==== Body pmc1 Introduction Towards the end of December 2019, an infectious disease that was later linked to the family of coronaviruses broke out in Wuhan, the capital city of Hubei province, China (Sulaymon et al., 2021). A cluster of coronavirus 2019 (COVID-19) cases was confirmed in Wuhan by the Chinese authority in January 2020. However, within a short time, it spread to the neighboring cities in Hubei province and beyond. To limit the spread of the pandemic, a nationwide lockdown was announced by the Chinese government on January 23, 2020. The lockdown measures were implemented primarily to reduce large gatherings and thereby control the spread of the virus (China State Council, 2020; Wang et al., 2020a). During the lockdown period, control measures included shutting down of all public transportation systems, schools, business centers, parks, non-essential industries, restaurants, and entertainment houses. There have been many studies from around the world on the impacts of COVID-19 lockdowns on air quality (Chen et al., 2020a; Li et al., 2020; Liu et al., 2020; Mahato et al., 2020; Muhammad et al., 2020; Sharma et al., 2020; Sulaymon et al., 2021; Wang et al., 2020b). Globally, about 1,216,357 deaths had been linked with COVID-19 as of November 6, 2020 (WHO, 2020). On the impacts of air pollution on human existence, several epidemiological studies have established strong correlations between high PM2.5 concentrations and severe human health risks (Croft et al., 2018; Hopke et al., 2019; Jimoda et al., 2018; Sulaymon et al., 2018, 2021). Also, high concentrations of PM2.5 pose adverse health effects on human health (Global Burden of Disease GBD, 2020; U.S. Environmental Protection Agency USEPA, 2019). Annually, about 4.2 million people die prematurely due to exposure to air pollution (Ambient air pollution, 2021). The important roles being played by the meteorological variables (wind speed, temperature, relative humidity, and planetary boundary layer height) in the formation, transportation, diffusion, and deposition of air pollutants cannot be overemphasized (Hu et al., 2016; Shi et al., 2020; Sulaymon et al., 2020, 2021; Wang et al., 2020b) as unfavorable meteorological conditions exacerbate high pollution. Such high pollution conditions are more frequent in winter even with limited reductions in anthropogenic emissions (Chen et al., 2020b; Fu et al., 2020; Liu et al., 2020; Shi et al., 2020; Wang et al., 2020b; Yang et al., 2019). In terms of its economy, industrialization, urbanization, and population growth, the Beijing-Tianjin-Hebei (BTH) region is one of the most developed regions in China. In recent decades, persistent high air pollution has been reported in the region (Chang et al., 2018, 2019; Zhao et al., 2019) especially during the winter period due to unfavorable meteorological conditions. During international events such as Beijing Olympic Games 2008, APEC Summit 2014, and the Military Parade 2015, several air pollution control policies were enacted by the Chinese authorities as a way of improving the air quality in the BTH region. Previous studies have documented the effectiveness of the emission control policies during the events in BTH region (Cheng et al., 2016; Wang et al., 2016; Wu et al., 2016; Xu et al., 2017; Yang et al., 2016; Zhao et al., 2016; Zhou et al., 2016a, 2016b). However, the duration of these control periods was short and the measures were not strict compared to this year's prolonged COVID-19 lockdown with very strict measures in BTH region and China as a country. To the best of our knowledge, this is the first study to evaluate the contributions of emissions reductions and meteorological conditions to PM2.5 concentrations before and during the COVID-19 pandemic lockdown periods in the 10 major cities of BTH region using chemical transport model. Hence, this study provide an assessment of these measures across multiple cities and can provide information to guide future control planning. This study investigated and quantified the contributions of anthropogenic emission reductions due to COVID-19 lockdown along with the impacts of meteorological conditions on air quality in the BTH region. Three different emission scenarios were formulated and simulated using the Community Multi-Scale Air Quality (CMAQ) model to investigate why the region was still characterized with several high-pollution days despite the lockdown being in place. 2 Materials and methods The Community Multiscale Air Quality (CMAQ V5.2) model was used to simulate air quality in the Beijing-Tianjin-Hebei (BTH) region. The model was configured with SAPRC07tic photochemical mechanism and the AERO6i aerosol module (Fu et al., 2020; Liu et al., 2020). A one-way, triple nested domain was used. The first domain (36 km horizontal resolution) covers China mainland and part of East and Southeast Asia; the second domain (12 km horizontal resolution) covers eastern China, while the innermost domain (4 km horizontal resolution) covers the study area (the BTH region). Default profiles provided by the CMAQ model were used as the initial and boundary conditions of the first domain while the results of the outer domains served as the initial and boundary conditions for the subsequent inner domains. The simulation began on December 27, 2019 and ended on February 29, 2020. In order to minimize the impact of initial conditions, the results of the first five days (spin-up) were not included in the analysis. Two simulation periods were defined: pre-lockdown (January 1st to January 22nd) and lockdown (January 23rd to February 29th). The meteorological fields were simulated with the Weather Research and Forecasting (WRF v4.0) model. Detailed configurations of WRF model adopted in this study have been described in previous related studies (Hu et al., 2015a, 2016; Zhang et al., 2012). To provide the anthropogenic emissions from China, the Multi-resolution Emission Inventory for China (MEIC) of year 2016 with resolution of 0.25° × 0.25° (http://www.meicmodel.org) was used. For other countries in the domain, emissions from the gridded Regional Emission inventory in Asia, version 2 (REAS2) with resolution of 0.25° × 0.25° were used (Kurokawa et al., 2013). Biogenic emissions were generated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1). For biomass burning emissions, the Fire INventory from NCAR (FINN) (Wiedinmyer et al., 2011) was used. During the CMAQ simulations, sea salt and windblown emissions were generated inline. Detailed descriptions of the emission processing are provided in Hu et al. (2016) and Qiao et al. (2015). To quantify the impacts of the reduction in anthropogenic emission on ambient air quality, three scenarios were simulated for comparison (Table 1 ). For the base case scenario (C1), the original anthropogenic emission inventory (MEIC16) was used throughout the whole simulation period. In scenario 2 (C2), a reduction in industry scaled with a factor of 0.80 and transportation with a scaling factor of 0.80 was implemented while the emissions from residential sources was adjusted by scaling factor of 1.10 since people were required to be at home. Emissions from agriculture were set to be the same as C1. For the third simulation scenario (C3), transportation emissions were scaled by factor of 0.20 while the remaining emission sources were those in C2. The emissions from power plants were held constant in the 3 scenarios since there was little change in the demand for electricity. Even though there was slight reduction in industrial activities during the lockdown period, the stay-at-home orders that were in place led to possibly higher demand and consumption of power for home heating and lighting since the lockdown period was in winter. In the absence of official emission inventory during the lockdown, the emission scaling factors used in this study followed the suggestions by the Chinese Research Academy of Environmental Sciences (CRAES, 2020) regarding the status of emission inventory during the lockdown and were also consistent with those of Wang et al. (2020a).Table 1 Emission scaling factors and the configuration of simulation scenarios. Table 1Scenario ID Residential Transportation Power Industry Agriculture C1 1.00 1.00 1.00 1.00 1.00 C2 1.10 0.60 1.00 0.80 1.00 C3 1.00 0.20 1.00 0.80 1.00 To investigate the effects caused by the changes in anthropogenic emissions, the difference between the concentrations in C3 and C1 was designated as the impact of the emissions reductions attributed to the COVID-19 lockdown since the same meteorology was used for the two simulations. The difference in pollutant concentrations between high-pollution days and low-pollution days in C3 during the lockdown was considered as the effects of changes in meteorology. 3 Results and discussion 3.1 Model validation 3.1.1 WRF model performance The significant role played by the meteorological variables in the formation, transportation, diffusion, and deposition of air pollutants has been documented in previous studies (Hu et al., 2015a, 2016). Measured data were downloaded from the National Climate Data Center (NCDC). The data were used for the validation of WRF performance, including relative humidity (RH) and temperature (T2) at 2 m above surface, and wind speed (WS) and wind direction (WD) at 10 m above the ground level. The statistical results of the model performance are shown in Table 2, Table 3 and include mean observation (OBS), mean prediction (PRE), mean bias (MB), gross error (GE), as well as the root mean square error (RMSE). The benchmarks used in this study were suggested by Emery et al. (2001). The WRF model slightly over-predicted WS (Fig. S1) with positive MB value of 1.2, which is beyond the benchmark. However, the GE and RMSE values of WS are within the benchmarks. With the MB value of −0.7, WD (Fig. S2) meets the benchmark of ≤±10° and the result is acceptable. GE value of WD was above the benchmark by 56%. T2 (Fig. S3) had an MB value of 3.1, indicating a slight over-estimation compared to the observations. The GE value of T2 was higher than the benchmark by 90%. RH (Fig. S4) was under-estimated with MB value of −4.9. Generally, air pollutants’ concentrations are associated with meteorological parameters especially in highly polluted areas in China (Shi et al., 2020; Wang et al., 2014). Also, bias in simulated meteorological variables largely contributes to bias in the predicted PM2.5 concentrations (Hu et al., 2015b; Shi et al., 2020). The performance of WRF model in this study is comparable to other previous studies in BTH region (Chang et al., 2018; Zhang et al., 2018, 2019) and China as a whole (Fu et al., 2020; Hu et al., 2016; Qiao et al., 2018, 2019; Wang et al., 2020b; Zhang et al., 2012).Table 2 Meteorology performance during January 01 to February 29, 2020 (OBS means observation; PRE means prediction; MB means mean bias; GE means gross error; RMSE is root mean square error). The values that do not meet the criteria are highlighted in bold. Table 2Parameters Indices Jan 01-Feb 29, 2020 Benchmarka T2(K) OBS PRE MB GE RMSE 272.6 275.8 3.1 3.8 4.8 ≤±0.5 ≤2.0 WS10(ms/s) OBS PRE MB GE RMSE 2.0 3.3 1.2 1.4 1.8 ≤±0.5 ≤2.0 ≤2.0 WD10(°) OBS PRE MB GE RMSE 178.1 177.3 −0.7 46.8 64.8 ≤±10 ≤±30 RH2(%) OBS PRE MB GE RMSE 64.0 59.1 −4.9 12.7 16.1 a The benchmarks used were suggested by Emery et al. (2001). 3.1.2 PM2.5 model performance Ten major cities (Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai) in BTH region were selected for analyses. The hourly PM2.5 observation data from the air quality monitoring stations in the cities were downloaded from the China's National Environmental Monitoring Center (http://www.cnemc.cn). Validation and necessary quality checks of the data followed the approach of Sulaymon et al. (2021). To identify the model performance in different cities of BTH region, model validations were performed separately for each city. PM2.5 model performance in different cities and periods are shown in Table 3 (C3) while those of C1 and C2 are in the Supplementary Material. The mean observations (OBS), mean predictions (PRE), mean fractional bias (MFB), mean fractional error (MFE), mean normalized bias (MNB), and mean normalized error (MNE) were estimated. The predicted and observed daily PM2.5 in C1, C2, and C3 in the ten major cities of BTH are illustrated in the Supplementary Material, while the predicted daily PM2.5 major components with observed daily PM2.5 in the cities in C1, C2, and C3 are shown in the Supplementary Material. In C1, PM2.5 was well predicted in all cities before lockdown except Hengshui with an MFB (0.63) above the criterion suggested by EPA (2007) while the model performance statistics of MFB fell within the EPA criterion for all cities during the lockdown period. MFE values for all cities during the two periods were within the EPA criterion value of ≤0.75. In addition, prior to lockdown period, negative MFB values were obtained for all cities except Beijing, Cangzhou, and Hengshui, indicating the model under-predicted the PM2.5 concentrations. A similar situation was obtained during the lockdown in all cities except Hengshui.Table 3 Model performance of PM2.5 in C3 before and during the lockdown (OBS is mean observation; PRE is mean prediction; MFB is mean fractional bias; MFE is mean fractional error; MNB is mean normalized bias; MNE is mean normalized error). The performance criteria for PM2.5 were suggested by EPA (2007). The values that do not meet the criteria are highlighted in bold. Table 3Before Beijing Tianjin Shijiazhuang Baoding Cangzhou Chengde Handan Hengshui Tangshan Xingtai Criteria PM2.5 (μg/m3) OBS 41.30 91.23 144.47 106.91 61.44 108.39 140.16 28.71 80.53 138.22 PRE 57.28 51.25 68.24 62.95 62.60 54.92 66.26 69.08 55.81 65.16 MFB 0.31 −0.21 −0.41 −0.30 0.13 −0.38 −0.42 0.57 −0.22 −0.43 ≤±0.6 MFE 0.48 0.43 0.46 0.33 0.37 0.38 0.44 0.65 0.32 0.45 ≤0.75 MNB 0.86 −0.14 −0.47 −0.36 0.41 −0.46 −0.48 1.61 −0.23 −0.49 MNE 1.07 0.62 0.55 0.41 0.71 0.46 0.52 1.71 0.42 0.53 During Beijing Tianjin Shijiazhuang Baoding Cangzhou Chengde Handan Hengshui Tangshan Xingtai Criteria PM2.5 (μg/m3) OBS 72.75 78.13 100.28 106.89 82.21 70.58 86.62 39.76 82.64 85.51 PRE 49.28 47.56 55.44 56.33 56.02 48.10 55.16 56.39 49.14 54.10 MFB −0.05 −0.16 −0.28 −0.25 −0.08 −0.19 −0.22 0.24 −0.24 −0.23 ≤±0.6 MFE 0.44 0.32 0.36 0.32 0.34 0.26 0.32 0.38 0.30 0.34 ≤0.75 MNB 0.25 −0.10 −0.27 −0.23 0.09 −0.18 −0.20 0.82 −0.24 −0.20 MNE 0.83 0.48 0.49 0.45 0.58 0.36 0.45 1.00 0.41 0.49 Generally, the predicted PM2.5 concentrations agreed well with observations, with the model performance statistics meeting the suggested criteria in all the cities, scenarios, and periods except Hengshui in C1 and C2 before the lockdown period. However, relatively large bias in model predicted concentrations were found in some cities especially before lockdown period. Model bias is mainly attributed to uncertainties associated with meteorological fields, emission inventory, model treatment, and configurations. Further studies are still needed to continue improving the model capability in accurately predicting air quality in China. In comparison with the predicted PM2.5 concentrations in C1 and C2, the simulated results in C3 were better since significant reductions in PM2.5 concentrations in all the cities before and during the lockdown periods were captured. Due to the shutting down of major sectors (public transportation systems, schools, business centers, parks, non-essential industries, restaurants, and entertainment houses) during the lockdown period, which led to reductions in anthropogenic emissions (Sulaymon et al., 2021), the results of scenario C3 had better predictions and were used in further discussion. 3.2 Impacts of emission reductions on PM2.5 in different cities The average predicted concentrations of PM2.5 major components and observed PM2.5 concentrations in the selected ten major cities under the three scenarios during the lockdown period are illustrated in Fig. 1 . The reduction in the anthropogenic emissions in C3 did not cause significant reduction in PM2.5 major components' concentrations across the cities. The highest reduction was recorded in Cangzhou (−3.00 μg/m3) while the lowest was found in Tianjin (−2.35 μg/m3). The reductions in PM2.5 major components’ concentrations in C3 when compared with C1 were −2.50, −2.66, −2.73, −2.74, −2.60, −2.73, −2.51, and −2.57 μg/m3 in Beijing, Shijiazhuang, Baoding, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Fig. 2 shows the spatial variations of the changes of simulated PM2.5 between the base case (C1) and the two emission reduction scenarios (C2 and C3) during the lockdown period. When compared to C1, PM2.5 increased by up to 10 μg/m3 in C2. This rise could be attributed to the increase in residential combustion sources (10%) in C2 as people were mandated to stay at home during the lockdown period. The spatial changes of PM2.5 concentrations, which increased by up to 20 μg/m3 in C2 before the lockdown period are illustrated in Fig. S15 in the Supplementary Material.Fig. 1 Average predicted daily PM2.5 with major components and observed PM2.5 in the 10 major cities under the three scenarios during the lockdown. Units are in μg/m3. Fig. 1 Fig. 2 Spatial distributions of predicted PM2.5 and the changes between the base case (C1) and the two emission reduction scenarios (C2 and C3) during the lockdown period. Units are in μg/m3. Fig. 2 The averaged predicted daily PM2.5 with its major components and observed PM2.5 concentrations in the ten major cities under study during the high-pollution and clean (low-pollution) days are displayed in Fig. 3 . According to the second level of Chinese NAAQS, high-pollution days are defined by daily PM2.5 concentrations above 75 μg/m3. During the lockdown period, the reductions in PM2.5 concentrations during high-pollution days in C3 when compared with C1 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai were 3.72, 4.27, 3.47, 3.92, 4.40, 3.98, 3.76, 9.03, 4.50, and 3.74 μg/m3, respectively. Overall, the changes were below 10%. Hengshui had the highest reduction of 6.10%. For other cities, the percentage reductions were 3.76, 3.54, 3.28, 3.22, 3.57, 3.56, 3.47, 3.61, and 3.67% in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Tangshan, and Xingtai, respectively. The percentage of high polluted days was highest in Shijiazhuang (64.9%) followed by Xingtai (56.8%) and the lowest was traced to Hengshui (10.8%).Fig. 3 Average predicted daily PM2.5 with major components and observed PM2.5 in the 10 major cities under the three scenarios on high-pollution days and low-pollution days during the lockdown. The number of days in high (H) and low (L) pollution days are indicated after the city names. Units are in μg/m3. Fig. 3 Alternatively, during low-pollution days, the reductions were 1.80, 0.72, 1.20, 1.53, 1.84, 1.91, 1.41, 1.97, 0.66, and 1.10 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. The reductions were less than 5% across the cities and the highest was found in Chengde (2.91%) while the lowest was observed in Tangshan (1.35%). For the low-pollution days, Hengshui had the highest percentage of clean days with 89.2%, followed by Beijing (62.2%) and the lowest was recorded in Shijiazhuang (35.1%). The benefits of emission reductions were counteracted by unfavorable meteorological conditions resulting in high PM2.5 pollution. Thus, this study showed that substantive reductions in transportation emissions and slight reductions in industrial emissions could not guarantee improved air quality in BTH region when unfavorable weather conditions occur. This study has also showed that the relationships between emissions and concentrations are not usually linear (Xing et al., 2020). Understanding and including the roles of both chemistry and meteorology in the formation of air pollutants is important as illustrated in this study. It is also important to use multi-pollutant nonlinear response models when designing effective emission control strategies as suggested by Xing et al. (2020). 3.3 Relative changes due to emission reductions Fig. 4 shows the relative changes of major PM2.5 components between C3 and C1 across the ten cities. Significant reductions were found in nitrate (NO3 −), elemental carbon (EC), and ammonium (NH4 +) while the changes in sulfate (SO4 2−) and primary organic aerosols (POA) were below 10% in all the cities. For instance, in Chengde where the highest reductions were observed, the concentrations of NO3 −, EC, NH4 +, SO4 2−, and POA decreased by 24.2, 22.6, 15.0, 9.0, and 4.3%, respectively. Other cities followed the same trends. Alternatively, the concentrations of secondary organic aerosols (SOA) and the group of compounds labelled as OTHER increased (rather than decreases) even though these increases were <3% across the cities.Fig. 4 Relative changes in concentrations of PM2.5 major components between C3 and C1 in the 10 major cities during the lockdown. Units are in %. Fig. 4 Fig. S16 presents the changes in PM2.5 major components concentrations before and during the lockdown in C3. In Tianjin, Baoding, Cangzhou, and Chengde. All the chemical species except SO4 2− and SOA decreased during the lockdown compared to their concentrations before the lockdown. There were reductions in the concentrations of all the species in the remaining six cities. Comparing the two periods (before and during the lockdown), significant reductions were found in NO3 −, POA, EC, and NH4 + while the changes in SO4 2− and SOA were less than 10% in all the cities. The highest reduction of NO3 − was observed in Chengde (−40.6%) while Shijiazhuang had the highest reductions of POA (−34.79%), EC (−31.69%), and NH4 + (−24.17%). The substantial reductions in NO3 − during the lockdown period could be attributed to reduction in its precursors (NOx) due to drastic reduction in vehicular movement and suspension of public transport across the country. The reduction in NH4 + could also be due to reduction in light-duty vehicles with catalytic converters (Heeb et al., 2006; Zhou et al., 2019). 3.4 Impacts on PM2.5 of emissions from festival activities During the spring festival, intensive fireworks are usually displayed beginning on the eve of the Chinese New year. The Chinese New Year began on January 25, 2020, while the Lantern festival was celebrated on February 8, 2020. In addition to this, there is an annual traditional event in the northern part of China called “Sheng Wang Huo” that is usually celebrated during the spring festival. As a way of celebrating Sheng Wang Huo annual event, a large pillar of wood and coal is usually ignited on New Year's Eve and burnt for about five days. Also, during the Lantern festival and other major cultural events, burning of wood and coal is a norm for the Chinese people. Although, the Chinese authorities at local government levels had banned this event as a way of reducing its impact on ambient air quality, a recent study by Dai et al. (2020) revealed that the people in the rural areas of provinces such as Hebei, Shanxi, and Inner Mongolia still observed Sheng Wang Huo during this year's Spring and Lantern Festivals. The atmospheric residence time of particulate matter from fireworks is around four days (Dai et al., 2020). The significant contributions due to fireworks and coal/wood burning were observed as higher PM2.5 pollution occurred starting from January 24 and lasted till January 31 in the cities except Hengshui where no record of high pollution was observed during the spring festival. For instance, the ranges of PM2.5 concentrations between January 24 to January 31 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Tangshan, and Xingtai were 76.29–169.67, 85.08–237.00, 129.38–206.58, 92.29–377.38, 80.75–247.29, 80.71–184.29, 100.25–157.25, 78.08–243.00, and 95.38–151.63 μg/m3, respectively. Also, during the Lantern festival, the contributions attributed to fireworks and coal/wood burning were significant and lasted until February 13 across the cities. Between February 7 and February 13, the ranges of PM2.5 concentrations were 119.83–205.67, 97.42–169.79, 103.13–213.17, 105.17–199.38, 82.46–194.82, 76.54–127.50, 77.50–167.63, 84.83–95.67, 75.75–190.71, and 90.13–170.5 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. The continuous high-pollution days recorded during the two festival periods could be attributed to the emissions from fireworks, coal/wood burning, and residential burning and were driven by poor meteorological conditions. 3.5 Impacts of meteorological conditions As a result of reduction in anthropogenic emissions in C3, reduction in the concentrations of PM2.5 were noticed across the ten cities of BTH region, though very low. As discussed earlier and shown in Fig. 3, high pollution persisted in all the cities except Hengshui despite reductions in anthropogenic activities during the lockdown period. It can be inferred that meteorological conditions such as low wind speed, high relative humidity, high temperatures, and low planetary boundary layer heights favored stagnation rather than ventilation, and enhanced the higher pollution formation especially during the festival periods as discussed in section 3.4. During the simulation period, there were high temperatures (Fig. S3), high relative humidity (Fig. S4), low wind speeds (Fig. S1), and low PBLHs (Fig. S5) across the cities in BTH region. The formation of secondary particulate matter was enhanced by these higher temperatures and RH (Li et al., 2019; Wang et al., 2019, 2020c, 2020c; Wu et al., 2019; Zhang et al., 2020) because reaction rates are higher without the temperatures reaching values that favor dissociation of the ammonium nitrate given that the mean temperature for the study period was ~0 °C. Alternatively, the dispersion of atmospheric pollutants was hindered by low wind speed and lower planetary boundary layer height (Li et al., 2019; Liu et al., 2017; Wang et al., 2020b). The efficiency of atmospheric dispersion was evaluated using the ventilation coefficient (VC). VC is the product of the wind speed times the mixed layer height and is reported in m2 s−1. Details of the VC can be found in Dai et al. (2020) and Tiwari et al. (2019). Due to poor dispersion, low VC values are seen for higher pollutant concentrations at a fixed emission rate. Fig. 5 shows the time series of PM2.5 concentrations and VC for Beijing, Tianjin, and Shijiazhuang while Figs S17 and S18 in the Supplementary Material show the time series for other cities. Lower values of VC led to high PM2.5 concentrations across the cities and subsequently resulted to several high-pollution days especially during the festival periods and up to six days after the festivals. Days with very high VC values had correspondingly low pollutant concentrations.Fig. 5 Time series of PM2.5 concentrations and VC for Beijing, Tianjin, and Shijiazhuang. Fig. 5 The changes due to meteorological conditions and reduction of anthropogenic emissions were calculated and illustrated in Fig. 6 . Reduction in emissions caused reductions in PM2.5 in all the cities. The changes were very small and less than 5 μg/m3 across the cities. Cangzhou (−3.30 μg/m3) had the highest and the lowest was observed in Tianjin (−2.53 μg/m3). The changes in other cities were −2.70, −2.90, −2.98, −2.81, −2.82, −2.98, −2.68, and −2.83 μg/m3 in Beijing, Shijiazhuang, Baoding, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Alternatively, substantive but opposite changes were observed for PM2.5 due to unfavorable meteorological conditions during the COVID-19 lockdown period. This situation led to increments in PM2.5 concentration across the cities. Tangshan had the highest contribution of 72 μg/m3, followed by Tianjin (66 μg/m3) while the lowest was found in Xingtai (19 μg/m3). Clearly, in all the ten cities, the significant increment caused by unfavorable meteorological conditions had counteracted the very low positive effects linked to emission reductions. Thus, severe high pollution occurred in BTH region during the COVID-19 lockdown period.Fig. 6 Contributions of emission reduction and meteorological changes to averaged PM2.5 concentrations in 10 major cities during the lockdown period. Units are in μg/m3. Fig. 6 4 Conclusions This study investigated the impacts of emission reductions and changes in meteorological conditions on PM2.5 concentrations during the COVID-19 lockdown in BTH region. The reductions in anthropogenic emissions led to decreased PM2.5 concentrations across the cities. The reduction in predicted PM2.5 concentrations were 2.70, 2.53, 2.90, 2.98, 3.30, 2.81, 2.82, 2.98, 2.68, and 2.83 μg/m3 in Beijing, Tianjin, Shijiazhuang, Baoding, Cangzhou, Chengde, Handan, Hengshui, Tangshan, and Xingtai, respectively. Across the cities, the levels of PM2.5 showed the opposite trend due to unfavorable meteorological conditions during the lockdown period. Tangshan had the highest contribution of 72 μg/m3, followed by Tianjin (66 μg/m3) while the lowest was found in Xingtai (19 μg/m3). During the lockdown, the percentage of days with high-pollution was 64.9% in Shijiazhuang, followed by Xingtai (56.8%) and the lowest was observed in Hengshui (10.8%). Obviously, in all the ten cities, the significant increment caused by unfavorable meteorological conditions had counteracted the very low positive effects attributed to emission reductions. Thus, high air pollution occurred in BTH region during the lockdown period. In designing effective emission control strategies to reduce PM2.5 level in BTH region, it is pertinent to understand and take into consideration the significant roles that both chemistry and meteorology play in the formation of air pollutants as highlighted in this study. Credit author contribution statement Ishaq Dimeji Sulaymon: Conceptualization, Methodology, Formal analysis, Data curation, Visualization, Writing - original draft, Writing - review & editing. Yuanxun Zhang: Conceptualization, Supervision, Funding acquisition, Writing - review & editing. Philip K. Hopke: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Jianlin Hu: Conceptualization, Writing - review & editing. Yang Zhang: Writing - review & editing. Lin Li: Data curation, Formal analysis. Xiaodong Mei: Data curation, Formal analysis. Kangjia Gong: Data curation, Formal analysis. Zhihao Shi: Data curation, Formal analysis. Bin Zhao: Writing - review & editing. Fangxin Zhao: Data curation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Acknowledgements This study was made possible with the support of CAS-TWAS President's Postgraduate Fellowship Program, the 10.13039/501100001809 National Natural Science Foundation of China (NSFC, No. 41877310), and partly by the 10.13039/501100012166 National Key Research and Development Program of China (No. 2016YFC0503600). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2021.111186. ==== Refs References Ambient air pollution https://www.who.int/airpollution/ambient/health-impacts/en 2021 Chang X. Wang S. Zhao B. Xing J. Liu X. Wei L. Song Y. Wu W. Cai S. Zheng H. 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35447126

PMC9750170

NO-CC CODE

2022-12-16 23:24:14

no

J Pediatr. 2022 Jul 18; 246:288-289

latin-1

J Pediatr

10.1016/j.jpeds.2022.04.018

oa_other

==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00671-3 10.1016/j.jpsychires.2021.11.022 Article Anxiety and depression in parents of children with autism spectrum disorder during the first COVID-19 lockdown: Report from the ELENA cohort Miniarikova Ela a Vernhet Christelle a Peries Marianne a Loubersac Julie ab Picot Marie-Christine bc Munir Kerim d Baghdadli Amaria abe∗ a Centre de Ressources Autisme Languedoc-Roussillon et Centre d'excellence sur l’Autisme et les Troubles Neurodéveloppementaux, CHU Montpellier, Montpellier, France b Université Paris-Saclay, UVSQ, Inserm, CESP, Team DevPsy, 94807, Villejuif, France c Clinical Research Unit, Department of Medical Information, CHU Montpellier, Montpellier, France d Developmental Medicine Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA e Faculté de Médecine, Université de Montpellier, France ∗ Corresponding author. Centre de Ressources Autisme Languedoc-Roussillon et Centre d'excellence sur l’Autisme et les Troubles Neurodéveloppementaux, CHU Montpellier, Montpellier, France. 8 11 2021 5 2022 8 11 2021 149 344351 26 4 2021 26 10 2021 6 11 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The Covid-19 pandemic had a strong impact on mental health in the general population. This study conducted during the first lockdown in France considered parents of children with Autism Spectrum Disorder (ASD) prospectively followed in the ELENA Cohort. Objectives We aimed to (1) compare the Anxiety and Depression (AaD) levels during the lockdown between mothers and fathers, (2) compare the parent's AaD between the lockdown and the last ELENA follow-up visit, and (3) identify risk factors for parental AaD during lockdown among socio-demographic and children's clinical characteristics. Methods The Hospital Anxiety and Depression Scale (HADS) was used to assess AaD in 134 parent's pairs. Parents also completed the Questionnaire about their living conditions during COVID-19, their child's interventions and perceived changes about their child's behaviors and sleep. Child's ASD severity, intellectual and socio-adaptive skills and parent's socio-demographic characteristics were collected from ELENA follow-up. Results The parents' AaD levels were lower during the lockdown compared to the last ELENA visit that coincided in 96% with the child's ASD diagnosis. The AaD levels were more pronounced in mothers and significantly associated with the child's challenging behaviors, parents' teleworking and perceived knowledge about COVID-19. The perception of an insufficient knowledge was the only risk factor for mothers' AaD. Conclusion Our findings highlighted the pertinence for an assessment of the mental health of main caregivers of children with ASD, consideration of their gender characteristics, and the importance of providing relevant information during pandemic. Future studies examining the pandemic long-term effects are needed. Trial registration number NCT02625116. Keywords Anxiety Depression Parents Autism spectrum disorder COVID-19 ==== Body pmc1 Introduction The coronavirus disease 2019 (COVID-19), first detected in December 2019 in Wuhan, China, spread rapidly across the globe (Adam, 2020). By March 2020, the World Health Organization acknowledged that Europe had become the epicenter of the pandemic. Facing a surge in case numbers, the French government introduced national containment and mitigation measures mandating the closure of schools, universities and all public venues. The impact of such unprecedented restrictions to limit the spread of COVID-19 had an important psychological impact on everyday public life (Brooks et al., 2020; Dubey et al., 2020). Initial reports emphasized the psychological effects of the pandemic on frontline healthcare workers, with increasing recognition of the more extensive psychological impact of mass quarantine on other vulnerable populations. In France, anxiety symptoms were reported in 26.7% of the general population a week following the beginning of the first lockdown, twice the average rate previously observed in the general population (Chan-Chee et al., 2020). Prior to the onset of the COVID-19 emergency, several factors were already known to be associated with the increased psychological burden of large-scale disease epidemics, notably being single, female, aged 16–24 years, having a low educational level, and financial hardship (Blendon et al., 2004; Brooks et al., 2020; Chan-Chee et al., 2020; Hyland et al., 2020; Liu et al., 2012; Mohammed et al., 2015). Protective factors included being male, being employed, living with a partner, having a high educational level, and favorable living conditions, e.g., larger living area and access to outdoors (Haesebaert et al., 2020; Webster et al., 2020). Although public health measures understandably focused on case fatality rates, especially among the elderly and those with pre-existing medical conditions, a handful of studies began to emphasize the substantial emotional toll of the containment measures, especially for parents living with children below 10-years during the COVID-19 lockdown (Haesebaert et al., 2020). Past studies have consistently reported that parents of children with neurodevelopmental disorders show higher levels of distress, especially anxiety and depression (AaD) compared to controls (Barker et al., 2011; Olsson and Hwang, 2008). Based on evaluation of AaD among parents of children with autism spectrum disorder (ASD) using the standardized Hospital Anxiety and Depression Scale (HADS) (Snaith, 2003), show that they are more likely to report AaD compared to parents of typically developing children or children with other neurodevelopmental disorders (Almansour et al., 2013; Hamlyn-Wright et al., 2007), with the period of initial diagnosis of ASD being especially emotionally challenging and stressful (Lerthattasilp et al., 2015). Women regularly report higher rates of AaD than men in the general population (Bekker and van Mens-Verhulst, 2007; Kuehner, 2003; McLean et al., 2011; Van de Velde et al., 2010) and not surprisingly past studies have also noted that mothers of children with neurodevelopmental disorders express lower levels of wellbeing (Olsson and Hwang, 2008) and higher levels of depression than fathers (Singer, 2006). Furthermore, mothers of children with ASD consistently report higher levels of stress and AaD than the fathers (Davis and Carter, 2008; Jones et al., 2013) and a significant positive link with maternal AaD was recently reported (Öz et al., 2020). Several studies have found that the severity of the children's behavioral problems is related to higher levels of AaD among mothers (Barker et al., 2011; Bitsika and Sharpley, 2004; Wiggins et al., 2019). Jones et al. (2013) suggested that behavioral problems and adaptive deficits in children with ASD are more strongly associated with anxiety in their mothers than in their fathers. The literature has also highlighted an association between the children's physiological behaviors, such as sleep, and ASD behavioral severity (Türkoğlu et al., 2020) and parental depressive symptoms (Meltzer, 2011). In terms of the impact of the pandemic in France, a survey showed that 38% of caregivers of children with ASD perceived the COVID-19 containment measures to be challenging (Centre de Ressources Autisme, 2020). Researchers recently reported an increase in challenging behaviors in children with ASD during the lockdown, particularly among younger children (Berard et al., 2021), suggesting a negative impact of the COVID-19 pandemic on the mental health of children with ASD and their parents (Guller et al., 2021). The first objective of the present study was to compare the levels of AaD between mothers and fathers caring for children with ASD during the first COVID-19 lockdown in France. Based on the existing literature, we therefore hypothesized that the levels of AaD of the mothers would be higher. A second objective was to compare the AaD levels between the lockdown and the last ELENA follow-up visit. The third objective was to identify risk factors for parental AaD during the lockdown among socio-demographic and clinical characteristics of children with ASD. 2 Methods 2.1 Study design The present study consisted of a cross-sectional parent survey carried out between April 27 and May 13, 2020, during the first COVID-19 lockdown in France (to facilitate the reading, the terms “first COVID-19 lockdown” and “lockdown” are used as synonyms in this paper). The study nested among families enrolled in the ELENA cohort (Baghdadli et al., 2019), a prospective multicenter study including 892 children with confirmed diagnosis of ASD. The data collected cover clinical, medical, social, and environmental variables collected at inclusion, and after 18, 36 and 72 months. 2.2 Data collection For the present study, a letter was sent to the parents via the ELENA database electronic system to invite them to participate in the online ELENA-COVID-19 study. A reminder was sent to parents two weeks later by e-mail or by telephone for those who did not have access to the internet. Approximately 30 questionnaires were administered by telephone by a clinical research assistant. 2.3 Participants Participants were parents of children with a confirmed diagnosis of ASD followed in the ELENA Cohort who both completed the HADS during the lockdown. The number of participants differed for each of the three objectives of the study based on the completed questionnaires (Fig. 1 ).Fig. 1 Flow chart of participants. Fig. 1 For the first objective, the sample included 134 pairs of parents who completed the HADS during the lockdown. For the second objective, the sample covered 94 mothers and 79 fathers who completed the HADS during the lockdown and before the pandemic (data collected from the last visit in the ELENA follow-up from April 2015 to March 2020). For the third objective, the sample included only 94 mothers who simultaneously completed the HADS and COVID-19 Questionnaire during the lockdown; the 10 fathers who completed the questionnaires were not included in this analysis. The online ELENA-COVID-19 study was approved by the Internal Review Board of the University Hospital of Montpellier (IRB-MTP_2020_04_202000453). 2.4 Variables 1. Parents' variables Data collected prior the lockdown, from the last ELENA follow-up visit (Baghdadli et al., 2019), were parental socio-demographic characteristics and the last HADS completed. The HADS was collected for 96% of the parents at the time of the inclusion in ELENA cohort (itself determined by the diagnosis of ASD to parents) and for 4% at another time but prior to epidemic. We also collected data during the lockdown: the COVID-19 Questionnaire developed by the authors and the HADS.2. Children's variables We used data collected prior the lockdown from the last ELENA follow-up visit: ADOS-2 CSS, best-estimate intellectual level and VABS-II scores, and behavioral data from the COVID-19 Questionnaire completed by parents during the lockdown. 2.5 Measures 1. COVID-19 Questionnaire Parents completed a structured online questionnaire constructed by the authors to collect data during lockdown and were asked to complete only one COVID-19 Questionnaire per child. The questionnaire consisted of the following four sections: (i) family environment: area of the living space of the house and household composition; (ii) parental professional activity: loss or reduction of employment and teleworking for the responder and his/her spouse; (iii) information on COVID-19 and containment measures; and (iv) child's status: health, need for care (related or not to COVID-19), and special education. Parents were also asked to rate the child's challenging behaviors and sleep as “unchanged”, “improved”, or “worsened” during the lockdown.2. The Hospital Anxiety and Depression Scale (HADS) The HADS (Zigmond and Snaith, 1983) was used to assess the AaD of the parents. This reliable tool has been widely used in community and primary care practice settings, but also in studies of parents of children with ASD (Almansour et al., 2013; Guller et al., 2021; Hamlyn-Wright et al., 2007; Lanyi et al., 2021; Reed et al., 2016). Both mothers and fathers were invited to self-rate the 14 items of the French-version (Lepine et al., 1985), including seven items about depression and seven about anxiety. The overall score and two sub-scores from the anxiety and depression subscales were determined. The thresholds for the sub-scores were: 0 to 7, absence of anxiety or depression; 8 to 10, suspected anxiety or depression; and 11 to 21, significant level of anxiety or depression. The thresholds for the combined scores were: 0 to 14, no anxiety/depression, and 15 to 42, presence of a significant level of combined anxiety + depression.3. Socio-demographic variables The parents’ age and education levels were extracted from the ELENA socio-demographic report.4. Children's clinical characteristics Children clinical characteristics were collected from the ELENA last follow-up visit. Symptoms severity was measured using the Calibrated Severity Score (CSS) of Autism Diagnosis Observation Schedule-2 (ADOS-2) (Gotham et al., 2007; Hus et al., 2014; Hus and Lord, 2014; Lord et al., 2012). The intellectual level was estimated for each child using age-appropriate tests to take into account the variability of skills among children by age (Howlin et al., 2014). A performance IQ was calculated if a standardized test could be administered (Wechsler scales (Wechsler, 2002, 2003, 2014a, b) or K-ABC II (Kaufman and Kaufman, 2013)). A developmental age was estimated from developmental scales if the child could not understand the test instructions (Brunet Lézine-Revised (Brunet et al., 1997) or PEP-3 (Schopler et al., 2004)) and a developmental quotient was calculated according to Stern's formula (Stern, 1912) by dividing the developmental age score by the chronological age x 100. The adaptive skills were assessed with the Vineland Adaptive Behavior Scale, Second Edition (VABS-II) (Sparrow et al., 2005). 2.6 Statistical analyses The outcome variable was the mothers' AaD levels during the lockdown. The following potential explanatory variables from the following sources were considered for the analysis: 1) the ELENA cohort: latest data collected concerning the child's CSS, VABS-II standard scores, and intellectual level and 2) the ELENA-COVID-19 Questionnaire: child's age during the lockdown, number of children living at home, number of outings, continuation of care during the lockdown, number of rooms in the house and the number of household inhabitants, single-parent family during the lockdown, parents' educational levels, parents' employment/loss of income during the lockdown, teleworking, parental perceived knowledge about COVID-19, and the perception of changes in their child's behaviors. The mean and standard deviation (SD) are reported for continuous variables and the frequency for categorical variables. Paired sample t-tests were used to compare: 1) HADS scores between the mothers and fathers during the lockdown and 2) the mothers' and fathers' HADS scores between the lockdown and the last ELENA follow-up visit. The association between potential explanatory factors and the mothers' AaD levels observed during the lockdown was studied using Pearson chi-square or Fisher exact tests for the categorical variables and Student's t-tests or Wilcoxon rank-sum tests for continuous variables. Due to the sample size, AaD risk factors were explored only for mothers by multivariate logistic regression. Variables with a p-value < 0.20 in univariate analysis were included in the model and backward selection was used to select the model that minimized the Akaike Information Criterion (AIC). The multivariate model was adjusted for the time since the diagnosis. Odds ratios (OR) with 95% confidence intervals are presented. The goodness-of-fit of the models was assessed using the Hosmer and Lemeshow test. All statistical tests were considered significant for p < 0 .05. Statistical analyses were performed using SAS Enterprise Guide V7.13 (SAS Institute Inc., Cary, NC, USA). 3 Results 3.1 Descriptive data 3.1.1 Parents’ characteristics The mean age was 41.1 years (±6.8) for the mothers and 44.2 years (±7.8) for the fathers. Overall, 60.2% (n = 74) of the mothers had a college/university education vs. 52.0% (n = 64) of the fathers. The parents’ characteristics were comparable between the samples of the ELENA-COVID-19 study and the ELENA cohort for age, educational level, and socio-economic status. 3.1.2 Children's characteristics The mean age of the children was 8.6 years (±4.0). There were 82.1% boys (n = 110). Overall, 40.2% of the children (n = 51) had an IQ < 70. The mean ADOS CSS-severity score was 7.34 (±1.7). The mean VABS-II scores were 70.8 (±15.3) for communication, 71.9 (±13.0) for daily living skills, and 69.2 (±12.9) for socialization. The children's characteristics were comparable between the samples of the ELENA-COVID-19 study and the ELENA cohort. 3.1.3 Socio-demographic characteristics during the lockdown During the COVID-19 lockdown, interventions from special education and care services were maintained for 72.0% (n = 95) of the children, interrupted for 23.5% (n = 31), and 4.5% (n = 6) of the children had no specialized interventions just before the lockdown. Other socio-demographic characteristics during the lockdown are presented in Table 1 .Table 1 Socio-demographic characteristics during the lockdown. Table 1 Mothers Fathers Parental characteristics Age (years) N = 134 N = 134 41.10 (±6.76) 44.22 (±7.84) Educational level N = 123 N = 123  Elementary . 3 (2.44%)  High school 49 (39.84%) 56 (45.53%)  College/University 74 (60.16%) 64 (52.03%) Environmental characteristics Parent living alone during containment N = 129 N = 96  No 114 (88.37%) 91 (97.79%)  Yes 15 (11.63%) 5 (5.21%) Number of children N = 129 N = 129 2.01 (±0.92) 2.03 (±0.91) Adequacy between room number and inhabitants: N = 129 N = 96  Room number < inhabitants 23 (17.83%) 20 (20.83%)  Rooms number ≥ inhabitants 106 (82.17%) 76 (79.17%) Access to outdoors N = 129 N = 96  No 8 (6.20%) 7 (7.29%)  Yes 121 (93.80%) 89 (92.71%) Going out during the lockdown with the child N = 128 N = 96  No 34 (26.56%) 27 (28.13%)  Yes 94 (73.44%) 69 (71.88%) Perceived knowledge about COVID-19 N = 128 N = 96  Highly insufficient or insufficient 32 (25.00%) 27 (28.13%)  Good 66 (51.56%) 44 (45.83%)  Very good 30 (23.44%) 25 (26.04%) Professional situation Professional situation N = 115 N = 80  Working 73 (63.48%) 73 (91.25%)  Continuity of activities 19 (31.15%) 26 (42.62%)  Complete shutdown of activity 11 (18.03%) 10 (16.39%)  Partial technical unemployment 5 (8.20%) 6 (9.84%)  Full technical unemployment 3 (4.92%) 3 (4.92%)  Telework 23 (37.70%) 16 (26.23%)  Retired . 2 (2.50%)  Job search 5 (4.35%) 3 (3.75%)  At home 37 (32.17%) 2 (2.50%) Spouse's professional situation N = 115 N = 83  Working 93 (80.87%) 56 (67.47%)  Continuation of activities 33 (42.31%) 12 (27.27%)  Complete shutdown of activity 12 (15.38%) 10 (22.73%)  Partial technical unemployment 8 (10.26%) 6 (13.64%)  Full technical unemployment 5 (6.41%) 3 (6.82%)  Telework 20 (25.64%) 13 (29.54%)  Retired 2 (1.74%) .  In search of employment 6 (5.22%) 3 (3.61%)  At home 2 (1.74%) 23 (27.71%)  Not concerned 12 (10.43%) 1 (1.20%) At least one adult at home working N = 128 N = 96  No 11 (8.59%) 6 (6.25%)  Yes 117 (91.41%) 90 (93.75%) At least one adult at home teleworking N = 128 N = 96  No 87 (67.97%) 66 (68.75%)  Yes 41 (32.03%) 30 (31.25%) Loss of income during containment N = 128 N = 96  No 78 (60.94%) 52 (54.17%)  Yes 50 (39.06%) 44 (45.83%) Data are presented as the mean (SD) or N (%). 3.1.4 Mothers' perception of changes in their children's behavior during the lockdown In terms of their children's sleep, 60.2% of the mothers (n = 56) described it as unchanged, 32.3% (n = 30) as worsened, and 7.5% (n = 7) as improved. In terms of their children's challenging behaviors, 32.3% of the mothers (n = 30) described them as unchanged, 50.5% (n = 47) as worsened, and 17.2% (n = 16) as improved. One mother did not answer the question. 3.2 Comparative analysis Objective 1: Comparison of AaD levels between mothers and fathers (n = 134 pairs) caring for children with ASD during the lockdown. Based on paired comparisons (Fig. 2 ), during lockdown, the HADS scores were significantly higher for the mothers than fathers for anxiety (mean difference = 2.0 (±4.6), p < 0.001), depression (mean difference = 0.9 (±4.7), p = 0.01), and anxiety + depression combined (mean difference = 3.0 (±8.0), p < 0.001). The same significant differences were found during the period coinciding with the announcement of the diagnosis (data not shown).Fig. 2 Paired comparison of AaD levels between mothers and fathers (n = 134 pairs) caring for children with ASD during the lockdown. Fig. 2 Objective 2: Comparison of parent's AaD levels (mothers, n = 94; fathers, n = 79) between the lockdown and the last ELENA follow-up visit. Paired comparisons of the HADS scores of the mothers (Fig. 3 ) showed them to be lower during the lockdown than from the last ELENA follow-up visit for anxiety (mean difference = −2.5 (±3.8), p < 0.001), depression (mean difference = −1.5 (±3.9), p < 0.001), and anxiety + depression combined (mean difference = −4.0 (±6.8), p < 0.001).Fig. 3 Paired comparison of parents' AaD levels between lockdown and the last ELENA follow-up visit. (a) Mother's HADS scores (n = 94). (b) Father's HADS scores (n = 79). Fig. 3 The fathers (Fig. 3) HADS scores were significantly lower during the lockdown than from the last ELENA follow-up visit for anxiety (mean difference = −2.3 (±4.1), p < 0.001) and anxiety + depression combined (mean difference = −3.0 (±7.8), p = 0.001), but were not significantly different for depression (mean difference = −0.7 (±4.6), p = 0.16). Objective 3: Identification of the risk factors for mothers’ AaD (n = 94) during the lockdown. 3.2.1 Univariate analysis Among the 94 mothers who simultaneously completed both the COVID-19 Questionnaire and HADS, 39.4% (n = 37) showed combined anxiety + depression. More mothers with combined anxiety + depression reported worsening of their child's challenging behaviors than those without (66.7% vs. 40.4%, p = 0.02). Telework was less common in families of mothers with combined anxiety + depression than in those of mothers without (19.4% vs. 40.4%, p = 0.04). In addition, 41.7% (n = 15) of mothers with combined anxiety + depression rated their perceived knowledge about COVID-19 as highly insufficient or insufficient versus 14.0% (n = 8) of the other mothers (p = 0.01). The child's IQ and sleep tended to be significantly associated with the mothers' combined anxiety + depression (p = 0.08 and p = 0.14, respectively). Mothers with combined anxiety + depression more often had children with an IQ < 70 and impaired sleep than those without. Other clinical characteristics of the children (age, sex, ADOS CCS-severity, and VABS-II scores), their interventions, and the parents' age or education level were not significantly associated with the mothers’ HADS scores. 3.2.2 Multivariate analysis Multivariate analysis showed that mothers who rated their perceived knowledge about COVID-19 as highly insufficient or insufficient compared to those who rated it as good had a significantly higher risk of having anxiety + depression combined [ORa = 4.58 (95%CI = 1.58–13.26), p = 0.01] (Table 2 ).Table 2 Risk factors for mothers’ AaD (n = 94). Table 2 N Crude OR Adjusted ORa OR 95%CI Pvalue OR 95%CI P value VABS-II Communication score  (units = 10) 92 0.79 [0.61–1.04] 0.09 Challenging behaviors  Improved vs unchanged 1.49 [0.39–5.78] 0.56  Worsened vs unchanged 93 3.43 [1.24–9.52] 0.02 Sleep  Improved vs unchanged 0.85 [0.15–4.78] 0.85  Worsened vs unchanged 93 2.41 [0.97–6.00] 0.06 Perceived knowledge about COVID-19  Very good vs good 1.13 [0.38–3.38] 0.23 1.12 [0.38–3.36] 0.23  Insufficient or highly insufficient vs good 93 4.55 [1.58–13.14] 0.01 4.58 [1.58–13.26] 0.01 At least one adult teleworking  Yes vs no 93 0.36 [0.13–0.95] 0.04 Best-estimate IQ 90  <70 vs ≥ 70 2.18 [0.91–5.19] 0.08 a Adjusted for the time since diagnosis. 4 Discussion This is one of the first studies to specifically investigate AaD levels of parents of children with a confirmed ASD diagnosis during the lockdown of the COVID-19 pandemic. The AaD levels were assessed using standardized measures in a relatively large sample of parents. In terms of the first study objective, to compare levels of AaD during the lockdown between mothers and fathers of the same child, our results showed the AaD levels to be higher for mothers than fathers, consistent with our hypothesis and results of previous research (Davis and Carter, 2008; Jones et al., 2013). Of note, women in the general population also report higher levels of AaD than men (Bekker and van Mens-Verhulst, 2007; McLean et al., 2011; Van de Velde et al., 2010). Among parents with a child with ASD, the difference between genders may also be related to differences in coping strategies (Luque Salas et al., 2017; Vernhet et al., 2019). Another interpretation is that mothers are more often the caregivers who are more highly involved in the daily care of the child, which may influence their AaD levels (McStay et al., 2014). For the second study objective, to compare AaD levels reported by parents during lockdown and the last ELENA follow-up visit, we assumed that the parents' AaD levels might be higher during the lockdown than assessments prior to the COVID-19 onset, as observed in the general population (Chan-Chee et al., 2020). As parents' AaD levels were not assessed just prior to the pandemic, interpretation of its effects on their mental health must be cautious. Actually, our findings suggest, that the measures of parents' AaD levels from the last follow-up visit were higher than during the COVID-19 lockdown. One possible explanation is that completion of the HADS prior to the pandemic coincided, in 96% of cases, with the time of inception into the cohort and communication of the children's diagnosis, which is a stressful period for the parents (Lerthattasilp et al., 2015). Another possible explanation is that the parents who agreed to participate in the ELENA-COVID-19 survey were those whose emotions were better preserved. Although it may be intriguing that the prevalence of depression was lower in our sample than in the general population over the same period (approximately 10% of fathers and mothers vs 19% of the general population), another interpretation may be that the parents of children with ASD have better coping skills, acquired through their caregiver status, that they mobilized during the pandemic (Zhao and Fu, 2020). A further explanation may be that the containment measures may have had a positive impact on families with ASD, who were in close proximity to their children and no longer exposed to stressful situations in their daily life related to recurrent transportation to children's interventions. For our third objective, to identify risk factors for parental AaD during the COVID-19 lockdown, the limited sample size restricted us to study risk factors only among the mothers. According to univariate analysis, the children's challenging behaviors, teleworking by the parents, and mothers' perceived knowledge about COVID-19 were significantly associated with the mothers' AaD levels. The role of the worsening of children's behaviors on the AaD of mothers, and their mental health has been previously reported in the literature (Baghdadli et al., 2014; Barker et al., 2011; Jones et al., 2013). We may also supposed that mothers with high levels of AaD have more difficulty in coping with their children's behaviors that they may perceive as worsened. A recent study about lockdown measures showed that teleworkers experienced lower well-being during lockdown than workers who remained in their usual office, with this perception being stronger among women (Escudero-Castillo et al., 2021). In contrast, our results suggest that parents' teleworking was associated with lower AaD levels in mothers. Since teleworking was widespread in France during the first lockdown, it can be assumed that mothers may perceived the benefits of the presence of their partner in daily life. Lockdown measures seem to reduce the organizational constraints around managing, for example, the child's accompaniments to therapies and so may have improved mother's sense of wellbeing. Furthermore, we found the mothers' AaD during lockdown was not associated with the child's socio-demographic or clinical characteristics, family living conditions, or continuity of care. In our sample, there was a trend towards an association between the level of AaD of the mothers and their child's intellectual level and sleep. This is consistent with the literature, which has shown that mothers who have a child with an intellectual disability have higher levels of AaD (Sheikh et al., 2018) and lower well-being (Olsson and Hwang, 2008). In addition, mothers who have reported significant levels of AaD have more often had children who sleep poorly (Meltzer, 2011; Waddington et al., 2020). The only risk factor for AaD among mothers identified from our multivariate analyses was their perceived lack of knowledge about COVID-19. This result is in accordance with those of previous reports showing that adequate knowledge about the disease during pandemics is associated with better adherence to containment measures and a greater sense of well-being (Webster et al., 2020). In our study, good perceived knowledge about COVID-19 was not significantly linked to an increased risk of AaD for mothers, in contrast to previous findings, suggesting that recurrent and excessive information about a disease may have a negative impact on anxiety levels (Everts, 2013; Roy et al., 2020). We suppose that parents with higher AaD levels, having stronger feeling of helplessness and vulnerability, perceived that they had less knowledge than parents with lower AaD. Finally, we assume that the AaD of the parents could be also related to factors that were not taken into account in our study, such as parental coping strategies that need to be studied. 4.1 Strengths and limitations The use of the HADS was a strength of our study in that it provided a structured, acceptable, and effective dimensional assessment of AaD (Snaith, 2003), in parents having a child with ASD (Almansour et al., 2013; Guller et al., 2021; Hamlyn-Wright et al., 2007; Lanyi et al., 2021; Reed et al., 2016). An important strength related to the relatively large sample of children with a confirmed and well-phenotyped ASD enrolled in an established cohort. In addition, this study is one of the first to assess AaD in both mothers and fathers with a child with ASD during a COVID-19 pandemic lockdown. However, our findings must be interpreted in the context of a number of limitations. First, only 27% of the families enrolled in the ELENA cohort participated in the ELENA-COVID-19 study, which may have introduced a response bias. We presume that the low response rate to the survey may have been related to the short completion lead period (15 days) and the need to assess AaD levels in real time especially in a context in which parents were less available due to school closures. One possible limitation of comparing AaD levels over time is that, parents' AaD levels were not assessed shortly before the pandemic, which may influence understanding of the real effects of the pandemic on parents' mental health. Another limitation was that the risk factors were analyzed only for mothers because of the lack of data for fathers. We did not use clinical interviews to assess parents' mental health, which should be proposed in future studies and, as the clinical analysis would be finer, it may probably lead to higher AaD rates. Finally, as the children's behaviors during lockdown were assessed by the parental questionnaires, we assume that parents' emotional states may have influenced their perception of children's behaviors. 4.2 Conclusion and implication This study suggests that AaD levels during the lockdown in France were higher in mothers than in fathers of children with ASD, as found in the general population, and in previous studies in parents of children with ASD. Our results also suggest that parents' AaD levels were lower during the COVID-19 lockdown than before the outbreak, a time that coincides in the present study with families' inclusion in the ELENA cohort and communication of the ASD diagnosis to parents. In addition, positive associations were found between mothers’ AaD and their perception of worsening child challenging behaviors, indicating the importance to prevent behavioral difficulties through parents training and supervision. The association between parental teleworking during lockdown and lower AaD in mothers may be the result of sharing the daily activities with the partner. The only significant risk factor for AaD found in mothers was their perceived knowledge about COVID-19, which is an additional argument to facilitate for caregivers access to updated and relevant information. This study leads to consider the mental health of caregivers of children with ASD (in our study, mainly mothers), through information, training and long-term support. Our study conducted at the beginning of the pandemic, could be extended by long-term studies of the effects of the pandemic on the mental health of parents. Funding Grant sponsor 1: 10.13039/501100009243 French Health Ministry (DGOS) PHRCN 2013; Grant number 1: 13-0232, and Grant sponsor 2: 10.13039/501100015721 Caisse Nationale de Solidarité pour l’Autonomie (CNSA) ; Grant number 2: 030319. Availability of data and material Research data are not shared due to the need for confidentiality. The corresponding author, Pr Amaria Baghdadli, confirm that she had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Ethical approval The study and informed consent procedure have been approved by the Internal Review Board of University Hospital of Montpellier. The ELENA cohort has been approved by the South Mediterranean Ethics Committee on the Research of Human Subjects of Marseille and the National Commission for Computing and Liberties (CNIL. number DR-2015-393). Informed consent Signed informed consent is obtained from all participating families included in the ELENA cohort. Declaration of competing interest The authors have no conflicts of interest to declare. Acknowledgments We sincerely thank the contributing families, the ELENA cohort staff (F. Dellapiazza, L. Audras-Torrent, M. Berard, C. Rattaz, C. Michelon, and L. Ferrando), and the centers participating in recruitment. We also express gratitude to the 10.13039/501100015721 CNSA and 10.13039/501100009243 DGOS for funding the ELENA Cohort. ==== Refs References Adam D. Special report: the simulations driving the world's response to COVID-19 Nature 580 7803 2020 316 318 32242115 Almansour M.A. Alateeq M.A. Alzahrani M.K. Algeffari M.A. Alhomaidan H.T. Depression and anxiety among parents and caregivers of autistic spectral disorder children Neurosciences 18 1 2013 58 63 23291799 Baghdadli A. Miot S. Rattaz C. Akbaraly T. Geoffray M.M. Michelon C. Loubersac J. Traver S. Mortamais M. Sonié S. Pottelette J. Robel L. Speranza M. Vesperini S. Maffre T. Falissard B. Picot M.C. Investigating the natural history and prognostic factors of ASD in children: the multicEntric Longitudinal study of childrEN with ASD - the ELENA study protocol BMJ Open 9 6 2019 e026286 Baghdadli A. Pry R. Michelon C. Rattaz C. 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Wessely S. Rubin G.J. How to improve adherence with quarantine: rapid review of the evidence Publ. Health 182 2020 163 169 Wechsler D. Wechsler Primary and Preschool Scale of Intelligence 2002 Wechsler D. Wechsler Intelligence Scale for Children fourth ed. 2003 (WISC-IV) Wechsler D. WISC-V: Administration and Scoring Manual 2014 NCS Pearson Incorporated Wechsler D. WPPSI-IV, échelle d'intelligence de Wechsler pour enfants 2014 ECPA Wiggins L.D. Rubenstein E. Daniels J. DiGuiseppi C. Yeargin-Allsopp M. Schieve L.A. Tian L.H. Sabourin K. Moody E. Pinto-Martin J. Reyes N. Levy S.E. A phenotype of childhood autism is associated with preexisting maternal anxiety and depression J. Abnorm. Child Psychol. 47 4 2019 731 740 30128718 Zhao M. Fu W. The resilience of parents who have children with autism spectrum disorder in China: a social culture perspective Int. J. Dev. Disabil. 2020 1 12 Zigmond A.S. Snaith R.P. The hospital anxiety and depression scale Acta Psychiatr. 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==== Front Lancet Lancet Lancet (London, England) 0140-6736 1474-547X Published by Elsevier Ltd. S0140-6736(22)02575-2 10.1016/S0140-6736(22)02575-2 Comment Community pandemic response: the importance of action led by communities and the public sector Byanyima Winnie a Lauterbach Karl b Kavanagh Matthew M a a United Nations Joint Programme on HIV/AIDS (UNAIDS), CH-1211 Geneva 27, Switzerland b Federal Ministry of Health of Germany, Government of Germany, Berlin 14 12 2022 14 12 2022 © 2022 Published by Elsevier Ltd. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcThe world faces multiple intersecting pandemics: COVID-19 and mpox (formerly known as monkeypox) have joined HIV/AIDS and a current outbreak of Ebola virus disease to create a dangerous global disease environment. Climate change is making outbreaks more likely.1 An important question for global health policy is which elements should be considered essential to effective pandemic prevention, preparedness, and response (PPR). As the world considers a new international PPR convention or agreement and financing mechanisms, we propose that strong community infrastructure is a necessary element that has been insufficiently addressed in PPR frameworks. Trust and equity are two factors that determine successful pandemic response.2 Mistrust undermines disease detection and health interventions. HIV and Ebola virus disease, for example, have spread partly because people avoided diagnosis for fear of stigma and discrimination.3 Mistrust and misinformation undercut COVID-19 vaccination4 and propel unsafe burial of loved ones who die of Ebola virus disease.5 Meanwhile, inequity also fuels pandemics. Viruses thrive when responses are unequal and some people are left behind—whether migrants in the COVID-19 response, people affected by poverty in informal settlements during cholera outbreaks, or populations of men who have sex with men, sex workers, and people who inject drugs who are at increased risk of HIV.6, 7 Community-led responses—particularly the work of organisations led by, and accountable to, people from affected communities—often bring trust, establish lines of communication, and reach marginalised groups when the state cannot.8, 9 Civil society can be a partner to prevent disease outbreaks becoming pandemics, as the Sustainable Development Goal 3 Global Action Plan and Accelerators recognised.10 Importantly, there are complementary but different roles for community-led organisations and government, which is responsible for its population's right to health. A government role in, for example, community engagement and employing community health workers is necessary but not sufficient. Community response therefore requires action led by both the public sector and community actors. However, as the Independent Panel for Pandemic Preparedness and Response highlighted, the role of community health workers and responses as well as “the potential for communities to shape the response at the decision-making table has been severely neglected”.11 Many PPR documents and plans, if they deal with community infrastructure, overlook community-led efforts. There is an opportunity to move to more comprehensive community engagement in such efforts, such as in preparedness assessments (eg, joint external evaluation12). We suggest community PPR infrastructure should include three elements: services and accountability led by communities, state-sponsored activities in communities, and the engagement of communities. Each component is crucial to develop community preparedness and infrastructure. At the UNAIDS Board meeting in December, 2022, outcomes were released from a consultative process over 2 years involving a multistakeholder task team of 11 governments, representing each region of the world, and 11 civil society representatives. The recommendations of the task team centre on implementing community-led responses that are “informed and implemented by and for communities themselves”.14 Focused on trust and equity capacity, a new definition in these recommendations could help funders and planners: “Community-led organizations, groups and networks, whether formally or informally organized, are entities for which the majority of governance, leadership, staff, spokespeople, membership and volunteers, reflect and represent the experiences, perspectives, and voices of their constituencies and who have transparent mechanisms of accountability to their constituencies. Community-led organizations, groups, and networks are self-determining and autonomous, and not influenced by government, commercial, or donor agendas. Not all community-based organizations are community led.” Registered Nurse Kerri Phithibeault gives Danny Garcia the Monkeypox vaccination at The Center in partnership with Orange County Department of Health in Orlando, Fla., Saturday, Aug. 13, 2022. (Willie J. Allen Jr./Orlando Sentinel/Tribune News Service via Getty Images)© 2022 Willie J Allen Jr./Orlando Sentinel/Tribune News Service via Getty Images 2022 The recommendations of the multistakeholder task team14 include developing better systems for financing community-led organisations, which often face legal, capacity, and eligibility barriers to government and donor funds; monitoring community-led capacity; and integrating data generated by community groups into response management. Community-led organisations meeting this definition will differ depending on disease and location, but might include local non-governmental organisations, women's groups, faith groups, and organisations that represent key populations, among others. Where community-led organisations have capacity and authorisation, we have learned in our work that they can provide cost-effective pandemic health services and improve accountability. For instance, in the response to mpox, HIV/AIDS community organisations in Germany and transgender women's groups in Peru both mobilised to raise awareness and inform the LGBTQI+ community, helping reduce spread even before vaccines were available. In Thailand, key-population-led health services have reached people at increased risk of HIV, achieving among the most equitable HIV responses in the region. In South Africa, community leaders with Ritshidze, which represents people living with HIV, visit clinics and communities to assess COVID-19, HIV, and tuberculosis services and hold administrators accountable for addressing issues such as long waiting times or confidentiality gaps that keep some people away from health services. In crises, such strategies are crucial because they sustain trust and access to services. Amid war, Ukraine's 100% Life, a network of people living with HIV, has used peer networks to communicate with displaced people, delivering medicines, food, and emergency assistance, including in front-line zones. International PPR agreements and funding should include specific goals for community-led capacity. Learning from countries and regions where community-led responses drove success on HIV/AIDS, COVID-19, Ebola, and beyond, governments and stakeholders can assess the density, capability, and funding of community organisations; presence of community-led accountability efforts; and the legal environment for organisations to provide services. The other side of community PPR infrastructure is the key pandemic activities in and with communities by the state and, depending on context, the private sector. These activities have been previously described and defined in international contexts.12 Community health workers are a crucial element of these efforts, reaching beyond health facilities with a role in prevention, detection, and response to pandemics.15 Two-way engagement with communities is another important area. The principle of “nothing about us without us” means moving beyond one-way risk communications to bring communities into decision making at all levels.12 There remain gaps in our understanding about how to do this effectively, so expanded social science research and political leadership in this area are needed;16 community-led organisations have a central role in these efforts. Ending AIDS, stopping COVID-19, mpox, and Ebola virus disease, and preparing for the next pandemic require expanded PPR infrastructure. Community-led and government-led efforts are synergistic, and both are indispensable parts of preparedness. When the next virus hits, community infrastructure can save many more lives if that is what governments and the global community set goals for, what we measure, and what we finance. WB is the Executive Director of UNAIDS and an Under-Secretary-General of the UN. KL is the Federal Minister of Health for the German Government. MMK is Interim Deputy Executive Director of UNAIDS. We declare no other competing interests. ==== Refs References 1 Atwoli L Baqui AH Benfield T Call for emergency action to limit global temperature increases, restore biodiversity, and protect health BMJ 374 2021 n1734 2 Yuan H Long Q Huang G Huang L Luo S Different roles of interpersonal trust and institutional trust in COVID-19 pandemic control Soc Sci Med 293 2022 114677 3 El-Sadr WM Platt J Bernitz M Reyes M Contact tracing: barriers and facilitators Am J Public Health 112 2022 1025 1033 35653650 4 Chen X Lee W Lin F Infodemic, institutional trust, and COVID-19 vaccine hesitancy: a cross-national survey Int J Environ Res Public Health 19 2022 8033 5 Masumbuko Claude K Underschultz J Hawkes MT Social resistance drives persistent transmission of Ebola virus disease in eastern Democratic Republic of Congo: a mixed-methods study PLoS One 14 2019 e0223104 6 UNAIDS In danger: UNAIDS global AIDS update 2022 2022 UNAIDS Geneva https://www.unaids.org/en/resources/documents/2022/in-danger-global-aids-update 7 World Bank Group Potential responses to the COVID-19 outbreak in support of migrant workers 2020 The World Bank Group Washington, DC https://documents1.worldbank.org/curated/en/428451587390154689/pdf/Potential-Responses-to-the-COVID-19-Outbreak-in-Support-of-Migrant-Workers-June-19-2020.pdf 8 Ayala G Sprague L van der Merwe LL-A Peer-and community-led responses to HIV: a scoping review PLoS One 16 2021 e0260555 9 Tejativaddhana P Suriyawongpaisal W Kasemsup V Suksaroj T The roles of village health volunteers: COVID-19 prevention and control in Thailand Asia Pacific J Health Manage 15 2020 18 22 10 WHO The Global Action Plan for Healthy Lives and Well-being for All—Accelerator https://www.who.int/initiatives/sdg3-global-action-plan/accelerator-discussion-frames 2019 11 The Independent Panel for Pandemic Preparedness and Response COVID-19: make it the last pandemic https://theindependentpanel.org/wp-content/uploads/2021/05/COVID-19-Make-it-the-Last-Pandemic_final.pdf 2021 12 WHO Joint External Evaluation Tool third edition 2022 World Health Organization Geneva 14 UNAIDS Final report on community-led AIDS responses based on the recommendations of the Multistakeholder Task Team 2022 UNAIDS Geneva https://www.unaids.org/en/resources/documents/2022/MTT-community-led-responses 15 Boyce MR Katz R Community health workers and pandemic preparedness: current and prospective roles Front Public Health 7 2019 62 30972316 16 Magaço A Munguambe K Nhacolo A Challenges and needs for social behavioural research and community engagement activities during the COVID-19 pandemic in rural Mozambique Global Public Health 16 2021 153 157 33125306 Uncited References 13 World Bank Group Establishment of a Financial Intermediary Fund for Pandemic Prevention, Preparedness and Response 2022 The World Bank Group Washington, DC https://documents.worldbank.org/en/publication/documents-reports/documentdetail/733191656685369495/Establishment-of-a-Financial-Intermediary-Fund-for-Pandemic-Prevention-Preparedness-and-Response

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==== Front Lancet Respir Med Lancet Respir Med The Lancet. Respiratory Medicine 2213-2600 2213-2619 Elsevier Ltd. S2213-2600(22)00479-9 10.1016/S2213-2600(22)00479-9 Comment PIONEER trial: favipiravir to treat moderate COVID-19 Shalhoub Sarah a a Schulich School of Medicine and Dentistry, Western University, London, ON N6A 5C1, Canada 14 12 2022 14 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcAs the COVID-19 pandemic claimed lives across the globe, several randomised clinical trials were conducted to evaluate the safety and efficacy of investigational and repurposed therapeutics for the treatment of COVID-19. As a result, a number of new antivirals are now licensed to treat COVID-19, such as nirmatrelvir–ritonavir (Paxlovid), molnupiravir, and remdesivir.1, 2, 3 Favipiravir is an RNA-dependant RNA polymerase inhibitor with activity against a range of RNA viruses. The agent is licensed in Japan to treat influenza virus and has been studied and subsequently used to treat SARS-CoV-2 infection in several Asian countries.4 This approval was driven by small studies that suggested a reduction of time to clinical improvement or cure in COVID-19 compared with standard of care in mostly mild or moderate cases of COVID-19 not requiring supplemental oxygen.5 Treatment initiation with favipiravir within 10 days from symptoms onset in moderate cases was allowed in one of the studies. However, those with moderate COVID-19 comprised 40% of the cohort, whereas the majority were mild cases and, therefore, it is difficult to draw conclusions on the basis of the small number of patients with moderate COVID-19.6 In The Lancet Respiratory Medicine, Pallav L Shah and colleagues7 report results of a multicentre, open-label, phase 3, randomised controlled trial of oral favipiravir for 10 days in patients newly hospitalised with COVID-19 in five centres in the UK (n=2), Brazil (n=2), and Mexico (n=1). 499 patients were randomly assigned to favipiravir and standard care (n=251) or standard care alone (n=248); an additional three patients had also been randomly assigned to hydroxychloroquine, azithromycin, and zinc before the withdrawal of the study group after instruction from the UK Medicines and Healthcare products Regulatory Agency, due to concerns regarding hydroxychloroquine and cardiac toxicity. Recruitment started in May, 2020, and concluded in May, 2021. During the initial phase of enrolment, uncertainty regarding effective therapies for COVID-19 dominated medical communities and the currently licensed antivirals with activity against SARS-CoV-2 were also being studied. Standard of care, including use of dexamethasone, remdesivir, and tocilizumab as recommended, after their authorisation for use to treat COVID-19, was allowed and was similar in both groups. The prespecified primary outcome was time to recovery, defined as improvement by two or more points on a seven-category ordinal scale from randomisation to day 28. Mortality and survival without ventilation at 28 days were secondary outcomes. SARS-CoV-2 infection was confirmed in 446 (89%) of 499 study participants by RT-PCR, whereas the diagnosis was only presumed on the basis of compatible clinical and radiological presentation, and absence of alternate diagnosis in the remaining 53 (11%) of 499 participants in the cohort. Notably, underlying malignancy at baseline was reported in only 17 (3%) of 502 patients and there were no immunocompromising conditions reported otherwise. Difference in time to recovery and survival without ventilation was similar between the two groups, except in a post-hoc analysis, in which there was a faster rate of recovery in patients younger than 60 years who received favipiravir and standard care compared with those who had standard care alone (HR 1·35 [95% CI 1·06–1·72]; p=0·01). Notably, there was no significant difference in mortality between the favipiravir and standard care group and the group that received standard care alone. A sensitivity analysis that only included RT-PCR-confirmed cases of COVID-19 yielded similar findings. Median time from symptoms onset to randomisation was 8·9 days (IQR 6·2–11·1), which is notably longer than the duration used for nirmatrelvir–ritonavir or molnupiravir.1, 2 415 (83%) of 502 participants in the cohort required oxygen supplementation at baseline and, thus, the majority of the included cohort had moderate or severe COVID-19. Adding favipiravir to standard care when managing COVID-19 that requires hospitalisation did not meet the primary endpoint in patients aged 60 years and older, a group at high risk of poor outcomes. Although the median duration of 8·9 days from symptoms onset to treatment initiation could be a factor in absence of benefit, several other studies explored early initiation of favipiravir within 7 days from symptoms onset in mild cases and reported no notable difference in time to clinical improvement or duration of viral shedding with favipiravir compared with standard care or other investigational therapies that were later proven ineffective in treating COVID-19.8, 9, 10 In a multicentre, open-label, randomised clinical trial,11 early initiation of favipiravir to treat mild COVID-19 did not substantially reduce progression or requirement of supplemental oxygen when compared with standard care. The authors stipulate that the dose used to treat SARS-CoV-2 is based on the approved dose to treat influenza virus, despite in-vitro evidence suggesting a higher dose might be required to effectively treat SARS-CoV-2 given a high half maximal effective concentration.12 These findings call for further work to explore the appropriate dosage to treat SARS-CoV-2, based on pharmacodynamic–pharmacokinetic studies and in-vitro testing. The evidence provided by Shah and colleagues, which supports findings in smaller cohorts, should motivate health authorities in countries where favipiravir is used to revise recommendations for its' use to treat patients who are hospitalised with COVID-19. Illustration of a coronavirus particle attacked by antibodies (immunoglobulin). Coronaviruses cause several diseases in humans, including covid-19, SARS and forms of the common cold.© 2022 Nobeastsofierce/Science Photo Library 2022 SS is a site investigator for the NOVATION-1 study to evaluate the safety and efficacy of aerosolized novaferon and standard of care versus placebo and standard of care in hospitalised adult patients with moderate to severe COVID-19. ==== Refs References 1 Hammond J Leister-Tebbe H Gardner A Oral nirmatrelvir for high-risk, nonhospitalized adults with COVID-19 N Engl J Med 386 2022 1397 1408 35172054 2 Jayk Bernal A Gomes da Silva MM Musungaie DB Molnupiravir for oral treatment of COVID-19 in nonhospitalized patients N Engl J Med 386 2022 509 520 34914868 3 Beigel JH Tomashek KM Dodd LE Remdesivir for the treatment of COVID-19—final report N Engl J Med 383 2020 1813 1826 32445440 4 Furuta Y Komeno T Nakamura T Favipiravir (T-705), a broad spectrum inhibitor of viral RNA polymerase Proc Jpn Acad Ser B Phys Biol Sci 93 2017 449 463 5 Shinkai M Tsushima K Tanaka S Efficacy and safety of favipiravir in moderate COVID-19 pneumonia patients without oxygen therapy: a randomized, phase iii clinical trial Infect Dis Ther 10 2021 2489 2509 34453234 6 Udwadia ZF Singh P Barkate H Efficacy and safety of favipiravir, an oral RNA-dependent RNA polymerase inhibitor, in mild-to-moderate COVID-19: a randomized, comparative, open-label, multicenter, phase 3 clinical trial Int J Infect Dis 103 2021 62 71 33212256 7 Shah PL Orton CM Grinsztejn B Favipiravir in patients hospitalised with COVID-19 (PIONEER trial): a multicentre, open-label, phase 3, randomised controlled trial of early intervention versus standard care Lancet Respir Med 2022 published online Dec 14. 10.1016/S2213-2600(22)00412-X 8 Solaymani-Dodaran M Ghanei M Bagheri M Safety and efficacy of Favipiravir in moderate to severe SARS-CoV-2 pneumonia Int Immunopharmacol 95 2021 107522 9 AlQahtani M Kumar N Aljawder D Randomized controlled trial of favipiravir, hydroxychloroquine, and standard care in patients with mild/moderate COVID-19 disease Sci Rep 12 2022 4925 10 Bosaeed M Alharbi A Mahmoud E Efficacy of favipiravir in adults with mild COVID-19: a randomized, double-blind, multicentre, placebo-controlled clinical trial Clin Microbiol Infect 28 2022 602 608 35026375 11 Chuah CH Chow TS Hor CP Efficacy of early treatment with favipiravir on disease progression among high-risk patients with coronavirus disease 2019 (COVID-19): a randomized, open-label clinical trial Clin Infect Dis 75 2022 e432 34849615 12 Du YX Chen XP Favipiravir: pharmacokinetics and concerns about clinical trials for 2019-nCoV Infection Clin Pharmacol Ther 108 2020 242 247 32246834

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Elsevier Inc. S0022-3476(20)31514-6 10.1016/j.jpeds.2020.12.032 Original Article Impact of the Coronavirus Disease 2019 Pandemic on Authorship Gender in The Journal of Pediatrics: Disproportionate Productivity by International Male Researchers Williams Wadsworth A. II MA 1 Li Alice BA 2 Goodman Denise M. MD, MS 13 Ross Lainie Friedman MD, PhD 45∗ 1 Feinberg School of Medicine, Northwestern University, Chicago, IL 2 Pritzker School of Medicine, University of Chicago, Chicago, IL 3 Division of Critical Care Medicine, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 4 MacLean Center for Clinical Medical Ethics, University of Chicago, Chicago, IL 5 Department of Pediatrics, University of Chicago, Chicago, IL ∗ Reprint requests: Lainie Friedman Ross, MD, PhD, Department of Pediatrics, University of Chicago, 5841 S Maryland Ave, MC 6082, Chicago, IL 60615. 22 12 2020 4 2021 22 12 2020 231 5054 13 8 2020 12 12 2020 14 12 2020 © 2020 Elsevier Inc. All rights reserved. 2020 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective To assess the impact of the coronavirus disease 2019 (COVID-19) pandemic on authorship gender in articles submitted to The Journal of Pediatrics. Study design Using gender-labeling algorithms and human inspection, we inferred the gender of corresponding authors of original articles submitted in January-February and April-May of 2019 and 2020 noting those articles related to the COVID-19 pandemic. We used Pearson χ2 tests to determine differences in gender proportions during the selected periods in the US and internationally. Results We analyzed 1521 original articles. Submissions increased 10.9% from January-February 2019 to January-February 2020 and 61.6% from April-May 2019 to April-May 2020. Women accounted for 56.0% of original articles in April-May 2019 but only 49.8% of original articles in April-May 2020. Original articles focused on COVID-19 represented a small percentage of additional articles submitted in January-February 2020 (1/33 or 3.0%) and (53/199 or 26.6%) in April-May 2020 compared with the number of submissions in the same months in 2019. International male corresponding authors submitted a significantly larger proportion of original articles compared with international female corresponding authors in April-May 2020 compared to April-May 2019 (P = .043). There was no difference in corresponding author gender proportion in the US (US in April-May of 2020 vs April-May of 2019; P = .95). There was no significant difference in final dispositions based on corresponding author gender for original articles from 2019 and 2020 (P = .17). Conclusions Original article submissions to The Journal increased in April-May 2020, with the greatest increase by international male corresponding authors. The majority of the submission growth was not related to COVID-19. Keywords gender gender bias female physicians women in pediatrics authorship publications COVID-19 pandemic international Abbreviation COVID-19, Coronavirus disease 2019 ==== Body pmcThe coronavirus disease 2019 (COVID-19) pandemic has had far-reaching global implications. As of November 1, 2020, the World Health Organization reports that there are more than 54 million confirmed cases and 1.3 million deaths from COVID-19.1 One area that has garnered attention is the disproportionate impact of the pandemic on academic authorship for women. Women submitted fewer articles to preprint servers in the fields of physical sciences, life sciences, and medicine in February-April 2020.2, 3, 4 There was a 7% reduction in women as corresponding authors for manuscripts submitted to JAMA Surgery when comparing April-May 2020 with April-May 2019.5 Early analyses of published articles concerning COVID-19 found a smaller proportion of women than expected.3 , 6 , 7 This observed authorship gap has no definitive causes but is theorized to stem from an unequal burden of domestic and child care responsibilities on women.2 , 5 , 6 To date, there have been no studies analyzing the effect of the COVID-19 pandemic on authorship gender in the field of pediatrics. Although in general children have mild symptoms and are less likely to require intensive care and are less likely to die,8 , 9 the field of pediatrics merits attention to gender equity, as it is the field of medicine with the greatest percentage of female physicians.10 According to data from the Association of American Medical Colleges, there were 63 902 active pediatricians in the US in 2017, and 61.7% were female.10 Data from the 2015 American Academy of Pediatrics Pediatrician Life and Career Experience Study show that US female pediatricians were responsible for a greater share of household responsibilities than their male peers and were more likely to report hiring help for cleaning, outdoor work, and childcare.11 As a result, female US academic pediatricians may be at significant risk of negative publishing and adverse career consequences created by the COVID-19 pandemic. Of note, corresponding data about international pediatricians, regarding both the number of men and women doing pediatric research and their work–life balance, are lacking. We aimed to study the impact of COVID-19 on the research productivity of women and men by comparing the proportion of female and male researchers submitting manuscripts to The Journal before and during the pandemic. Methods We were given access to Elsevier's database for original articles submitted to T he Journal of Pediatrics for the months of January-February and April-May for both 2019 and 2020. The year 2019 served as a control to compare with 2020. April-May 2020 were used as the months affected by the pandemic. We distinguished original manuscripts submitted that concerned the COVID-19 pandemic by examining articles that included the key words “COVID-19,” “SARS-CoV-2,” “coronavirus,” or “pandemic” (hereinafter referred to as pandemic original manuscripts). We evaluated final dispositions of the 2019 and 2020 original articles. Our final update of dispositions was October 30, 2020. In addition, we compared this 2019 and 2020 period with data from The Journal that we published in 2015 and 2016 to establish a longitudinal trend.12 Corresponding Authors The Journal retains the names of first authors and corresponding authors of all submitted articles but does not retain the names of senior authors. The only other identifying information retained about rejected articles is the country-of-origin of the corresponding authors. Therefore, we chose to analyze the gender and country-of-origin (US vs international) of corresponding authors to account for potential global differences from the pandemic. Gender Inference We rigorously inferred the gender of corresponding authors for all analyzed manuscripts through the use of gender inference algorithms and human identification. As outlined in the methods of our previous research,12 the initial screen began with a gender-inference program created by the researchers with MATLAB (R2020a, MathWorks Inc). Our internal inference program was set with high specificity and has an error rate of <1%. For all names that were not inferred by the program, authors searched the internet for academic profiles and online accounts (such as Research Gate, LinkedIn, Doximity, and institutional websites) to find pronouns or images identifying the gender of the author. One challenge in gender inference research is accurately labeling the gender of Asian names because of a lack of reliable census information, regional and ethnic variations of gender-association with specific names, and a difference in how self-identified “first” and “last” names are reported to The Journal. We used an outside algorithm to identify gender for those names that were not labeled after human inspection. Following research by Santamaría and Mihaljević, comparing commercially available databases for gender inference research, we used Gender API to minimize false-positive identification, as it has the largest sample of names and the lowest misidentification rate of studied databases.13 We included a name as a positive identification if it was labeled with 100% accuracy according to the Gender API, or if it had a >70% rated accuracy from a sample of more than 50 names. We used this previously validated Gender API percentage accuracy threshold of 70%,14 , 15 and additionally imposed a sample size requirement to help minimize type I errors. All names were treated the same, regardless of author's country-of-origin, to avoid biasing the gender inference. Statistical Analyses We analyzed gender proportion for submissions and dispositions of articles from 2019 and 2020 using the Pearson χ2 test. For the comparison of dispositions for pandemic-focused original articles, we used a Fisher exact test. Significance was set at P = .05. The University of Chicago institutional review board exempted this research and waived the need for informed consent. Results Original Articles Submitted Between 2019 and 2020 There were 1521 original articles submitted to The Journal during the studied time periods of January-February and April-May of 2019 and 2020. Only 39 (2.6%) corresponding author genders could not be inferred and were excluded from gender analysis. Of the 1482 original articles studied in 2019 and 2020, the first author was the corresponding author for 876 (59.1%) of submitted articles. The majority of articles (925/1482 or 62.4%) were submitted by international corresponding authors (Table I ). In 2019 and January-February of 2020, women corresponding authors represented an average of 55.0% of all original articles submitted (53.0% international corresponding authors, 56.0% US corresponding authors). However, in April-May of 2020, during the COVID-19 pandemic, women were the corresponding author of only 49.8% of all submitted original articles (46.9% international corresponding authors, 55.9% US corresponding authors).Table I Comparing gender proportion of corresponding authors for original articles submitted—select months of 2019 vs 2020 Corresponding author origin—time periods Total corresponding authors, no. Female corresponding authors, no. (%) Male corresponding authors, no. (%) P value All—January-February 2019 302 161 (53.3) 141 (46.7) .53 All—January-February 2020 335 187 (55.8) 148 (44.2) International—January-February 2019 175 91 (52.0) 84 (48.0) .74 International—January-February 2020 203 109 (53.7) 94 (46.3) US—January-February 2019 127 70 (55.1) 57 (44.9) .52 US—January-February 2020 132 78 (59.1) 54 (40.9) All—April-May 2019 323 181 (56.0) 142 (44.0) .078 All—April-May 2020 522 260 (49.8) 262 (50.2) International—April-May 2019 195 109 (55.9) 86 (44.1) .043 International—April-May 2020 352 165 (46.9) 187 (53.1) US—April-May 2019 128 72 (56.3) 56 (43.8) .95 US—April-May 2020 170 95 (55.9) 75 (44.1) Value in bold are statistically significance at P value. Original articles submitted in January-February 2019 compared with January-February 2020 showed a 10.2% increase. In contrast, submissions in April-May 2020 increased by 61.6% compared with April-May 2019 (international corresponding authors growth +80.5%, US corresponding authors growth +32.8%). Female corresponding authors during April-May 2020 demonstrated a year-over-year growth of 43.6% (international female corresponding authors 51.4%, US female corresponding authors 31.9%) compared with 84.5% for male corresponding authors (international male corresponding authors 117.4%, US male corresponding authors 33.9%). There was no significant difference in the proportion of corresponding author gender for all original articles submitted between January-February of 2019 compared with January-February of 2020 (P = .53, Table I). There was also no significant difference in the gender proportion of corresponding authors when comparing all original article submissions between April-May of 2019 vs April-May 2020 (P = .078). However, there was a significantly lower proportion of original article submissions by international female corresponding authors in April-May 2020 compared with April-May 2019 (P = .043). Final dispositions were assigned for 99.3% (1472/1482) of analyzed original articles submitted in the select months of 2019 and 2020. As shown in Table II , there was no significant difference in final manuscript dispositions for original articles submitted during these months based on corresponding author gender. All female corresponding authors had an acceptance rate of 16.3% compared with male corresponding authors (13.6%, P = .17). International female and international male corresponding authors were accepted at a lower rate (7.7% and 8.0%, respectively) compared with the acceptance rate of US female and US male corresponding authors (29.2%, P < .00001 and 24.0%, P < .00001 respectively).Table II Final dispositions of all original articles—January-February and April-May of 2019 and 2020 Corresponding author origins—dispositions Female corresponding authors Male corresponding authors P value No. Gender rate % No. Gender rate % All—Accept 129 16.3 94 13.6 .13 All—Reject 653 82.8 594 85.7 International—Accept 37 7.7 36 8.0 .93 International—Reject 435 91.8 414 91.8 US—Accept 92 29.2 58 24.0 .17 US—Reject 218 69.2 180 74.4 COVID-19 Pandemic–Related Research: January-February 2020 and April-May 2020 We analyzed 54 original manuscripts concerning the COVID-19 pandemic that were submitted to The Journal in January-February and April-May 2020 (Table III ). The first pandemic manuscript submitted was from China on February 13, 2020, which was the only pandemic original article in January-February. The gender for all 54 of these manuscripts was inferred. International authors submitted 87.0% of all pandemic original manuscripts. Female corresponding authors authored 48.1% of all pandemic-focused original articles. Manuscripts were submitted primarily by China in February but by April and May, submissions began to increase around the globe, with Italy leading all pandemic original manuscripts submissions in April and the US in May.Table III Combined pandemic-focused original articles (January-February and April-May 2020) Origins of corresponding authors No. submitted/no. accepted All Female corresponding author Male corresponding author All 54/24 26/1 28/2 International 48/1 22/0 26/1 US 6/2 4/1 2/1 Discussion There was a large increase in the number of original article submissions to The Journal in April-May 2020 compared with April-May 2019 as the COVID-19 pandemic spread across the globe. However, original manuscripts related to the pandemic only accounted for approximately one-quarter of the increase in articles submitted. Although submissions increased globally, international men had the greatest increase in submissions. Most submissions in April-May 2020 would have involved research conducted before the emergence of COVID-19, and researchers may have availed themselves of the shelter-in-place rules to complete manuscripts already in progress. More analysis is necessary to understand why international female researchers and male and female US researchers were comparatively less productive. What impact COVID-19 is having on initiating and completing new projects (both COVID-19 and non–COVID-19–related) in both the short and long term still remains to be seen. Given the long timeline of research projects (from idea generation and procuring funding to conducting the research and reporting on the findings), the full impact on researcher productivity and whether there is a gender difference will not be known for months to years.16 , 17 Our data show that international male corresponding authors had the largest productivity gain, as measured in the number of submissions to The Journal early in the pandemic. The change in gender proportion for international corresponding authors of original articles from a previous female majority (average of 53.0% during nonpandemic months of 2019 and 2020) to a female minority (46.9%) in April-May 2020, is noteworthy and may suggest a comparative disproportionate harm to international female researchers. US male and female researchers had a smaller increase in publication submissions to The Journal at this point in the pandemic, suggesting a comparative productivity disadvantage for US researchers compared with their international counterparts. Research is needed to determine whether this comparative productivity disadvantage is true in other medical specialty and subspecialty fields and whether it persists over time. The gendered impact of the pandemic on international authors is not fully appreciated in the medical literature to date, which focuses mainly on authorship gender of US researchers.3 , 6 , 7 For example, Andersen et al only analyzed authors affiliated with US institutions,6 Frederickson used only US Social Security Administration data to infer gender,4 and Vincent-Lamarre et al and Kibbe did not differentiate international from US authors in their analyses.3 , 5 Many top-tier academic journals, including The Journal, receive a large volume of submissions from international authors.18, 19, 20 As researchers continue to uncover the far-reaching implications of the pandemic on gender, the long-term effects on gender in different global regions should be explored. In contrast with their international colleagues, male and female US corresponding authors had a similar comparatively smaller increase in submissions. A variety of factors may help explain why we are not seeing the adverse impact on US women pediatric researchers that has been described for female researchers in other areas of science and medicine.2, 3, 4, 5 First, the field of pediatrics has had a lesser clinical burden of COVID-19–related illness, although other societal disruptions such as school closures would still be expected to affect pediatricians. Second, male and female US pediatric researchers may be taking on similar family childcare responsibilities such that their productivity is being equally affected (growth by US male corresponding authors [+34%] and US female corresponding authors [+32%] are lower than the increased productivity of international male corresponding authors [+117%] and female corresponding authors [+51%]). Alternatively, the studied period of April-May 2020 may be too early to see the full impact on gender. Although not seen in April-May 2020, the potential for a differential impact of the pandemic on US female researchers requires vigilance because comparatively lower productivity may still occur.16 Increased familial childcare burden persists due to the failure of many K-12 schools to resume school fully in-person in August-September 2020,21 and the increasing cases occurring in October-November 2020 suggest that full resumption of in-person schooling for many children may not occur this academic year.22 , 23 Our study has several limitations. First, this manuscript is limited to 1 pediatric journal. We only used data from The Journal because it allowed us access to its internal database, which includes all submitted manuscripts, both those accepted and rejected. Thus, despite the limitation, we were able to analyze not only those manuscripts accepted but also those rejected. We have previously shown that manuscript acceptance at The Journal is gender-neutral,12 and we were able to show that this was still the case during the pandemic. Second, we only analyzed the gender of corresponding authors, not first or last authors, because The Journal's database does not retain information for the last author nor the country-of-origin of the first author for those articles that are rejected. It is possible that there is a differential gender effect of the pandemic on authorship gender of first authors (more junior authors who may be younger and have younger children) than last authors (who may be older and have older children). However, 59.1% of corresponding authors were first authors. Third, we recognize that gender is a societal construct and inferring gender from an image may not represent the gender of the individual. In addition, name inference from database and image identification may marginalize groups that do not conform to the concept of binary genders. Fourth, we only studied a few discrete segments of time, so we were not able to model any trends over time. Given that the pandemic shows no signs of ending soon, further research needs to be done. COVID-19 has created massive changes to society and medicine. Equity in career opportunities and advancement requires that the academic community is vigilant about the potential differential impact on the productivity of female and male researchers in both the short-term and long-term in both the US and around the world. We thank the editors and publishers of The Journal, especially Meghan McDevitt, for their access and willingness to explore the role of gender in publication. We also thank Dr Yinghan Shi, Clinical Associate of Pediatrics at the University of Chicago, for her assistance in inferring the gender of Chinese researchers. Data Statement Data sharing statement available at www.jpeds.com. Supplementary Data DataProfile D.G serves an associate editor of The Journal of Pediatrics. L.R. serves on the Editorial Board of The Journal of Pediatrics and also serves on the Editorial Boards of the Journal of Clinical Ethics, the Journal of Empirical Research in Human Research Ethics (JERHRE), and Perspectives in Biology and Medicine. The other authors declare no conflicts of interest. ==== Refs References 1 WHO Coronavirus Disease (COVID-19) Dashboard | WHO Coronavirus Disease (COVID-19) Dashboard https://covid19.who.int/?gclid=CjwKcorresponding authorjw1K75BRAEEiwAd41h1GKNiVofUSj45ZGnxmGgj9Rs4iodDoLSzb2J3wrI5kggPMo6XwVMQhoCVSIQAvD_BwE 2 Viglione G. Are women publishing less during the pandemic? Here’s what the data say Nature 581 2020 365 366 32433639 3 Vincent-Lamarre P. Sugimoto C.R. Lariviere V. The decline of women’s research production during the coronavirus pandemic | Nature Index https://www.natureindex.com/news-blog/decline-women-scientist-research-publishing-production-coronavirus-pandemic 4 Frederickson M. pandemic-pub-bias/README.md at master drfreder/pandemic-pub-bias GitHub https://github.com/drfreder/pandemic-pub-bias/blob/master/README.md 2020 5 Kibbe M.R. Consequences of the COVID-19 pandemic on manuscript submissions by women JAMA Surg 155 2020 803 804 32749449 6 Andersen J.P. Nielsen M.W. Simone N.L. Lewiss R.E. Jagsi R. COVID-19 medical papers have fewer women first authors than expected Elife 9 2020 1 7 7 Pinho-Gomes A.C. Peters S. Thompson K. Hockham C. Ripullone K. Woodward M. Where are the women? Gender inequalities in COVID-19 research authorship BMJ Glob Heal 5 2020 3 6 8 Zimmermann P. Curtis N. Coronavirus infections in children including COVID-19: an overview of the epidemiology, clinical features, diagnosis, treatment and prevention options in children Pediatr Infect Dis J 39 2020 355 368 32310621 9 Sisk B. Cull W. Harris J.M. Rothenburger A. Olson L. National Trends of Cases of COVID-19 in Children Based on US State Health Department Data Pediatrics 146 2020 e2020027425 10 Active Physicians by Sex and Specialty, 2017 | AAMC https://www.aamc.org/data-reports/workforce/interactive-data/active-physicians-sex-and-specialty-2017 11 Starmer A.J. Frintner M.P. Matos K. Somberg C. Freed G. Byrne B.J. Gender discrepancies related to pediatrician work-life balance and household responsibilities Pediatrics 144 2019 12 Williams W.A. Garvey K.L. Goodman D.M. Lauderdale D.S. Ross L.F. The role of gender in publication in The Journal of Pediatrics 2015-2016: equal reviews, unequal opportunities J Pediatr 200 2018 254 260.e1 30029860 13 Santamaría L. Mihaljević H. Comparison and benchmark of name-to-gender inference services Peer J Comput Sci 2018 2018 1 29 14 Nielsen M.W. Andersen J.P. Schiebinger L. Schneider J.W. One and a half million medical papers reveal a link between author gender and attention to gender and sex analysis Nat Hum Behav 1 2017 791 796 31024130 15 Andersen J.P. Schneider J.W. Jagsi R. Nielsen M.W. Gender variations in citation distributions in medicine are very small and due to self- citation and journal prestige Elife 8 2019 1 17 16 Rahman M. Fukui T. A decline in the U.S. share of research articles N Engl J Med 347 2002 1211 1212 12374892 17 Hellems M.A. Gurka K.K. Hayden G.F. A review of The Journal of Pediatrics: the first 75 years J Pediatr 155 2009 16 20.e1 18926546 18 Huang M.H. Chang H.W. Chen D.Z. The trend of concentration in scientific research and technological innovation: a reduction of the predominant role of the U.S. in world research & technology J Informetr 6 2012 457 468 19 Flaherty C. Women are falling behind | Inside Higher Ed https://www.insidehighered.com/news/2020/10/20/large-scale-study-backs-other-research-showing-relative-declines-womens-research 20 North A. Covid-19 and school reopenings, explained in 10 facts. Vox https://www.vox.com/2020/10/1/21493602/covid-19-schools-reopening-nyc-florida-hybrid 21 Smith M. The U.S. Hits the 9-Million Mark as Infections Keep Surging - The New York Times https://www.nytimes.com/live/2020/10/29/world/covid-19-coronavirus-updates 22 Kramer J. The Virus Moved Female Faculty to the Brink. Will Universities Help? - The New York Times https://www.nytimes.com/2020/10/06/science/covid-universities-women 23 COVID-19 Map - Johns Hopkins Coronavirus Resource Center https://coronavirus.jhu.edu/map.html

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2022-12-16 23:24:15

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J Pediatr. 2021 Apr 22; 231:50-54

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J Pediatr

10.1016/j.jpeds.2020.12.032

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==== Front Lancet Psychiatry Lancet Psychiatry The Lancet. Psychiatry 2215-0366 2215-0374 Elsevier Ltd. S2215-0366(22)00410-2 10.1016/S2215-0366(22)00410-2 Comment The effect of the COVID-19 pandemic on health-care workers Billings Jo a a Division of Psychiatry, University College London, London, UK 14 12 2022 1 2023 14 12 2022 10 1 35 © 2022 Elsevier Ltd. All rights reserved. 2023 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcThe COVID-19 pandemic has had a profound impact on the health-care workforce in the UK and worldwide.1, 2 However, in The Lancet Psychiatry, Hannah Scott and colleagues3 report the results of a two-phase epidemiological survey of health-care workers in England, which suggest that prevalence rates based on self-report screening measures might have inflated estimates of mental disorders among health-care staff during the COVID-19 pandemic. The authors report that prevalence rates from a sample of clinical interviews (conducted between March 1 and Aug 27, 2021) were lower than those obtained using screening tools (administered between April 24, 2020, and Jan 15, 2021). The combined population prevalence of common mental disorders (generalised anxiety disorder and depression) was 21·5% (95% CI 16·9–26·8) by clinical interview compared with 52·8% [51·7–53·8] by screening tools, and the estimated population prevalence of post-traumatic stress disorder (PTSD) was 7·9% (4·0–15·1) by clinical interview compared with 25·4% (24·3–26·5) by screening tools. The conclusions of this study raise several discussion points. First, the screening data were collected between April, 2020 and January, 2021, which corresponded to the peaks of the first and second waves of COVID-19 infection in the UK. By contrast, the clinical interview data were collected between March, 2021, and August, 2021, which coincided with the easing of social restrictions, mass vaccination of health-care workers and vulnerable adults, and markedly lower rates of mortality than observed earlier in the pandemic.4 It is possible that health-care workers were experiencing higher rates of anxiety, depression, and PTSD during the screening evaluation, but that prevalence had fallen by the time of the clinical interviews, especially considering that symptoms of mental disorders associated with exposure to traumatic experiences are expected to remit over time due to processes of natural recovery.5 Scott and colleagues did not readminister screening tools at the time of the clinical interview, therefore it is not possible to definitively attribute differences observed to methods of measurement or changes over time. However, health-care workers in England, and across the world, have continued to face considerable challenges in the workplace, with little time to recover from the effects of the COVID-19 pandemic. Health-care workers are now dealing with a backlog of patients who experienced interruptions to clinical care due to the pandemic, continued staff and resource shortages, and ongoing disputes over working conditions and pay. More high quality longitudinal data are needed to understand the impact of the COVID-19 pandemic and these other adverse circumstances on the health-care workforce over time, and further findings from the NHS CHECK Team and other longitudinal studies are anticipated in the future. Second, what, and whom, are we missing? The study by Scott and colleagues included measures of common mental disorders (anxiety and depression) and PTSD. Frontline health-care workers from several countries participating in qualitative research are also reporting experiences of stress, burnout, moral injury, and vicarious traumatisation.6, 7 Despite these experiences not being classified as mental disorders, they are often associated with the onset of mental health problems and incur a considerable mental health burden on those affected.8 Although understandably outside of the remit of the study by Scott and colleagues, it is crucial for future research to investigate and quantify these experiences. The current study is commendable for including clinical and non-clinical staff from both acute hospital and mental health Trusts, but other groups were particularly affected by the pandemic. Family members of health-care workers also report a considerable detriment to their own wellbeing due to their loved ones working on the frontline during the pandemic9 and mental health professionals who were specifically mobilised to support health-care workers have described feeling ill prepared for this work, overwhelmed, and vicariously traumatised.10 Third, is subjective distress important? Scott and colleagues rightly point out that normal distress should not be medicalised and that it is not necessarily the remit of, or best use of, mental health professionals to intervene where individuals are not meeting clinical thresholds for mental disorders. Nevertheless, is it reasonable to expect a workforce to work in a context where more than half are reporting significant distress and over a quarter of individuals are reporting traumatic stress, of sufficient severity to meet cutoffs on mental health screening measures for common mental disorders and PTSD (even if they do not subsequently fulfil diagnostic criteria for a mental disorder)? Should mental health professionals have a role in holding organisations to account for better protecting the mental health and wellbeing of their staff, mitigating preventable distress, and putting appropriate primary prevention strategies in place? This novel and well conducted study highlights the importance of not relying on screening tools as measures of prevalence and urges against medicalising normal distress. The results also point to the clinical utility of using screening tools to identify staff who are potentially at risk, who can then be followed up with more specific clinical diagnostic interviews. Mental health resources can be targeted at staff most in need, but perhaps also towards trying to effect change at an organisational level. I declare no competing interests. ==== Refs References 1 Bell V Wade D Mental health of clinical staff working in high-risk epidemic and pandemic health emergencies a rapid review of the evidence and living meta-analysis Soc Psychiatry Psychiatr Epidemiol 56 2021 1 11 33245379 2 Billings J Ching BCF Gkofa V Experiences of frontline healthcare workers and their views about support during COVID-19 and previous pandemics: a systematic review and qualitative meta-synthesis BMC Health Serv Res 21 2021 923 34488733 3 Scott HR Stevelink SAM Garoor R Prevalence of post-traumatic stress disorder and common mental disorders in health-care workers in England during the COVID-19 pandemic: a two-phase cross-sectional study Lancet Psychiatry 10 2023 40 49 4 UK Government Deaths in the United Kingdom https://coronavirus.data.gov.uk/details/deaths 5 Santiago PN Ursano RJ Gray CL A systematic review of PTSD prevalence and trajectories in DSM-5 defined trauma exposed populations: intentional and non-intentional traumatic events PLoS One 8 2013 e59236 6 Berkhout SG Billings J Abou Seif N Shared sources and mechanisms of healthcare worker distress in COVID-19: a comparative qualitative study in Canada and the UK Eur J Psychotraumatol 13 2022 2107810 7 Hegarty S Lamb D Stevelink SAM ‘It hurts your heart’: frontline healthcare worker experiences of moral injury during the COVID-19 pandemic Eur J Psychotraumatol 13 2022 2 8 Williamson V Stevelink SAM Greenberg N Occupational moral injury and mental health: systematic review and meta-analysis Br J Psychiatry 212 2018 339 346 29786495 9 Tekin S Glover N Greene T Lamb D Murphy D Billings J Experiences and views of frontline healthcare workers' family members in the UK during the COVID-19 pandemic: a qualitative study Eur J Psychotraumatol 13 2022 2057166 10 Billings J Biggs C Ching BCF Gkofa V Singleton D Bloomfield M Greene T Experiences of mental health professionals supporting front-line health and social care workers during COVID-19: qualitative study BJPsych Open 7 2021 e70 33752774

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PMC9750182

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Lancet Psychiatry. 2023 Jan 14; 10(1):3-5

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Lancet Psychiatry

10.1016/S2215-0366(22)00410-2

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Elsevier Inc. S0022-3476(21)00030-5 10.1016/j.jpeds.2021.01.023 Special Communication Reflections on Ethics and Advocacy in Child Health (REACH): Creating a Forum for Ethical Dialogue Ross Lainie Friedman MD, PhD 1∗ Ott Mary A. MD, MA 23 1 Department of Pediatrics, MacLean Center for Clinical Medical Ethics, Institute for Translational Medicine, University of Chicago, Chicago, IL 2 Department of Pediatrics, Section of Adolescent Medicine, Indiana University School of Medicine, Indianapolis, IN 3 Department of Philosophy, Indiana University-Purdue University Indianapolis, Indianapolis, IN ∗ Reprint requests: Lainie Friedman Ross, MD, PhD, University of Chicago Department of Pediatrics, 5841 S Maryland Ave MC 6082, Chicago, IL 60615. 18 1 2021 4 2021 18 1 2021 231 56 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Abbreviations COVID-19, Coronavirus disease-2019 REACH, Reflections on Ethics and Advocacy in Child Health ==== Body pmcWe are pleased to announce a new section of The Journal of Pediatrics entitled REACH: Reflections on Ethics and Advocacy in Child Health. REACH-themed articles will be published quarterly and feature articles that examine a pediatric ethics or child health policy issue from multiple perspectives. The first installment features 3 articles that address the ethics and policy questions about child and adolescent vaccination. Vaccines have been controversial since their inception.1 Although vaccines have saved millions of lives worldwide, vaccine hesitancy is a growing global phenomenon.2 , 3 As vaccine-preventable diseases decrease, rare vaccine adverse events, distrust in government policies, and large-scale misinformation on the web have led to vaccine refusals.4 Most concerning from a public health perspective is that refusals are not random, but tend to cluster, leading to the loss of herd immunity in certain communities and resulting in pockets of disease.5 The need to combat vaccine hesitancy and refusal has taken on greater urgency during the coronavirus disease-2019 (COVID-19) pandemic, which will require billions of people to be immunized with vaccines that have been developed and tested in a short period of time.6 Although pediatric testing of these vaccines for children younger than 16 years is still in early stages, once a COVID-19 vaccine is found safe and effective in children, questions will arise about whether to make these vaccines mandatory for school entry.7 Erin Talati Paquette discusses the ethics of mandatory vaccination for school entry in the United States, state exemption policies, and the American Academy of Pediatrics 2016 policy statement recommending the elimination of nonmedical exemptions. Paquette argues that the broad elimination of exemptions is not justified. She applies a contextualized approach to childhood vaccination that better aligns with public health law. She concludes that “Both legal and ethical (social justice) arguments also support allowing exemption based on public health ethics and public health law.”8 Julian Savulescu et al from Australia and the UK discuss the issue of mandatory vs voluntary vaccination of children for COVID-19 based on the actual risks and benefits that severe acute respiratory syndrome novel corona virus-2 infection poses to children and society at large. Balancing self-interest with duty to others, Savulescu et al examine the decision based upon the principles of liberty and usefulness. While awaiting pediatric safety and efficacy COVID-19 vaccine data, they consider the case for mandatory influenza vaccination in children during the pandemic, given that children are known to be a major source of influenza spread. They conclude that the case is strong for mandating influenza vaccine to decrease pressure on hospital resources: “By preventing spread to vulnerable patients who may require hospitalization for influenza, vaccination for children can free up limited resources for use by COVID-19 patients.”9 To round out the discussions about vaccine controversies, the article by Gregory Zimet et al discusses the ethical, legal, and practical issues related to adolescent self-consent in the US for the human papillomavirus vaccination.10 They then contextualize the issue in light of the COVID-19 pandemic, which underscores the need for vaccine policy changes to be pursued with clear communication and consistent with ethical principles. For future REACH section articles, we are interested in both conceptual and empiric proposals, with the twin goals of providing a platform for rigorous theoretical arguments for controversies in pediatric ethics, as well as enlarging the evidence base for future policymaking. REACH themes will encompass the broad expanse of ethics controversies in pediatric health, across the domains of clinical care, research, and social determinants of health. The section will address applied ethics issues in pediatric health, such as the limits of parental authority, the evolving role of the children and adolescents in health care decision-making, healthcare disparities and racism, equity issues in the pediatric workforce, translational research ethics, child advocacy, and professionalism. We will issue calls for proposals in advance of REACH issues. See the most recent call for proposals on The Journal's website (https://www.jpeds.com/reach). L.R. and M.O. serve on the Editorial Board of The Journal of Pediatrics and are the Section Editors of REACH. References available at www.jpeds.com. ==== Refs References 1 Stern A.M. Markel H. The history of vaccines and immunization: familiar patterns, new challenges Health Aff 24 2005 611 621 2 The power of vaccines: still not fully utilized Accessed February 24, 2021 www.who.int/publications/10-year-review/vaccines/en/ 3 De Figueiredo A. Simas C. Karafillakis E. Paterson P. Larson H.J. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study Lance 396 2020 898 908 4 Fortuna G. Vaccines are victim of their own success; global health expert says. EURACTIV.com Accessed February 24, 2021 www.euractiv.com/section/health-consumers/interview/vaccines-are-victim-of-their-own-success-global-health-expert-says/ 5 Estep K. Greenberg P. Opting out: individualism and vaccine refusal in pockets of socioeconomic homogeneity Am Sociol Rev 85 2020 1 35 6 Lazarus J.V. Ratzan S.C.C. Palayew A. Gostin L.O. Larson H.J. Rabin K. A global survey of potential acceptance of a COVID-19 vaccine Nat Med 20 2020 1 4 7 Opel D.J. Diekema D.S. Ross L.F. Should we mandate a COVID-19 vaccine for children? JAMA Pediatr 175 2021 125 126 32926083 8 Paquette E.T. In the wake of a pandemic: revisiting school approaches to non-medical exemptions to mandatory vaccination J Pediatr 231 2021 17 23 33484695 9 Savulescu J. Giubilini A. Danchin M. Global ethical considerations regarding mandatory vaccination in children J Pediatr 231 2021 10 16 33484698 10 Zimet G.D. Silverman R.D. Bednarczyk R.A. English A. Adolescent consent for HPV vaccine: ethical, legal, and practical considerations J Pediatr 231 2021 24 30 33484694

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J Pediatr. 2021 Apr 18; 231:5-6

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J Pediatr

10.1016/j.jpeds.2021.01.023

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Elsevier Inc. S0022-3476(20)31471-2 10.1016/j.jpeds.2020.11.059 Letter to the Editor Willingness of parents to vaccinate their children against influenza and the novel coronavirus disease-2019 AlHajri Bedour BSc Alenezi Deema BSc Alfouzan Heba BSc Altamimi Saba BSc Alzalzalah Sayed BSc Almansouri Waleed BSc Alqudeimat Yosor BSc Almokhaizeem Zain BSc Ziyab Ali H. PhD Department of Community Medicine and Behavioral Sciences, Faculty of Medicine, Kuwait University, Safat, Kuwait 30 11 2020 4 2021 30 11 2020 231 298299 © 2020 Elsevier Inc. All rights reserved. 2020 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcTo the Editor: Goldman et al showed a 15.8% increase in parents' willingness to vaccinate their children against influenza after the coronavirus disease 2019 (COVID-19) pandemic.1 Herein, we report results of an online survey disseminated to adults living in Kuwait between August 26 and September 1, 2020 (n = 2368; 1038 participants with children aged <18 years). The survey collected information on parents' willingness to vaccinate their children against influenza and COVID-19 once a vaccine is available, and the child's prior uptake of influenza vaccine. In total, 17.6% of parents (183/1038) reported that their children have received the influenza vaccine in the last influenza season. Such an uptake is substantially lower than influenza vaccine coverage of 63.8% among US children in the 2019-2020 season.2 Of the participating parents, 33% (342/1038) indicated that they definitely/probably will vaccinate their children against influenza in the coming influenza season (Figure, A) hence representing a 15.4% increase in comparison with uptake in the last season. Moreover, parents' intention to vaccinate their children against influenza in the coming season was higher among those who vaccinated their children in the last influenza season compared with those who did not (Figure, A). These observations are similar to those reported by Goldman et al.1 Figure Willingness of parents to vaccinate their children against influenza and COVID-19 in the total study sample and stratified according to whether the child did/did not receive influenza vaccine in the last influenza season. A, Parents' willingness to vaccinate their children against influenza in the coming influenza season. B, Parents' willingness to vaccinate their children against COVID-19 once a vaccine is approved and available. Moreover, 44.2% of the participating parents (459/1038) indicated that they will definitely/probably vaccinate their children against COVID-19 once a vaccine is approved and available (Figure, B). This estimate is lower than a previously reported estimate of 70%.3 Parents who vaccinated their children against influenza were more willing to vaccinate their children against COVID-19 (Figure, B). Overall, our results show a low willingness of parents to vaccinate their children against influenza and COVID-19, and prior influenza vaccine uptake is related to greater willingness. Public health strategies are needed to increase parents’ vaccine acceptance for their children. ==== Refs References 1 Goldman R.D. McGregor S. Marneni S.R. Katsuta T. Griffiths M.A. Hall J.E. Willingness to vaccinate children against influenza after the coronavirus disease 2019 pandemic J Pediatr 228 2020 87 93.e2 32771480 2 Centers for Disease Control and Prevention (CDC) [homepage on the Internet]. Flu vaccination coverage, United States, 2019–20 influenza season. Atlanta, GA www.cdc.gov/flu/fluvaxview/coverage-1920estimates.htm 3 Dror A.A. Eisenbach N. Taiber S. Morozov N.G. Mizrachi M. Zigron A. Vaccine hesitancy: the next challenge in the fight against COVID-19 Eur J Epidemiol 35 2020 775 779 32785815

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J Pediatr. 2021 Apr 30; 231:298-299

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J Pediatr

10.1016/j.jpeds.2020.11.059

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Published by Elsevier Ltd. S0022-3956(21)00405-2 10.1016/j.jpsychires.2021.06.031 Article Prior trauma, PTSD long-term trajectories, and risk for PTSD during the COVID-19 pandemic: A 29-year longitudinal study Solomon Zahava a∗ Mikulincer Mario b Ohry Avi c Ginzburg Karni a a The Bob Shapell School of Social Work, Tel Aviv University, Tel Aviv, Israel b School of Psychology, Interdisciplinary Center (IDC) Herzliya, Herzliya, Israel c Sackler Faculty of Medicine, Tel Aviv University and the Reuth Medical and Rehabilitation Center, Tel Aviv, Israel ∗ Corresponding author. The Bob Shapell School of Social Work, Tel Aviv University, P.O.B. 39040, Ramat Aviv, Tel Aviv, 6997801, Israel. 16 6 2021 9 2021 16 6 2021 141 140145 24 11 2020 4 5 2021 14 6 2021 © 2021 Published by Elsevier Ltd. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This study assessed the contributions of prior war captivity trauma, the appraisal of the current COVID-19 danger and its resemblance to the prior trauma, and long-term trajectories of posttraumatic stress disorder (PTSD) to risk for PTSD during the COVID-19 pandemic. Capitalizing on a 29-year longitudinal study with four previous assessments, two groups of Israeli veterans – ex-Prisoners-of-War (ex-POWs) of the 1973 Yom Kippur War and comparable combat veterans of the same war – were reassessed during the COVID-19 pandemic. Previous data were collected on their PTSD trajectory 18, 30, 35, and 42 years after the war and exposure to stressful life events after the war. Currently, we collected data on their PTSD during the COVID-19 pandemic and their appraisal of similarities of past trauma with the current pandemic. Previously traumatized ex-POWs were found to be more vulnerable and had significantly higher rates of PTSD and more intense PTSD during the current pandemic than comparable combat veterans. Moreover, veterans in both groups who perceived the current adversity (captivity, combat) as hindering their current coping were more likely to suffer from PTSD than veterans who perceived it as a facilitating or irrelevant experience. In addition, chronic and delayed trajectories of PTSD among ex-POWs increased the risk for PTSD during the pandemic, and lifetime PTSD mediated the effects of war captivity on PTSD during the current pandemic. These findings support the stress resolution perspective indicating that the response to previous trauma – PTSD and its trajectories – increased the risk of PTSD following subsequent exposure to stress. Keywords COVID-19 PTSD PTSD trajectories Ex-POWs Veterans ==== Body pmc1 Introduction The COVID-19 pandemic is a public health emergency of international concern (World Health Organization, 2020). Given its highly infectious nature, in many countries Covid-19 has led to quarantine, which entails social distancing and restriction of movement. As a result, people have had to make significant changes in their daily routines. These include the curtailment of daily activities and interaction with others. The COVID-19 pandemic and its restrictions are even more distressing for the elderly who are seen as a high-risk group for both morbidity and mortality. While many young people continue to work, community support and senior citizen centers are closed. This is especially difficult for those who live alone and are unable to socialize. While the pandemic may be stressful for all elderly people, there is one group in particular that could be exceptionally affected – elderly individuals with a history of trauma. One such group is former prisoners-of-war (ex-POWs) who endured severe deprivations, torture, and mock executions repeatedly for extended periods of time. Ex-POWs traumatic experiences often leave them with severe and debilitating psychological damage (Sutker et al., 1990), in particular posttraumatic stress disorder (PTSD; Solomon et al., 2012). Yet, what these trauma survivors lived through may not end with repatriation. Studies have shown that prior exposure to trauma increases the risk of future stressful life incidents and exposure to more traumatic events (Breslau et al., 2008). According to the sensitivity perspective (Selye, 1976; Solomon, 1993), when traumatized individuals are faced with subsequent adversity they are more likely to suffer from heightened psychological vulnerability and distress than individuals without a history of trauma. Moreover, previously traumatized individuals are likely to endure reactivation following further exposure to stressors, particularly if there is a resemblance to the initial trauma (Christenson et al., 1981; Solomon, 1993). Reactivation is also known to be prevalent among the elderly, who tend to shift their attention from present and future activities to reviewing and reminiscing about their lives. In fact, studies conducted among previously traumatized older veterans (Christenson et al., 1981; Solomon, 1993) have shown that ensuing stressful experiences serve as triggers that unmask and accelerate latent PTSD. Although trauma history is a risk factor for traumatized survivors, some may be more vulnerable than others. Those who perceive the current adversity as resembling their previous trauma are likely to assess the risk to be greater and, thus, are more likely to experience reactivation and exacerbation of their posttraumatic symptoms (Hantman et al., 1994). The stress resolution perspective contends that it is not merely previous exposure but rather the psychological impact of the previous trauma that affects the outcome of the subsequent adversity (Solomon, 1993). Namely, survivors who already suffered from PTSD are more vulnerable than those who did not and, therefore, are at the greatest risk for recurrent PTSD upon additional traumatic exposure (Breslau et al., 2008). When applied to the current COVID-19 pandemic, elderly veterans who had previously suffered from PTSD are more likely to experience reactivation and exacerbation of PTSD than those who had similar trauma exposure but did not develop PTSD. PTSD is recognized as a labile disorder, with a heterogeneous and fluctuating course (Bonanno et al., 2012; Bryant, 2019), and both increases and decreases over time. Indeed, several studies have identified distinct PTSD trajectories (American Psychiatric Association, 1994), with a predominant trajectory of resilience (Bonanno et al., 2012) alongside chronic, recovered, delayed, and reactivated (Magruder et al., 2016). In this study, we capitalized on data from a 29-year longitudinal study comprising of Israeli ex-POWs of the 1973 Yom Kippur War and comparable combat veterans of the same war, who were evaluated at four previous time points −1991 (T1), 2003 (T2), 2008 (T3), and 2015 (T4) – and then during the COVID-19 outbreak at April–May 2020 (T5), with the initial four waves identifying PTSD trajectories (Solomon et al., 2012). While both ex-POWs and controls had similar trajectories, the two groups differed in proportions of the trajectories, with more ex-POWs exhibiting ongoing and severe clinical profiles (chronic and delayed) and less mild trajectories (resilient and recovered) than controls. Given that the various trajectories may represent different levels of vulnerability, we aim to examine the role of PTSD trajectories in predicting war-related PTSD during the COVID-19 pandemic. The study aims to (1) assess the implication of previous traumatic exposure (war captivity) in PTSD and PTSD clusters during the current COVID-19 pandemic, that is, whether ex-POWs were at increased risk for PTSD during the pandemic; (2) examine the extent to which veterans' appraisal of their wartime experiences increases the risk for PTSD during the pandemic; and (3) assess whether lifetime PTSD and PTSD trajectories were associated with PTSD during the pandemic. 2 Methods 2.1 Participants and procedure 240 Israeli ground forces were captured during the 1973 Yom Kippur War. 164 of these ex-POWs participated at T1, 103 participated at T2 (41 could not be located/refused, 4 had died, and 6 could not participate due to a deteriorated mental status), 183 at T3 (29 could not be located/refused, 20 had died, and six could not participate due to mental deterioration), and 158 at T4 (49 could not be located/refused, 30 had died, and three suffered from physical or mental problems). One-hundred and twenty of these ex-POWs participated in the assessment conducted during the COVID-19 outbreak (T5; 66 could not be located/refused, 36 had died, and 18 could not participate due to mental deterioration). In addition, 280 veterans were sampled from the Israel Defense Forces (IDF) computerized database (Solomon et al., 1994). These individuals also participated in the Yom Kippur War on the same fronts, but were not taken captive, and were matched to ex-POWs on military background and socio-demographic variables. Among them, 185 participated at T1, 106 took part at T2 (78 could not be located/refused and 1 had died), 118 took part at T3 (20 could not be located/refused, and five had died), and 101 participated at T4 (60 could not be located/refused, 1 was abroad, and 18 had died). At T5, the target group included 136 controls; of those, 65 participated at the study (65 could not be located/refused, 3 had died, and 3 could not participate due to mental deterioration). Data on exposure to stressful life events were assessed at T1, T2, T3, and T4. Level of exposure to COVID-19 and participants' appraisals of the extent to which their war-related experiences affected their current adjustment were assessed at T5. PTSD was assessed at T1, T2, T3, T4, and T5. The study was approved by the institutional review board (IRB) and all participants signed a consent form. 2.2 Measures 2.2.1Background variables Participants were asked their age, education, occupational status, and with whom they lived. Additionally, participants were asked in T5 for their appraisals of whether their wartime experiences affected the way they adjusted to the current lockdown and social restrictions (1 = it facilitated their adjustment, 2 = it did not affect their adjustment, 3 = it hindered their adjustment). 2.2.2Exposure to COVID-19 At T5, 10 questions were included to assess exposure to COVID-19 (Tsur and Abu-Raiya, 2020; Zhen and Zhou, 2020). Participants were asked whether they had been exposed to COVID-19-related incidents (e.g., getting infected, quarantined, a family member got infected or quarantined, knowing someone who died from COVID-19). Overall exposure was calculated by summing all positive answers, with higher scores indicating higher exposure to COVID-19. 2.2.3Exposure to stressful life events At T1, T2, and T3 participants completed a brief scale on exposure to stressful life events (Ginzburg, 2006) and reported whether they experienced a targeted event (e.g., death of a significant other, motor vehicle accident). At T4, participants were asked to list the events they experienced since T3. For each participant we computed the overall number of reported stressful events. 2.2.4PTSD PTSD was measured at all assessments using the PTSD-Inventory (Solomon, 1993; Solomon et al., 2012), a 17-item self-report scale corresponding to DSM PTSD symptom criteria (American Psychiatric Association, 1994). Each of the PTSD symptoms was anchored to the participants' war experiences. Participants indicate whether they experienced the symptom in the past month, on a four-point scale ranging from 1 (not at all) to 4 (I usually did). An answer of 3 or above was considered a positive endorsement. PTSD trajectories were derived from PTSD status (meeting/not meeting DSM criteria). In addition, intensity of PTSD and of each of its symptom clusters (intrusion, avoidance, and arousal) was calculated as the mean of the relevant items. Lifetime PTSD was defined as meeting clinical criteria at least one wave of measurement. Previous studies have supported the validity and reliability of the PTSD Inventory (Solomon et al., 1993). Rate of agreement between diagnoses made by the PTSD Inventory and by clinical interviews reached 85% (Solomon et al., 1993). Reliability of the scale's score was high at all assessments (Cronbach's alpha ranging from 0.91 to 0.96). 2.3 Data analysis A series of Chi square analyses examined group differences (ex-POWs vs. controls) in PTSD rates at T1-T5. To examine whether ex-POWs are at an increased risk for PTSD during the COVID-19 pandemic (T5), we conducted a binomial logistic regression, examining the effect of group (ex-POWs vs. control) to the prediction of PTSD at T5, controlling for stressful life events and exposure to COVID-19. This analysis was followed by multivariate analysis of variance (MANOVA) examining the effect of group on PTSD symptom intensity (total, intrusion, avoidance, hyperarousal), while controlling for stressful life events. The effects of captivity and appraisal of the extent to which war-related experiences affected adjustment to COVID-19 on PTSD at T5 were examined by (a) a Chi square analysis assessing group differences (ex-POWs vs. controls) in appraisals, and (b) a two-way ANOVA assessing the effects of group, appraisal, and their interaction on intensity of PTSD at T5. To examine the effect of PTSD trajectories (T1-T4) on PTSD at T5, participants were divided into five groups according to their PTSD classification at T1-T4: chronic PTSD (participants who met the criteria for PTSD in all four waves), delayed PTSD (participants who did not endorse the PTSD criteria in the first wave/s but did in subsequent wave/s), recovered PTSD (participants who endorsed PTSD criteria in either of the first waves but not in the later/last waves), resilient (veterans who never endorsed the criteria for PTSD), and reactivation (participants who initially had PTSD, recovered, and then had a delayed reactivation of PTSD at a later measurement). Chi square analysis examined differences in the trajectory rates between ex-POWs and controls. Another Chi-square analysis, conducted among the ex-POWs, examined differences between PTSD trajectories in rates of PTSD at T5. And, an one-way ANOVA assessed differences between PTSD trajectories groups in intensity of PTSD at T5. Finally, a logistic regression examined the contribution of different factors to rates of PTSD at T5. The predictors entered were group (step 1), life events since the war (step 2), lifetime PTSD (step 3), and appraisal of the effect of war-related experiences on current adjustment as a dummy variable (1 = hindering effect; 0 = appraisal as facilitating of irrelevant). 3 Results 3.1 Are ex-POWs at an increased risk for PTSD during the COVID-19 pandemic? A series of Chi square (χ2) analyses indicated higher rates of PTSD among ex-POWs as compared to controls across all assessments (T1-T5; see Table 1 ).Table 1 Differences between ex-POWs and controls in rates of PTSD across all assessments, and intensity of PTSD symptoms at T5. Table 1Rates of PTSD Ex-POWs n (%) Controls n (%) χ2 p-value PTSD T1 21 (12.7%) 6 (3.3%) 10.82** 0.001 PTSD T2 75 (61.9%) 7 (6.7%) 73.47*** <0.001 PTSD T3 107 (60.5%) 5 (4.3%) 94.27*** <0.001 PTSD T4 67 (47.7%) 8 (8%) 35.54*** <0.001 PTSD T5 48 (42.1%) 4 (6.3%) 25.00*** <0.001 Intensity of PTSD T5 Ex-POWs M (SD) Controls M (SD) F (1, 163) p-value PTSD total 2·37 (0·74) 1·58 (0·55) 49·18*** <0·001 Intrusion 2·33 (0·90) 1·47 (0·62) 40·03*** <0·001 Avoidance 2·19 (0·77) 1·47 (0·56) 39·13*** <0·001 Hyperarousal 2·65 (0·84) 1·86 (0·73) 35·04*** <0·001 Notes. **p < 0.01; ***p < 0.001. The logistic regression revealed that, overall, the model was significant, Chi square (df = 3) = 27.73, p < 0.001, Cox & Snell R 2 = 15.4%. Captivity had a significant contribution to rates of PTSD at T5 (B = 2.33, SE = 0.55, Wald = 17.64, p < 0.001). Chi square with Fisher exact showed a significant difference, as the ex-POW group presented 10.31 times higher risk for PTSD (41.6%), compared to controls (6.1%) (95% CI 3.47, 30.643). Life events since the war did not contribute to PTSD at T5 (B = 0.01, SE = 0.03, Wald = 0.16, p = 0.69) nor did the level of exposure to COVID-19 (B = −0.12, SE = 0.13, Wald = 0.85, p = 0.36). The comparison between ex-POWs and controls in the intensity of total PTSD and symptom clusters at T5, while controlling for life events since the war, resulted in a significant multivariate effect F(4,160) = 12.59, p < 0.001. Further examination yielded significant group differences in each of the symptom clusters as well as the total PTSD score (Table 1). Ex-POWs reported higher intensity of total PTSD, intrusion, avoidance, and hyperarousal than controls. The effects of life events since the war were not significant, F(4, 160) = 0.33, p = 0.855. 3.2 The effect of captivity and appraisal of the extent to which war-related experiences affected adjustment to COVID-19 on PTSD at T5 Overall, ex-POWs and controls differed in their appraisal of the extent to which their war-related experiences affected their adjustment to COVID-19, χ2(2) = 23.02, p < 0.001. Forty-three (38.1%) of the ex-POWs perceived their war-related experiences as hindering their adjustment to COVID-19 compared to 11.3% (n = 7) of the controls; 20.4% (n = 23) of the ex-POWs appraised it as facilitating adjustment compared to 9.7% (n = 6) of the controls; and, 41.6% (n = 47) of ex-POWs perceived their war-related experiences as irrelevant to their current adjustment compared to 79% (n = 49) of the controls. A two-way ANOVA on intensity of PTSD during COVID-19 yielded a significant main effect for captivity, F(1,169) = 22.19, p < 0.001 with higher PTSD intensity for ex-POWs (M = 2.37, SD = 0.73) than controls (M = 1.58, SD = 0.55). Analysis also revealed a significant main effect for the appraisal of the effect of war-related experiences on current adjustment, F(2,169) = 16.99, p < 0.001. That is, participants who appraised their experiences as hindering adjustment (M = 2.81, SD = 0.59) had a higher intensity of PTSD compared to veterans who perceived it as facilitating adjustment (M = 1.96, SD = 0.61) or irrelevant (M = 1.75, SD = 0.63). However, the interaction was not significant, F(2,169) = 0·55, p = 0.58, indicating that the effect of appraisal on PTSD intensity at T5 was similar among ex-POWs and controls. 3.3 Are the PTSD trajectories of ex-POWs' related to PTSD during the COVID-19 pandemic? First, participants were divided into five groups according to their PTSD classification from the first four measurements (T1-T4): Ten participants (5.4%) were included in the chronic PTSD trajectory, 65 (35.1%), in the delayed PTSD trajectory, six (3.2%) in the recovered PTSD trajectory, 101 (54.6%) in the resilient trajectory, and three (1.6%) in the reaction trajectory. Further examination yielded a significant difference between groups in the distribution of the trajectories, χ2(4) = 49.38, p < 0.001. The resilient trajectory was less prevalent in the ex-POWs (n = 43, 35.8%) compared to controls (n = 58, 89.7%). However, the chronic and delayed trajectories were more prevalent in the ex-POWs group (n = 8, 6.7% and n = 60, 50%, respectively) compared to controls (n = 2, 3.1% and n = 5, 7.7%, respectively). The recovery and reactivation trajectory were similar for the ex-POWs (n = 6, 5.0% and n = 3, 2.5%, respectively) compared to controls (n = 0, 0% and n = 0, 0%, respectively). Chi-square analysis revealed that PTSD trajectories of ex-POWs were associated with different rates of PTSD at T5, χ2(4) = 30.09, p < 0.001. Specifically, of the resilient ex-POWs, only 11.6% developed PTSD during the COVID-19 pandemic, while of the recovery trajectory, 16.7% had PTSD during the pandemic. Of the ex-POWs in the chronic and delayed trajectories groups, 50% and 63.3% had PTSD during the pandemic. Accordingly, of the ex-POWs in the reactivation trajectory group, 66.7% had PTSD during the pandemic. The ANOVA, assessing differences between PTSD trajectories groups in PTSD intensity at T5, yielded a significant effect, F(4,139) = 16.19, p < 0.001. Bonferroni post hoc tests showed that resilient ex-POWs had the lowest levels of PTSD intensity during the pandemic (M = 1.73, SD = 0.52); chronic (M = 3.04, SD = 0.45) and delayed (M = 2.90, SD = 0.49) trajectories had the highest levels of PTSD intensity; and the recovery (M = 2.10, SD = 0.59) and reactivation (M = 2.55, SD = 0.51) trajectories were in the mid and significantly differed from the resilient trajectory. 3.4 Predicting risk for PTSD during the COVID-19 pandemic – a holistic model The logistic regression examining the contribution group, stressful life events, lifetime PTSD, and appraisals of wartime experiences to PTSD at T5 yielded a significant model, χ2(df = 4) = 79.04, p < 0.001, Cox & Snell R 2 = 36.2%. In the final step, lifetime PTSD was associated with a greater risk of 8.15 times more to develop PTSD at T5 (see Table 2 ). Appraisal war-related experiences were not significant predictors of PTSD rates at T5. Finally, the significant association between captivity trauma and PTSD rates during the COVID-19 pandemic was reduced when lifetime PTSD was entered into the model (step 3). This reduction, depicted in Table 2 was significant according to the Sobel test, Z = 2.00, p = 0.02, indicating that the indirect effect was significant. In other words, there is a higher risk for ex-POWs to develop PTSD during the pandemic as mediated by lifetime PTSD.Table 2 Logistic regression predicting PTSD rates at T5. Table 2 b SE Wald p-value OR OR CI 95% Step 1  Group 1.60 0.60 7.20 0.007 4.96 1.54, 15.99 Step 2  Group 1.59** 0.59 7.16 0.007 4.96 1.53, 15.94  Life events since war 0.01 0.04 0.08 0.78 1.01 0.93, 1.10 Step 3  Group 0.27 0.75 0.13 0.81 1.32 0.30, 5.71  Life events since war 0.01** 0.05 0.05 0.001 1.01 0.92, 1.11  Lifetime PTSD 2.84*** 0.85 11.09 0.000 7.15 3.22, 9.15 Step 4  Group 0.052 0.065 0.642 0.423 1.05 0.927, 1.19  Life events since war −1.252 1.460 0.736 0.391 0.29 0.016, 4.99  Lifetime PTSD 2.097* 0.992 4.472 0.034 8.15 1.166, 9.911  Appraisal of previous trauma as related to COVID-19 adjustment 1.100 0.805 1.866 0.172 3.01 0.62, 11.568 Notes. ***p < 0.001; **p < 0.01; *p < 0.05. 4 Discussion The findings of this study demonstrated that previously traumatized ex-POWs were more vulnerable during the current pandemic and had significantly higher rates and intensity of PTSD than comparable combat veterans. Moreover, veterans in both groups who perceived their war-related experiences as hindering their current coping with the COVID-19 related stress were more likely to suffer from PTSD during the pandemic than veterans who perceived it as a facilitating or irrelevant experience. Most importantly, the results of this longitudinal study showed that chronic and delayed trajectories of PTSD among ex-POWs increased the risk for PTSD during the pandemic and lifetime PTSD mediated the effects of war captivity on PTSD during the current pandemic. In keeping with the vulnerability perspective (Selye, 1976), and supported by numerous studies (Hantman et al., 1994; Kessler et al., 2018), the previously traumatized ex-POWs were more vulnerable to PTSD decades later when faced with the current stressors of COVID-19. Their prior trauma undermined their sense of safety (Janoff-Bulman, 2010), and taxed and eroded their self-efficacy and trust in their own abilities (Titchener and Ross, 1974). As a result, their coping rendered them less prepared to cope with subsequent stressors. Moreover, our findings indicated that the current vulnerability of ex-POWs cannot be attributed to the life events that they experienced after their repatriation. It is plausible that the brutal and extreme experiences of war captivity, in which human lives are of no consequence, are likely to dwarf the meaning and effects of more common and mundane subsequent life events (Ruch et al., 1980). Our findings revealed considerable variability in both the ex-POWs and the combatants regarding the effects of prior trauma on their current perceptions. In both groups, some survivors felt that their previous trauma (captivity, combat) negatively affected their adjustment to the current COVID-19 and thus made it more difficult to endure. Interestingly, more ex-POWs felt that their previous trauma affected their perception of the current adversity in comparison to control combatants. It transpired that the vast majority of the controls and most of the ex-POWs reported that their war experiences had no relevance in the context of COVID-19. However, when we assessed the relationship between the participants' evaluation of the effects of previous trauma as enhancing or hindering coping with the current stress, we found that in both groups their attribution was significantly associated with their current PTSD. In both study groups, individuals who felt that their prior trauma made the current stress easier to endure had lower rates of PTSD than those who felt that their trauma history made COVID-19 more difficult to endure. This is consistent with an earlier Israeli study of Holocaust survivors who reportedly perceived the 1992 Gulf War as similar to their prior trauma and, as such, reacted with intense distress (Hantman et al., 1994). These findings clearly underscore the role of meaning-making of the prior trauma in the psychiatric response to current trauma. 4.1 Lifetime PTSD While many of participants met PTSD symptom criteria, many others did not. One-third of the ex-POWS, and almost 90% of the controls, were not identified as having PTSD at any of the first four times. Consistent with the crisis resolution perspective (Solomon, 1993) and earlier studies (Solomon, 1993; Solomon et al., 1987), our findings indicated that it is not the history of prior exposure to trauma per se but rather the psychological outcome that affects reactions to subsequent trauma. We found that a lifetime prevalence of PTSD (that in endorsing PTSD at lead once in previse assessments was associated with an increased risk for PTSD during the COVID-19 pandemic among ex-POWs but not in the control group. In other words, the elevated risk of current PTSD among ex-POWs was accounted for by their lifetime PTSD. Inspection of the regression analysis clearly demonstrated that lifetime PTSD prevalence, rather than trauma exposure, is implicated in risk for PTSD upon subsequent stress. This finding is consistent with a prospective systematic study of young adults (Breslau et al., 2008), which found that the presence of PTSD as a result of subsequent trauma was limited to respondents with a history of PTSD. Why did their prior PTSD increase the risk of PTSD in response to the subsequent trauma? One cannot negate the possibility that a pre-existing vulnerability predated the first trauma (war captivity) and led to PTSD following other traumas. Yet, as Titchner and Ross (1974) argued as well as numerous studies of various populations (Solomon and Mikulincer, 2006), the initial psychological rupture set in motion a process of posttraumatic decline leaving permanent effects, particularly a proneness to anxiety reactions entrenched in vulnerability. This vulnerability is likely to give rise to the reactivation and exacerbation of PTSD upon exposure to subsequent trauma (Christenson et al., 1981; Solomon, 1993; Solomon and Mikulincer, 2006). 4.2 Trajectories of PTSD PTSD, like other anxiety disorders, is not a stable entity. To the best of our knowledge, this is the first study that examined not only PTSD following prior trauma but also the implication of PTSD trajectories measured prospectively at four time points over 29 years. As PTSD symptoms fluctuate over time, they are likely to tax and deplete trauma survivors' psychiatric resources differently and thus have a differential effect on their ability to cope with subsequent stress. Accordingly, our results reflect differential risk for PTSD during the COVID-19 pandemic as a function of PTSD trajectories over a four waves of measurement (24 years). At the greatest risk and most severely affected were those who had not recovered and suffered for decades from chronic debilitating PTSD. Second were those with delayed onset PTSD who lived for years after the war with residual subclinical symptoms, however, over time and due to aging their PTSD symptoms were reactivated and exacerbated, leaving them emotionally depleted and vulnerable. The most robust were those in the resilient trajectory who were initially better emotionally equipped and, therefore, subsequent stress only had limited pathogenic effects. 4.3 Limitations The findings of the current study should be considered in light of its limitations. The sample size, especially that of the controls, is modest. Additionally, an initial assessment was not conducted within the first few years following the war, as the first assessment was conducted 18 years after the war. Although self-report symptom checklists based on the DSM criteria, as the PTSD Inventory used in the current study, were evaluated as valid and reliable effective for research purposes, especially when the questionnaire refers to specific traumatic events, such as captivity or combat-related events (McDonald and Calhoun, 2010; Wilkins et al., 2011), the use of self-report questionnaires to identify PTSD might be considered another limitation. Finally, as the study was conducted among Israeli ex-POWs and combat veterans, generalizing from these results to other populations, in other times and cultures, should be undertaken cautiously. 4.4 Clinical implications The results call for a need to monitor and provide support and clinical help to previously traumatized individuals during the current COVID-19 pandemic. This is particularly needed for elderly trauma survivors who suffer from PTSD and are currently in double jeopardy as an identified high-risk group for COVID-19. Given the lockdowns and social restrictions that compound the already restricted movement of the elderly, it is incumbent upon the medical staff and other helping professionals, in care homes as well as in the community, to pay close attention to the psychological distress and needs of those with a history of PTSD following an event which may bear a resemblance to the current stressful experience, who are at risk for reactivation and exacerbation. Furthermore, as previous studies indicated that PTSD is often comorbid with other disorders (Ginzburg et al., 2010), other manifestations of distress should also be monitored and targeted. Support and evidence-based trauma treatments should be available and offered when relevant. Given that this pandemic is not over and another potentially worse wave may return, preparedness and precautions are called for. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors, therefore, no funding sources had any involvement in this study. CRediT authorship contribution statement Zahava Solomon: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. Mario Mikulincer: Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Avi Ohry: Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Karni Ginzburg: Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. Declaration of interest None of the authors have any conflicts of interest to report. ==== Refs References American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV) 1994 American Psychiatric Press Washington, DC Bonanno G.A. Mancini A.D. Horton J.L. Powell T.M. LeardMann C.A. Boyko E.J. Wells T.S. Hooper T.I. Gackstetter G.D. Smith T.C. Trajectories of trauma symptoms and resilience in deployed US military service members: prospective cohort study Br. J. Psychiatry 200 4 2012 317 323 22361018 Breslau N. Peterson E.L. Schultz L.R. A second look at prior trauma and the posttraumatic stress disorder effects of subsequent trauma: a prospective epidemiological study Arch. Gen. Psychiatr. 65 4 2008 431 437 18391131 Bryant R.A. Post‐traumatic stress disorder: a state‐of‐the‐art review of evidence and challenges World Psychiatr. 18 3 2019 259 269 Christenson R.M. Walker J.I. Ross D.R. Maltbie A.A. Reactivation of traumatic conflicts Am. J. Psychiatr. 138 7 1981 984 985 7258364 Ginzburg K. Life events and adjustment following myocardial infarction Soc. Psychiatr. Psychiatr. Epidemiol. 41 10 2006 825 831 Ginzburg K. Ein-Dor T. Solomon Z. Comorbidity of posttraumatic stress disorder, anxiety and depression: a 20-year longitudinal study of war veterans J. Affect. Disord. 123 1–3 2010 249 257 19765828 Hantman S. Solomon Z. Prager E. How the Gulf war affected aged Holocaust survivors Clin. Gerontol. 14 3 1994 27 37 Janoff-Bulman R. Shattered Assumptions 2010 Simon and Schuster New York Kessler R.C. Aguilar-Gaxiola S. Alonso J. Bromet E.J. Gureje O. Karam E.G. Koenen K.C. Lee S. Liu H. Pennell B.-E. The associations of earlier trauma exposures and history of mental disorders with PTSD after subsequent traumas Mol. Psychiatr. 23 9 2018 1892 1899 Magruder K.M. Goldberg J. Forsberg C.W. Friedman M.J. Litz B.T. Vaccarino V. Heagerty P.J. Gleason T.C. Huang G.D. Smith N.L. Long‐term trajectories of PTSD in Vietnam‐Era Veterans: the course and consequences of PTSD in twins J. Trauma Stress 29 1 2016 5 16 26764215 McDonald S.D. Calhoun P.S. The diagnostic accuracy of the PTSD checklist: a critical review Clin. Psychol. Rev. 30 8 2010 976 987 20705376 Ruch L.O. Chandler S.M. Harter R.A. Life change and rape impact J. Health Soc. Behav. 21 1980 248 260 7410801 Selye H. The Stress of Life 1976 McGraw-Hill New York Solomon Z. Combat Stress Reaction: the Enduring Toll of War 1993 Plenum Press New York Solomon Z. Benbenishty R. Neria Y. Abramowitz M. Ginzburg K. Ohry A. Assessment of PTSD: validation of the revised PTSD Inventory Isr. J. Psychiatry Relat. Sci. 30 2 1993 110 115 8270385 Solomon Z. Horesh D. Ein-Dor T. Ohry A. Predictors of PTSD trajectories following captivity: a 35-year longitudinal study Psychiatr. Res. 199 3 2012 188 194 Solomon Z. Mikulincer M. Trajectories of PTSD: a 20-year longitudinal study Am. J. 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Synthesis of the psychometric properties of the PTSD checklist (PCL) military, civilian, and specific versions Depress. Anxiety 28 7 2011 596 606 21681864 World Health Organization Coronavirus Disease 2019 (COVID-19): Situation Report - 90 2020 https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200419-sitrep-90-covid-19.pdf?sfvrsn=551d47fd_4 Zhen R. Zhou X. Predictive factors of public anxiety under the outbreak of COVID-19 Chinese J. Appl. Psychol. 26 2020 99 107

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==== Front J Pediatr J Pediatr The Journal of Pediatrics 0022-3476 1097-6833 Elsevier Inc. S0022-3476(20)31460-8 10.1016/j.jpeds.2020.11.048 Notes from the Association of Medical School Pediatric Department Chairs, Inc. Pediatric Departmental Advocacy: Our Experience Addressing the Social Challenges of Coronavirus Disease 2019 and Racism Ramirez Melanie R. BA 12∗ Bruce Janine S. DrPH, MPH 12 Ball Alexander J. MD, MPH 13 Gambhir Simran MD 13 Zabrocka Katarzyna MD, MA 13 Sahak Omar MD, MPH 34 Dali Salma MD 13 Jones Kamaal A. MD 13 Chamberlain Lisa J. MD, MPH 12 1 Department of Pediatrics, Stanford School of Medicine, Stanford, CA 2 Division of General Pediatrics, Stanford School of Medicine, Stanford, CA 3 Pediatric Residency Program, Stanford School of Medicine, Stanford, CA 4 Department of Psychiatry Stanford School of Medicine, Stanford, CA ∗ Reprint requests: Melanie R. Ramirez, BA, Department of Pediatrics, 1265 Welch Rd, x240, Stanford, CA 94305. 8 12 2020 4 2021 8 12 2020 231 79.e3 © 2020 Elsevier Inc. All rights reserved. 2020 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Abbreviations AAP, American Academy of Pediatrics COVID-19, Coronavirus disease 2019 CPTI, Community Pediatrics Training Institute ==== Body pmcThe coronavirus disease 2019 (COVID-19) pandemic and the unearthing of existing racism nationwide have revealed how health is inextricably linked to the community we call “home.” COVID-19 shelter-in-place regulations resulted in millions of jobs lost,1 rising food insecurity,2 increased difficulty paying for basic needs,2 and school closures that disrupted child learning.3 These indirect effects of COVID-19 will have long-term implications for child poverty and health.3 Simultaneously, racial unrest exploded in America, revealing the persisting injustice from 400 years of unchecked racism. From police violence to every day racially charged interactions, to racial and ethnic disparities in COVID-19 mortality,4 underrepresented minorities struggle to live and thrive. Academic medical centers and are not immune to racism, thus, a critical reckoning must begin. Responding to challenges set forth by the intersectionality of racism and COVID-19 requires Departments of Pediatrics to engage with local communities to advance multilevel, community-engaged support, and antiracist advocacy. We will discuss how pre-existing infrastructure supporting community engagement and advocacy enabled one Department of Pediatrics to nimbly respond to dynamic challenges using a framework to map key assets and organizing responsive actions into 7 guiding principles. Building from a History of Community Pediatrics There has been momentum to advance community pediatrics over the last 2 decades.5 The Accreditation Council for Graduate Medical Education continues to incorporate community engagement and advocacy for child health within its program requirements.6 In accordance with these requirements, the American Academy of Pediatrics (AAP) has provided Community Access to Child Health grants to over 1700 programs to support academic and community partnerships advancing child health since 1993.5 The AAP also established the Community Pediatrics Training Institute (CPTI) in 2005, providing guidance to residency programs, including their six drivers of success (Table I; available at www.jpeds.com).7 Community pediatric training results in increased community-engaged physicians8 and CPTI framework incorporates residency curricula, faculty development, and community interventions.9 To address the intersectionality between racism, poverty, and child health disparities, the AAP and the Journal of Adolescent Health have released policy statements with recommendations to train health professionals to address social determinants of health of youth by working effectively with disadvantaged communities, collaborating with community organizations to support families, and advocating for essential benefits programs.10 , 11 The Stanford School of Medicine and the Lucile Packard Children's Hospital established the Pediatric Advocacy Program in 2002 that has directed a community pediatrics and child advocacy rotation and a track in community-engagement and advocacy. Over time, the Advocacy Program expanded its reach, incorporating all 6 CPTI components to provide comprehensive structures for community-engagement and advocacy (Table I). The Advocacy Program maintains decade's long partnerships with community organizations and outpatient providers to address child health needs at a population level. In 2016, Advocacy Program directors formed a pediatric advocacy coalition across 5 community healthcare centers and 4 healthcare systems to address child health disparities. The Pediatric Residency Advocacy Council was formed in 2018 to coordinate resident-led, grassroot advocacy efforts, education, and skills-building open to all pediatric residents and fellows. In parallel, a new leadership position, Associate Chair of Policy and Community Engagement, was established to coordinate department-wide, community-engaged activities, support faculty developing careers in advocacy, and provide opportunities for participation in policy initiatives to advance child health equity. These structures, teams, and partnerships allowed the Department of Pediatrics to respond to the challenges of 2020. Seven Guiding Principles Responding to COVID-19 and Racial Injustice Principle 1: Community Engagement Requires Sustained Commitment When the economic devastation of COVID-19 posed a threat to children, the Advocacy Program immediately mobilized long-term partnerships12 , 13 to address urgent pandemic-related child health needs. The week after shelter in place was enacted, the Advocacy Program convened 15 community organizations and 5 clinical partners (Table II; available at www.jpeds.com). Together they prioritized challenges and defined avenues for collective action. Pre-existing coalitions, prior work with community organizations, and the longstanding nature of these partnerships allowed immediate mobilization of trusted entities to address COVID-driven challenges, linking families to key resources (principle 2) and organizing physician advocacy to support community-identified policy solutions (principle 3). Principle 2: Departments of Pediatrics Must Listen First Community organizations expressed being overwhelmed by the influx of new resources to support an increasing number of struggling families. In response, the Advocacy Program catalogued COVID-19 resources, which were then vetted by community partners. The result was a series of multilingual, COVID-19 resource guides highlighting relevant family resources. The guide was made available in both hard-copy and digital formats (using scannable QR codes linking to websites), to ensure families have access to the most updated, evolving information, in either mode. Moving from community to clinical dissemination, the Advocacy Program worked with the children's hospital social work team and information technology department to make these resource guides available for inpatients via smart phrases in the electronic health record. Meanwhile, the resident Advocacy Council disseminated guides in outpatient clinics and the school of medicine's emergency department. These resources have been disseminated in over 13 000 flyers via school meal distributions and patient mail and accessed 2300 times via QR scan. In addition, the Advocacy Council built a Nursery Navigation Program in the Well-Baby Nursery where residents on the Community Pediatrics Rotation are “on call” to meet with parents, review the resource guide, and conduct warm handoffs to community organizations. The resource guide was sought after because we started by listening, developing a relevant resource guide requires community input and understanding evolving community dynamics. Principle 3: Policy Engagement Requires Coordination Community organizations voiced COVID-19–driven challenges that required policy solutions, including eviction moratoriums. The Advocacy Program's collaboration with the children's hospital Government Relations Office has persisted for 20 years and works closely with our AAP chapter and district leadership. Prior to COVID-19, the Advocacy Program built an email distribution list of over 250 faculty, trainees, and staff who receive monthly child policy updates. When community partners shared that an eviction moratorium was on the agenda in local jurisdictions and asked for physicians to weigh in, we reached out to AAP district leadership who provided a letter of support as housing security is critical for child well-being. With coordination from the hospital's Government Relations Office, the AAP letter was shared in an Action Alert. Similar requests from local organizations included initiatives to protect child welfare, distribute personal protective equipment to essential food workers, and expand public food benefits. The Department of Pediatrics' powerful collective voice was quickly leveraged to support child-friendly policy needs, but this work would be reckless in isolation: it must be done in coordination with community partners, advocacy groups, the AAP and in trusting relationship with the institution's Government Relations Office (Table II). Principle 4: Departments of Pediatrics’ Longstanding Commitment Generates Resources The local philanthropic community was moved by the news of economic stress and increasing food insecurity. Early in the pandemic, like many institutions, the Associate Chair of Policy and Community Engagement presented a grand rounds talk on the economic impacts of COVID-19, which was viewed by over 600 providers and community members. The visibility of the Advocacy Program's mobilization of our pediatric community in partnership with local organizations inspired donors to financially support the work. The Advocacy Program utilized funding to provide over 17 000 pounds of food, 45 000 diapers, and 200 thermometers to a wide range of community organizations and clinics. An academically based, community-engaged program can serve as a trusted link between community donors and the local organizations surrounding the children's hospital, which is an important emerging role for development offices to consider. Principle 5: Resident Advocacy Requires Faculty Engagement Infrastructure The resident Advocacy Council provided a critical capacity to address COVID-19 and spearhead activities addressing racism. The Advocacy Council, led by peer-elected residents and open to all, is mentored by community and policy-engaged faculty and the children's hospital Director of Government Relations. They organized resident conferences on the indirect impacts of COVID-19, disseminated pandemic resources, and provided education on legislative issues. The Council's weekly advocacy updates, read widely by the residency program, encouraged all residents to participate in advocacy projects and opportunities. Resident involvement expanded use of the resource guides (principle 2) to represent 6 counties, include 3 languages, and expand within 2 hospital systems, increasing the guide's utility and reach. On the national stage, residents wrote op-eds and sent letters of gratitude to support colleagues in areas hard-hit by the pandemic. After responding to COVID-19 demands, the Advocacy Council pivoted to respond to George Floyd's murder. Residents had worked with faculty to organize demonstrations to protect Medicaid and protest separation of immigrant families, among other issues. Expanding this activism in solidarity with the Black Lives Matter movement, the Council, with faculty support from the Leadership Education for Advancing Diversity (LEAD) program, organized over 800 members of the Stanford Medicine community for a Rally for Racial Justice.14 Two local news outlets covered the event where the Council called for broad individual, structural, and community level antiracism efforts. They subsequently released a letter of antiracism proposals for the Pediatric Residency program, which resulted in an academic half-day dedicated for antiracism education. Such successful resident advocacy is not accidental: it must emerge from existing infrastructure, long-term faculty support, trusting relationships with departmental and hospital leadership, and a culture of taking a stand on pressing issues. Principle 6: Departmental Commitment to Equity Requires Ongoing Self-Scrutiny and Action After the national outrage against racism and the Advocacy Council's Rally for Racial Justice, the department's need to mobilize a response was clear. The Associate Chair of Policy and Community Engagement, joined by leadership from Stanford's LEAD program, developed an initiative to involve all members (faculty, staff, learners) and across all arms (clinical care, education, research, etc). Consistent with principle 1, the initial step was a “listening campaign” to understand issues of racism and gauge solutions to move the Department of Pediatrics toward being an antiracist community. Hour-long confidential, small group conversations were held and qualitatively analyzed to extrapolate key themes. Subsequently, a modified Delphi process identified solutions for an antiracism action plan. Actions centered around 7 domains: diverse faculty and staff recruitment and promotion, human resources and measuring, training, communication, leadership representation, community engagement and research, and staff engagement (Appendix; available at www.jpeds.com). Teams of faculty, staff, and trainees have been assembled to lead each domain, focused on increasing the diversity of faculty, number of underrepresented minorities in leadership, and instilling mandatory antiracism and allyship trainings. The department leadership's quick response to investigate departmental racism and engage the entire department for opportunities for change was critical in starting to build an antiracist community. Principle 7: Structural Changes Need to Support Emerging ScholarshipAround Advocacy A new leadership role, the Associate Chair of Policy and Community-engagement elevates a focus on advocacy and community-engagement, working with the School of Medicine's Appointment and Promotion Committee to articulate a career path for junior faculty. The Associate Chair meets with various divisions to identify faculty champions and scholarly avenues for advocacy specific to each subspecialty.15 In response to the COVID-19 pandemic, the Associate Chair raised department awareness of the COVID-19–driven challenges of local communities and played the crucial role of generating resources to support the community (principle 4). In the department efforts to address racism (principle 6), the Associate Chair helps coordinate 7 antiracism teams led by faculty and staff dyads, enabling department members at every level to lead the charge to devise antiracist solutions. While creating pathways for members of the department to lead these efforts, the Associate Chair functions to ensure faculty and staff are supported by navigating key contacts, providing guidance for systems change, and working with senior leadership to move solutions from ideation to action. Departments of Pediatrics benefit from structural leadership—including a chair position—to ensure that advocacy and associated scholarship is supported. Conclusions The events of 2020 have challenged Departments of Pediatrics to self-scrutinize current practices to promote health equity for all children. The shift by many pediatric academic centers to incorporate community-engaged, advocacy infrastructure has prepared the field of pediatrics to respond. As next steps, we recommend an assessment of the percentage of pediatric programs that provide structure for advocacy and community engagement and a priori-driven research to discern best practice for community-engaged advocacy. With the lessons from the COVID-19 pandemic and unveiling of racism fresh in our minds, now is the time to advance Departments of Pediatrics to incorporate community engagement, advocacy, and antiracism as a central fabric of our work. Appendix Appendix Table I Pediatric Advocacy Program and CPTI 6 drivers of success for community health and advocacy education in pediatrics (1) Faculty champions (2) Effective teams (3) Community partnerships (4) Leadership support (5) Curriculum (6) Sustainable capacity • Medical Director (Associate Chair of Policy and Community Engagement) • Public health trained Program Director • Clinical Professors (2) • Community pediatrics advocacy collaborative • Resident advocacy council • School districts (2) • Early childhood programs (3) • Food service programs (3) • Social service agencies (3) • Legal/advocacy groups (3) • Local libraries • Associate Chair of Policy and Community Engagement • Government relations • Track in community engagement and advocacy • Community pediatrics rotation • Departmental funding • Support from local philanthropic foundations (7) Table II Partner organizations and coordinated COVID-19 response Organizations Partners Coordinated response Institutional Emergency departments Children's hospital subspecialists Social work University/School of Medicine/hospital Government relations Hospital information technology 1. Resource navigation guide 2. Advocacy (letters of support, petitions, contacting legislators) Community School districts Legal/advocacy groups Food banks Social service agencies Early childhood programs County libraries County Board of Supervisors 1. Resource navigation guide 2. Advocacy (letters of support, petitions, contacting legislators) 3. Direct material support 4. Direct clinic communications Community Pediatrics Advocacy Coalition County clinics Academic medical center clinic Federally qualified health center 1. Resource navigation guide 2. Direct material support Professional AAPs 1. Advocacy (letters of support) ==== Refs References 1 Employment Situation Summary [Internet] https://www.bls.gov/news.release/empsit.nr0.htm 2 Karpman M, Zuckerman S, Gonzalez D, Kenney GM. The COVID-19 Pandemic Is Straining Families’ Abilities to Afford Basic Needs: Low-Income and Hispanic Families the Hardest Hit. Urban Institute 21. https://www.urban.org/research/publication/covid-19-pandemic-straining-families-abilities-afford-basic-needs. Accessed January 5, 2021. 3 Lancker W.V. Parolin Z. COVID-19, school closures, and child poverty: a social crisis in the making Lancet Public Health 5 2020 e243 e244 32275858 4 Gross C.P. Essien U.R. Pasha S. Gross J.R. Wang S. Nunez-Smith M. Racial and ethnic disparities in population-level COVID-19 mortality J Gen Intern Med 4 2020 1 3 5 Oettgen B. Ruch-Ross H. Barrett H.A. Bennett-Tejes D. Palmer K. Hobson W.L. The Community Access to Child Health (CATCH) Program: a 25-year retrospective Pediatrics 143 2019 e20182551 31142579 6 ACGME Program Requirements for Graduate Medical Education in Pediatrics 2020 7 Community Pediatrics Training Initiative (CPTI) [Internet]. AAP.org http://www.aap.org/en-us/advocacy-and-policy/aap-health-initiatives/CPTI/Pages/default.aspx 8 Minkovitz C.S. Goldshore M. Solomon B.S. Guyer B. Grason H. on behalf of the Community pediatrics training initiative workgroup. Five-year follow-up of community pediatrics training initiative Pediatrics 134 2014 83 90 24982098 9 Hoffman B.D. Rose J. Best D. Linton J. Chapman S. Lossius M. The Community pediatrics training initiative project planning tool: a practical approach to community-based advocacy Med Ed Portal 13 2017 10630 10 Barkley L. Kodjo C. West K.J. Vo D.Z. Chulani V.L. Svetaz M.V. Promoting equity and reducing health disparities among racially/ethnically diverse adolescents: a position paper of the society for adolescent health and medicine J Adolesc Health 52 2013 804 807 23701890 11 AAP Council on Community Pediatrics Poverty and child health in the United States Pediatrics 137 2016 e20160339 26962238 12 Bruce J.S. De La Cruz M.M. Moreno G. Chamberlain L.J. Lunch at the library: examination of a community-based approach to addressing summer food insecurity Public Health Nutr 20 2017 1640 1649 28318465 13 Peterson J.W. Huffman L.C. Bruce J. Prata N. Harley K.G. Chamberlain L.J. A clinic-based school readiness coaching intervention for low-income Latino children: an intervention study Clin Pediatr 59 2020 1240 1251 14 “Street Medics” provide emergency care and compassion at protests [Internet]. https://www.mercurynews.com/2020/06/21/street-medics-provide-emergency-care-and-compassion-at-protests/ 15 Chamberlain L. Promoting Clinical Faculty: Emerging Advocacy Scholarship. AMSPDC 2020 Annual Meeting; February 29 2020 Long Beach, CA

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J Pediatr. 2021 Apr 8; 231:7-9.e3

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10.1016/j.jpeds.2020.11.048

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00182-5 10.1016/j.jpsychires.2021.03.032 Correspondence Intolerance of uncertainty and mental health during the COVID-19 pandemic: The role of anger as a moderator Hamama-Raz Yaira School of Social Work, Ariel University, Ariel, Israel Goodwin Robin Department of Psychology, University of Warwick, Coventry, United Kingdom Leshem Elazar Ben-Ezra Menachem ∗ School of Social Work, Ariel University, Ariel, Israel ∗ Corresponding author. 25 3 2021 6 2021 25 3 2021 138 5052 8 12 2020 9 3 2021 19 3 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords COVID-19 Anger Intolerance of uncertainty Depressive symptoms ==== Body pmc1 Introduction Beyond the immediate health implications, the COVID-19 pandemic has also led to mounting financial losses with little immediate prospects for recovery. Lockdowns and other extreme restrictions have also added additional mental health and economic stresses that may generate considerable uncertainty. This has been exacerbated by conflicting messages from governments and public health authorities. Intolerance of uncertainty (UI) refers to an individual's negative emotions, cognitions, and behaviors when uncertainty is experienced and is defined as: "an individual's dispositional incapacity to endure the aversive response triggered by the perceived absence of salient, key, or sufficient information, and sustained by the associated perception of uncertainty."(Carleton et al., 2012; p. 31). Research suggests that intolerance of uncertainty may play a central role in the etiology and maintenance of worry and rumination, which may explain its transdiagnostic associations with variety of psychological disorders (Yook et al., 2010). Indeed, intolerance of uncertainty has been associated with poor mental health during the COVID-19 pandemic (Rettie and Daniels, 1037), while intolerance of uncertainty was found to explain the positive connection between anxiety symptoms and anger (Fracalanza et al., 2014). The array of losses accompanying COVID-19 (e.g. health of self or loved ones, finances, routines and opportunities to see friends and family) might be expected to arouse anger and civil unrest (Galea and Abdalla, 2020). This anger in itself is associated with depression (Busch, 2009) and can include greater symptom severity and worse treatment response (Cassiello‐Robbins and Barlow, 2016). Both expressed and suppressed anger can be a source of conflict and become self-directed which may lead to coronary heart diseases, diabetes, bulimic behaviors and road accidents (Staicu and Cuţov, 2010). In accordance with the above, we explore the role of anger as a moderator between intolerance of uncertainty and depression in the light of the ongoing coronavirus pandemic. 2 Methods We deployed the Israeli Ipanel company to deploy the COVID-19 Mental Health Survey. The panel is a probability-based panel with 100,000 members designed to be representative of the adult Jewish population in Israel. Data were collected from August 3 to August 30, 2020. The sample was administered online, and all participants signed an electronic informed consent. The study was approved by Ariel University Institutional Review Board (AU-SOC-YHR-20200616). Out of 1350 invitations sent, 1030 responded (response rate = 76.2%). We conducted a priori sensitivity analyses and found no statistically significant differences between those who answered the survey and those who did not for each of the demographic variables in the study. The sample mean age was 40.75 (SD = 14.75; range 18–75) with 521 (50.6%) women, 645 (62.6%) who are in intimate relationship, 465 (45.1%) who have an academic degree. We employed the Intolerance of Uncertainty Scale (IUS-12) (Carleton et al., 2007), a 12-item self-report measure of negative beliefs about and reactions to uncertainty (e.g., “Uncertainty makes life intolerable”). Items on the IUS-12 are rated on a five-point, Likert-type scale ranging from 1 (Not at all characteristic of me) to 5 (Entirely characteristic of me) (IUS-12 scale, α = 0.91). The IUS-12 skewness was 0.425 (Skewness SD = .076) and Kurtosis was -.189 (Kurtosis SD = .152). Anger was measured by the Short Anger Measure (SAM) (Gerace and Day, 2014), a 12-item self-report measure of angry feelings and aggressive impulses (SAM scale, α = 0.90). Items on the SAM are rated on a five-point, Likert scale ranging from 1(never) to 5 (almost always). The SAM skewness was 0.813 (Skewness SD = .076) and Kurtosis was 0.401 (Kurtosis SD = .152). Depression was measured using the Patient Health Questionnaire-9 (PHQ-9) (Kroenke et al., 2001). This 9-item self-report measure asks participants indicate how often they have been bothered by each symptom over the last two weeks using a four-point Likert scale ranging from 0 (Not at all) to 3 (Nearly every day). Possible scores range from 0 to 27, with higher scores indicative of higher levels of depression. A cut-off score of ≥10 is used to identify those who are likely to meet the criteria for depressive disorder. This cut-off produces good sensitivity (0.88) and specificity (0.88) (PHQ-9; α =.88). The PHQ-9 skewness was 1.222 (Skewness SD = .076) and Kurtosis was 1.303 (Kurtosis SD = .152). The analytic plan used moderation analysis to examine the potential moderating effect of anger on the association between intolerance of uncertainty and depressive symptoms. No multicollinearity was found in the study. For IUS and SAM the VIF was 1.208 and the tolerance was 0.828. In addition, we have added Cohen's d for measuring effect size. Cohen suggested that d = 0.2 be considered a ‘small’ effect size, 0.5 a ‘medium’ effect size, and 0.8 a ‘large’ effect size (Cohen, 2013). 3 Results The prevalence rate of probable depression in August 2020 showed that 20.5% (95% CI, 18.1%–23.1%) of respondents had met reached the cut-off point and above for depression. This compares to a 2007 Israel National Health Survey which reports a prevalence of 9.8% (95% CI, 9.0%–10.6%) for major depression (Levinson et al., 2007). Anger was examined as a moderator of the association between intolerance of uncertainty and depression. Using SPSS process module Model 1(Hayes, 2017), intolerance of uncertainty and anger were entered in the first step of the regression analysis, in the second step the interaction term between intolerance of uncertainty and anger explained a significant increase in variance in depression, ΔR2 = .01, F (1, 1030) = 16.51, p < .001. Thus, anger was a significant moderator of the relationship between intolerance of uncertainty and depression. The unstandardized simple slope for depression 1 SD below the mean of anger was 0.11 (t = 6.93; p < .001), for depression with a mean level of anger was 0.16 (t = 11.28; p < .001), and for depression 1 SD above the mean of anger was 0.20 (t = 10.26; p < .001) (Fig. 1 ). Finally, Cohen's d measuring effect size in the regression for the association between intolerance of uncertainty and depression was 0.75 (r = 0.35) and 1.21 (r = 0.52) for the association between anger and depression.Fig. 1 The moderation of Anger on the relationship between Uncertainty and depression. Fig. 1 4 Discussion The current findings demonstrated a direct link between intolerance of uncertainty and depression during COVID-19. This result highlights the need to provide structured and targeted psychological support and guidance to reduce intolerance of uncertainty during COVID (Busch, 2009). As uncertainty continues, governments should strive to provide clearer information regarding COVID-19 management, including the use of lockdowns and other restrictions, in order to provide relief and reduce uncertainty. Anger had an indirect effect on the association between intolerance of uncertainty and depression, stressing the importance of targeting ways to reduce anger to increase wellbeing. Anger management through cognitive behavior therapy techniques have been shown to be very effective for anger reduction (Bradbury and Clarke, 2007). By integrating assessment of anger into COVID-19 care settings depression may be identified at an early stage, ensuring the capacity for timely consultation with mental health specialists. The current study was limited by its cross-sectional design and possible response bias introduced by participation through an online application. In addition, the sample consist of a high percentage of subjects with an academic degree. Comparison with 2007 data might be difficult as there could be an underestimation of depression due to more stigmatization or lesser awareness. Findings demonstrate the increased mental health burden associated with uncertainty during a pandemic. In doing so they highlight the importance of identifying and targeting mediator variables such as anger in order to facilitate the management of psychological health during uncertain times. Ethics approval The study was approved by Ariel University Institutional Review Board (AU-SOC-YHR-20200616). Funding/support The study was supported via internal research grant from Ariel University Research Authority (RA2000000302). Role of the funder/sponsor The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Declaration of competing interest The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. ==== Refs References Bradbury K.E. Clarke I. Cognitive behavioural therapy for anger management: effectiveness in adult mental health services Behav. Cognit. Psychother. 35 2 2007 201 208 Busch F.N. Anger and depression Adv. Psychiatr. Treat. 15 2009 271 278 Carleton R.N. Norton M.P.J. Asmundson G.J. Fearing the unknown: a short version of the intolerance of uncertainty scale J. Anxiety Disord. 21 1 2007 105 117 16647833 Carleton R.N. Mulvogue M.K. Thibodeau M.A. McCabe R.E. Antony M.M. Asmundson G.J. Increasingly certain about uncertainty: intolerance of uncertainty across anxiety and depression J. Anxiety Disord. 26 3 2012 468 479 22366534 Cassiello-Robbins C. Barlow D.H. Anger: the unrecognized emotion in emotional disorders Clin. Psychol. Sci. Pract. 23 1 2016 66 85 Cohen J. Statistical Power Analysis for the Behavioral Sciences 2013 Academic press Fracalanza K. Koerner N. Deschênes S.S. Dugas M.J. Intolerance of uncertainty mediates the relation between generalized anxiety disorder symptoms and anger Cognit. Behav. Ther. 43 2 2014 122 132 24579760 Galea S. Abdalla S.M. COVID-19 pandemic, unemployment, and civil unrest: underlying deep racial and socioeconomic divides JAMA 324 3 2020 227 228 32530457 Gerace A. Day A. The Short Anger Measure: development of a measure to assess anger in forensic populations J. Forensic Nurs. 10 2014 44 49 24263349 Hayes A.F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach 2017 Guilford publications Kroenke K. Spitzer R.L. Williams J.B. The PHQ‐9: validity of a brief depression severity measure J. Gen. Intern. Med. 16 2001 606 613 11556941 Levinson D. Zilber N. Lerner Y. Grinshpoon A. Levav I. Prevalence of mood and anxiety disorders in the community: results from the Israel National Health Survey Isr. J. Psychiatry Relat. Sci. 44 2 2007 94 103 18080646 Rettie H, Daniels J. Coping and Tolerance of Uncertainty: Predictors and Mediators of Mental Health during the COVID-19 Pandemic. American Psychologist Advance online publication. 10.1037/amp0000710. Staicu M.L. Cuţov M. Anger and health risk behaviors J. Med.life 3 4 2010 372 375 21254733 Yook K. Kim K.H. Suh S.Y. Lee K.S. Intolerance of uncertainty worry, and rumination in major depressive disorder and generalized anxiety disorder J. Anxiety Disord. 24 6 2010 623 628 20439149

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J Psychiatr Res. 2021 Jun 25; 138:50-52

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J Psychiatr Res

10.1016/j.jpsychires.2021.03.032

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Elsevier Ltd. S0022-3956(21)00258-2 10.1016/j.jpsychires.2021.04.031 Correspondence Letter to the Editor: Targeting adverse stress-related consequences of the COVID-19 crisis in individuals with psychotic disorders and childhood maltreatment Fares-Otero Natalia E. ∗ Department of Psychiatry, Biomedical Research Institute, University Hospital 12 de Octubre (imas12), Madrid, Spain Trautmann Sebastian Department of Psychology, Medical School Hamburg, Hamburg, Germany Pfaltz Monique C. Department of Consultation-Liaison Psychiatry and Psychosomatic Medicine, Faculty of Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland Rodriguez-Jimenez Roberto Department of Psychiatry, Biomedical Research Institute, University Hospital 12 de Octubre (imas12), Faculty of Medicine, Complutense University of Madrid (UCM), Madrid, Spain CIBERSAM (Biomedical Research Networking Centre in Mental Health), Spain ∗ Corresponding author. Department of Psychiatry, Biomedical Research Institute, University Hospital 12 de Octubre (imas12), Avda. de Córdoba s/n, 28041, Madrid, Spain. 30 4 2021 6 2021 30 4 2021 138 453455 8 12 2020 1 4 2021 20 4 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords COVID-19 pandemic Childhood abuse Stress Schizophrenia Cognitive-behavioural therapy ==== Body pmcThe novel coronavirus disease (COVID-19) has created unprecedented global health and social challenges. Between December 2019 and April 2021, over 149 million cases of COVID-19 have been diagnosed worldwide (WHO, 2021). As the number of infections and deaths continues to surge, governments have (re)introduced restrictions such as lockdowns, no-contact regulations and quarantine. Despite the fact that the pandemic affects the life of the vast majority of individuals, several subgroups are at particularly high risk for adverse consequences. Psychosis is a mental health condition that requires specific attention in the context of the COVID-19 pandemic. It has been demonstrated that stressful life events are an important risk factor for the exacerbation of psychotic symptoms (Norman and Malla, 1993). Given their vulnerability to social determinants of mental health (Anglin et al., 2020), people with psychotic disorders (PD) may be at risk for adverse consequences of stress associated with physical distancing and reduction in social connectedness during the COVID-19 pandemic. In turn, abrupt changes in life circumstances disproportionally affect people with PD (Brown et al., 2020), while lockdown and social distancing have disrupted much of the professional support available to them under normal circumstances. During this prolonged period of crisis, the need for mental health and psychosocial assistance will likely increase in individuals with PD, due to the complex and multiple stressors and difficulties associated with the pandemic. In fact, many of the critical psychosocial stressors of the COVID-19 crisis will probably remain for a longer period of time. It is, therefore, a critical time window for the development of novel interventions for these patients, in order to reduce the burden and costs associated with adverse stress-related consequences of the COVID-19 crisis. We want to highlight that among the vulnerable group of individuals with PD, those with a history of childhood maltreatment (CM), i.e., physical, emotional and sexual abuse, and physical and emotional neglect, need specific attention. We argue that there is a need for 1) further research on the cumulative effects of CM and COVID-19-related stressful events (Fares-Otero et al., 2020) in individuals with PD, and (2) for designing specific treatments to account for these specific effects on the course of PD. Such interventions for the particularly vulnerable group of individuals with PD who have a history of CM would make a difference during the COVID-19 crisis. In addition to its important role in the aetiology and course of PD (Kelleher et al., 2013), CM is a highly prevalent vulnerability factor that can put individuals with PD at higher risk for stress. At least one form of CM is reported in 50% of patients with schizophrenia (Morgan and Fisher, 2007). CM is one of the most important environmental stressor (Lardinois et al., 2011) associated with brain alterations (Teicher et al., 2016) that can put individuals with PD at particularly high mental health risks when exposed to a stressful environment such as the COVID-19 pandemic and corresponding measures. More specifically, CM plays a significant role in PD, increasing the risk of neurocognitive (Mørkved et al., 2020; Schalinski et al., 2018), social cognition (Kilian et al., 2018), behavioural problems and functioning (Copeland et al., 2018). Therefore, individuals with PD and CM survivors may be particularly vulnerable when facing the COVID-19 pandemic (Hamam et al., 2021). That is, COVID-19 related measures might contribute to a more severe and unstable illness course, characterized by an increased risk of neurocognitive difficulties (e.g. attention, planning) and behavioural problems (e.g. impulsivity, risk-taking behaviours increasing the risk of becoming infected with COVID-19) in individuals with PD with (versus those without) CM. To date, no specific treatment targeting stress-related consequences of COVID-19 exists for patients with PD and CM. However, Cognitive-Behavioural Therapy (CBT) approaches are highly promising in this regard and might increase resilience to the stress caused by the COVID-19 pandemic. Against the above outlined background (see also Fig. 1 ), in this letter we suggest that the CBT program has to meet certain requirements accounting for the specific characteristics of individuals with PD and a history of CM and to include specific components to be effective. Specific components that might prove effective are psychoeducation, verbal or written recounting of traumatic experiences, cognitive restructuring, behavioural activation, problem-solving, emotion regulation and communication strategies.Fig. 1 Conceptual scheme about stress-related consequences of COVID-19 crisis and targeted Cognitive-Behavioural Therapy for individuals with psychotic disorders and childhood maltreatment. Fig. 1 Psychoeducation as part of such a CBT program for individuals for psychotic disorders and childhood maltreatment (CBTPD-CM) could include information on both psychosis diagnosis and aftereffects of CM and the rationale for CBT techniques with a special focus on stressors and experiences related to the COVID-19 pandemic. Furthermore, the CBTPD-CM program should provide a supportive treatment environment in which PD patients with CM are encouraged to talk about their past (and current) stressful experiences, including verbal or written recounting of events (Jaeger et al., 2014). The trauma exploration, formulation, historical review and attributional work can be carried out as long as the patient is stable enough to tolerate the experience (van den Berg et al., 2016; van den Berg et al., 2018). We propose to combine cognitive restructuring and behavioural activation (practicing new resources) with problem-solving skills to develop new perspectives to deal with fear and brooding. In turn, it is important that patients are given the possibility to process stressful and adverse events, including traumatic experiences (where necessary, preceded by psychological stabilization), to account for the specific characteristics of patients with PD and CM. A sense of the world being dangerous in individuals with CM can lead to hypervigilance to (potential) threat related stimuli and to increased attention to unpleasant events. This may, in turn, be related to concentration problems, irritability, anger, withdrawal, and difficulties in thinking about the future (Lu et al., 2017). In addition, it therefore seems crucial to include attention training, planning strategies as well as relaxation strategies. Moreover, emotion regulation plays a mediating role between CM and distressing psychotic experiences. In fact, difficulties in emotion regulation and higher symptom distress exacerbate each other (Lincoln et al., 2017). The experiences of physical distancing and loneliness (effects of contact restrictions and isolation) can trigger memories of past losses experienced earlier in life in PD patients with CM. Affected individuals may fear that things will never return to normal or that the future will mean further losses. Therefore, reinforcing emotion regulation strategies (Guimond et al., 1017), including (online) relaxation, mindfulness and imagination techniques to identify and modify automatic thoughts related to distressing emotions might be effective skills to cope with COVID-19 related stressors. In this respect, therapists may want to stress that core (negative) beliefs and emotions (e.g. worry about COVID-19 infection) are situation specific, rather than being fundamental to the patient, thus helping with re-attribution, encouraging alternative ways of coping and reducing distress. Importantly, there is a need to improve social (media) interactions and interpersonal communication strategies of patients. Individuals with PD are particularly socially isolated, and commonly face stigma and discrimination. Likewise, a fear that people will treat them differently because of CM is common (Hardy et al., 2016). Thus, CBTPD-CM delivered in groups may be especially advantageous, which can present individuals with PD and CM with an opportunity to interact with peers and benefit from the interpersonal feedback to improve their ability to express emotions, as well as to maintain social relationships and deal with grief for the loss of loved ones. Both content and setting have to meet several requirements. Where necessary, remote or internet-based digital platforms can provide viable routes for the delivery of CBTPD-CM during limited access to health services during lockdowns. Important, clinicians will need training in (remote) CBTPD-CM. We also recommend (online) supervision to keep them on track with the delivery of CBTPD-CM. Finally, research is needed to test the feasibility, acceptability, and effectiveness of our proposed approach in reducing adverse effects of COVID-19 in this vulnerable population. While our suggestions are specific for individuals with PD and CM, they might also in part be applicable, possibly in an adapted format, to a broader range of COVID-19 stress related disorders. In summary, individuals with PD and CM constitute a high-risk group for adverse consequences of the COVID-19 crisis, which has been underrecognized so far. Past experiences of CM in these patients may influence their responses to the pandemic and related measures, increasing the risk of difficulties in cognitive and social functioning. Further research is needed on the cumulative effects of prior chronic (traumatic) stress and the COVID-19 crisis in individuals with PD. We point out that PD patients with CM are in need for tailored treatments and propose to develop and test the effectiveness of specific CBTPD-CM programs that might be particularly effective for these individuals to improve their mental health and psychosocial wellbeing during and after the pandemic and future crises. Declaration of competing interest Dr. R. Rodriguez-Jimenez has been a consultant for, spoken in activities of, or received grants from: Institute of Health Carlos III, Sanitary Research Fund (FIS), Biomedical Research Networking Centre in Mental Health (CIBERSAM), Madrid Regional Government (S2010/BMD-2422 AGES; S2017/BMD-3740), Janssen Cilag, Lundbeck, Otsuka, Pfizer, Ferrer, Juste, Takeda, Exeltis, Angelini, and Casen-Recordati. Acknowledgements The first author is supported by the Madrid Regional Government (R&D activities in Biomedicine, grant number S2017/BMD-3740 - AGES-CM 2-CM) and Structural Funds of the European Union. ==== Refs References Anglin D.M. Galea S. Bachman P. Going upstream to advance psychosis prevention and improve public health JAMA Psychiatry 77 7 2020 665 666 10.1001/jamapsychiatry.2020.0142 32236511 Brown E. Gray R. Lo Monaco S. The potential impact of COVID-19 on psychosis: a rapid review of contemporary epidemic and pandemic research Schizophr. Res. 222 2020 79 87 10.1016/j.schres.2020.05.005 32389615 Copeland W.E. Shanahan L. Hinesley J. Association of childhood trauma exposure with adult psychiatric disorders and functional outcomes JAMA Netw Open 1 7 2018 e184493 10.1001/jamanetworkopen.2018.4493 Fares-Otero N.E. Pfaltz M.C. Estrada-Lorenzo J.-M. Rodriguez-Jimenez R. COVID-19: the need for screening for domestic violence and related neurocognitive problems J. Psychiatr. Res. 130 2020 433 434 10.1016/j.jpsychires.2020.08.015 32891919 Guimond S, Ling G, Drodge J, et al. Functional connectivity associated with improvement in emotion management after cognitive enhancement therapy in early-course schizophrenia. Psychol. Med.. Published online undefined/ed:1-10. doi:10.1017/S0033291720004110. Hamam A.A. Milo S. Mor I. Shaked E. Eliav A.S. Lahav Y. Peritraumatic reactions during the COVID-19 pandemic – the contribution of posttraumatic growth attributed to prior trauma J. Psychiatr. Res. 132 2021 23 31 10.1016/j.jpsychires.2020.09.029 33038562 Hardy A. Emsley R. Freeman D. Psychological mechanisms mediating effects between trauma and psychotic symptoms: the role of affect regulation, intrusive trauma memory, beliefs, and depression Schizophr. Bull. 42 2016 S34 S43 10.1093/schbul/sbv175 Suppl 1(Suppl 1) 27460616 Jaeger J. Lindblom K.M. Parker-Guilbert K. Zoellner L.A. Trauma narratives: it's what you say, not how you say it Psychol Trauma 6 5 2014 473 481 10.1037/a0035239 25379123 Kelleher I. Keeley H. Corcoran P. Childhood trauma and psychosis in a prospective cohort study: cause, effect, and directionality Am. J. Psychiatr. 170 7 2013 734 741 10.1176/appi.ajp.2012.12091169 23599019 Kilian S. Asmal L. Chiliza B. Childhood adversity and cognitive function in schizophrenia spectrum disorders and healthy controls: evidence for an association between neglect and social cognition Psychol. Med. 48 13 2018 2186 2193 10.1017/S0033291717003671 29268811 Lardinois M. Lataster T. Mengelers R. Van Os J. Myin-Germeys I. Childhood trauma and increased stress sensitivity in psychosis Acta Psychiatr. Scand. 123 1 2011 28 35 10.1111/j.1600-0447.2010.01594.x 20712824 Lincoln T.M. Marin N. Jaya E.S. Childhood trauma and psychotic experiences in a general population sample: a prospective study on the mediating role of emotion regulation Eur. Psychiatr. 42 2017 111 119 10.1016/j.eurpsy.2016.12.010 Lu W. Mueser K.T. Rosenberg S.D. Yanos P.T. Mahmoud N. Posttraumatic reactions to psychosis: a qualitative analysis Front. Psychiatr. 8 2017 129 10.3389/fpsyt.2017.00129 Morgan C. Fisher H. Environment and schizophrenia: environmental factors in schizophrenia: childhood trauma--a critical review Schizophr. Bull. 33 1 2007 3 10 10.1093/schbul/sbl053 17105965 Mørkved N. Johnsen E. Kroken R.A. Does childhood trauma influence cognitive functioning in schizophrenia? The association of childhood trauma and cognition in schizophrenia spectrum disorders Schizophr Res Cogn 21 2020 100179 10.1016/j.scog.2020.100179 32461919 Norman R.M. Malla A.K. Stressful life events and schizophrenia. I: a review of the research Br. J. Psychiatry 162 1993 161 166 10.1192/bjp.162.2.161 8435685 Schalinski I. Teicher M.H. Carolus A.M. Rockstroh B. Defining the impact of childhood adversities on cognitive deficits in psychosis: an exploratory analysis Schizophr. Res. 192 2018 351 356 10.1016/j.schres.2017.05.014 28576548 Teicher M.H. Samson J.A. Anderson C.M. Ohashi K. The effects of childhood maltreatment on brain structure, function and connectivity Nat. Rev. Neurosci. 17 10 2016 652 666 10.1038/nrn.2016.111 27640984 van den Berg D.P.G. de Bont P.A.J.M. van der Vleugel B.M. Trauma-Focused treatment in PTSD patients with psychosis: symptom exacerbation, adverse events, and revictimization Schizophr. Bull. 42 3 2016 693 702 10.1093/schbul/sbv172 26609122 van den Berg D. de Bont P.A.J.M. van der Vleugel B.M. Long-term outcomes of trauma-focused treatment in psychosis Br. J. Psychiatry 212 3 2018 180 182 10.1192/bjp.2017.30 29436320 World Health Organization Coronavirus disease (COVID-19) 2021 Retrieved (30 Apr 2021) from https://www.who.int/emergencies/diseases/novel-coronavirus-2019

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==== Front J Psychiatr Res J Psychiatr Res Journal of Psychiatric Research 0022-3956 1879-1379 Pergamon Press S0022-3956(21)00203-X 10.1016/j.jpsychires.2021.03.051 Article Clinical and functional effects of the COVID-19 pandemic and social distancing on vulnerable veterans with psychosis or recent homelessness Wynn Jonathan K. ab∗ McCleery Amanda c Novacek Derek a Reavis Eric A. ba Tsai Jack def Green Michael F. ba a Research Enhancement and Award Program on Enhancing Community Integration for Homeless Veterans, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA b Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA c Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, USA d VA National Center on Homelessness Among Veterans, Washington, DC, USA e School of Public Health, University of Texas Health Science Center at Houston, TX, USA f Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA ∗ Corresponding author. VA Greater Los Angeles Healthcare System, MIRECC, Bldg. 210, 11301 Wilshire Blvd., Los Angeles, CA, 90073, USA. 29 3 2021 6 2021 29 3 2021 138 4249 14 1 2021 26 2 2021 24 3 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic has upended the lives of everyone in the United States, negatively impacting social interactions, work, and living situations, and potentially exacerbating mental health issues in vulnerable individuals. Within the Department of Veterans Affairs (VA) healthcare system, two vulnerable groups include those with a psychotic disorder (PSY) and those who have recently experienced homelessness (recently housed Veterans, RHV). We conducted phone interviews with PSY (n = 81), RHV (n = 76) and control Veterans (CTL, n = 74) between mid-May – mid-August 2020 (“initial”) and between mid-August – mid-October 2020 (“follow-up”). At the initial period, we also collected retrospective ratings relative to January 2020 (“pre-COVID-19”). We assessed clinical factors (e.g., depression, anxiety, loneliness) and community integration (e.g., social and role functioning). All groups reported worse clinical outcomes after the onset of the COVID-19 pandemic. However, PSY and RHV exhibited improvements in depression and anxiety from initial to follow up, whereas CTL continued to exhibit elevated levels. There was little change in community integration measures. Our results indicate that all groups reported increased mental health problems after the onset of the pandemic, but vulnerable Veterans were not disproportionately affected and had better mental health resilience (i.e., for depression and anxiety) as the pandemic progressed compared to CTL. This effect could be due to the availability and utilization of VA services for PSY and RHV (e.g., housing and financial support, medical and mental health services), which may have helped to mitigate the impact of the pandemic. Keywords COVID pandemic Veterans Homeless Psychosis Mental health Community integration ==== Body pmc1 Introduction The global coronavirus disease (COVID-19) pandemic has upended the daily lives of most people throughout the world and raised widespread mental health concerns. The stay-at-home orders and social distancing recommendations that have been put in place by public health authorities in response to COVID-19 have dramatically impacted people's daily social interactions, work, and living situations. These impacts are likely to have affected a range of mental health issues. In terms of psychological consequences, the current pandemic and resulting public health measures have increased levels of anxiety, depression, suicidal ideation, and loneliness across a wide swath of the world population (Bauerle et al., 2020; Jewell et al., 2020; Killgore et al., 2020; Newby et al., 2020; Salari et al., 2020; Shah et al., 2020), similar to prior pandemics (Peng et al., 2010; Wheaton et al., 2012; Zortea et al., 2020). Other aspects of mental health may also be impacted. For example, concerns about contamination and obsessive behaviors (such as hand washing) are likely to have increased for obvious reasons (Abba-Aji et al., 2020). In addition, safer-at-home and social distancing mandates have severely disrupted aspects of community functioning, including a reduction in social and family contacts, substantial job loss and reduction in pay, and potential loss of housing (Tsai and Wilson, 2020). Two populations of special concern during the pandemic within the U.S. Department of Veterans Affairs (VA) system are those who have a psychotic disorder (PSY) and those who have recently experienced homelessness (recently housed Veterans, RHV). These groups generally do not have strong social contacts to begin with and might be particularly vulnerable to mental health and social impacts of the pandemic (Kozloff et al., 2020). Therefore, in the current, ongoing longitudinal study we aimed to determine the impact of social distancing and COVID-19 restrictions on clinical factors (e.g., anxiety, depression) and community integration factors (e.g., social networks, family networks, work) in these two vulnerable Veteran groups, in addition to control Veterans (CTL) who have never experienced psychosis or homelessness. We also examined whether any of the negative impacts on clinical and community integration factors would be moderated by age. Some studies have found that older adults are relatively resilient to the effects of the pandemic and not show negative impacts on community integration or mental health (Vahia et al., 2020). We hypothesized that all three groups would experience negative impacts on clinical and community integration factors due to the pandemic. We further hypothesized that the two vulnerable groups of Veterans (PSY, RHV) would experience disproportionate negative impacts of the pandemic on these factors compared to controls. 2 Methods Data collection occurred between mid-May – mid-August 2020 for the initial period (“initial”) and mid-August – mid-October 2020 for the follow up period (“follow-up”). Potential participants were recruited through two main sources: 1) two VA administrative datasets (the Corporate Data Warehouse and Homeless Veteran Registry) from the VA Informatics and Computing Infrastructure (VINCI) platform; and 2) Veterans who have participated in prior studies in our lab and agreed to be contacted for future studies. For RHV, we utilized the VA Computerized Patient Record Systems (CPRS) and VINCI to determine if the participant had a current housing voucher administered by the Housing and Urban Development – VA Supportive Housing (HUD-VASH) initiative. For PSY, we examined CPRS to determine if a psychotic disorder was listed in their medical record to verify eligibility. All procedures were approved by the VA Greater Los Angeles Institutional Review Board. Selection criteria for the groups were intentionally broad and relied on the chart diagnoses from VA medical records. For PSY, participants required a psychotic disorder diagnosis (other than substance-induced psychosis), such as schizophrenia (n = 42), schizoaffective disorder (n = 22), depressive disorder with psychotic features (n = 1), bipolar disorder with psychotic features (n = 9), or psychotic disorder not otherwise specified (n = 7). For RHV, participants required a history of homelessness and attainment of housing within the past 12 months with a HUD-VASH voucher. Of the RHV, eight received a diagnosis for a psychotic disorder, which was permissible for this group. For the control group (CTL), participants required no history of a psychotic disorder or evidence of homelessness based on codes in VINCI and review of medical records. Once enrolled in the study, we examined CPRS for all participants for current mental health and alcohol/substance use disorder diagnoses. Based on this review, we report the percentage of participants in each group with a mood disorder, posttraumatic stress disorder (PTSD), and a current alcohol or substance use disorder in addition to providing demographic information. PTSD and mood disorders were present in all three groups, though to a lesser degree in PSY. Alcohol and substance use were comparatively high in PSY and RHV compared to CTL. We identified 956 potentially eligible participants who were contacted by phone by a lab research assistant. After a brief description of the project, participants who agreed to participate provided verbal informed consent. The participant's contact information was then provided to one of ten clinically trained interviewers. Interviewers typically conducted the initial assessment in two parts on separate days. In the first part, interviewers obtained information on demographics (e.g., age, gender, race/ethnicity, personal education, etc.), finances (e.g., monthly income, work hours/pay reductions, furloughs, etc.) and COVID-specific questions (e.g., COVID-19 positive test, self-quarantine, etc.). In the second part, interviewers administered several questionnaires assessing clinical, risk/protective, and community integration factors (details below). Most questionnaires for the clinical and community integration factors were assessed for three rating periods: initial, follow-up, and a retrospective evaluation collected at initial, in which participants were asked to give ratings for January 2020 (“pre-COVID”). The schedule of assessments along with scales and questionnaires used are detailed in Table 2 . For clinical factors, we assessed depression, anxiety, obsessive-compulsive traits, paranoid thoughts, self-report motivation, suicidal ideation, substance and alcohol use, and loneliness. We also administered the Fear of Illness and Virus Evaluation (FIVE) questionnaire (Ehrenreich-May 2020) which assessed people's fears and behaviors about contamination and illness, social distancing, and the impact of COVID-19 on their lives. For the assessment of alcohol and substance use, we administered the Addiction Severity Index (ASI) (McLellan et al., 1980), which assessed the number of days in the past 30 days that participants endorsed using alcohol or a substance (e.g., cannabis, methamphetamine). As few participants endorsed using any substance other than cannabis, only alcohol and cannabis use are reported in the Results.Table 1 Demographics and clinical diagnoses. Table 1 Control (n = 74) Recently Housed (n = 76) Psychosis (n = 81) Statistic (F or χ2) Demographics Age 56.5 (9.5) 51.6 (12.5) 54.4 (9.8) F2,228 = 4.03, p = 0.019 C > RH Gender (M:F) 63:11 66:10 72:9 χ2(2) = 0.485, p = 0.785 Personal Education (years) 14.6 (2.0) 13.4 (1.5) 13.4 (1.6) F2,228 = 12.94, p < 0.001 C > RH, P Parental Education 13.0 (3.1) 13.5 (3.1) 12.9 (3.9) F2,228 = 0.74, p = 0.477 Ethnicity (H:NH) 19:55 21:55 16:63 χ2(2) = 1.23, p = 0.541 Race (B:W:O) 28:38:8 34:31:9 40:29:10 χ2(4) = 3.47, p = 0.482 Clinical Diagnoses from Medical Records Mood Disorder 47.3% 60.5% 23.5% −−− PTSD 39.2% 42.1% 22.2% −−− Alcohol Use Disorder 4.1% 22.4% 23.5% −−− Substance Use Disorder 9.5% 38.2% 33.3% −−− Note: M = male, F = Female, H = Hispanic, NH = Non-Hispanic, B = Black, W = White, O = Other, PTSD = posttraumatic stress disorder. Table 2 List of questionnaires and interviews to assess clinical factors, vulnerability and protective factors, and community integration. Table 2Measure (Reference) Scoring Pre-COVID Initial Follow Up Clinical Factors Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001) Higher score = greater depression X X X General Anxiety Disorder (GAD-7) (Spitzer et al., 2006) Higher score = greater anxiety X X X Dimensional Obsessive-Compulsive Scale (DOCS) (Abramowitz et al., 2010) Higher score = greater endorsement of obsessive-compulsive behaviors X X X Revised Paranoid Thoughts Scale (RGPTS) (Freeman et al., 2019) Higher score = greater level of paranoid thoughts X X X Motivation and Pleasure Scale – Self-Report (MAP-SR) (Llerena et al., 2013) Higher score = diminished motivation and pleasure X X Montgomery-Åsberg Depression Rating Scale (MADRS) (Montgomery and Asberg, 1979) Ratings≥4 indicate moderate to severe suicidal ideation X X Fear of Illness and Virus Evaluation (FIVE) – Adult Report Form (Ehrenreich-May 2020) Higher score = greater fear and distress related to COVID and quarantine orders X X Revised UCLA Loneliness Scale (ULS) (Russell et al., 1980) Higher score = greater loneliness X X X Community Integration Lubben Social Network Scale (LSNS) (Lubben et al., 2002) Higher score = better family and social functioning X X X Role Functioning Scale (RFS) – Family and Social Subscales (Goodman et al., 1993) Higher score = better family and social functioning X X X Role Functioning Scale (RFS) – Work and Ind. Living Subscales (Goodman et al., 1993) Higher score = better work and better independent living outcomes X X X 2.1 Analytical approach For demographics and the risk and protective factors, we used either Chi-square or analysis of variance (ANOVA) tests to examine group differences at the initial assessment. Group (CTL, PSY, RHV) by Time (pre-COVID, initial, follow-up) effects for the clinical and community integration factors were analyzed with linear mixed-effects models using R version 4.0.2 and the lme4 package version 1.1–23 (Bates et al., 2015). Separate models for each measure were fit using restricted maximum likelihood (REML), and no missing value imputation was utilized. We entered time and group as fixed effects, with participant as a random effect. The formula for each analysis was thus DV ~ Group*Time + (1|Participant). P-values were calculated using the Satterthwaite method using the afex package version 0.28–0 (Singmann et al., 2020). If a significant main effect or interaction was identified, we performed post-hoc comparisons using false-discovery rate (FDR) correction with the emmeans package version 1.5.1 (Lenth et al., 2020). For these analyses, we present in tables the F-value, degrees of freedom, and estimated p-value for main effects and interactions; we present the full summary statistics, including parameter estimates, confidence intervals, p-values, and degrees of freedom, in the supplementary files. For the ASI, we conducted a repeated measures ordinal logistic regression using the GENLIN function in SPSS version 26 (IBM SPSS Statistics, Armonk, NY, USA). Finally, we conducted exploratory analyses using age as a covariate to determine if age moderated any of the negative effects of the pandemic on the clinical or community integration factors. For these analyses, we included age as a covariate in the model with the resulting formula, DV ~ Group*Time + Age + (1|Participant). Because some participants did not answer all questions, there are minor differences in degrees of freedom from measure to measure. 3 Results 3.1 Demographics Demographic information and the statistical results and p-values are presented in Table 1. We collected data on 81 PSY, 76 RHV, and 74 CTL for both the pre-COVID and initial periods. Retention for the follow-up period was relatively high (>82% in each group), with 74 PSY, 63 RHV, and 66 CTL assessed during this period. As is typical of studies consisting solely of Veterans, the samples included a relatively high proportion of Black participants and the majority (87.1%) were male. Groups did not differ significantly on ethnicity, race, or gender. There were significant group differences in age and in personal education. The control group was significantly older than the RHV group; there were no other significant age differences. Controls also had significantly higher personal education levels compared to the other two groups; there were no significant differences between RHV and PSY. However, there were no significant differences in parental education, which serves as a proxy measure of familial socioeconomic status, across the three groups. Regarding direct COVID-related impacts, fewer than 2.7% of participants in each group reported being diagnosed with COVID-19 at initial and none reported a new diagnosis at follow-up; fewer than 20% of participants in each group reported having to self-quarantine due to potential COVID-19 exposure at initial, and fewer than 9% in each group reported subsequent need to self-quarantine at follow-up. 3.2 Clinical factors For clinical measures, descriptive statistics as well as inferential statistics and p-values are reported in Table 3 . For depression, there was a significant main effect of Time (Fig. 1 A) and a significant Time × Group interaction; the main effect of Group was not significant. The interaction was driven by CTL showing significantly increased depression at initial and follow-up relative to pre-COVID ratings, with no change between initial and follow-up. Both RHV and PSY showed a significant increase in depression from pre-COVID to initial, followed by a significant decrease from initial to follow-up. For anxiety (Fig. 1B), there were significant main effects of Group and Time, and a significant Group × Time interaction. The interaction was driven by CTL reporting increased anxiety at initial and follow-up relative to pre-COVID, with no change between initial and follow-up. Both RHV and PSY showed a significant increase in anxiety from pre-COVID to initial, followed by a significant decrease from initial to follow-up.Table 3 Descriptive statistics and statistical test results for clinical factors, including depression (PHQ-9), anxiety (GAD-7), obsessive-compulsive traits (DOCS), suspiciousness (RGPTS), alcohol and cannabis use, motivation and pleasure (MAPS-SR), suicidal ideation (MADRS), and Fear of Illness and Virus Evaluation (FIVE). Values are raw means and standard deviations. Table 3 Control Recently Housed Psychosis Statistics Pre Initial Follow Up Pre Initial Follow Up Pre Initial Follow Up Depression (PHQ9) 5.22 (5.00) 7.16 (5.76) 7.02 (6.00) 6.41 (5.64) 9.43 (6.53) 7.29 (5.30) 6.95 (6.07) 8.61 (6.32) 6.60 (5.40) Time: F2,424.09 = 28.6, p = 2.2 x 10−12 Group: F2,225.11 = 1.48, p = 0.230 T x G: F4,424.08 = 2.65, p = 0.033 Anxiety (GAD7) 3.96 (4.58) 6.55 (6.08) 6.18 (5.62) 6.80 (5.85) 9.15 (6.28) 6.37 (5.25) 5.53 (5.38) 6.92 (6.15) 5.47 (5.40) Time: F2,423.55 = 22.5, p = 5.01 x 10−10 Group: F2,223.85 = 3.36, p = 0.037 T x G: F4,423.53 = 2.75, p = 0.028 OCD – Contamination (DOCS) 2.88 (3.38) 8.38 (5.54) 7.27 (5.22) 3.89 (3.86) 8.38 (5.11) 6.84 (4.39) 3.03 (3.38) 7.95 (5.03) 6.10 (3.90) Time: F2,424.62 = 137.3, p < 2.0 x 10−16 Group: F2,222.84 = 0.69, p = 0.505 T x G: F4,424.60 = 1.013, p = 0.400 Suspiciousness (RGPTS) 10.29 (11.09) 10.89 (10.58) 10.71 (13.26) 15.64 (16.20) 15.62 (16.76) 14.34 (16.23) 17.83 (17.21) 17.16 (16.23) 17.81 (18.08) Time: F2,415.67 = 0.08, p = 0.920 Group: F2,217.37 = 5.53, p = 0.005 T x G: F4,415.66 = 0.58, p = 0.676 ASI Alcohol+ Time: Wald χ2(2) = 5.21, p = 0.074 Group: Wald χ2(2) = 4.73, p = 0.094 T X G: Wald χ2(4) = 5.52, p = 0.24 None 69.4 61.1 65.2 50.7 46.4 58.3 64.9 64.9 65.8 Occasional 26.4 31.9 25.8 33.3 37.7 25.0 22.1 24.7 24.7 Frequent 4.2 7.0 9.1 16.0 15.9 16.7 13.0 10.4 9.6 ASI Cannabis+ Time: Wald χ2(2) = 8.50, p = 0.014 Group: Wald χ2(2) = 17.88, p < 0.001 T X G: Wald χ2(4) = 14.85, p = 0.005 None 91.7 93.1 95.5 65.2 59.4 76.7 74.0 81.8 80.8 Occasional 4.2 2.8 0.0 8.7 11.6 1.7 10.4 7.8 9.6 Frequent 4.1 4.1 4.5 26.1 29.0 21.7 15.6 10.4 9.6 Motivation (MAPS-SR) – 39.64 (12.52) 38.89 (13.77) – 32.48 (14.13) 35.90 (12.66) – 34.83 (14.51) 36.48 (13.31) Time: F1,200.25 = 2.65, p = 0.105 Group: F2,214.72 = 3.68, p = 0.027 T x G: F2,200.23 = 1.61, p = 0.202 Loneliness (ULS) 14.74 (12.71) 15.54 (11.43) 14.61 (13.87) 23.68 (15.45) 27.05 (16.18) 21.47 (15.39) 22.73 (15.46) 24.52 (14.99) 20.52 (15.48) Time: F2,418.03 = 11.39, p = 1.53 x 10−5 Group: F2,219.81 = 10.7, p = 3.74 x 10−5 T x G: F4,418.02 = 0.98, p = 0.419 Fear of Illness (FIVE) – 77.68 (19.96) 74.56 (19.14) – 73.39 (17.85) 72.16 (20.00) – 72.66 (17.19) 68.84 (15.64) Time: F1,200.30 = 6.11, p = 0.014 Group: F2,215.351 = 1.58, p = 0.207 T x G: F2,200.27 = 0.43, p = 0.654 ASI = Addiction Severity Index; + Percent participants reporting use in past 30 days for the categories of None, Moderate (1–8 days), and Severe (9+ days). Fig. 1 Raw means and ± 1 standard error bars for clinical factors assessed pre-COVID (light purple), initial (aqua), and follow up (blue) for control Veterans (CTL), Veterans with psychosis (PSY), and recently-housed Veterans (RHV). A) Anxiety symptoms (GAD7); B) Depression symptoms (PHQ9); C) loneliness (ULS); and D) obsessive-compulsive behaviors (DOCS). For all ratings a higher score indicates increased pathology. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Fig. 1 For suspiciousness, there was a significant main effect of Group in that both RHV and PSY had higher levels of suspiciousness compared to CTL, though this effect did not survive FDR correction. The main effect of Time and the Group × Time interaction were not significant. For OCD-like features, there was a significant main effect of Time (Fig. 1C). The Group main effect and the Group × Time interaction were not significant. Follow-up tests for the Time main effect revealed significant differences between each pair of timepoints (p's < 0.001), with OCD-like features highest at the initial assessment. For Motivation and Pleasure, the Group main effect was significant, in that CTL reported significantly greater levels compared to RHV but not PSY; the main effect of Time and the Group × Time interaction were not significant. For Fear of Illness, the Time main effect was significant, with a small but significant decrease from initial to follow up; the main effect of Group and the Group × Time interaction were not significant. For loneliness, there were significant main effects of Time and Group (Fig. 1D). The Group × Time interaction was not significant. Follow-up tests for the Time main effect revealed significant differences between each pair of timepoints (p's < 0.05), with loneliness highest at the initial assessment. Follow-up tests for the Group main effect revealed significantly higher loneliness in RHV and PSY compared to CTL (p's < 0.0005), with no significant difference between RHV and PSY. Regarding the MADRS, most participants endorsed no or mild thoughts of suicidal ideation at initial or follow up (>85% in all three groups had ratings of 0 or 1). Only five participants at initial (1 CTL, 1 RHV, 3 PSY) and one participant at follow up (1 PSY) had a score of 4 indicating moderate levels of suicidal ideation. No participant in any group had a rating higher than 4. For the ASI we assigned participants to one of three severity categories based on inspection of the distribution of reported days used: None (0 days), Occasional (1–8 days), and Frequent (9 or more days). For alcohol use, there were no significant main effects of Time or Group, and the Group × Time interaction was not significant. For cannabis use, there was a significant main effect of Group, Wald χ2 (2) = 17.88, p < 0.001, and a significant main effect of Time, Wald χ2 (2) = 8.50, p = 0.014. However, there was a significant Group × Time interaction, Wald χ2 (4) = 14.85, p = 0.005. The significant interaction was driven by the RHV reporting a significant increase in cannabis use at Initial relative to pre-COVID; there were no significant changes in the PSY or CTL groups. Age was a significant covariate for only two measures: anxiety and motivation. Adding age as a covariate only changed the significance of one result, compared to the models that did not include an age covariate: for anxiety, the Group main effect changed from significant (p = 0.037) to a trend (p = 0.095). Adding age as a covariate did not change the significance of any other main effects or interactions. All results of the models including age covariates can be found in the Supplement. 3.3 Community integration For community integration measures, descriptive statistics as well as inferential statistics and p-values are reported in Table 4 . The Lubben Social Network Scale showed a significant main effect of Group; CTL reported significantly higher levels of social networks compared to either the RHV or PSY (p's < 0.02), with no significant differences between the latter two groups. The main effect of Time and the Time × Group interaction were not significant. For Social Network functioning from the RFS, there was a significant main effect of Group; CTL reported significantly higher social network scores compared to both the RHV and PSY groups (p's < 0.0005), with no significant differences between the latter two groups (Fig. 2 A). The Time main effect and Group × Time interaction were not significant. For Family functioning from the RFS, there was a significant main effect of Group (Fig. 2B); all three groups differed from each other significantly (p's < 0.02), with functioning highest in CTL, lowest in PSY, and intermediate in RHV. The main effect of Time and Group × Time interaction were not significant.Table 4 Descriptive statistics and statistical test results for Community Integration factors. Values are raw means and standard deviations. Table 4 Control Recently Housed Psychosis Statistics Pre Initial Follow Up Pre Initial Follow Up Pre Initial Follow Up Social Networks (Lubben) 31.25 (10.29) 31.07 (10.17) 31.11 (9.90) 26.52 (11.34) 26.28 (11.65) 27.34 (10.66) 26.19 (13.04) 25.91 (13.06) 25.15 (13.46) Time: F2,411.86 = 0.23, p = 0.795 Group: F2,215.76 = 5.82, p = 0.003 T x G: F4,411.86 = 1.61, p = 0.172 Role Functioning Work 5.56 (2.33) 5.17 (2.56) 5.08 (2.58) 2.65 (2.24) 2.52 (2.07) 2.68 (2.13) 2.58 (2.14) 2.51 (2.08) 2.36 (2.01) Time: F2,358.73 = 1.75, p = 0.175 Group: F2,195.99 = 36.00, p = 4.65 x 10−7 T x G: F4,358.71 = 1.27, p = 0.281 Independent Living 6.49 (1.06) 6.50 (0.92) 6.50 (1.04) 5.43 (1.54) 5.72 (1.28) 6.10 (1.15) 5.49 (1.42) 5.37 (1.47) 5.45 (1.47) Time: F2,412.25 = 7.26, p = 7.98 x 10−4 Group: F2,216.49 = 15.61, p = 4.65 x 10−7 T x G: F4,412.25 = 6.35, p = 5.77 x 10−5 Family 6.07 (1.23) 6.06 (1.15) 6.00 (1.26) 5.04 (1.62) 5.19 (1.68) 5.18 (1.76) 4.42 (1.78) 4.45 (1.82) 4.63 (1.82) Time: F2,412.16 = 0.88, p = 0.417 Group: F2,216.34 = 20.58, p = 6.56 x 10−9 T x G: F4,412.16 = 1.00, p = 0.407 Social 5.93 (1.48) 5.81 (1.39) 5.86 (1.32) 5.01 (1.68) 4.82 (1.77) 4.69 (1.79) 4.51 (1.81) 4.45 (1.80) 4.59 (1.90) Time: F2,411.07 = 1.85, p = 0.159 Group: F2,215.54 = 15.51, p = 5.09 x 10−7 T x G: F4,411.07 = 0.46, p = 0.767 Fig. 2 Raw means and ± 1 standard error bars for community integration factors assessed with the Role Functioning Scale (RFS) pre-COVID (light yellow), initial (light orange), and follow up (dark orange) for control Veterans (CTL), Veterans with psychosis (PSY), and recently-housed Veterans (RHV). A) Family network functioning (RFS Family); B) Social network functioning (RFS Social); C) Work functioning (RFS Work); and D) Independent living (RFS Independent Living). For all ratings a higher score indicates better functioning. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Fig. 2 Work functioning showed a significant main effect of Group (Fig. 2C); CTL had significantly greater work functioning than both RHV and PSY (p's < 0.001), but there was no significant difference between RHV or PSY. There was no significant effect of Time nor a Group × Time interaction. For independent living, there were significant main effects of Time and Group, and a significant Group × Time interaction (Fig. 2D). The interaction was due to a small but significant increase in independent living ratings in RHV from pre-COVID to initial to follow-up (p's < 0.015), with no significant changes in CTL or PSY. Exploratory models that included age as a covariate showed broadly consistent results to the primary models. Age was a significant covariate for only one measure, social functioning from the RFS, but the age covariate did not change the significance of any main effects or interactions found using the primary model that did not include age. The results of all models including age covariates are described in the Supplement. 4 Discussion In the current study, we found evidence of negative mental health impacts related to the COVID-19 pandemic on Veterans from two vulnerable groups (i.e., Veterans with psychosis, recently housed Veterans), as well as Veterans from a control group. Consistent with our hypotheses, all three groups reported increased levels of depression, anxiety, loneliness, and concerns about contamination relative to the retrospective report of their pre-COVID state. However, contrary to our hypotheses, we did not observe a disproportionate worsening of clinical symptoms in the two vulnerable Veteran groups compared to the Veteran controls. On the contrary, we found that for depression and anxiety both the PSY and RHV groups showed significant decreases from initial to follow up, whereas CTL maintained higher levels at both time periods relative to pre-COVID. The mental health effects in the current study are consistent with published reports in non-Veteran populations (e.g., Jewell et al., 2020). While this finding was unexpected it is consistent with previous research showing that humans are quite resilient and often do not show increased psychopathology after natural disasters, including pandemics (Pfefferbaum and North, 2020). Further, there is evidence that adversity in vulnerable Veteran groups might actually result in improvements in mental health outcomes and general health (Tsai et al., 2015). In addition, we found no increased use of alcohol in any groups, and only the RHV showed an increase in cannabis use. As expected, community integration (strength of family and social networks, work, independent living) was lower overall in the two vulnerable groups, however, the pandemic did not have a noticeable effect in these areas in any group. Moreover, we did not find that age influenced any of the findings in any substantial way. Finally, we did not detect any signs of imminent suicide risk. There are some potential reasons for the lack of disproportionate effect on vulnerable Veterans. First, our sample was older and consisted of more males than most published reports to date. Some studies have found a disproportionate mental health impacts in the domains assessed in the current study among younger or female participants relative to older or male participants (Barros et al., 2020; Groarke et al., 2020; Jia et al., 2020). Second, it is likely that many of the vulnerable Veterans in the current study were engaged with mental health and case management services available to them through the VA. The VA very rapidly adapted telehealth service in homeless, mental health, and medical services when in-person visits became difficult due to the pandemic (Connolly et al., 2020; Heyworth et al., 2020). As we collect additional follow-up data, we will examine the role of specific types of VA mental health services and their role in mitigating negative effects of the pandemic. Finally, population-specific factors may have led to improvements in functioning and symptoms in the two vulnerable groups relative to the control group. For example, it is possible that those with psychosis may already prefer to spend more time indoors or avoid social situations due to social impairments in this population. Thus, they may have been less impacted over time by social distancing measures. For the recently housed group, their levels of symptoms may have been mitigated by the fact that they have less anxiety and depression over time that are associated with being housed after a period of homelessness. While these are intriguing possibilities, we unfortunately did not directly assess for these possibilities. The study had several limitations. First, the study relied on retrospective self-reports of functioning prior to the onset of the pandemic for some measures. While the participants might not have been able to estimate a particular point in time from a few months prior, their responses are an indication of their impression of their own functioning pre-COVID, and thus could serve as a comparison to the follow-up assessment. However, as with other self-report measures there are biases that may affect the validity of the data, including poor recall and any cognitive impairments that can influence the participant's reporting. Thus, it is possible that the vulnerable participants, many of whom have cognitive impairments and past or present substance use issues, provided somewhat biased estimates of their pre-COVID mental health. However, if this were the case, we would expect to see similar biases on both the clinical and community integration measures. Instead, we found that the community integration measures were relatively unchanged for the current vs. pre-COVID assessment period. Second, all of the participants were Veterans so we cannot make comparisons to the general population. Third, as mentioned earlier, nearly all participants were male, making it impossible to examine for any possible gender effects. Finally, Veterans with a history of homelessness may be at greater risk of testing positive for COVID-19 (Tsai et al., in press), but we were not able to examine this possibility in our data because very few participants reported having been diagnosed with COVID-19 (<3% at initial, none at follow-up) and few reported that they needed to self-quarantine (<20% at initial and <9% at follow up). Given that we had too few participants directly impacted by COVID-19 (diagnosed or needing to self-quarantine) to examine for these possibilities, we are unable to determine the direct impact of COVID-19 on the clinical factors we examined. Despite these limitations, the current results suggest a negative impact of the pandemic on mental health and daily functioning in all three groups of Veterans we examined. However, the “vulnerable” groups (PSY and RHV) actually showed more resilience in their mental health than non-vulnerable control Veterans in the follow-up period. Nevertheless, the COVID-19 pandemic continues, and infection rates across much of the United States have worsened in late 2020 and early 2021. Going forward, it will be important to understand how mental health and community integration in these groups may continue to change as the pandemic wears on over an extended period of time. We are continuing to conduct follow-up interviews with the same participants and can examine in future papers how measures of vulnerability and protective factors (e.g., resilience, perceived stress, coping skills, etc.) we assessed moderated the effects of the pandemic on mental health and community functioning we found in the vulnerable groups in the current study. CRediT authorship contribution statement Jonathan K. Wynn: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing, Supervision. Amanda McCleery: Conceptualization, Methodology, Writing – review & editing, Supervision, Project administration. Derek Novacek: Methodology, Writing – review & editing, Investigation. Eric A. Reavis: Conceptualization, Methodology, Writing – review & editing. Jack Tsai: Conceptualization, Methodology, Writing – review & editing. Michael F. Green: Conceptualization, Methodology, Writing – review & editing, Funding acquisition. Declaration of competing interest None of the authors report any conflicts of interest for this publication. Funding statement This study was funded by the Research Enhancement Award Program to Enhance Community Integration in Homeless Veterans Rehabilitation Research and Development grant D1875-F from the Department of Veterans Affairs to Dr. Green and by the VA National Center on Homelessness Among Veterans. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Acknowledgments We extend our gratitude to our recruiters and interviewers without whom this work would not have been possible: Lauren Catalano, PhD, Gerard De Vera, Arpi Hasratian, Julio Iglesias, Brian Ilagan, Mark McGee, Jessica McGovern, PhD, Ana Ceci Myers, Megan Olsen, and Michelle Torreliza. We would like to thank Catherine Sugar, Ph.D. for statistical consultation. Finally, we thank our Veteran volunteers for taking the time to participate in this research. The contents of this article do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpsychires.2021.03.051. ==== Refs References Abba-Aji A. Li D. Hrabok M. Shalaby R. Gusnowski A. Vuong W. Surood S. Nkire N. Li X.M. Greenshaw A.J. Agyapong V.I.O. 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The impact of infectious disease-related public health emergencies on suicide, suicidal behavior, and suicidal thoughts Crisis 2020 1 14

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==== Front Environ Res Environ Res Environmental Research 0013-9351 1096-0953 Elsevier Inc. S0013-9351(21)00670-8 10.1016/j.envres.2021.111376 111376 Article Exploring the predictors of health-protective behavior during the COVID-19 pandemic: A multi-country comparison Jadil Yassine a∗ Ouzir Mounir b a University Mohammed V, Rabat, Morocco b Faculty of Science, University Mohammed V, Rabat, Morocco ∗ Corresponding author. 25 5 2021 8 2021 25 5 2021 199 111376111376 3 3 2021 19 5 2021 19 5 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In recent years, examining the determinants of health behaviors on a multi-country level remains limited. Therefore, the purpose of this study is to explore the key factors that may enhance the adoption of health-protective behaviors during the COVID-19 pandemic in Morocco and India. A theoretical framework derived from the health belief model (HBM) was used for this research. Data was collected from a sample of 444 adult individuals split across Morocco (n = 215) and India (n = 229). Data analysis was carried out using two-stage multiple-analytic techniques. First, structural equation modeling (SEM) was employed to test the hypothesized relationships. Second, an artificial neural network (ANN) model was employed to rank the significant independent variables obtained from SEM analysis. The results of SEM showed that perceived benefit is the key predictor of the protective behavior in Morocco, followed by self-efficacy, and then perceived severity. By contrast, ANN analysis showed that perceived severity was the most vital factor for predicting the protective behavior in Morocco, followed by perceived benefits, and then self-efficacy. For the Indian sample, both SEM analysis and the ANN model revealed that the impact of perceived susceptibility on the adoption of the protective measure is stronger than that of cues to action. Theoretical contributions and managerial implications are also discussed toward the end. Keywords COVID-19 Protective behavior Health belief model Structural equation modeling Artificial neural network ==== Body pmc1 Introduction The coronavirus disease 2019, commonly known as COVID-19, is a zoonotic disease, transmitted initially from animals to humans and caused by the virus SARS-CoV-2 (WHO, 2020a, 2020b). The person-to-person COVID-19 transmission occurs directly through coughing or sneezing and indirectly through contaminated surfaces or objects (WHO, 2020c). As of December 31, 2019, the first case of COVID-19 in the world was reported (WHO, 2020d). The outbreak rapidly propagated around the world and was declared on the 30th of January of 2020 as an international public health threat, by which time the vaccine against COVID-19 was unavailable (WHO, 2020e). Accordingly, the adoption of health measures was widely recommended by the WHO to control the global pandemic (WHO, 2020f). A thorough analysis of the scientific literature suggests that health-protective behaviors against respiratory infections encompass three different types: (1) preventive behaviors such as hand-washing, mask-wearing, and vaccine uptake, (2) avoidant behaviors such as social distancing and avoidance of crowds, and (3) management of illness behaviors such as taking antiviral medications (Gutiérrez-Doña et al., 2012; Karademas et al., 2013; Kim et al., 2015; Wang et al., 2016). An examination of existing works also reveals that considerable effort has been invested to further the current knowledge on the behavioral determinants that influence adherence to protective measures. In particular, perceived benefits (Liao et al., 2011a; Gaygısız et al., 2012), anticipated regret (Liao et al., 2011b; Penţa et al., 2020), knowledge (Liao et al., 2011a; Zottarelli et al., 2012), self-efficacy (Payaprom et al., 2011; Prue et al., 2019), subjective norm (Yardley et al., 2011; Ng et al., 2020), perceived susceptibility (Zottarelli et al., 2012; Liao et al., 2013), cues to action (Ho et al., 2013; Zhang et al., 2015), attitude (Ahmad et al., 2020; Bae and Chang, 2020), trust (Yang et al., 2014; D'Antoni et al., 2019), and perceived severity (Lee and You, 2020; Penţa et al., 2020) were among the most salient factors in shaping the adoption of health recommendations. Extant studies have applied several social cognition models in various health-protective actions. For example, the health belief model (HBM) has been applied for explaining the antecedents of vaccine uptake (Penţa et al., 2020), hand hygiene, and social distancing (Zottarelli et al., 2012). The existing empirical studies are focused on explaining the compliance with health preventive measures in the context of a variety of respiratory infectious diseases such as seasonal flu (Yardley et al., 2011), MERS (Yoo et al., 2016), H1N1 (Payaprom et al., 2011), H5N1 (Liao et al., 2011a), and COVID-19 (Ahmad et al., 2020). Although findings from the existing empirical studies may provide valuable insights to manage future respiratory epidemics, these studies also show some boundaries. First, most studies of behavioral response to infectious disease were conducted in a single country, including Germany (Reuter and Renner, 2011), Thailand (Payaprom et al., 2011), the United States (Zottarelli et al., 2012), Hong Kong (Liao et al., 2011a), and the United Kingdom (Yardley et al., 2011). Yet, examining the determinants of health behaviors on a multi-country level remains limited. To our knowledge, no study has compared how the determinants of health-related behaviors can differ in the context of Morocco and India. Thus, the first aim of this study was to compare the adoption of avoidant protective measures among Moroccan and Indian adults. Second, some studies have used only one single data analysis tool to validate the hypothesized models. These include, for example, structural equation modeling (Liao et al., 2013; Yoo et al., 2016; Ng et al., 2020) or multiple regression analysis (Wang et al., 2016; D'Antoni et al., 2019; Penţa et al., 2020). This is problematic because these studies might not assess non-linear associations between preventive measures adoption and predictor constructs. To our knowledge, there have been relatively few attempts in behavioral medicine literature to use a two-stage multi-analytical approach combining SEM with ANN. Hence, the second aim of this study is to apply a two-staged SEM and ANN modeling technique to examine both linear and non-linear relationships among HBM variables. The rest of this study is arranged as follows. Section 2 provides a brief overview of the HBM and a review of existing studies on health preventive behaviors. Section 3 presents the theoretical framework and develops the research hypothesis. Section 4 explains the data collection procedure, describes the study instruments, and illustrates the two-stage SEM-ANN approach. Section 5 outlines the characteristics of respondents and reports the outcomes of SEM-ANN modeling. Section 6 outlines a discussion of the results, reports the implications of the research findings, elucidates the boundaries of this study, and suggests directions for future research. Section 7 concludes this study by summarizing the results. 2 Theoretical background and related work In recent years, investigating the determinants of health-related behaviors during the outbreak of infectious respiratory diseases has been the focus of the scientific literature in behavioral medicine (Gutiérrez-Doña et al., 2012; Zottarelli et al., 2012). A variety of health behavior theories have been widely tested to investigate the essential elements affecting the behavioral response to health-protective actions. Prominent among these precautionary measures are vaccination uptake (Liao et al., 2011b), covering mouth when sneezing (Liao et al., 2011a), hand hygiene (Reuter and Renner, 2011), home confinement (Teasdale et al., 2012), mask-wearing (Yoo et al., 2016), participating in sports activities (Chirico et al., 2020), and safer traveling (Han et al., 2020). In this work, the HBM has been adopted as the conceptual framework to examine the determinants leading to the adoption of health behaviors in the context of the COVID-19 pandemic in two countries (i.e., Morocco and India). HBM is one of the most popular frameworks in predicting and explaining health-related behaviors (Penţa et al., 2020). This belief-based model postulates that health prevention behaviors are predicted by six behavioral determinants: perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (Rosenstock et al., 1988). HBM has been used to examine health-related behavior in various infectious respiratory epidemics (Zottarelli et al., 2012; Bae and Chang, 2020; Penţa et al., 2020). In a longitudinal study, Penţa et al. (2020) extended the applicability of the HBM to understand factors that predict vaccination intention among 401 young adults in Romania. Results suggested that anticipated regret, perceived benefits, and perceived susceptibility to being affected by seasonal flu were positively and significantly associated with the individuals' intention to vaccine against seasonal flu. They also demonstrated that the modified HBM explained 60% of the variance in vaccination intention. A cross-sectional study carried out during the 2009 H1N1 outbreak in the United States includes past behavior and knowledge in the HBM (Zottarelli et al., 2012). Data collected from 909 students showed that knowledge, perceived susceptibility, past behavior, and perceived threat were all significant determinants of the student's adherence to non-pharmaceutical interventions. The study also concluded that the extended version of HBM predicts over 17% of behavior-related preventive measures. In the context of COVID-19 in South Korea, an empirical research by Bae and Chang (2020) draws inspiration from the HBM in conjunction with supplemental factors of the TPB such as attitude, subjective norm, and perceived behavioral control. By applying the SEM approach the authors affirmed that perceived susceptibility is a fundamental variable in boosting subjective norm and intention towards untact tourism. Findings of this study also evinced that worry has a significant impact in the development of attitude towards untact tourism. 3 Research model and hypotheses development The theoretical framework of this study is derived from the HBM (Rosenstock et al., 1988) and it consists of six latent variables. More specifically, perceived severity, perceived susceptibility, perceived benefits, cues to action, and self-efficacy were used as the exogenous constructs in the structural model. On the other hand, avoidant protective behavior was treated as the only endogenous construct in this model. For simplicity, the five hypothetical relationships between dependant and independent latent variables of the research model are represented visually in Fig. 1 .Fig. 1 Research model (Adapted from Rosenstock et al. (1988)). Fig. 1 3.1 Perceived severity Perceived severity is often described as the individual's subjective assessment of the seriousness of a given illness (Penţa et al., 2020). The idea that the perceived severity of the disease has a positive impact on determining the preventive measures during pandemics has received repeated support from extant empirical studies. For example, Lee and You (2020) evaluated a health behavior model to predict which factors drive the adoption of preventative actions in South Korea. Results from multivariate linear regression revealed that perceived severity was linked to higher intention to engage in protective behaviors during the COVID-19 pandemic. Prue et al. (2019) in their work demonstrated that individuals with a high belief in the seriousness of the Ebola virus are more likely to comply with the protective recommended behaviors in the United States. However, a study conducted by Gaygısız et al. (2012) in Turkey suggests that the individuals' perception of possible negative consequences of H1N1 was positively but non-significantly associated with carrying out health-protective measures against the disease. Karademas et al. (2013) also concluded that the individual's belief in the severity of H1N1 influenza is a positive but not significant predictor of performing the protective health behaviors in Greece. Thereby, the above inconsistency in the existing empirical evidence led the present study to hypothesize that:H1 Perceived severity will have a significant and positive impact on the adoption of protective behavior. 3.2 Perceived susceptibility Perceived susceptibility is defined as the extent to which an individual estimates the likelihood and chance of becoming infected with the disease (Ng et al., 2020). The literature related to respiratory infectious disease has suggested that individuals are more likely to comply with health-recommended actions if they feel personally vulnerable to the illness. A survey of individuals in the United States investigating peoples' adoption of protective behaviors indicated that perceived susceptibility was an important factor in predicting health-protective decisions against H1N1 influenza (Zottarelli et al., 2012). Moreover, Liao et al. (2013) reported that the person's perception of the likelihood of getting H1N1 affects positively and significantly the adoption of recommended preventives measures among the adult population in China. Similarly, Wang et al. (2016) suggested that beliefs about the subjective probability of contracting H7N9 infection were positively and significantly related to recommended behaviors against the illness. However, Lee and You (2020) founded in their empirical study within South Korean respondents, that perceived susceptibility to COVID-19 was not significantly associated with hand hygiene, mask wearing, and social distancing practices. Hence, the contradictory results in the literature mentioned above leads to propose the following hypothesis for investigation:H2 Perceived susceptibility will have a significant and positive impact on the adoption of protective behavior. 3.3 Perceived benefits Perceived benefits refer to the degree to which individuals believe that adopting the recommended preventive measures will protect them from contracting the illness (D'Antoni et al., 2019). Scholars in psychological responses to infectious disease suggest that perceived benefits to be a crucial predictive element of preventive measures adherence. For instance, Liao et al. (2011a) revealed that in addition to the relationship between H5N1-related knowledge about the disease and the adoption of the recommended protective behaviors, beliefs about the benefits of prevention actions increase individual's adherence to preventive recommendations against H5N1 influenza. Moreover, an investigation conducted by Gaygısız et al. (2012) across Turkey in the context of the H1N1 influenza pandemic concluded that adherence to preventive measures was influenced by the positive consequences of the recommended protective behavior. Penţa et al. (2020) examined the determinants of vaccination in the influenza virus outbreak in Romania. Results suggested that perceptions of benefits alongside the anticipated regret were significant determinants of the likelihood of protective behavior. In contrast, findings from a recent study conducted by Harris and Armién (2020) in a national sample from Panama indicated that the individual's beliefs in the effectiveness of the health recommendation were positively but non-significantly associated with adopting preventive measures. To further explore these contradictions in past studies, the current research hypothesizes that:H3 Perceived benefits will have a significant and positive impact on the adoption of protective behavior. 3.4 Cues to action Cues to action refer to formal or informal health education that might trigger carrying out preventive measures, such as exposure to information obtained from traditional media, social media, or health-related communication with friends, family, doctors, and coworkers (Ho, 2012; Li and Cao, 2019). The construct of cues to action has been demonstrated to be an important determinant of health-protective actions in a health emergency. According to a study developed in the context of the H1N1 epidemic in Singapore (Ho et al., 2013), a higher level of interpersonal discussion on H1N1 triggers more adoption of recommended preventive measures. Similarly, Zhang et al. (2015) assume a positive association between cues to action and protective behaviors against infectious diseases. These authors suggest that exposure to prevention information against H1N1 on mass media leads college students in the United States to undertake precautionary behaviors. Along this line, Yoo et al. (2016) showed that receiving information from social network sites that promote awareness about the MERS infectious disease enhances the individual's intention to comply with handwashing and mask-wearing practices in South Korea. However, Harris and Armién (2020) recently indicated that exposure to messages from physicians, government, and leaders was positively but non-significantly associated with the adherence to prevention measures in Panama. The above divergence among previous work leads to the following assumption:H4 Cues to action will have a significant and positive impact on the adoption of protective behavior. 3.5 Self-efficacy Self-efficacy is defined as the individuals' confidence in their ability to perform the required preventive health measures against a particular illness (Prue et al., 2019). Recent work on health decision-making related to respiratory epidemics indicates self-efficacy as a necessary precursor of protective behaviors. In the context of the H1N1 outbreak, Payaprom et al. (2011), for example, attempted to predict vaccination uptake and revealed that individuals' confidence in their ability to successfully perform the preventive measure is positively associated with the behavioral response to public health recommendation in Thailand. Likewise, Wang et al. (2016) have investigated several antecedents that influence health-protective decisions in response to H7N9 Influenza. Based on data collected from 762 Chinese adults, they propose that self-efficacy is among the prominent factors that encourage increased individuals’ compliance with precautionary behaviors. Moreover, Prue et al. (2019) argued that if travelers in the United States believe that they could perform preventive behaviors, then they will be more likely to comply with protective action recommendations taken by the public authorities. More recently, Ng et al. (2020) assumed that when healthcare workers in Hong Kong believe that they can successfully execute a recommended action, a high degree of adherence to preventive recommendations will be generated. Consistent with the evidence described below, the following is postulated:H5 Self-efficacy will have a significant and positive impact on the adoption of protective behavior. 4 Materiel and methods 4.1 Participants and procedure A cross-sectional online survey was used for data collection in the period between May 08, 2020, and June 26, 2020. During this period, an e-mail invitation was sent to English-speaking, Arabic-speaking, and French-speaking adults who reside in Morocco and India. The rationale of selecting participants from Morocco and India is to assess the individual's beliefs and protective health behaviors during the COVID-19 pandemic across two culturally different countries (see Fig. 2 ). Participation was voluntary and those who responded to the survey received the results of the study at their email address. About 1000 completed the online survey and 444 valid observations were used for data analysis after exclusion of incomplete answers and non-serious responses.Fig. 2 Hofstede cultural differences across Morocco and India (Source: www.hofstede-insights.com). Fig. 2 Before the main data collection, an academic researcher in neuroscience was requested to review the wording of each item and the format of the questionnaire to ensure content validity. Also, a pretest was conducted among a sample of 100 individuals. As a result, the wording of questionnaire measures and response scales has been refined according to the comments and feedback of the expert and respondents. Furthermore, reliability and validity tests were performed for each construct. Following the results, six poor measures have been excluded from the initial measurement model. The final online survey contains four sections. The first section consisted of a filter question asking participants whether they were above 18 years of age. Only those who had answered affirmatively were conducted in the next section. The second section comprised of an informed consent explaining the purpose of the study, the procedures to be followed, and the risks and benefits that can be expected of taking part in the survey. At this point, participants were made aware that they free to withdraw from the survey at any time and that all the data will be completely stored and analyzed anonymously. The third section measures the health belief model's constructs. Finally, the fourth section captures the respondents' characteristics such as gender, age, level of education, health status, etc. 4.2 Measures The reflective indicators were based on validated measures from prior empirical studies in the context of MERS and H1N1 influenza outbreaks and were adjusted to fit the context of the COVID-19 pandemic. Perceived severity of COVID-19 consists of two items on a 5-point Likert scale that going from strongly disagree (1) to strongly agree (5). Manifest variables were adopted from the study conducted by Yoo et al. (2016). Perceived susceptibility to COVID-19 was measured with two items on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Observed variables were derived from the study conducted by Yoo et al. (2016). The construct of perceived benefits was captured by three items on a 5-point Likert scale going from strongly disagree (1) to strongly agree (5). Measurement items were picked from the study conducted by Liao et al. (2011b). The construct of cues to action was assessed with two items on a 5-point Likert scale ranging from no attention (1) to very close attention (5). Measures of cues to action were originally developed by Yang et al. (2014) and Yoo et al. (2016). Self-efficacy of protective behavior was measured with two measures on a 5-point Likert scale going from strongly disagree (1) to strongly agree (5). Indicator variables were drawn from those proposed by Yang et al. (2014). At long last, avoidant protective behavior was measured using a single item (i.e., avoiding crowds during the COVID-19 outbreak) on a 5-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Item measurements were based on the work of Kim et al. (2015). 4.3 SEM-ANN approach Inspired by how the neurons of the human brain are interconnected, the artificial neural network (ANN) is capable of (1) acquiring knowledge through learning (training), and (2) storing that knowledge through interneuron connection strengths (Haykin, 2009). The architecture of a multi-layer ANN operates as follows: three types of layers, namely, input, hidden, and output layers constitute the ANN model. Each layer is made up of artificial neurons called nodes or units (Alam et al., 2020). Every input node has a corresponding synaptic weight of its own that is transferred to all hidden nodes through synaptic links. The hidden nodes then convert the weighted sum of the inputs to some output values with the help of an activation function (Haykin, 2009). In the present study, the model was empirically analyzed using a combination of two techniques: structural equation modeling (SEM) and ANN. SEM is suitable for testing the hypothesized relationships and determining the significant antecedent of the dependant constructs (Hair et al., 2018). One key advantage of using such a model is that besides assessing the measurement relationships between observed and latent variables, it can also help to examine the structural relationships linking the exogenous and endogenous latent variables (Hair et al., 2018). Furthermore, SEM has been widely used in prior research on behavioral medicine, particularly in empirical studies about health-protective behaviors that limit the spread of respiratory infection diseases (Liao et al., 2011b; Gutiérrez-Doña et al., 2012; Teasdale et al., 2012; Ho et al., 2013). Nevertheless, this multivariate technique only can analyze linear associations between endogenous and exogenous variables (Hair et al., 2018). On the other hand, the neural network technique allows us to use linear as well as nonlinear causal relationships between behavioral predictors and the dependant constructs (Haykin, 2009). However, ANN is not suitable for theory testing as it is based on the “black-box” mechanism (IBM SPSS, 2012). Furthermore, it is often difficult for the neural nets to assess the reliability and validity of the constructs. Therefore, to complement each other, a two-stage multi-analytical approach combining SEM with ANN was adopted within the two country datasets of the present study (i.e., Morocco and India). At the primary stage, this research study employed a two-step SEM approach in AMOS 24 to analyze the proposed framework as recommended by Hair et al. (2018). Specifically, the measurement model (CFA model or outer model) was first evaluated, and the structural model (inner model) was then assessed. In the second stage of SEM-ANN analysis, this research applied an ANN technique in IBM SPSS 21 to identify the relative importance of each significant variable obtained by SEM in predicting the avoidant protective behavior (IBM SPSS, 2012). 5 Results 5.1 Characteristics of participants The socio-demographic characteristics of the two subsamples can be found in Table 1 . A total of 444 participants were included in the study, 51.6% of them were Indians (n = 229) and 48.4% were Moroccans (n = 215). The majority of the participants were from an urban area (89.9%) and single (85.1%). The gender distribution was almost balanced: 46.8% were women (n = 208) and 53.2% were men (n = 236). More than half of the participants were aged 18–24 years (61.5%) and lived with 4–6 people in the same household (59.7%). Approximately half of the participants were students (49.5%) with a Master's degree (43.5%). Also, 70.5% of the participants were listed as low-income individuals with less than $ 300 per month. 85.4% of the participants reported having good or very good health conditions. The most common sources to get COVID-19 information were social media (17.5%), followed by TV (16.1%), Web sites (15.3%), and the Ministry of Health (12.8%) (see Table 2 ).Table 1 Socio-demographic characteristics. Table 1Category Whole sample (n = 444) Morocco (n = 215) India (n = 229) n % n % n % Area Urban area 399 89.9 195 90.7 204 89.1 Rural area 45 10.1 20 9.3 25 10.9 Gender Male 236 53.2 101 47.0 135 59.0 Female 208 46.8 114 53.0 94 41.0 Marital status Single 378 85.1 165 76.7 213 93.0 Married 49 11.0 39 18.1 10 4.4 No answer 17 3.8 11 5.1 6 2.6 Household size 1 person (self) 12 2.7 10 4.7 2 0.9 2 people 24 5.4 18 8.4 6 2.6 3 people 65 14.6 30 14.0 35 15.3 4-6 people 265 59.7 125 58.1 140 61.1 7 people and more 67 15.1 29 13.5 38 16.6 No answer 11 2.5 3 1.4 8 3.5 Age group 18–24 years 273 61.5 85 39.5 188 82.1 25–34 years 144 32.4 107 49.8 37 16.2 35–44 years 19 4.3 15 7.0 4 1.7 45–54 years 6 1.4 6 2.8 0 0 55–60 years 2 0.5 2 0.9 0 0 Occupation Student 220 49.5 47 21.9 173 75.5 Job seeker 81 18.2 61 28.4 20 8.7 Employed part time 26 5.9 23 10.7 3 1.3 Employed full time 105 23.6 80 37.2 25 10.9 Self-employed 11 2.5 4 1.9 7 3.1 Retired 1 0.2 0 0 1 0.4 Education High school 39 8.8 12 5.6 27 11.8 Technician degree 27 6.1 23 10.7 4 1.7 Bachelor's degree 151 34.0 49 22.8 102 44.5 Master's degree 193 43.5 114 53.0 79 34.5 Doctorate 10 2.3 9 4.2 1 0.4 Professional degree 24 5.4 8 3.7 16 7.0 Monthly income Below 300 USD 313 70.5 119 55.3 194 84.7 301 - 600 USD 73 16.4 52 24.2 21 9.2 601 - 900 USD 43 9.7 33 15.3 10 4.4 901 - 1200 USD 12 2.7 8 3.7 4 1.7 Above 1200 USD 3 0.7 3 1.4 0 0 Health condition Very bad 1 0.2 1 0.5 0 0 Bad 6 1.4 6 2.8 0 0 Neutral 50 11.3 27 12.6 23 10.0 Good 205 46.2 115 53.5 90 39.3 Very good 174 39.2 61 28.4 113 49.3 No answer 8 1.8 5 2.3 3 1.3 Information source TV 357 16.1 164 17.1 193 15.2 Radio 82 3.7 48 5.0 34 2.7 Newspapers 160 7.2 21 2.2 139 11.0 Web sites 341 15.3 167 17.4 174 13.7 Social media 390 17.5 186 19.4 204 16.1 Doctors 76 3.4 32 3.3 44 3.5 Ministry of Health 284 12.8 138 14.4 146 11.5 Poster and brochure 54 2.4 15 1.6 39 3.1 Friends 239 10.7 95 9.9 144 11.4 Family 241 10.8 92 9.6 149 11.8 Table 2 Measurement model fit statistics. Table 2Fit indexes Cut-off values Whole sample (n = 444) Morocco (n = 215) India (n = 229) Results Remarks Results Remarks Results Remarks CMIN/df <3 1.83 Good 1.65 Good 2.18 Acceptable GFI >0.90 0.97 Good 0.95 Good 0.95 Good RMSEA <0.08 0.04 Good 0.06 Acceptable 0.07 Acceptable RMR <0.10 0.02 Good 0.03 Good 0.02 Good NFI >0.90 0.96 Good 0.93 Acceptable 0.92 Acceptable TLI >0.90 0.97 Good 0.95 Good 0.92 Acceptable CFI >0.90 0.98 Good 0.97 Good 0.95 Acceptable AGFI ≥0.90 0.95 Good 0.90 Good 0.90 Good PCFI >0.50 0.60 Good 0.59 Good 0.58 Good [Legend CMIN/df = Normed Chi-Square; GFI = Goodness-of-Fit Index; RMSEA = Root Mean Square Error of Approximation; RMR = Root Mean Square Residual; NFI = Normed Fit Index; TLI = Tucker Lewis Index; CFI = Comparative Fit Index; AGFI = Adjusted Goodness of Fit Index; PCFI = Parsimony Comparative Fit Index]. 5.2 Measurement model According to Hair et al. (2018), the assessment of the measurement model depends on three conditions (1) testing the measurement model fitness, (2) examining the constructs' reliability, and (3) evaluating the constructs’ validity. According to the results, the measurement model indices provided an acceptable fit of the data for the whole sample, the Moroccan sample, and the Indian sample. As the measurement model used in the study achieved adequate fit indices, the reliability and validity of the constructs should be examined (Hair et al., 2018). 5.2.1 Reliability analysis The constructs' reliability is examined using three criteria: First, evaluating the composite reliability (CR) of each construct. Second, assessing the values of Cronbach's alpha. Third, evaluating the average variance extracted (AVE) estimates. The reliability values for Cronbach's α, CR, and AVE are presented in Table 3 .Table 3 Mean, standard deviation, correlation, reliability, and validity analysis. Table 3Constructs M SD CA CR AVE Inter-correlation matrix 1 2 3 4 5 6 Whole sample (n = 444) 1. Perceived severity 4.09 0.85 0.79 0.70 0.54 0.73 2. Perceived susceptibility 4.57 0.69 0.76 0.81 0.69 0.34 0.83 3. Perceived benefits 4.57 0.59 0.81 0.84 0.64 0.36 0.51 0.80 4. Cues to action 4.22 0.73 0.81 0.74 0.58 0.29 0.27 0.38 0.76 5. Self-efficacy 4.10 0.85 0.72 0.75 0.60 0.29 0.32 0.51 0.19 0.77 6. Protective behavior 4.73 0.57 N/Aa N/Aa N/Aa 0.37 0.43 0.47 0.31 0.39 1.00 Morocco (n = 215) 1. Perceived severity 3.86 0.93 0.82 0.83 0.72 0.85 2. Perceived susceptibility 4.59 0.68 0.71 0.72 0.56 0.35 0.75 3. Perceived benefits 4.65 0.52 0.77 0.77 0.53 0.42 0.62 0.73 4. Cues to action 4.11 0.82 0.84 0.84 0.73 0.14 0.28 0.47 0.85 5. Self-efficacy 4.07 0.91 0.71 0.72 0.57 0.15 0.30 0.37 0.05 0.75 6. Protective behavior 4.74 0.58 N/Aa N/Aa N/Aa 0.36 0.38 0.49 0.28 0.34 1.00 India (n = 229) 1. Perceived severity 4.31 0.69 0.70 0.79 0.65 0.81 2. Perceived susceptibility 4.55 0.69 0.80 0.76 0.61 0.45 0.78 3. Perceived benefits 4.50 0.64 0.83 0.81 0.59 0.46 0.43 0.77 4. Cues to action 4.32 0.61 0.74 0.81 0.68 0.45 0.31 0.38 0.82 5. Self-efficacy 4.12 0.79 0.72 0.73 0.57 0.45 0.37 0.64 0.42 0.76 6. Protective behavior 4.71 0.55 N/Aa N/Aa N/Aa 0.42 0.48 0.45 0.40 0.45 1.00 [Legend M = Mean; SD = Standard deviation; CA = Cronbach's alpha; CR = Composite reliability; AVE = Average variance extracted; the diagonal elements (in bold) represent the square root of AVE; the off-diagonal elements represent the corresponding inter-construct-correlation; N/Aa = Single item does not permit to check for the construct reliability]. As shown in Table 3, CR takes a range of values from 0.70 to 0.84 for the full sample, from 0.72 to 0.84 for the Moroccan sample, and from 0.73 to 0.81 for the Indian sample. According to Hair et al. (2018), the minimum recommended reliability values range from 0.60 to 0.70. Thus, the CR of each construct is achieved. Furthermore, the values of coefficient alpha range from 0.72 to 0.81 for the full sample, from 0.71 to 0.84 for the Moroccan sample, and from 0.70 to 0.83 for the Indian sample. That means Cronbach's α for the six latent variables is either equal to or higher than 0.70 which exceeds the lower limit of acceptability (from 0.60 to 0.70) as advocated by Hair et al. (2018). The values associated with AVE ranged from 0.54 to 0.69 for the full sample, from 0.53 to 0.73 for the Moroccan sample, and from 0.57 to 0.68 for the Indian sample. All AVE exceed the 50 percent rule of thumb. Therefore, the measured indicators of the study are highly interrelated with their respective latent variables, providing evidence of constructs' reliability. 5.2.2 Validity analysis To assess the validity of unobserved latent constructs, both convergent and discriminant validity were checked in the current research. As seen in Fig. 3 , the factor loadings of all un-removable items range from 0.74 to 0.90 for the full sample, from 0.71 to 0.94 for the Moroccan sample, and from 0.71 to 0.90 for the Indian sample. Thus, according to Hair et al. (2018), all factor loadings of the study are higher than the 0.70 rule of thumb. Moreover, the results suggest that all standardized loading estimates of each observed variable are statistically significant at the 0.001 level as recommended by Hair et al. (2018). Thus, the convergent validity of the measurement model for the full sample, the Moroccan, and Indian samples is achieved. Concerning the discriminant validity, the inter-correlation matrix in Table 3 shows that the square root of the AVE of each latent variable (bold diagonal values) was superior to the corresponding inter-constructs-correlation (off-diagonal values) in samples considered in this study. Therefore, it can be said that the CFA model of this study has adequate discriminant validity.Fig. 3 SEM results for (a) the whole sample, (b) the Moroccan sample, and (c) the Indian sample. Fig. 3 5.3 SEM outcomes Fig. 3 and Table 4 presents the SEM analyses for all hypothesized paths. Based on the maximum likelihood estimation (MLE), all the direct hypotheses are supported in the full sample, three out of five direct hypotheses (H1, H3, and H5) are supported in the Moroccan sample, while only two out of five direct hypotheses (H2 and H4) are confirmed in the Indian sample. More specifically, perception in the seriousness of COVID-19 was significantly associated with greater compliance with protective measure for Moroccan respondents (H1: β = 0.183; t = 2.413; p < 0.05) but not for Indian respondents (H1: β = 0.100; t = 1.103; p > 0.05). Moreover, perceived susceptibility to COVID-19 was significantly associated with the adoption of the protective avoidant behavior in India (H2: β = 0.269; t = 3.446; p < 0.001) but not for the Moroccan sample (H2: β = 0.073; t = 0.727; p > 0.05). Furthermore, perceived benefits were significantly associated with protective avoidant behavior for Moroccan respondents (H3: β = 0.253; t = 2.074; p < 0.05) but not for Indian respondents (H3: β = 0.127; t = 1.369; p > 0.05). In addition, cues to action was associated with an increase in the adoption of the protective measure for Indian respondents (H4: β = 0.162; t = 2.013; p < 0.05), but not for Moroccan respondents (H4: β = 0.100; t = 1.292; p > 0.05). Self-efficacy was positively and significantly related to the protective avoidant behavior for Moroccan respondents (H5: β = 0.193; t = 2.342; p < 0.05) but not for Indian respondents (H5: β = 0.155; t = 1.591; p > 0.05). As shown in Table 4 and Fig. 3, the HBM constructs contribute jointly to explain 33.1% of the variance in the adoption of the protective measure for the whole sample, 31.1% for Moroccan respondents, and 36.1% for Indian respondents.Table 4 Hypothesis testing results. Table 4Hypothesized path Whole sample (n = 444) Morocco (n = 215) India (n = 229) β Supported? β Supported? β Supported? H1 PSE ➔ PRB 0.148** Yes 0.183* Yes 0.100ns No H2 PSU ➔ PRB 0.207*** Yes 0.073ns No 0.269*** Yes H3 PBE ➔ PRB 0.178** Yes 0.253* Yes 0.127ns No H4 CTA ➔ PRB 0.114* Yes 0.100ns No 0.162* Yes H5 SEF ➔ PRB 0.172** Yes 0.193* Yes 0.155ns No R-squared 33.1% 31.1% 36.1% [Legend β = Standardized path coefficient; ns = not significant; *p<0.05; **p<0.01; ***p<0.001; PSE = Perceived severity; PSU = Perceived susceptibility; PBE = Perceived benefits; CTA = Cues to action; SEF = Self-efficacy; PRB = Protective behavior]. 5.4 ANN results Following the suggestions by Alam et al. (2020), a multi-layer perceptron (MLP) method was employed in the present research to train and predict the neural network model as can be seen in Fig. 4 , Fig. 5 , and Fig. 6 . Similar to a previous study by Chong (2013), the significant results in SEM analysis (see Table 4) were given as inputs in the three ANN models to overcome the overfitting of the ANN. Specifically, the ANN model of the full sample has five input nodes (perceived severity, perceived susceptibility, perceived benefits, cues to action, and self-efficacy). The ANN model of the Moroccan sample comprises three input nodes (perceived severity, perceived benefits, and self-efficacy). The ANN model of the Indian sample has two input nodes (perceived susceptibility and cues to action). As stated by Chong (2013), the output layer consisting of one output node for the three ANN models was represented by the dependent variable namely avoidant health behavior. The three neural network models considered in the present study have one hidden layer that intervenes between the input layer and the output layer (Liébana-Cabanillas et al., 2018).Fig. 4 MLP neural network for the whole sample. Fig. 4 Fig. 5 MLP neural network for the Moroccan sample. Fig. 5 Fig. 6 MLP neural network for the Indian sample. Fig. 6 5.4.1 Validation of ANN Table 5 summarizes the results of the neural network validation for the full sample, the Moroccan sample, and the Indian sample. Similar to the approach by Talukder et al. (2020), this research used seventy percent of data sets to train the NN model and thirty percent of data sets to test the trained NN model. In this study, the sigmoid function was applied as the activation function associated with both the hidden layer and output layer of the ANN (Liébana-Cabanillas et al., 2018). To measure the accuracy of the ANN model, the Root Mean Square Error (RMSE) is computed using the formula RMSE=MSE=SSEn where MSE is the mean square of errors, SSE is the sum square of errors, and n refers to the sample size for both training and testing data points. To properly address the ANN overfitting issue, the RMSE values were obtained from a 10-fold cross-validation procedure (Liébana-Cabanillas et al., 2018). Following the suggestions by Chong (2013), a number of hidden nodes varying from 1 to 10 was used to examine the neural networks. In the training model, the average RMSE values for the full sample was 0.105, for the Moroccan sample was 0.110, while for the Indian sample was 0.152. In the testing model, the mean values for RMSE for the full sample was 0.103, for the Moroccan sample was 0.108, while for the Indian sample was 0.159. Therefore, it can be concluded that the ANN employed in the present study has a high predictive precision since the RMSE values of the three samples were quite small.Table 5 Neural networks validation. Table 5Hidden Nodes Whole sample (n = 444) Morocco (n = 215) India (n = 229) Inputs: PSE. PSU. PBE. CTA. SEF Inputs: PSE. PBE. SEF Inputs: PSU. CTA Output: PRB Output: PRB Output: PRB Training Testing Training Testing Training Testing 1 0.108 0.100 0.110 0.115 0.149 0.171 2 0.100 0.111 0.103 0.122 0.150 0.161 3 0.101 0.120 0.110 0.116 0.126 0.194 4 0.109 0.090 0.118 0.087 0.172 0.112 5 0.106 0.102 0.109 0.102 0.150 0.166 6 0.103 0.113 0.103 0.126 0.162 0.141 7 0.105 0.097 0.117 0.095 0.158 0.144 8 0.104 0.102 0.111 0.104 0.138 0.190 9 0.102 0.103 0.107 0.114 0.158 0.144 10 0.111 0.093 0.116 0.103 0.154 0.165 Average RMSE 0.105 0.103 0.110 0.108 0.152 0.159 SD 0.003 0.009 0.005 0.012 0.012 0.023 [Legend RMSE = Root Mean Square Error; SD = Standard deviation; PSE = Perceived severity; PSU = Perceived susceptibility; PBE = Perceived benefits; CTA = Cues to action; SEF = Self-efficacy; PRB = Protective behavior]. To gain more insights, the coefficient of determination (R2) was then calculated by using the formula R2=1−RMSESSE. Hence, the ANN developed in this research has been able to predict approximately a variance of 93%, 86.1%, and 91.3% in avoidant protective behavior for the full sample, Moroccan sample, and the Indian sample, respectively. 5.4.2 Sensitivity analysis The results of the sensitivity analysis for the three samples are displayed in Table 6 . The purpose of performing a sensitivity analysis was to identify the most important input variables in predicting the output variable. The normalized importance, which is presented as percentages, is the ratio of the average importance of each independent variable to the highest importance value of the predictor (IBM SPSS, 2012). In this study, the sensitivity for the full sample was found to be 100% for perceived benefits, 79% for self-efficacy, 74% for perceived susceptibility, 59% for perceived severity, and 50% for cues to action. For the Moroccan sample, sensitivity was found to be 100% for perceived severity, 99% perceived benefits, and 51% self-efficacy. For the Indian sample, sensitivity was found to be 100% for perceived susceptibility and 79% for cues to action (see Table 6).Table 6 Neural networks sensitivity analysis. Table 6Hidden Nodes Whole sample (n = 444) Morocco (n = 215) India (n = 229) PBE SEF PSU PSE CTA PSE PBE SEF PSU CTA 1 0.225 0.166 0.182 0.249 0.179 0.376 0.377 0.247 0.587 0.413 2 0.294 0.241 0.217 0.154 0.094 0.441 0.372 0.186 0.533 0.467 3 0.257 0.171 0.278 0.097 0.198 0.363 0.329 0.308 0.490 0.510 4 0.275 0.195 0.161 0.262 0.108 0.577 0.169 0.254 0.439 0.561 5 0.298 0.326 0.161 0.089 0.125 0.254 0.555 0.191 0.596 0.404 6 0.307 0.239 0.230 0.054 0.171 0.188 0.631 0.181 0.554 0.446 7 0.288 0.241 0.201 0.151 0.120 0.430 0.374 0.196 0.616 0.384 8 0.250 0.210 0.237 0.165 0.138 0.385 0.471 0.144 0.637 0.363 9 0.297 0.184 0.182 0.213 0.124 0.554 0.295 0.151 0.526 0.474 10 0.272 0.201 0.187 0.210 0.131 0.431 0.388 0.181 0.594 0.406 AI 0.276 0.217 0.204 0.164 0.139 0.400 0.396 0.204 0.557 0.443 NI 100% 79% 74% 59% 50% 100% 99% 51% 100% 79% [Legend AI = Average importance; NI = Normalized importance; PSE = Perceived severity; PSU = Perceived susceptibility; PBE = Perceived benefits; CTA = Cues to action; SEF = Self-efficacy]. A comparison between the findings obtained from ANN-based models and SEM is presented in Table 7 . For the Moroccan sample, the order of path coefficient from SEM is not the same as the order of normalized importance obtained from the neural network. More specifically, the findings from the neural network model showed that perceived severity (NI = 100%) was the most important predictor of the avoidant protective behavior in Morocco, followed by perceived benefits (NI = 99%), and then self-efficacy (NI = 51%). In contrast, SEM results evidenced that perceived benefits (β = 0.253) is the strongest predictor of the adoption of the avoidant protective measure in Morocco, followed by self-efficacy (β = 0.193), and then perceived severity (β = 0.183). A possible reason behind this difference in the ranking for the Moroccan sample is that ANN can capture linear as well as nonlinear associations between perceived severity, perceived benefits, self-efficacy, and avoidant protective behavior. For the Indian sample, there is no change in the relative importance ranking of the independent variables between SEM and ANN results. Specifically, the NN-sensitivity analysis demonstrated that perceived susceptibility (NI = 100%) is the most influential determinant of the adherence to protective actions in India followed by cues to action (NI = 79%). Similarly, SEM analysis revealed that the adoption of the protective measure in India was most influenced by perceived susceptibility (β = 0.269) followed by cues to action (β = 0.162). Therefore, the key antecedents obtained from SEM results for the Indian sample are supported by the ANN model results.Table 7 SEM and ANN results comparison. Table 7Significant variables SEM ANN Matched? β Ranking NI Ranking Whole sample (n = 444) Perceived susceptibility 0.207 1 74% 3 No Perceived benefits 0.178 2 100% 1 No Self-efficacy 0.172 3 79% 2 No Perceived severity 0.148 4 59% 4 Yes Cues to action 0.114 5 50% 5 Yes Morocco (n = 215) Perceived benefits 0.253 1 99% 2 No Self-efficacy 0.193 2 51% 3 No Perceived severity 0.183 3 100% 1 No India (n = 229) Perceived susceptibility 0.269 1 100% 1 Yes Cues to action 0.162 2 79% 2 Yes [Legend β = Standardized path coefficient; NI = Normalized importance]. 6 Discussion In this study, the percentage of variance in ANN was 93% for the full sample, 86.1% for the Moroccan sample, and 91.3% for the Indian sample. On the other hand, the percentage of variance in SEM was 33.1%, 31.1%, and 36.1% for the full sample, Moroccan sample, and the Indian sample, respectively. Therefore, the ANN performed better than SEM models with respect to the prediction of avoidant protective behavior. As a result, the findings from ANN sensitivity analysis presented in Table 7 were used instead of the findings obtained by SEM to conclude the final ranking among the adoption factors. 6.1 Major findings in Morocco As expected, the ANN results showed that perception of the COVID-19 severity is the most important factor with a significant positive effect on the adoption of protective behavior among Moroccan residents. This is consistent with the findings of studies conducted in the United States and South Korea that support the strong influence of perceived severity on the behavioral responses to health-related recommendations (Prue et al., 2019; Lee and You, 2020). In Morocco, this may imply that the greater individual's assessment of the negative consequences of COVID-19 such as death, leads to a higher level of compliance with the health-protective measures. Furthermore, the finding of the present study revealed that COVID-19 infection-likelihood perception was not related to compliance with the health preventive measure in Morocco. This is in line with the finding of Lee and You (2020) who claimed that perceived susceptibility is not significant in determining the adherence to health practices in South Korea. However, this is in sharp contrast with studies set in China by Liao et al. (2013) and Wang et al. (2016), which identified a significant relationship between beliefs about the likelihood of infection and compliance with the protective measures. With 81.9% of the respondents reported that their health condition is good or very good, it is understandable that Moroccan people might do not believe that the chance of contracting the COVID-19 for oneself and the people around them will motivate them to avoid crowds during the COVID-19 outbreak. In line with findings from the study in Hong Kong by Liao et al. (2011a) and the study in Romania by Penţa et al. (2020), the perceived benefits of the preventive measure was the second major factor with a significant positive impact on the adherence to avoidant protective actions in Morocco. A plausible explanation could be that Moroccan individuals may avoid crowds during the COVID-19 pandemic because they believe in the effectiveness of the recommended protective behavior to help protect themselves, neighbors, family, and friends against the COVID-19. Also, self-efficacy was the third strongest predictor after perceived severity and perceived benefits for Moroccan individuals. This finding is in agreement with the empirical evidence, showing that adherence to preventive measures in Thailand and China is significantly enhanced by self-efficacy (Payaprom et al., 2011; Wang et al., 2016). This may further suggest that Moroccan respondents who believe subjectively that they had the ability to adopt the preventive behaviors against COVID-19 will avoid crowds during the COVID-19 pandemic. Surprisingly, an unexpected result in the current study confirmed that the construct of cues to action has an insignificant impact on the decision to avoid crowds during the COVID-19 pandemic in Morocco. Evidence in support of this non-significant relationship between cues to action and protective behavior can be found in a study conducted in Panama by Harris and Armién (2020). It is possible that although health information on COVID-19 was received ubiquitous coverage in mass media and social media, the attention to these external stimuli may have nothing to do with the adoption of the protective measure among the Moroccan participants in this study. 6.2 Major findings in India The results of this research showed that adherence to avoidant protective behavior in India is not significantly predicted by the perceived severity of COVID-19. This finding concurs with the studies carried out by Gaygısız et al. (2012) and Karademas et al. (2013), which have also established a non-significant relationship between perceived severity and compliance with the health preventive measures in Turkey and Greece. As the present study was conducted in the early phase of the COVID-19 pandemic, one possibility is that even if 79.5% of the Indian participants were university degree holders, they may not have prior knowledge about the ways of handling this new disease. As a result, regardless of how they believe that the COVID-19 to be deadly this may not be necessarily important to comply with the protective measures. Similarly, the compliance with recommended protection measures was not significantly influenced by the perceived benefits, which is in line with a previous study by Harris and Armién (2020), who found that perceived benefits and the adherence with recommended protective actions in Panama were not significantly related. One explanation for this insignificant relationship is that Indian individuals may have not prior experience with the adoption of the protective measure, and thus they are not familiar with the perceived effectiveness of such health recommendations to protect them from getting the COVID-19. Moreover, this study found that self-efficacy does not encourage Indian individuals to comply with protective action recommendations in the case of the COVID-19 pandemic. The present finding is not consistent with studies set in Thailand, the United States, and Hong Kong which reported that the individual's self-efficacy is a significant antecedent of compliance with recommended behaviors (Payaprom et al., 2011; Prue et al., 2019; Ng et al., 2020). It may be that a higher increase in an individual's ability to avoid crowds in India does not necessarily lead to greater adoption of preventive health actions against the COVID-19 infection. On the other hand, the results indicated that the perception of susceptibility is a potent predictor of preventive behavior in India, which support previous studies in the United States and China arguing that the perception of susceptibility to infection plays a crucial role in determining the adherence to health-protective actions (Wang et al., 2016; Zottarelli et al., 2012). From this, it can be deduced that Indian respondents who believe more in their susceptibility of becoming infected with COVID-19 were the most concerned about adopting health-protective measures. Likewise, the construct of cues to action was strongly correlated with the individual's engagement in preventive measures in India. This result is in line with previous studies conducted in Singapore and the United States (Ho et al., 2013; Zhang et al., 2015), demonstrating the influence of cues to action on the adoption of precautionary measures. Thus it seems that respondents who paid greater attention to the information provided by TV, newspaper, radio, and Internet-based media were more prone to avoid crowds during the COVID-19 pandemic in India. 6.3 Implications for theory The findings of this study have many valuable contributions for scholars working on studies related to behavioral medicine, especially those explaining behavioral responses to infectious disease. More specifically, the current research can serve as a baseline for understanding the reasons that motivate people to engage in preventative measures during the COVID-19 pandemic. The results of this study demonstrated the validity of the HBM constructs in two countries with different cultural dimensions proposed by Hofstede's findings. Therefore, by showing variation in protective measures adherence at a multi-country level, this study adds more knowledge regarding cultural differences within Moroccan and Indian settings. To follow on, SEM-ANN modeling is a relatively new approach in the context of health measures adoption. From a methodological viewpoint, researchers in this field can use this multi-analytical technique to obtain good prediction accuracy and enhanced theory testing. Lastly, the findings of this research confirm previous infectious disease studies by showing the importance of including perceived severity, perceived benefits, self-efficacy, perceived susceptibility, and cues to action as the key components of preventive measures adherence. 6.4 Implications for practice From a practical perspective, the current study provides several useful health communication strategies for the stakeholders involved in containing and managing future infectious disease pandemics. SEM-ANN analysis indicates that perceiving the COVID-19 as being severe is the most crucial determinant of the adherence to avoidant protective measures in Morocco. Therefore, creating health messages about the possible complications of the disease on the individual's health along with highlighting the possible negative consequences of that illness on the individual's economic situation and their social life seems to be crucial for preventing the spread of future infectious disease at a practical level. The present study also found that perceived benefit is the second important predictor of the decision to engage in health official actions in Morocco. This suggests that local health authorities should communicate continuously about the positive consequences of the protective measures during times of future pandemics, such as decreasing the chance of contracting a given illness and reducing the degree of transmission to one's family, friends, and neighbors. Furthermore, data collected during the COVID-19 outbreak in Morocco demonstrate that self-efficacy is the third most important antecedent of behavioral compliance with protective measures. Thus, when attempting to motivate Moroccans to adopt the preventive measure in the case of future global infectious outbreaks, health risk communicators must focus on encouraging individuals to believe that they can easily perform the recommended action and should constantly provide guidance to overcome the possible drawbacks of applying that preventive health measure. When it comes to India, the results emphasize that the individuals' perceptions of the probability of getting COVID-19 are the most important predictor that led people to perform preventive behavior. Therefore, in the case of preparedness for future respiratory infectious diseases, it would be useful to expose credible testimony of confirmed cases to raise the individual's awareness about the chances of becoming infected with the health threat if no preventative action was undertaken. Finally, sensitivity analysis suggests that the attention to COVID-19-related information is the second most important factor in the adoption of protective measures in India. In this sense, the government officials in India should provide funding, infrastructure, and resources to doctors so that they can train other professionals to deliver public health recommendations over multiple channels such as traditional media (e.g., television, newspapers, radio) and Internet media (e.g., websites and social media). Such actions are necessary to raise awareness when facing future pandemic threats in India. 6.5 Limitations and future research directions Although this study strengthens our understanding of the drivers of avoidant protective measure compliance in Morocco and India, it should be emphasized that some weaknesses need to be addressed in future research. The first limitation is the use of one single indicator to assess the avoidant protective behavior, which precludes us from checking for the construct's reliability. Although previous studies have used one single indicator in their research models (Wang et al., 2016; Yoo et al., 2016; Penţa et al., 2020), it must be acknowledged that future research needs to adopt multiple observed variables to measure adequately complex concepts such as avoidance behavior. For instance, it would be useful to take into account three other measurement indicators: 1) avoidance of public transport, 2) travel avoidance, and 3) avoidance of office work. The second limitation is that this study could not make a clear distinction as to whether avoidance of crowds served as a proxy for social distancing or as a dimension of home confinement. Thus, it is of great importance for behavioral scientists to add more precision to the operationalization of the concept of home confinement to avoid potential overlapping with other constructs. The third limitation concerns the cross-sectional design of this study. As the COVID-19 situation develops over time, it may be worth noting that perceptions of individuals may also change over the different phases of the COVID-19 pandemic. Capturing changes in health beliefs at different COVID-19 pandemic phases deserve to be tested more thoroughly in future longitudinal studies. The fourth limitation is that the population representativeness may be compromised due to online surveys that were employed for data collection. Thus, it can be assumed that only well-educated individuals who have access to the Internet were able to participate in the study. To avoid selection bias, future research should also use a paper version of the survey to reach large and diverse respondents who may not have Internet access and with limited literacy. The fifth limitation is that the current study has underrepresented peoples in the 35–60 age groups (6.2%), which raises the question of the generalization of the findings to other age groups in Morocco and India. Future studies should resolve this issue by considering representative samples comprised of research subjects of different age groups. The sixth limitation is that this study was conducted on a total sample of 444 adult individuals from just two countries. Thus, the results of the present research may not be generalizable to other populations living in European or American countries for example. Accordingly, continued endeavors would be needed to replicate the finding of this study in other societies, as well as with larger sample sizes, which may be particularly important to investigate the moderating impact of culture on the adoption of precautionary measures. 7 Conclusion The purpose of this study was to explore the behavioral determinants of the individuals’ decision to avoid crowds during the COVID-19 pandemic at a multi-country level. To meet this goal, a research framework based on HBM was empirically tested among 215 and 229 adults in Morocco and India, respectively. By using an integrative SEM-ANN approach, this research demonstrated that the perceived severity of COVID-19 is the strongest antecedent of the protective avoidant behavior in Morocco, whereas perceived susceptibility to COVID-19 contributes the most in the protective avoidant behavior in India. In light of these findings, health risk communicators should put more emphasis on such cognitive risk perceptions when tailoring health education messages to curb future infectious disease pandemics. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Statement regarding informed consent Informed consent was obtained from all individual participants included in the study. Statement regarding ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Statement regarding the welfare of animals This article does not contain any studies with animals performed by any of the authors. Data availability statement Data supporting the findings of this study are openly available via the OSF platform (see osf.io/rbnsk). CRediT author statement Yassine Jadil: Investigation, Methodology, Formal analysis, Writing - Original Draft. Mounir Ouzir: Supervision, Writing - Review & Editing, Validation. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2021.111376. ==== Refs References Ahmad M. Iram K. Jabeen G. Perception-based influence factors of intention to adopt COVID-19 epidemic prevention in China Environ. Res. 190 2020 109995 10.1016/j.envres.2020.109995 32739626 Alam M.Z. Hu W. 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Ho S.S. Lwin M.O. Promoting preventive behaviors against influenza: comparison between developing and developed countries Asian J. Commun. 24 2014 567 588 10.1080/01292986.2014.927894 Yardley L. Miller S. Teasdale E. Little P. Using mixed methods to design a web-based behavioural intervention to reduce transmission of colds and flu J. Health Psychol. 16 2011 353 364 10.1177/1359105310377538 20929941 Yoo W. Choi D.-H. Park K. The effects of SNS communication: how expressing and receiving information predict MERS-preventive behavioral intentions in South Korea Comput. Hum. Behav. 62 2016 34 43 10.1016/j.chb.2016.03.058 Zhang L. Kong Y. Chang H. Media use and health behavior in H1N1 flu crisis: the mediating role of perceived knowledge and fear Atl. J. Commun. 23 2015 67 80 10.1080/15456870.2015.1013101 Zottarelli L.K. Sunil T.S. Flott P. Karbhari S. College student adoption of non-pharmaceutical interventions during the 2009 H1N1 influenza pandemic: a study of two Texas universities in Fall 2009 Prev. Med. 55 2012 497 499 10.1016/j.ypmed.2012.08.009 22940037

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==== Front Industrial Marketing Management 0019-8501 0019-8501 Elsevier Inc. S0019-8501(21)00136-X 10.1016/j.indmarman.2021.07.007 Article Managing the supply chain during disruptions: Developing a framework for decision-making Kumar Bipul a Sharma Arun b⁎ a Marketing Department, Indian Institute of Management Indore, Prabandh Shikhar, Rau-Pithampur Road, Indore 453331, India b Miami Herbert Business School, University of Miami, 5250 University Drive, Coral Gables, Florida, USA ⁎ Corresponding author. 24 7 2021 8 2021 24 7 2021 97 159172 3 7 2020 30 6 2021 15 7 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Business-to-business firms have traditionally encountered disruptions, but the scale of the COVID-19 pandemic was extraordinary as it interrupted global supply chains by causing unprecedented shocks to supply and demand. Firms experienced extraordinary pressure and struggled to minimize the immediate and long-term impact of supply chains disruptions. Applying chaos theory, this study employs a single-case method to understand the disruptions to the business-to-business oil and gas supply chain. We make three major contributions. First, we examine firm decision-making during significant disruptions. Second, we use chaos theory to better understand the decision-making process. Finally, we develop a framework to explicate the decision-making process and provide guidelines for decision-making during disruptions. Our findings provide theoretical insights and have important implications for practitioners addressing supply chain disruptions during crises. Keywords Disruption COVID-19 Crisis Supply chain Chaos theory Strategic decision-making Tactical decision-making ==== Body pmc1 Introduction Given the dramatic increase in global connectivity between people and markets, the potential for global industry disruptions has increased (Sharma, Rangarajan, & Paesbrugghe, 2020). Recently, the world has faced crises of finance, security, and health that threaten connectivity and the globalized economy (Biggs, Biggs, Dakos, Scholes, & Schoon, 2011). The COVID-19 crisis is an unprecedented disruption; it has caused firms to shift to remote work, reduce new endeavors, and change communication patterns. In post-World War II history, global economies have not witnessed a disruption comparable to the COVID-19 pandemic (König & Winkler, 2021). Disruption management is an important consideration in supply chain management (Bode & Wagner, 2015; Snyder et al., 2016), but the impact of the COVID-19 pandemic has been dramatic as it has disrupted global supply chains due to changing supply and customer demand, new government lockdown regulations, and extreme uncertainty (Haren & Simch-Levi, 2020; Pedersen & Ritter, 2020). Uncertainty affects the perception of context based on the collective experience of individuals who represent various stakeholders (Artinger, Petersen, Gigerenzer, & Weibler, 2015; Guercini & Medlin, 2020), and intra-country complexities exacerbated uncertainty. The scale and scope of the impact of the Covid-19 pandemic on supply chains require a reexamination of supply chain performance during global disruptions. The objective and contribution of this study are threefold. First, we examine the decision-making by firms during significant disruptions. COVID-19 is regarded as the most significant disruption since World War II in both its scope and duration. Second, we use chaos theory to better understand the decision-making process during an unprecedented crisis. We also aim to understand the strategic, tactical, and operational decision-making that firms can use to address supply chain disruptions in times of crisis. Finally, we develop a framework to understand the decision-making process and provide guidelines for decision-making during disruptions. Specifically, the framework can answer questions such as “What can we do?” and “How can we do it?” when faced with a crisis similar to COVID-19. We conduct an in-depth investigation of a Fortune 500 oil and gas firm in India that faced significant disruptions to its supply chain due to COVID-19 and examine its successful response by applying chaos theory. The unprecedented combination of supply and demand shocks during COVID-19 dramatically affected the oil and gas supply chain, and the upstream, midstream, and downstream supply chains faced unprecedented challenges. Also, governmental decisions to implement lockdowns led to a dramatic reduction in oil and gas usage. The immediate impact on the supply chain was compounded by restricted logistics (movement of products) and the uneven demand for petroleum products across industries. We examine this context, investigate the disruption's antecedents, and collect data on decision-making, tactical plans to address the revenue and profit shortfall, and strategies to address potential threats. The following section provides a brief overview of the literature on supply chain disruptions, followed by a literature review on chaos theory, including its stages. In the research methodology section, we present a single-case approach and discuss the data collection and analysis processes. This is followed by our findings and a discussion. We conclude with managerial implications, theoretical implications, the limitations of the study, and future research directions. 2 Literature review 2.1 Supply chain disruptions Disruptions are “a low-probability, high-impact event that threatens the viability of the organization and is characterized by ambiguity of cause, effect, and means of resolution, as well as by a belief that decisions must be made swiftly” (Pearson & Clair, 1998, p. 60). Such crises have a low probability of occurrence, but they pose threats to the organization's survival, as the organization has limited time to successfully respond to the disruptions (Quarantelli, 1988; Ritter & Pedersen, 2020; Shrivastava, Mitroff, Miller, & Miclani, 1988). Disruptions in supply chains due to events such as tsunamis and financial crises have led researchers to study the resiliency of systems at both the firm and network levels (e.g., Bode, Wagner, Petersen, & Ellram, 2011; Kim, Chen, & Linderman, 2015). Key factors examined by this research include design, agility, and risk management culture (Christopher & Peck, 2004), collaborations in the supply chain (Datta & Christopher, 2011; Tang, 2006), the visibility and accuracy of information (Li et al., 2006; Sheffi & Rice Jr, 2005), the design and structure of supply chain networks (Craighead, Blackhurst, Rungtusanatham, & Handfield, 2007; Kim, Realff, & Lee, 2011), and the self-assessment of all stakeholders and the role of reverse logistics channels (Pettit, Croxton, & Fiksel, 2019). The literature on crisis management has predominantly focused on crisis failures detailing why and how organizational activities were affected by a crisis (Bierly III & Spender, 1995; Hittle & Leonard, 2011; Liska, Petrun, Sellnow, & Seeger, 2012; Ma & Xie, 2018; Pearson & Clair, 1998; Wilding, 1998). Key learnings from the research on crisis failures are that an incident in an isolated system can lead to system failure (Bierly III & Spender, 1995); failures have psychological, social-political, and technological-structural perspectives (Pearson & Clair, 1998); uncertainty and chaotic decision-making systems drive crises (Ma & Xie, 2018; Wilding, 1998); and, environmental aspects, bankruptcies, and loss of clients drive supply chain crisis (Richey, Natarajarathinam, Capar, & Narayanan, 2009; Wilson, 2007). There has been extensive learning from studying failures during crises. An under-researched area is understanding how firms successfully navigated crises and developing key learning for firms (Bundy, Pfarrer, Short, & Coombs, 2017). For example, Zhang, Bai, and Gu (2018) examined how contracts can be adjusted ex-post an exchange disruption to renew interfirm relationships to enhance our understanding of contractual exchanges. Our study extends our understanding of successfully addressing a major supply chain crisis. 2.2 The nature of the COVID-19 pandemic The degree of disruption created by the COVID-19 pandemic is unprecedented since World War II. The pandemic dramatically impacted most countries and industries due to its global nature, its impact on economies due to shut-downs, and dramatic supply chain disruptions. The COVID-19 pandemic also severely affected traditional supply chain functions, such as warehousing, transportation, and labor (Araz, Choi, Olson, & Salman, 2020; Cankurtaran & Beverland, 2020), leading to declining industrial productivity and affecting labor markets (Brinca, Duarte, & Faria-e-Castro, 2020; Harris, 2020). For example, the healthcare system was under tremendous stress, as the supply and demand of essential items, including ventilators, were unpredictable (Govindan, Mina, & Alavi, 2020; Ivanov & Dolgui, 2020). The COVID-19 pandemic has highlighted challenges beyond our current understanding of supply chain disruptions. These challenges focus on maintaining inventories, backup supply, flexibility in production, and real-time monitoring systems (Ivanov & Dolgui, 2020). Cankurtaran and Beverland (2020) suggest that such disruptive situations are wicked problems whose understanding and solutions require unconventional thinking. 2.3 The nature of supply chain disruption research Our literature review suggests that studies examining supply chain disruptions have focused on individual elements of supply chains but have not comprehensively examined the antecedents and consequences of significant disruptions (Bundy et al., 2017). Examples of focus areas include disaster management centers, infrastructural restoration, and transportation management (Dekle, Lavieri, Martin, Emir-Farinas, & Francis, 2005; Richey et al., 2009). There has been a call to enhance the theoretical perspective on supply chain disruptions and develop a framework for a deeper understanding (Bundy et al., 2017; Hittle & Leonard, 2011; Richey et al., 2009). The non-linear nature of major disruptions requires a fresh perspective, and we propose that chaos theory can be used to develop a deeper understanding of disruptions in the supply chain. 2.4 Gaps addressed by this study This study addresses three gaps in the literature by developing a deeper understanding of disruptions in the supply chain. The first gap that we address is examining supply chain disruptions during an unprecedented period (COVID-19 pandemic). The second gap that we address is using a fresh perspective to better understand major disruptions. Earlier research has focused on the use of chaos theory to understand supply chain issues (e.g., Hwarng & Xie, 2008; Ma & Xie, 2018; Shih, Hsu, Zhu, & Balasubramanian, 2012; Wilding, 1998), and we propose that chaos theory is ideal for studying unprecedented disruptions. Additionally, studies used chaos theory in qualitative and case-study-based research (e.g., McBride, 2005; Paraskevas, 2006; Speakman & Sharpley, 2012), guiding our study. Finally, there is also no comprehensive framework that examines and addresses major supply chain disruptions. Therefore, the third gap we address is the development of a comprehensive framework that seeks to enhance our understanding of major supply chain disruptions. 2.5 Review of chaos theory We suggest that the impact of the COVID-19 pandemic can be uniquely understood from the perspective of chaos theory, which is the theoretical underpinning of this study. Chaos theory is ideal to understand strategy when long-term planning is very difficult; industries do not reach a stable equilibrium and solutions are complex; dramatic change can occur unexpectedly; short-term forecasts and predictions of patterns are made, albeit with inaccuracies; and adaptive guidelines are needed to cope with complexity (Levy, 1994). Conducting business through traditional processes may be impossible under chaotic conditions, and chaos theory can help understand disruptions and decision-making (Cartwright, 1991; Liska et al., 2012; Murphy, 1996; Sellnow, Seeger, & Ulmer, 2002), including in the supply chain (e.g., Levy, 1994; Stapleton, Hanna, & Ross, 2006). Researchers have applied chaos theory to analyze the impact of health-related crises, including the Mexican AH1N1 influenza outbreak's effect on destination marketing (Speakman & Sharpley, 2012) and the role of public administration in the Ebola virus context (Keyes & Benavides, 2018). Chaos theory, originally proposed by Lorenz (1963), is contrary to the linear and causal perspectives and highlights the collapse of the balanced perspective (Tsoukas, 2005). With its genesis in physical science, chaos theory characterizes a nonlinear dynamic system that reconciles the elements of unpredictability (Cartwright, 1991). It is synonymous with the postmodern paradigm that questions deterministic positivism, which ignores most systems' complexity and diversity (Hassard & Parker, 1993; Levy, 1994). Chaos theory accentuates the basic tenets of unpredictability and suggests that nonlinear systems are difficult to model and forecast. Understanding how to address a crisis based on less severe disruptions may not be relevant in a major crisis such as COVID-19, leading to exploring a new paradigm (Kuhn, 1962). The pandemic's disruptions posed new tactical and strategic challenges for organizations, and chaos theory can help understand complex systems and managerial decision-making (Alshammari, Pavlovic, & Qaied, 2016). Building on Laszlo's (1987) work on biological evolution, Goerner and Combs (1998) argued that chaotic conditions can self-organize into extremely complex structures that can mutate beyond common understanding. Researchers across different disciplines have used chaos theory to understand disruptive contexts, and Table 1 provides examples from the social sciences. The table finds that chaos theory is increasingly used to understand uncertain and dynamic environments as well as major disruptions.Table 1 Sample research that uses chaos theory in social science research. Table 1Author(s) Journal Context Major finding(s) Levy (1994) Strategic Management Journal Dynamic evolution of industries and the complex interactions among industry actors in the context of the supply chain of a California-based computer company. Understanding industries as complex systems, organizations can improve their decision-making process and find innovative solutions. Thietart and Forgues (1995) Organization Science Organizational evolution in a chaotic and unstable condition. In chaotic conditions, small changes in organizations can have significant consequences that are beyond prediction. Wilding (1998) The International Journal of Logistics Management Uncertainty in the supply chain scenario. The paper defines deterministic chaos and exhibits how supply chains can display some key attributes of chaotic systems. Stapleton et al. (2006) Supply Chain Management: An International Journal The examination of forecasting, product design, and inventory management challenges faced by supply chain practitioners. Chaos theory explains why unpredictability occurs within nonlinear systems and helps researchers develop better and more accurate models to understand supply chain management decisions. Hwarng and Xie (2008) European Journal of Operational Research The examination of supply chain factors in complex dynamics and chaotic behaviors in a beer distribution model. The adjustment parameters for inventory and supply line discrepancies need to be comparable to manage the quantum of chaos in the supply chain. The paper also suggests a phenomenon of chaos amplification as the bullwhip effect. Speakman and Sharpley (2012) Journal of Destination Marketing & Management Destination marketing during the Mexican AH1N1 influenza crisis. A chaos theory-based perspective on crisis management helps destinations respond to disruptions. Liska et al. (2012) Southern Communication Journal Crisis communication during the Kingston Coal Ash Spill in 2008 at Tennessee. A major element of chaos theory, bifurcation, revealed a significant failure in an organization's policies and procedures in dealing with the crisis. Shih et al. (2012) Information & Management Examination of the importance of knowledge sharing in a downstream two-echelon supply chain. Knowledge-sharing practices can be beneficial for downstream operations of a supply chain. Hung and Tu (2014) Research Policy The technological progress of incremental, continuous change and radical, discontinuous change at the industrial level. The paper examines the non-linearity of the processes of technological change, suggesting that under the conditions of chaotic dynamics, even an incremental change can generate disproportionate results leading to a new paradigm. Hwarng and Yuan (2014) European Journal of Operational Research Application of chaos theory in a time series when the underlying structure is unknown. The result exhibits chaos characterization aids in deterministic and stochastic categories of demand. Ma and Xie (2018) Communications in Nonlinear Science and Numerical Simulation The examination of a supply chain system's stability comprising one supplier and one bounded rational retailer. The paper finds that adaptive exponential smoothing does not affect system stability. In contrast, bounded rationality expectations render the system stability susceptible to the retailers' loss sensitivity and the decision adjustment speed. Keyes and Benavides (2018) International Journal of Organization Theory & Behavior Public administration in the Ebola virus situation and coordinating learning for organizations to overcome situations of uncertainty. The findings suggest that public entities were more likely to arrange organizational learning by coordinating professionals, access to quality information, and participation in daily communication in a crisis. Yuan and Nishant (2019) Journal of Business Research Chaotic behavior shown by firms having growth driven by their R&D. The findings indicate that the investment in R&D has more complex impacts on growth than on the firms, and decisions about such investments can cause fluctuations and erratic growth patterns in a nonlinear and complex business environment. In the context of supply chains, the actions taken by one supply chain member would be known and predictable. However, in a time of crisis, external conditions are uncontrollable and individual reactions to the crisis vary. This causes the supply chain to enter a state of chaos and leads to outcomes that follow an unpredictable and nonlinear path. Based on our previous discussions, chaos theory would be ideal for studying supply chain disruptions during unprecedented disruptions. Chaos theory suggests that crises follow four stages: bifurcation, fractals, self-organization, and strange attractors (e.g., Freimuth, 2006; Liska et al., 2012; Murphy, 1996; Sellnow et al., 2002). These stages are described in the subsections that follow. 2.5.1 Bifurcation Bifurcation signifies a basic disturbance of the status quo. When an increasing number of variables with differing frequencies come together, the basic state of equilibrium changes. When the relative strength of the variables changes, the overall system moves from a state of equilibrium to a periodic and then a chaotic situation (Thietart & Forgues, 1995). The ever-increasing number of variables with different frequencies creates more complicated behavior, leading to apparent randomness or chaos. The shift indicates a fundamental change in the existing system wherein stakeholders are left in a complete state of disorientation. Once in a chaotic condition, organizational actors can only predict short-term impacts due to changes in the underlying factors. Even a small change at this stage initiates a multiplier effect that causes exponential instability in the system. This is described as a state where “previous assumptions, methods, patterns, and relationships can no longer function” (Seeger, Sellnow, & Ulmer, 2003, p. 31), and it leads to a reexamination of many assumptions used to address the issues. The bifurcation state warrants a salvage plan from organizational actors who, themselves, do not understand such disruptions. 2.5.2 Fractals Fractals help to identify the emerging pattern that follows the state of bifurcation. They act as a source of information in a state of crisis. At the fractal stage, understanding and gathering evidence about a challenging situation is very important, as it assists in addressing failures that arise during bifurcation. The fractal stage requires truthful and accurate data, as their absence may lead to confusion at a later stage (Sellnow et al., 2002). The stage involves organizational actors who describe and measure the impact of the complex system. These actors are required to implement a holistic approach to decipher the larger picture. It is important to note that the typically readily available cues to understand disruptions may not exist (Liska et al., 2012), and executives may use misleading information or proxy heuristics to make incorrect inferences (Guercini, 2019; Murphy, 1996). The key to attaining success in this phase is identifying the stage and quickly understanding and responding to the disruption. Research suggests that it is better to focus on information and communication flows and make deliberate decisions (Speakman & Sharpley, 2012). 2.5.3 Self-organization Self-organization is a key stage in chaos theory. It allows the chaotic system to reorient with the help of communicative structures and relevant procedures (Sellnow et al., 2002). A new structural form emerges to restore the overall system through complexity, new structures, processes, and hierarchies. Although the relationship between order and chaos is dynamic, inherently complex systems exert a pull towards each other to achieve balance. As this stage is quasi-evolutionary, organizations may make short-term tactical decisions that lead to strategic decision-making with a long-term perspective (Kauffman, 1995). In the context of a business crisis, firms must adapt by changing their structure, processes, and routines (Horsley, 2008). The concept of self-organization is critical for recognizing and managing crises. Wheatley (2007) identified three conditions for self-organization to excel: identity, information, and relationships. In this phase, firms rely on a combination of tactical and operational tasks to repair the damage caused by the disruption. 2.5.4 Strange attractors The strange attractors, a central idea of chaos theory, proposes that order will emerge from the chaotic state (Thietart & Forgues, 1995). Attractors are the basic values and principles that unite individuals in attaining their common goals. Managers act as strange attractors by developing vision and facilitating appropriate communication structures and cooperative relationships and by creating conducive conditions for new orders to prevail (Speakman & Sharpley, 2012; Zahra & Ryan, 2007). Firms need to quickly develop policies to restore the confidence of stakeholders, such as customers and employees (Beirman, 2003). Wheatley (2007) suggested that the attractors' ability to maintain the underlying thread among organizational members lies in communicating the correct meaning or action through fractals. To ensure that the underlying values are established, a deployment strategy is required to enhance the community and avoid misinterpretation. 3 Research methodology 3.1 Research strategy We used a single-case-study approach as the research strategy in this study. According to Yin (2014), a case study approach is the preferred research method when exploring a real-life phenomenon in which the boundary between the context and phenomenon is blurred. A single-case research method is used, which is relevant for investigating a rare and extreme context (e.g., Easton, 2010; Eisenhardt & Graebner, 2007; Järvinen & Taiminen, 2016; Miles, Huberman, & Saldaña, 2013). In this study, “case” refers to the supply chain disruption caused by the COVID-19 pandemic. 3.2 Selection of the case company As suggested by Järvinen and Taiminen (2016), we followed an “extreme case sampling” approach. This approach is akin to purposive sampling, wherein the selected cases outline notable success. We chose a leading Indian oil and gas Fortune 500 firm that is well regarded for implementing best practices in handling supply chain disruptions and successfully addressing the COVID-19 disruption. The firm's net profit increased in the second quarter of the crisis (July–September 2020) compared to the previous year, and its gross refining margin increased by 17% during the first two quarters of the crisis (April–September 2020) compared to the previous year. The case company's selection was based on the impact of the disruption on the supply chain, the firm's response, and our access to key informants. Hereafter, “case company” refers to the company that witnessed the supply chain disruptions and about which this study was conducted. 3.3 Data collection We collected the primary data by conducting semi-structured telephone interviews. Following Eisenhardt and Graebner (2007), we interviewed knowledgeable key informants in the case company who held senior positions and were involved in various supply chain planning and management stages during the crisis. We used snowball sampling to identify key informants and then interviewed them (Järvinen & Taiminen, 2016; Salganik & Heckathorn, 2004). This process resulted in seven interviews with informants. Among them, five belonged to the marketing department, and two were from the operations department. The marketing and operations departments are responsible for supply chain elements, including the receipt, storage, and dispatch, and delivery of the products. The marketing department regularly interacts with customers and defines the supply chain parameters. The interview process included open-ended questions centered on the subject of the study. The interview questions were based on the following: recognition of the antecedents of the supply chain disruption; gathering information on the extent of the damage to various links of the supply chain; the impact of the crisis on the businesses of the company's customers; the role of prior relationships with the customers; the short-term response by the company to repair the damage; the company's plan to manage the potential threat to the supply chain in the immediate future; and the impact of the crisis on the physical and psychological well-being of the people involved in operations and the supply chain. Using data from multiple sources helped us in triangulation, which enhanced the study's reliability and the saturation of the data (Dubois & Gibbert, 2010; Fusch, Fusch, & Ness, 2018; Stavros & Westberg, 2009). Apart from interviews with key informants, we also gathered secondary data from media coverage of the oil and gas industry and the case company, the social media pages of the case company, and the company's website to understand its disruption management practices during the COVID-19 pandemic. The secondary data allowed for a deeper understanding of the actions taken by the firm. To further substantiate the informants' narratives, we also interviewed two customers and one supply chain partner of the case company. One of the customers belonged to the pharmaceutical industry and the other to the construction industry. We also interviewed a supply chain consultant (referred to as the “expert”) who specializes in supply chain issues and has consulted with different firms on supply chain management during the crisis. The interview with the consultant also helped us evaluate the external validity of the results. The details of the primary and secondary data collection are summarized in Table 2 .Table 2 Source of the primary data – Interview of the respondents. Description of respondents. Table 2Title and role/responsibility Representative of Interview duration General Manager: Responsible for overall supply chain planning and strategy at a state level Marketing department 32 Minutes Deputy General Manager-1: Responsible for overall supply chain planning and monitoring at a state level Marketing department 26 Minutes Deputy General Manager-2: Responsible for overall supply chain planning and monitoring at a state level Marketing department 25 Minutes Chief Manager: Responsible for supply chain planning, monitoring, and customer contact at a division level Marketing department 22 Minutes Senior Manager: Responsible for ensuring the smooth supply of products to customers. The first line of contact for customers. Marketing department 38 Minutes Chief Operations Manager: Responsible for operations –ensures planning and supply of products to customers. Operations department 35 Minutes Senior Operations Manager: Responsible for day-to-day operations–ensures timely supply of products to customers. Operations department 30 Minutes Purchase Manager (Pharmaceutical firm): Point of contact for the marketing department of the case company. Purchases petroleum products from the case company. Customer–pharmaceutical firm 18 Minutes Purchase Manager (Construction firm): Point of contact for the marketing department of the case company. Purchases petroleum products from the case company. Customer–construction firm 16 Minutes Supply chain partner of the case company: A key logistic partner involved in the transportation of petroleum products from the case company to the customers Supply chain partner–transportation 20 Minutes Supply chain consultant (Mentioned as “Expert”): An expert on various aspects of supply chain Supply chain consulting firm 35 Minutes Source of secondary data: Media coverage of the oil & gas industry and the case company on the topic of research. Contents from the social media pages of the case company on the topic of research. Website of the case company. 3.4 Data analysis The data analysis process started with a review of the entire dataset by the authors and two independent researchers unfamiliar with the study to enhance confidence in the coding scheme (Harris, 2001; Miles & Huberman, 1994; Myhal, Kang, & Murphy, 2008). Differences among coders, if any, were resolved through mutual discussion until an agreement was achieved (Miles & Huberman, 1994). The role of the independent researchers was limited to the check-coding process; the rest of the analysis was done by the authors themselves, as they were familiar with the context and the theory used in the study. Following de Casterlé, Gastmans, Bryon, and Denier (2012), the data analysis is described in Table 3 and exemplars are provided in Table 4 . First, we conducted a preliminary but comprehensive preparation of the coding process, including reading and understanding the data, followed by identification and evolution, verification, and comparison of the concepts. Second, we focused on the coding process and analysis by listing, coding, and analyzing concepts, designing the structure, and describing the findings. The process matches the thematic analysis suggested by Miles et al. (2013), which includes data condensing, data display, and drawing inferences. Since data collection and analysis cannot be completely segregated, the process was iterative and involved delving deeper into the data and moving back and forth between various stages (de Casterlé et al., 2012; Froggatt, 2001). The analysis was continued until data saturation was reached. The details of the process are described in Table 3. Using a part of the overall data, Table 4 depicts an illustrative example of the different stages of the coding and analysis process relating to chaos theory in the context of the current study.Table 3 Steps in data analysis. Table 3Stage Details Comprehensive preparation for the coding process 1 Reading the interview data. We transcribed the interviews verbatim and read them carefully to gain a preliminary understanding of their content. During the process, important phrases were underlined, and the details needed to understand the analysis at a later stage were written down. We also made observational notes on all other secondary information, such as media coverage and social media content. 2 Understanding the interview data. Following de Casterlé et al. (2012), we proceeded with this stage after conducting a few interviews. We then began articulating our understanding of the content. 3 Identification and evolution of concepts. In this stage, we began to filter important data and aggregate them into concepts. This stage was critical, as it helped us arrive at a more abstract analysis based on the interview narratives. As shown in Table 4, this stage highlighted the relevant concepts that aided our understanding of the phenomena and addressed the research objective. 4 Verification of concepts. In this stage, we reexamined interviews with the highlighted concepts to identify additional meaningful concepts and connect the conceptual scheme with the interview data. This stage was also the beginning of the iterative process involving forward and backward movement in examining the data. 5 Comparison of concepts. We reexamined the conceptual schemes by comparing them with the data emerging from the interviews. Any themes or concepts present in the new interviews were reviewed to determine if they were similar to those in previous interviews and were refined as necessary. Coding and analysis 6 Listing of concepts. We drew up a list of concepts representing different ideas. These concepts were reviewed for any overlap or ambiguity; conflicts, if any, were resolved through mutual consensus. 7 Coding of concepts. Following de Casterlé et al. (2012), all interviews were reread with the list of concepts. The concepts aided in identifying important paragraphs and quotes in the interviews, which were highlighted. The coding ensured that the concepts encompassed all important ideas and implicit messages. This stage was a refinement of the previous stage, which focused on listing concepts. 8 Analysis, description, and aggregation of concepts. We analyzed and carefully explored the coding process, examining all relevant information across all the interviews. This step allowed us to examine whether concepts needed to be aggregated or split into sub-concepts. As a result, the concepts were further refined and described based on the available interview data (see Table 4). 9 Shaping the structure. We integrated the concepts by structuring them into a meaningful conceptual framework to address the research objective. In the context of this study, we structured the findings using the 4-C framework of crisis management under the tenets of chaos theory. 10 Description of the findings. Using the conceptual framework and analysis of the concepts, we systematically delineated the findings to achieve the research objective. Following de Casterlé et al. (2012), we added notable quotes to explain the concepts and the framework. Table 4 Exemplars of the coding and analysis process in the context of the chaos theory. Table 4 3.5 Evaluation of study quality As suggested by Yin (2014), we considered three criteria—construct validity, external validity, and reliability—to ascertain the quality of the study. Construct validity indicates the “extent to which a study investigates what it claims to investigate” (Dubois & Gibbert, 2010, p. 132). We used multiple information sources in the present study, including interviews with case company informants, customers, and an expert. We also referred to secondary sources, including media coverage and social media content, to triangulate the data by examining the research phenomenon from various perspectives (e.g., Beverland & Lindgreen, 2010; Järvinen & Taiminen, 2016; Piekkari, Plakoyiannaki, & Welch, 2010). Following Yin's (2014) approach, we provided evidence demonstrating the study's progression from its objective to its findings. We also discussed the findings with the case company informants to verify the validity of the results. Finally, following Guba and Lincoln (1981), we conducted a member check to ensure the quality of the analysis. External validity is described as whether there is a “domain to which a study's findings can be generalized” (Yin, 2014, p. 46). We used chaos theory and a process model (Shrivastava, 1993) to frame our findings. The respondents' responses were largely in support of the framework. Transparent and systematic data collection and analysis enhanced the study's reliability (Batt, 2012; Dubois & Gibbert, 2010). Additionally, to ensure reliability and validity, we also followed the verification strategies suggested by Morse, Barrett, Mayan, Olson, and Spiers (2002). We ensured “methodological coherence” by establishing congruence between the research objective and selecting the appropriate methodology. In our study, the single-case research methodology helps achieve the research objective of understanding disruptions in the supply chain. As suggested, we also checked the “appropriateness of the sample.” This methodology is mentioned in detail in Section 3.3 on data collection. The other aspects, like “collecting and analyzing data concurrently,” “thinking theoretically,” and “theory development,” were followed extensively in our data analysis process, which is also reflected in Table 4. 4 Findings and discussion Based on the analysis of all the available data, we present our findings in the subsequent subsections. The findings also form the basis of a framework that can be used to understand the entire crisis scenario, from awareness to developing a management approach (Fig. 1 ). To provide a structure for our findings, we utilized the 4-C framework of crisis management proposed by Shrivastava (1993), also referred to as the process model. This model consists of cause, signifying the event that triggered the crisis, including the antecedent conditions; consequences, signifying the immediate and long-term impacts; coping, describing measures taken to respond to a crisis that has already occurred; and caution, indicating measures taken to prevent or minimize the impact of a potential crisis.Fig. 1 Framework. Fig. 1 The overall findings are structured using elements of the 4-C framework of crisis management with the theoretical underpinning of chaos theory, which comprises four factors: bifurcation, fractals, self-organization, and strange attractors. The framework was developed from a process model proposed by Shrivastava (1993), but the attributes of the framework have been driven by our analysis. We discuss each element of the findings in the subsequent sections. 4.1 Scrutinizing the causes and consequences of the crisis We focused on gathering the firm's understanding of the disruption and its consequences. The findings were based on responses to the interview questions. The details are described in the following section. 4.1.1 Bifurcation The oil and gas supply chain system and buyer-seller relationships were severely affected by the COVID-19 pandemic. The entire industry was in shock, and the pandemic left stakeholders confused. One of the senior managers who was interviewed for this study made a similar observation:Well…this goes beyond our normal understanding. I can't believe what I am watching currently [sic]. None of us ever dreamed of something like this. I don't think we can conduct business as usual in this situation. (Deputy General Manager-1). The crisis created unpredictability for the oil and gas supply chain, as demand declined sharply when many customers of the case company suspended their operations. One of the managers made the following observation:Many customers' businesses were also closed temporarily during pandemic…so that was also a reason of concern as where to send our products. We cannot shut down production from our refinery, a minimum production level will be maintained always. (Deputy General Manager-2). The firm in the case study supplies petroleum products to different industries, and it was unable to understand the demands of its customers, at least in the immediate term. These issues were amplified by the important role of the case company's logistics partners, who faced initial curbs in cross-country transportation due to the lockdowns imposed by central or state governments and had concerns about their people being exposed to COVID-19. A supply chain partner expressed these feelings:We are facing difficulties in cross-country transportation due to a lot of confusion. Although we are transporting an essential product, many issues arise when we are on the way…yes, our crewmembers are scared of the exposure to the disease…no one knows what may happen. (supply chain partner). A similar concern was expressed by one of the managers of the case company:Our supply chain partners, their crew are highly concerned about saving their life first from covid. They travel long distance and if everything is closed across the country, where will they get food …where will they get other necessary things. (Deputy General Manager-2). The overall ambiguity, confusion, and lack of understanding of the situation were visible in the narratives of one of the case company informants:We have seen many ups and downs, but that did not ever hinder our efforts in serving our customers. Although cultivated over long time and great efforts [sic], we really faced a tough time in managing our relationship with our key customers. It was a time when we were uncertain about supplying and serving our customers. (Chief Manager). The interviews also revealed that the way information about the disease was disseminated created confusion among different stakeholders, leading to constrained decision-making. One of the customers interviewed for this study mentioned the following:We did not have accurate information, at least at the start of [the] pandemic. The lack of knowledge about the nature of [the] disease and inadequate information were reasons behind holding up some decision-making at our end. (Purchase Manager, construction firm). One of the managers of the case company also voiced a similar narrative:Towards the start, neither we nor our customers had clarity about what is going on…customers did not know what to operate what not to operate…information management at their end was also not clear. (General Manager). Overall, the magnitude and unpredictability of the COVID-19 crisis led to supply chain disruptions, following the principles of bifurcation. 4.1.2 Fractals Respondents were asked about their understanding of the situation and their initial reaction to the disruption due to the COVID-19 crisis. We gathered information on the extent of the damage to various links in the supply chain. Based on the analysis of interviews and other sources, we first examined how the company observed the pattern of the crisis and evaluated the company's initial actions. During our interviews, one of the managers stated the following:Our long-term relation with some of our key customers helped us in understanding the real crisis [that the] coronavirus pandemic could create at their end…our customers faced an emergent issue of shortage of products needed to run their operations. (Deputy General Manager-2). The case company quickly assessed customers' issues because of its investment in relationships and related infrastructure. One respondent described this investment as follows:Our firm invested in information technology–enabled infrastructure, such as enterprise resource planning and automation, that could even track the status of stocks [sic] at our customers' end in [sic] real-time basis. (Senior Operation Manager). With the disruption of the supply chain, customers expected the company to respond rapidly. As one customer stated, one immediate solution was the timely delivery of critical products:We told the company to supply at least some minimum quantity of products so that we could run our operations. We had shared a valued relationship, and we expect[ed] the company to help us in this time of crisis. (Purchase Manager, construction firm). Another customer of the case company also raised concerns about the impact on their supply chain if the lockdown condition continued for a longer duration, as described by its purchase manager:We need many raw materials as ingredients for manufacturing the products at our factory and we do the centralized purchasing…like many solvents, fuel…other products. We fear that continuation of the current condition will affect our supply chain arrangements. (Purchase Manager, pharmaceutical firm). The case company quickly realized that to help its customers, it needed to support its logistics partners, who were the backbone of the supply chain. One of the managers stated the following:Our petroleum product supply chain is highly dependent on our logistic[s] partners who provide tank trucks, and their crew members are at the core of timely delivery of the product in the right quality and quantity. I found them also in a state of confusion, and, at the same time, they are worried about health issues, too. (Deputy General Manager-1). There were also health-related concerns at the case company's supply location, creating pressure on the supply chain infrastructure. A senior executive responsible for supplying products to customers explained this challenge:Although we are trying our best, our operating staff and other human power involved in [the] supply[ing] of products to our customers are also in a state of anxiety due to uncertainty and related health issue[s] due to coronavirus spread. (Senior Operations Manager). Through our secondary sources, we also observed discussion of the stress on the oil and gas supply chain in the context of COVID-19:The pandemic risk can and has not only triggered and amplified the already recognized risks such as economic risk, financial risk, security risk, but also created new risks such as stressed supply chains, high percentage of workforce exposed to risk. (Secondary source: IIFL Securities, 2020). The pandemic also affected the internal supply chain arrangements of the case company, which was evident in the narratives of one of the managers:At the start of pandemic, we faced some issues due to overall confusion about how to work at our locations in view of fear of exposure to coronavirus and also due to hindrances faced in moving tank trucks to different supply locations from our large oil terminals. (Chief Manager). Based on information from interviews and secondary sources, we found that the crisis interrupted supply chains and caused confusion among stakeholders, including logistics partners. A significant reason for the company's quick understanding of the crisis was its preparedness for such situations and its balanced approach towards risk mapping. 4.2 Managing the crisis through coping and caution Organizational crisis management is a systematic attempt by organizational members in partnership with external stakeholders to avert crises or effectively manage those that occur (Pearson & Clair, 1998 ). In the next sections, we focus on organizational crisis management to describe the steps taken by the company to manage the COVID-19 crisis, including tactical, strategic, and operational decision-making. 4.2.1 Self-organization Self-organization is the process of recognizing and coping with the unprecedented changes that a crisis causes. It is synonymous with a company's short-term response to disruption. In the present study, the content of the interviews and data from other sources revealed the important role of the firm's tactical and operational decision-making. Three conditions are necessary for self-organization to excel: identity, information, and relationships (Wheatley, 2007). We present our findings on managing the crisis from this perspective. In a crisis, organizational leaders often need to revise or reinvent their organization's identity to respond to the altered dynamics (Hearit, 2006). Leaders may also reference their past encounters with a crisis to handle current challenges. One of the respondents acknowledged this:We have many hierarchical levels in our organization[al] structure, and everyone works as per the delegation of authority…I think our top management is visionary enough to create a quick decision-making process to handle critical supplies for our customers. Many of us didn't need to look towards our bosses, as we were delegated with [the] right authority to take action in this time of crisis. (Chief Manager). The case company created a decentralized structure that facilitated rapid decision-making. A key executive of the case company told us:We have set up command centers in our offices, which keep [sic] a tab on information provided by the government agencies. We also pass on useful information to our customers and our logistic[s] partners. (Deputy General Manager-1). The company also focused on managing information about the pandemic and the availability of products for its customers:It was important to gather [the] right information about [the] coronavirus disease, as our logistic[s] partners were worried…things were very uncertain, and the crew members were really worried about their exposure to the disease. We also have many small customers who needed our support regarding exact information about supply of critical products to maintain their operations. (Senior Manager). Another important component of self-organization, relational effort, facilitates creating and forming organizational identity, which further benefits stakeholders. An important pharmaceutical industry customer of the case company validated our earlier observations:Medicines being the essential necessity, we were required to run our operations despite all problems in the lockdown condition…our established relationship with the company helped us in continuing our business operations. They ensured the supply of critical products despite all problems. (Purchase Manager, pharmaceutical firm). One of the essential aspects of the relational effort was to assist people involved in business continuity efforts, which included taking care of the physical and psychological well-being of employees and supply chain partners. One senior executive of the case company described this initiative:We got the medical insurance done for all crew members of our logistic[s] partners involved in transportation of the products. We also distributed necessary products for their hygiene, like sanitizer, masks, and hand gloves. The same was done for our employees, too, who were handling supply operations at our end. (Chief Operations Manager). These efforts were also discussed in the media and on social media platforms. One respondent from the case company stated the following:The crew members need to travel to [sic] long distance to supply products to our customers…as it was difficult to get food on such long hauls in lockdown condition, we also facilitated [sic] them by providing food packets for en-route consumption. We also arranged food for them in their long haul through our other business partners. (Chief Manager). This tactical facilitation helped preserve the physical and psychological well-being of the people involved in the supply chain and smoothed operational decision-making. Additionally, valuing past relationships with key customers, a senior executive shared some innovative approaches they used:In lockdown, in a few instances, we faced some challenges due to restricted movement of our employees and logistic[s] partners…For many of our valuable customers, we shifted the base of our supply location by identifying alternate sources to provide uninterrupted supplies. (Deputy General Manager-2). A case company senior executive described an incident that reflects collaborative buyer-supplier relationships:In this time of crisis, we parked the excess products beyond our capacity in the storage tanks of our key customers. This was a unique decision never taken before…but you know unique times need unique approaches. All this was possible as we cultivated great relationships with our customers. (General Manager). We also observed (through secondary sources) that the case company invested heavily in forecasting and intelligence gathering, which strengthened its operational decision-making during the crisis. Overall, tactical and operational decision-making allowed the company to recover to a great extent from the disruptions in its supply chain. 4.2.2 Strange attractors This section focuses on understanding the measures used to prevent or minimize the impact of a potential crisis. Strange attractors aim to build and enhance resilience in the entire system to manage a future crisis successfully and develop strategic and operational decision-making structures to reduce the potential impact of a future crisis. Strategic decision-making is preceded by strategic planning and management with four main elements: strategic analysis, strategic direction and choice, strategy implementation and control, and strategic evaluation and feedback (Richardson & Richardson, 1992; Ritchie, 2004; Viljoen, 1994). Strange attractors are important as they provide an understanding of the strategy that emerges to address future disruption. Strange attractors are also important because they reflect the basic values and principles that unite individuals in attaining their common goals. This facet of designing a strategic response to disruptions has attracted limited attention in previous supply chain research, requiring a deeper examination of strange attractors. After analyzing the data from the case company, the expert, and other sources, we observed some emerging patterns. We present our findings by structuring them under the purview of strategic planning and management. The executives of the case company used their learnings during the crisis in their strategic planning process. One key respondent from the case company stated the following:We are continuously watching the government guidelines and understanding their meaning for ours [sic] as well as for our customers' business[es]. We are contemplating when there can be a conducive environment for safe movement of our supply chain partners and what infrastructure we need to put in place. (Deputy General Manager-2). The expert believed that firms must have a strong risk analysis and forecasting mechanism to handle any potential threat in a crisis:The risk analysis and forecasting shall also be complemented with a right contingency and emergency plan to avert any potential threat. (Expert). To understand the strategic direction and choice in a given situation, firms must resort to scenario planning and formulate and evaluate strategic alternatives to sustain their business. One senior manager at the case company described the company's strategy:We don't want a potential crisis to keep us paralyzed…we are getting better with each learning [sic] and hence working with different possibilities to cope up [sic] any future eventuality. We value our relationship with our customers and hence [are] keeping all alternatives open to sustain our relationship and business. (General Manager). Once the strategy is clear, firms need to focus on implementation. The expert whom we interviewed for this study also validated this narrative:Organizations need to remove any barrier in strategy implementation in a crisis. They need to really act fast, and their inherent properties, like the structure of the organization, shall be responsive enough to act quickly. (Expert). We observed that the case company had facilitated a decentralized organizational structure for fast decision-making. Another important factor was the ability to mobilize and manage appropriate resources:We are dedicating appropriate resources to fight with [sic] any potential crisis emanating in future [sic]. We are also in the process of identifying permanent alternative sources that could be activated in the time of crisis for serving our customers…yes, we also aim to prepare our workforce to cope up [sic] with such kind of potential eventualities. (General Manager). The expert interviewed for the study identified evaluation and feedback on the entire process as important:Firms need to strictly monitor and evaluate the strategies that they put in place. It [sic] should be augmented with regular feedback, and they shall also be prepared to do course corrections if results are not as anticipated. (Expert). Since strategic decision-making affects operational decision-making, we examined the structure of the operational decision-making process and its various factors. The expert noted the following:The strategic decision-making process shall ensure that internal operations at the supply company shall not be hampered in any case. [The] company shall be looking at its competitors and other stakeholders to collaborate in the time of crisis, and a formal mechanism shall be kept in place. (Expert). In addition to operational decision-making, the case company's manager pointed out the company's new approach:Our firm is taking one step forward and creating a cooperative approach to help our supply chain partners. We will continue to invest in their physical and psychological well-being. (Deputy General Manager-1). The insights about the strange attractors in this study were distinct from the previous research on crisis response. Most studies limit the discussion on strange attractors on restoring trust (Liska et al., 2012), structure (Speakman & Sharpley, 2012), and chaotic and random behavior (Hung & Tu, 2014). In contrast, the current study provides a more in-depth and multi-layered structure of strange attractors focusing on long-term strategic and operational decision-making. We summarize all the findings in the form of a framework (Fig. 1). This framework advances the theoretical understanding of supply chain disruptions and their management during crises. 4.3 The crisis management framework We developed a crisis management framework using chaos theory and a process model. The cause and consequence components of the process model (Shrivastava, 1993) reflect the bifurcation and fractal stages of chaos theory. The causes of supply chain disruption include the unpredictability of the crisis, conflicting and noncoherent information dissemination, and health and psychological problems. The consequence of the crisis is the failure of the supply chain infrastructure, which immediately affects the company's operations and customers. The overall situation triggers a state of confusion among stakeholders. The coping and caution components of the process model (Shrivastava, 1993) reflect the self-organization and strange attractors stages of chaos theory. We examined self-organization in terms of identity, information, and relationships (Wheatley, 2007). Our framework highlights the importance of quickly and accurately disseminating information to all stakeholders on the nature of the crisis and immediate responses. The business continuity plan and the physical and psychological well-being of the people involved in the process are equally important in this stage. Operational decision-making includes implementing a network utilizing supply from an alternate location and strengthening operational forecasting and intelligence systems. In the framework, caution focused on long-term strategic decision-making. The elements of strategic decision-making are strategic analysis, strategic direction and choice, strategic implementation and control, and strategic evaluation and feedback. Macro- and micro-level environmental issues are important, including understanding government regulations and guidelines; and customers, competitors, and supply chains. Other important attributes in long-term strategic decision-making include the role of scenario and contingency planning, emergency planning, and developing strategic alternatives. The framework also emphasizes implementing a responsive organizational structure, displaying an effective leadership style, and mobilizing appropriate resources, including crisis communication and control modalities. Organizations need effective, continuous monitoring strategies and systems to improve and perform course corrections when required. Within operational decision-making, the framework highlights the importance of collaborating with competitors and other stakeholders to ensure uninterrupted supplies and a robust operational forecasting and intelligence system. In summary, traditionally, the focus of supply chains during periods of disruption is on supply chain elements such as production, warehousing, stocking, and inventory. Our framework suggests that firms need to take a more holistic view in addressing disruptions. First, the framework indicates that to manage disruptions, firms must recognize and understand their antecedents. The framework suggests that this information is usually unclear, and the path of disruptions is unpredictable. The second issue facing firms during disruptions is that the disruptions' impact on supply chains and firms is poorly understood. The framework advises that firms structure tactical decision-making and operational decision-making in a specific manner to cope with disruptions. Finally, to prevent and minimize future disruptions, firms must evolve the structure of strategic and operational decision-making in a particular way. The framework provides additional insight from extant literature in three ways. First, the comprehensive nature of the theoretical framework examines different stakeholders and integrates knowledge from various disciplines such as marketing, organization behavior, strategy, and operations management. The cross-functional perspective has not been observed in extant literature and helps develop important insights about addressing a major disruption. Second, the framework provides a comprehensive theoretical understanding of supply chain during disruptions and suggests the necessary steps to handle the different facets of the disruption, an area that has not been addressed by extant literature. Third, the framework specifies a temporal perspective, from a short-term (self-organization) perspective to a long-term (strange attractor) perspective. A temporal perspective has not been observed in the literature. 5 Implications The contribution of this paper is in three areas. First, we examine decision-making by firms facing significant disruptions. The COVID-19 disruption is regarded as the most significant disruption since World War II in both its scope and duration. Second, we use chaos theory to better understand decision-making processes under major disruptions. Finally, we develop a framework to understand the decision-making process during major disruptions and provide guidelines for decision-making. 5.1 Theoretical implications There are four key theoretical implications of our research. First, we develop a comprehensive framework utilizing chaos theory to deepen our understanding of organizational responses to a major crisis like COVID-19. We suggest four major elements of our framework—causes, consequences, coping, and caution. Our research suggests that this is the first comprehensive framework that examines supply-chain disruption during a major crisis. This framework addresses the call by researchers to develop comprehensive frameworks that address the successful transformation of organizations (Bundy et al., 2017). Second, our research theoretically delineates short-term and long-term decision-making during a major crisis. Short-term decision-making strategies focus on tactics, and long-term decision-making focuses on strategies. We did not observe this temporal division in previous research. We find that identity, information, and relational efforts structure short-term decision-making. We find strategic analysis, strategic direction and choice, strategy implementation and control, and strategic evaluation and feedback structure strategic decision-making. Third, the paper explores supply chain disruptions by employing two theories/frameworks–chaos theory (Lorenz, 1963) and the 4-C crisis management framework (Shrivastava, 1993). There has been a call to unify and apply extant frameworks/theories to examine phenomena, and our study successfully applied two theories/frameworks to develop a deeper understanding of crisis management. Finally, this study contributes to the literature by its applicability to buyer-supplier relationships and examining suppliers' responses during a disruption. In examining transactional versus relationship strategies, buyers seek some sellers for transactions and some for relationships. Applying the framework suggests that some firms are better at bifurcation, fractals, self-organization, and strange attractors. Firms that are better at these attributes will be better partners for long-term relationships. Buyers may seek to evaluate how firms performed on bifurcation, fractals, self-organization, and strange attractors attributes to evaluate the relationship potential. Firms that underperform on these attributes may be limited to transactional relationships or may be required to provide a dramatically higher level of offerings to satisfy customers. 5.2 Managerial implications We undertook this study to understand how business-to-business firms could manage supply chain disruptions during crises like the novel coronavirus pandemic. Managers should recognize that there are four distinct aspects of managing crises: recognizing the antecedents of the crisis (bifurcation), recognizing its consequences (fractals), taking measures to respond to a crisis that has already occurred (coping), and taking measures to prevent or minimize the impact of potential crises (caution). Recognizing the antecedents of the crisis. Our study suggests that firms sense crises but cannot accurately comprehend their size. We suggest that firms create a strategic initiatives team that is given the task of monitoring shifts in the environment. Such teams should meet frequently and discuss changes in the environment to quickly diagnose the beginning of a crisis. The teams can be trained to separate the signal from the noise by conducting scenario planning (Oliver & Parrett, 2018). Recognizing the consequences. Our study suggests that firms that sense a crisis are often unable to accurately comprehend its consequences. We suggest that open communication can help mitigate this lack of understanding. First, as suggested earlier, a strategic initiatives team can help recognize the crisis's consequences. In addition, our findings suggest a closer relationship between supply chain partners. We suggest that firms create strategic teams with their supply chain customers and partners. Such teams should frequently meet to discuss current issues and should meet more frequently during a crisis. Responding to the current crisis. Firms should develop plans to respond to a crisis when it is recognized. Our findings suggest that firms should set up a crisis command center to address two types of issues: tactical decisions (identity, information, and relationships) and operational decisions (continuity, network, customers, and intelligence). Firms need to revisit their corporate identity, enforce this identity in all decisions, and decentralize certain decision-making processes. Firms also need to collect accurate information and disseminate the information through the crisis command center. Finally, firms must ensure the welfare of their customers (through continuity plans) and employees (financial, physical, and psychological well-being). In operational decision-making, firms must first ensure the continuity of their internal operations. They need to use a network-based approach (using networks to ensure supply) and ensure that supply chain partners are operational. Finally, firms must develop stronger intelligence to forecast the immediate future of the crisis. Responding to prevent and minimize the impact of future crises. Firms need to make fundamental changes to minimize the impact of future crises. First, firms must enhance their ability to conduct strategic analysis by developing better skills for understanding the macro-environment (economy, health, and regulations) and micro-environments (customers, supply chain partners, and competitors). We suggest that firms create a group to examine the environment and perform risk analysis and forecasting. Second, we suggest that firms conduct scenario and contingency planning, plan for emergencies, and develop strategic alternatives. Third, we suggest that firms create a responsive organizational structure and develop crisis leadership skills. Fourth, we suggest that firms consistently examine their crisis strategies and conduct dry runs. Finally, firms need to develop supply chains with more resiliency, which may include redundancy, including suppliers, inventory, and other logistics. 6 Limitations and directions for future research We have focused on the supply chain's three main linkages: supplier, buyer, and supply chain partner. Future studies may include these context-specific intermediaries and their role in crisis management strategies. We used different stages of chaos theory to understand the disruption in the supply chain and the management of disruptions in a limited time frame. Future studies could explore the interrelation between different stages of chaos theory under varying contextual factors and a long-term perspective. We use the single-case-study method by selecting a case company as a sample. Future studies may explore the supply chain disruption or any similar disruption by collecting samples from multiple companies from diverse industries to create additional knowledge. Moreover, future studies may look beyond the case research method and incorporate other research techniques when studying crisis management. Future research questions may include the following: the impact of risk management strategies on buyer-seller relationships; leadership required for developing and implementing an effective crisis management strategy; collaborative approaches during a crisis; effective coping strategies at functional and business level; and, the applicability of the resource-based view in a time of crisis. We hope that this paper provides the impetus for future research in this area. ==== Refs References Alshammari M. Pavlovic M. Qaied B.A.A. Chaos theory in strategy research American Journal of Business and Management 5 1 2016 1 13 Araz O.M. Choi T.M. Olson D. Salman F.S. 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Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions Journal of Operations Management 36 2015 215 228 Bode C. Wagner S.M. Petersen K.J. Ellram L.M. Understanding responses to supply chain disruptions: Insights from information processing and resource dependence perspectives Academy of Management Journal 54 4 2011 833 856 Brinca P. Duarte J.B. Faria-e-Castro M. Measuring sectoral supply and demand shocks during COVID-19 FRB St. Louis Working Paper, (2020−011) 2020 Bundy J. Pfarrer M.D. Short C.E. Coombs W.T. Crises and crisis management: Integration, interpretation, and research development Journal of Management 43 6 2017 1661 1692 Cankurtaran P. Beverland M.B. Using design thinking to respond to crises: B2B lessons from the 2020 COVID-19 pandemic Industrial Marketing Management 88 2020 255 260 Cartwright T.J. Planning and chaos theory Journal of the American Planning Association 57 1 1991 44 56 de Casterlé B.D. Gastmans C. Bryon E. Denier Y. QUAGOL: A guide for qualitative data analysis International Journal of Nursing Studies 49 3 2012 360 371 21996649 Christopher M. Peck H. Building the resilient supply chain International Journal of Logistics Management 15 2 2004 1 13 Craighead C.W. Blackhurst J. Rungtusanatham M.J. Handfield R.B. The severity of supply chain disruptions: Design characteristics and mitigation capabilities Decision Sciences 38 1 2007 131 156 Datta P.P. Christopher M.G. Information sharing and coordination mechanisms for managing uncertainty in supply chains: A simulation study International Journal of Production Research 49 3 2011 765 803 Dekle J. Lavieri M.S. Martin E. Emir-Farinas H. Francis R.L. A Florida county locates disaster recovery centers Interfaces 35 2 2005 133 139 Dubois A. Gibbert M. From complexity to transparency: Managing the interplay between theory, method and empirical phenomena in IMM case studies Industrial Marketing Management 39 1 2010 129 136 Easton G. Critical realism in case study research Industrial Marketing Management 39 1 2010 118 128 Eisenhardt K. Graebner M. Theory building from cases: Opportunities and challenges Academy of Management Journal 50 1 2007 25 32 Freimuth V.S. Order out of chaos: The self-organization of communication following the anthrax attacks Health Communication 20 2 2006 141 148 16965251 Froggatt K.A. The analysis of qualitative data: Processes and pitfalls Palliative Medicine 15 5 2001 433 438 11591096 Fusch P. Fusch G.E. Ness L.R. Denzin’s paradigm shift: Revisiting triangulation in qualitative research Journal of Social Change 10 1 2018 2 Goerner S. Combs A. Consciousness as a self-organizing process: An ecological perspective BioSystems 46 1–2 1998 123 127 9648683 Govindan K. Mina H. Alavi B. A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19) Transportation Research Part E: Logistics and Transportation Review 138 2020 101967 32382249 Guba E.G. Lincoln Y.S. Effective evaluation: Improving the usefulness of evaluation results through responsive and naturalistic approaches 1981 Jossey-Bass Guercini S. Heuristics as tales from the field: The problem of scope Mind & Society 18 2 2019 191 205 Guercini S. Medlin C.J. A radical constructivist approach to boundaries in business network research Industrial Marketing Management 91 2020 510 520 Haren P. Simch-Levi D. How coronavirus could impact the global supply chain by mid-march Harvard Business Review 28 2020 Harris H. Content analysis of secondary data: A study of courage in managerial decision making Journal of Business Ethics 34 3 2001 191 208 Harris R. Covid-19 and productivity in the UK 2020 Durham University Business School Hassard J. Parker M. Postmodernism and organizations 1993 Sage Thousand Oaks, CA Hearit K.M. Crisis management by apology 2006 Lawrence Erlbaum Malawah, NJ Hittle B. Leonard K.M. Decision making in advance of a supply chain crisis Management Decision 49 7 2011 1182 1193 Horsley S. Seeking reliability in chaos: The crisis adaptive public information model Conference Papers--International Communication Association, 1–26 2008 Hung S.C. Tu M.F. Is small actually big? The chaos of technological change Research Policy 43 7 2014 1227 1238 Hwarng H.B. Xie N. Understanding supply chain dynamics: A chaos perspective European Journal of Operational Research 184 3 2008 1163 1178 Hwarng H.B. Yuan X. Interpreting supply chain dynamics: A quasi-chaos perspective European Journal of Operational Research 233 3 2014 566 579 IIFL Securities Management discussions Retrieved April 25, 2021, from https://www.indiainfoline.com/company/indian-oil-corporation-ltd/management-discussions/12002 2020 Ivanov D. Dolgui A. A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0 Production Planning & Control 2020 1 14 Järvinen J. Taiminen H. Harnessing marketing automation for B2B content marketing Industrial Marketing Management 54 2016 164 175 Kauffman S. The search for laws of self-organization and complexity 1995 Oxford University Press Oxford Keyes L.M. Benavides A.D. Chaos theory, uncertainty, and organizational learning International Journal of Organization Theory & Behavior 21 4 2018 226 241 Kim J. Realff M.J. Lee J.H. Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty Computers & Chemical Engineering 35 9 2011 1738 1751 Kim Y. Chen Y.S. Linderman K. Supply network disruption and resilience: A network structural perspective Journal of Operations Management 33 2015 43 59 König M. Winkler A. COVID-19: Lockdowns, fatality rates and GDP growth Intereconomics 56 1 2021 32 39 33518787 Kuhn T.S. The structure of scientific revolutions 1962 University of Chicago Press Chicago Laszlo E. Evolution: The grand synthesis 1987 Shambhala Press Boston, MA Levy D. Chaos theory and strategy: Theory, application, and managerial implications Strategic Management Journal 15 S2 1994 167 178 Li X. Chang K.S. Dooley J.J. Deshpande A.A. Greene T.A. Hakeman D.J. Supply chain visibility for real-time tracking of goods U.S. Patent No. 7,136,832 2006 U.S. Patent and Trademark Office Washington, DC Liska C. Petrun E.L. Sellnow T.L. Seeger M.W. 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Verification strategies for establishing reliability and validity in qualitative research International Journal of Qualitative Methods 1 2 2002 13 22 Murphy P. Chaos theory as a model for managing issues and crises Public Relations Review 22 2 1996 95 113 Myhal G.C. Kang J. Murphy J.A. Retaining customers through relationship quality: A services business marketing case Journal of Services Marketing. 22 6 2008 445 453 Oliver J.J. Parrett E. Managing future uncertainty: Reevaluating the role of scenario planning Business Horizons 61 2 2018 339 352 Paraskevas A. Crisis management or crisis response system? A complexity science approach to organizational crises Management Decision. 44 7 2006 892 907 Pearson C.M. Clair J.A. Reframing crisis management Academy of Management Review 23 1 1998 59 76 Pedersen C.L. Ritter T. Preparing your business for a post-pandemic world 2020 Harvard Business Review Digital Articles Pettit T.J. Croxton K.L. Fiksel J. The evolution of resilience in supply chain management: A retrospective on ensuring supply chain resilience Journal of Business Logistics 40 1 2019 56 65 Piekkari R. Plakoyiannaki E. Welch C. “Good” case research in industrial marketing: Insights from research practice Industrial Marketing Management 39 1 2010 109 117 Quarantelli E.L. Disaster crisis management: A summary of research findings Journal of Management Studies 25 4 1988 373 385 Richardson B. Richardson R. Business planning: An approach to strategic management 2nd ed. 1992 Pitman London Richey R.G. Natarajarathinam M. Capar I. Narayanan A. Managing supply chains in times of crisis: A review of literature and insights International Journal of Physical Distribution & Logistics Management 39 7 2009 535 573 Ritchie B.W. Chaos, crises and disasters: A strategic approach to crisis management in the tourism industry Tourism Management 25 6 2004 669 683 Ritter T. Pedersen C.L. Assessing Coronavirus’s impact on your business model 2020 Harvard Business Review Digital Articles Salganik M. Heckathorn D. Sampling and estimation in hidden populations using respondent-driven sampling Sociological Methodology 34 1 2004 193 239 Seeger M.W. Sellnow T.L. Ulmer R.R. Communication and organizational crisis 2003 Praeger Westport, CT Sellnow T.L. Seeger M.W. Ulmer R.R. Chaos theory, informational needs, and natural disasters Journal of Applied Communication Research 30 4 2002 269 292 Sharma A. Rangarajan D. Paesbrugghe B. Increasing resilience by creating an adaptive salesforce Industrial Marketing Management 88 2020 238 246 Sheffi Y. Rice J.B. Jr. A supply chain view of the resilient enterprise MIT Sloan Management Review 47 1 2005 41 Shih S.C. Hsu S.H. Zhu Z. Balasubramanian S.K. Knowledge sharing—A key role in the downstream supply chain Information & Management 49 2 2012 70 80 Shrivastava P. Crisis theory/practice: Towards a sustainable future Industrial & Environmental Crisis Quarterly 7 1 1993 23 42 Shrivastava P. Mitroff I.I. Miller D. Miclani A. Understanding industrial crises Journal of Management Studies 25 4 1988 285 303 Snyder L.V. Atan Z. Peng P. Rong Y. Schmitt A.J. Sinsoysal B. OR/MS models for supply chain disruptions: A review IIE Transactions 48 2 2016 89 109 Speakman M. Sharpley R. A chaos theory perspective on destination crisis management: Evidence from Mexico Journal of Destination Marketing & Management 1 1–2 2012 67 77 Stapleton D. Hanna J.B. Ross J.R. Enhancing supply chain solutions with the application of chaos theory Supply Chain Management: An International Journal 11 2 2006 108 114 Stavros C. Westberg K. Using triangulation and multiple case studies to advance relationship marketing theory Qualitative Market Research: An International Journal. 12 3 2009 307 320 Tang C.S. Robust strategies for mitigating supply chain disruptions International Journal of Logistics: Research and Applications 9 1 2006 33 45 Thietart R.A. Forgues B. Chaos theory and organization Organization Science 6 1 1995 19 31 Tsoukas H. Complex knowledge: Studies in organizational epistemology 2005 Oxford University Press Oxford, UK Viljoen J. Strategic management: Planning and implementing successful corporate strategies 2nd ed. 1994 Longman Melbourne Wheatley M.J. Leadership for an uncertain time 2007 Barrett-Koehler San Francisco, CA Wilding R.D. Chaos theory: Implications for supply chain management The International Journal of Logistics Management 9 1 1998 43 56 Wilson M.C. The impact of transportation disruptions on supply chain performance Transportation Research Part E: Logistics and Transportation Review 43 4 2007 295 320 Yin R.K. Case study research: Design and methods 5th ed. 2014 Sage Thousand Oaks CA Yuan X. Nishant R. Understanding the complex relationship between R&D investment and firm growth: A chaos perspective Journal of Business Research 129 2019 666 678 Zahra A. Ryan C. From chaos to cohesion—Complexity in tourism structures: An analysis of New Zealand's regional tourism organizations Tourism Management 28 3 2007 854 862 Zhang C. Bai X. Gu F.F. Contract learning in the aftermath of exchange disruptions: An empirical study of renewing interfirm relationships Industrial Marketing Management 71 2018 215 226

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==== Front Clin Imaging Clin Imaging Clinical Imaging 0899-7071 1873-4499 Published by Elsevier Inc. S0899-7071(22)00318-7 10.1016/j.clinimag.2022.12.003 Breast Imaging Axillary lymphadenopathy following bivalent COVID-19 booster vaccination Lane Elizabeth G. Babagbemi Kemi Mema Eralda Dodelzon Katerina ⁎ Weill Cornell Medicine at NewYork-Presbyterian, Department of Radiology, New York, NY, USA ⁎ Corresponding author. 15 12 2022 15 12 2022 6 12 2022 9 12 2022 9 12 2022 © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords Breast imaging COVID-19 vaccine Booster Axillary lymphadenopathy Bivalent ==== Body pmc1 Introduction With the recent FDA emergency use authorizations of the bivalent Pfizer-BioNTech and Moderna COVID-19 vaccines, in conjunction with documented lymphadenopathy in clinical trials, vaccine history should continue to be considered when encountering unilateral axillary lymphadenopathy. With the rise of the Omicron subvariants came a reduced effectiveness of the monovalent COVID-19 vaccines, leading to the development of the bivalent COVID-19 vaccines (Original and Omicron B.4/B.5) [1]. The Moderna bivalent booster contains 25 μg of the nucleoside-modified mRNA that was used in the original vaccine that encodes the “prefusion stabilized Spike glycoprotein (S) of the SARS-CoV-2 Wuhan-Hu-1 strain” and 25 μg of mRNA that encodes “the pre-fusion stabilized S-protein of the SARS-CoV-2 Omicron variant lineages BA.4 and BA.5”. [2] Similarly, the Pfizer bivalent booster contains 15 μg of the original mRNA and 15 μg of the nucleoside-modified mRNA that encodes for BA.4 and BA.5 variants. [3]. Although vaccine related adenopathy ipsilateral to the vaccination site following monovalent COVID-19 vaccination has been well documented in the literature 4., 5., 6., reaction to bivalent COVID-19 vaccines is not well documented. Similar lymph node reactivity is suggested, given reports of lymphadenopathy in bivalent COVID-19 vaccine trials. 7., 8. In this case series, we present five patients with incidental axillary lymphadenopathy identified on breast ultrasound, PET-CT, or breast MRI, following the administration of the Pfizer or Moderna bivalent COVID-19 booster vaccine. 2 Case descriptions 2.1 Case 1 46-year-old female presented for baseline screening mammogram and ultrasound. Bilateral mammogram was assessed as BI-RADS 1. Prominent left axillary lymph nodes were identified on screening ultrasound. The patient received the Moderna Bivalent Booster (50 MCG/0.5 mL) Sars-CoV-2-Vaccination to the left deltoid two days prior to breast imaging. The ultrasound was assessed as BI-RADS 0. The patient was recalled for a diagnostic, targeted ultrasound of the left axilla for further evaluation. Diagnostic breast ultrasound imaging, 21 days post-bivalent booster vaccination, demonstrated an interval decrease in the size of the previously prominent lymph nodes and as such lymphadenopathy was attributed to reactive vaccine related etiology and assessed as BI-RADS 2. The patient had previously received three doses of the Pfizer COVID-19 vaccine, which included the initial series, and one monovalent booster. 2.2 Case 2 75-year-old female presented for bilateral diagnostic mammogram and ultrasound as a follow up of recent left lumpectomy for DCIS with positive margins. Patient received the Pfizer Bivalent Booster (30 MCG/0.3 mL) Sars-CoV-2-Vaccination to the right deltoid three days prior to mammogram and ultrasound. Bilateral prominent axillary lymph nodes were identified on ultrasound, the largest on the right, measuring 1.1 × 0.7 × 1.1 cm which, given history of recent vaccination with a bivalent booster were attributed to reactive vaccine related etiology. Prominent left axillary lymph nodes were assessed as consistent with recent postoperative status. The ultrasound was assessed as BI-RADS 2. The patient had previously received four doses of the Pfizer COVID-19 vaccine, the initial series, and two monovalent booster vaccines. The patient did not demonstrate axillary lymphadenopathy on breast imaging performed within six months following the initial COVID-19 vaccine series. 2.3 Case 3 73-year-old female with right-sided breast cancer status post bilateral mastectomy presented for surveillance PET-CT skull base to mid-thigh. The patient received the Moderna Bivalent Booster (50 MCG/0.5 mL) Sars-CoV-2-Vaccination to the left upper extremity, four days prior to imaging. Mildly FDG avid, non-enlarged, left supraclavicular and axillary lymph nodes were identified. Lymphadenopathy ipsilateral to vaccination site was attributed to reactive vaccine related etiology. The patient had previously received four doses of the Moderna COVID-19 vaccine, the initial series, and two monovalent booster vaccines without enlarged or FDG avid axillary lymphadenopathy noted on prior surveillance PET-CTs performed within four and eight months of vaccination. 2.4 Case 4 70-year-old female presented for routine screening mammogram and breast ultrasound. The patient received the Pfizer Bivalent Booster (30 MCG/0.3 mL) Sars-CoV-2-Vaccination to the left deltoid two days prior to mammogram and ultrasound. The ultrasound demonstrated multiple prominent left lymph nodes and was assessed as BI-RADS 0. The patient returned for additional imaging four days later. The subsequent diagnostic ultrasound demonstrated multiple left axillary lymph nodes with cortical thickening, up to 0.4 cm. The diagnostic imaging was assessed as BI-RADS 3 and six month follow up sonographic evaluation was recommended. The patient had previously received four doses of the Pfizer COVID-19 vaccine, the initial series, and two monovalent booster vaccines. The patient did not demonstrate axillary lymphadenopathy on breast imaging performed one day after the first monovalent COVID-19 booster vaccine. 2.5 Case 5 62-year-old female with a history of right-sided breast cancer, status post lumpectomy, presented for bilateral, screening breast MRI. The patient received the Moderna bivalent booster (50 MCG/0.5 mL) Sars-CoV-2-vaccination to the left upper extremity 26 days prior to imaging. MRI demonstrated a mildly prominent left axillary lymph node. The imaging was assessed as BI-RADS 2. The patient had previously received three doses of the Moderna COVID-19 vaccine, the initial series, and one monovalent booster vaccine. The patient did not demonstrate axillary lymphadenopathy on breast imaging performed within five months of the initial COVID-19 vaccine series. 3 Discussion Lymphadenopathy has been reported in clinical studies investigating both the Pfizer and Moderna bivalent COVID-19 vaccines 7., 8.. In one study, 21.3% of participants (age 18 to 64) and 11.5% (65+) reported axillary swelling and tenderness within seven days of receiving a second booster dose (Moderna bivalent booster (Original and Omicron BA.1)) compared to 18.5% of participants (age 18 to 64) and 10.7% (65+) who received the monovalent Moderna vaccine as a second booster dose [7]. In a study evaluating a Pfizer bivalent booster (Original and Omicron BA.1) 0.3% of participants (age 55+) reported lymphadenopathy [8]. One should note that the bivalent boosters in these studies contained the Omicron variant lineage BA.1, rather than BA.4/BA.5, variants, which are present in the current bivalent booster vaccines. As delay of screening mammography post COVID vaccination is no longer recommended [9] nor prudent, breast radiologists should be aware of the continued COVID-19 vaccine related adenopathy, in particular within a few days following bivalent COVID-19 booster. As with the monovalent COVID-19 vaccines, we expect to encounter large numbers of patients with axillary lymphadenopathy following the administration of the COVID-19 bivalent booster vaccines. This underscores the importance of continuing to collect “information on patient COVID-19 vaccination status including the date and side of vaccination on patient intake forms” as advised by the Society of Breast Imaging (SBI) guidelines [9]. Given appropriate vaccine related history, as per revised SBI guidelines [9], a follow-up of 12 or more weeks is recommended, or patients can be returned to the screening pool if lymphadenopathy is assessed as benign in the absence of other suspicious breast findings (Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5 ).Fig. 1 a. Transverse gray-scale static sonographic image of the left axilla demonstrates a lymph node (arrow) with diffusely thickened cortex. b. Follow up ultrasound performed 19 days subsequent to the initial screening ultrasound. Transverse gray-scale static sonographic image of the left axilla demonstrates interval resolution of cortical thickening of a previously noted axillary lymph node (arrow) compatible with benign reactive etiology. Fig. 1 Fig. 2 a. Transverse gray-scale static sonographic image of the left axilla demonstrates a lymph node (arrow) with diffusely mildly thickened cortex, assessed as reactive and compatible with recent lumpectomy. b. Transverse gray-scale static sonographic image of the right axilla demonstrates a lymph node (arrow) with diffusely thickened cortex and obliteration of the fatty hilum. Fig. 2 Fig. 3 a. Fused axial PET/CT image demonstrates FDG avid left axillary lymph nodes (arrow). b. Fused axial PET/CT image demonstrates FDG avid left supraclavicular lymph nodes (arrow). Fig. 3 Fig. 4 Transverse gray-scale static sonographic image of the left axilla demonstrates multiple enlarged lymph nodes (arrow) with diffusely thickened cortices. Fig. 4 Fig. 5 a. Axial T1-weighted fat suppressed post contrast subtracted sequence demonstrates a single left axillary lymph node (arrow) with obliteration of the fatty hilum. b. Sagittal T1-weighted fat suppressed post contrast sequence demonstrates a single left axillary lymph node (arrow) with obliteration of the fatty hilum. Fig. 5 Declaration of competing interest None. ==== Refs References 1. Chalkias S. Harper C. Vrbicky K. A bivalent omicron-containing booster vaccine against Covid-19 N Engl J Med 387 14 2022 1279 1291 10.1056/NEJMoa2208343 36112399 2. Moderna HCP FS for bivalent booster for 6 years and older (Gray Label) 10122022. The U.S. Food and Drug Administration https://www.fda.gov/media/161318/download 3. Pfizer HCP FS bivalent booster grey 10122022. The U.S Food and Drug Administration https://www.fda.gov/media/161327/download 4. Mehta N. Sales R.M. Babagbemi K. Unilateral axillary adenopathy in the setting of COVID-19 vaccine Clin Imaging 75 2021 12 15 10.1016/j.clinimag.2021.01.016 33486146 5. Lane E.G. Eisen C.S. Drotman M.B. Time for resolution of COVID-19 vaccine-related axillary lymphadenopathy and associated factors AJR Am J Roentgenol 219 4 2022 559 568 10.2214/AJR.22.27687 35583425 6. Wolfson S. Kim E. Plaunova A. Axillary adenopathy after COVID-19 vaccine: no reason to delay screening mammogram [published correction appears in Radiology. 2022 Sep;304(3):E57] Radiology 303 2 2022 297 299 10.1148/radiol.213227 35133198 7. Centers for Disease Control and Prevention website. The Moderna COVID-19 vaccine's local reactions, systemic reactions, adverse events, and serious adverse events Accessed 28 October 2022 www.cdc.gov/vaccines/covid-19/info-by-product/moderna/reactogenicity.html 8. Centers for Disease Control and Prevention website. Pfizer-BioNTech COVID-19 vaccine reactions & adverse events www.cdc.gov/vaccines/covid-19/info-by-product/pfizer/reactogenicity.html 9. Revised SBI Recommendations for the Management of Axillary Adenopathy in Patients with Recent COVID-19 Vaccination. Society of Breast Imaging Patient Care and Delivery Committee https://www.sbi-online.org/Portals/0/Position-Statements/2022/SBI-recommendations-for-managing-axillary-adenopathy-post-COVID-vaccination_updatedFeb2022.pdf

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==== Front Mol Ther Mol Ther Molecular Therapy 1525-0016 1525-0024 American Society of Gene & Cell Therapy S1525-0016(22)00712-2 10.1016/j.ymthe.2022.12.008 Original Article SARS-CoV-2 viral protein ORF3A injures renal tubules by interacting with TRIM59 to induce STAT3 activation Cai Hong ab Chen Ya ab Feng Ye a Asadi Morad a Kaufman Lewis a Lee Kyung a Kehrer Thomas cd Miorin Lisa c Garcia-Sastre Adolfo cefg Gusella G. Luca a Gu Leyi b Ni Zhaohui b Mou Shan b∗∗∗ He John Cijiang a∗∗ Zhou Weibin a∗ a Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, NY 10029, USA, b Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Renji Hospital, Uremia Diagnosis and Treatment Center, Jiao Tong University School of Medicine, Shanghai, China c Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA; and Global Health Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, NY 10029, USA d Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA e Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA f Department of Medicine, Division of Infectious Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA g The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA ∗ Correspondence should be addressed to: Weibin Zhou, Ph.D, Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA Phone: +1 (212)241-2318 Fax: +1 (212)987-0389 ∗∗ Correspondence should be addressed to: John Cijiang He, MD/PhD, Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA Phone: +1 (212)241-2318 Fax: +1 (212)987-0389 ∗∗∗ Correspondence should be addressed to: Shan Mou, MD, Department of Nephrology, Molecular Cell Lab for Kidney Disease, Shanghai Peritoneal Dialysis Research Center, Renji Hospital, Uremia Diagnosis and Treatment Center, Jiao Tong University School of Medicine, Shanghai, China 15 12 2022 15 12 2022 9 8 2022 12 12 2022 © 2022. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Acute kidney injury occurs frequently in COVID-19 patients infected by the coronavirus SARS-CoV-2 and infection of kidney cells by this virus has been reported. However, little is known about the direct impact of the SARS-CoV-2 infection upon the renal tubular cells. We report that SARS-CoV-2 activated Signal Transducer and Activator of Transcription 3 (STAT3) signaling and caused cellular injury in the human renal tubular cell line (HK-2). Mechanistically, the viral protein ORF3A of SARS-CoV-2 augmented both NF-κB and STAT3 signaling and increased the expression of kidney injury molecule 1(KIM-1). SARS-CoV-2 infection or expression of ORF3A alone elevated the protein level of Tripartite motif-containing protein 59 (TRIM59), a ubiquitin E3 ligase, which interacts with both ORF3A and STAT3. The excessive TRIM59 in turn dissociated the phosphatase TCPIP from binding to STAT3 and hence inhibited the dephosphorylation of STAT3, leading to persistent STAT3 activation. Consistently, ORF3A induced renal injury in zebrafish and mice. In addition, expression of TRIM59 was elevated in the kidney autopsies of COVID-19 patients with AKI. Thus, the aberrant activation of STAT3 signaling by TRIM59 plays a significant role in the renal tubular cell injury caused SARS-CoV-2, which suggest a potential targeted therapy for the renal complications of COVID-19. Graphical abstract SARS-CoV-2 infection of renal tubular cells increases TRIM59 proteins via the viral protein ORF3A, resulting in the inhibition of STAT3 dephosphorylation and excessive activation of STAT3 that leads to renal tubular injury. This reveals a novel mechanism underlying the acute kidney injury occurring in severe COVID-19 patients. ==== Body pmc

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==== Front Comput Biol Med Comput Biol Med Computers in Biology and Medicine 0010-4825 1879-0534 The Author(s). Published by Elsevier Ltd. S0010-4825(22)01125-8 10.1016/j.compbiomed.2022.106417 106417 Article Improving COVID-19 CT classification of CNNs by learning parameter-efficient representation Xu Yujia Lam Hak-Keung ⁎ Jia Guangyu Jiang Jian Liao Junkai Bao Xinqi Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom ⁎ Corresponding author. 15 12 2022 15 12 2022 1064178 8 2022 22 11 2022 4 12 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improve the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. The achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset. Source code is available at https://github.com/YujiaKCL/COVID-CT-Similarity-Regularization. Keywords COVID-19 Computed tomography CNNs Deep learning Similarity regularization ==== Body pmc1 Introduction The Coronavirus Disease 2019 (COVID-19) has become a worldwide pandemic and infected over 493 million people till April 2022 [1]. Its increasingly high infectivity and fatality rate due to strain variation are threatening human health and damaging the global economy [2], [3], [4]. The efficient reproductive number of the virus in many countries remains high, as reported in [5], indicating COVID-19 continues spreading quickly around the world. Thereby, a timely and efficient diagnosis is crucial for the treatment of COVID-19 positive patients and the control of further disease spread. In the early diagnosis of COVID-19 infection, real-time reverse transcription polymerase chain reaction (RT-PCR) is the primary choice due to its convenience and high specificity. However, research results [6], [7], [8] have suggested that RT-PCR is not sensitive enough that some infected patients turned out to be positive even after several negative tests. These false-negative cases might continue to infect their close contacts without isolation or develop into severe illness. Chest computed tomography (CT) has the potential to identify pathological lung changes and thus can serve as a supplementary screening tool to RT-PCR in infection detection, as indicated by [9], [10], [11]. Since the pandemic started, researchers have been exploring the potential of convolutional neural networks (CNNs) in COVID-19 CT classification and reported high accuracy without clinician intervention. CNNs are a kind of deep learning technique dominating computer vision tasks. For example, Gunraj et al. [12] introduced a large-scale open-access COVID-19 CT dataset (COVIDx CT-1) and trained a COVID-19-specific tailored CNN. Panwar et al. [13] utilized transfer learning to inherit cross-domain knowledge to improve the model performance. All these researches reveal that CNNs have the potential to serve as an assistant to help clinicians in COVID CT diagnosis. Although CNNs have achieved remarkable results in CT diagnosis, challenges remain before they can be put into practical use. Deep learning methods often require large-scale standard datasets, while the existing COVID-19 CT datasets are insufficient. Also, CT scans collected from different institutes have inconsistent characteristics like orientation, brightness, etc. The trained models might be more sensitive to these irrelevant information than the pneumonic pathologies that really matter. Furthermore, the increasingly great capability of CNN-based models may not be fully fulfilled when learning from the limited data sources. Hence, methods for learning more parameter-efficient representations are crucial for mitigating the data insufficiency issue and improving the classification performance. By addressing these problems above, a more reliable COVID-19 CT classification system can reduce the workload of clinicians and provide more accurate and sensitive computer-aided diagnoses. Motivated by these factors, this study aims to use deep learning techniques to improve the COVID-19 CT classification performance of commonly used CNNs. Particularly, to alleviate the data insufficiency and enhance the data diversity, we design and apply augmentation of incremental levels on the currently largest COVID-19 CT benchmark dataset (COVIDx CT-2 A) [14]. Meanwhile, to find the optimal selection of CNN architectures and augmentation combinations, we explore seven commonly used CNN architectures under seven augmentation settings. The CNNs include SqueezeNet1.1 [15], MobileNetV2 [16], DenseNet121 [17], ResNet-18/34/50 [18], and InceptionV3 [19]. Meanwhile, contrastive learning is one promising self-supervised method for enabling deep learning models to learn more parameter-efficient features. We propose the similarity regularization (SR) derived from contrastive learning to learn more parameter-efficient representations and improve CNN classification. The experimental results demonstrate that SR can improve the classification performance of CNNs stably and surpass conventional contrastive learning. Our main contributions are summarized as follows: (a) We investigate the impacts of augmentation and model selection in COVID-19 CT classification for three classes, including normal, non-COVID-19 pneumonia (NCP), and COVID-19 pneumonia (CP). (b) We propose SR as a regularization term for learning more parameter-efficient representations. Comparisons between seven models with or without SR are conducted. The experimental results demonstrate that SR can improve classification stably without extra introduced model parameters during the test interface. (c) Our proposed model, DenseNet121-SR, achieves 99.44% test accuracy, 98.40% precision, 99.59% sensitivity and 99.50% specificity for COVID-19 positive class, achieving the state-of-the-art. (d) On other COVID-19 CT datasets, i.e., SARS-CoV2 and COVIDx CT-1, our DenseNet121-SR outperforms the existing methods in terms of efficiency and accuracy. (e) We extend the study to seven classic natural datasets and find that our DenseNet121-SR is superior to the original DenseNet121 for all tasks, indicating that our method can be generalized to general classification problems. The rest of this paper is organized as follows. In Section 2, we review the related works and analyze their pros and cons. Section 3 describes our proposed method. We demonstrate and analyze the experimental results in Section 4 and conduct ablation studies in Section 5. Section 6 discusses our achievements, limitations, and future works. Section 7 draws the conclusion. 2 Related works 2.1 COVID-19 related researches CNNs are increasingly improving the COVID-19 CT classification with advanced algorithms and enhanced datasets. Numerous CNN-based methods achieving high accuracy have been proposed, indicating the potential of CNNs in assisting practical diagnosis. Some representative methods on four benchmark datasets are listed in Table 1.Table 1 Comparison of classification metrics between multiple deep learning methods in four datasets. The precision, sensitivity, and specificity metrics are for COVID-19 positive class only. (The decimal places are kept consistent as reported in the publications.). Dataset Method Params. (M) Accuracy (%) Precision (%) Sensitivity (%) Specificity (%) SARS-CoV-2 [20] Alshazly et al. [21] 86.74 99.4 99.6 99.8 99.6 Silva et al. [22] 4.78 98.99 99.20 98.80 – Kundu et al. [23] 132.86 98.93 98.93 98.93 98.93 Jaiswal et al. [24] – 96.25 96.29 96.29 96.21 Panwar et al. [13] 20.55 94.04 95.30 94.04 95.86 Jangam et al. [25] 202.87 93.5 89.91 98 – Wang et al. [26] – 90.83 95.75 85.89 – COVID-CT [27] Chen et al. [28] – 88.5 89.9 88.6 – He et al. [29] 0.55 86 – – – Polsinelli et al. [30] 1.26 85.03 85.01 87.55 81.95 Jangam et al. [25] 202.87 84.73 78.15 94.9 – Wang et al. [26] – 78.69 78.02 79.71 – COVIDx CT-1 [12] Gunraj et al. [12] 1.40 99.1 99.7 97.3 99.9 Ter-Sarkisov [31] 34.14 91.66 90.80 94.75 – COVIDx CT-2A [14] Zhao et al. [32] 23.51 99.2 98.5 98.7 99.5 Gunraj et al. [14] 0.45 98.1 97.2 98.2 98.8 Gunraj et al. [12] 1.40 94.5 90.2 99.0 95.7 In the COVID-19 CT classification, there exist no gold standard datasets so far. The four widely employed open-access datasets [12], [14], [20], [27] in Table 1 differ in many aspects, including patient/scan distribution, collection sources, dataset size, class numbers, labelling quality, etc. Particularly, COVID-CT [27] and SARS-CoV-2 [20] are two small binary-classification datasets containing 812 and 2,482 CT scans for COVID-19 positive and non-COVID classes, respectively. Gunraj et al. released a larger dataset COVIDx CT-1 [12] consisting of 104,009 scans for normal, NCP and CP classes upon which the authors later built COVIDx CT-2 [14]. COVIDx CT-2 is the largest existing dataset containing 194,922 CT scans, combined from multiple data sources. Generally, data-driven methods like CNNs depend heavily on dataset size. This can be drawn from the classification metrics in Table 1 that the methods trained on larger datasets can roughly achieve higher performance. To ensure both the data diversity and satisfactory results, our study employs COVIDx CT-2 A [14] as our target dataset. Despite the various datasets, many CNN-based methods have been developed to continuously boost classification performance. In particular, researchers often use transfer learning [13], [21], [22], [23], [24], [25], [33], [34], [35] or ensemble learning [22], [23], [25], [33], [36], [37] to overcome data insufficiency in small-scale datasets like SARS-CoV-2 and COVID-CT. For example, Jaiswal et al. [24] and Panwar et al. [13] utilized transfer learning to pre-train the weights of VGG19 and DenseNet201 on ImageNet and then fine-tuned on SARS-CoV-2, achieving 96.25% and 94.04% accuracy, respectively. Besides, ensemble learning, merging the decisions from multiple models into a more balanced decision, has been widely applied in some works through different merging approaches like weighted sum [25], voting [22], and fuzzy rank-based fusion [23]. However, ensemble learning is rarely applied in large-scale datasets like COVIDx CT-1/2 [12], [14]. Specifically, COVID-Net CT-1/2 L [12], [14] are two light-tailored CNNs whose architectures are finely designed by automatic neural architecture searching. The two models are extremely parameter-efficient and achieved 94.5% and 98.1% accuracy with only 0.45MB and 1.40MB parameters, respectively. Another research [32] employed ResNet-50x1 pre-trained on ImageNet-21k and fine-tuned on COVIDx CT-2 A, achieving 99.2% accuracy. Drawn from the reviewed research works above, deep learning models can achieve higher performance in COVID-19 CT classification through the approaches that: (1) train models over data of higher diversity; (2) with finely designed neural networks; (3) ensemble the decisions from multiple models; (4) inherit out-of-domain classification knowledge. Although models can benefit from these aspects, the expensive computational cost of neural architecture searching and large-scale pre-training, and long execution time caused by over-parameterization should be considered as well. 2.2 Contrastive learning In recent years, supervised deep learning models of increasing complexity and depth have shown great progress in many large-scale applications like ImageNet classification [18], [19]. However, directly applying these models to COVID-19 datasets of smaller scales might cause over-parameterization. It means that model capacities cannot be fully fulfilled, and the extracted representations are not parameter-efficient. One promising approach to addressing the issue is contrastive learning. In the deep learning field, it is widely recognized that the model performance depends on the quality of their learned representation. Contrastive learning, also known as contrastive self-supervised representation learning, is one framework aiming at learning efficient representations without human-specified labels. In general, the main idea of contrastive learning is to project inputs into an embedding space where the embedded vectors of similar samples are closer while dissimilar ones are apart. More formally, for visual tasks, a pair of views augmented from one image is considered a positive pair, while pairs of views from different images are considered negative pairs. Hence, contrastive learning models aim to maximize the representation similarity between positive pairs and minimize that between negative pairs. In practical tasks, contrastive learning often pre-trains the front representation extractors of deep learning models in a self-supervised manner, and then fine-tunes the pre-trained weights in a conventional supervised manner. The state-of-the-art contrastive learning frameworks include MoCo  [38], [39], SimCLR [40], [41], SimSiam [42], SwAV [43], BYOL [44], etc. These frameworks mainly differ in terms of the loss function, representation projection, and negative pair formation [42]. And the differences further determine their requirements on the complexity of augmentation policies and batch size. Normally, in order to obtain a satisfactory result, contrastive methods depend on a large batch size to cover enough negative pairs [38], [39], [40], [41]. Among these models, BYOL, SwAV and SimSiam are the contrastive frameworks requiring no negative pairs. In ImageNet linear classification experiments [42], BYOL achieves relatively better performance. This explains that we select BYOL as the basic framework for SR calculation as in Section 3.2. The success of contrastive learning has emerged some applications in COVID-19 CT diagnosis [28], [29], [45]. He et al. [29] employed a MoCo-like [38] framework to enhance the CT scan representations extracted by DenseNet169 and fine-tuned the network, achieving 86% accuracy on COVID-CT [27]. Similarly, Chen et al. employed the MoCo-v2-like [39] framework on the same dataset and reached 88.5% accuracy within six shots. Li et al. [45] put the contrastive loss as a regularization term and trained their CMT-CNN in an end-to-end manner, obtaining 93.46% accuracy. These studies suggest contrastive learning can boost classification performance by learning more efficient representations. 3 Method 3.1 Augmentation of incremental levels Data augmentation is vital for improving the performance of deep learning models, especially for contrastive learning [39], [40]. However, the optimal selection for COVID-19 CT augmentation has not been studied. Inspired by the literature in Section 2, we design and evaluate a series of augmentation operations of incremental levels as follows where “+” denotes the appended augmentation based on the previous level: Level 0 No augmentation. Level 1 + RandomResizedCrop: Randomly obtain an image crop of size in the range [0.08,1] of the original size 256 × 256, and then randomly scale the crop according to an aspect ratio in the range [3/4,4/3]. The scaled crop is finally resized to the original size. Level 2 + Horizontal Flip. Randomly flip the input image horizontally with 50% using probability. Level 3 + RandAugment [46]: Randomly apply rand augment twice with magnitude 9 and magnitude standard deviation 0.5. Level 4 + Random Erasing [47]: Select a rectangle region of the input image and do pixel-wise erasing with 25% using probability. The size of the selected region are randomly picked in the range [0.02,1/3] of the image size. Level 5 + Mixup [48]: Mix two in-batch images up with a ratio λ subjecting to a beta distribution, λ∼B(1,1). The mixup process for images IA and IB can be formatted as IA(x,y)=λIA(x,y)+(1−λ)IB(x,y), where (x,y) denotes the pixel coordinate. Level 6 + CutMix [49]: Switch from Mixup to Cutmix with 50% probability. Randomly replace a square region in the original image with a region in another in-batch image. The region size is randomly determined, subject to the squared root of a beta distribution B(1,1). The visualization of the augmented scans is demonstrated in Fig. 2. Specifically, RandomResizedCrop and horizontal flip are two commonly used augmentation operations in both supervised [18], [19] and self-supervised learning [39], [40], [41], [44]. Since contrastive learning requires more complicated augmentation [40], the two stronger augmentations, RandAugment and Random Erasing, are further introduced in levels 3 and 4. Their implementations and parameters refer to [50], [51]. In levels 5 and 6, mixup and cutmix are two augmentations enabling higher data diversity by fusing in-batch images. In these two levels, we mainly experiment on whether such sample-fusing augmentations can improve COVID-19 classification. By comparing the performance of models under these incremental augmentation levels, an appropriate augmentation strategy for COVID-19 CT scans can be established.Fig. 1 The overall structure of the models with our proposed similarity regularization in training interface. The two projectors, g1 and g2, and the online predictor p are implemented by non-linear MLPs. After training, only the online encoder f1 and fully connected layer FC are preserved in testing. Fig. 2 Illustration of applied incremental augmentation of six levels. On the right side, six groups of images are augmented from the same left COVID-19 positive scan. These groups from top to down are in the augmentation levels from 1 to 6. The bottom left normal scan is the auxiliary original image that only participated in Mixup/Cutmix augmentation in levels 5 and 6. The left two scans are from COVIDx CT-2 A. 3.2 Similarity regularization Most mainstream conventional CNNs contain two parts, a representation extractor f and a followed fully connected layer FC. The extractor f aims to extract the distinguishable representations of given inputs, and FC predicts the class probability distribution by summarizing the extracted representations. This forward propagation is demonstrated as the top branch in Fig. 1. More formally, the input image x is first transformed to a view v by a random on-the-fly augmentation operation t∼T where T denotes an infinite collection of augmentation operations. Subsequently, the representation extractor f converts the input view v to a representation embedding vector h=f(v). FC predicts the class probability distribution based on its obtained representation, yˆ=FC(h). The training target of such a classifier is to minimize the class probability distribution distance between the prediction yˆ and the ground truth y according to the cross-entropy loss in Eq. (1), where i∈{0,1,2} denotes the class index. (1) H(y,yˆ)=−∑i=02yilogyˆi In this conventional fully supervised scenario, the trained representations aim at better projecting to human-specified class distribution. However, this manner affects data efficiency, robustness or generalization [52]. Instead, contrastive learning enables learning more parameter-efficient representations from inputs themselves instead of the specified annotations. We thus incorporate it in common CNNs to improve their representation learning ability. The overall structure of our method is illustrated in Fig. 1. We keep the conventional supervised classifier unchanged in the top branch while introducing a contrastive learning framework in the bottom branch. As in Section 2.2, contrastive learning is to maximize the representation similarity between positive pairs. We punish the positive-pair representation distance as a regularization term beside the cross-entropy loss, naming the term similarity regularization (SR). Particularly, the contrastive framework is a siamese network like most mainstream frameworks [40], [41], [42], [44], consisting of an online network and a target network. The target network can be seen as a moving average of the online one. Given two views v1 and v2 augmented from the same input image x, the representation extractors f1 and f2 in two networks extract their corresponding latent representation vectors, h1=f1(v1) and h2=f2(v2). To avoid representations heavily affected by SR, the representation vectors then projected to another embedding space where z1=g1(h1) and z2=g2(h2), as in [41], [44]. Since the projectors g1 and g2 share slightly different feature spaces, the online projection z1 is further projected to p(z1) of same dimension via online predictor p. The cosine representation similarity S of value in range [−1,1] can be measured according to Eq. (2). (2) S(p(z1),z2)=〈p(z1),z2〉‖p(z1)‖2⋅‖z2‖2 where 〈⋅,⋅〉 and ‖⋅‖2 are inner product and L2 norm notations, respectively. A higher value indicates two vectors are of higher similarity. To penalize a low cosine similarity between positive pairs and scale the penalty in range [0,1], SR can be calculated as Eq. (3). (3) D(p(z1),z2)=2−2S(p(z1),z2)=2−2〈p(z1),z2〉‖p(z1)‖2⋅‖z2‖2 Hence, for a positive pair (v1,v2), its total loss containing both cross-entropy loss and SR is written as in Eq. (4). (4) L(z1,z2,y,yˆ)=(1−γ)H(y,yˆ)+γD(p(z1),z2) where γ is a scale factor for balancing the conventional cross-entropy loss and the introduced SR. (v2,v1) is the symmetric positive pair with respect to (v1,v2). We calculate the losses for both symmetric pairs and take their mean as the final loss for fast convergence. SR as a regularization term may raise concern if it will dominate the combined loss and thus degrade the classification. To remove the concern and find an appropriate scheduler for γ, we design three strategies as listed below. i denotes the current training iteration number. Constant (default) : γ is set to be a constant value during all training iterations, 0.5 by default. Linear Decay : γ decays linearly to a minimum value γmin=0.01 along N training iterations according to Eq. (5). (5) γi=γmin+(1−iN)(1−γmin) Cosine Decay : γ decays to a minimum value γmin=0.01 along N training iterations according to cosine annealing scheduler as in Eq. (6). (6) γi=γmin+12(1+cosiπN)(1−γmin) Besides, after training, we throw away all the components except the online representation extractor f1 and the fully connected layer FC. Hence, introducing SR in training will not slow down the test interface. The training pseudocode of models with SR is demonstrated in Algorithm 1. 4 Results and analysis 4.1 Dataset description In this paper, we mainly train and evaluate our proposed method using the largest existing open-access COVID-19 CT dataset, COVIDx CT-2 A.1 Specifically, the dataset contains three classes, including normal, non-COVID-19 pneumonia (NCP), and COVID-19 pneumonia (CP). Its class distribution is summarized in Table 2. The dataset is of high diversity, containing scans of 3,745 patients from eight open-access sources. It should be noted that the scans from the same patient are in one subset, preventing information leakage from training to validation or testing. Table 2 Class distribution of the employed COVIDx CT-2A dataset. Set Normal NCP CP Total Training 35,996 25,496 82,286 143,778 Validation 11,842 7400 6244 25,486 Testing 12,245 7395 6018 25,658 4.2 Experimental setting In this paper, we keep the hyper-parameters consistent in all experiments for fair comparisons. The codes are implemented by PyTorch. We implement the CNN backbones and image augmentation by torchvision and timm [51] libraries, respectively. For acceleration, we train models on Torch distributed data parallelism on four Nvidia V100 GPUs with apex mixed precision of level O1. Besides, to alleviate the randomness concern, we obtain the experimental statistics by averaging the measurements in three distinct trials. During training, CT scans are resized to 256 × 256 in 3 channels using bicubic interpolation and normalized by ImageNet mean and std. In the test interface, 256 × 256 CT scans are cropped from the center of resized 293 × 293 original images. This is empirically good as the center crop can preserve the main lung regions. To avoid models from being too confident in one-class prediction, label smoothing [19] of smoothing factor 0.1 is applied in the cross-entropy in augmentation levels 0−4. While in augmentation level 5 or 6, in-batch paired labels are mixed up based on mixed inputs (See [48], [49] for more details). The optimizer we used is Adam with 10−6 weight decay. After a 5-epoch linear warmup [53] from 5×10−7, we use cosine annealing scheduler to decay the learning rate from 5×10−4 to 5×10−7 in the later 45 epochs. The batch size is set to 64 in each process. Besides, the gradients are clipped to be no larger than 5.0 to avoid overflow. In the SR calculation, the projectors g1,g2 and predictor p have the same multi-layer perceptron (MLP) architecture that consists of two linear layers connected by a batch normalization layer and a ReLU activation layer. The front linear layer projects the inputs to 512-D embedding vectors and the later linear layer outputs 128-D vectors. The analysis for the dimension setting is in Section 5.3. The momentum rate β for updating f2 and g2 is 0.99, a median value among contrastive frameworks [38], [42], [43], [44]. 4.3 Results of ResNets under incremental augmentation levels We first compare the performance of ResNets with or without SR under the incremental augmentation levels designed in Section 3.1 to determine an appropriate augmentation policy for the coming experiments. The averaged test accuracies are listed in Table 3. Since SR requires calculating the similarity between two augmented views, models with SR cannot be implemented under augmentation level 0. Table 3 shows that the original ResNets achieve the highest accuracy in level 2 and cannot be improved in the following levels, heavily degraded in levels 5 and 6. The degradation may result from the fact that sample-fusing augmentation sometimes transfers the pneumonic pathologies from CP/NCP cases to normal cases. We thus do not perform SR in levels 5 and 6. Different from the original ResNets, ResNets with SR continue to improve after level 2 and achieve the highest accuracy in level 4. This is consistent with the findings in many contrastive learning research works that contrastive learning requires stronger augmentation than supervised models [39], [40], [54]. Hence, we select level 4 as the basic augmentation level for the following experiments. Overall, it is observed that SR can improve the classification performance of ResNets stably under all augmentation levels from 1 to 4.Table 3 Test accuracy comparison between original ResNets and ResNets with proposed SR under incremental augmentation levels. ResNet is abbreviated as R. The scale factor scheduler for scaling SR is the default constant scheduler γ=0.5. Augmentation Method CNN backbone R18 R34 R50 Level 0 Original 91.28 92.28 91.57 Level 1 Original 99.10 99.00 98.89 ＋SR(Ours) 99.23 99.27 99.09 Level 2 Original 99.17 99.11 99.00 ＋SR(Ours) 99.27 99.39 99.19 Level 3 Original 99.11 99.06 99.08 ＋SR(Ours) 99.26 99.29 99.20 Level 4 Original 99.12 99.09 99.13 ＋SR(Ours) 99.40 99.43 99.31 Level 5 Original 97.49 97.34 97.66 Level 6 Original 98.50 98.69 98.91 Table 4 Test accuracy of seven CNNs with or without SR under augmentation level 4, ordered by the number of parameters. The scale factor scheduler for scaling SR is the default constant strategy γ=0.5. Note that the extra parameters introduced by SR will be discarded in test interface after training. Method Metric CNN backbone SqueezeNet MobileNet DenseNet ResNet18 ResNet34 InceptionV3 ResNet50 Original Params. (M) 0.69 2.12 6.63 10.66 20.30 20.78 22.42 Acc. (%) 98.22 99.02 99.05 99.08 99.06 99.22 99.12 ＋SR(Ours) Params. (M) 1.13 2.94 7.33 11.10 20.74 21.97 23.62 Acc. (%) 98.41 99.18 99.44 99.39 99.43 99.32 99.31 Table 5 Comparison of DenseNet121-SR with the state-of-the-art methods on COVIDx CT-2A dataset. Method Accuracy (%) Precision (%) Sensitivity (%) Specificity (%) Normal NCP CP Normal NCP CP Normal NCP CP COVID-Net CT-1 [12] 94.5 96.1 97.6 90.2 98.8 80.2 99.0 96.3 99.4 95.7 COVID-Net CT-2 S [14] 97.9 99.3 96.4 97.0 98.9 95.7 98.1 99.3 98.9 98.8 COVID-Net CT-2 L [14] 98.1 99.4 96.7 97.2 99.0 96.2 98.2 99.5 99.0 98.8 Bit-M [32] 99.2 99.8 98.9 98.5 99.3 99.6 98.7 99.8 99.6 99.5 DenseNet121-SR (Ours) 99.44 99.89 99.55 98.40 99.12 99.83 99.59 99.91 99.82 99.50 4.4 Results under augmentation level 4 In this section, we extend the experiments to seven widely used CNNs, including SqueezeNet1.1 [15], MobileNetV2 [16], DenseNet121  [17], ResNet-18/34/50 [18], and InceptionV3 [19]. The experiments are under augmentation level 4 and a constant scale factor scheduler γ=0.5. From the results in Table 4, it can be seen that all our models with SR surpass the original models in terms of averaged test accuracy. The best model, DenseNet121 with SR, achieves 99.44% accuracy with 7.33M parameters. Note that the extra parameters will be thrown away after training so that the parameters in the test interface are consistent for a model with or without SR. Another observation is that, in COVID-19 CT classification, the model performance is not strictly proportional to its capacity despite model architecture. This suggests that the fine design of model architecture rather than simply expanding depth or width is more valuable in this task, as supported in [12], [14], [30]. Fig. 3 shows the confusion matrices for DenseNet121-SR in three training trials. Based on the matrices, we measure the performance of the model in terms of averaged accuracy, precision, sensitivity, and specificity, as listed in Table 5. The results show that our DenseNet121-SR has outperformed the state-of-the-art models in nearly all measurements. Specifically, DenseNet121-SR achieves a high sensitivity 99.59% for COVID-19 positive class, indicating that the model has the potential to efficiently avoid COVID-19 positive patients from being wrongly diagnosed.Fig. 3 Confusion matrix for DenseNet121-SR in three trials. To better understand the behavior of our model, we visualize the attention of DenseNet121-SR on three CT scans in different classes as in Fig. 4. It can be observed that our model mainly focuses its attention on some suspicious regions where the pathologies may exist. Fig. 4 Attention of DenseNet121-SR visualized by Grad-CAM. The three groups of CT scans and heatmaps from left to right are in class normal, NCP, and CP, respectively. The highlighted parts are the regions based on which CNNs classify the CT scans. Table 6 Classification accuracy of DenseNet121 with or without SR over seven classic natural datasets. Aircraft CIFAR10 CIFAR100 DTD Flowers102 OxfordIIITPet StanfordCars DenseNet121 88.15 94.45 85.08 70.60 93.17 92.47 92.46 DenseNet121-SR (Ours) 88.18 94.47 85.08 71.01 94.42 92.88 92.55 4.5 Results on other datasets On other COVID-19 CT datasets Based on the experimental results aforementioned, we extend our method to the other two COVID-19 CT datasets, i.e., SARS-CoV2 and COVIDx CT-1. It should be noted that, for SARS-CoV2, we train DenseNet121-SR over 200 epochs with weights pre-trained on ImageNet because SARS-CoV2 contains much fewer CT scans than the others. The results as listed in Table 7 show that our method can be generalized to other datasets and can achieve a high classification performance. Comparing to the methods listed in Table 1, our DenseNet121-SR with only 6.63 MB parameters is more parameter-efficient and outperforms the reviewed methods. On classic natural datasets Besides, extensive experiments are conducted over seven natural datasets to further evaluate the generalization ability of our method. To evaluate the effect of SR fairly, we keep the setting unchanged as in Section 4.2 and initialize the model weights as pre-trained on ImageNet. Table 6 demonstrates the classification accuracy of DenseNet121 with or without SR on the seven datasets, including FGVC Aircraft [55], CIFAR10/100 [56], Describable Textures Dataset (DTD) [57], Oxford 102 Flowers [58], Oxford-IIIT Pets [59], and Stanford Cars [60]. It shows that DenseNet121-SR is superior to the original model in all the tasks, indicating our proposed SR can be generalized to general classification problems.Table 7 Classification results of DenseNet121-SR on SARS-CoV2 and Covidx CT-1 datasets. The precision, sensitivity, and specificity metrics are for COVID-19 positive class only. Dataset Acc (%) Prec (%) Sens (%) Spec (%) SARS-CoV2 99.20 99.47 98.93 99.46 COVIDx CT-1 99.78 99.56 99.84 99.74 5 Ablation study The following ablation studies are conducted to better investigate the effects of our proposed SR. 5.1 Ablation to self-supervised learning Fully self-supervised learning Contrastive learning is widely adopted in pre-training CNNs that are fine-tuned later for downstream tasks. In our methods, we turn the process to an end-to-end manner by regularizing CNNs with proposed SR derived from contrastive learning. Hence, a comparison between SR and conventional contrastive learning is necessary. Specifically, we design and measure the following methods for comparison as follows. (a) Linear Evaluation. First pre-train the representation extractor f weights of which are frozen in the later FC fine-tuning. The pre-training process is equivalent to setting γ=1 in all training epochs as in Algorithm 1, and then fine-tuning only the linear layers FC as usual. The fine-tuning hyper-parameters include: 256 batch size, learning rate decays from 40 to 4×10−6 according to cosine decay scheduler [53]. The optimizer used is SGD. Linear evaluation is simply conducted for verifying the effects of contrastive learning in this task. (b) Two-stage training (self-supervised contrastive learning followed by conventional supervised learning). This way first pre-trains the representation extractor f and then trains the entire CNN with pre-trained weights. The hyper-parameters are consistent with others as in Section 4.2. (c) Apply SR to ResNets with a default constant γ=0.5. The results for the designs above are listed in Table 8. It can be observed that contrastive learning can learn efficient representations that even a simple linear evaluation on the pre-trained representation extractor can achieve over 92% test accuracy. For the second method, two-stage contrastive learning, the pre-trained weights from the representation extractor might be hard to maintain in the later training phase. Our introduced SR maintains the representation by explicitly penalizing the representation difference for positive pairs. The results in Table 8 verify that ResNets with SR surpass the two-stage contrastive learning method in most experiments. Besides, it is worth noting that the end-to-end training with SR does not require pre-training and thus saves computational resources.Table 8 Test accuracy comparison between two-stage training and end-to-end training with SR under incremental augmentation levels. The scale factor scheduler for scaling SR is the default constant scheduler γ=0.5. Augmentation Method CNN backbone R18 R34 R50 Level 1 Linear Eval 95.47 92.89 92.32 Two-stage 99.17 99.17 99.04 ＋SR(Ours) 99.23 99.27 99.09 Level 2 Linear Eval 95.76 92.03 94.02 Two-stage 99.25 99.23 99.20 ＋SR(Ours) 99.27 99.39 99.19 Level 3 Linear Eval 93.92 93.88 95.08 Two-stage 99.26 99.29 99.24 ＋SR(Ours) 99.26 99.29 99.20 Level 4 Linear Eval 93.12 93.23 94.53 Two-stage 99.38 99.30 99.18 ＋SR(Ours) 99.40 99.43 99.31 5.2 Ablation to decay strategy for γ The two-stage contrastive learning method can be approximated to run the SR Algorithm 1 with γ=1 in pre-training and γ=0 in fine-tuning. The sharp fall of γ may destroy the maintained representation space obtained in contrastive pre-training. To avoid the potential negative impact, we designed two mild γ decay strategies in Section 3.2 despite the constant γ strategy. From the results demonstrated in Fig. 5, we can conclude that SR with all designed γ strategies can stably improve classification accuracy. And SR is insensitive to the γ strategy setting since all strategies have comparable performance. Due to the simplicity of the γ strategy (γ=0.5 in all iterations) and its slight superiority in level 4 augmentation, we select it as the default strategy in our experiments. The ablation studies find that a constant strategy, γ=0.5, can achieve the relatively highest performance among the three strategies under level 4 augmentation.Fig. 5 Accuracy of ResNets obtained with different γ decay strategies under incremental augmentation levels from 1 to 4. The baselines are the original models without introduced SR. The γ value for the constant strategy is 0.5 by default while γ decays from 1.0 to 0.01 in linear and cosine strategies. The constant γ value still requires studies for finding its effects on model performance. We thus vary the γ value in constant strategy from 0.1 to 0.9 with 0.2 interval and repeat the experiments for CNNs with SR under augmentation level 4. As shown in Fig. 6, SR can improve the CNN classification performance when γ value is in an appropriate range near [0.5,0.7]. In particular, a smaller γ cannot fully fulfils the advantage of SR and sometimes even degrades the model capacity as in SqueezeNet1.1 case. Meanwhile, setting γ to a large value like 0.9 is also risky since SR dominates the total loss while the primary cross entropy for classification is slighted. Fig. 6 Accuracy of seven CNNs with SR controlled by constant γ scale factor strategy, under augmentation level 4. γ varies from 0.1 to 0.9 with 0.2 interval. 5.3 Ablation to projection size in SR The output dimension or named projection size, of both the projector and predictor in SR calculation is set to 128 as default. We keep the hidden dimension 512 unchanged to avoid redundant computation while varying the projection size to analyze its effect in terms of classification accuracy. As visualized in Fig. 7, the differences between the classification accuracy for models except for SqueezeNet are small (≤0.2%). This indicates that the hyper-parameter setting in our proposed SR is robust.Fig. 7 Impact of projection size in SR calculation. 6 Discussion Since COVID-19 grows rapidly worldwide, designing efficient and accurate classification systems is essential. Although some methods [12], [14], [21], [22], [32] have claimed a high classification accuracy (≈99%) on multiple datasets, we argue that even a slight improvement can mitigate further infection. Meanwhile, some high-performance methods require considerable computational resources, making them hard to be deployed into practical healthcare systems. Hence, designing more efficient models with affordable training parameters should also be noted. This paper mainly proposes an incremental augmentation strategy and SR to improve the CNN classification performance on three COVID-19 CT datasets. The results illustrate that appropriate augmentation can significantly alleviate the data limitation problem in COVID-19 CT classification. Meanwhile, our proposed SR further improves the classification performance of seven CNNs by enhancing their representation learning ability. Specifically, on the largest dataset COVIDx CT-2 A, our model DenseNet121-SR achieves 99.44% accuracy and 99.59% sensitivity with only 6.63 MB parameters in the test interface, outperforming all the reviewed state-of-the-art methods. Overall, the designed augmentation strategy enhances the COVID-19 CT dataset diversity approximately and the proposed SR better fulfils the representation learning ability of CNNs within definite parameters, both leading to the high classification performance of our work. Besides, we evaluate DenseNet121-SR on the other two datasets, achieving 99.78% and 99.20% accuracy on COVIDx CT-1 and SARS-CoV2, respectively. To further justify the effect of SR, we extend the DenseNet121-SR to seven classic natural datasets, illustrating SR can be generalized to general classification tasks. Furthermore, since SR derives from contrastive learning, we compare traditional contrastive learning and end-to-end training with SR in ablation studies. The comparison demonstrates that SR is superior in classification accuracy and training efficiency and is robust to its hyper-parameter setting. Despite the achieved promising performance, the limitations of our method exist. Our method requires either large amounts of training data or pre-training on other large-scale datasets. The high performance of our models with SR partly owes to the efforts of workers collecting numerous CT scans. For smaller-scale datasets like SARS-CoV2, the backbone of our method requires pre-training. The pre-training on ImageNet helps improve the accuracy from around 98% to 99.20% in the DenseNet121-SR case. Besides, we can hardly evaluate other contrastive frameworks due to the lack of computational resources. Meanwhile, we cannot redesign the CNN backbones to better balance computational efficiency and classification performance because of the substantial computational loads of neural architecture searching and pre-training. Before deploying to clinical practice, computer-aided methods must be evaluated regarding their robustness and generalization. Compared to the existing online CT datasets, CT scans in practical scenarios are obtained by more diverse imaging devices under changeable environments. It is significant for the methods to adapt to these unfamiliar scenarios. Also, the interpretability of the methods for inferring the decision principle can make the methods more trustable. Besides, with the evolution of coronavirus, whether the pathological changes can still be observed clearly in CT scans should be concerned as well. Computer-aided methods can convince clinicians and patients as assisting tools in practical uses only if the issues above are addressed. For future work, we will explore redesigning the network backbone, pre-training the redesigned backbones on large-scale datasets, and making networks more explainable in COVID-19 CT diagnosis. 7 Conclusion This paper aims to improve the CNN performance for COVID-19 CT classification by enabling CNNs to learn parameter-efficient representations from CT scans. We propose the SR technique derived from contrastive learning and apply it to seven commonly used CNNs. The experimental results show that SR can stably improve the CNN classification performance. Together with a well-designed augmentation strategy, our model DenseNet121-SR with 6.63 MB parameters outperforms the existing methods on three COVID-19 CT datasets, including SARS-CoV2, COVIDx CT-1, and COVIDx CT-2 A. Specifically, on the largest available dataset COVIDx CT-2 A, DenseNet121-SR achieves 99.44% accuracy, 98.40% precision, 99.59% sensitivity, and 99.50% specificity for the COVID-19 pneumonia category. Furthermore, the extensive experiments on seven classic natural datasets demonstrate that SR can be generalized to common classification problems. CRediT authorship contribution statement Yujia Xu: Conceptualization, Methodology, Writing – original draft. Hak-Keung Lam: Method improvement, Review & editing, Supervision. Guangyu Jia: Method improvement. Jian Jiang: Review & editing. Junkai Liao: Review & editing. Xinqi Bao: Review & editing. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment This work was supported by King’s College London and has been performed using resources from the Cirrus UK National Tier-2 HPC Service at EPCC funded by the University of Edinburgh and EPSRC (EP/P020267/1). 1 https://www.kaggle.com/hgunraj/covidxct. ==== Refs References 1 WHO WHO Coronavirus (COVID-19) dashboard 2022 https://covid19.who.int/. (Online; Last accessed 7 April 2022) 2 McKibbin W. Fernando R. The economic impact of COVID-19 Economics in the Time of COVID-19 45 10.1162 2020 3 Iacobucci G. COVID-19: New UK variant may be linked to increased death rate, early data indicate Br. Med. J. 372 230 2021 n230 33500262 4 Mahase E. COVID-19: Where are we on vaccines and variants? Br. Med. J. 372 2021 n597 10.1136/bmj.n597 33653708 5 Gu Y. COVID-19 infections tracker 2022 https://covid19-projections.com/infections-tracker/. (Online; Last Accessed 17 August 2021) 6 Kucirka L.M. Lauer S.A. Laeyendecker O. Boon D. Lessler J. 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==== Front Soc Sci Med Soc Sci Med Social Science & Medicine (1982) 0277-9536 1873-5347 Published by Elsevier Ltd. S0277-9536(22)00926-1 10.1016/j.socscimed.2022.115620 115620 Article The associations of everyday and major discrimination exposure with violence and poor mental health outcomes during the COVID-19 pandemic Raj Anita ab∗ Chatterji Sangeeta d Johns Nicole E. a Yore Jennifer a Dey Arnab K. a Williams David R. c a Center on Gender Equity and Health, University of California San Diego School of Medicine, 9500 Gilman Drive #0507, La Jolla, CA, 92093, USA b School of Social and Political Science, University of Edinburgh, Chrystal Macmillan Building, 15a George Square, Edinburgh EH8 9LD United Kingdom c Department of Education Studies, University of California, 3350 La Jolla Village Dr, San Diego, CA 92161, USA d Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA ∗ Corresponding author. Center on Gender Equity and Health, University of California San Diego School of Medicine, 9500 Gilman Drive #0507, La Jolla, CA, 92093, United States. 15 12 2022 15 12 2022 11562017 4 2022 11 10 2022 13 12 2022 © 2022 Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Research on discrimination and risks for violence and mental health issues under the pandemic is notably absent. We examined the relative effects of perceived everyday discrimination (e.g., poorer service, disrespectful treatment in a typical week) and major experiences of race-based discrimination (e.g., racial/ethnic discrimination in housing or employment at any point in the lifetime) on experiences of violence and the PHQ-4 assessment of symptoms of depression and anxiety under the pandemic. We analyzed state-representative cross-sectional survey data from California adults (<I>N</I> = 2114) collected in March 2021. We conducted multivariate regression models adjusting for age, race/ethnicity, gender, sexual identity, income, and disability. One in four Californians (26.1%) experienced everyday discrimination in public spaces, due most often to race/ethnicity and gender. We found that everyday discrimination was significantly associated with past year physical violence (single form Adjusted Odds Ratio [AOR] 5.0, 95% CI 2.5–10.3; multiple forms AOR 2.6, 95% CI 1.1–5.8), past year sexual violence (multiple forms AOR 2.5, 95% CI 1.4–4.4), and mental health symptoms (e.g., severe symptoms, multiple forms AOR 3.3, 95% CI 1.6–6.7). Major experiences of race-based discrimination (reported by 10.0% of Californians) were associated with past year sexual violence (AOR 2.0, 95% CI 1.1–3.8) and severe mental health symptoms (AOR 2.7, 95% CI 1.2–6.2). Non-race-based major discrimination (reported by 23.9% of Californians) was also associated with violence and mental health outcomes Everyday discrimination, more than major experiences of discrimination, was associated with higher risk for violence and poor mental health outcomes during the pandemic. Non-race-based forms of major discrimination independently were also associated with these negative outcomes. Findings indicate that efforts to reduce and ultimately eliminate discrimination should be a focus of public health and COVID-19 rebuilding efforts. Keywords Sexual violence Sexual harassment Gender-based violence COVID-19 Economic deprivation Poverty ==== Body pmcThe authors report no conflicts of interest. 1 Introduction Global evidence documents a 25% increase in depression and anxiety disorders as a consequence of the social isolation and economic and health stressors of the COVID-19 pandemic (WHO, 2022). In the United States (U.S.), these mental health consequences are occurring in tandem with an increase in violence, with some indication that these disproportionately affected women and racial/ethnic minorities (Connor et al., 2020; FBI, 2021, June, 2021; GEH, May 13, 2021). Racially motivated hate crimes and racial discrimination also increased in this same timeframe, as did those based on sexual identity, religion and gender (FBI, 2021, August 30, 2021; Strassle et al., 2022). Racial discrimination is a driver of major health inequities including experiences of violence and poor mental health (Marmot, 2017; McCartney et al., 2019; Williams and Cooper, 2019). However, research has not examined the associations between discrimination and these outcomes in the pandemic. The American Psychological Association describes discrimination as “the unfair or prejudicial treatment of people and groups based on social characteristics or identities such as race, gender, age or sexual orientation.” (APA, October 31, 2019). Such discrimination can be in the form of “everyday discrimination,” which can include being treated with lesser courtesy and respect in everyday interactions (Williams et al., 1997) or “‘microaggressions’ such as snubs, slights and misguided comments that suggest a person doesn't belong or invalidates his or her experiences” (APA, October 31, 2019). In contrast, major episodes of discrimination are those that are similar to major life events and instrumental in adversely affecting opportunities for advancement or triggering retrogression/harm (Williams et al., 2008). These include discrimination occurring at the institutional level, with resultant disadvantage based on a social attribute by the system (e.g., refusal of a loan from a bank) or within the institution (e.g., denial of promotion or salary inequity in one's place of employment) (Lincoln and Stanley, 2021), as well as institutional violence such as discriminatory policing (Williams et al., 2008). Limited research has examined the relative effects of these forms of discrimination, though both forms are associated with poorer health, particularly mental health (Gee, 2002, 2008; Williams et al., 2019). Discrimination may also be associated with the exacerbation of poorer mental health outcomes resulting from the pandemic (Hossain et al., 2020), particularly given evidence of elevation in racial/ethnic discriminatory attacks over the past few years in the U.S. (Laster Pirtle and Wright, 2021). Prior research indicates that experiences of violence are associated with COVID-19 related mental health effects (A. Raj et al., 2020a, Raj et al., 2020b), but discrimination has not been examined in this regard. This is a notable absence given that discrimination itself can be considered a form of violence (Lombardi et al., 2002; Sanders-Phillips, 2009; SteelFisher et al., 2019). Further, similar to community and family violence (Bacchus et al., 2018; Baranyi et al., 2021; Norman et al., 2012), discrimination has been implicated in creating chronic stress and poorer mental health outcomes among socially marginalized populations (APA, October 31, 2019; Bailey et al., 2017; Berger and Sarnyai, 2015; Ruth A. Hackett et al., 2020; Paradies et al., 2015; Williams et al., 2019). Racial/ethnic discrimination experiences may have increased under the pandemic (Laster Pirtle and Wright, 2021) and may be linked with both violence and poorer mental health outcomes. Other forms of social discrimination also may have increased under the pandemic, but consideration of both racial and non-racial discrimination simultaneously is not typically done in the literature, despite calls for more intersectional analysis (Fagrell Trygg et al., 2019). Research that has examined associations between discrimination and victimization from violence has mostly focused on self-reported perceptions of gender discrimination and experiences of violence against women and lesbian, gay, bisexual, transgender, queer/questioning, intersex, asexual (LGBTQIA+) individuals (Gordon and Meyer, 2007; Jackson et al., 2019; Lombardi et al., 2002; Rafferty, 2013; SteelFisher et al., 2019). Similarly, there is extensive evidence regarding gender and mental health. There has long been evidence regarding higher levels of depression for women relative to men, but research also documents associations between gender discrimination and poor mental health outcomes (Hackett et al., 2019). Studies also show that experiences of discrimination against LGBTQIA + individuals areassociated with poorer mental health outcomes, and further, that minority stress contributes to this increased risk in ways similar to that seen for racial/ethnic minority communities (Hatzenbuehler and Pachankis, 2016; Meyer, 2003; Tan et al., 2020). Intersectional minority stresses, such as being gay and Black, likely compound stress responses and increase risk for consequent mental health concerns (Parra and Hastings, 2018). Certainly, other characteristics could contribute to experiences of discrimination as well, such as disability; research shows that disability-based discrimination is also linked to both violence, including hate crime violence, and poorer mental health outcomes (Clement et al., 2011; R. A. Hackett et al., 2020a, Hackett et al., 2020b). Age discrimination, based mostly on older age, is also correlated with worse physical and mental health outcomes (Jackson et al., 2019). So here, too, we see the potential harms of discrimination across attributes, and the aggregate and intersectional risks that can occur for those who are, for example, older and living with disability. Racial discrimination and victimization from violence has received less attention, despite extensive research documenting the association between this form of discrimination and other negative social and health outcomes, including mental health trauma (Bailey et al., 2017; Lewis et al., 2015; Williams and Cooper, 2019; Williams et al., 2019). Mental health trauma is highly correlated with violence in racial/ethnic minority communities (Williams, 2018). Lack of focus on this issue is particularly of concern given the availability of research on race/ethnicity and violent crime (Burt et al., 2012) and the demonstrated interconnections of violence victimization, violence perpetration, and mental health (Choe et al., 2008; Hong et al., 2015; Russell et al., 2010). Further, while there is an increasing recognition of intersectional discrimination – i.e., discrimination based on multiple social factors in combination, such as race/ethnicity and gender (Fagrell Trygg et al., 2019), and differential effects of everyday compared with major experiences of discrimination, as noted above, little research has examined multiple forms of discrimination simultaneously. A study that did assess the independent and intersectional associations of self-reported perceptions of racial/ethnic and gender discrimination with experiences of victimization from violence found that both were associated with increased risk for victimization from dating violence among adolescents (Roberts et al., 2018). The literature connecting violence and mental health outcomes shows that violence can be causal or resultant of poor mental health and is often embedded in social contexts of vulnerability and social and economic alienation (Chatterji and Heise, 2021). Meta-analyses using empirical research with youth and adults show a causal association between violence, experiences most often occurring for the first time in youth, and outcomes of depression and anxiety symptoms and diagnosis (Bellis et al., 2019; LeMoult et al., 2020). Studies also show that perpetrators are more likely to hold an anxiety attachment style, indicating that poor mental health outcomes may precede or follow violence experiences (Velotti et al., 2022). Additional review studies highlight that contexts of social alienation and economic marginalization increase risk for both perpetration and victimization from violence, and exacerbate harmful effects of violence on mental health (Gao et al., 2017, 2021). Taken together, these findings highlight that violence and its negative health effects occur in and are affected by social and economic marginalization. Discrimination can be a mechanism through which marginalization occurs. This study examines the associations between everyday and major experiences of discrimination [measured by self-perception reports] and outcomes of victimization from violence [past year physical and sexual violence] and negative mental health symptoms [past two-week depression and anxiety symptoms] during the pandemic among a state-representative sample of California adults. This work can provide insight into the relative effects of everyday versus major discrimination on violence and mental health, and the relative effects of race-based and non-race based major experiences of discrimination on these outcomes. Such findings can offer greater insight into the ways in which multiple aspects of systemic racism can affect health disparities related to trauma and mental health (Boynton-Jarrett et al., 2021). We examine these issues in the context of a study from California, a state that showed both a significant increase in violence from 2020 to 2022 and early in the pandemic adverse mental health consequences (GEH, May 13, 2021; Anita Raj et al., 2020; A. Raj et al., 2020a, Raj et al., 2020b; Raj et al. September 2022). Findings can help guide how to address pandemic impacts with considerations of social inequalities and health disparities. 2 Methods 2.1 Data source We analyzed cross-sectional data from a state-representative online survey of California residents aged 18 and older (N = 2203) conducted in March 2021 as part of the California Study on Violence Experiences Across the Lifespan 2021 (Cal-VEX 2021) (GEH, May 13, 2021). The Cal-VEX 2021 survey built upon prior annual surveys with an additional focus on impacts of the COVID-19 pandemic (Anita Raj et al., 2020). NORC at the University of Chicago obtained the survey sample from a general population sample of California adults age 18 and older selected from their probability-based AmeriSpeak Panel and supplemented by non-probability panels to reach desired sample size. NORC funds and operates the AmeriSpeak Panel of randomly selected US households, inviting selected households into the study using US mail, telephone, and field interviewers (face to face). This panel provides sample coverage of approximately 97% of the U.S. household population. Households with P.O. Box only addresses, addresses not listed in the USPS Delivery Sequence File, and some newly constructed dwellings are excluded from this sample. Most AmeriSpeak households participate in online surveys, but non-internet households can participate by telephone or smartphone. For this study, NORC sampled from the California portion of the AmeriSpeak panel sample and supplemented it with respondents from nonprobability online opt-in panels to achieve the sample size of approximately 2000 participants. NORC conducted a statistical calibration to combine these samples and create a survey-weighted final sample that is representative of the California adults with regard to gender, age, race/ethnicity, income, education, employment status and region of the state. The recruitment rate for this study was 20%, and the response rate was 28%. These are standard for online panel surveys, which hover around 20–25% (Callegaro and DiSogra, 2009; Nulty, 2008). The NORC team contacted participants to invite them into the 15-min online survey. Respondents were offered the cash equivalent of USD$2 for completion of this survey. All participation was voluntary, and the participant was allowed to decline questions or stop the survey at any time. Participants in the survey panels provided written informed consent at the time of enrolment in the panel, and agreed to privacy policies provided by NORC. Due to the sensitive nature of topics covered in the survey, the survey included a prompt on all pages with the following text, “If you are experiencing distress or discomfort, see this website for services in the state https://victims.ca.gov/resources.aspx.” To ensure confidentiality, our team only had access to completely anonymized data. Both NORC/University of Chicago and the University of California San Diego (Project #201780) Institutional Review Boards approved these study procedures. 2.2 Measures Our dependent variables were past year experience of physical violence, past year experience of sexual violence, and past two-week mental health symptom severity. We assessed participants’ past year experiences regarding three types of <i>physical violence</i> (physical abuse, threat or violence with a knife, threat or violence with a gun) and six types of <i>sexual violence</i> (verbal sexual harassment, homophobic or transphobic comments, cyber sexual harassment, physically aggressive sexual harassment, <i>quid pro quo</i> or coercive sexual harassment, and forced sex) (Anita Raj et al., 2020). We categorized the physical violence and sexual violence outcomes as yes/no based on whether they said yes to any of the specific subtypes of violence assessed or no to all items assessed. We assessed depression and anxiety symptoms, and severity of symptoms, using the Patient Health Questionnaire-4 (PHQ-4), which assesses number of days in the past two weeks they experienced specific symptoms (e.g., “not being able to stop or control worrying” (Kroenke et al., 2009). Response options ranged from “Not at all” = 0 to “Nearly every day” = 3, allowing for a range of 0–12 for the summated score. The Cronbach alpha for these four symptom items was 0.90. The severity of mental health symptom score as stipulated by the PHQ-4 tool is the sum of the four items, categorized as normal (0–2), mild (3–5), moderate (6–8), and severe (9–12) (Kroenke et al., 2009). Our independent variables of interest were perceived experiences of everyday discrimination and perceived major experiences of racial/ethnic discrimination. We assessed <i>everyday discrimination</i> using a modified five-item version of the Everyday Discrimination scale. The original scale has been previously validated in the US. (Williams et al., 1997) We asked if the participants experienced any of the following specific forms of everyday discrimination in a typical week, yes/no. Example items included: “People treat me as if I am not intelligent.” And “People treat me like I am dishonest.” The Cronbach alpha for these five types of everyday discrimination was 0.62. We categorized responses as experiencing no forms of everyday discrimination, one form, or multiple forms. The perceived reason for the discrimination was not assessed. We assessed <i>major experiences of discrimination</i> using the six-item Major Experiences of Discrimination Scale (abbreviated) (Sternthal et al., 2011). Example items included: “unfairly fired or denied a promotion” and “unfairly prevented from moving into a neighborhood because the landlord/realtor refused to rent/sell a house/apartment.” We did not conduct a Cronbach's alpha for this measure, because the measure was not designed to assess a unitary construct. Hence, there is no expectation of these items to show good inter-correlation. If respondents indicated experience of one or more forms of major discrimination, we then asked what the primary reason for this discrimination was, with answer choices: race/ethnicity, age, gender, religion, immigration situation (or assumption thereof), physical appearance, sexual orientation or gender identity, income level/social class, or other. Respondents could only select one primary reason; we categorized those who selected race/ethnicity as experiencing <i>race-based major discrimination</i>. We ultimately used a binary measure of race-based major discrimination, any experience vs none. We categorized participants who experienced at least one of the six forms of major discrimination but reported a primary reason other than race/ethnicity as having experienced <i>non-race-based major discrimination</i>. A binary measure was used for this predictor, any experience vs none. Because the primary reason for discrimination follow-up question allowed for a single response on the social factor most associated with these major experiences of discrimination, race-based major discrimination and non-race-based major discrimination were mutually exclusive. We also included past year experience of policing as a separate form of major discrimination, based on the extensive data indicating that police are more likely to track males and racial/ethnic minorities (PPI, May 14, 2019) and its alignment with the definition of major discrimination (Williams et al., 2008). This measure was tied to past year experience. Using a single item measure, we asked participants whether they had been stopped or approached by the police: in the past six months, in the past year but not past six months, ever but not in the past year, or had never been stopped by the police. We dichotomized responses as stopped in the past year or not, i.e., <i>policing</i> or no policing exposure. For those who had policing exposure in the past year, we also asked, “On the last occasion you were approached by the police, how do you think you were treated?” Response options were ‘very badly,’ ‘somewhat badly,’ ‘neither well nor badly,’ ‘reasonably well,’ or ‘very well.’ We provide these data descriptively for the 27 participants reporting it. We included socio-demographic covariates for the social factors that could be the basis of discrimination experiences in adjusted models: self-defined gender, race/ethnicity, age, income, sexual identity, and disability status. Details on questions and variable constructions for these covariates are outlined in prior reports (GEH, May 13, 2021; Raj et al. September 2022). We categorized race/ethnicity as White, Black, Asian, Hispanic, and Other/multiple races due to small cell sizes for other racial/ethnic groups. 2.3 Data analysis We present frequency data on all key variables for the total sample, overall and by each outcome. We also present correlation between the measures of discrimination. We then conducted unadjusted and adjusted logistic regressions to assess associations between experiences of discrimination and past year physical and sexual violence. We conducted unadjusted and adjusted multinomial logistic regressions to assess associations between experiences of discrimination and mental health symptom severity. Adjusted models included all measures of discrimination, as well as gender, race/ethnicity, age, income, sexual identity, and disability. All analyses accounted for survey design and weighting to produce state-representative findings, and were conducted using STATA 15.1. Statistical significance was set at p < 0.05 for all odds ratios (ORs), adjusted odds ratios (AORs), relative risk ratios (RRRs), and adjusted relative risk ratios (aRRRs); 95% confidence intervals (CIs) are reported throughout. 3 Results 3.1 Sample and characteristics The total number of Cal-VEX 2021survey participants was 2203, but the analytic sample was restricted to participants providing responses to all discrimination, outcome, and demographic items, resulting in a final analytic sample of N = 2114 individuals. (Note: Non-binary participants were too small in number (n = 13) for inclusion in gender-stratified analyses.) One quarter of participants (26.1%) regularly experienced everyday discrimination in public spaces in an average week (See Table 1 .). One in ten respondents (10.0%) reported major experiences of race-based discrimination. Of these, 38% attributed this discrimination primarily to race/ethnicity, 18% to physical appearance, 10% age, and 9% gender. One in four respondents (23.9%) reported major experiences of discrimination for reasons other than race/ethnicity; of these, the most common reasons for discrimination were age (22%), income level/socioeconomic status (15%), physical appearance (14%), and gender (14%). One in eight (13.6%) were approached or stopped by police in the prior year; of those who were approached, 65.7% reported that they were treated reasonably or very well, 23.0% reported neutral treatment, and 11.3% reported that they were treated somewhat or very badly (result not shown). Though theoretically related, our measures of discrimination were very weakly correlated in the study sample; everyday discrimination and major experiences of race-based discrimination: rho = 0.26; everyday discrimination and policing: rho = 0.30; and major experiences of race-based discrimination and policing: rho = 0.12. Nonetheless, they were significantly associated, suggesting inter-relationships across forms of discrimination.Table 1 Experiences of discrimination, past year victimization from violence, recent depression/anxiety symptoms, and socio-demographic characteristics among a state representative sample of California adults in March 2021 (N = 2114). Table 1 Unweighted N Weighted % Total 2114 100% Experiences of discrimination Everyday discrimination  None 1603 73.9%  Single Form 298 14.2%  Multiple Forms 213 11.9% Major experiences of race-based discrimination  No 1931 90.0%  Yes 183 10.0% Major experience of non-race-based discrimination  No 1537 76.1%  Yes 577 23.9% Policing in past year  No 1766 86.4%  Yes 348 13.6% Outcomes Physical violence, past year  No 1943 93.0%  Yes 171 7.0% Sexual harassment or violence, past year  No 1840 86.6%  Yes 274 13.4% Depression/anxiety symptoms, past 2 weeks  Normal 1229 56.1%  Mild 523 25.4%  Moderate 223 10.7%  Severe 139 7.8% Socio-demographics Gender  Female 989 51.0%  Male 1125 49.0% Race  White 1436 44.5%  Black 85 5.6%  Asian 151 12.5%  Hispanic 339 31.4%  Other/multiple races 103 6.1% Age (continuous; mean SD) 48.5 17.2 Income Quintile  Lowest 375 25.5%  Second Lowest 369 19.9%  Middle 351 17.0%  Second Highest 472 18.6%  Highest 547 19.1% Sexual identity  Heterosexual 1891 90.2%  Gay/Lesbian/Bisexual/Other 223 9.8% Disability  No 1465 71.3%  Yes 649 28.8% We also assessed each form of discrimination reported by racial/ethnic group and found white participants least likely and Black participants most likely to report everyday discrimination (16.5% and 43.7%, respectively) and race-based major discrimination (3.2% and 45.2%, respectively). Hispanic participants were most likely to report past-year policing across racial/ethnic groups (20.8% vs. 9.1–13.3% for the other racial/ethnic groups categorized in this study). Past year physical violence was reported by 7% of respondents, and 13.4% had experienced past year sexual violence (See Table 1.). More than half of participants (56.1%) reported normal levels of depression and/or anxiety symptoms in the prior two weeks, 25.4% reported mild levels, 10.7% reported moderate levels, and 7.8% reported severe levels of depression and/or anxiety symptoms in the past two weeks (See Table 1.). 3.2 Associations between discrimination and outcomes In unadjusted regression models, everyday discrimination, non-race-based major discrimination, and policing experience were associated with significantly greater odds of past-year physical violence experience (ps < 0.001) (See Table 2 .). In fully adjusted models, experiences of everyday discrimination (single form AOR 5.0, 95% CI 2.5–10.3, p < 0.001; multiple forms AOR 2.6, 95% CI 1.1–5.8, p = 0.02) remained significantly associated with increased odds of physical violence, but major experiences of race-based and non-race-based discrimination were not significantly associated with the outcome. Policing exposure remained associated with greater odds of past-year physical violence in fully adjusted models (AOR 3.8, 95% CI 2.0–7.1, p < 0.001).Table 2 Frequencies, unadjusted and adjusted logistic regression analyses to assess associations between experiences of discrimination and past year physical violence among a state representative sample of California adults in March 2021 (N = 2114). Table 2 Tabulations by outcome Unadjusted regression Adjusted regression Physical Violence Subsample No Physical Violence Subsample OR p-value 95% CI AOR p-value 95% CI Unwt N Wt % Unwt N Wt % Lower Upper Lower Upper Everyday Discrimination  None 44 26.8% 1559 77.4% Ref Ref Ref Ref Ref Ref Ref Ref  Single Form 72 47.4% 226 11.7% 11.67 <0.001 6.39 21.31 5.03 <0.001 2.46 10.31  Multiple Forms 55 25.7% 158 10.8% 6.85 <0.001 3.49 13.44 2.57 0.02 1.14 5.79 Major experiences of race-based discrimination  No 137 84.7% 1794 90.4% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 34 15.3% 149 8.6% 1.70 0.16 0.82 3.53 0.94 0.90 0.34 2.57 Major experience of non-race-based discrimination  No 87 51.9% 1450 77.9% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 84 48.1% 493 22.1% 3.27 <0.001 1.95 5.48 1.95 0.054 0.99 3.83 Policing in Past Year  No 53 46.8% 1713 89.4% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 118 53.3% 230 10.7% 9.56 <0.001 5.57 16.41 3.82 <0.001 2.04 7.14 Gender  Male 126 57.3% 999 48.4% Ref Ref Ref Ref Ref Ref Ref Ref  Female 45 42.7% 944 51.6% 0.70 0.18 0.41 1.18 0.69 0.21 0.39 1.24 Race  White 113 29.1% 1323 45.6% Ref Ref Ref Ref Ref Ref Ref Ref  Black 4 5.3% 81 5.6% 1.47 0.54 0.43 4.99 1.23 0.74 0.35 4.31  Asian 5 4.3% 146 13.1% 0.52 0.24 0.17 1.56 0.39 0.20 0.09 1.63  Hispanic 44 59.2% 295 29.3% 3.17 <0.001 1.88 5.35 1.76 0.11 0.88 3.50  Other/multiple races 5 2.1% 98 6.4% 0.52 0.23 0.18 1.52 0.32 0.051 0.10 1.00 Age      Continuous - mean (SD) 34.7 (11.2) 49.5 (17.0) 0.94 <0.001 0.92 0.95 0.96 <0.001 0.94 0.98 Income Quintile  Lowest 27 37.0% 348 24.6% Ref Ref Ref Ref Ref Ref Ref Ref  Second Lowest 17 14.2% 352 20.3% 0.46 0.07 0.20 1.07 0.39 0.03 0.17 0.93  Middle 14 10.7% 337 17.5% 0.41 0.04 0.17 0.98 0.54 0.20 0.21 1.38  Second Highest 42 19.8% 430 18.5% 0.71 0.36 0.35 1.47 1.02 0.97 0.47 2.21  Highest 71 18.3% 476 19.2% 0.64 0.19 0.32 1.25 1.14 0.76 0.49 2.65 Sexual identity  Heterosexual 132 71.8% 1759 91.6% Ref Ref Ref Ref Ref Ref Ref Ref  Gay/Lesbian/Bisexual/Other 39 28.2% 184 8.4% 4.28 <0.001 2.33 7.85 2.43 0.01 1.19 4.96 Disability  No 50 32.1% 1415 74.2% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 121 67.9% 528 67.9% 6.09 <0.001 3.54 10.47 2.57 0.004 1.36 4.84 In unadjusted regression models, everyday discrimination, non-race-based major discrimination, and policing experience were associated with significantly greater odds of past-year sexual violence experience (ps < 0.001) (See Table 3 .). Experience of race-based major discrimination was also associated with greater likelihood of sexual violence in unadjusted comparisons (OR 2.1, 95% CI 1.2–3.5, p = 0.01). In fully adjusted models, experiences of everyday discrimination (single form AOR 1.7, 95% CI 1.0–2.9, p = 0.047, multiple form AOR 2.5, 95% CI 1.4–4.4, p = 0.002) and experiences of race-based major discrimination (AOR 2.0, 95% CI 1.1–3.8, p = 0.03) remained significantly associated with increased odds of sexual violence. Experience of non-race-based major discrimination (AOR 2.4, 95% CI 1.5–3.9, p = 0.001) and policing (AOR 2.6, 95% CI 1.6–4.2, p < 0.001) were also associated with greater odds of past-year sexual violence.Table 3 Frequencies, unadjusted and adjusted logistic regression analyses to assess associations between experiences of discrimination and past year sexual harassment and violence among a state representative sample California adults in March 2021 (N = 2114). Table 3 Tabulations by outcome Unadjusted regression Adjusted regression Sexual Violence Subsample No Sexual Violence Subsample OR p-value 95% CI AOR p-value 95% CI Unwt N Wt % Unwt N Wt % Lower Upper Lower Upper Everyday Discrimination  None 110 43.9% 1493 78.5% Ref Ref Ref Ref Ref Ref Ref Ref  Single Form 87 27.6% 211 12.2% 4.06 <0.001 2.49 6.60 1.70 0.047 1.01 2.87  Multiple Forms 77 28.5% 136 9.3% 5.49 <0.001 3.36 8.96 2.48 0.002 1.41 4.36 Major experiences of race-based discrimination  No 224 83.2% 1707 91.0% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 50 16.8% 133 9.0% 2.05 0.01 1.19 3.53 2.03 0.03 1.08 3.81 Major experience of non-race-based discrimination  No 157 59.2% 1380 78.7% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 117 40.8% 460 21.3% 2.54 <0.001 1.71 3.78 2.36 0.001 1.45 3.85 Policing in Past Year  No 142 64.3% 1624 89.8% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 132 35.7% 216 10.2% 4.87 <0.001 3.16 7.51 2.60 <0.001 1.61 4.19 Gender  Male 141 31.8% 984 51.6% Ref Ref Ref Ref Ref Ref Ref Ref  Female 133 68.2% 856 48.4% 2.29 <0.001 1.55 3.37 3.19 <0.001 2.09 4.86 Race  White 167 32.3% 1269 46.3% Ref Ref Ref Ref Ref Ref Ref Ref  Black 13 6.3% 72 5.5% 1.65 0.22 0.74 3.67 0.92 0.84 0.38 2.19  Asian 14 8.3% 137 13.1% 0.91 0.79 0.43 1.91 0.72 0.42 0.32 1.60  Hispanic 62 44.1% 277 29.4% 2.15 <0.001 1.40 3.30 1.12 0.67 0.67 1.88  Other/multiple races 18 8.9% 85 5.7% 2.25 0.02 1.13 4.50 1.57 0.34 0.62 3.96 Age  Continuous - mean (SD) 37.3 (13.0) 50.2 (17.1) 0.95 <0.001 0.94 0.96 0.96 <0.001 0.94 0.97 Income Quintile  Lowest 46 29.7% 329 24.8% Ref Ref Ref Ref Ref Ref Ref Ref  Second Lowest 37 20.5% 332 19.8% 0.86 0.62 0.49 1.53 0.98 0.96 0.53 1.82  Middle 33 13.7% 318 17.5% 0.65 0.19 0.34 1.23 1.05 0.90 0.51 2.17  Second Highest 70 18.6% 402 18.6% 0.84 0.54 0.48 1.48 1.41 0.30 0.73 2.71  Highest 88 17.6% 459 19.3% 0.76 0.33 0.44 1.32 1.58 0.12 0.88 2.85 Sexual Identity  Heterosexual 213 78.5% 1678 92.0% Ref Ref Ref Ref Ref Ref Ref Ref  Gay/Lesbian/Bisexual/Other 61 21.6% 162 8.0% 3.17 <0.001 1.95 5.16 2.16 0.003 1.30 3.60 Disability  No 128 48.9% 1337 74.7% Ref Ref Ref Ref Ref Ref Ref Ref  Yes 146 51.1% 503 25.3% 3.08 <0.001 2.09 4.54 1.76 0.01 1.14 2.72 Individuals who experienced severe depression and/or anxiety symptoms in the past two weeks more frequently reported experiences of everyday discrimination than those who reported normal symptom levels (48.3% vs 16.2%) and more frequently reported race-based major discrimination (19.2% vs 9.0%) (See Table 4 a.). Those who experienced severe symptoms also more frequently reported non-race-based major discrimination (31.3% vs 17.7%) and being approached or stopped by the police in the past year (23.6% vs 8.6%). In unadjusted multinomial regression models, everyday discrimination, non-race-based major discrimination, and policing experience were associated with significantly greater risk of mild, moderate, and severe mental health symptoms (ps < 0.05) (See Table 4 b.). Experience of race-based major discrimination was also associated with greater risk of severe mental health symptoms in unadjusted comparisons (RRR 2.4, 95% CI 1.2–4.9, p = 0.02). In fully adjusted models, experience of multiple forms of everyday discrimination (aRRR 3.3 95% CI 1.6–6.7, p = 0.001) and experience of race-based major discrimination (aRRR 2.7, 95% CI 1.2–6.2, p = 0.02) remained significantly associated with increased risk of severe depression and/or anxiety symptoms (See Table 4 c.). Experience of a single form of everyday discrimination was also associated with greater risk of moderate mental health symptoms (aRRR 2.0, 95% CI 1.1–3.6, p = 0.02), and experience of multiple forms of everyday discrimination was associated with greater risk of mild mental health symptoms (aRRR 2.0, 95% CI 1.1–3.6, p = 0.02). Experience of non-race-based major discrimination was significantly associated with mild (aRRR 1.7, 95% CI 1.2–2.5, p = 0.005) and moderate (aRRR 1.9, 95% CI 1.2–2.9, p = 0.007) mental health symptom severity. Past year policing experience was not associated with any level of mental health symptom severity.Table 4a Distributions of discrimination experiences and demographics by recent depression/anxiety symptoms among a state representative sample of California adults in March 2021 (N = 2114). Table 4a Normal Symptoms Mild Symptoms Moderate Symptoms Severe Symptoms Unwt N Wt % Unwt N Wt % Unwt N Wt % Unwt N Wt % Everyday Discrimination  None 1063 83.8% 357 64.8% 121 59.8% 62 51.7%  Single Form 111 9.9% 100 18.2% 58 26.9% 29 14.9%  Multiple Forms 55 6.3% 66 16.9% 44 13.3% 48 33.4% Major experiences of race-based discrimination  No 1144 91.0% 469 88.9% 204 93.8% 114 80.8%  Yes 85 9.0% 54 11.1% 19 6.2% 25 19.2% Major experience of non-race-based discrimination  No 980 82.4% 340 69.7% 128 63.9% 89 68.7%  Yes 249 17.7% 183 30.4% 95 36.1% 50 31.3% Policing in Past Year  No 1110 91.4% 414 81.6% 158 78.8% 84 76.4% Yes 119 8.6% 109 18.4% 65 21.2% 55 23.6% Gender  Male 675 53.0% 266 46.8% 118 45.5% 66 32.5%  Female 554 47.0% 257 53.3% 105 54.5% 73 67.5% Race  White 867 48.8% 340 39.8% 139 36.2% 90 39.4%  Black 51 5.6% 21 5.4% 6 5.3% 7 6.2%  Asian 86 12.3% 43 15.2% 18 11.8% 4 5.5%  Hispanic 168 27.9% 90 30.7% 47 41.0% 34 45.0%  Other/multiple races 57 5.3% 29 8.8% 13 5.7% 4 4.0% Age  Continuous - mean (SD) 52.6 (17.4) 45.6 (15.4) 44.0 (16.2) 34.9 (11.6) Income Quintile  Lowest 183 20.3% 98 25.0% 56 36.9% 38 47.9%  Second Lowest 218 21.0% 80 17.1% 42 18.2% 29 23.5%  Middle 212 17.9% 88 17.3% 37 17.9% 14 8.4%  Second Highest 290 19.7% 124 20.8% 35 12.7% 23 11.5%  Highest 326 21.1% 133 19.8% 53 14.3% 35 8.7% Sexual Identity  Heterosexual 1137 94.2% 451 86.1% 194 90.9% 109 73.9%  Gay/Lesbian/Bisexual/Other 92 5.8% 72 13.9% 29 9.1% 30 26.1% Disability  No 1015 85.3% 312 61.5% 101 50.0% 37 31.4%  Yes 214 14.7% 211 38.5% 122 50.0% 102 68.6% Table 4b Unadjusted multinomial logistic regression analysis to assess associations between experiences of discrimination and recent depression/anxiety symptoms among a state representative sample of California adults in March 2021 (N = 2114).* Table 4b Mild Moderate Severe RRR p-value 95% CI RRR p-value 95% CI RRR p-value 95% CI Lower Upper Lower Upper Lower Upper Everyday Discrimination None Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Single Form 2.39 <0.001 1.55 3.69 3.82 <0.001 2.23 6.53 2.44 0.02 1.15 5.16 Multiple Forms 3.47 <0.001 2.11 5.71 2.96 0.001 1.59 5.48 8.58 <0.001 4.49 16.39 Major experiences of race-based discrimination     No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Yes 1.27 0.31 0.80 2.03 0.67 0.32 0.30 1.48 2.41 0.02 1.18 4.90 Major experience of non-race-based discrimination No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Yes 2.03 <0.001 1.46 2.82 2.64 <0.001 1.71 4.07 2.13 0.007 1.23 3.68 Policing in Past Year No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Yes 2.38 <0.001 1.54 3.69 2.85 <0.001 1.67 4.86 3.28 <0.001 1.78 6.04 Gender Male Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Female 1.28 0.19 0.96 1.71 1.35 0.15 0.90 2.02 2.34 0.001 1.39 3.95 Race White Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Black 1.19 0.62 0.61 2.32 1.27 0.64 0.46 3.49 1.36 0.57 0.47 3.91 Asian 1.52 0.07 0.97 2.38 1.29 0.46 0.66 2.51 0.55 0.34 0.17 1.86 Hispanic 1.35 0.10 0.95 1.92 1.98 0.004 1.25 3.14 1.99 0.01 1.15 3.47 Other/multiple races 2.05 0.01 1.16 3.64 1.45 0.37 0.65 3.22 0.93 0.91 0.25 3.38 Age Continuous 0.98 <0.001 0.97 0.98 0.97 <0.001 0.96 0.98 0.93 <0.001 0.91 0.95 Income Quintile Lowest Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Second Lowest 0.66 0.09 0.41 1.06 0.48 0.02 0.26 0.87 0.47 0.03 0.24 0.94 Middle 0.79 0.33 0.49 1.27 0.55 0.06 0.30 1.03 0.20 0.001 0.08 0.50 Second Highest 0.86 0.50 0.55 1.34 0.36 0.001 0.19 0.67 0.25 <0.001 0.12 0.52 Highest 0.77 0.22 0.50 1.17 0.38 0.002 0.20 0.69 0.18 <0.001 0.08 0.37 Sexual Identity Heterosexual Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Gay/Lesbian/Bisexual/Other 2.62 <0.001 1.64 4.20 1.63 0.11 0.89 2.96 5.73 <0.001 3.04 10.80 Disability No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Yes 3.63 <0.001 2.62 5.02 5.80 <0.001 3.77 8.91 12.68 <0.001 7.19 22.34 *Reference is normal symptoms. Table 4c Adjusted multinomial logistic regression analysis to assess associations between experiences of discrimination and recent depression/anxiety symptoms among a state representative sample of California adults in March 2021 (N = 2114). Table 4c Mild Moderate Severe aRRR p-value 95% CI aRRR p-value 95% CI aRRR p-value 95% CI Lower Upper Lower Upper Lower Upper Everyday Discrimination  None Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Single Form 1.38 0.19 0.85 2.23 2.02 0.02 1.13 3.60 0.74 0.55 0.27 2.00  Multiple Forms 2.01 0.02 1.14 3.55 1.57 0.24 0.74 3.33 3.29 0.001 1.62 6.68 Major experiences of race-based discrimination  No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Yes 1.19 0.55 0.67 2.13 0.61 0.33 0.23 1.63 2.71 0.02 1.19 6.19 Major experience of non-race-based discrimination  No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Yes 1.71 0.005 1.17 2.48 1.87 0.01 1.19 2.94 1.79 0.11 0.88 3.64 Policing in Past Year    No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Yes 1.51 0.10 0.92 2.47 1.63 0.14 0.85 3.14 1.27 0.51 0.62 2.61 Gender    Male Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Female 1.44 0.02 1.05 1.98 1.34 0.21 0.85 2.10 2.40 0.005 1.29 4.46 Race    White Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Black 0.77 0.48 0.38 1.57 1.02 0.97 0.38 2.75 0.39 0.08 0.13 1.13  Asian 1.54 0.08 0.95 2.51 1.50 0.29 0.71 3.15 0.65 0.58 0.14 2.99  Hispanic 0.92 0.69 0.61 1.39 1.15 0.62 0.66 2.02 0.69 0.29 0.35 1.37  Other/multiple races 1.54 0.18 0.82 2.90 1.20 0.69 0.49 2.93 0.49 0.34 0.11 2.13 Age    Continuous 0.98 <0.001 0.97 0.99 0.97 <0.001 0.96 0.99 0.93 <0.001 0.91 0.95 Income Quintile  Lowest Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Second Lowest 0.75 0.27 0.45 1.26 0.55 0.08 0.29 1.06 0.57 0.14 0.27 1.20  Middle 0.99 0.96 0.60 1.64 0.73 0.37 0.37 1.45 0.36 0.07 0.12 1.10  Second Highest 1.08 0.75 0.68 1.72 0.47 0.03 0.24 0.91 0.45 0.07 0.19 1.06  Highest 0.99 0.97 0.62 1.59 0.54 0.07 0.28 1.04 0.33 0.02 0.13 0.81 Sexual Identity  Heterosexual Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Gay/Lesbian/Bisexual/Other 1.88 0.008 1.18 3.01 0.92 0.81 0.45 1.86 2.84 0.01 1.34 6.00 Disability  No Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref Ref  Yes 3.45 <0.001 2.44 4.88 5.25 <0.001 3.27 8.42 11.40 <0.001 5.98 21.73 *Reference is normal symptoms. 4 Discussion Findings from this study demonstrate that experiences of everyday discrimination, major experiences of racial discrimination, and heavy policing are associated with higher odds of experiencing physical violence, sexual violence, and severe symptoms of depression and anxiety. These findings are consistent with prior research implicating discrimination as a key risk factor for chronic stress and poor mental health outcomes among racial/ethnic minority populations (APA, October 31, 2019). Our analyses extend this work by documenting the associations between discrimination and victimization from violence during the pandemic. Prior research suggests that both violence victimization and mental health issues have increased during the pandemic (Connor et al., 2020; de Figueiredo et al., 2021; A. Raj et al., 2020a, Raj et al., 2020b), and this study suggests that previously documented increases in experiences of race/ethnicity related discrimination during the pandemic (Strassle et al., 2022) may have exacerbated violence and mental health risks. Additionally, results indicate that experiences of non-race/ethnicity-based discrimination – most commonly tied to age, income/class, and gender or sexual identity-also contribute to victimization and adverse health outcomes. Findings support the use of an intersectional analysis in our understanding of discrimination and its impacts (Fagrell Trygg et al., 2019). An important finding is that everyday experiences of discrimination tend to have a larger association with all our outcomes as compared with major race-based discrimination. This difference could be attributed, at least partially, to the different timeframes for both measures. Everyday discrimination assesses current experiences of discrimination, whereas the measure for major discrimination assesses lifetime experiences of discrimination. Because such experiences could have occurred months, years, or decades prior to the survey, they may be less significant relative to more immediate and chronic experiences of everyday discrimination. These findings are consistent with prior research showing stronger associations between everyday discrimination and mental health outcomes as compared to major discrimination (Ayalon and Gum, 2011). Nevertheless, it is important to note that race-based major discrimination was significantly associated with increased risk for sexual violence and severe mental health symptoms. Major race-based discrimination remains a key risk factor, as it can affect the socioeconomic stability and well-being of individuals and perceptions of options to help ensure safety. We found that policing exposure is also associated with greater risk for violence, though not with mental health concerns. It may be that policing is more likely to happen in contexts where violence occurs. Environments with heavy policing and police surveillance may also be places where victimization from violence is more likely. Racial residential segregation is linked with abuses from police for Black and Latinx residents (Johnson et al., 2019). As a result, policing in these neighborhoods may be an everyday reality for these residents and may not be associated with mental health outcomes but are associated with an increased risk of victimization (Lodge et al., 2021). Regardless, given the history of racial discrimination in policing, and growing concerns regarding abusive police during the pandemic (Sewell, 2020), more research is needed on this. We also need more research on the exact mechanisms underlying the association between discrimination and victimization. Our study results may be indicative of the dual risk of discrimination and victimization among racial/ethnic minority populations. Discrimination may increase the risk for violence, or may co-occur with violence. More research is needed to understand whether experiences of discrimination are directly associated with specific forms of victimization. For example, instances of verbal discrimination may escalate into instances of physical aggression, increasing the risk for physical and sexual violence. These results also highlight the importance of employing an intersectional lens when assessing the risk for victimization and poor mental health outcomes. Prior research documents that violence often occurs in multiple forms against those who experience victimization. Multiple forms of everyday racism can take an even bigger toll on mental health. 5 Limitations We must consider the findings in light of certain study limitations. As noted, the study is cross-sectional, so we cannot assume temporal ordering or causality. Our data relied on self-report and are thus subject to recall and social desirability biases. Recall for violence is likely high given the salience of the issue, and recall of mental health symptoms is likely high due to the recall time being the past two weeks. However, participants may under-report both outcomes given the stigma attached to both victimization and mental health concerns. We used previously validated discrimination scales, but these too may not fully reflect all experiences of discrimination for participants. The Everyday Discrimination scale used in this study did not allow for clarity on what characteristics resulted in discrimination, and there is some indication of variability in the scale by demographic characteristics (Harnois et al., 2019). We also only have measures of perception of discrimination and not objective measures of discrimination. Meta-analysis of subjective (perception) versus more objective measures of discrimination show stronger effects of objective measures on well-being including mental health outcomes (Schmitt et al., 2014). Hence, our findings are likely yielding conservative estimates. The study used an online probability panel that facilitates engagement of a state representative sample, but the participation rate is low (32%), which is typical of online studies (Callegaro and DiSogra, 2009; Nulty, 2008). At the same time, random sampling approaches would be better to reduce potential biases inevitable in on-line rapid surveys, because standard approaches likely under-represent those affected by violence and mental health issues (Pierce et al., 2020). Additionally, while this study is weighted to yield a state representative sample, it is also a convenience sample of online panel participants, though efforts were made to reduce some of the biases from typical online surveys as much as possible, including area probability and address-based recruitment and inclusion of non-internet and non-cell phone households. A non-response follow-up campaign was also used to increase participation and representation. Additionally, generalizability of findings may be limited to adults in California and may not reflect younger populations or populations in other states. 6 Conclusion In summary, this cross-sectional study of discrimination, violence, and mental health of California adults, undertaken during the COVID-19 pandemic in 2021, demonstrates that experiences of discrimination, particularly everyday discrimination, are associated with increased risk for physical and sexual violence as well as depression and anxiety symptoms during the pandemic. Further, we see that everyday discrimination, which can manifest as regularly occurring microaggressions, more than major racial discrimination experiences (e.g., discrimination resulting in non-hiring or denial of bank loans from financial institutions), may be driving these vulnerabilities. Experiences with different types of discrimination, including policing, are also associated with an increased risk for violence victimization. This work provides greater insight into some aspects of systemic racism and health disparities related to trauma and mental health (Boynton-Jarrett et al., 2021) and documents the need to focus on anti-racist care and service provision as part of COVID-19 rebuilding efforts. Importantly, given the other attributes linked to discrimination, in particular age and sex/gender, more work is needed to recognize that these forms of discrimination also persist and yield harm. We also need further methodological work to disentangle the impacts of everyday versus major experiences of discrimination in addition to identifying mechanisms underlying the discrimination-victimization link. Nonetheless, the findings emphasize the need to address social determinants of health with an intersectional lens and as part of strengthening community health for both pandemic management and post-pandemic rebuilding (Bleser et al., 2022). Further, these findings support the growing body of evidence that shows that we cannot achieve health equity and human dignity without ending all forms of discrimination, including racial/ethnic discrimination (Bleser et al., 2022). Sources of funding 10.13039/100001002 Blue Shield of California Foundation Grants RP-1907-13755 & P-2006-14747; 10.13039/100005977 Kaiser Permanente National Community Benefit Fund at the East Bay 10.13039/100008174 Community Foundation Grants 20202903 & 118910; 10.13039/100000865 Bill and Melinda Gates Foundation INV018007. Credit author statement Anita Raj: Conceptualization, Writing – original draft, Review and Editing. Sangeeta Chatterji: Writing – original draft, Review and Editing, Validation. Nicole E Johns: Formal analysis, Writing – original draft, RFeview and Editing. Jennifer Yore: Project administration. Arnab Dey: Writing – review & editing. David R Williams: Methodology, Writing – review & editing. 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High Educ. 33 3 2008 301 314 10.1080/02602930701293231 Paradies Y. Ben J. Denson N. Elias A. Priest N. Pieterse A. Gupta A. Kelaher M. Gee G. Racism as a determinant of health: a systematic review and meta-analysis PLoS One 10 9 2015 e0138511 10.1371/journal.pone.0138511 Parra L.A. Hastings P.D. Integrating the Neurobiology of Minority Stress with an Intersectionality Framework for LGBTQ-Latinx Populations 2018 New Dir Child Adolesc Dev 10.1002/cad.20244 Pierce M. McManus S. Jessop C. John A. Hotopf M. Ford T. Hatch S. Wessely S. Abel K.M. Says who? The significance of sampling in mental health surveys during COVID-19 Lancet Psychiatr. 7 7 2020 567 568 10.1016/S2215-0366(20)30237-6 Rafferty Y. International dimensions of discrimination and violence against girls: a human rights perspective J. Int. Wom. Stud. 14 1 2013 1 23 http://vc.bridgew.edu/jiws/vol14/iss1/1 Raj A. Johns N. Ramirez L. Barker K. California Experiences of Violence across the Lifespan (CalVEX) 2020 2020 https://gehweb.ucsd.edu/wp-content/uploads/2020/09/Cal-VEX-2020-Report.pdf Raj A. Johns N.E. Barker K.M. Silverman J.G. Time from COVID-19 shutdown, gender-based violence exposure, and mental health outcomes among a state representative sample of California residents EClinicalMedicine 26 2020 100520 10.1016/j.eclinm.2020.100520 Raj A. Johns N.E. Dehingia N. Cheung W.L. September California Study on Violence Experiences across the Lifespan (CalVEX): Findings from the March 2022 Survey 2022 https://gehweb.ucsd.edu/wp-content/uploads/CalVEX-09.06.22.pdf Roberts L. Tamene M. Orta O.R. The intersectionality of racial and gender discrimination among teens exposed to dating violence Ethn. Dis. 28 Suppl. 1 2018 253 260 10.18865/ed.28.S1.253 30116095 Russell P.L. Nurius P.S. Herting J.R. Walsh E. Thompson E.A. Violent victimization and perpetration: joint and distinctive implications for adolescent development Vict. Offenders 5 4 2010 329 353 10.1080/15564886.2010.509655 Sanders-Phillips K. Racial discrimination: a continuum of violence exposure for children of color Clin. Child Fam. Psychol. Rev. 12 2 2009 174 195 10.1007/s10567-009-0053-4 19466544 Schmitt M.T. Branscombe N.R. Postmes T. Garcia A. The consequences of perceived discrimination for psychological well-being: a meta-analytic review Psychol. Bull. 140 4 2014 921 948 10.1037/a0035754 24547896 Sewell A.A. Policing the block: pandemics, systemic racism, and the blood of America City Community 19 3 2020 496 505 10.1111/cico.12517 SteelFisher G.K. Findling M.G. Bleich S.N. Casey L.S. Blendon R.J. Benson J.M. Sayde J.M. Miller C. Gender discrimination in the United States: experiences of women Health Serv. Res. 54 S2 2019 1442 1453 10.1111/1475-6773.13217 31663120 Sternthal M.J. Slopen N. Williams D.R. Racial disparities in health: how much does stress really matter? Du. Bois Rev. 8 1 2011 95 113 10.1017/s1742058x11000087 29887911 Strassle P.D. Stewart A.L. Quintero S.M. Bonilla J. Alhomsi A. Santana-Ufret V. Maldonado A.I. Forde A.T. Nápoles A.M. COVID-19-Related discrimination among racial/ethnic minorities and other marginalized communities in the United States Am. J. Publ. Health 112 3 2022 453 466 10.2105/ajph.2021.306594 Tan K.K.H. Treharne G.J. Ellis S.J. Schmidt J.M. Veale J.F. Gender minority stress: a critical review J. Homosex. 67 10 2020 1471 1489 10.1080/00918369.2019.1591789 30912709 Velotti P. Rogier G. Beomonte Zobel S. Chirumbolo A. Zavattini G.C. The relation of anxiety and avoidance dimensions of attachment to intimate partner violence: a meta-analysis about perpetrators Trauma Violence Abuse 23 1 2022 196 212 10.1177/1524838020933864 32608337 WHO Mental health and COVID-19: early evidence of the pandemic's impact (scientific brief Issue 2022 Williams D.R. Stress and the mental health of populations of color: advancing our understanding of race-related stressors J. Health Soc. Behav. 59 4 2018 466 485 10.1177/0022146518814251 30484715 Williams D.R. Cooper L.A. Reducing racial inequities in health: using what we already know to take action Int. J. Environ. Res. Publ. Health 16 4 2019 10.3390/ijerph16040606 Williams D.R. Yan Y. Jackson J.S. Anderson N.B. Racial differences in physical and mental health: socio-economic status, stress and discrimination J. Health Psychol. 2 3 1997 335 351 10.1177/135910539700200305 22013026 Williams D.R. Gonzalez H.M. Williams S. Mohammed S.A. Moomal H. Stein D.J. Perceived discrimination, race and health in South Africa Soc. Sci. Med. 67 3 2008 441 452 10.1016/j.socscimed.2008.03.021 1982 18486292 Williams D.R. Lawrence J.A. Davis B.A. Racism and health: evidence and needed research Annu. Rev. Publ. Health 40 2019 105 125 10.1146/annurev-publhealth-040218-043750 Williams D.R. Lawrence J.A. Davis B.A. Vu C. Understanding how discrimination can affect health Health Serv. Res. 54 Suppl. 2 2019 1374 1388 10.1111/1475-6773.13222 31663121

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==== Front Enferm Intensiva (Engl Ed) Enferm Intensiva (Engl Ed) Enfermeria Intensiva 2529-9840 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC). Published by Elsevier España, S.L.U. S2529-9840(21)00019-7 10.1016/j.enfie.2020.09.003 Special Article: education Essential elements to elaborate a study with the (e)Delphi method☆ Elementos esenciales para elaborar un estudio con el método (e)DelphiRomero-Collado A. RN, PhD Facultad de Enfermería, Universidad de Girona, Girona, Spain 20 5 2021 April-June 2021 20 5 2021 32 2 100104 © 2020 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC). Published by Elsevier España, S.L.U. All rights reserved. 2020 Sociedad Española de Enfermería Intensiva y Unidades Coronarias (SEEIUC) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcIntroduction What method enables me to establish a competency framework for specialist critical care nurses?1 How can I exercise a protocol, based on the best evidence available, for the care of a person who suffers from a pilonidal sinus wound healing by secondary intent?2 What is the best method for compiling a basic minimum data set for the prevention, diagnosis and treatment of venous conditions of the lower limb in the adult population?3 Undoubtedly on some occasions we have asked ourselves similar questions, related to our field of expertise. Sometimes it is difficult to find sets of recommendations for a situation, setting, specific problem, since evidence is insufficient to encompass the whole process or simply, we wish to reach an agreement about which competences should have a professional figure. One of the options available are the expert group consensus methods, understood to be a systematic medium of measurement and development of consensus. Its aim is to establish to what extent experts agree on a specific subject.4 The two most commonly used methods of consensus are the nominal group technique (NGT) and the Delphi method,4, 5 followed by the RAND/UCLA method, which is a cross between the previous two.6 The NGT takes place through group interaction. It is a highly structured, face-to-face method, where participants express their opinion and the other members listen to their opinions,5 allowing for the possibility to debate issues where there is little consensus and to generate a more robust idea.6 The Delphi method also provides the opinion of a group of experts, called a panel, on a subject matter in a structured format,5, 7 although interaction between the different members is through a questionnaire.5 Due to Internet use application, several authors have suggested calling this e-Delphi, since pen and paper have been replaced by the benefits offered through the online platform that helps to organise and provide communication between the researchers and the experts.8 Both terms are used in the literature, however. The advantage of this method is it allows for a larger number of people to participate, eliminating geographical distance whilst maintaining anonymity of participants, with avoidance of any influence from the response of any panel member.6, 9 Furthermore, it is relatively cheap, helps to organize data and its importation for database analysis.9 There are also disadvantages. For example, there is no certainty whether the people who respond to the questionnaires are experts. To resolve this, a single link may be sent which only one expert can use.8, 9 Another disadvantage is the use of electronic mail firewalls, an aspect that may prevent the invitation to participate in the study from arriving. Essential elements of the Delphi method Delphi is a qualitative technique, although there are authors who defend that it is a mix and others who say it is quantiative10 in its final phase. The first step is to organize a group of researchers who are responsible for creating the whole process and for carrying out follow-up. One person should be appointed to act as the visible head of the group. They will direct communication with the panel of experts, resolve any possible incidences and drive the whole process. The essential elements of the Delphi method, as may be observed in Fig. 1 may be summarized as six6, 11: identification of the research problem, construction of the questionnaire, selection of the panel of experts, provision of information to the panel members to help with their decision, questionnaire distribution, data analysis, provision of feedback to the panel and report on findings.Figure 1 Summary of the Delphi method’s essential elements. Figure 1 Identification of the research problem As with any investigation, the first step is to be clear on what the research objective is.4, 6, 11 We could start with any of the questions with which we introduced this article. For example, with the COVID-19 pandemic where critical care nursing is in demand, we could ask: what are the specific competences that must be acquired to be considered specialist critical care nurses1 in a certain country? Construction of the draft questionnaire To construct the draft questionnaire one frequently used option is the creation of a bibliographic review2, 3 which includes aspects of grey literature.6, 11 The information obtained allows us to create items: the number and ideal nature of them must be agreed upon among the researcher group, in keeping with the initial objective. One aspect which will help us to organise the items, if there are many, is to group them into categories. It is important to record the whole step-by-step process of questionnaire creation as this is the basis of the study. Some studies also obtain the draft questionnaire from information obtained from focus groups,1 made up of the panel of experts they have invited11 or of the same experts who are in charge of the study.12 For example, in the study by Raurell-Torredà et al.12 the researchers themselves, experts in nursing simulation and taxonomy, created the initial group of nursing interventions classification (NIC) to be used in the design of high quality simulation clinical case studies for the training of nursing students in non technical skills. Once the items have been created, we must determine their grading using a Likert type scale5; those most used are item 3, 5, 7 and 9, and the expert has to indicate their level of agreement with the statement from “totally disagree” to “totally agree” passing through intermediate terms, depending on the number of response options on the scale. One example of an evaluation scale of 5 items was found in the study by Zhang et al.1 on the creation of a competence framework for the critical care specialty in China. In the complex decision domain, the expert should indicate the degree of agreement in three competences, giving them a score on the scale from 1 = totally disagree to 5 = totally agree (Table 1 ).Table 1 Example of the Likert type assessment scale from the complex decision domain from the study by Zhang et al.1 Table 1Complex decisions Totally disagree Disagree Neither agree nor disagree Agree Totally agree Evidence-based decision Clinical judgement Integrated reasoning An open-ended response option should be present in the questionnaire to offer the respondent the opportunity of adding new items or for the experts to make a comment on any aspect considered appropriate or relevant. The decision to determine what percentage we consider consensus to be reached should be established in this phase. Despite the fact there is no existing definition of consensus, depending on the type of study conducted, justification of why this decision is taken must be made.11 Some authors consider consensus in one item with 70% of experts responding that they “agree” or “very much agree” with that item.4 It is necessary to consider the number of items contained in the questionnaire and their complexity, with a pilot study9 with a small number of experts, to assess aspects such as comprehension or duration time.4 A questionnaire which has not been tested may affect expert participation, especially in successive rounds. There are mechanisms for facilitating the experience of response to the questionnaires: creating definition with simple language, grouping items into categories and arranging them in alphabetical order whenever possible.13 Panel of expert selection In consensual methods the experts are those people who have knowledge and experience on the study subject.5, 11 A good descriptions of the defining characteristics of whom we consider an expert is required, similar to the inclusion criteria of any study. Following on with the example of the study by Zhang et al.,1 to participate in the panel the expert must have: a) a graduate or higher degree; b) a deputy director qualifications or higher; c) over 10 years of professional experienced and working in the field of critical care with a solid theoretical base; d) the ability to give comprehensive opinions and make suggestions and e) be highly motivated and easily able to participate in the study. There is no particular minimum number of experts for a panel, but results will be more stable the higher the number of experts.11 A minimum number of 6 and maximum of 12 is desirable,4 and if they are from the same discipline Toronto9 considers that from 12 to 20 experts are sufficient. It is difficult for all experts to continuously participate throughout the whole process, with this ranging between 35% and 87%, and it is therefore advisable to invite a minimum of 30 experts.9 The percentage of expert participation must be monitored in each round, from first to last. To recruit the experts several options are available: sending an invitation to different scientific organisations related to the study theme, requesting they notify their members1, 3, 12, 13; searching in health databases, identifying authors with publications which are relevant to the field of study, from recent years9, 13; sometimes it may be necessary to create a snowball effect when access to the study population is more complicated. Distribution of the questionnaire with information to the panel members to aid evaluation At present questionnaires may be sent online. Different platforms exist to help us in this phase, some of which are free (like for example Google forms)14 and others to be paid for (like SurveyMonkey).15 Responses may also be subsequently downloaded onto a database or software may be used to analyse the responses. In this part of the process strict safety measures must be used to maintain participant anonymity, since group mailing of the questionnaire link can reveal identities through email mailing, if the right measures of concealment are not used. The questionnaire must contain instructions for completion and information on the review of the literature undertaken with an explanation, using definitions, of several aspects to help with evaluation.4, 11 A time interval must be programmed for response. An interval of 30–45 days may be sufficient, although in some case flexibility is required so as not to lose experts.13 it is also recommended to send personalized reminders, if possible, a fortnight and one week prior to the finalisation of the response period, indicating the importance of the experts’ participation.13 An initial definition of the number of rounds planned for the study should be made to avoid reaching a “false consensus” due to exhaustion of the experts, who agree to finalise the process. Although there is no unanimity, most Delphi studies used between two and three rounds,13 and therefore a minimum of two and maximum of four rounds should be considered.4, 16 Data analysis and feedback to the panel Data obtained from the expert panel responses must be previously defined through “consensus” on the item. There is no single definition, but the one most used is the consensus percentage.13 Humphrey-Murto et al.4 consider that when 70% of the experts “agree” or “very much agree” over an item then it is reasonable. 70% of response “in disagreement” or “totally in disagreement” are sufficient to eliminate the item, whilst the rest should be reassessed in the next round. There is also the possibility of using central tendency measures, correlation coefficients, the kappa index, Cronbach’s alpha, etc.7 At the end of each round a report on findings obtained is required. A good option is to provide information on each item, with the response from the author and the mean obtained by the rest, so that its positioning with the rest can be viewed and opinion may vary, if applicable, in the following round.11 Feedback must be provided at the end of each round, with as little time as possible passing from its termination, so as not to delay the following round and remainder of the process. Report on findings The results from several rounds of a Delphi study may be difficult to comunicate.11 One very summarised version when there are few items would be to only show those that reached consensus, although it is also advisable to express added items, eliminated ones and those which were altered.11, 16 A diagram like the one in Fig. 2 may be used for the report, where it is observed that through the bibliographic review the authors started with 7 categories and 70 items. Using two evaluation rounds, the final version reached a consensus in 57 items, with the whole process being observed and the degree of consensus in all items. For example, we observe that in category 1, where there were 12 items in the first round, consensus was reached regarding 11, the experts added another 3 items and reassessed one in the next round. In round 2 these 4 items were assessed, for which a consensus was reached in 3, eliminating one due to the absence of the necessary consensus. At the end of this category, where there had been 12 items, the final number was 14.Figure 2 Graphic examples on the evolution of the items during the whole process for presenting in the report n findings. n1: number of items assessed in round 1. n2: number of items assessed in round 2. nf: number of final items for which consensus was reached. ✓: number of items accepted (agree > 70%). х number of items removed (disagree > 70%). ±: number of items to be reassessed. +: number of items added by the experts. Figure 2 Manuscript publication phase When the final phase is to be published of the results obtained by consensus, Diamond et al.7 suggest a series of key methodological criteria which should be responded to in the writing up of the manuscript:▪ Those relating to the objective: does the Delphi study address consensus as its objective? Is the objective to present the findings which reflect group consensus or to quantify the degree of consensus? ▪ Those relating to the participants: how were the experts selected or excluded? ▪ The definition of consensus: how will the consensus be defined? If applicable, what will the threshold be to consider that consensus has been reached? What criteria have been used to determine the termination of the Delphi in absence of consensus? ▪ In the Delphi process: were items eliminated? What criteria were used to eliminate them? What criteria were used to determine study termination or were used during a specific number of rounds? Final considerations Unlike other methods, in Delphi there is no standardisation of definitions, and its use and presentation of reports may lead to a sensation of it being a method which supplies little evidence. This guideline wishes to offer basic aspects for the creation of a Delphi study, offering maximum guarantees. With regard to the type of evidence provided, this depends on approach. A good review of the literature, combined with the experience of the experts with systematization of the whole process may obtain a final product with the maximum rigour possible and provide a type of evidence which is impossible to obtain using regular research methods. Conflict of interest The author has no conflict of interest to declare. ☆ Please cite this article as: Romero-Collado A. Elementos esenciales para elaborar un estudio con el método (e)Delphi. Enferm Intensiva. 2021;32:100–104. ==== Refs References 1 Zhang X. Meng K. Chen S. Competency framework for specialist critical care nurses: a modified Delphi study Nurs Crit Care 25 2020 45 52 10.1111/nicc.12467 31373155 2 Harris C.L. Holloway S. Development of an evidence-based protocol for care of pilonidal sinus wounds healing by secondary intent using a modified reactive Delphi procedure. Part 2: methodology, analysis and results Int Wound J 9 2012 173 188 10.1111/j.1742-481X.2011.00925.x 22296455 3 Homs-Romero E. Romero-Collado A. Development of a minimum data set registry for chronic venous insufficiency of the lower limbs J Clin Med 8 2019 1779 10.3390/jcm8111779 31653084 4 Humphrey-Murto S. Varpio L. Gonsalves C. Wood T.J. Using consensus group methods such as Delphi and Nominal Group in medical education research Med Teach 39 2017 14 19 10.1080/0142159X.2017.1245856 27841062 5 McMillan S.S. King M. Tully M.P. How to use the nominal group and Delphi techniques Int J Clin Pharm 38 2016 655 662 10.1007/s11096-016-0257-x 26846316 6 Humphrey-Murto S. Varpio L. Wood T.J. Gonsalves C. Ufholz L.-A. Mascioli K. The use of the Delphi and other consensus group methods in medical education research: a review Acad Med 92 2017 1491 1498 10.1097/ACM.0000000000001812 28678098 7 Diamond I.R. Grant R.C. Feldman B.M. Bencharz P.B. Ling S.C. Moore A.M. Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies J Clin Epidemiol 67 2014 401 409 10.1016/j.jclinepi.2013.12.002 24581294 8 Donohoe H. Stellefson M. Tennant B. Advantages and limitations of the e-Delphi technique Am J Health Educ 43 2012 38 46 9 Toronto C. Considerations when conducting e-Delphi research: a case study Nurse Res 25 2017 10 15 10.7748/nr.2017.e1498 10 Sekayi D. Kennedy A. Qualitative Delphi method: a four round process with a worked example Qual Rep 22 2017 2755 2763 11 Jorm A.F. Using the Delphi expert consensus method in mental health research Aust N Z J Psychiatry 49 2015 887 897 10.1177/0004867415600891 26296368 12 Raurell-Torredà M. Llauradó-Serra M. Lamoglia-Puig M. Rifà-Ros R. Díaz-Agea J.L. García-Mayor S. Standardized language systems for the design of high-fidelity simulation scenarios: a Delphi study Nurse Educ Today 86 2020 104319 10.1016/j.nedt.2019.104319 13 Hall D.A. Smith H. Heffernan E. Fackrell K. Recruiting and retaining participants in e-Delphi surveys for core outcome set development: evaluating the COMiT’ID study PLoS One 13 2018 e0201378 10.1371/journal.pone.0201378 14 Google LLC Google forms. Mountain view Available from: 2020 https://www.google.com/forms/about/ 15 SurveyMonkey. Available from: https://es.surveymonkey.com/. 16 Humphrey-Murto S. Wood T.J. Gonsalves C. Mascioli K. Varpio L. The Delphi method Acad Med 95 2020 168 10.1097/ACM.0000000000002887 31335812

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==== Front Phys Chem Earth (2002) Phys Chem Earth (2002) Physics and Chemistry of the Earth (2002) 1474-7065 1873-5193 Elsevier Ltd. S1474-7065(22)00243-1 10.1016/j.pce.2022.103350 103350 Article In silico evaluation of potential intervention against SARS-CoV-2 RNA-dependent RNA polymerase Kapoor Shreya 1 Singh Anurag 1 Gupta Vandana ∗ Department of Microbiology, Ram Lal Anand College, University of Delhi, Benito Juarez Road, New Delhi, 110021, India ∗ Corresponding author. 1 Both, Shreya Kapoor and Anurag Singh contributed equally to the manuscript. 15 12 2022 15 12 2022 10335026 10 2021 17 9 2022 10 12 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. With few available effective interventions, emergence of novel mutants responding poorly to existing vaccines and ever swelling newer waves of infection, SARS-CoV-2 is posing difficult challenges to mankind. This mandates development of newer and effective therapeutics to prevent loss of life and contain the spread of this deadly virus. Nsp 12 or RNA-dependent RNA polymerase (RdRp) is a suitable druggable target as it plays a central role in viral replication. Catalytically important conserved amino acid residues of RdRp were delineated through a comprehensive literature search and multiple sequence alignments. PDB Id 7BV2 was used to create binding pockets using SeeSAR and to generate docked poses of the FDA approved drugs on the receptor and estimating their binding affinity and other properties. Result In silico approach used in this study assisted in prediction of several potential RdRp inhibitors; and re-validation of the already reported ones. Five molecules namely Inosine, Ribavirin, 2-Deoxy-2-Fluoro-D-glucose, Lamivudine, and Guaifenesin were shortlisted which exhibited reasonable binding affinities, with neither torsional nor intermolecular or intramolecular clashes. Conclusion This study aimed to widen the prospect of interventions against the SARS-CoV-2 RdRp. Our results also re-validate already reported molecules like 2-Deoxy-D-glucose as a similar molecule 2-deoxy-2-fluoro-D-glucose is picked up in this study. Additionally, ribavirin and lamivudine, already known antivirals with polymerase inhibition activity are also picked up as the top leads. Selected potent inhibitors of RdRp hold promise to cater for any future coronavirus-outbreak subject to in vitro and in vivo validations. Keywords CADD COVID-19 Docking Drug repurposing RdRp SARS-CoV-2 Abbreviations CNS, Central nervous system NTPs, Nucleoside triphosphates RdRp, RNA-dependent RNA polymerase ==== Body pmc1 Introduction SARS-CoV-2 (the seventh known human coronavirus) has a spherical or pleomorphic shape with 125 nm diameter and belongs to the order Nidovirales, family Coronaviridae, genus Betacoronavirus. Coronaviruses are known to have zoonotic origin and cause respiratory, CNS, hepatic and gastrointestinal infections (Mirza and Froeyen, 2020). SARS-CoV-2 has a 29903 nucleotide long positive sense single stranded RNA genome sharing homology with SARS-CoV (79.5% identity) and Bat-CoV RaTG13 (96.2% identity) (Baek et al., 2020; Chen et al., 2020; Ludwig and Zarbock, 2020). RNA-dependent RNA polymerase (RdRp) is a multidomain protein that catalyses the synthesis of viral RNA and is essential for viral propagation. It consists of three domains: N-terminal domain, C-terminal domain, which are linked by an interface domain (Gao et al., 2020). The C-terminal domain further contains three subdomains: finger, palm and a thumb subdomain. The RdRp of SARS-CoV-2 shares 96.4% sequence identity and sequence similarity of 99.4% with SARS-CoV. It exists as a complex bound to a Nsp7-Nsp8 heterodimer and a Nsp8 monomer, that acts as cofactors and stimulates the polymerase activity of RdRp by reducing its dissociation rate from RNA (Yoshimoto, 2020). The Nsp7-Nsp8 heterodimer is bound to the thumb subdomain (Tian et al., 2021) and serves as primase (Konkolova et al., 2020). Nsp8 acts as an allosteric activator and molecular connector responsible for holding the replication machinery together (Romano et al., 2020). RdRp can be inhibited by broadly two classes of inhibitors: nucleoside analogue and non-nucleoside analogue inhibitors. Nucleoside analogues (like Remdesivir) mimics the natural substrate of RdRp and once incorporated in the growing RNA chain, blocks further addition of NTPs. While non-nucleoside analogues (like Galidesivir) interact with sites other than catalytic site and change the conformation of the active site of the polymerase rendering it ineffective (Mishra and Rathore, 2021). Studies have demonstrated that targeting residues like ASP760 and ASP761 in RdRp catalytic site have potential to stall its activity (Aftab et al., 2020). In this study we undertook an in silico approach to delineate such SARS-CoV-2 RdRp inhibitors. 1.1 Rationale of the study Drug repurposing exploits the off-targeting property of a drug. In silico screening approach that we used in our study provides scope to hasten drug development in a limited timeframe and at lower costs, thus providing an edge over conventional routes especially in times of a pandemic. RdRp, the core component of viral replication, catalyses RNA synthesis and serves as an appropriate drug target owing to its conserved nature across coronavirus' family with respect to several amino acid residues of the catalytic site. Furthermore, drugs targeting RdRp are associated with fewer or no off-target effects due to the absence of its counterpart in mammalian cells. Leads against this protein may possibly help us prepare for any future outbreak of different Coronaviruses. 2 Structure of RdRp Of the three domains of RdRp, the N-terminal domain occupies the residues from the first 249 amino acids and is also referred to as the NiRAN (nidovirus RdRp-associated nucleo-tidyltransferase) domain due to the presence of nucleo-tidyltransferase activity (Gao et al., 2020). It exhibits kinase-like folds (Romano et al., 2020) and is attached to the rear side of the right-hand shaped polymerase domain. In addition to the replication of RNA, it also plays an important role in protein primed RNA synthesis, mRNA capping and nucleic acid ligation (Ortiz-Prado et al., 2020). The sequence of the NiRAN domain in SARS-CoV-2 is homologous to that of SARS-CoV with 93.2% identity. N-terminal also consists of a β-hairpin motif (residues 29–50) embedded in a groove formed by the NiRAN domain and palm subdomain of the polymerase domain [ Table S1 ]. The NiRAN domain is connected to the finger subdomain of the C terminal polymerase domain by the interface domain consisting of residues 250–365. C-terminal/polymerase domain extends from amino acids 367–920. This domain has a structure similar to that of a cupped human right hand and consist of a finger subdomain (366–581 and 621–679 amino acids), a palm subdomain (582–620 and 680–815 amino acids), and a thumb subdomain (816–920 amino acids) [ Table 1 ]. These subdomains perform various functions viz. Polymerization, template binding, facilitate entry of nucleoside triphosphates (NTP) etc. (Gao et al., 2020; Mirza and Froeyen, 2020). The polymerase domain consists of an active site formed at the centre by the finger and thumb subdomains. This active site is highly conserved because of the presence of seven conserved motifs (A-G) which are responsible for the catalytic role of Nsp12 (Mirza and Froeyen, 2020). 5 motifs A-E are found in the palm subdomain while the remaining two F and G occur in the finger subdomain (Gao et al., 2020) [ Table 2 ]. RdRp encompasses a closed ring structure formed due to the intersection of the thumb subdomain with the extended finger subdomain (Peng et al., 2020). The polymerase also possesses various channels namely: template entry, nucleotide entry, RNA exit channel. The entry and exit channels have positively-charged residues and enable passage of RNA strands (Gao et al., 2020).Table 1 Functions performed by different domains of RdRp. Table 1S. No. Domain Functions Reference 1 N-terminal domain NiRAN domain (60–249 amino acids) and β-hairpin motif (29–50 amino acids) Provides assistance in mRNA capping, RNA synthesis, nucleic acid ligation Gao et al. (2020); Zhang et al. (2020) 2 Interface domain (250–365 amino acids) Connect the NiRAN domain and C-terminal domain Gao et al. (2020); Zhang et al. (2020) 3 C-terminal domain (367–920 amino acids) Thumb subdomain (816–920 amino acids) Stabilizes the initiating NTPs on the template and facilitates polymerization Gao et al. (2020); Venkataraman et al. (2018) Finger subdomain (366–581 and 621–679 amino acids) Holds the template in correct position and thus aids in polymerization; also plays crucial role in the recognition and binding due to its interaction with the major groove of the template Gao et al. (2020); Venkataraman et al. (2018); Wang et al. (2020) Palm subdomain   (582–620 and 680–815 amino acids) Known for catalysing the phosphoryl transferase reaction and also for choosing NTPs over deoxy NTPs. One of its residues along with a few residues of the finger subdomain stabilize the phosphate backbone of the template. Gao et al. (2020); Venkataraman et al. (2018); Wang et al. (2020) Table 2 The 7 conserved motifs of the catalytic pocket of the C-terminal domain along with their functions.  Table 2 Motifs Functions References PALM  Motif A (residues 611–626)  Has cation binding residue (ASP618); ASP623 of this motif together with asparagine residue of motif B forms hydrogen bond with incoming NTP (with its 2′OH) and facilitate selection of NTPs over deoxy NTPs Gao et al., (2020); Venkataraman et al., (2018);    Motif B (residues 678–710)  Facilitates translocation of RNA owing to the dynamic interaction existing between the nascent dsRNA and characteristic loop region (having GLY683); recognises NTP ribose Wang et al., (2020);    Motif C (residues 753–767)  Has catalytic residues (SER759, ASP760 and ASN761) that interact with the metal ions (Mg2+). Gao et al., (2020); Jiang et al., (2021); Venkataraman et al., (2018)    Motif D (residues 771–796)  Essential for conformational changes occurring during the binding of the correct NTP and it allows the movement of the thumb subdomain during elongation. Venkataraman et al. (2018)    Motif E (residues 810–820)  Exists as beta hairpin and plays a significant role in positioning the 3′ hydroxyl group of the primer correctly; together with thumb subdomain aids in imparting support to the primer strand Gao et al., (2020); Venkataraman et al. (2018) FINGER  Motif F (residues 544–560)  Interact with the phosphate group of incoming NTP via its hydrophilic residues (LYS545, ARG553 and ARG 555) that make up the NTP entry channel; forms an extended fingertip projecting into the catalytic chamber Venkataraman et al., (2018); Jiang et al., (2021)    Motif G (residues 499–511)  A part of template entry channel interacting with the phosphate group of newly synthesised RNA strand and 5′OH of the template via its ASP499 residue; guides template strand towards the active site Jiang et al., (2021); Venkataraman et al., (2018); Wang et al., (2020) 3 RdRp inhibitors Since the arrival of COVID-19, scientists have been working round the clock to identify effective therapeutics to tackle the virus. A multitude of investigations have been undertaken across the globe to uncover the potency of antivirals and other compounds against RNA-dependent RNA polymerase of SARS-CoV-2. The results have been encouraging and reliable as will be discussed in this section. Based on their mechanism of action, antivirals are broadly categorised as Nucleoside Analogue Inhibitors (NIs) and Non-Nucleoside Analogue Inhibitors (NNIs) (Vardanyan and Hruby, 2016). Nucleoside Analogue Inhibitors impede viral proliferation by promoting chain termination. Because of their structural resemblance with the native RdRp substrates, these compounds compete for incorporation into the nascent RNA chain and once incorporated, they prevent the insertion of subsequent nucleotides, thereby halting replication (Tian et al., 2021). Remedesivir (RDV) which is an adenosine triphosphate analogue is the only antiviral drug currently approved for treatment of SARS-CoV-2 by the FDA (US Food and Drug Administration, 2021). It is a prodrug, triphosphate form (RDV-TP) of which serves as the substrate for RdRp because of its resemblance with ATP and delays termination of nascent RNA thereby acting as a non-obligate chain terminator (Amirian and Levy, 2020). RDV is highly selective for the RNA polymerases because of the presence of 1’ cyano group (Gordon et al., 2020; Romano et al., 2020). Recent phase 2 clinical trial data on Molnupiravir (a drug originally intended to target influenza) on COVID patients has demonstrated reduction in the propagation of SARS-CoV-2. It has an acceptable safety profile, making it a promising candidate as an oral anti-COVID-19 intervention (Fischer et al., 2021; Borbone et al., 2021). Galidesivir (adenosine analogue) has shown efficacy in preclinical tests (EC50 value ∼ 3–68 μM) and phase 1 clinical tests against SARS-CoV-2 with favourable safety profiles and is currently under phase 2 clinical studies (Celik et al., 2021). Favipiravir is a prodrug acting in a way similar to RDV. Its efficacy and low toxicity has been demonstrated in several clinical investigations. In addition, it has been shown to lessen viral clearance time and provide cough relief in patients with mild infections (Celik et al., 2021). Non-nucleoside inhibitors interact with the allosteric region of the polymerase, altering its spatial conformation and thereby suppressing its function. The allosteric site can be found in either the thumb or the palm subdomains. HCV-NS5B polymerase inhibitors targeting deep hydrophobic pockets in the palm subdomain can be repurposed to target SARS-CoV-2 Nsp12 (Tian et al., 2021). For example: Tegobuvir occupies the interface between Nsp7 and 12 hindering the process of RNA synthesis by the replication complex. Results of in vitro experiments published recently by Dejmek et al. (2021) revealed the potency of another NNI: HeE1-2 Tyr and related compounds (originally proposed as inhibitors of flaviviruses’ RdRp) against SARS-CoV-2 RdRp. These compounds can be enhanced for physicochemical parameters and potency before being employed to combat SARS-CoV-2 (Dejmek et al., 2021). 3.1 Phytochemicals Swertiapuniside and Amarogentin both obtained from Swertia chirayita; Cordifolide A derived from Tinospora cordifolia and Sitoindoside IX from Withania somnifera exhibited effective interaction with Nsp12 (Koulgi et al., 2021). Additionally, theaflavin is considered to be effective against RdRp because of its ability to obstruct the active site (Lung et al., 2020). The antiviral properties of tea polyphenols were demonstrated in a docking study undertaken by a group of Indian researchers. Their studies revealed that theaflavin (interacting with SER607) and EGCG (interacting with GLU106) have better binding affinities than remdesivir and favipiravir and exhibited both polar and hydrophobic interactions (Mhatre et al., 2021). 4 Result The amino acid residues important for RdRp functioning and those participating in replication-transcription complex were discerned through extensive literature search. Following this, multiple sequence alignments were performed using Clustal Omega to identify conserved residues, while the 3-Dimensional protein structure was extracted from RCSB-PDB. PDB 7BV2 (Yin et al., 2020) consisting of Nsp12-Nsp7-Nsp8 complex in association with primer-template RNA and Remdesivir triphosphate was selected for undertaking this in silico study. 7BV2 is a 2.50 Å resolved structure determined by electron microscopy (Yin et al., 2020). In this research, we used only the Nsp12 chain and deleted other protein chains using SeeSAR (SeeSAR version 11.0.2). 47 crucial residues in Nsp12 were demarcated through literature search and alignment studies [ Tables S2 and S3 ]. Following this a suitable auto-detected active site pocket was created. And was modified to include some crucial residues based on literature studies, to form a deep seated final pocket of 28 residues: GLN541, ASN543, LYS545, ARG553, ARG555, THR556, VAL557, ALA558, GLY559, VAL560, ASP618, TYR619, LYS621, CYS622, ASP623, ARG624, THR680, SER682, GLY683, ASP684, ALA685, THR687, ALA688, TYR689, ASN691, ASP760, ASP761, SER814 (the crucial residues are highlighted in a bold face) [ Fig. 1 ].Fig. 1 The 28 residue docking pocket created within the RdRp (PDB 7BV2). Fig. 1 The library of 1615 FDA approved drugs was downloaded from https://zinc15.docking.org in mol2 file format and was imported to SeeSAR to generate docking poses (10 poses for each molecule) on the receptor and their affinity and other properties were estimated. A number of compounds could dock on this pocket with reasonable binding affinities. 23 leads with binding affinity <250 nM, without torsional and inter-molecule clashes, suitable current therapeutic use, side effects and route of administration were selected and were redocked to generate 100 iterations. Finally 5 best molecules based on their interaction with key residues were shortlisted [ Table 3 , Fig. 2 ]. Table 3 Shortlisted molecules based on docking studies. Table 3Molecular name and Zinc ID Binding Affinity (nM) Key Interactions Current therapeutic use(s) Inosine ZINC8855117 2–159 TYR619, ASP760, Mg 1004, Mg 1005 Nutritional supplement; may have some neurorestorative, anti inflammatory, immunomodulatory and cardio-protective effects Ribavirin ZINC1035331 3–251 TYR619, ASP760, Mg 1004, Mg 1005 Guanosine nucleotide used for HCV infection treatment 2-Deoxy-2-Fluoro-D-glucose ZINC1846431 4–438 TYR619, ASP760, Mg 1004, Mg 1005 Glucose analogue; used as a radiotracer for glucose in medical diagnostics and for treating cancer, cardiovascular diseases, Alzheimer disease, etc Guaifenesin ZINC394284 37–3725 Mg 1004 OTC products for symptomatic relief from congested chests and coughs associated with colds, bronchitis and other breathing illnesses. Lamivudine ZINC12346 57–5658 ASP 761, SER814, Mg 1004, Mg 1005 Reverse transcriptase inhibitor used against HIV and hepatitis B infections Fig. 2 Interaction and docking analysis of RdRp inhibitors: (I.) Showing positioning of the leads in a surface view created using SeeSAR. (II.) Interaction with docking site residues in a 2-D view created using ProteinsPlus (https://proteins.plus/), respectively. A. Inosine (ZINC8855117), B. Ribavirin (ZINC1035331), C. 2-Deoxy-2-Fluoro-D-glucose (ZINC1846431), D. Lamivudine (ZINC12346), and E. Guaifenesin (ZINC394284). Fig. 2 Our current study found 2-deoxy-2-fluoro-D-glucose to have a good binding affinity for RdRp. A similar molecule 2-Deoxy-D-glucose (2DG) is in use in India for treating SARS-CoV-2. This study also corroborates other reported polymerase inhibitors like ribavirin and lamivudine (Singh et al., 2021; The Hindu, 2021; Wu et al., 2020). 5 Conclusion and future enhancement The SARS-CoV-2 outbreak, first recognized in December 2019 took a heavy toll on the naive human population and continues to wreak havoc with newer waves of infection across several countries. With little to no effective therapeutic intervention and unreliable vaccine supplies, rapid evolution of the virus as it continues to mutate poses critical challenges to overcoming this pandemic. The rationale of choosing RNA-dependent RNA polymerase as a target protein was its integral role in virus replication, among others. This 932 amino acid, multidomain protein is largely conserved, and hence makes one of the chief targets for SARS-CoV-2 drug development. Besides, in silico screening and repurposing of approved drugs provides scope to hasten drug development in a limited timeframe. Computer Aided Drug Discovery provides an interdisciplinary approach to researchers for fast-track drug discovery with several proven successes. In this research we propose 5 compounds with suitable binding affinity which are potential SARS-CoV-2 RdRp inhibitors. This study widens the scope of drug development against the said virus and aims to further enhance the identified leads to augment its efficacy with in-vitro wet lab analysis. Studying the effect of mutations on the drug target also makes up another aspect of future studies. It is also suggested to recore the molecules with biocompatible groups to further optimize the lead and to undertake MD simulations to analyse the stability of ligand binding using numerical methods. CRediT authorship contribution statement Shreya Kapoor: Investigation, Data curation, Writing – original draft, Visualization. Anurag Singh: Investigation, Data curation, Writing – original draft, Visualization. Vandana Gupta: Conceptualization, Methodology, Resources, Writing – review & editing, Supervision, Project administration. All authors have read and approved the final version of the manuscript. Uncited references SeeSAR, 2021. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Supplementary Information TABLE S1 Amino acid residues of beta hairpin interacting with those of NiRAN domain and palm subdomain of RdRp domain. The gray coloured ones are conserved (based on alignment results) (Gao et al., 2020) TABLE S1β- Hairpin NiRAN Palm R33 R55 D711 F35 K121 N713 D36 Y122 H725 V38 D126 Y728 V42 Y129 E729 F45 T206 R733 F48 D208 S236 TABLE S2 The aminoacid residues from different subdomains interacting with the template, product and primer RNA strand. Reference: Template (Yin et al., 2020); Product (Hillen et al., 2020); Primer (Yin et al., 2020) TABLE S2Finger subdomain Palm subdomain Thumb subdomain Template Product Primer Template Product Primer Template Product Primer S501 N507 Q541 K545 N543 R513 D499 R513 N496 K500 V557 A558 G559 V560 R569 K577 A580 G590 S592 F594 Y595 S682 G683 D684 A685 Y689 L758 S759 D760 D761 C813 S814 S759 D760 C813 S814 Q815 I864 F920 M924 R836 M855 E857 R858 S861 D865 R836 A840 K849 L854 R858 S861 L862 D865 Table S3 Crucial amino acid residues from RdRp domain which are conserved as well (based on literature and alignment studies). Residues highlighted in boldface are included in the final binding pocket. Table S3N496 A558 D623 Y689 D761 M855 D499 G559 N628 N691 C813 E857 K500 K577 T680 N713 S814 R858 S501 A580 S682 Y728 Q815 S861 N507 G590 G683 R733 R836 L862 R513 F594 D684 L758 A840 I864 R555 Y595 A685 S759 K849 D865 V557 D618 T687 D760 L854 Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Data availability No data was used for the research described in the article. Acknowledgement We would also like to acknowledge support from the 10.13039/501100001407 Department of Biotechnology , India under the star college scheme. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.pce.2022.103350. ==== Refs References Aftab S.O. Ghouri M.Z. Masood M.U. Analysis of SARS-CoV-2 RNA-dependent RNA polymerase as a potential therapeutic drug target using a computational approach J. Transl. Med. 18 1 2020 275 10.1186/s12967-020-02439-0 32635935 Amirian E.S. Levy J.K. 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Phys Chem Earth (2002)

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==== Front Clin Chim Acta Clin Chim Acta Clinica Chimica Acta; International Journal of Clinical Chemistry 0009-8981 1873-3492 Elsevier B.V. S0009-8981(22)01411-5 10.1016/j.cca.2022.12.009 Article Evaluation of reverse transcriptase-polymerase spiral reaction assay for rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2 Prerana Sharan a Ashwini Pai a Padyana Anupama Karanth a Shankaranarayana Prajna Valakkunja a Suresh Prithvisagar Kattapuni a Nayak Ashwath a Rai Praveen a⁎ Rohit Anusha b Karunasagar Indrani c Karunasagar Iddya c Maiti Biswajit a⁎ a Nitte (Deemed to be University), Nitte University Centre for Science Education and Research (NUCSER), Division of Infectious Diseases, Paneer Campus, Deralakatte, Mangalore- 575018, India b Madras Medical Mission, Department of Microbiology, Dr. J. J. Nagar, Mogappair, Chennai- 600037 c Nitte (Deemed to be University), University Enclave, Medical Sciences Complex, Deralakatte, Mangalore- 575018, India ⁎ Corresponding authors at: Nitte University Centre for Science Education and Research, Paneer Campus, Deralakatte, Mangalore- 575018, India. Nitte University Centre for Science Education and Research, Paneer Campus, Deralakatte, Mangalore- 575018, India. 15 12 2022 15 12 2022 19 10 2022 12 12 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background and aim Existing real-time reverse transcriptase PCR (RT-PCR) has certain limitations for the point-of-care detection severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since it requires sophisticated instruments, reagents, and skilled laboratory personnel. In this study, we evaluated an assay termed the reverse transcriptase-polymerase spiral reaction (RT-PSR) for rapid and visual detection of SARS-CoV-2. Methods The RT-PSR assay was optimized using RdRp gene and evaluated for the detection of SARS CoV-2. The time of 60 min and a temperature of 63°C was optimized for targeting the RNA-dependent RNA polymerase gene of SARS-CoV-2. The sensitivity of the assay was evaluated by diluting the in-vitro transcribed RNA, which amplifies as low as ten copies. Results The specific primers designed for this assay showed 100% specificity and did not react when tested with other lung infection-causing viruses and bacteria. The optimized assay was validated with 190 clinical samples in two phases, using automated RT PCR based TrueNat test, and the results were comparable. Conclusions The RT-PSR assay can be considered for rapid and sensitive detection of SARS-CoV-2, particularly in resource-limited settings. To our knowledge, there is as yet no RT-PSR-based kit developed for SARS-CoV2. Keywords COVID-19 RT-PSR SARS-CoV-2 coronavirus detection TrueNat test ==== Body pmc1 Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a novel coronavirus, the causative agent of coronavirus disease 2019 (COVID-19). It is a single-stranded positive-sense RNA (+ssRNA) virus belonging to the genus β-coronavirus [1]. The genome of the virus codes for four structural proteins, the spike (S) protein, nucleocapsid (N) protein, membrane “matrix” (M) protein, and envelope (E) proteins [2]. SARS-CoV-2 is aerosol borne and can spread from person to person mainly through respiratory droplets. Infected persons may be asymptomatic, and yet shed the virus. Early diagnosis is crucial to combat COVID-19, for which the availability of tools for rapid detection of the virus holds the key. The nucleic acid amplification tests (NAAT, such as probe-based reverse transcriptase (RT) - real-time polymerase chain reaction (PCR) (RT-qPCR), are considered the gold standard globally for the detection of SARS-CoV-2 [3], [4], [5], [6], [7]. The assay is accepted globally as it has a high level of sensitivity and offers a low limit of detection (LOD). However, performing RT-qPCR requires sophisticated equipment, the Real-Time PCR machine, coupled with high-quality reagents, and skilled laboratory personnel, all of which contribute to it being cost-intensive [8]. In addition, the turnaround time is more. Additionally, a low viral concentration limits the effectiveness of the test [9]. Hence, there is a need for an alternative rapid, portable, and affordable NAAT-based point-of-care assay to detect SARS-CoV-2. Unlike the PCR-based method, isothermal amplification is relatively simple in that it rapidly amplifies the nucleic acids at a constant temperature without the need for a programmable thermocycler [10]. The continuous search for novel isothermal amplification techniques has contributed to the development of RT- polymerase spiral reaction (RT-PSR) as diagnostic assays that do not require sophisticated equipment [11]. The RT-PSR nucleic acid amplification is carried out with 1-2 pairs of primers, a strand displacing DNA polymerase enzyme, and reverse transcriptase enzyme. This technique can be performed using a simple dry bath without the need for a thermal cycler. The validation of the assay using clinical swab samples from COVID-19 patients showed this assay to be highly sensitive, specific [12], and one which can lend itself as a POC test of high utility through low cost. We developed and optimized the reaction conditions of RT-PSR assay for the detection of SARS CoV-2 that can be sensitive, specific, and detected visually. Further, the assay was independently validated with clinical samples and compared with automated RT-PCR based TrueNat test results. 2 Material and Methods 2.1 Ethical and bio-safety statement Ethical approval for the study was taken by the Central Ethics Committee, Nitte (Deemed to be University), and Madras Medical Mission, Chennai, India. The biosafety approval was taken from the institutional biosafety committee (IBSC), Nitte (Deemed to be University). 2.2 Primer designing for RT-PSR assay The RNA-dependent RNA polymerase gene (RdRp) sequence of SARS-CoV-2 was retrieved from the NCBI GenBank database. The obtained sequences were subjected to multiple sequence alignment (MSA) tool MultAlin to find the conserved regions in each gene. Primers for RT-PSR were designed using the Primer3Plus software. The best pair was selected as forward and reverse primers (PS-F & PS-R) for the assay, and an unrelated sequence from a plant source was linked to it. To amplify the target gene, an extra pair of primers were designed using Primer3Plus software as auxiliary accelerated outer primers (PS-OF & PS-OR). Sequence of primers used for the test is listed in Table.1 .Table 1 List of primer used in this study Primer name Sequence (5’3’) Purpose Reference RdRp-F ATGGCCTCACTTGTTCTTGC For PCR assay This study RdRp-R GGCCCCTAGGATTCTTGATG RdRp- T7F TAATACGACTCACTATAGGGATGGCCTCA CTTGTTCTTGC For in-vitro RNA synthesis This study RdRp-PS-F ACGAATTCGTACATAGAAGTATAGGTAGCTTGT CACACCGTT For RT-PSR assay This study RdRp-PS-R GATATGAAGATACATGCTTAAGCAGCATTAACA TTGGCCGTG RdRp-PS-OF TCTGACGATGCTGTTGTGTGT RdRp-PS-OR CAGTCTCAGTCCAACATTTTGC 2.3 SARS-CoV-2 and RNA extraction Lysed SARS-CoV-2 samples with protocol were kindly provided by the National Institute of Virology, Pune, Viral RNA was extracted using QIAampviral RNA extraction kit (Qiagen, Germany, and the concentration was measured using Nanophotometer®NP80 (Implen, Germany) and stored at -80°C deep freezer (Panasonic, Japan) for further use. 2.4 Ligation and transformation of the target gene RT-PCR was performed by a One-step RT-PCR Kit (Qiagen, Germany) using gene-specific primers and the extracted viral RNA. The amplified products were purified using the QIAquick PCR purification kit (Qiagen, Germany). The RdRp gene was further ligated to pDrive vector using a Quick-start QIAGEN PCR cloning kit (Qiagen, Germany). Competent cells of E. coli DH5α were used to transform the ligated product by heat shock method and confirmed by blue-white screening followed by colony PCR using gene/vector-specific primers. Plasmid DNA was then extracted from the positive clones using the QIAprep Spin Miniprep kit (Qiagen, Germany), and Sanger sequencing was performed (M/S Eurofins Genomics India Pvt Ltd., Bangalore, India). The sequences obtained were analyzed using various bioinformatics tools, and the integrity of the gene was confirmed. 2.5 In-vitro RNA synthesis The PCR of the target sequence was performed using a T7 promoter containing forward primer and gene-specific reverse primer, and the product was used for in-vitro RNA synthesis with HiScribe™ T7 quick high yield RNA synthesis kit (New England BioLabs, USA). The RNA obtained was purified using a Monarch® RNA cleanup kit (New England BioLabs, USA), and the presence was confirmed by treating with RNase A (New England BioLabs, USA) followed by agarose gel electrophoresis. Purified RNA was stored at –80 °C (Panasonic, Japan) for further use. The concentration and purity of the in-vitro transcribed RNA were estimated using Nanophotometer® NP80 (Implen, Germany). 2.6 Optimization of RT-PSR for the detection of SARS-CoV-2 In-vitro transcribed SARS-CoV-2 RNA was used for the optimization of the RT-PSR assay (Fig. 1 ). The temperature was optimized using 60℃, 63℃, and 65℃. Likewise, the assay time was optimized by considering 15 min, 30 min, 45min, and 60 min time points. The requirement of magnesium sulphate was also optimized using varying concentrations. The assay was optimized in a microcentrifuge tube using 25 µl of reaction mixture comprising 1.6 µM each of forward and reverse primers (FP and RP), 0.2 µM each of outer primers (OF and OR), 2.5 µl of 10X isothermal amplification buffer, 1.6 M betaine (Sigma-Aldrich, USA), 4 mM magnesium sulphate, 1.4 mM of each of the four dNTPs, 0.8 µl WarmStart® RTx Reverse Transcriptase (NewEngland BioLabs), and 1 μl Bst 2.0 DNA Polymerase (New England BioLabs). Two microliters of in-vitro synthesized RNA were used as the template for the reaction.Fig. 1 Schematic representation of workflow for the detection of SARS-CoV-2 using RT-PSR assay targeting RdRp gene 2.7 Visual detection of the amplicons and agarose gel electrophoresis Visual detection of the amplicon was based on an enzymatic reaction using hydroxynapthol blue (HNB) dye at a concentration of 120 µM in the 12.5 µl reaction mixture [13]. The change in colour of the reaction mixture from dark blue to sky blue was considered positive. Further confirmation was by agarose gel electrophoresis, gel stained with ethidium bromide (0.5 μg/ml), and visualized using a gel documentation system (Gel Doc XR+, Bio-Rad, USA). 2.8 Sensitivity test for RT-PSR assay The sensitivity test was performed by diluting the in-vitro synthesized RdRp RNA to yield varying concentrations (5 ng/µl, 25 ng/µl, 50 ng/µl, and 200 ng/µl), which served as a template for the RT-PSR assay. Further, the analytical sensitivity of RT-PSR assay was performed by serially diluting the in-vitro synthesized RdRp RNA to contain varying RNA copy numbers (from 2.65 x10 11 to 2.65 x 1 copies of RNA). 2.9 Specificity test for RT-PSR assay The specificity assay was performed using both in-vitro and in-silico analysis. For in-silico analysis, sequences of SARS-CoV-2 reference genome (Wuhan City, Hubei, 2019-12-26, GenBank ID MN908947), other SARS-CoV-2 emerging sequence variants from around the world, other coronaviruses, and related RNA viruses were retrieved from NCBI GenBank and GISAID (global initiative on sharing all influenza data EpiFlu database). The MSA analysis was performed against the RT-PSR primer sequences using the bioinformatics software Geneious (https://www.geneious.com/) to check for any mismatches. In-vitro specificity test was performed using RNA sample of influenza virus and other bacterial infectious agents causing lung infection such as Burkholderia cepacia (ATCC 25416), Mycobacterium marinum (ATCC 11565), Acinetobacter baumannii (ATCC 19606) and Pseudomonas aeruginosa (Lab isolate, NUCSER). 2.10 Internal validation of RT-PSR with clinical samples The developed RT-PSR assay was validated in two phases, with clinical samples (nasopharyngeal swab samples) at the tertiary care hospital, Madras Medical Mission, Chennai, India. During the first phase, 90 random clinical samples were selected, of which 60 were positive, and 30 were negative based on TrueNat test results. The positive samples comprised 36 that were collected in July and August 2021 (stored samples), and 24 were drawn in September and October 2021 (freshly collected samples). All the 30 negative samples were collected during October 2021, and validation study was performed end of October 2021. During the second phase, 100 samples were collected during January 2022, of which 80 were positive and 20 negative by TrueNat. All the samples were validated at the end of January 2022. The total viral RNA was extracted from the samples using the High Pure Viral RNA kit (Roche, Switzerland) as per the manufacturer’s instructions, for use as template, and results of RT-PSR correlated with that of TrueNat (Table 2 ).Table 2 The results of first and second phases of clinical sample validation Phase of clinical sample validation Period of sample collection TrueNat positive samples TrueNat negative samples Total positive samples True positive byRT-PSR True positive sample for RT-PSR (%) Total negative samples Truenegative byRT-PSR True negative forRT-PSR (%) First phase July-August 2021 36 28* 77% - - - September-October 2021 24 23‘ns’ 95.8% 30 29‘ns’ 96% Second phase January 2022 80 75* 93.7% 20 20‘ns’ 100% *Significant difference in RT-PSR results in comparison to TrueNat test ‘ns’No significant difference in RT-PSR results in comparison to TrueNat test 2.11 Statistical analysis The two-sample proportion test with a significance of p<0.05 was used to assess the significant difference between the RT-PSR assay and the TrueNat test and was calculated using online software, Mathcracker. The graphs were generated using Prism version 5.0 software (Graph Pad, Inc., La Jolla, USA). 3 Results 3.1 Cloning and in-vitro transcription of SARS CoV-2gene The sequence and MSA analysis showed a 100% match between all the aligned sequences with the primer sequence. The RNA concentration obtained for the RdRp target was > 450 ng/µl. Treatment of RNA with RNase resulted in detection of no band in electrophoresis study, whereas all the untreated RNA showed a band in the gel. 3.2 Optimization of RT-PSR assay to amplify RdRp gene Four primers (FP, BP, OFP, ORP) were designed to target RdRp gene. The optimized temperature and time for the assay were found to be 63°C and 60 minutes, respectively. The HNB dye at a concentration of 120 µM showed a colour change from dark blue to sky blue in positive samples and no change in negative samples (Fig. 2 ).Fig. 2 A representative image showing the visual detection of RT-PSR amplified product followed by agarose gel electrophoresis analysis. Lane 1: Non template control; Lane 2: Positive control (in-vitro synthesized RNA) 3.3 Sensitivity test for RT-PSR assay The RT-PSR assay sensitivity was evaluated by agarose gel electrophoresis and HNB dye-based visual detection. Ten-fold serial dilutions of the in-vitro transcribed viral RNA ranging from 2.65 x 1011 to 2.65 x10 copies could be detected (Fig. 3 ). The lowest concentration of RNA at which the RdRp gene target showed amplification was 5 ng/ µl RNA.Fig. 3 Optimization of temperature, sensitivity, specificity of RT-PSR assay. A. Temperature optimization using visual detection followed by 2% agarose gel analysis of RT-PSR assay. Lane 1: Non template control; Lane 2: Amplification at 60˚C for 60 min; Lane 3: Amplification at 63˚C for 60 min, Lane 4: Amplification at 65 ˚C for 60 min. B. Determination of sensitivity of RT-PSR assay using visual detection followed by 2% agarose gel analysis. Figure showing the amplification using different RNA concentration for RdRp gene (expressed in copy number). Lane 1: Non template control, Lanes 2: 2.65x1011 RNA copies; Lanes 3: 2.65x104RNA copies; Lanes 4: 2.65x103RNA copies; Lanes 5: 2.65x102RNA copies; Lanes 6: 2.65x10RNA copies. C. Determination of specificity using visual detection followed by 2% agarose gel analysis. Lane 1: Non template control; Lane 2: In-vitro RNA (positive control); Lane 3: Influenza virus; Lane 4: B. cepacia; Lane 5: M. marinum; Lane 6: A. baumannii and Lane 7: P. aeruginosa. 3.4 Specificity test for RT-PSR assay The in-silico analysis for the RT-PSR primers revealed high specificity to the SARS-CoV-2 and its variants. For the primer, no mismatch with SARS-CoV-2 sequences from the world database was recorded, while 100% mismatch was seen with other coronavirus, and other related RNA virus sequences included for the analysis. No amplification was observed for influenza virus and lung infection, causing bacterial pathogens like B. cepacia, M. marinum, A. baumannii, and P. aeruginosa (Fig. 3). 3.5 Clinical validation of RT-PSR assay The two-sample Z test statistical analysis was applied with the validated TrueNat test used by Madras Medical Mission, as the reference method. In the first phase of clinical validation, 52 samples (87%) showed amplification using RT-PSR assay, which included 29 old positive stored samples (80.5%) and 23 (95%) fresh positive samples. The 29 (97%) negative samples did not amplify, by visual detection or gel electrophoresis (Fig. 4 ). Likewise, during the second phase, 75 samples (93.7%) showed amplification for the RdRp gene. All 20 (100%) true negative samples showed no amplification, either by visual observation or by gel electrophoresis.Fig. 4 Results of internal clinical validation of the optimized RT-PSR assay. A, and B: Visual detection of amplicons followed by agarose gel analysis of clinical samples for first and second phase, respectively. A1: Lane 1: Non template control, Lane 2: Positive control (in-vitro synthesized RNA), Lanes 3-7: Samples showing amplifications. A2: Lane 1: Non template control, Lane 2: Positive control (in-vitro synthesized RNA), Lanes 3-7: Samples showing no amplifications. B1: Lane 1: Non template control, Lane 2: Positive control (in-vitro synthesized RNA), Lanes 3-7: Samples showing amplifications. B2: Lane 1: Non template control, Lane 2: Positive control (in-vitro synthesized RNA), Lanes 3-7: Samples showing no amplifications. C. Results for first phase of sample validation compared to TrueNat confirmed old positive (80.5%), new positive (95.8%) and negative (96.6%). D. Results for second phase of clinical validation compared to TrueNat confirmed positive (93.7%) and negative (100%) samples. E. Comparison of results between TrueNat and RT-PSR assay for first phase sample validations. ‘*’ indicates Significant difference between RT-PSR and TrueNat (p = <0.05). ‘ns’ No significant difference between RT-PSR and TrueNat assay (p = >0.05); F. Comparison of results between TrueNat and RT-PSR assay for second phase sample validation. ‘*’ indicates Significant difference between RT-PSR and TrueNat (p = <0.05). ‘ns’ No significant difference between RT-PSR and TrueNat assay (p = >0.05). 4 Discussion The pandemic due to SARS-CoV-2 has caused an unprecedented situation globally for health and the economy [14]. The development and application of highly specific, rapid, and sensitive point-of-care diagnostics for timely detection and contact tracing have been considered most important tool as of now [15], [16]. Although the gold standard RT-qPCR is highly sensitive and specific, there are some limitations, such as the need for sophisticated equipment, skilled research personnel, complex protocol, and long waiting time [6], [17]. In this study, we developed, optimized, and evaluated the suitability of a colorimetric RT-PSR assay for the detection of SARS-CoV-2 without compromising on the specificity, sensitivity, and rapidity. To the best of our knowledge, this is the first study on the use of RT-PSR for the detection of SARS-CoV-2 with the attributes mentioned above. In the initial phase of development of RT-PSR, we designed and tested primers targeting the various SARS-CoV-2 genes like nucleocapsid, spike, envelope, and RDP genes. However, it was observed that RdRp gene primer worked the best for this assay. An additional feature with regard to RdRp gene was its highly conserved nature [18]. As compared to other isothermal amplification-based assays, such as loop-mediated isothermal amplification (LAMP), the primer design for RT-PSR assay is far simpler [9], [11]. and offers greater detection efficiency [19], [20]. Further, the assay has been optimized using HNB dye for the visual detection of the amplification product [21]. During the amplification, the insoluble magnesium pyrophosphate products formed at the end of the isothermal amplification facilitate the direct visual determination of the result for a given reaction because of the dye complex resulting in an easy to read coloured format for a POC test [22]. Previous studies on porcine epidemic diarrhoea virus, canine parvovirus 2, and coxsackie virus have shown RT-PSR assay to be highly sensitive with an ability to amplify even a single copy of RNA [12], [23], [24]. This study also reflected the high sensitivity of the test with the ability to amplify RNA as low as 5 ng/μL. The limit of detection in terms of copy numbers was as low as ten copies of RNA. The in-silico analysis demonstrated the specificity of the primer for the detection of SARS-CoV-2. The primer binding region was highly conserved and specific with ability to detect all the major variants of the virus. Further, the assay was specific as the influenza virus, and other respiratory infection-causing bacterial species showed no cross-reactivity with the PSR primers. This was further confirmed by our observation that all TrueNAT negative clinical samples were negative in this assay. The key feature of the assay relates to the short time and isothermal temperature of 63°C for about 60 min, significantly reducing the clinical testing time, thus making it faster than RT-qPCR. Additionally, the reaction results can be determined visually by the colour change, without the need for fluorescent probes, making it simple and cost-effective. The practicability of the developed RT-PSR assay was validated using clinical samples and compared with the widely accepted TrueNat test results. In the first phase of clinical samples received and tested during July-October 2021, the sample positivity rate was low (87%) for the stored samples. There were also few false-negative results by RT-PSR obtained in true-positive (TrueNat positive) samples. Interestingly, majority of these samples were relatively old samples stored at -80 (collected during July and August 2021). However, no false-negative result was obtained in the samples collected during September-October 2021. We surmise the old stored false-negative results of true positive samples to be due to the degradation during the storage period. Likewise, during the second testing phase, a total of 75 samples (93.7%) showed amplification for the RdRp gene. All the 20 (100%) true negative samples showed no amplification when results were read both visually and by gel electrophoresis. In the second phase of sample validation, we tried to address the issue false-negative result of the first phase. For this, 100 fresh samples were selected based on TrueNat test results (collected and tested end of January 2022). The RT-PSR assay results were comparable with TrueNat results. The RT-PSR assay targeting the RdRp gene showed amplification for 94% of samples that are positive by TrueNat, and 100% of true-negative samples did not show any amplification. We conclude that the RT-PSR assay results are comparable with TrueNat test results and can be a point-of-care diagnostic assay for detecting SARS-CoV-2 in a low-resource setting with the cardinal features of specificity, sensitivity, rapidity, and affordable. Author Contributions SP and PA performed experiments, interpreted the results, analyzed the data, and wrote the manuscript. KPA performed experiments. VSP performed experiments. KSP and AN sample validated and analyzed the data. PR designed the experiments, supervised, and reviewed the manuscript. AR sample validated, provided resources, and reviewed the manuscript. IK and IK conceptualized, supervised, and reviewed the manuscript. BM conceptualized, designed the experiments, supervised, provided funds and resources, and reviewed the manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgments This study was supported by the Science and Engineering Research Board (SERB), DST-Government of India, through the COVID-19 project (CVD/2020/000150). ==== Refs References 1 Raj V.S. Osterhaus A.D. Fouchier R.A. Haagmans B.L. MERS: emergence of a novel human coronavirus Curr. Opin. Virol. 5 2014 58 62 24584035 2 Mariano G. Farthing R.J. Lale-Farjat S.L. Bergeron J.R. Structural characterization of SARS-CoV-2: Where we are, and where we need to be Front. Mol. Biosci. 7 2020 605236 3 Dhama K. Khan S. Tiwari R. Sircar S. Bhat S. Malik Y.S. Singh K.P. Chaicumpa W. Bonilla-Aldana D.K. Rodriguez-Morales A.J. Coronavirus disease 2019–COVID-19 Clin. Microbiol. Rev. 33 2020 e00028 e120 32580969 4 Yüce M. Filiztekin E. Özkaya K.G. COVID-19 diagnosis—a review of current methods Biosens. Bioelectron. 72 2021 112752 5 Islam K.U. Iqbal J. An update on molecular diagnostics for COVID-19 Front. Cell Infect. Microbiol. 10 2020 560616 6 P. Rai, B.K. Kumar, D.V. Kumar, P. Kumar, A. Kumar, S.K. Shetty, B. Maiti, The Evolution of COVID-19 Diagnostics. COVID-19: From Bench to Bedside. ISBN 9781032040622 (2022). 7 Rai P. Kumar B.K. Deekshit V.K. Karunasagar I. Karunasagar I. Detection technologies and recent developments in the diagnosis of COVID-19 infection Appl. Microbiol. Biotechnol. 105 2021 441 455 33394144 8 Xu W. J, Gao, H, Zheng, C, Yuan, J, Hou, L. Zhang, G. Wang, Establishment and application of polymerase spiral reaction amplification for Salmonella detection in food J Microbiol Biotechnol. 29 2019 1543 1552 31546299 9 Sethuraman N. Jeremiah S.S. Ryo A. Interpreting diagnostic tests for SARS-CoV-2 JAMA. 323 2020 2249 2251 32374370 10 Zhao Y. Chen F. Li Q. Wang L. Fan C. Isothermal amplification of nucleic acids Chem. Rev. 115 2015 12491 12545 26551336 11 Liu W. Dong D. Yang Z. Zou D. Chen Z. Yuan J. Huang L. Polymerase spiral reaction (PSR): A novel isothermal nucleic acid amplification method Sci. Rep. 5 2015 1 8 12 Maiti B. Anupama K.P. Rai P. Karunasagar I. Karunasagar I. Isothermal amplification-based assays for rapid and sensitive detection of severe acute respiratory syndrome coronavirus 2: Opportunities and recent developments J Med. Virol. 32 2022 e2274 13 Anupama K.P. Nayak A. Karunasagar I. Maiti B. Rapid visual detection of Vibrio parahaemolyticus in seafood samples by loop-mediated isothermal amplification with hydroxynaphthol blue dye World J Microbiol. Biotechnol. 36 2020 1 14 Riley W.T. Borja S.E. Hooper M.W. Lei M. Spotts E.L. Phillips J.W. Gordon J.A. Hodes R.J. Lauer M.S. Schwetz T.A. Perez-Stable E. National Institutes of Health social and behavioral research in response to the SARS-CoV2 Pandemic Transl. Behav. Med. 10 2020 857 861 32716038 15 Hellewell J. Abbott S. Gimma A. Bosse N.I. Jarvis C.I. Russell T.W. Munday J.D. Kucharski A.J. Edmunds W.J. Funk S. Eggo R.M. Sun F. Flasche S. Quilty B.J. Davies N. Liu Y. Clifford S. Klepac P. Jit M. Diamond C. Gibbs H. van Zandvoort K. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts Lancet Glob. Health 8 4 2020 e488 e496 32119825 16 Younes N. Al-Sadeq D.W. Al-Jighefee H. Younes S. Al-Jamal O. Daas H.I. Yassine H. Nasrallah G.K. Challenges in laboratory diagnosis of the novel coronavirus SARS-CoV-2 Viruses 12 2020 582 32466458 17 Dramé M. Teguo M.T. Proye E. Hequet F. Hentzien M. Kanagaratnam L. Godaert L. Should RT-PCR be considered a gold standard in the diagnosis of Covid-19? J Med. Virol. 92 2020 2312 2313 32383182 18 Nawattanapaiboon K. Pasomsub E. Prombun P. Wongbunmak A. Jenjitwanich A. Mahasupachai P. Vetcho P. Chayrach C. Manatjaroenlap N. Samphaongern C. Watthanachockchai T. Colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) as a visual diagnostic platform for the detection of the emerging coronavirus SARS-CoV-2 Analyst 146 2021 471 477 33165486 19 Milton A.A. Momin K.M. Ghatak S. Thomas S.C. Priya G.B. Angappan M. Das S. Sanjukta R.K. Puro K. Shakuntala I. Sen A. Development of a novel polymerase spiral reaction (PSR) assay for rapid and visual detection of Staphylococcus aureus in meat LWT 139 2021 110507 20 A.A. Milton, K.M. Momin, S. Ghatak, G.B. Priya, M. Angappan, S. Das, K. Puro, R.K. Sanjukta, I. Shakuntala, A. Sen, B.K. Kandpal, Development of a novel polymerase spiral reaction (PSR) assay for rapid and visual detection of Clostridium perfringens in meat. Heliyon 7 (2021) e05941. 21 Goto M. Honda E. Ogura A. Nomoto A. Hanaki K.I. Colorimetric detection of loop-mediated isothermal amplification reaction by using hydroxy naphthol blue Biotechniques 46 2009 167 172 19317660 22 Mori Y. Nagamine K. Tomita N. Notomi T. Detection of loop-mediated isothermal amplification reaction by turbidity derived from magnesium pyrophosphate formation Biochem. Biophys. Res. Commun. 289 2001 150 154 11708792 23 Wang X. Xu X. Hu W. Zuo K. Li Z. Kan Y. Yao L. Ji J. Bi Y. Visual detection of porcine epidemic diarrhea virus using a novel reverse transcription polymerase spiral reaction method BMC Vet. Res. 15 2019 1 7 30606179 24 He S. Huang Y. Zhao Y. Pang B. Wang L. Sun L. Yu H. Wang J. Li J. Song X. Li H. A reverse transcription-polymerase spiral reaction (RT- PSR)-based rapid coxsackievirus A16 detection method and its application in the clinical diagnosis of hand, foot, and mouth disease Front. Microbiol. 11 2020 734 32477283

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==== Front Saf Health Work Saf Health Work Safety and Health at Work 2093-7911 2093-7997 Occupational Safety and Health Research Institute, Published by Elsevier Korea LLC. S2093-7911(22)00159-7 10.1016/j.shaw.2022.12.002 Original Article The Effect of Occupational Moral Injury on Career Abandonment Intention Among Physicians in the Context of the COVID-19 Pandemic sert-ozen Arzu PhD 1∗ kalaycioglu Ozan PhD 2 1 Department of Business Administration, Istanbul Kent University, Istanbul, Turkey 2 Turk Eximbank, Trabzon Branch, Trabzon, Turkey ∗ Corresponding author. Department of Business Administration, Istanbul Kent University.Address: Cihangir, Siraselviler Cd. No:71, 34433 Beyoglu, Istanbul, Turkey. Tel.: +902126101010 15 12 2022 15 12 2022 18 3 2022 3 12 2022 7 12 2022 © 2022 Occupational Safety and Health Research Institute 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Since the advent of the COVID-19 pandemic, physicians have been the unsung heroes of the pandemic. However, many are about to give up the battlefield. This study investigated the mediating role of emotional exhaustion in the relationship between occupational moral injury and physicians’ career abandonment intention. Methods Cross-sectional data collected from 201 physicians were analyzed using the partial least squares structural equation modeling (PLS-SEM) with Smart-PLS to determine the relationship among physicians’ moral injuries, emotional exhaustion, and career abandonment intention. Results The results indicated that occupational moral injury was positively related to emotional exhaustion and career abandonment intention. In addition, emotional exhaustion was found to play a mediating role in the relationship. Conclusion To reduce physicians’ intention to leave their career, physicians should be prepared for moral injury and psychological issues by offering psychological support and meeting their needs early at both the individual and organizational level during and after the pandemic. Keywords career abandonment intention emotional exhaustion moral injury physicians ==== Body pmc1 Introduction Since the new coronavirus disease (Covid-19) was declared as a worldwide pandemic by the World Health Organization on March 11, 2020, 364 million people have been infected by the disease and 5.6 million of them have died as of February 2022. At the forefront of this pandemic, healthcare professionals assume a crucial responsibility to challenge a virus whose behavior is unpredictable and even the diagnosis and treatment of infected patients are made in the shadow of uncertainty [1]. This uncertainty comes with extraordinary stress on healthcare professionals who are on the frontline to face one of the most serious disasters in history in terms of hospitalizations and deaths. Working under these challenging conditions with the risk of becoming infected and dying, there is also an increased risk for many healthcare workers to have mental health symptoms such as work-related stress, depression, post-traumatic stress disorder and anxiety [2]. Moreover, as healthcare professionals have to adapt to new protocols and continual changes in disease management, they are experiencing a significant increase in the volume and intensity of their work causing extreme stress and anxiety [3, 4]. Within such a difficult period, healthcare professionals often experience moral injury, when a legitimate authority betrays "what is right" in a high-risk situation [5]. Studies have found that moral injury exists mostly in the populations such as rape victims, military personnel, first responders, war veterans and police officers, experiencing severe trauma [6,7,8]. However, limited research has examined the effect of moral injury among healthcare professionals [9,10,11]. Therefore, the literature highlights the importance of examining moral injury among healthcare professionals working in the context of the COVID-19 pandemic [12, 13, 14] and indicates that during and after the COVID-19 pandemic, healthcare professionals may be at risk of moral injury, post-traumatic stress disorder [12, 15] and the risk of emotional exhaustion is higher among the healthcare professionals than other workers because they face with difficult moral decisions in the processes of treating the patients in suffer from life-threatening infections and by the nature of their job requires emotional involvement [11, 16]. From an organizational perspective, previous research has examined emotional exhaustion as an important work-related stressor that has an impact on well-being and health in the workplace, as well as on important work-related outcomes such as turnover, performance and job satisfaction, career abandonment intention [17, 18]. However, limited research has been conducted to identify the mediating effect of emotional exhaustion on health professionals' attitudes towards their work [19]. Especially career abandonment intention, which refers to the intention to leave the current profession, should be examined since emotional exhaustion is the main predictor of the leaving and career abandonment intention. In addition, intention to leave the profession is not just an individual issue, but also an important outcome for organizations as it represents actual turnover [20]. As a result of that, it has been suggested by various studies, such as Austin et al [21], Laurs et al [ 22], that moral injury has a relationship with career abandonment intention and there is a mediating role of emotional exhaustion in this relationship as affirmed by a number of studies [10, 11, 23, 24, 25, 26]. Emotional exhaustion as a potential mediator on healthcare professionals' attitudes toward the work have not received significant attention in many studies, but such a consideration might enhance our theoretical understanding and offer empirical evidence on how emotional exhaustion affect the relationship between moral injury and career abandonment intention among physicians in the context of covid-19. 2 Materials and methods 2.1 Measures The responses were rated on a five-point Likert scale, ranging from 1 ‘‘completely disagree’’ to 5 ‘‘completely agree’’. In addition, a double-blind back-translation process [27] was used to translate the items in the survey into Turkish. 2.1.1 Occupational Moral Injury To assess occupational moral injury among physicians, we adopted a 4-item scale, which labeled perceived transgressions by self from Nash et al [28]. The original scale was assessed for military service to measure moral injury. The participants answered the questionnaire by taking into consideration their medical experience since the Covid-19 out-break. 2.1.2 Emotional Exhaustion We used a six-item scale developed by Wharton [29] to measure job-related emotional exhaustion. 2.1.3 Career Abandonment Intention Career abandonment intention was assessed using the three-item scale adapted from Krausz et al [30]. 2.2 Research hypotheses The term moral injury has increasingly attracted attention since it was first introduced to the literature by psychiatrist Johnathan Shay [31]. The definition of moral injury can be split into a three-component definition as (1) betrayal of what is right (2) by a legitimate authority (3) in a high-risk situation [5]. Moral injury occurs when someone must commit or witness an action that violates their moral belief system. Healthcare professionals often complain that they cannot work under desired conditions for reasons such as hospital and insurance practices. Furthermore, a particularly disturbing aspect of the pandemic period for healthcare professionals is that they are commonly defined as "front line" warriors in society. The term “front line” that contributes to burnout syndrome evokes images of soldiers in combat and mental health problems they often suffer, such as post-traumatic stress disorder [32]. While moral injury is a little-known term in non-military communities, the COVID-19 pandemic has shown that the phenomenon of moral injury exists not just in the battlefield [12]. Potential moral injury events occur in high-risk environments. In such environments, one's own actions can violate his/her own values or moral codes by doing something that shouldn't be done or not doing something that should be done. In the current literature that focuses primarily on military issues, these events include wounding or killing enemy combatants, being unable to prevent the suffering of fellow soldiers or civilians or being betrayed by a leader [33]. In the context of COVID-19, withdrawing care from a less promising patient in intensive care to save a more promising patient is an example of an action with the potential moral injury. In response to potential moral injury events, people may develop feelings of disgust, guilt, shame, and anger, as well as feelings of blaming themselves and/or others. When these distressing moral feelings are suppressed or avoided, moral injury occurs and negatively affects a person's functioning [34]. Recently, the effects of moral injury on healthcare workers' emotional exhaustion and turnover intention have attracted attention in the literature [35]. During the COVID-19 pandemic, physicians were faced with difficult moral decisions because of the increase in the number of patients with life-threatening infections, an insufficient number of personal protective equipment, ventilators, and life-saving medicines. During this period, physicians often faced pressure to select patients for whom they would use limited health equipment, resulting in moral injury [11]. One empirical research reported that moral injury was strongly associated with burnout among physicians and nurses regardless of the clinical attributes, sociodemographic factors, and religious characteristics [9]. In addition, studies conducted by Wang et al [10], Zhizhong et al [11] on the healthcare professionals have reported that moral injury symptoms are strongly correlated with higher clinician burnout during the COVID-19 period. Furthermore, moral distress negatively affects healthcare workers’ intention to remain in the profession [21, 22]. Based on these factors, moral injuries experienced by physicians will have an impact on their emotional exhaustion and leaving the profession.H1 Physicians' moral injuries are positively associated with their emotional exhaustion. H2 Physicians' moral injuries are positively associated with their career abandonment intention. Emotional labor refers to the effort to manage emotions when the job role requires the display of certain emotions and the suppression of others. Since it is not always possible to demonstrate the "appropriate" emotional response, employees must suppress emotional responses that are inappropriate for the job role such as disgust and disappointment and display more adaptive ones such as empathy and patience. Emotional dissonance between the emotions experienced by individuals leads to exhaustion by depleting emotional resources [36]. Conservation of Resources (COR) theory provides valuable insights for studies of emotional exhaustion [37]. Resources are defined as conditions, energies, or personal characteristics valued by the individual and these valued resources should be conserved to meet current job demands as the main motivation source for employees. According to the theory, individuals have a limited number of resources such as emotional, physical, and mental, for response to situations. The individual will try to protect these resources in order not to lose them. When loss occurs, or when resources are threatened, employees will be more stressful, and they will experience a loss of energy in the form of emotion due to high job demands. As a coping mechanism, employees conserve the remainder of resources by decreasing their commitment to the workplace and leaving their current job [38, 39]. Research in the literature provides increasing empirical evidence that emotional exhaustion is strongly associated with important work attitudes, such as career abandonment intention [23], and employee turnover intention [40, 41]. Emotional exhaustion is an emotional response to demanding working conditions and results in emotional withdrawal from profession [24]. Emotionally exhausted healthcare workers will experience lower job satisfaction, which will reduce their job performance [16]. Research shows that healthcare workers' levels of commitment to their organizations decrease and their desire to leave positions increase as a result of increasing job demands [42, 43]. In an empirical study conducted by Blau [17], the depletion of emotional energy needed to meet job demands was found to be a significant correlate for career abandonment intention. In addition, Laschinger and Fida [24] found that emotional exhaustion has a direct effect on career abandonment intention.H3 Physicians' emotional exhaustion is positively related to their career abandonment intention. When social exchange relationships with organizations are developed by employees, they will be more prone to perform higher levels of organizational citizenship behavior, higher job performance, and lower turnover intention [44]. However, jobs that cause moral injury and emotional exhaustion will undermine this process. First, emotional exhaustion can be viewed as the cost of benefits obtained through employment for the organization. Second, when employees are exposed to overwork to the point of emotional burnout and moral injury, they tend to take a negative attitude towards the organization they work for [45]. In addition, moral distress leads to more stress and lower employee productivity [24]. Some research concludes that emotional exhaustion is the main predictor of turnover [46, 47] and career abandonment intention [25]. In a field study conducted by Lee and Ashforth [26], emotional exhaustion was found to be a mediator in the burnout process, leading to turnover intention. In line with these empirical findings and conceptual frameworks, the following hypothesis has been developed regarding the mediating role of emotional exhaustion in the relationship between moral injury and career abandonment intention of physicians.H4 Emotional exhaustion has a mediating role in the relationship between moral injuries of physicians and their career abandonment intention. 3 Results 3.1 Data Collection and participants We collected data using a self-administered questionnaire survey to test the hypotheses in this cross-sectional study. Study participants were physicians working in city hospitals in Turkey. The sample comprises 201 physicians and 58.2% of whom were male (Table 1 ). Most participants have 20 years and above (35.8%) of professional experience, followed by 1-4 years (15.4%), 5-9 years (14.9%), 10-14 years (16.4%) and 15-19 years (17.4%). Most participants fell in the 35-44 years age group (37,8%). In addition, participants were predominantly specialists (79%).Table 1 Demographic characteristics of participants (n=201) Table 1Features Category N % Gender Female Male 84 117 41,8 58,2 Age (years) 25-34 35-44 45-54 55 and above 58 76 54 13 28,9 37,8 26,9 6,5 Years in profession 1-4 5-9 10-14 15-19 20 and above 31 30 33 35 72 15,4 14,9 16,4 17,4 35,8 Professional status GP Assistant Specialist Medical Student 14 20 159 8 7 10 79 4 3.2 Analysis and results In this research, the structural model was tested with partial least squares (PLS) analysis. Like covariance-based SEM techniques (e.g, AMOS, LISREL), PLS is a second-generation statistical technique (e.g, SmartPLS, WarpPLS) that calculates the measurement model and the structural theoretical model simultaneously. The main difference of PLS from covariance-based algorithms is that PLS is variance-based and allows working with a small sample [48, 49, 50]. In addition, PLS-SEM is a suitable method for examining complex models with many items and mediating variables with a small sample [48, 51]. Therefore, the PLS-SEM method is widely used in small-sample studies [e.g., 49, 52, 53]. In this study, the research data and the significance of hypotheses were tested by using bootstrapping resampling methods in SmartPLS software [48]. 3.2.1 Measurement Model Result All the latent variables have reflective indicators in this study [48]. Sarstedt et al [54] suggested that indicator reliability, internal consistency reliability, convergent validity and discriminant validity should be used to evaluate the quality of reflectively specified measurement models. The evaluation process begins by examining the indicator loadings to test indicator reliability. The loadings should be at least 0.708 for each indicator. Loadings above 0.708 indicate the construct explains more than 50 percent of the indicator’s variance [54]. All the indicator loadings in the study have values above 0.708 as shown in Table 2 . Hence, the loadings are confirmed as exhibiting acceptable indicator reliability. Afterwards, it is recommended to use composite reliability (CR) to evaluate the internal consistency reliability [48, 55]. A CR value of 0.70 or higher proves sufficient composite scale reliability [56], but it should not exceed 0.95 [57]. CR values for career abandonment intention, emotional exhaustion and moral injury were 0.93, 0.95 and 0.93, respectively. Hence, the results indicate that the internal consistency reliability is quite satisfactory. The next step in evaluating the model measurement involves assessment of convergent validity of each construct. Convergent validity is the extent to which a construct converges in its indicators by explaining the items’ variance. To evaluate convergent validity, the average variance extracted (AVE) for all indicators is used. The recommended threshold value for the AVE value is 0.50 [56, 58]. The AVE values ​​for career abandonment intention, emotional exhaustion and moral injury are 0.806, 0.768, and 0.771, respectively, providing support for convergent validity. Table 2 presents the indicator reliability, internal consistency reliability, average variance extracted and convergent validity for the latent variables.Table 2 Measurement model assessment result Table 2 Variables Items Indicator Loadings Average variance extracted (AVE) Composite Reliability (CR) Career abandonment intention Cai1. I am seriously thinking of leaving the hospital. Cai2. I am actively searching for another career out of the hospital. Cai3. I will leave the profession in the near future. 0.919 0.871 0.903 0.806 0.926 Emotional exhaustion Eexh1. I feel emotionally drained from my work Eexh2. I feel used up at the end of the day Eexh3.I dread getting up in the morning and having to face another day on the job Eexh4. I feel burned out from my work Eexh5. I feel frustrated by my job Eexh6. I feel I’m working too hard on my job 0.918 0.900 0.834 0.945 0.876 0.775 0.768 0.952 Moral injury Minj1. I acted in ways that violated my own moral code or values. Minj2. I am troubled by having acted in ways that violated my own morals or values. Minj3. I violated my own morals by failing to do something that I felt I should have done. Minj4. I am troubled because I violated my morals by failing to do something, I felt I should have done. 0.897 0.835 0.889 0.888 0.771 0.931 Finally, discriminant validity tests were performed to test whether a construct is empirically distinct from other constructs by assessing Fornell-Larcker criterion and Heterotrait - Monotrait (HTMT) [54, 55]. The Fornell-Larcker criterion [55] is widely used to evaluate discriminant validity. According to this criterion, the AVE square root of each variable should be greater than the correlation coefficients of the variables. The results of the discriminant validity are met for this research because the square roots of AVE on the diagonal lines are larger than the correlation between the constructs in the model (Table 3 ) [57]. Sarstedt et al [54, 58] suggested that HTMT criteria can provide better results for discriminant validity. For conceptually very distinct constructs, the values below the threshold of 0.85 indicate that discriminant validity is established [59]. All HTMT values are all below the threshold of 0.85 as shown in Table 4 . Hence, this study achieved adequate discriminant validity. Since the assessment provides support for the measurement quality, the structural model evaluation will be performed in the next step.Table 3 Discriminant validity (Fornell-Larcker), correlations among variables, means and standard deviations. Table 3Variables 1 2 3 M SD 1. Career Abandonment Intention 0.90 2.64 1.13 2. Emotional Exhaustion 0.61∗ 0.88 3.76 0.98 3. Moral Injury 0.33∗ 0.38∗ 0.88 2.35 1.07 Square root of AVE on the diagonal (bold). ∗ Correlation is significant at the 0.01 level (2-tailed). Table 4 Heterotrait - Monotrait Criteria (HTMT) Table 4Variables 1 2 3 1. Career Abandonment Intention 2. Emotional Exhaustion 0.647 3. Moral Injury 0.360 0.416 3.2.2 Structural Model Result In the assessment of the structural model with reflective indicators of each construct, Hair et al [60] and Sarstedt et al [54] recommended evaluating path coefficients and the significance values, explanatory power, and predictive power respectively. First, as suggested by Chin [48], PLS-SEM was used to estimate both the main and the mediating effects in the model. To determine the significance of the relationship between variables in SmartPLS, a bootstrapping run was performed with 1000 bootstrap samples with 201 cases [48]. After running bootstrap, Smart PLS provided t-values for structural model estimates obtained from the procedure [57]. Hypothesis 1 predicted that physicians’ moral injuries are positively related to emotional exhaustion. The results affirmed that physicians’ moral injuries were positively related to emotional exhaustion (β = 0.38, t = 6.377, p = 0.000). Therefore, Hypothesis 1 was supported. Hypothesis 2 suggested that physicians’ moral injuries are positively related to career abandonment intention. The result showed that physicians’ moral injuries were positively related to career abandonment intention (β = 0.33, t = 4.938, p = 0.000). Hence, Hypothesis 2 was supported. Hypothesis 3 anticipated that physicians’ emotional exhaustion is positively related to career abandonment intention. The results indicated that physicians’ emotional exhaustion was positively related to career abandonment intention (β = 0.56, t =11.090, p = 0.000). Thus Hypothesis 3 was supported (Fig. 1, 2 ).Fig.1 Direct effect of moral injury on emotional exhaustion and career abandonment intention. Fig.1 Fig. 2 Mediating effect of emotional exhaustion on the relationship between moral injury and career abandonment intention. Fig. 2 Furthermore, Hypothesis 4 proposed that emotional exhaustion has a mediating role in the relationship between moral injuries of physicians and their career abandonment intention. The Sobel test, which is widely used to measure the mediating effect, cannot provide consistency with the non-parametric PLS-SEM method. For this reason, bootstrapping is used instead of the Sobel test which cannot be applied within the scope of PLS-SEM, to test the mediating effect [57, 61]. First, following the studies by Hair et al [57] and Klarner et al [61], the significance level of the direct effect was examined without the mediating variable of emotional exhaustion using the bootstrapping process in SmartPLS. Later, emotional exhaustion was included in the model and the path coefficients, t values, and the significance level of the indirect effect were investigated. The direct path of moral injury to career abandonment intention was significant at p = 0.000. The relationship between moral injury and career abandonment intention became insignificant with the inclusion of emotional exhaustion in the model and the path coefficient decreased (from β = 0.33, t = 4.938, p = 0.000 to β = 0.11, t = 1.783, p > 0.05) indicating that emotional exhaustion had a full mediation role in the moral injury – career abandonment intention relationship according to Baron and Kenny [62] procedure, so H4 was supported. After testing the significance and relevance of path coefficients, the next step involves the examining of R2 (coefficient of determination). The R2 represents the model’s explanatory power, also referred to as in-sample predictive power [54]. All the two exogenous latent variables (moral injury and emotional exhaustion) explained 0.378 % of the variance in career abandonment intention. Thus, the explanatory power of the exogenous latent variables is large for career abandonment intention [63; small: .02 ≤ R2 < .13, medium: .13 ≤ R2 < .26 and large: .26 ≤ R2]. The most recent recommendation for model validation is to use the PLSpredict procedure, also referred to as out of sample predictive power [58, 64, 65]. To determine how the model would perform if applied to predict a new observation, the PLSpredict routine with 10 folds and 10 repetitions was used as Shmueli et al [65] recommended. The predictive power assessment in this study is based on the root mean squared error (RMSE) because PLS-SEM errors are distributed symmetrically. The RMSE errors are compared to the LM errors in this process [65]. In the PLS-SEM analysis, all indicators have lower RMSE values except for one indicator (Eexh6) compared to the naïve LM benchmark as shown in Table 5 . Thus, the model has high predictive power [54].Table 5 Differences of RMSE values Table 5 PLS LM PLS – LM Indicators RMSE Q2 RMSE Q2 RMSE Q2predict CAI3 1.192 0.085 1.211 0.057 -0.019 0.028 CAI2 1.179 0.031 1.194 0.007 -0.015 0.024 CAI1 1.250 0.105 1.259 0.092 -0.009 0.013 Eexh4 1.099 0.102 1.108 0.086 -0.009 0.016 Eexh1 1.073 0.110 1.078 0.102 -0.005 0.008 Eexh3 1.152 0.094 1.157 0.087 -0.005 0.007 Eexh6 1.939 0.088 0.936 0.094 1.003* -0.006* Eexh5 1.148 0.113 1.158 0.096 -0.01 0.017 Eexh2 0.949 1.118 0.951 0.108 -0.005 0.01 4 Discussion In this cross-sectional study, we examined the effect of moral injury on emotional exhaustion and career abandonment intention and tested whether emotional exhaustion has a mediating role in the relationship between moral injury and career abandonment intention among physicians during the Covid-19 pandemic. We found that moral injury was positively associated with emotional exhaustion and career abandonment intention. These findings are consistent with previous studies indicating that moral injury was positively related to emotional exhaustion among healthcare professionals [10, 11]. Furthermore, the results showed that moral injury was positively related to career abandonment intention. Studies conducted by Austin et al [21] and Laurs et al [22] reported similar findings. In addition, we found that emotional exhaustion fully mediated the relationship between moral injury and career abandonment intention. The finding is supported by other studies indicating that moral injury is related to emotional exhaustion [10, 11] and in turn, emotionally exhausted physicians perform withdrawal behaviors such as career abandonment intention [24, 25, 29]. The results of this study on career abandonment intentions among Turkish physicians are not surprising. According to the Turkish Medical Association, due to psychological and financial problems, 197 physicians emigrated abroad only in January 2022. This number was 1,405 in 2021. It is predicted that more than 2 thousand physicians will leave Turkey by the end of 2022. The number increases exponentially as those who leave their jobs lead the other physicians to leave their professions [66] and look for brighter opportunities especially in Europe [67]. Physicians face ethical challenges as they are confronted with patient suffering and death daily, and they have a sense of inability to help the patients adequately. This situation causes ethical dilemmas and moral injuries. The limited number of empirical studies on the moral injury of healthcare professionals, especially in the last two years of the pandemic, reveals that it must be imperative to consider the phenomenon of moral injury in terms of health services. Physicians may blame themselves as they are surviving in the face of an unexpected number of deaths from deadly viruses. This feeling of guilt starts to become evident when a physician asks himself the question of “what I could do more” as a result of the loss he experienced, despite putting all his strength into saving his patient. Witnessing the loss of lives of their patients and the feeling of helplessness they felt afterward can cause a mental collapse. This study makes contributions to the literature which has very limited empirical studies on moral injury in the healthcare context and the understanding of its relation to emotional exhaustion and career abandonment intention. To our knowledge, no research has yet examined moral injury among physicians in Turkey. One of the main limitations of this study is that it is a cross-sectional study. For this reason, it is not possible to examine causal relationships. To test causal relationships more precisely, a longitudinal study should be conducted. Second, the study examined moral injury only among physicians and other healthcare workers such as nurses were excluded. Therefore, other healthcare workers should be included in future studies. Third, potential situations that might cause moral injury need to be evaluated. Finally, although the questionnaire was anonymous in nature, external factors may affect the reporting of symptoms, hence the accuracy of the answers cannot be guaranteed. 5 Conclusion Based on the results outlined in the study, Covid-19 pandemic caused moral injury and emotional exhaustion among the physicians under contagious conditions because they had to make extraordinary choices that caused them to experience dilemmas between their careers and moral values in the contagious environment. Additionally, negative aspects of psychological health may continue to exist after the pandemic. Therefore, new intervention programs can be created to protect physicians from moral injury and emotional exhaustion by offering psychological support and meeting their needs early at both the individual and organizational level. In turn, their intention to stay in their career will increase. Declaration of Competing Interest The authors whose names are listed immediately below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. ==== Refs References 1 Vahidi E. Jalili M. Why COVID-19? Frontiers in Emergency Medicine 4 2s 2020 e36 2 Lai J. 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Evaluating structural equation models with unobservable variables and measurement error Journal of Marketing Research 18 1 1981 39 50 56 Bagozzi R.P. Yi Y. On the evaluation of structural equation models Journal of the Academy of Marketing Science 16 1 1988 74 94 57 Hair Jr, JF, Hult GTM, Ringle CM, Sarstedt, M. A primer on partial least squares structural equation modeling (PLS-SEM). 2nd ed. Los Angeles (USA): Sage Publications; 2017. 58 Sarstedt M. Hair J.F. Pick M. Liengaard B.D. Radomir L. Ringle C.M. Progress in partial least squares structural equation modeling use in marketing research in the last decade Psychology & Marketing 39 5 2022 1035 1064 59 Henseler J. Ringle C.M. Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling Journal of the academy of Marketing science 43 1 2015 115 135 60 Hair JF, Hult GTM, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS‐SEM) (3rd ed.) Sage; 2022. 61 Klarner P. Sarstedt M. Hoeck M. Ringle C.M. Disentangling the effects of team competences, team adaptability, and client communication on the performance of management consulting teams Long Range Planning 46 3 2013 258 286 62 Baron R.M. Kenny D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations Journal of personality and social psychology 51 6 1986 1173 3806354 63 Cohen J. Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates Inc; 1988. 64 Hair JF, Hult GT, Ringle CM, Sarstedt M, Danks NP, Ray S. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R, Springer; 2021. 65 Shmueli G. Sarstedt M. Hair J.F. Cheah J. Ting H. Vaithilingam S. Ringle C.M. Predictive model assessment in PLS‐SEM: Guidelines for using PLSpredict European Journal of Marketing 53 11 2019 2322‐47 66 Anna [Internet] Politic Headlines 2022 https://politicsheadlines.com/physicians-go-on-strike-what-are-their-demands/ 67 Inanc YS. [Internet]. Middle East Eye. 2021 [cited 2022 March 5]. Available from: https://www.middleeasteye.net/news/turkey-doctors-abandon-posts-abroad-violence-low-pay-covid

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PMC9750509

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2022-12-16 23:24:16

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Saf Health Work. 2022 Dec 15; doi: 10.1016/j.shaw.2022.12.002

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Saf Health Work

10.1016/j.shaw.2022.12.002

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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Pergamon Press S0022-3999(21)00082-9 10.1016/j.jpsychores.2021.110437 110437 Article The experience of healthcare workers facing COVID-19 crises: A qualitative study in a primary care university setting in Switzerland Tzartzas Konstantinos Graells Madison Schmutz Elodie Bodenmann Patrick Blaser Jeremie Petitgenet Isabelle Marion-Veyron Régis Zozaya Javier Sanchis Vermeulen Brigitte Pahud Kokkinakis Ioannis Favrat Bernard Grazioli Véronique Center for Primary Care and Public Health (Unisanté), University of Lausanne, Switzerland 5 5 2021 6 2021 5 5 2021 145 110437110437 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcObjective The COVID-19 pandemic has pushed health systems to their limits. Healthcare Providers (HP) are facing extreme working conditions and major changes in their usual work context (WC), potentially leading to a risk of developing mental health problems. Their ability to cope with these stressing conditions could be supported by different workplace interventions. Before effective supporting approaches are proposed to HP, their lived experiences in their specific WC have to be investigated. Methods We explored qualitatively the experience of HP of a university center for primary care and public health in Switzerland. Individual semi-structured interviews were conducted. A thematic content analysis was realized. Results 20 interviews were conducted (85% female HP; mean age = 40.55; 35% physicians, 35% nurses, 30% pharmacists). Six major themes emerged regarding HP’s experience when facing COVID-19 pandemic: i) professional complexity (risk of contamination, material shortage, communication difficulties, etc.); ii) new types of collaboration and work organization; iii) wide range of feelings (positive, negative, mixed, blurry feelings); iv) perceived benefits of the crisis (opportunities, new helping factors and resources,); v) emerging needs and confrontation with basic needs; vi) private life complexity (family organization, caring for loved ones, relationships changing). Conclusions Participants reported numerous individual, relational and institutional difficulties (both private and professional) related to COVID-19 pandemic, leading to multiple and mixed feelings. Constant changes in the WC forced them to keep adapting to find new balances. Individual and structural approaches, tailored to the WC, need to be proposed, promoting specific helping factors and minimizing emerging difficulties.

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PMC9750612

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2022-12-16 23:24:16

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J Psychosom Res. 2021 Jun 5; 145:110437

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J Psychosom Res

10.1016/j.jpsychores.2021.110437

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==== Front J Pediatr Surg J Pediatr Surg Journal of Pediatric Surgery 0022-3468 1531-5037 Published by Elsevier Inc. S0022-3468(21)00214-1 10.1016/j.jpedsurg.2021.02.068 Correspondence Journal of pediatric surgery letter to the editor: Operating room limited resources utilization stratification system Johansen Mathias a⁎ Emil Sherif b Engelhardt Thomas a a Department of Pediatric Anesthesia, Montreal Children's Hospital, 1001 Decarie Blvd, Montreal, Canada b Department of Pediatric Surgery, Montreal Children's Hospital, 1001 Decarie Blvd, Montreal, Canada ⁎ Corresponding author. 18 3 2021 8 2021 18 3 2021 56 8 14711471 23 2 2021 24 2 2021 Crown Copyright © 2021 Published by Elsevier Inc. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcDear editor, In follow up to the recent article by Defazio et al.[1] we would like to report the pediatric operating room utilization system we used at the Montreal Children's Hospital at the peak of the COVID-19 pandemic. Patients were stratified as follows. All bookings were screened by each surgical division chief and forwarded to the anesthesiologist in charge. Class 1: Operative intervention required immediately Category 1: Immediate (e.g. active bleeding) Category 2: Within 6–8 h (e.g. appendicitis) Class 2:Operative intervention required within 24 h Fracture, infected hardware, dialysis access etc. Class 3:Operative intervention required within 7 days Vascular access, tumor resection, etc. Class 4: Operative intervention required within 14 days Premature inguinal hernia, feeding access, etc. Class 5: Cases that represent operations ideally performed within a specific time frame for optimal outcome or because the child has reached optimal age for the procedure. These cases should generally be completed within 6 weeks of diagnosis The aim of this stratification system was to maintain operating room access while ensuring safe conditions for staff and patients, as well as simultaneously liberating human and equipment resources. Using this system, we were able to maintain a 72% efficiency rate throughout the peak of the pandemic (2310 h of operative activity from March to June 2020 compared to 3225 h the previous year). We believe our stratification system allows a pediatric operating room to optimize productivity and resource utilization during periods of limited operating room resources. Declaration of competing interest None The Montreal Children's Hospital, McGill University Health centre, Montreal, Quebec, Canada ==== Refs Reference 1 Development of pediatric surgical decision-making guidelines for COVID-19 in a New York City children's hospital. Jennifer R DeFazio, Anastasia Kahan, Erica M Fallon et al J Pediatr Surg 55 8 2020 1427 1430 32553456

33781556

PMC9750615

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2022-12-16 23:24:16

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J Pediatr Surg. 2021 Aug 18; 56(8):1471

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J Pediatr Surg

10.1016/j.jpedsurg.2021.02.068

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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Elsevier Inc. S0022-3999(21)00078-7 10.1016/j.jpsychores.2021.110433 110433 Article Finding the power within and without: How can we strengthen resilience against symptoms of stress, anxiety, and depression in Australian parents during the COVID-19 pandemic? Mikocka-Walus Antonina ab⁎ Stokes Mark ab Evans Subhadra ab Olive Lisa abc Westrupp Elizabeth abd a School of Psychology, Deakin University, Melbourne, Australia b Faculty of Health, The Centre for Social and Early Emotional Development, Victoria, Australia c Institute for Innovation in Mental and Physical Health and Clinical Treatment, Deakin University, Geelong, Australia d Judith Lumley Centre, La Trobe University, Victoria, Australia ⁎ Corresponding author at: School of Psychology, Deakin University, 221 Burwood Highway, Burwood 3125, VIC, Australia. 23 3 2021 6 2021 23 3 2021 145 110433110433 15 9 2020 16 3 2021 21 3 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective The present study investigated the association between resilience and indicators of mental health in a large sample of Australian parents at the time of the COVID-19 pandemic. Methods Data were from a large longitudinal cohort study of Australian parents of a child aged 0–18 years collected during the COVID-19 pandemic. The Brief Resilience Scale (BRS) was used to measure resilience, the Depression Anxiety and Stress Scale (DASS) measured distress (i.e., composite of stress, anxiety and depression scales). Other factors assessed included: age, gender, being born overseas, number of children, self-assessed introversion, social, educational and economic variables, family resources, positive affect and emotional regulation, external social support, and partner social support. Hierarchical regression models and a moderation analysis were used to assess the aims. Results Of 2110 parents, 1701 (80.6%) were female. The mean age was 38 years old (SD = 7, range = 19–69). High loneliness was a key contributor to distress. The level of social support received did add significantly to distress, with greater assistance associated with lower stress and anxiety (both p < .01). Partner support significantly moderated the relationship between resilience and depression; however, this relationship is of unlikely clinical significance due to its small statistical effect. Conclusion Interventions targeting resilience against distress and mental health of parents at the time of pandemics should focus on reducing loneliness while working with the constraints of imposed social isolation and might include partners. Qualitative studies are needed to understand the various useful and not useful aspects of partner's support. Keywords Anxiety COVID-19 Depression Families Parents Resilience Stress ==== Body pmc1 Introduction 1.1 COVID-19 pandemic and mental health The World Health Organization (WHO) has declared novel coronavirus (2019-nCoV) a public health emergency of international concern, with >2.2 million deaths to date [1]. While in Australia the response to the pandemic has been more successful than in many other countries, with the total number of cases over 28,800 and > 900 deaths [2], Australians have been and will continue to be affected by the pandemic. Nearly 30% of the workforce have experienced unemployment or under-employment [3], with associated socio-economic stressors, such as housing and economic uncertainty, and threats to family wellbeing [4,5]. Between March and May 2020, the federal and state governments introduced a range of measures to slow the rate of infection by isolating members of the community [6]. While effective in mitigating the spread of the virus, these measures may present risks for adult mental health, family relationships and conflict, and child health and development. In early reports from China (n = 1210) on mental health during the pandemic, over 50% of general public respondents reported moderate to severe symptoms of stress (8.1%), anxiety (28.8%) and depression (16.5%) [7]. Higher distress in comparison to pre-COVID-19 rates was also noted in the New Zealand population (n = 1003) [8]. Worry about loved ones, prolonged isolation, increase in the rates of domestic violence, rising unemployment, and home schooling combined with occupational demands are common contributors to the mental health burden of the COVID-19 pandemic [9]. However, centuries of learnings and research from disasters and crisis events have shown that there are individual differences in how people adapt in response to adversity, such as the COVID-19 pandemic [[10], [11], [12], [13], [14]]. The success of adaption has often been associated with an individual's resilience, which is influenced by internal and external resources [15]. Given that resilience is responsive to psychological interventions, understanding the factors that strengthen adult resilience in the face of major disasters, such as the current COVID-19 pandemic, will assist in designing treatments to reduce the long-term impact of the current pandemic, as well as the impacts of future crises. 1.2 What is resilience? Resilience (sometimes referred to as ‘psychological resilience’ or ‘psychological hardiness’) has traditionally been defined as the ability to cope effectively with adversity [16]. The more current definitions conceptualise resilience as a process of adaptation to function and be well in times of significant stress [17,18]. All humans have a capacity for resilience, however, individuals' levels of resilience are determined by an interplay between individual and intraindividual factors, with a number of identified protective factors (e.g., skills, abilities) strengthening resilience [19]. The socioecological model provides a useful framework to conceptualise the complex interplay between these factors and to understand the personal and intrapersonal characteristics that protect individuals in the face of adversity [20], with the social and physical ecologies highlighted as particularly important modifiers of outcomes when one faces major stresses [21]. Indeed, the socioecological model posits that behaviours are shaped by the environment, and therefore, a supportive environment enables people to adopt healthy behaviours, consequently expanding their resilience capital. Within a socioecological framework, at the individual level, resilience involves cognitive, behavioural, emotional, and physiological processes. However, at the same time, resilience is very much a developmental process shaped by the environment, including factors such as social support and family dynamics [22]. When considering environmental influences as key determinants of one's resilience, prior studies have proposed the important role of informal and formal social capital in promoting resilience [23]. Such social capital has focused on personal and social competence, family coherence, social support, and personal structure [24], or combined social support with public policy influences [25]. In an attempt to counteract the historically overemphasized role of individual characteristics in understanding resilience in favour of the broader context, it seems useful to frame resilience in socio-ecological terms; for example, by considering the combination of social inclusion, family attachment and support, as well as cultural identity, spirituality, and individual competencies [26]. 1.3 Resilience against stress, anxiety and depression at the time of disasters Resilience has been observed in a variety of contexts, frequently in relation to unexpected events, including natural disasters, such as fires, floods, and volcanic eruptions, as well as human-orchestrated disasters, such as terrorism [15]. Resilience is changeable over time and influenced by protective factors [19]. In adult samples, resilience has been shown to be higher in those with minimal exposure to the disaster, male sex, older age, higher income and education, not being a member of ethnic minorities, free from secondary stressors, good prior mental health, and the personality trait of ‘harm avoidance’, among other factors [15,27]. Other protective factors have also been identified in disaster research, including use of multiple coping strategies (as opposed to only problem- or emotion-based coping), higher perceived social support, self-efficacy, mastery or self-esteem, perceived control, hopefulness, acceptance, and sense of coherence [15,28]. However, individual personal and intrapersonal strengths have largely not been explored to date in the context of the COVID-19 pandemic, which we conceptualise as a traumatic, disaster-like, event. Further, resilience in parents, specifically, has not yet been examined. COVID-19 has been exceptionally stressful for parents. Australia's state and federal restrictions at the time of this study included working from home, home schooling children, and the closure of playgrounds, and community sport. This meant that children were prevented from engaging in their usual activities and outlets, with parents required to take unprecedented responsibility for children's schooling, activity and social needs, and general welfare while working from home. Two thirds of parents have reported that they are unable to meet the dual needs of work and their child's wellbeing during the pandemic [29]. Drawing on the socioecological model [20] and based on previous research on personal [15,27] and intrapersonal [15,30] correlates of resilience in the context of mental health and disasters, the current study examined the relationship between resilience and mental health in a large sample of Australian parents in context of the COVID-19 pandemic. Specifically, through exploratory analyses, we sought to identify potential protective factors across individual (e.g., positive affect) and interpersonal levels (e.g., social support) of the socioecological model that may elucidate the relationship between resilience and indicators of parental mental health. We conceptualise that resilience happens in a dynamic interplay between a stressor (COVID-19 pandemic), protective and promotive processes and factors (e.g. individual and interpersonal factors), and outcomes (stress, anxiety, depression). Since the long-term mental health of parents and children living through the COVID-19 pandemic is likely to be strengthened by promoting parent resilience [31], it is timely to examine parental personal and intrapersonal traits associated with resilience. Specifically, the study aims to:1) Explore the unique associations between individual and interpersonal strengths and distress (stress, anxiety and depression), while accounting for resilience; 2) Explore whether the association between resilience and distress (stress, anxiety and depression) is moderated by individual and interpersonal factors. 2 Materials and methods 2.1 Design This cross-sectional study was nested within a large longitudinal cohort study of Australian parents of a child aged 0–18 years (see study protocol, [32]). 2.2 Selection criteria Participants were currently residing in Australia and speaking English, were 18 years or over, and a current parent of a child aged 0–18 years. Participants were under no obligation to participate and free to withdraw from the study at any time without consequences. 2.3 Recruitment Families were recruited during the COVID-19 pandemic (April, 2020) via social media advertisements and paid online recruitment platforms, e.g., Facebook, Twitter, Instagram. A range of methods were used to target specific groups to increase the representativeness of the sample (e.g., targeting postcodes and demographic factors in Facebook's advertising manager software). We targeted based on parent gender, languages spoken, geographic location, child age, and being a current parent. 2.4 Measures 1. Primary outcome measure Symptoms of stress, anxiety and depression were measured on the Depression and Anxiety Scale (DASS) 21-item version [33]. The DASS includes three subscales: Depression (α = 0.89), Stress (α = 0.82), Anxiety (α = 0.87), 7 items each. The DASS is rated on a 4-point scale from ‘did not apply to me at all’ to ‘applied to me very much, or most of the time’. Example item: “I found it hard to wind down.”2. Covariates / factors 2a. Individual factors Resilience was measured on the Brief Resilience Scale (BRS) [34]. The BRS has 6 items and is rated on a 5-point scale from ‘strongly disagree’ to ‘strongly agree’ (α = 0.88). Example item: “I tend to bounce back quickly after hard times.” Positive affect was measured on the Positive Affect Subscale (PANAS) derived from the Positive and Negative Affect Schedule Short Form [35]. The PANAS is a 5-item scale rated on a 5-point scale from ‘very slightly or not at all’ to ‘extremely’ (α = 0.80). Example item: “Thinking about yourself in the past four weeks, about how often did you feel… alert?” Emotion regulation was measured on the Difficulties in Emotion Regulation Scale-16 (DERS) Item Version [36]. This is a 16-item scale rated on a 5-point scale from ‘almost never’ to ‘almost always’ (α = 0.95). Example item: “I have difficulty making sense out of my feelings.” We interpreted the data using the strengths-based approach with a low score on difficulties regulation considered a strength. Introversion/Extraversion was measured on the investigator's developed scale. This is a 1-item measure: “Do you consider yourself an introvert?” rated on a 7-point scale from ‘introvert’ to ‘extrovert’. Attachment-related anxiety and avoidance were measured with the Experiences in close relationships scale–relationship structures (ECR-RS) [37]. ECR-RS is a 9-item scale Rated on a 7-point scale from ‘strongly disagree’ to ‘strongly agree.’ Example item: “It helps to turn to people in times of need.” We interpreted the data using the strengths-based approach with a low score on the subscales considered a strength. 2b. Interpersonal factors Couple relationship quality measured on the Perceived Relationships Quality Component (PRQC) Questionnaire [38]. The PRQC is a 6-item measure rated on a 7-point scale from ‘not at all’ to ‘extremely’ (α = 0.89). Example item: “How satisfied are you with your relationship?” External social support (1 item) from the Longitudinal Study of Australian Children (LSAC) [39]. It is rated on a 4-point scale from ‘I get enough help’ to ‘I don't get any help at all’; and ‘I don't need any help’. Item: “Overall how do you feel about the amount of support or help you get from family or friends living elsewhere?” Partner social support was from the Social Provisions Scale [40] (1 item selected). It is rated on a 7-point scale from ‘strongly disagree’ to ‘strongly agree’. Item: “When I am feeling stressed about a new or unknown situation, I can rely on my partner to comfort me.” This variable is further measured on the Secure Base Characteristics Scale [41] (1 item selected). It is rated on a 7-point scale from ‘strongly disagree’ to ‘strongly agree’. Item: “My partner encourages me to draw on my skills and abilities to deal with challenges”. Loneliness was measured using the UCLA Loneliness Scale [42] (6 items). It is rated on a 4-point scale from ‘never’ to ‘always’ (α = 0.83). Item: “Indicate how often each of the statements below is descriptive of you. I lack companionship.” Positive family expressiveness was measured on the Adapted short-form of the Self-Expressiveness in the Family Questionnaire (SEFQ) [43] (11 items were selected according to a consensus of three independent expert ratings evaluating item relevance in relation to the COVID-19 pandemic) (α = 0.87). It is rated on a 9-point scale from ‘not at all frequently in my family’ to ‘very frequently in my family’, with two subscales: Positive and negative expressiveness. Example item: “Showing contempt for another's actions.” Mindful parenting was measured on the Interpersonal Mindfulness in Parenting (IEM-P) [44]. It is a 3-item scale rated on a 5-point scale from ‘almost never’ to ‘almost always’. Example item: “When I'm upset with my child, I notice how I am feeling before I take action.” Other variables: age, gender, education, marital status, place of birth, number of children. 2.5 Analysis and data preparation We tested Aims 1 and 2 via hierarchical regression models, with indicators of mental health (depression, anxiety, stress) entered as dependent outcome variables. In the first step, we entered personal variables (age, gender, born overseas, number of children, extraversion). In the second step, social and educational variables (relationship status, loneliness, completion of high school) were added to the model to test whether these variables were associated with distress while adjusting for the effects of age, gender, being born overseas, number of children, and extraversion. In the third step, personal resources: positive affect and emotional regulation were added to the model to test whether these variables were associated with distress while adjusting for the effects of age, gender, being born overseas, number of children, extraversion and social and educational variables. In the fourth step, family resources (couple relationship quality, positive and negative aspects of self-expressiveness in the family) were added to the model to test whether these variables were associated with distress while adjusting for the effects of age, gender, being born overseas, number of children, extraversion, social and educational variables, and positive affect and emotional regulation. In the fifth step, resilience, external social support and partner social support were added to the model to test whether these variables were associated with distress while adjusting for the effects of all other variables. In the sixth step, the interactions between resilience and external social support and partner social support, after centring these effects to remove covariance with their constituent variables, were tested. Partner and external social support were identified as key moderators following the examination of factor structure and correlations in the data, with the variable choice dictated by the socio-ecological model. 2.6 Inclusion criteria and approach to missing data Data analyses were conducted using Stata 16 [45] and SPSS 26 [46]. The data consisted initially of 2365 cases. Little's MCAR test was undertaken on variables of interest for the analysis, and was found to be significant, indicating that the data were not missing completely at random, and needed replacement (χ2 (29) = 150.30, p < .001). Variables ranged from no missing data to 16.1%. All missing data were replaced using multiple imputation with Markov Chained Monte Carlo procedures, with 500 case draws, 500 parameter draws, and up to 7000 model parameters across 100 imputed data sets. All reported results are from the multiply imputed data sets. 2.7 Ethical approval The current study was approved by the Deakin University Human Ethics Advisory Group (Project number: HEAG-H 52_2020). Participants indicated their consent to participate in the study at the start of the online questionnaire. Participants of the longitudinal cohort study in which the present sub-study is nested (see study protocol, [32]) have been entered into a prize draw for 1 of 10 AU $50 online gift vouchers if they have completed at least one survey for every month of the survey. 3 Results 3.1 Demographic characteristics Of 2110 respondents included, 1701 (80.6%) were female. The mean age was 38 years old (SD = 7), ranging from 19 to 69. Overall, 380 (18%) were born outside Australia and 4% spoke language other than English at home, 40 (2%) were Aboriginal or Torres Strait Islander Peoples. Ninety-one per cent (n = 1918) completed high school, with 68% reporting a university degree. In terms of family structure, 1901 (91%) reported having a partner. The majority had more than one child, with 46% reporting having two, 18% having three, and 7% having more than three living in the household. On average the children were 8.6 (SD = 5.1) years old (Table 1 ).Table 1 Demographic details of sample and mean responses to items included in the analyses. Table 1Variable Male Female Parent gender 409 1701 Mean SD Min Max Age 38.25 7.07 19 69 Yes No Born overseas 380 (18%) 1726 (82%) Did not complete high school 192 (9%) 1918 (91%) Having a partner 1901 (91%) 192 (9%) 0 1 2 3 4+ Number of children in household 0 608 974 379 148 Mean SD Min Max DASS total 15.5 10.5 0 63 DASS anxiety 2.9 3.4 0 21 DASS depression 4.9 4.2 0 21 DASS stress 7.7 4.4 0 21 Extraversion 3.6 1.6 1 7 Positive affect 14.7 3.7 5 25 Emotion regulation 31 12.2 8 80 Resilience 3.5 0.8 1 5 Attachment anxiety 3.3 1.8 1 7 Attachment avoidance 3.4 1.3 1 7 Couple relationship quality 33.9 6.5 6 42 Loneliness 1.8 0.9 1 4 Partner support: when stressed about a new or unknown situation, my partner will comfort me 5.2 1.7 1 7 Partner support: my partner encourages me to use my skills and abilities to deal with challenges 5.2 1.7 1 7 External social support 0.7 0.4 0 1 Positive expressiveness 7.05 1.614 1 9 Mindful parenting 10.3 2.234 3 15 3.2 Aim 1 and 2 Social and educational variables (loneliness, completion of high school) accounted for 12.1% of anxiety, 24.6% of depression, and 13.1% of stress while personal variables (age, gender, born overseas, number of children, extraversion) had only a minor contribution to mental health variables (Table 2 ).Table 2 Model summary output from imputed data for the analysis of distress together with 95% confidence intervals at each model step. Table 2Model R2 95% CI Adj'd R2 95% CI ΔR2 95% CI ΔF df1 df2 p Anxiety 1 0.037 0.037–0.037 0.035 0.034–0.035 0.037 0.037–0.037 16.045 5 2099 <0.001 2 0.157 0.156–0.159 0.155 0.153–0.156 0.121 0.119–0.122 150.256 2 2097 <0.001 3 0.365 0.363–0.366 0.362 0.360–0.363 0.207 0.205–0.209 341.485 2 2095 <0.001 4 0.369 0.368–0.370 0.365 0.364–0.366 0.004 0.004–0.005 3.606 4 2091 <0.01 5 0.373 0.372–0.374 0.368 0.367–0.369 0.004 0.004–0.005 2.907 5 2086 0.01 6 0.375 0.373–0.376 0.368 0.367–0.370 0.001 0.001–0.001 2.050 2 2084 0.13 Depression 1 0.023 0.023–0.024 0.021 0.021–0.021 0.023 0.023–0.024 10.023 5 2099 <0.001 2 0.269 0.267–0.271 0.267 0.265–0.269 0.246 0.244–0.248 353.112 2 2097 <0.001 3 0.565 0.563–0.567 0.563 0.561–0.565 0.296 0.293–0.298 712.513 2 2095 <0.001 4 0.568 0.566–0.569 0.565 0.563–0.566 0.003 0.002–0.003 3.219 4 2091 0.01 5 0.570 0.568–0.571 0.566 0.564–0.567 0.002 0.002–0.002 1.969 5 2086 0.08 6 0.574 0.572–0.575 0.569 0.568–0.571 0.004 0.004–0.004 9.563 2 2084 <0.001 Stress 1 0.045 0.044–0.045 0.042 0.042–0.043 0.045 0.044–0.045 19.576 5 2099 <0.001 2 0.175 0.174–0.177 0.173 0.171–0.174 0.131 0.130–0.132 166.322 2 2097 <0.001 3 0.424 0.422–0.426 0.422 0.420–0.424 0.249 0.247–0.251 453.106 2 2095 <0.001 4 0.437 0.435–0.440 0.434 0.432–0.436 0.013 0.013–0.014 12.294 4 2091 <0.001 5 0.442 0.440–0.444 0.437 0.435–0.439 0.005 0.004–0.005 3.593 5 2086 <0.01 6 0.443 0.440–0.445 0.437 0.435–0.439 <0.001 <0.001 - <0.001 0.648 2 2084 0.52 Step 1: personal variables (age, gender, born overseas, number of children, extraversion), Step 2: social and educational variables (relationship status, loneliness, completion of high school), Step 3: personal resources: positive affect and emotional regulation, Step 4: family resources (couple relationship quality, positive and negative aspects of self-expressiveness in the family, and mindful parenting), Step 5: resilience, external social support, and partner social support, Step 6: interactions between resilience and external social support and partner social support. Level of education and loneliness were both important to anxiety and depression (see Table 3 ), but only loneliness was significantly associated with stress. Personal resources (positive affect, and difficulties in emotional regulation) added considerably to the explanation of anxiety (ΔR 2 = 20.7%), depression (ΔR 2 = 29.6%), and stress (ΔR 2 = 24.9%), with both positive affect and difficulties in emotional regulation significantly contributing to the model (see Table 3). The level of external social support received did add significantly to distress, with greater assistance associated with lower anxiety and stress (Table 2).Table 3 Coefficients, correlations, and semi-partial correlations at the sixth level of the model for distress. Table 3Entry Variable Anxiety Depression Stress b SE sr b SE sr b SE sr 1 Constant −2.371⁎ 1.049 −1.402 1.152 4.001⁎⁎ 1.433 1 Age −0.017 0.009 −0.032 0.013 0.010 0.021 −0.039⁎⁎ 0.011 −0.058 1 Gender (0: F, 1: M) −0.410⁎ 0.160 −0.045 0.286 0.168 0.025 −0.329 0.203 −0.028 1 Born overseas (0: N, 1: Y) −0.194 0.159 −0.022 0.137 0.164 0.012 −0.458⁎ 0.195 −0.039 1 Number of children living in the household 0.084 0.069 0.022 0.048 0.071 0.010 0.091 0.085 0.018 1 Extraversion (1: I, 7: E) −0.023 0.041 −0.010 0.020 0.045 0.007 0.073 0.054 0.025 2 Highest level of schooling (0: Completed high school, 1: Did not complete) 0.845⁎⁎⁎ 0.215 0.070 0.633⁎⁎ 0.220 0.042 −0.124 0.263 −0.008 2 Loneliness (High = high loneliness) 0.075⁎⁎ 0.025 0.063 0.200⁎⁎⁎ 0.028 0.135 0.092⁎ 0.036 0.060 3 Positive Affect (High = High Positive Affect) −0.068⁎⁎ 0.020 −0.066 −0.297⁎⁎⁎ 0.022 −0.231 −0.163⁎⁎⁎ 0.029 −0.121 3 Emotion Regulation (High = High Difficulties) 0.125⁎⁎⁎ 0.007 0.343 0.160⁎⁎⁎ 0.009 0.356 0.163⁎⁎⁎ 0.011 0.348 4 Couple relationship quality (High = High Quality) 0.010 0.015 0.014 0.017 0.015 0.020 0.014 0.019 0.015 4 Family expressiveness Negative (High = High Negative Express) 0.072 0.042 0.034 0.035 0.045 0.013 0.246⁎⁎⁎ 0.054 0.089 4 Family expressiveness Positive (High = High Positive Express) 0.072 0.050 0.029 −0.021 0.054 −0.007 0.209⁎⁎ 0.069 0.065 4 Mindfulness (high = high mindful) 0.064 0.035 0.043 0.063 0.045 0.035 −0.011 0.058 −0.005 5 Resilience (high = low resilience) −0.231⁎ 0.094 −0.046 −0.027 0.103 −0.004 −0.160 0.130 −0.025 5 Partner Support (High = High Support) 0.009 0.061 0.003 −0.042 0.060 −0.012 −0.049 0.076 −0.013 5 External Social Support (0 = none, 1 = get help) −0.278 0.158 −0.034 −0.117 0.168 −0.011 −0.476⁎ 0.208 −0.044 5 Attachment-Related Avoidance (high = high avoidance) −0.025 0.060 −0.009 −0.032 0.081 −0.009 −0.019 0.121 −0.005 5 Attachment-Related Anxiety (high = high anxiety) 0.019 0.049 0.008 0.083 0.054 0.028 0.042 0.071 0.014 6 Interaction Resilience with External Social Support 0.040 0.048 0.016 0.012 0.050 0.004 −0.016 0.064 −0.005 6 Interaction Resilience with Partner Support 0.233 0.167 0.026 0.664⁎⁎⁎ 0.180 0.060 0.124 0.219 0.011 ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001. In terms of the moderation analysis conducted in Step 6, partner support significantly moderated the relationship between resilience and depression (Table 2, Table 3). However, Fig. 1 demonstrates that while the moderation was statistically significant, the magnitude of the effect was small.Fig. 1 Plot of the interaction between resilience (BRS) and partner support for depression. Fig. 1 4 Discussion This baseline survey from a large longitudinal cohort of Australian families explored the relationship between resilience and mental health at the time of the COVID-19 pandemic. The study made several original and important observations on mental health in the context of post-disaster trauma. Importantly, we demonstrated that loneliness was a key contributor to distress, over and above the influence of resilience. There has been a growing body of evidence linking loneliness with poor health, such as increased rates of coronary heart disease and stroke [47], increased all-cause mortality, and poor mental health [48]. People who are lonely tend to report more symptoms of depression [49]. However, the studies on loneliness and health are largely conducted with older cohorts, with very few studies focused on young adults or families with young children; our study is original in describing that parents can feel lonely even in the presence of others (family members and children). Further, in the context of disasters, loneliness has been found to be associated with resilience in one small study (n = 216) [50]. However, the COVID-19 pandemic has been unique in terms of enforced isolation, with people living in Melbourne, Victoria, currently entering their sixth month of isolation. Documenting the relationship between loneliness, mental health and resilience is particularly important for future pandemics. Interventions targeting mental health at this time should focus on reducing the sense of loneliness while working with the constraints of imposed social isolation. This could be achieved by drawing on the services which remain open throughout isolation such as sports clubs, social clubs, mothers' groups but also workplaces which have the capacity to hold social gatherings online. In clinical practice, it is important to watch clients expressing sense of loneliness and those with little social support particularly carefully for worsening psychological wellbeing during future disasters and to offer preventive as well as remedial approaches including scheduling regular social activities (online or via social bubbles if permitted). The level of social support received did add significantly to distress, with greater assistance associated with lower stress and anxiety. Further, while the presence of a partner in a respondent's life had a significant effect on the relationship between resilience and depression in our moderation analysis, its size was small and unlikely to be clinically meaningful. This may be counterintuitive as one would assume social support would strengthen resilience and reduce depression, however, in the context of wide-spread loneliness we observed (even when surrounded by the family), perhaps the constant presence of the partner nearby (i.e., over months of working from home) is more stressful than helpful, or perhaps its usual beneficial effect is diluted. In addition, other family and parenting variables were not significant factors in our analysis. This could be because other parenting variables are more applicable to the children's mental health rather than that of the parents, however, we could not verify that as part of the present study. Nevertheless, partner support has been previously found to promote resilience in pregnant and postpartum women at the time of hurricane Katrina (n = 514) [51]. Therefore, it may be important to include partners in any future resilience-strengthening interventions, particularly in the context of parenting and caring for young children during a crisis. Such interventions should ideally be co-designed with consumers to understand which aspects of partner support might be useful and which unhelpful. Qualitative studies might shed further light on this controversy. 4.1 Strengths and limitations This large study was nested within an existing prospective cohort which utilised multiple sampling strategies to increase representativeness. However, while documenting a large cohort, the present data are cross-sectional and thus it was not possible to determine the directionality of the assessed associations. In addition, the variables we have studied could be bidirectionally linked. For example, greater depression could also lead to greater loneliness, withdrawal, and lower partner satisfaction. Likely, depression and loneliness are associated with one another in a bidirectional manner over time. However, assessing causal relationships was beyond the scope of the present paper. Further, most of the sample identified as female. While this is a common occurrence in online surveys, it may create a gender bias. However, since the present study was focused on families with children, it is perhaps unsurprising that women, who tend to be main carers of children, participated in greater numbers. Nevertheless, this poorer representation of male views may limit the generalisability of our findings. In order to reduce the study burden, we had to limit the number of questions in our survey. While many scales have been previously used and validated, some constructs (e.g. personality, social support) were measured using 1-item questionnaires developed by our team or derived from other scales. Further, all the scales we used in the study were based on self-report and thus prone to reporting bias. Nevertheless, psychological studies usually rely on subjective measures as objective measures in this context are very limited. In addition, mental health was measured using a screening measure rather than a psychological interview. This was done for practical reasons, to avoid delays in capturing mental health of our population of interest during the COVID-19 pandemic but means that we can only comment about symptoms of mental illness rather than refer to specific diagnoses. Finally, resilience was measured once only during the present study and since resilience is increasingly conceptualised as a process of adaptation it would be useful for the future studies to include multiple time points to measure the construct prospectively. 5 Conclusion Loneliness was a key contributor to mental health outcomes, over and above resilience. Interventions targeting resilience to distress and mental health of parents at the time of pandemics should focus on reducing loneliness, while working with the constraints of imposed social isolation. The level of social support was associated with distress. However, the presence of a partner in a respondent's life had only a small statistical effect on the relationship between resilience and depression, likely with little clinical meaning. It may be important to include partners in any future resilience-strengthening interventions, particularly in the context of parenting during a crisis, however, such interventions should be co-developed with consumers to ensure only the useful aspects of partner support are included. Data availability statement The data that support the findings of this study are available on request from the senior author, EW. The data are not publicly available due to restrictions. Declaration of Competing Interest We have no interests to report. There is no funding to report. ==== Refs References 1 ECDC Download Today's Data on the Geographic Distribution of COVID-19 Cases Worldwide Stockholm European Centre for Disease Prevention and Control Available from: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide 2020 2 AustralianGovernment Coronavirus (COVID-19) 2020 Australian Government Canberra 3 Article No. 8363: Extra 1.4 Million Australians out of Work in Wake of COVID-19 pandemic – 3.92 Million (27.4% of workforce) Now Unemployed or Under-employed 8 April 2020 (press release) 4 Conger R. Conger K.J. Martin M.J. Socioeconomic status, family processes, and individual development J. Marriage Fam. 72 3 2010 685 704 20676350 5 Ensminger M.E. Fothergill K.E. Bornstein M. Bradley R. A Decade of Measuring SES: What it Tells Us and Where to Go From Here. 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Resilience after hurricane Katrina among pregnant and postpartum women Women’s Health Issues 20 1 2010 20 27 20123173

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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Pergamon Press S0022-3999(21)00095-7 10.1016/j.jpsychores.2021.110450 110450 Article Burnout and resilience among hospital staff during the COVID-19 pandemic: Cross-sectional results from the international Cope-Corona study Müller Markus ape Stein Barbara a Baillès Eva c Blanch Jordi d Conti Chiara f Dunne Pádraic J. g Stanculete Mihaela Fadgyas h Farré Josep Maria i Font E. d Puntonet Mireia Forner j Fritzsche Kurt k Gayán Elena i Guagnano Maria Teresa l König Sarah b Lanzara Roberta m Lobo Antonio n Nejatisafa Ali-Akbar q Obach Amadeu d Offiah Gozie r Parramon Gemma j Peri Josep Maria d Rosa Ilenia o Rousaud Araceli d Schuster Sara Katharina b Torres Xavier d Waller Christiane a a Paracelsus Medical University, General Hospital Nuremberg, Department of Psychsomatic Medicine and Psychotherapy, Germany b Catholic University Eichstätt-, Ingolstadt, Social and Organizational Psychology, Germany c Hospital Nostra Senyora de Meritxell, Servei de Salut Mental, Andorra d Hospital Clínic de Barcelona, Servei de Psiquiatria i Psicologia, Spain e University of Barcelona, Spain f Università degli Studi G. d'Annunzio Chieti e Pescara, Dept. of Psychological, Health and Territorial Sciences, Italy g Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Centre Of Positive Psychology and Health, Ireland h University of Medicine and Pharmacy Iuliu Hatieganu, Romania i Dexeus University Hospital, Department of Psychiatry Psychology and Psychosomatics, Spain j Hospital Universitari Vall d’Hebron, Department of Psychiatry, Spain k Universitätsklinikum Freiburg, Klinik für Psychosomatische Medizin und Psychotherapie, Germany l Università degli Studi G. d'Annunzio Chieti e Pescara, Department of Internal Medicine, Italy m 'Sapienza' University of Rome, Department of Dynamic and Clinical Psychology, Italy n Universidad de Zaragoza, Departamento de Medicina y Psiquiatría, Spain o Instituto de Investigación Sanitaria Aragón, Spain p CIBERSAM, Spain q Tehran University of Medical Sciences, Psychosomatic Research Center, Department of Psychiatry, Teheran r Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Ireland 5 5 2021 6 2021 5 5 2021 145 110450110450 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcObjective The COVID-19 pandemic has had an enormous impact on hospital staff. The aim of this study was to understand what individual and organizational factors are related to stress and burnout as a reaction to the pandemic. Methods An online survey was distributed to hospital staff in seven countries (Germany, Andorra, Ireland, Spain, Italy, Romania, Iran) in summer 2020. Burnout (exhaustion and depersonalization) was measured with two items. A set of variables was used to measure individual, coronavirus-related, and work-related factors, as well as demographics and occupational characteristics. Results In total, 2188 respondents answered more than 50 per cent of the survey (73.3 % women). Staff from a wide range of functions responded (MD, nurses, medical-technical personnel, psychologists, pastoral care, rescue service, administration, service, research, trainees, social work). Exhaustion (d = 0.33, 95% CI: 0.24 to 0.41) and depersonalization (d = 0.41, 95% CI: 0.32 to 0.50) were higher for staff working directly with infected patients. Among no-contact staff, rescue services and service personnel had highest levels of exhaustion. Multiple regression analyses revealed that support at the workplace, self-compassion and sense of coherence reduced the risk of exhaustion, while perceived stress and risk perception predicted exhaustion for all groups. Among staff with COVID-19 contact, workplace safety additionally predicted exhaustion. Conclusion Burnout can be a problematic consequence of the COVID-19 pandemic. A supportive work environment, the availability of protective equipment, but also an organizational climate that promotes self-compassion and sense of coherence can help foster resilience against staff burnout.

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J Psychosom Res. 2021 Jun 5; 145:110450

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==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 Elsevier Inc. S0022-3999(21)00127-6 10.1016/j.jpsychores.2021.110482 110482 Article Subjective wellbeing in parents during the COVID-19 pandemic in Australia Westrupp Elizabeth M. abc⁎ Stokes Mark A. a Fuller-Tyszkiewicz Matthew a Berkowitz Tomer S. a Capic Tanja a Khor Sarah a Greenwood Christopher J. a Mikocka-Walus Antonina a Sciberras Emma abcd Youssef George J. a Olsson Craig A. abd Hutchinson Delyse abde a Deakin University, Centre for Social and Early Emotional Development, School of Psychology, Victoria, Australia b University of Melbourne, Department of Paediatrics, Victoria, Australia c La Trobe University, Judith Lumley Centre, Victoria, Australia d Murdoch Children's Research Institute, Melbourne Royal Children's Hospital, Victoria, Australia e The University of New South Wales, The National Drug and Alcohol Research Centre, Australia ⁎ Corresponding author at: School of Psychology, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia. 1 4 2021 6 2021 1 4 2021 145 110482110482 1 12 2020 2 3 2021 29 3 2021 © 2021 Elsevier Inc. All rights reserved. 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives To examine (1) the subjective wellbeing of Australian parents raising children and adolescents (0–18 years) during April 2020 ‘stage three’ COVID-19 restrictions, in comparison with parents assessed over 18-years prior to the pandemic; and (2) socio-demographic and COVID-19 predictors of subjective wellbeing during the pandemic. Methods Cross-sectional data were from the COVID-19 Pandemic Adjustment Survey (CPAS, N = 2365 parents of a child 0–18 years, 8-28th April 2020); and a pre-pandemic national database containing 18 years of annual surveys collected in 2002–2019 (N = 17,529 parents). Results Levels of subjective wellbeing during the pandemic were considerably lower than ratings prior to the pandemic (Personal Wellbeing Index, mean[SD] = 65.3 [17.0]; compared to [SD] = 75.8 [11.9], p < 0.001). During the pandemic, lower subjective wellbeing was associated with low education (adjusted regression coefficient, 95% confidence interval [95% CI] = −5.19, −0.93), language other-than-English (95% CI = -7.22, −1.30), government benefit (95% CI = -6.99, −0.96), single parents (95% CI = -8.84, −4.59), child neurodevelopmental condition (95% CI = -3.44, −0.76), parent physical/mental health problems (95% CI = -3.23, −0.67), COVID-environmental stressors (95% CI = -3.48, −2.44), and fear/worry about COVID-19 (95% CI = -8.13, −5.96). Unexpectedly, parent engagement with news media about the pandemic was associated with higher subjective wellbeing (95% CI = 0.35, 1.61). Conclusion Subjective wellbeing in parents raising children aged 0–18 years appears to be disproportionately impacted by the COVID-19 pandemic and restrictions in Australia. Specific at-risk groups, for which government intervention may be warranted, include parents in socially disadvantaged contexts, parents with pre-existing mental health difficulties, and parents facing significant COVID-19-related work changes. Keywords Australia COVID-19 pandemic Families Parent Subjective wellbeing ==== Body pmcThe global spread of SARS-Cov-2 (i.e., the virus that causes COVID-19) has led to the rapid implementation of population-wide lockdown measures which have raised widespread public health and clinical concern about impacts on health, and mental health, in particular. Evidence describing the impact of the pandemic has indicated increased rates of mental health problems in Australia [20,37] and internationally [36,41]. A review of 68 studies (N = 288,830 participants, 19 countries) found the prevalence of adult anxiety and depression during the pandemic to be 30–33%, with women, younger adults, and low socio-economic status most vulnerable to experiencing higher levels of psychological distress [49]. Less is known about broader impacts on overall subjective wellbeing; yet widespread subclinical mental health difficulties carry significant implications for the subjective wellbeing of the population particularly in the absence of sufficient resources [12,21]. The population level of subjective wellbeing is relatively stable and positive, yet vulnerable population groups such as carers or single parents typically report lower than normal levels [11,27]. One notable group missing from COVID research undertaken so far is that of the family unit. This is despite families representing one of the largest demographics of any given population, one of the most resource-intensive periods of the life course, and a key point of exchange between generations that can have intergenerational consequences (both positive and negative). The current study thus compares parent subjective wellbeing during lockdown to pre-pandemic normative data from a series of cross-sectional surveys of Australian adults collected annually over 18 years [27].Australia had early success in slowing the infection rate of SARS-Cov-2 via social distancing measures implemented from March through May 2020, with a ‘stage three’ lockdown requiring that Australians avoid leaving their house except for four reasons: (1) shopping for food and supplies, (2) care and caregiving, (3) exercise, and (4) study or work – if unable to do so from home [15]. In the first week of lockdown (and of our data collection), on the 5th April 2020, Australia had experienced 5687 cases, including 34 deaths; by the end of our data collection on 28th April 2020, Australia had a total of 6731 cases, including 84 deaths. Although the stay-at-home orders were applied nationally, individual states lowered lockdown restrictions at different rates progressively throughout early May to mid-June 2020 [14]. The period of lockdown and restrictions was accompanied by a rapid increase in job losses and unemployment, with two-thirds of Australians having their employment affected (Roy [35]). Based on data from Life in Australia (~N = 3000), a representative longitudinal sample of Australian residents aged 18 years and over [24], there is emerging evidence of lower rates of adult subjective wellbeing during the pandemic [4,5]. Specifically, pre-pandemic, Global Life Satisfaction, i.e., measured on a 0–10 scale where 0 is ‘not at all satisfied’ and 10 is ‘completely satisfied’, changed from 7.05 in October 2019, to 6.90 in January 2020. This period in January saw the most widespread and destructive bushfire season ever to occur in Australia. Global Life Satisfaction then dropped again to 6.51 in April 2020 (coinciding with restrictive COVID-19 social distancing measures), rising to 6.86 in May 2020, at a period when social distancing measures were on the cusp of being relaxed. This finding was not replicated in neighbouring New Zealand over a similar time period, and under a more restrictive ‘stage four’ lockdown. Specifically, Sibley et al. [44] assessed subjective wellbeing from March to April 2020, in a nationally representative sample of 1003 New Zealanders, and found no change over this period on items from both the Personal Wellbeing Index [23] or the Satisfaction With Life Scale [17]. The reason for differential impacts between Australia and New Zealand remains unclear, but may be related to overall lower infection rates in New Zealand. It is also likely that impacts on subjective wellbeing are being felt in both countries, but are concentrated in more particular populations characterised by higher demands (or higher needs) prior to the pandemic, and not modelled in either study. Families, in particular, have been hard hit by SARS-Cov-2 containment lockdowns and restrictions. Parent mental health, parenting practices, and the quality of the couple relationship are all fundamental to parent subjective wellbeing and healthy child development [40,54]. However, their protection depends on access to high quality supports, which have been substantially reduced in the pandemic. During lockdown, playgrounds and campus-based schooling were closed, requiring that parents supervise children and/or manage children's distance education from home, often while juggling their own paid work [15]. Emerging findings from Australian and USA research suggest that many parents, and particularly mothers, were forced to reduce their paid work hours during lockdown [9,31,43]. Further, pandemic data from 1500 parents of children aged 4–16 years in the United Kingdom's Co-SPACE Study showed that two thirds of parents reported they were not meeting the needs of both work and their child(ren) during lockdown in late March [48]. Data from Singapore also shows associations between work-family conflict, parenting stress and couple conflict in parents juggling work while supervising children during lockdown [8]. Despite being at high risk, research specifically examining parents' wellbeing and functioning throughout the pandemic has been limited [16], and has tended to focus on child outcomes. Data from the first lockdown in Spain demonstrates links between higher levels of parenting distress, less structured parenting, and child mental health problems [42]. A study in the USA showed that parent anxiety and depression were associated with parent stress and child abuse potential [7]. However, these studies examined associations at the time of the pandemic, and did not estimate whether there were changes in family functioning prior to, compared to during, the pandemic. Just one preprint has examined this to-date; findings from the Born in Bradford study (N = 1860) show increases in parent depression and anxiety assessed before and during the first COVID-19 lockdown in the United Kingdom, and also that parent loneliness, financial insecurity, lack of physical activity poor partner relationship were predictors of poorer mental health functioning [16]. Here, we address a need to estimate the impact of the SARS-Cov-2 containment measures on parent subjective wellbeing, by comparing parent-report of subjective wellbeing using the Personal Wellbeing Index, in: (1) cross-sectional pandemic data from 2365 parents of a child 0–18 years in the COVID-19 Pandemic Adjustment Survey (CPAS), collected in April 2020 during ‘level three’ restrictions; and, (2) national pre-pandemic data from 17,529 parents living with children of all ages in the Australian Unity Wellbeing Index (AUWI), collected in 36 annual cross-sectional surveys over 2002–2019 [13,28]. Specifically, our aims were threefold:1. To compare subjective wellbeing assessed in CPAS parents during the pandemic with subjective wellbeing assessed in AUWI parents prior to the pandemic. 2. Within CPAS parents, to investigate whether subjective wellbeing in parents reporting ‘high mental health risk’ is lower compared to other parents, with ‘high mental health risk’ defined according to pre-existing or current parent physical or mental health problem, and/or having a child with a neurodevelopmental or mental health condition; all of which have been associated with lower parent subjective wellbeing in previous research [10,30]. 3. Within CPAS parents, to examine the extent to which pre-pandemic factors (demographic, socio-economic, parent and child diagnosis) and COVID-related stressors (i.e., environmental risks such as financial or housing insecurity; working from home with children; food shortages; media use, as well as feelings and attitudes about COVID-19) are associated with compromised parent subjective wellbeing during the pandemic. 1 Method 1.1 Participants and study design 1.1.1 The COVID-19 pandemic adjustment survey (CPAS) We used baseline data from a longitudinal cohort study of Australian parents, the COVID-19 Pandemic Adjustment Survey (CPAS, N = 2365). Data were collected over 3 weeks from the 8th to the 28th of April 2020 (see study protocol, [50]). Parents were eligible to participate if they were a parent of a child aged 0–18 years, an Australian resident, and 18 years or over. Parents were recruited via paid and unpaid social media advertisements, which contained a web link directing participants to a Qualtrics survey. The study was approved by the Deakin University Human Ethics Advisory Group (Project number: HEAG-H 52_2020). 1.1.2 Normed data from the Australian Unity Wellbeing Index (AUWI) We used normative data on subjective wellbeing from 36 cross-sectional surveys collected annually over the period 2002–2019 as part of the Australian Unity Wellbeing Index (for access to cross-sectional data, see: www.acqol.com.au). We analysed data from a sub-sample of parents with one or more child living in the same household (total sub-sample, N = 17,529) [26]. Data collection for the AUWI surveys was carried out via telephone interview. The sample for each survey was stratified to match the demographic distribution of the population by gender and geographic location. Participants were aged 18 years and older and fluent in English. The AUWI was approved by the Deakin University Human Ethics Advisory Group (Project number: HEAG-H 45). 1.2 Measures Please see Table 1 for a summary of parent, socio-demographic and COVID-19 risk measures used in this study.Table 1 Study measures in the COVID-19 pandemic adjustment survey (CPAS). Table 1Construct Measure Parent subjective wellbeing The Personal Wellbeing Index (PWI) [23] (7 items) comprises seven domains measuring satisfaction with Standard of Living, Health, Achieving in Life, Relationships, Safety, Community-Connectedness, and Future Security. Example item: “How satisfied are you with… your standard of living?”. The PWI correlates strongly with the Satisfaction with Life Scale [17] (r = 0.78; α = 0.70–0.85) [23]. The items are intended to be rated on an end-defined, unipolar, 11-choice scale from zero ‘no satisfaction at all’ to 10 ‘completely satisfied’. The CPAS applied a 10-choice scale, ranging from 1 ‘no satisfaction at all’ to 10 ‘completely satisfied’. For consistency with the AUWI datasets, the CPAS version of the PWI scale was transformed to a 11-point scale. Domain scores are converted to a percentage of scale maximum and summing them together to produce a PWI score on a scale from 0 to 100 percentage points. Participant data with a score 0 or 100 percentage points on the PWI were removed [23]. Demographic factors (CPAS only) Participants were instructed to complete the following items as they pertained to their situation prior to the pandemic: their own and their child's age and gender, parent country of birth and Aboriginal and Torres Strait Islander status, whether a language other than English was spoken at home, geographical location (i.e., postcode), and number of children in the household, single parent status (i.e., have no partner or not living with partner). Social disadvantage (CPAS only) Education level (i.e., non-high school completion versus completion), household income (low income defined as AU $52,000 per year or less), receipt of government benefits. Participants were also asked about money shortages in the 12 months prior to the pandemic (e.g., unable to pay bills, mortgage or rent, unable to heat home, went without meals, pawned or sold something, asked for financial help). These 7 items were summed to form a financial deprivation index [51]. Parent and child diagnosis (CPAS only) Parents also reported whether they have a pre-existing physical or mental health condition, and whether their child has a neurodevelopmental or mental health condition (Attention Deficit Hyperactivity Disorder; Autism, Asperger's, or other Autism Spectrum; Oppositional Defiant or Conduct Disorder; Speech or Language Disorder; Reading or Learning Disorder; Head Injury; Epilepsy/Head Injury/Other Neurological Diagnosis; Disability). High mental health risk (CPAS only) High pre-existing mental health risk was defined as parents with any of the following risk factors: in the severe range for current anxiety or depression symptoms, reporting a previous physical or mental health diagnosis, or having a child with a pre-existing neurodevelopmental or mental health condition. COVID-19 environmental risk index Four items were adapted from the CoRonavIruS Health Impact Survey (CRISIS) V1.0 [34] to measure: (1) COVID-19 participant or family member diagnosis, hospitalisation, self-quarantine, family member passed away; (2) financial problems; (3) housing; and (4) food insecurity related to COVID-19. In addition, participants were asked whether they had experienced (5) job loss; (6) reduced employment; or, (7) redeployment to new roles and responsibilities in their work. Each of the 7 COVID-19 risk factors was converted to a binary variable (0 = no risk; 1 = risk), and then summed to form a COVID-19 environmental risk index score. Participants were also asked about the frequency of their use of media news sources (newspapers, television, social media, radio, rated on 6-point scale from ‘not at all’ to ‘multiple times per day’); two items assessed participants' appraisals of COVID-19 as a serious health risk, whether they were likely to catch COVID-19 (both items rated on a 7-point scale from ‘strongly disagree’ to ‘strongly agree’). Participants were also asked about their feelings about COVID-19 (worry, fear, confidence, hope) rated on a 4-point scale from ‘not at all’ to ‘a great deal’. The two positive feelings were reverse coded and the 4 items were summed to a total score with higher scores reflecting more negative feelings. Participants reported on whether they were working from home, and whether children who were usually in a childcare or a formal education setting were also at home with them. 1.3 Data procedures 1.3.1 Overview The aims of the present study emphasize comparison of several datasets (Aims 1 and 2) and further modelling of wellbeing outcomes for the CPAS dataset (Aim 3). Accordingly, datasets were not pooled, and preparatory steps for intended analyses were conducted for each dataset separately. 1.3.2 Population weighting We derived post-stratification weights in the CPAS dataset to compensate for differences between the final sample and the national population of parents. We generated post-stratification weights through a raking approach [29], using six demographic factors: (1) geographic location (major city, inner and outer regional areas, and remote areas); (2) child age groups (0–4, 5–9, 10–12, 13–14, and 15–18 years); (3) parent gender (male, female), (4) family structure (single parent, couple family), (5) parent education (Did not complete high school, high school completion; and (6) parent employment status (employed, unemployed). The CPAS datasets was weighted to be equivalent to a subpopulation of Australian adults; i.e., parents of a child 0–18 years, with an estimated total population size of 8.4 million parents. Australian Bureau of Statistics population level statistics for parents of dependent children were included for comparison to the AUWI and CPAS demographic characteristics [2]. 1.3.3 Missing data The AUWI dataset had minimal missing data (0 to 0.8% on PWI variables), thus no missing data treatment was applied. In the CPAS dataset, item level missing data ranged from 0 to 8% on individual variables. In the CPAS dataset, multivariate multiple imputation using chained equations was performed to account for missing data. All variables from the final analytic models and weights were included in the multiple imputation model to create 100 imputed datasets. All CPAS reported results are from the multiply imputed datasets. 1.3.4 Data analysis Analyses were conducted in Stata version 16 [45]. To address Aims 1 and 2, means and standard deviations were calculated for the CPAS and AUWI samples, overall, and for CPAS parents with and without high mental health risk. CPAS weighted data are presented to ensure reported outcomes are as close to population representation as possible, but we primarily focus on unweighted results in our interpretation. Data from the two studies were not pooled but rather analysed separately. We conducted a series of one sample t-tests (CPAS compared to AUWI norms), and independent samples t-tests (CPAS high risk versus low risk groups) and calculated Cohen's d effect sizes to assess unweighted differences between samples/groups on each domain. To address Aim 3, a series of unadjusted linear regression analyses were conducted (unweighted) with the total PWI score and PWI domains entered as dependent outcome variables, and pre-pandemic and COVID-related stressors separately entered as independent variables into each univariate model. Next, adjusted models were conducted to assess the unique contribution of each of these independent variables in relation to each indicator of parent subjective wellbeing, while simultaneously accounting for the contribution of all the other pre-pandemic and COVID-related stressor variables in the model. Variables were included in adjusted models where there was evidence for an unadjusted association with the outcome (p < 0.1). In line with Perneger [39], we describe all results without adjusting for multiple comparisons. 2 Results 2.1 Sample characteristics Characteristics of the CPAS and AUWI samples are shown in Table 2 . Parents in the CPAS sample were on average 38 years with a primary school aged child. The majority of the CPAS sample were cisgender women; and just over half of their children were cisgender boys. Parents in the AUWI norm sample were on average 46 years of age, with an even distribution of cisgender men and women. The CPAS sample was broadly representative of the Australian parent population in terms of geographic location, number of children, parents born overseas, and single parent households, but was somewhat under-representative of families with a low income and low education [2].Table 2 Sample characteristics for the Australian Unity Wellbeing Index (AUWI) and COVID-19 Pandemic Adjustment Survey (CPAS) samples. Table 2 Australianpopulation$ AUWI Norms% CPAS§ N (%) % Parent age, m(sd) n/a 45.9 (11.8) 38.3 (7.1) Child age, m(sd) n/a n/a 8.7 (5.1) Parent gender  Cisgender men 46% 8255 (47%) 19%  Cisgender women 54% 9274 (53%) 81%  Transgender or nonbinary n/a n/a <1% Child gender  Cisgender boy n/a n/a 51%  Cisgender girl n/a n/a 49%  Transgender or nonbinary n/a <1% Geographic location  Major Cities of Australia 74% 70%  Inner Regional Australia 17% 23%  Outer Regional Australia 7% 6%  Remote Australia 2% 1% Number of children  1 child 42% n/a 28%  2 children 39% n/a 46%  3 children 14% n/a 18%  4 or more children 5% n/a 7% Single parent household 11% 11% Parent born overseas 21% n/a 18% Aboriginal or Torres Strait Islander 4% n/a 2% Did not complete high school 40% n/a 9% Receiving government benefit n/a n/a 6% Low household income  $52,000 or less per year 21% 14%  $60,000 or less per year 13% Parent mental health condition n/a n/a 37% Parent chronic health condition n/a n/a 30% Child neurodevelopmental or mental health condition n/a n/a 31% COVID-19 related factors  Deprivation index, m(sd) n/a n/a 0.4 (1.0)  Child home while working n/a n/a 50%  COVID-19 environmental risk index, m(sd) n/a n/a 1.4 (1.2) Notes: m(sd) = Mean (standard deviation); AUWI = Australian Unity Wellbeing Index; CPAS = COVID-19 Pandemic Adjustment Survey. $ Data from the Australian Bureau of Statistics summarising characteristics of Australian parents living with a dependent child (usually defined as 0–14 years). % Norms from 2002 to 2019, N = 17,529 parents living with children (includes children of all ages living in the same household as their parent/s). § Data collected 8th–28th April 2020, N = 2365 parents of a child 0–18 years, data are multiply imputed and thus can only be presented as percentages. 2.2 Measurement sensitivity analysis As explained in the attached Supplementary Analysis, we conducted two sensitivity analyses related to the measurement of parent subjective wellbeing to explore whether the estimates of subjective wellbeing were influenced by slight methodological changes in the presentation of items (see Supplementary Analysis for details). First, we assessed the placement of the PWI items after emotive measures within the CPAS survey, which may have influenced PWI scores. We found a small placement effect of 0.75 PWI units (t = 36.15, df = 2107.60, p < 0.001). Second, we assessed the restricted range of the CPAS PWI response scale and found that the variance had not been reduced by the reduced score range. Given the difference in the gender balance between the CPAS and AUWI samples, we conducted a third sensitivity analysis to investigate socio-demographic differences between cisgender women and men within the CPAS sample. Compared to men, we found that women were more likely to report COVID-19 diagnosis, hospitalisation, employment changes, and financial impacts, to be a single parent, and to be receiving a government benefit, and less likely to report that they had not completed high school, and were supervising a child at home while they worked from home. There were no differences in reported rates of pre-pandemic financial deprivation. 2.2.1 Aim 1: comparing parent subjective wellbeing before and during the pandemic Fig. 1 and Supplementary Table 1 compares parent PWI total and individual domain scores for the AUWI norms (pre-pandemic) and CPAS results (during the pandemic). According to CPAS unweighted results, subjective wellbeing levels in parents during the pandemic were notably lower than pre-pandemic AUWI norms. The largest difference was evident for the PWI, where CPAS results were close to one standard deviation lower than the norm data, t(2159) = −29.92, p < 0.001, Cohen's d = -62. There was evidence for differences across all domains, with small differences for Standard of Living and Personal Safety (Cohen's d − 0.21; −0.32 respectively, both p < 0.001), moderate differences for Achieving in Life, Personal Relationships, Community Connectedness, and Future Security (Cohen's d − 0.50 to −0.55, all p < 0.001), and a large difference for Health (Cohen's d = −0.60, p < 0.001). CPAS weighted results showed consistently lower subjective wellbeing than the unweighted results, suggestive of even greater differences between pre-pandemic and pandemic figures.Fig. 1 Means and standard deviations (i.e., error bars) for the Australian Unity Wellbeing Index (AUWI) norm and unweighted and weighted COVID-19 Pandemic Adjustment Survey (CPAS) samples on the Personal Wellbeing Index (PWI) and PWI individual domains. Fig. 1 2.2.2 Aim 2: associations with parent and offspring physical and mental health Table 3 presents comparisons for CPAS parents with heightened risk for compromised subjective wellbeing, i.e., current parent anxiety or depression symptoms, a pre-existing mental and/or physical health diagnosis, or child neurodevelopmental or mental health conditions (72%), compared to other parents (28%). Parents at risk consistently reported lower subjective wellbeing across all domains. The largest differences were evident for Personal Health, t(2176) = −9.74, p < 0.001, Cohen's d = −0.47, and the PWI, t(2176) = −8.71, p < 0.001, Cohen's d = −0.42, but group differences were evident across all domains (p < 0.001, Cohen's d − 0.20 to −0.33). We compared levels of subjective wellbeing in CPAS parents not reporting high mental health risk with AUWI norms, and found evidence for lower levels of subjective wellbeing across the PWI, t(609) = −9.05, p < 0.001, Cohen's d = −0.36) and six of the seven domains (Cohen's d, −0.09 to −0.39), whereas no differences were evidence for Standard of Living, t(609) = −1.50, p = 0.135.Table 3 Differences for COVID-19 Pandemic Adjustment Survey (CPAS) participants with and without high pre-existing mental health risk: Unweighted and weighted comparisons for the Personal Wellbeing Index (PWI). Table 3 CPAS: High risk (72%) CPAS: Not high risk (28%) Weighted Unweighted Weighted Unweighted Mean (sd) Mean (sd) Mean (sd) Mean (sd) Personal Wellbeing Index 59.88 (18.61) 63.42 (17.50) 68.65 (14.59) 70.40 (14.80) Standard of Living 67.76 (22.02) 71.76 (21.08) 72.98 (19.12) 75.92 (18.77) Health 56.22 (22.39) 60.10 (21.87) 68.15 (19.52) 69.91 (18.99) Achieving in Life 57.06 (23.33) 60.81 (22.33) 64.95 (19.91) 67.37 (20.20) Personal Relationships 65.23 (24.83) 66.78 (23.60) 73.35 (20.09) 74.23 (20.11) Personal Safety 69.79 (24.11) 71.76 (22.68) 78.22 (18.55) 78.80 (18.82) Community Connectedness 50.87 (27.25) 56.12 (25.73) 60.35 (23.37) 62.45 (23.54) Future Security 52.24 (26.63) 56.63 (25.14) 62.54 (21.48) 64.13 (22.19) Notes: Data are multiply imputed. sd = Standard deviation; CPAS = COVID-19 Pandemic Adjustment Survey. High risk defined as parents with any of the following risk factors, including severe range for current anxiety or depression, reporting a previous physical or mental health diagnosis, or having a child with a pre-existing neurodevelopmental or mental health condition. 2.2.3 Aim 3: associations with socioeconomic and COVID-19 stressors Table 4, Table 5, Table 6 present results from unadjusted and adjusted regression analyses testing the associations between parent subjective wellbeing on the PWI and subdomains, and a range of demographic, socio-economic, parent and child diagnosis, and COVID-19 related stressors. Parent Aboriginal and Torres Strait Islander status, speaking a language other than English at home, receiving a government benefit, low education, single parents, younger child age, and higher financial deprivation prior to the pandemic were all associated with lower subjective wellbeing. Parents living in regional areas,raising a child with a neurodevelopmental or mental health condition, and having a physical or mental health diagnosis themselves, were also associated with lower subjective wellbeing. The one protective association was with higher frequency of a variety of news media use. The COVID-19 pandemic-related factors were all associated with lower parent subjective wellbeing, including supervising children while working from home, higher levels of COVID-19 risk factors; i.e., diagnosis, hospitalisation, employment changes, and financial impacts; beliefs about being likely to catch COVID-19, more negative feelings about COVID-19; i.e., higher fear and worry, and low confidence and hope; and the belief that COVID-19 is a serious health risk.Table 4 Unadjusted and adjusted associations between COVID-19 Pandemic Adjustment Survey (CPAS) demographic characteristics prior to the pandemic and parent subjective wellbeing domains: Personal Wellbeing Index, Standard of Living and Health. Table 4 Personal Wellbeing Index Standard of Living Health Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted B B LL UL B B LL UL B B LL UL Demographic factors  Parent age −0.01 0.06 −0.04  Child age −0.23** −0.17* −0.32 −0.03 −0.26** −0.12 −0.30 0.05 −0.30** −0.17 −0.36 0.03  Parent gender  Cisgender women$ 0.23 1.98 3.83** 1.83 5.83 0.25  Transgender or nonbinary$ 18.29 24.53 26.91 −7.83 61.64 7.16  Aboriginal or Torres Strait Islander −13.96** −7.92** −12.12 −3.71 −11.82** −6.25* −11.64 −0.87 −13.10** −6.73* −12.45 −1.00  Language other than English −3.48* −4.26** −7.22 −1.30 −6.92** −7.48** −11.25 −3.70 −1.24 Socio-economic risk factors  Receiving government benefit −17.34** −3.98* −6.99 −0.96 −20.42** −7.47** −11.34 −3.60 −20.42** −3.55 −7.63 0.53  Did not complete high school −9.73** −3.06** −5.19 −0.93 −9.64** −2.45 −5.19 0.28 −10.74** −4.35** −7.24 −1.46  Single parent household −13.53** −6.72** −8.84 −4.59 −12.34** −4.49** −7.27 −1.70 −9.91** −2.60 −5.51 0.31  Geographic location  Inner Regional Australia% −3.15** −1.03 −2.49 0.43 −2.90** −0.65 −2.55 1.26 −4.95** −3.25** −5.21 −1.28  Outer Regional Australia% −2.99* −0.17 −2.67 2.34 −2.32 0.61 −2.62 3.84 −4.44* −1.58 −4.99 1.83  Remote Australia% 2.71 2.97 −3.26 9.19 4.43 3.12 −4.76 11.01 2.14 3.03 −5.36 11.42  Number of children −0.58 −1.17* −0.16 −1.10 0.77 −0.60  Financial deprivation index −5.83** −3.09** −3.78 −2.41 −7.38** −4.94** −5.82 −4.06 −5.72** −3.28** −4.22 −2.35 Parent and child diagnosis  Child neurodevelopmental diagnosis −5.42** −2.10** −3.44 −0.76 −4.58** −1.57 −3.29 0.15 −6.04** −1.83* −3.65 −0.01  Parent physical/mental health diagnosis −6.35** −1.95** −3.23 −0.67 −3.21** 0.94 −0.72 2.59 −10.27** −5.85** −7.59 −4.12 COVID-19 related stressors  Child home while working −1.70* 1.34 −0.12 2.80 −1.30 −2.69** 0.71 −1.28 2.71  COVID-19 environmental risk index −5.00** −2.96** −3.48 −2.44 −5.09** −3.35** −4.02 −2.67 −3.91** −1.50** −2.22 −0.78  Believe likely to catch COVID-19 −0.89** −0.04 −0.43 0.34 −0.25 −1.28** −0.12 −0.65 0.40  Fear and worry about COVID-19 −10.08** −7.04** −8.13 −5.96 −6.57** −4.09** −5.50 −2.69 −9.13** −4.92** −6.41 −3.43  Believe COVID-19 serious health risk −1.96** −0.45* −0.82 −0.07 −1.34** −0.20 −0.67 0.27 −3.18** −1.86** −2.37 −1.35  Higher frequency of news media use 0.93* 0.98** 0.35 1.61 1.27** 1.25** 0.45 2.06 0.78 Notes: Data are multiply imputed. Variables were included in adjusted models where there was evidence for an unadjusted association with the outcome (p < 0.1). B = unweighted regression coefficient; LL = lower limit of a 95% confidence interval; UL = upper limit of a 95% confidence interval; * p < 0.05; ** p < 0.01. $ Compared to cisgender men. % Compared to Major Cities of Australia. Table 5 Unadjusted and adjusted associations between COVID-19 Pandemic Adjustment Survey (CPAS) demographic characteristics prior to the pandemic and parent subjective wellbeing domains: Achieving in Life, Personal Relationships, and Personal Safety. Table 5Demographic variables Achieving in Life Personal Relationships Personal Safety Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted B B LL UL B B LL UL B B LL UL Demographic factors  Parent age −0.05 −0.25** −0.33** −0.51 −0.15 −0.13 −0.11 −0.27 0.05  Child age −0.30** −0.16 −0.38 0.06 −0.36** 0.19 −0.09 0.48 −0.37** −0.17 −0.38 0.04  Parent gender  Cisgender women$ 1.94 4.37** 2.15 6.58 −0.61 −2.37* 1.69 −0.44 3.82  Transgender or nonbinary$ 18.72 23.67 −16.02 63.36 22.89 16.39 23.52 −13.38 60.41  Aboriginal or Torres Strait Islander −11.52** −5.42 −11.38 0.54 −8.96** −2.37 −8.69 3.96 −17.37** −10.86** −16.49 −5.23  Language other than English −5.53* −6.45** −10.68 −2.22 −0.20 −5.50* −5.42** −9.41 −1.43 Socio-economic risk factors  Receiving government benefit −17.81** −7.62** −11.96 −3.28 −16.52** −1.24 −5.78 3.30 −13.24** −0.67 −4.70 3.36  Did not complete high school −8.85** −2.13 −5.14 0.87 −5.87** −0.12 −3.32 3.08 −7.78** −1.62 −4.49 1.24  Single parent household −10.24** −2.99 −6.07 0.10 −20.41** −16.24** −19.44 −13.04 −13.55** −8.53** −11.41 −5.65  Geographic location  Inner Regional Australia% −3.18** −1.26 −3.34 0.82 −2.76* −1.05 −3.23 1.13 −1.53  Outer Regional Australia% −6.03** −3.36 −6.89 0.17 −3.67 −1.88 −5.63 1.86 −0.87  Remote Australia% 0.82 −0.06 −8.66 8.53 −1.03 −1.53 −10.83 7.77 0.50  Number of children −0.87 0.25 −0.81 1.31 0.25 −0.87 −2.00 0.26 −0.16  Financial deprivation index −5.92** −3.33** −4.30 −2.36 −3.33 −2.30** −3.34 −1.26 −4.61** −2.11** −3.04 −1.19 Parent and child diagnosis  Child neurodevelopmental diagnosis −5.57** −2.29* −4.19 −0.40 −6.72** −3.46** −5.46 −1.45 −5.21** −1.59 −3.38 0.20  Parent physical/mental health diagnosis −5.73** −1.89* −3.67 −0.10 −6.83** −2.97** −4.88 −1.06 −6.27** −1.69 −3.40 0.02 COVID-19 related stressors  Child home while working −3.15** −0.38 −2.48 1.72 −0.38 0.11 −2.14 2.36 −1.24  COVID-19 environmental risk index −4.91** −3.03** −3.76 −2.29 −3.03 −1.81** −2.60 −1.02 −4.87** −2.45** −3.16 −1.75  Believe likely to catch COVID-19 −0.49 0.09 −0.45 0.63 0.09 −0.38 −0.96 0.20 −1.87** −0.75** −1.26 −0.23  Fear and worry about COVID-19 −8.57** −6.59** −8.14 −5.04 −6.59 −5.13** −6.76 −3.51 −14.33** −11.83** −13.31 −10.36  Believe COVID-19 serious health risk −1.38** 0.16 −0.37 0.69 0.16 0.44 −0.12 1.00 −2.54** −0.28 −0.78 0.23  Higher frequency of news media use 0.60 0.66 0.26 Notes: Data are multiply imputed. Variables were included in adjusted models where there was evidence for an unadjusted association with the outcome (p < 0.1). B = unweighted regression coefficient; LL = lower limit of a 95% confidence interval; UL = upper limit of a 95% confidence interval; * p < 0.05; ** p < 0.01. $ Compared to cisgender men. % Compared to Major Cities of Australia. Table 6 Unadjusted and adjusted associations between COVID-19 Pandemic Adjustment Survey (CPAS) demographic characteristics prior to the pandemic and parent subjective wellbeing domains: Community Connectedness and Future Security. Table 6Demographic variables Community connectedness Future security Unadjusted Adjusted Unadjusted Adjusted B B LL UL B B LL UL Demographic factors  Parent age 0.28** 0.26** 0.11 0.41 0.04  Child age 0.13 −0.17 −0.12 −0.33 0.09  Parent gender  Cisgender women$ 2.16 6.58** 3.94 9.22 −1.77  Transgender or nonbinary$ 29.78 34.73 −11.73 81.20 8.57  Aboriginal or Torres Strait Islander −16.09** −9.82** −16.79 −2.85 −18.86** −11.96** −18.11 −5.81  Language other than English −0.67 −4.31 −3.86 −8.10 0.38 Socio-economic risk factors  Receiving government benefit −15.17** −2.42 −7.37 2.53 −22.44** −6.46** −10.85 −2.06  Did not complete high school −13.40** −6.99** −10.48 −3.50 −11.82** −3.07 −6.13 0.00  Single parent household −11.75** −6.90** −10.43 −3.37 −16.53** −7.79** −10.86 −4.73  Geographic location  Inner Regional Australia% −3.88** −2.88*  Outer Regional Australia% −2.11 −1.47  Remote Australia% 9.08 3.00  Number of children −0.26 −0.02  Financial deprivation index −5.13** −2.23** −3.36 −1.09 −7.31** −3.38** −4.37 −2.39 Parent and child diagnosis  Child neurodevelopmental diagnosis −4.32** −1.91 −4.08 0.27 −5.49** −1.44 −3.39 0.51  Parent physical/mental health diagnosis −5.85** −1.87 −4.00 0.26 −6.27** −0.09 −1.94 1.77 COVID-19 related stressors  Child home while working 1.60 −1.99 1.58 −0.57 3.72  COVID-19 environmental risk index −4.35** −2.25** −3.11 −1.38 −8.41** −6.08** −6.84 −5.31  Believe likely to catch COVID-19 −0.59 0.16 −0.47 0.80 −0.95** 0.25 −0.30 0.81  Fear and worry about COVID-19 −10.73** −8.91** −10.73 −7.09 −14.48** −9.99** −11.56 −8.42  Believe COVID-19 serious health risk −1.69** −0.33 −0.94 0.29 −2.61** −0.54* −1.08 0.00  Higher frequency of news media use 2.03** 1.89** 0.86 2.93 0.94** 1.05* 0.15 1.95 Notes: Data are multiply imputed. Variables were included in adjusted models where there was evidence for an unadjusted association with the outcome (p < 0.1). B = unweighted regression coefficient; LL = lower limit of a 95% confidence interval; UL = upper limit of a 95% confidence interval; * p < 0.05; ** p < 0.01. $ Compared to cisgender men. % Compared to Major Cities of Australia. These results were mostly consistent across the PWI domains, with some exceptions. Cisgender women were more likely than cisgender men to report higher subjective wellbeing on the Standard of Living, Achieving in Life, and Community Connectedness domains. The most consistent findings were evident for Aboriginal and Torres Strait Islander status, single parents, government benefits, pre-pandemic financial deprivation, the COVID-19 environmental risk index, and negative feelings about COVID-19. 3 Discussion We found that subjective wellbeing in CPAS parents during the April 2020 ‘level three’ lockdown in Australia was lower (0.5–1 SD) than pre-pandemic levels, as assessed annually over the previous two decades in AUWI parents. Pre-pandemic to pandemic differences equated to more than a 10-point reduction in scores on the total PWI in CPAS parents compared with AUWI parents. Within the CPAS parent sample, the most profound differences in subjective wellbeing were in parents with pre-existing challenges that predated the pandemic; including physical or mental health problems, having a child with a neurodevelopmental or mental health condition, living in a socially disadvantaged context, and being a young parent, as well as parents reporting current challenges; including anxiety and depression, and direct impacts of COVID-19, such as job loss. We also found that parents from Aboriginal and Torres Strait Islander backgrounds reported lower subjective wellbeing in the CPAS sample. We consistently found that indicators of pre-pandemic socio-economic disadvantage, such as financial deprivation, government benefit, single parent status, speaking a language other than English, and low education, were associated with lower subjective wellbeing. Results also indicated that younger parent age was associated with lower ratings of subjective wellbeing, personal relationships, and community connectedness. These results suggest that already vulnerable families struggling with socio-economic disadvantage prior to the pandemic, or parents in disadvantaged demographic groups, are likely to have suffered the most during the April 2020 ‘level three’ restrictions in Australia. Further, parents directly impacted by the COVID-19 pandemic reported consistently lower subjective wellbeing, over and above the influence of pre-pandemic socio-economic disadvantage. Direct COVID-19 impacts included the experience of COVID-19-related illness, job loss or employment changes, financial hardship, negative feelings and attributions about COVID-19, and juggling child supervision/remote learning whilst working from home. All of these factors are likely to have placed parents under additional strain as suggested by their low scores on indicators of subjective wellbeing. Together, these findings highlight a need for targeted supports and services for families experiencing socio-demographic risk, both in relation to pre-existing disadvantage, and in relation to risk that has occurred as a result of the pandemic and the related social distancing measures put in place to contain the virus in Australia. We found consistent associations between lower subjective wellbeing and parent report of high mental health risk, demonstrating additional strain associated with parents having pre-existing or concurrent mental or physical health problems or having a child with a neurodevelopmental or mental health condition. These findings are in line with previous pre-pandemic research showing lower subjective wellbeing in these groups [10,30]. It is also notable that rates of child neurodevelopmental or mental health conditions were higher than other Australian population estimates [1,19,32,33], suggesting that CPAS represents a higher-risk sample of parents from the Australian community. When we compared rates of subjective wellbeing within CPAS parents, we found consistently lower wellbeing in parents with mental health risks compared to other parents. Yet the elevated rates of mental health risks did not account for the overall differences in subjective wellbeing evident in parents prior to and during the pandemic; that is, when we excluded parents with high mental health risk, differences remained evident for CPAS and AUWI data on almost all domains of subjective wellbeing. These findings are suggestive of additional strains related to the pandemic that are not fully explained by pre-existing or concurrent mental health risk in families. Unexpectedly, we found that parents engaging more with news media about the pandemic were more likely to report higher subjective wellbeing. This is counter-intuitive and requires further investigation, but it may be that being informed leads to a higher sense of control, and thus improved subjective wellbeing, in context of a crisis such as the COVID-19 pandemic. This association may also be related to the quality and type of media that parents engage with, factors that were not assessed in this study. 3.1 Policy implications and future research Taken together, the results of the current study suggest that parents are an important group of Australians that need extra government resources for support in the current (and potential future) pandemics. Results specifically suggest that parents already struggling with socio-economic adversity and/or other disadvantage may benefit from targeted support. This is consistent with well-established evidence showing that families with pre-existing social disadvantage are much more likely to experience adversity, and have fewer resources to buffer the negative impact of stressful life events, such as challenges related to the COVID-19 pandemic and social distancing measures [38,53]. Our findings also support recent calls for additional government support during and beyond the pandemic [6], such as extending income support for parents experiencing financial stress. Further, given our findings indicating that juggling child supervision with work was associated with parent depression and irritability, and the documented negative impact of poor parent mental health and irritable parenting on children's future outcomes [25,40,55], steps to alleviate the stress of working parents are likely to be beneficial. This could include leave entitlements for parents juggling work with home-schooling or caring for children [6], alongside workplace interventions including flexible work arrangements or workload assistance during the pandemic. Research and policy is often, and understandably, focussed on mental illnesses such as depression and anxiety, but these can be viewed as end-states of more mild symptoms that may be captured in measures of general wellbeing. Longitudinal studies could be useful to confirm whether lower than usual (or lower than normal) subjective wellbeing scores are an early marker for later mental health problems. Such information could help with prevention and early intervention efforts, which have been argued to be more cost-effective than treating once full blown disorder is present. Further, prospective studies are needed to improve understanding of family outcomes longitudinally. Our data are suggestive of negative impacts associated with the COVID-19 pandemic for parents, children, and families. In particular, the state of Victoria in Australia has experienced one of the world's most stringent and lengthy periods of social distancing restrictions. It is imperative that future research investigate the longer-term impact of the COVID-19 pandemic on families, with a focus on how impacts vary relative to the nature and duration of social distancing measures worldwide. 3.2 Limitations Our study had a number of limitations. There were systematic differences between the CPAS sample and the AUWI normed sample, where parents in the normed sample were on average eight years older, more socially advantaged, and with a higher proportion of fathers. It was also not possible to limit the AUWI comparison to parents of children 0–18 years, so this sample included parents living with children of all ages. CPAS participants were recruited online, while AUWI participants were recruited via telephone. There is a precedent for greater enrolment of at-risk individuals via online research [3]; but there may also be emerging bias associated with telephone recruitment in an opposite direction, perhaps resulting in a disproportionately low-risk population, particularly in younger populations. We did not investigate whether these parents were also more likely to experience COVID-19 related risk factors, but this should be considered in future research given established associations between pre-existing health risks, socio-economic adversity, and vulnerability to crisis events [53]. 4 Summary Results indicated substantially lower rates of subjective wellbeing in a cohort of Australian parents raising children and adolescents (0–18 years) during ‘level-three’ COVID-19 restrictions in April 2020, compared to pre-pandemic levels. We identified three high-risk groups of parents for whom we recommend direct assistance through government, public health, and clinical services. These include: (1) parents reporting their own or their child as having mental health risk; (2) parents experiencing socioeconomic disadvantage prior to the pandemic; and, (3) parents affected by COVID-19 related factors, such as illness, financial or housing insecurity, changes to employment, and juggling childcare with work from home, who were also more vulnerable to the pandemic. Our findings specifically point to the need for targeted supports and services for parents and their families in these three high risk categories of Australian parents in the community. The following are the supplementary data related to this article.Supplementary Fig. 1 Cumulative density for PWI data for the CPAS data contrasted with normative PWI data from the AUWI for 68,011 cases. Supplementary Fig. 1 Supplementary Fig. 2 Cumulative density for PWI data for normative PWI data from the ACQOL for 2110 cases where one group were administered the PWI with emotionally leading questions (n = 1011; M = 73.75, SD = 0.496), and the other were not (n = 1099; M = 74.50, SD = 0.463). Supplementary Fig. 2 Supplementary Table 1 Unweighted and weighted comparisons between the AUWI norm and CPAS samples on the Personal Wellbeing Index (PWI) and PWI individual domains. Supplementary Table 1 Supplementary material Image 1 Declaration of Competing Interest All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf and declare that the authors have no competing interests to report. Acknowledgements We would like to thank all the parents who have contributed valuable data to the project. The current study was supported by funding from the Centre for Social and Early Emotional Development, School of Psychology, Victoria, Australia. EW & AM were supported by Deakin Faculty of Health Mid-Career Fellowships. DH was supported by a NHMRC Investigator Grant (1197488). ES was supported by an NHMRC Career Development Fellowship (1110688), a Medical Research Future Fund Investigator Grant (1194297) and a veski Inspiring Women’s Fellowship. ==== Refs References 1 Australian Bureau of Statistics National Survey of Mental Health and Wellbeing 2007: Summary of Results 2008 ABS 2 Australian Bureau of Statistics. (2017) 2016 Census of Population and Housing: General Community Profile Catalogue number 2001.0. 3 Batterham P.J. Recruitment of mental health survey participants using internet advertising: content, characteristics and cost effectiveness Int. J. Methods Psychiatr. Res. 23 2014 184 191 24615785 4 Biddle N. Edwards B. Gray M. Hardship, Distress, and Resilience: The Initial Impacts of COVID-19 in Australia 2020 ANU Centre for Social Research and Methods, Australian National University Canberra 5 Biddle N. Edwards B. 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J Psychosom Res. 2021 Jun 1; 145:110482

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10.1016/j.jpsychores.2021.110482

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==== Front J Biomech J Biomech Journal of Biomechanics 0021-9290 1873-2380 Elsevier Ltd. S0021-9290(21)00162-7 10.1016/j.jbiomech.2021.110382 110382 Article Hemodynamic performance limits of the neonatal Double-Lumen cannula Rasooli Reza a1 Jamil Muhammad a1 Rezaeimoghaddam Mohammad a Yıldız Yahya b Salihoglu Ece c Pekkan Kerem a⁎ a Mechanical Engineering, Koc University, Turkey b School of Medicine, Medipol University, Turkey c School of Medicine, Biruni University, Turkey ⁎ Corresponding author at: Mechanical Engineering Department, Koç University, Rumeli Feneri Campus, Sarıyer, Istanbul, Turkey.. 1 This manuscript is prepared by two first authors whose names are listed with no particular order. All parametric cannulae in this article are designed by MJ. 15 4 2021 24 5 2021 15 4 2021 121 110382110382 10 3 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Venovenous extracorporeal membrane oxygenation (VV-ECMO) is the preferred surgical intervention for patients suffering from severe cardiorespiratory failure, also encountered in SARS-Cov-2 management. The key component of VV-ECMO is the double-lumen cannula (DLC) that enables single-site access. The biofluid dynamics of this compact device is particularly challenging for neonatal patients due to high Reynolds numbers, tricuspid valve location and right-atrium hemodynamics. In this paper we present detailed findings of our comparative analysis of the right-atrial hemodynamics and salient design features of the 13Fr Avalon Elite DLC (as the clinically preferred neonatal cannula) with the alternate Origen DLC design, using experimentally validated computational fluid dynamics. Highly accurate 3D-reconstructions of both devices were obtained through an integrated optical coherence tomography and micro-CT imaging approach. Both cannula configurations displayed complex flow structures inside the atrium, superimposed over predominant recirculation regimes. We found that the Avalon DLC performed significantly better than the Origen alternative, by capturing 80% and 94% of venous blood from the inferior and superior vena cavae, respectively and infusing the oxygenated blood with an efficiency of more than 85%. The micro-scale geometric design features of the Avalon DLC that are associated with superior hemodynamics were investigated through 14 parametric cannula configurations. These simulations showed that the strategic placement of drainage holes, the smooth infusion blood stream diverter and efficient distribution of the venous blood capturing area between the vena cavae are associated with robust blood flow performance. Nevertheless, our parametric results indicate that there is still room for further device optimization beyond the performance measurements for both Avalon and Origen DLC in this study. In particular, the performance envelope of malpositioned cannula and off-design conditions require additional blood flow simulations for analysis. Keywords Cardiovascular devices Congenital heart defects Hemodynamics Biofluid mechanics micro-CT Hemolysis and thrombogenicity ==== Body pmc1 Introduction Extracorporeal membrane oxygenation (ECMO) is a life-sustaining therapy that has saved numerous lives since it was first successfully development in 1970 (Baffes et al., 1970), with high survival rate (Peek et al., 2009) for patients suffering from severe cardiorespiratory failure. Blood perfusion during ECMO therapy is typically achieved by either venoarterial (VA) or venovenous (VV) access. VV-ECMO is the currently preferred intervention in neonatal population due to its superior outcomes compared to conventional ventilator therapy (Palmér et al., 2016) and the lower risk of serious acute complications as encountered in VA-ECMO (Koerner et al., 2019). Double lumen cannula (DLC), which has been shown to play a pivotal role in advancing VV-ECMO therapy, is basically a single catheter with two separated channels. One channel is responsible for the infusion of oxygenated blood and the other channel functions to drain venous blood from the right atrium. Utilizing DLC during VV-ECMO provides single-site surgical access, resulting in better patient mobilization and rehabilitation, lower recirculation levels (Klein et al., 1985) and lower risk of infection, bleeding, thrombosis and sepsis rates (Hamilton and Foxcroft, 2007). During VV-ECMO, DLC is inserted percutaneously in the right internal jugular vein and is advanced into the right atrium under ultrasound guidance. Infusion lumen in the DLC directs the oxygenated blood flow to the tricuspid valve while drainage lumen is responsible for the suction of the venous return blood. The overall efficiency of the VV-ECMO therapy is firmly tied to DLC performance due to the simultaneous operation of both infusion and drainage channels inside the compact atrium, which is characterized by complex hemodynamics that can potentially result in the mixing of different blood streams. Despite the significant advantages that VV-DLC provides, it can also lead to high levels of recirculation (Chacon and Shillcutt, 2017). Recirculation fraction is defined as the fraction of blood from the infusion port that does not enter the pulmonary circulation, which is immediately drained through the drainage side before being systemically circulated. Extensive research has shown that DLC design (Rasooli et al., 2020) and positioning (Clements et al., 2008, Jamil et al., 2020, Wang et al., 2008, Xie et al., 2016)[9, 10], the volume of the atrium (Jamil et al., 2020) and ECMO flow rate (Sreenan et al., 2000, van Heijst et al., 2001), are the key factors that have a significant impact on recirculation levels. Our recent computational study revealed high recirculation fraction for Origen DLC even with correct caval positioning (Muhammad et al., 2018) which underscores the significance of DLC design parameters. Adequate perfusion of DLC in VV-ECMO requires drainage of venous blood from the right atrium to the ECMO circuit and delivery of oxygenated blood to the tricuspid valve for systemic circulation. Neonatal population present a unique challenge for the DLC assisted VV-ECMO therapy as the smaller physiology entails smaller DLC sizes to be considered. Hence, the interplay between limited space and small DLC sizes can lead to complicated and potentially different hemodynamic challenges. Significant research effort has been directed at addressing the design considerations and has been realized in the form of various DLC designs that are being utilized in routine clinical practice. Our primary aim in this study was to determine the current performance levels of two clinically approved and widely used cannulas: Origen and Avalon DLC. Most importantly, we set out to quantify the potential performance improvement of both cannulas through extensive parametric hemodynamic analysis of their key design features. This involved meticulously scrutinizing the DLC design principles under the purview of providing adequate venous unloading and oxygenated blood delivery. Conventional device characterization relies on in vitro bench testing or in vivo monitoring; however, in this study, we used our recently developed combined experimental and patient-specific computational fluid dynamics (CFD) strategy (Muhammad et al., 2018). This enabled us to thoroughly investigate the impact of different design parameters on the final device characteristics of recirculation fraction and blood damage. In the first phase of our study, we were able to identify and compare the relevant key design features of commercial cannulas using the CFD-based analysis of existing DLCs. We then parametrized these design features in the second phase of this study to extend the design rationale and to develop and test novel DLC prototypes that satisfy the design considerations while also providing better performance in hemodynamic parameters compared to the existing ones. 2 Methodology 2.1 Image-based 3D device reconstruction Two 13 French (Fr) DLC devices (both Avalon and Origen) were selected for this study as recommended by our collaborating neonatal intensivists and newborn intensive care unit (NICU) clinicians. Both devices were scanned using micro-CT with an extremely fine resolution (~150 μm) and image data was segmented in slicer software to obtain the 3D reconstruction (Çakmak et al., 2020). To resolve the exact geometry of the infusion and drainage holes, DLCs were scanned under optical coherence tomography (OCT) reaching less than 2 μm resolution. Fig. 1 summarizes the details of this 3D reconstruction approach.Fig. 1 Snapshots of the integrated micro-CT and optical coherence tomography (OCT) based 3D reconstruction of the dual lumen cannula (DLC) Avalon Elite 13Fr shown in (a). Commercially available device is acquired from the market and scanned under micro-CT at high resolution (150 µm) and reconstructed through 3D slicer software (b). For further refinement, device features were scanned under OCT (1–2 µm resolution). Section planes of OCT scanning lines are shown in red for infusion holes (c) and (e), and for drainage holes (g). The corresponding raw OCT images are provided in Figure inserts (d), (f) and (h), respectively. The final model is processed in Geomagic software to obtain an accurate CFD-ready geometry (i). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 2.2 Patient-specific right-atrial anatomy The 3D reconstruction of the right atrium (RA) geometry of a neonate (weight = 3 kg, height = 50 cm, body surface area = 0.2 m2) having normal venous return was acquired from MRI scans through approved IRB. The DLC models were oriented in the RA model in situ to replicate the confined in vivo atrial hemodynamics (Fig. 2 ). We evaluated the atrial hemodynamic performance of new parametric design features (Table 1 ), subjected to the identical methodology and performance metrics used for the two commercially-available designs.Fig. 2 (A) Correct position of 13 Fr Avalon DLC in atrium and (B) Origen DLC in optimal location where infusion hole points towards the tricuspid valve and distal tip is placed in the IVC. In Avalon DLC eight drainage holes (DH) and the tip are placed inside the inferior vena cava (IVC) while 3 drainage holes are situated proximal to the superior vena cava (SVC). DH 1–3 located proximal to SVC are responsible for drainage of venous blood from SVC while the tip and two sets of 4-holes arranged circumferentially and staggered at 90 to each other DH 4–7, DH 8–11, are responsible for drainage of venous blood from IVC. Origen DLC consists of single infusion hole intended to direct the oxygenated blood to the tricuspid annulus while the tip and three drainage holes arranged in line are responsible for drainage of venous blood from the IVC and SVC respectively. Solid bar in (A) corresponds to a length scale of 4 mm. Drainage hole diameter (ø) of the two devices are provided in the figure. Both designs represent two completely different design structures. IH: infusion hole. Tr: Tricuspid Valve. Table 1 Design matrix for the novel DLC design configurations. For each design configuration, the tabulated key performance parameters of (i) Recirculation (Recir.), (ii) Superior Vena Cava (SVC) and (iii) Inferior Vena Cava capture efficiency values were computed by post-processing the CFD solutions. The clinical applicability of each design configuration is also evaluated in the last column. # Design drawing Design features Recir. SVC eff. IVC eff. 1 Single hole (lengthwise) 35 100 54 2 Two holes (lengthwise) 32 100 51 3 Two holes closer to tip 32 100 51 4 Expanded tip 26 100 61 5 4 holes (arranged circumferentially) 28 100 55 6 Smaller inlet size 26 100 58 7 Triple lumen 25 100 60 8 4 holes (circumferentially) 15 96 69 9 4 small holes (circumferentially) 23 100 61 10 4 holes (lengthwise) 22 100 62 11 8 holes (lengthwise) 12 96 51 12 8 holes further apart (lengthwise) 23 100 59 13 Larger 3rd lumen with 8 holes further apart (lengthwise) 25 100 60 Numerical simulations were carried out in FLUENT solving steady RANS turbulent and 2nd order SIMPLE algorithm. Boundary conditions were identical to our earlier study (Muhammad et al., 2018). To summarize briefly, for the infusion channel (120 ml/kg/min), two hydraulic diameter extension inlet flowrate as plug flow (due to the short entrance length of ECMO circuit) was assigned for IVC/SVC (50/50 split of neonatal total cardiac output), nine hydraulic diameter extension and pressure outlet was specified for the tricuspid valve annulus and the outflow condition was considered for the drainage channel. A mesh independence study similar to our earlier study (Muhammad et al., 2018) was performed and a high-quality polyhedral mesh was prepared, which consisted of 0.685 million cells. Evaluation parameters, such as recirculation fraction (as in (Jamil et al., 2020)), superior vena cava (SVC) and inferior vena cava (IVC) blood capture efficiencies, were calculated by post-processing the computational results. We defined the SVC and IVC blood capture efficiencies as the fraction of the total venous blood taken from the respective conduits, which are dependent on the design and drainage mechanism of the DLC. Common clinical practices were used to minimize recirculation fraction as much as possible (ideally below 20% (Locker et al., 2003)). In addition, other hemodynamic parameters such as shear stress, hemolysis and residence times were also computed. 2.3 CFD solver validation Following the protocols for standard mesh sensitivity and CFD verification tests, we tested our solver against the FDA nozzle benchmark test (Supplementary Fig. 1). Next, we validated our jet-direction computations by connecting the DLC infusion port to a pump running water-glycerin blood analog fluid and then captured the images of free jet via high-speed camera on benchtop. Finally, we conducted particle image velocimetry (PIV) measurements at the operating Reynolds (Re) number of 1845. The details of this experimental approach are available in the literature [9] and briefly stated as follows. A closed loop flow loop is employed where the DLC was placed in a tank the size of 25 × 30 × 50 cm. Water was used as working fluid and seeded with silver coated spherical glass particles. A laser sheet the thickness of less than 1 mm was placed congruent with the mid-plane of the cannula. Time-resolved planar PIV was performed at a frame rate of 200 Hz. 3 Results 3.1 Experimental validation The numerical results of jet direction demonstrated excellent agreement with the bench experimental tests, as shown Fig. 3 , where the numerical and free-jet bench-top experimental results are superimposed. More specifically, Fig. 3 compares the PIV-measured and CFD-predicted velocity fields. A semi-instantaneous PIV velocity profile is also presented to show the turbulent flow characteristics and justify our CFD turbulent solver.Fig. 3 Comparison of the infusion jet direction for the bench experiment (Top Left) and computational fluid dynamics (CFD) simulation over-plotted with experimental camera image (Top Right). Bottom Row: Comparison of flow fields measured via particle image velocimetry (left) and computed with the CFD solver (right) for the neonatal Origen DLC cannula. In order to illustrate the unsteadiness in experimental flow fields an instantaneous velocity field is provided on purpose, while CFD provides time-averaged results. Time-averaged PIV is in good agreement with CFD as reported elsewhere (Rasooli et al., 2020). 3.2 Internal flow, velocity and wall shear stress The two DLC designs demonstrated similar velocity patterns as displayed in Fig. 4 (top). While the jet directions were similar, the IVC drainage flow of the Avalon design allowed more freedom for its jet wake development. Furthermore, the Avalon jet wake region was significantly longer and allowed better gradual expansion than Origen. The peripheral IVC side hole arrangement also resulted in a more balanced flow compared to Origen. Although the flow percentage through the drainage hole sets can vary, the flow through individual holes of the same sets was balanced. It was observed that the highest wall shear stress (WSS) zone was located at the infusion conduit across all the designs, although lower values of WSS were evident for the Avalon DLC (Fig. 4 - bottom). Similarly, the drainage holes closest to the drainage outlet resulted in high WSS hotspots. The internal flow quality of the drainage pathway in Avalon was slightly better than Origen as indicated by the corresponding WSS distributions.Fig. 4 Comparison of velocity magnitude on a cut-plane for Avalon (top) and Origen DLC (bottom). Higher velocities are observed in the infusion port and the drainage hole proximal to the drainage port. Comparison of wall shear stress (WSS) distribution on the Avalon (top) and Origen DLC (bottom). A region of high wall shear stress (WSS) is observed adjacent to the infusion port exit. The aspiration hole closer to the drainage port experiences the highest WSS on the venous side of blood flow. Tr: Tricuspid Valve, IVC: Inferior Vena Cava, SVC: Superior Vena Cava. 3.3 Right atrial flow characteristics As depicted in Fig. 5 , both of the commercially-available cannula designs generated the characteristic flow circulation inside the right atrium. In the Avalon DLC, an additional secondary flow circulation was induced due to its IVC side-hole flow. From a device design perspective, flow residence time characteristics of both designs were significantly different and worth discussing. The infusion jet wake region of the Avalon design exits the atrial flow domain significantly faster than the Origen. This jet-core region is associated with high blood flow rate which results in efficient infusion. However, the remaining few flow streams that cannot exit from the tricuspid annulus caused significantly higher blood residence times compared to the Origen design. It is important to note that these few remaining flow streams have the potential to increase thrombogenicity, but this also depends on positioning For both designs, the recirculation fractions varied significantly. For the Origen DLC, the recirculation fraction was 38% compared to only 15% for the Avalon. Furthermore, IVC efficiency of the Avalon DLC was 80% compared to 40% for the Origen. SVC efficiency was measured at 94% for both Avalon and Origen DLC designs.Fig. 5 Flow streamlines for correct position of Origen DLC and Avalon DLC. Color represents the three-dimensional blood residence time distribution plotted on flow streamlines. Higher red regions signify longer residence times making them more susceptible to blood clotting and thrombogenicity. Tr: Tricuspid Valve, IVC: Inferior Vena Cava, SVC: Superior Vena Cava. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 3.4 Parametric cannula configurations Table 1 lists all of the new design parameters that were analyzed in this study, including drainage hole numbers, hole size, hole arrangement, cannula tip size and SVC pathway separation from IVC drainage. By comparing the computational data presented in Section 3.3 with the new design parameters listed in Table 1, it is evident that these new designs were able to deliver higher SVC efficiency and lower recirculation fraction levels compared to the clinically-approved cannulae. Our results show that the optimal arrangement of tip holes can provide up to 70% IVC capture efficiency (Table 1: Design 8), which is identified to be the most challenging performance parameter for the neonatal cannulae. Likewise, the tip hole location is found to have a much more significant impact on the IVC performance when compared to the other design parameters, such as hole size, number of holes and SVC drainage pathways separation. However, separation of the SVC drainage pathways did not produce any significant benefit to either recirculation or IVC capture efficiency (Table 1: Designs 7–13). Our findings also show that IVC capture efficiency is significantly influenced by the orientation of the DLC cannula relative to the right atrium. Unfortunately, in actual clinical scenarios, the optimal positioning of the DLC is rarely possible. Therefore, configurations that are less sensitive to cannula positioning are desired. One possible novel configuration that can address the aforementioned problem is inspired from turbo-jet engine inlets (Fig. 6 ). The fuselage-shaped tip insert attached to the conical diffuser allows for improved IVC flow capture independent of the main external atrial flow direction due to its unique “flow attachment” function regardless of the inflow direction (see also Supplementary Fig. 2). In this design, a more uniform infusion jet flow is achieved due to the precise orientation of the IH towards the tricuspid valve annulus, the smooth internal cannula pathway and extremely efficient IVC and SVC capture (Fig. 6). The recirculation fraction, and IVC and SVC efficiencies for this novel design were calculated to be 13%, 84% and 100%, respectively.Fig. 6 Flow streamlines for one of the new designs (Table 1: Design 4) compared with the novel “flow-capturing cannula” concept (right column). An insert, indicated with an arrow, facilitates flow attachment and improves IVC drainage efficiency independent of its canula position relative to IVC flow. Top Row: Only the flow streamlines emerging through the infusion hole are plotted for both designs. Bottom Row: Corresponding IVC drainage is plotted. The innovative flow capturing design represents a novel approach where infusion lumen is located co-axially to facilitate better infusion flow guidance while drainage holes are arranged circumferentially for efficient venous blood capture from the SVC (flow streamlines originating from the SVC are omitted as they were similar for both designs) IVC: Inferior vena cava, SVC: Superior vena cava, Tr: Tricuspid valve annulus. 4 Discussion We computationally evaluated two clinically-approved DLCs with the Avalon and Origen designs and our findings show that the Avalon DLC is clinically preferred to the Origen DLC in terms of providing more effective hemodynamic support for neonatal VV-ECMO therapy. Our comprehensive CFD analyses using an inverse-design approach reveal the RA hemodynamics of the Avalon DLC in situ for the first time in the literature. A series of parametric simulations indicate that the Avalon design has more optimal performance in terms of recirculation, and IVC and SVC blood capture efficiencies. Importantly for patients with cardiorespiratory failure, this study enabled us to identify the main features of DLC design that are critical for more effective surgical procedures with the potential for even more optimal DLC designs in the future. 4.1 Avalon and Origen DLC performance inside the atrium In the Avalon DLC, venous blood drainage is achieved through its tip and drainage holes DH1-11, where DH1-3 are intended to remove blood from the SVC while the tip and remaining sections of the cannula drain the blood from IVC. For DH 1–3, the shape of the drainage lumen restricts their size and circumferential location as it allows holes to be placed in only half of the circumference. Nonetheless, it does provide an equally-distributed flow through these holes, as they are located at the same distance from the suction side, which results in lower wall shear stress values. In relation to the Origen DLC, three equal-sized drainage holes (DH1-DH3) are arranged in a straight line, which results in unequal flow through the holes. For equal-sized holes DH1-3, the tendency is for the maximum flow to pass through the initial DH1 and then decreases steadily for the other holes located distally. It should also be noted that although DH1-3 allows for sufficient drainage for both DLCs in normal conditions, this arrangement can make the Origen DLC vulnerable to complete blockage of IVC blood drainage when there is any malposition of these holes. A full circumferential arrangement, such as DH4-7 and DH8-11, is more suitable to ensure a balanced blood flow and avoid blockages. The Avalon DLC also provides more sufficient redundancy of SVC drainage than the Origen DLC. In the event that the tip or any of the holes DH4-11 are blocked, IVC blood can still be drained via the other holes or the tip in the Avalon design. In comparison, a single tip is responsible for IVC blood drainage in the Origen design and there is no alternative mechanism to drain the venous blood from the IVC if the tip is blocked. Similarly for SVC drainage, the Avalon DLC is less likely to be blocked than the Origen DLC due to the circumferential arrangement of DH 1–3, which are located on the same side in the Avalon design and therefore provide better redundancy. In addition to the drainage mechanics of DLC design, infusion jet characteristics also play a dominant role in shaping atrial hemodynamics. However, realizing the optimal jet infusion characteristics is challenging as there are many dependent factors to take into account. The size and shape of the infusion lumen are extremely important on the basis they control the infusion velocities and WSS (Rasooli et al., 2020). In the multi-hole design of the Origen DLC, the infusion lumen is relatively small which entails higher WSS values. In comparison, the larger size of the infusion lumen in the Avalon DLC show lower values of WSS. These characteristics are critical in determining the maximum ECMO flow rate that can be achieved with acceptable blood damage. The guiding mechanism of the jet also plays an important role as it dictates jet energy dissipation, expansion characteristics and jet direction capabilities. The jet expansion cross-sectional area in the Origen DLC is not optimal and the bulk flow in the infusion lumen encounters a rapid directional change. Furthermore. It is also important to note that the suboptimal jet direction mechanism in the Origen DLC prevents efficient utilization of the jet area. In more practical terms, this means that the Origen DLC has a higher exit area but only a fraction of it is efficiently utilized at exit. In comparison, the Avalon DLC embodies better jet direction strategy, allowing for smooth transition and expansion inside the atrium. However, slight flow disturbances can nevertheless be observed near the infusion holes. For the Avalon DLC, three drainage holes are located in the SVC while the rest of the eight holes and tip are located in the IVC. This is a good design strategy since the three holes with high suction pressure are able to capture the SVC blood while the eight holes and the tip provide a larger suction surface area that makes up for the decrease in suction pressure towards the proximal end. Fig. 7 shows the distribution of blood capturing area on the drainage side for Avalon DLC, with only 15% of the blood capturing area dedicated to SVC and the other 85% of the area intended for drainage of blood from the IVC. The larger tip (45% of the total blood capturing area) provides efficient suction efficiency by making up for the reduced suction pressure. In addition, the longer distance between the infusion jet and drainage holes discourages recirculation.Fig. 7 IVC drainage design is important in DLC performance. Here the electrical analog circuits of the drainage side of the Origen DLC and an alternate design known as the Avalon DLC is compared. Labelled values in blue indicate the percentage of total flow area on the drainage side for each design. Red squares correspond to the intended location of the respective components. Our measurements indicate that the Avalon DLC allows for more flow area in the IVC that may be attributed to an efficient blood capture of the IVC blood. Total flow area on the drainage side is 13.5 mm2 for Origen DLC compared to 15.7 mm2 for Avalon DLC. For the Origen DLC, the infusion and drainage lumen perimeters are 4.31 and 7.87 mm and their respective areas are 1.08 mm2 and 4.54 mm2. These areas are based on micro-CT and OCT imaging of single device samples, thus variations are expected from the manufacturer propriety design drawings. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) The relatively poor performance of the Origen DLC can be attributed to design features that do not conform to RA anatomy. In the ideal caval position, the drainage holes are located inside the atrium while the tip is positioned at the cavo-atrial junction. However, the Origen design arrangement not only allows the venous blood from the SVC to pool in the atrium but also increases the risk of blood shunting from the infusion hole to the drainage holes, which are located almost in the wake of the infusion jet. Moreover, the three large-sized drainage holes claim almost all of the suction side pressure and provide little stimulus to draw blood from the tip section, which results in very poor IVC blood capture efficiency. Fig. 7 highlights the key differences in suction area distribution between the Avalon and Origen DLC respectively. While most of the drainage exit orifice area in the Avalon DLC is reserved only for the SVC and IVC, the available drainage area in the Origen DLC is distributed to capture blood from the SVC, IVC and the atrium. In fact, the blood capturing surface area of the Origen is design in such a biased way that blood drains from the atrium preferentially. The smaller distances between the infusion hole and the drainage holes provides a strong stimulus for the proximal atrium chamber flow to move towards the drainage holes, which then in turn can increase further recirculation. 4.2 Novel DLC design configurations and performance As we discovered in this study, the geometric optimization of DLC design requires a comprehensive evaluation of DLC functionality in the actual right atrium. While it is possible to examine DLC functionality in idealized cuboidal domains to provide geometry-dependent jet characteristics and balanced flow distributions, this research approach will not guarantee optimal performance when the device is placed inside the right atrium. The functional requirements of an effective DLC involve the drainage of venous blood from the SVC and IVC and the infusion of oxygenated blood to the tricuspid valve. The precise nature of this cardiorespiratory function necessitates that the design characteristics of the DLC, such as the number and location of holes, are synchronized with the surrounding physiological conditions. In developing more optimal novel DLC configurations, we were committed to overcoming the apparent shortcomings of the two commercially-available DLC designs examined in this study. Therefore, several novel design configurations explored to test the limits of DLC performance are listed in Table 1. One of the main features of these configurations is a more efficient jet infusion guiding mechanism. This was achieved by using the co-axial arrangement of the infusion lumen inside the drainage lumen instead of the side-by-side arrangement used in the Avalon or Origen DLC. This novel co-axial arrangement was found to possess multiple functional design advantages. It was able to more efficiently guide the infusion jet with a longer guidance length. It also resulted in minimal losses and smoother expansion of the infusion jet into the atrium. More importantly, the new DLC designs achieved much better jet direction performance with more efficient utilization of the infusion hole area. The superior characteristics of new DLC designs can be attributed to longer guidance lengths and minimum area change of the infusion hole. Based on our results it can be deduced that geometric features can manipulate jet characteristics and simpler mechanisms of jet control is more efficient and possible. These complexities may cause instabilities in terms of flow control and can limit maximum ECMO flow rate possible. Another salient feature of these novel design configurations is that the outer lumen that makes up the outer body of the cannula allows for full coverage of the circumference where equal-sized drainage holes can be placed for balanced drainage flow. It is also possible to vary the size of the holes depending on the requirements. Moreover, such a circumferential arrangement of holes is less liable for anatomic obstruction due to the surrounding environment. Even if there is an obstruction to any one of the holes, the rest of the holes can make up for that deficiency. Moreover, the in-situ arrangement for these configurations is when the drainage holes are placed in the SVC while the longer distance between the infusion and drainage holes ensures minimal mixing of venous and oxygenated blood, hence less recirculation. Among all our design configurations and the commercial ones examined in this study, the design with a fuselage-shaped insert attached to the cannula tip was found to significantly enhance the cannula performance in terms of recirculation fraction, and IVC and SVC blood capture efficiencies. However, we also acknowledge that these performance indices correspond to current atrial anatomy and significant changes should be expected for varied surrounding anatomy or off-design conditions, such as anatomic obstruction and malposition. Further in depth and randomized studies are warranted. Methodological limitations are discussed in Ref. (Jamil et al., 2020). 5 Conclusions This study provides a functional explanation of the prevailing complications of the DLC use in VV-ECMO. A thorough CFD analysis of two existing designs revealed deficiencies in their functional design and overall performance in the atrium. Our findings clearly show that the superior performance of the Avalon DLC (replicated geometry) can be linked to its more effective design features, yet Avalon DLC had short-commings Compared to the Origen DLC (replicated geometry), the superior performance of the Avalon DLC can be attributed to more efficient distribution of the drainage surface area, the size and location of the drainage holes, and the efficient guiding mechanism of the infusion jet. Problems associated with recirculation and poor SVC/IVC efficiencies in the Origen DLC can be traced back to obvious design shortcomings, such as imbalanced drainage flow inside the DLC itself and in situ positions that apparently do not conform to the surrounding anatomy. Yet despite the encouraging performance of the Avalon design features, we identified certain key aspects in our 14 novel design configurations that can be modified to further improve DLC functionality. Our findings indicate that these features will expand the performance envelope of DLC off-design for malposition scenarios; however, we also acknowledge that further in-vitro studies are required to confirm this concept. CRediT authorship contribution statement Reza Rasooli: Conceptualization, Data curation, Writing, Discussions, Methodology, Validation. Muhammad Jamil: Original draft preparation, Discussions, Methodology, Validation. Mohammad Rezaeimoghaddam: Writing, Discussions, Methodology, Validation. Yahya Yıldız: Conceptualization, Methodology, Clinical Insight, Writing. Ece Salihoglu: Conceptualization, Methodology, Clinical Insight, Writing. Kerem Pekkan: Supervision, Conceptualization, Writing, Reviewing, Editing and Funding acquisition. Appendix A Supplementary data The following are the Supplementary data to this article:Supplementary data 1 Supplementary data 1 Acknowledgments Funding was provided by TUBITAK Grant 118M369 (PI: Kerem Pekkan). Language editing is performed by Anthony Townley. Conflict of interest Authors have no relations with the companies and have no conflicts of interest. Geometries are not from the actual design drawings but reconstructed with best tools available. Appendix A Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.afjem.2018.07.005. ==== Refs References Baffes T.G. Fridman J.L. Bicoff J.P. Whitehill J.L. Extracorporeal circulation for support of palliative cardiac surgery in infants The Annals of thoracic surgery 10 1970 354 363 4195759 Çakmak B. Ermek E. Jamil M. Horasan A. Pekkan K. Applications of micro-CT in cardiovascular engineering and bio-inspired design, Micro-computed Tomography (micro-CT) in Medicine and Engineering Springer 2020 171 181 Chacon M.M. Shillcutt S.K. Intraoperative transesophageal echocardiography–guided placement of bicaval dual-lumen extracorporeal membrane oxygenation cannula CASE: Cardiovascular Imaging Case Reports 1 2017 116 30062259 Clements D. Primmer J. Ryman P. Marr B. Searles B. Darling E. Measurements of recirculation during neonatal veno-venous extracorporeal membrane oxygenation: clinical application of the ultrasound dilution technique The journal of extra-corporeal technology 40 2008 184 18853830 Hamilton H.C. Foxcroft D. Central venous access sites for the prevention of venous thrombosis, stenosis and infection in patients requiring long-term intravenous therapy Cochrane Database of Systematic Reviews 2007 Jamil M. Rezaeimoghaddam M. Cakmak B. Yildiz Y. Rasooli R. Pekkan K. Salihoglu E. Hemodynamics of neonatal double lumen cannula malposition Perfusion 35 2020 306 315 31580212 Klein M.D. Andrews A.F. Wesley J.R. Toomasian J. Nixon C. Roloff D. Bartlett R.H. Venovenous perfusion in ECMO for newborn respiratory insufficiency. A clinical comparison with venoarterial perfusion Annals of surgery 201 1985 520 3977454 Koerner M.M. Harper M.D. Gordon C.K. Horstmanshof D. Long J.W. Sasevich M.J. Neel J.D. El Banayosy A. Adult cardiac veno-arterial extracorporeal life support (VA-ECMO): prevention and management of acute complications Annals of cardiothoracic surgery 8 2019 66 30854314 Locker G.J. Losert H. Schellongowski P. Thalhammer F. Knapp S. Laczika K.F. Burgmann H. Staudinger T. Frass M. Muhm M. Bedside exclusion of clinically significant recirculation volume during venovenous ECMO using conventional blood gas analyses Journal of clinical anesthesia 15 2003 441 445 14652122 Muhammad J. Rezaeimoghaddam M. Cakmak B. Rasooli R. Salihoglu E. Yıldız Y. Pekkan K. Patient-specific atrial hemodynamics of a double lumen neonatal cannula in correct caval position Artificial Organs 42 2018 401 409 29572879 Palmér O. Palmér K. Hultman J. Broman M. Cannula design and recirculation during venovenous extracorporeal membrane oxygenation Asaio Journal 62 2016 737 27660904 Peek G.J. Mugford M. Tiruvoipati R. Wilson A. Allen E. Thalanany M.M. Hibbert C.L. Truesdale A. Clemens F. Cooper N. Efficacy and economic assessment of conventional ventilatory support versus extracorporeal membrane oxygenation for severe adult respiratory failure (CESAR): a multicentre randomised controlled trial The Lancet 374 2009 1351 1363 Rasooli R. Yıldız Y. Jamil M. Pekkan K. Infusion jet flow control in neonatal double lumen cannulae Journal of Biomechanical Engineering 142 2020 Sreenan C. Osiovich H. Cheung P.-Y. Lemke R.P. Quantification of recirculation by thermodilution during venovenous extracorporeal membrane oxygenation Journal of pediatric surgery 35 2000 1411 1414 11051139 van Heijst A.F. van der Staak F.H. de Haan A.F. Liem K.D. Festen C. Geven W.B. van de Bor M. Recirculation in double lumen catheter veno-venous extracorporeal membrane oxygenation measured by an ultrasound dilution technique Asaio Journal 47 2001 372 376 11482489 Wang D. Zhou X. Liu X. Sidor B. Lynch J. Zwischenberger J.B. Wang-Zwische double lumen cannula—toward a percutaneous and ambulatory paracorporeal artificial lung Asaio Journal 54 2008 606 611 19033774 Xie A. Yan T.D. Forrest P. Recirculation in venovenous extracorporeal membrane oxygenation Journal of critical care 36 2016 107 110 27546757

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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Elsevier Inc. S0022-4804(22)00019-1 10.1016/j.jss.2021.12.047 Thoracic Surgery Pre-COVID-19 National Mortality Trends in Open and Video-Assisted Lobectomy for Non-Small Cell Lung Cancer Dezube Aaron R. MD a1∗ Hirji Sameer MD, MPH a1 Shah Rohan MD, MPH a Axtell Andrea MD, MPH b Rodriguez Maria MD c Swanson Scott J. MD a Jaklitsch Michael T. MD a Mody Gita N. MD, MPH d a Division of Thoracic and Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts b Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts c Department of Thoracic Surgery, Clinica Universidad de Navarra, Madrid, Spain d Division of Cardiothoracic Surgery, Department of Surgery, University of North Carolina, Chapel Hill, North Carolina ∗ Corresponding author. Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115. Tel.: +1 617-306-0961; fax: +1 617-730-2898. 1 These authors are co-first authors. 26 1 2022 6 2022 26 1 2022 274 213223 28 5 2021 4 11 2021 27 12 2021 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Introduction In the current era of episode-based hospital reimbursements, it is important to determine the impact of hospital size on contemporary national trends in surgical technique and outcomes of lobectomy. Methods Patients aged >18 y undergoing open and video-assisted thoracoscopic surgery (VATS) lobectomy from 2008 to 2014 were identified using insurance claims data from the National Inpatient Sample. The impact of hospital size on surgical approach and outcomes for both open and VATS lobectomy were analyzed. Results Over the 7-y period, 202,668 lobectomies were performed nationally, including 71,638 VATS and 131,030 open. Although the overall number of lobectomies decreased (30,058 in 2008 versus 27,340 in 2014, P < 0.01), the proportion of VATS lobectomies increased (24.0% versus 46.9%), and open lobectomies decreased (76.0% versus 53.0%, all P < 0.01). When stratified by hospital size, small hospitals had a significant increase in the proportion of open lobectomies (6.4%-12.2%; P = 0.01) and trend toward increased number of VATS lobectomies (2.7%-12.2%). Annual mortality rates for VATS (range: 1.0%-1.9%) and open (range: 1.9%-2.4%) lobectomy did not significantly differ over time (all P > 0.05) but did decrease among small hospitals (4.1%-1.3% and 5.1%-1.1% for VATS and open, respectively; both P < 0.05). After adjusting for confounders, hospital bed size was not a predictor of in-hospital mortality. Conclusions Utilization of VATS lobectomies has increased over time, more so among small hospitals. Mortality rates for open lobectomy remain consistently higher than VATS lobectomy (range 0.4%-1.4%) but did not significantly differ over time. This data can help benchmark hospital performance in the future. Keywords Lobectomy Lung cancer Outcomes Thoracotomy Utilization Video-assisted Thoracoscopic Surgery ==== Body pmcIntroduction Lung cancer is the number one cause of cancer-related mortality in the United States and worldwide, with an estimated 228,150 new cases in the United States in 2019 and 2.09 million new cases and 1.76 million deaths worldwide, despite a declining incidence over the last 2 decades from 70.2 cases per 100,000 people in 1999 to 55.2 per 100,000 people in 2017 within the United States.1, 2, 3 To date, lobectomy remains the gold standard for the management of early stage non–small cell lung cancer (NSCLC).4 Over the last decade, there has been a substantial growth in the use of video-assisted thoracoscopic surgery (VATS) in the management of patients, albeit with varying levels of uptake across different institutions. Recent studies have confirmed that VATS lobectomy is a safe and effective alternative to traditional lobectomy.5, 6, 7 In the contemporary era, there is growing literature demonstrating the impact of operative volume and/or hospital size on overall patient outcomes.8, 9, 10, 11 These studies have shown a strong correlation between lobectomy volume and patient outcomes, especially in patients treated at high-volume surgery centers.8 , 12 , 13 Although these findings are encouraging, volume alone has been shown to be an inadequate proxy of quality assessment after lobectomy.14 To address these inadequacies, the Society of Thoracic Surgeon's recently used the General Thoracic Surgery Database (GTSD) to develop a composite quality measure scores for measuring performance of hospitals performing lobectomy, which does not include hospital size.15 Although the utility and reliability of GTSD have been validated previously,16 , 17 the composite scores were not generalizable (as they only included voluntary institutions participating in the database) and were also prone to selection bias,16 , 17 leaving the question of how to predict and judge surgical quality for lobectomy patients at institutions with different characteristics. To our knowledge, there exists no study that has specially examined the impact of overall hospital size as a function of open or VATS lobectomies and outcomes (morbidity and mortality). We hypothesized that hospital size may play a significant role in terms of outcomes, in addition to known factors such as surgeon and hospital operative volume. This research may provide useful benchmarking data to guide resource utilization and provide a framework to improve quality and transparency at a national level. Thus, in this study, we sought to (1) describe contemporary national trends in open and VATS surgery volume and mortality, (2) examine the impact of hospital size on patient outcomes, and (3) develop a predictive model for in-hospital mortality after open and VATS surgery. Methods Data source The National Inpatient Sample (NIS) is the largest publicly available all-payer database of hospitalized patients in the United States and is sponsored by the Agency for Healthcare Research and Quality (AHRQ) as a part of the Healthcare Cost and Utilization Project (HCUP). It is composed of more than 45 state databases and includes anonymized data on discharge diagnoses and procedures on about eight million hospitalizations from about 1000 hospitals sampled annually.18 Although the NIS dataset constitutes a 20% stratified sample of US hospitals, it provides reliable sampling weights to calculate robust national estimates that represent more than 95% of the US population. This study adhered to best practices required by AHRQ for design and conduct of research using the NIS19 and followed recommendations for reporting statistics that are based on HCUP data. This study was considered exempt from institutional review board approval. Study population We identified hospitalizations in patients aged ≥18 y who underwent either open or VATS lobectomy between 2008 and 2014 for lung cancer. The 2014 endpoint was chosen, given the sampling design of the NIS, as assessment of studies with volume–outcome within the NIS is only valid until 2015.20 To isolate this cohort, we used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), procedure codes highlighted in Table e1 in the Data Supplement. We then excluded patients who did not undergo a lobectomy to avoid both confounding and due to the heterogeneity of reporting of ICD-9-CM codes for sublobar resection within the NIS. We also excluded patients who underwent concurrent thoracic surgery procedures (i.e., additional procedures during the same operation) or those who underwent wedge resection or pneumonectomy. Because of the deidentified nature of hospitals in the NIS, we were unable to control for these coding practices at the hospital level. Finally, the possibility of variability in technique and volume across different hospitals cannot be adequately assessed, given the deidentified nature of the NIS. A consort flowchart is included as Supplemental Figure. Study outcomes Our primary outcomes of interest were national trends in procedures and all-cause, in-hospital mortality. Secondary outcomes of interest included specific adverse events such as acute myocardial infarction (MI), stroke, major bleed, acute kidney injury (AKI), hospital length of stay (LOS), inpatient cost, discharge disposition following surgery, and predictors of in-hospital outcomes. A list of ICD-9-CM codes used to define in-hospital complications is also included in Table e1 in the Data supplement. Statistical analysis Cochran-Armitage test for trend was conducted to determine significant differences in open and VATS volume and mortality over time. Open and VATS surgeries were then stratified by hospital bed size (small, medium, and large) as defined by the NIS.21 Briefly, bed size categories were based on the number of hospital beds and were specific to the hospital's region (Northeast, Midwest, South, and West) and teaching status (teaching versus nonteaching), as the NIS uses different cutoff points for rural, urban nonteaching, and urban teaching hospitals by region (see Data Supplement Table e2). Patient demographic information, clinically relevant diagnoses, surgical and in-hospital outcomes, hospital LOS and costs, and discharge disposition were extracted and compared by bed size. AHRQ comorbidities were used both as single categories and as Charlson comorbidity index to serve indirectly as an indicator for frailty. In addition to bed size, hospital location and teaching status were also examined. Normally distributed continuous variables were expressed as a mean with standard deviation and compared using one-way analysis of variance tests. Categorical variables were presented as number and percentages and compared using χ2 tests. We then adjusted outcomes for the small-sized hospitals using multivariable logistic and linear regression to control for patient demographics, comorbidities, admission, and hospital-level factors. Independent predictors for in-hospital mortality were determined by including all preoperative variables in a backward selection, parsimonious multivariable logistic regression model with P < 0.05 as the threshold for inclusion. The final model contained preoperative variables that met the threshold for inclusion along with hospital bed size. This second logistic regression model was performed similarly to the previous model. Missing data were rare (<1% for all variables). P value of <0.05 was the threshold criterion for statistical significance for all tests and models. Analysis was conducted using STATA version 13.1 (StataCorp LP, College Station, TX). Results Overall patient sample Over the 7-y period, 202,668 lobectomies were recorded nationally in the NIS, with 131,030 done open (65%) and 71,638 (35%) done via VATS. Overall number of annual lobectomies decreased from 30,058 in 2008 to 27,340 in 2014 (P < 0.01; Fig. 1 ). At the same time, the proportion of VATS lobectomies increased from 24% to 47%; meanwhile, open lobectomies decreased from 76% of all cases to 53% (P < 0.01). Furthermore, no statistical difference in overall in-hospital mortality for either VATS or open lobectomy was observed for all cases during the study period; however, mortality for VATS lobectomy remained consistently lower than open lobectomy (range 0.41% in 2010 to 1.42% in 2012; Fig. 2 ).Fig. 1 Temporal trend in open versus VATS lobectomy from 2008 to 2014 within the National Inpatient Sample. Figure 1 demonstrates decreased performance of overall lobectomies performed by open technique (n = 22,843 between 2008 and 2009 to n = 15,000 in 2013-2014) with a statistically significant increased utilization of video-assisted thoracoscopic lobectomy (n = 7215 in 2008-2009 to 12,015 in 2013-2014) over the same period from 2008 to 2014 (both P < 0.01). Fig. 2 Temporal trend in mortality between open versus VATS lobectomy from 2008 to 2014 within the National Inpatient Sample. Figure 2 depicts decreasing mortality rates for VATS and open lobectomy between 2008 and 2014; however, the temporal improvement in mortality for each method of resection did not achieve statistical significance (both P > 0.05). Open lobectomy Table 1 describes rates and demographics of patients undergoing open lobectomy. In terms of open lobectomy, rates were highest at small hospitals. Although the median age was statistically different, it was clinically similar (68 [IQR 10.2] versus 67 [IQR 9.9] versus 67 [IQR 9.8] y for small, medium, and large hospitals, respectively; P < 0.01). Patients at large hospitals had higher Charlson comorbidity indices, rates of metastatic cancer, and MI history but lower rates of chronic obstructive pulmonary disease (COPD) compared with smaller hospitals. No differences were found between hospital size with observed rates of in-hospital outcomes (AKI, MI, major bleeding, pulmonary embolism, stroke, and death) except for LOS (median 8.3 versus 8.6 versus 8.6 d for small, medium, and large hospitals, respectively; P < 0.01). Overall, patients at larger hospitals had more costly stays ($33,813 versus $33,538 versus $34,839 for small, medium, and large hospitals, respectively), but after adjustment for inflation, cost was highest at small hospitals (inflation-adjusted cost $31,212 versus $28,093 versus $30,569 for small, medium, and large hospitals, respectively; P < 0.01). However, patients at large hospitals were less likely to be discharged to a nonhome facility (P < 0.01; Table 2 ). Although annual mortality rates for open procedures did not statistically vary over time (1.9%-2.4%), a significant improvement in mortality for small hospitals did occur (P < 0.01; Fig. 3 A).Table 1 Demographics, comorbidities, and hospital factors by hospital size for patients undergoing open lobectomy from 2008 to 2014 in the National Inpatient Sample. Variable Small (n = 11,162) Medium (n = 27,461) Large (n = 92,407) P value Demographics, n (%)  Age (y), median (IQR) 68 (10.2) 67 (9.9) 67 (9.8) <0.01∗  Female 5769 (51.7) 13,824 (50.4) 45,886 (49.7) 0.20  Race 0.77  White 7924 (83.7) 20,402 (84.3) 67,472 (84.2)  Black 786 (8.3) 1891 (7.8) 6314 (7.9)  Hispanic 334 (3.5) 785 (3.2) 3017 (3.8)  Asian or Pacific Islander 197 (2.1) 550 (2.3) 1733 (2.2)  Payer 0.07  Medicare 7069 (63.6) 17,044 (62.2) 57,620 (62.4)  Medicaid 658 (5.9) 1503 (5.5) 5036 (5.5)  Private 3161 (28.4) 7916 (28.9) 26,161 (28.3)  Self-pay 115 (1.0) 336 (1.2) 1481 (1.6)  Median household income quartile per ZIP code 0.04∗  1 2386 (21.7) 7198 (26.8) 23,742 (26.2)  2 3470 (31.6) 7584 (28.2) 24,471 (27.0)  3 2827 (25.7) 6551 (24.3) 22,334 (24.7)  4 2308 (21.0) 5576 (20.7) 19,946 (22.0) Comorbidities, n (%)  Alcohol abuse 450 (4.0) 966 (3.5) 2853 (3.1) 0.03∗  Atrial fibrillation 2157 (19.3) 4971 (18.1) 17,707 (19.2) 0.22  Deficiency anemias 1476 (13.2) 3916 (14.3) 11,878 (12.9) 0.12  Congestive heart failure 598 (5.4) 1283 (4.7) 4063 (4.4) 0.13  Chronic pulmonary disease 5891 (52.8) 14,804 (53.9) 46,836 (50.7) <0.01∗  Coagulopathy 282 (2.5) 956 (3.5) 2575 (2.8) 0.03∗  Diabetes, uncomplicated 1961 (17.6) 5015 (18.3) 16,487 (17.8) 0.71  Dyslipidemia 3961 (35.5) 10,635 (38.7) 34,387 (37.2) 0.12  HTN 6749 (60.5) 16,787 (61.1) 54,877 (59.4) 0.09  Liver disease 155 (1.4) 370 (1.3) 1494 (1.6) 0.32  Fluid and electrolyte disorders 2231 (20.0) 5552 (20.2) 18,346 (19.9) 0.9  Metastatic cancer 2044 (18.3) 5044 (18.4) 18,532 (20.0) 0.04∗  Obesity 962 (8.6) 2320 (8.4) 7593 (8.2) 0.77  Peripheral vascular disorders 877 (7.9) 2699 (9.8) 7862 (8.5) <0.01∗  Psychoses 295 (2.6) 682 (2.5) 2489 (2.7) 0.71  Pulmonary circulation disorders 319 (2.9) 579 (2.1) 2085 (2.3) 0.14  Renal failure 614 (5.5) 1653 (6.0) 4916 (5.3) 0.15  Weight loss 570 (5.1) 1542 (5.6) 4299 (4.7) 6410 (4.9)  Smoking 6233 (55.8) 16,528 (60.2) 53,561 (58.0) 0.03∗  Prior MI 2996 (26.9) 8265 (30.1) 25,316 (27.4) <0.01∗  Prior transient ischemic attack/stroke 2698 (24.2) 6916 (25.2) 21,939 (23.7) 0.11  APRDRG_Risk mortality 2.0 (0.9) 1.9 (0.9) 1.9 (0.9) <0.01∗  APRDRG_Severity 2.2 (0.8) 2.3 (0.8) 2.2 (0.8) <0.01∗  Charlson comorbidity index 4.2 (2.6) 4.2 (2.5) 4.3 (2.6) <0.01∗  Sum of Elixhauser comorbidities 3.8 (1.6) 3.8 (1.6) 3.7 (1.6) <0.01∗ Hospital factors, n (%)  Hospital region <0.01∗  Northeast 1986 (17.8) 4689 (17.1) 16,572 (17.9)  Midwest 4279 (38.3) 7609 (27.7) 23,610 (25.6)  South 3722 (33.4) 10,643 (38.8) 37,454 (40.5)  West 1175 (10.5) 4521 (16.5) 14,772 (16.0)  Control/ownership of hospital  Government or private, collapsed category 5520 (49.5) 13,018 (47.4) 40,244 (43.6) <0.01  Government, nonfederal 527 (4.7) 763 (2.8) 5727 (6.2)  Private, nonprofit 3366 (30.2) 10,240 (37.3) 38, 645 (41.8)  Private, invest own 1725 (15.5) 3404 (12.4) 6225 (6.7)  Location/teaching status of hospital <0.01∗  Rural 56 (0.5) 318 (1.2) 6780 (7.3)  Urban nonteaching 2631 (23.6) 8910 (32.5) 34,646 (37.5)  Urban teaching 8475 (75.9) 18,233 (66.4) 50,981 (55.2) APRDRG = All Patients Refined Diagnosis Related Groups. ∗ P < 0.05. Table 2 Outcomes related to hospital size in patients undergoing open lobectomy in the National Inpatient Sample from 2008 to 2014. Variable Small (n = 11,162) Medium (n = 27,461) Large (n = 92,407) P value In-hospital outcomes, n (%)  Acute MI 153 (1.4) 301 (1.1) 872 (0.9) 0.13  AKI 627 (5.6) 1721 (6.3) 5472 (5.9) 0.51  Major bleed 492 (4.4) 1193 (4.3) 3913 (4.2) 0.89  Pulmonary embolism 104 (0.9) 176 (0.6) 697 (0.8) 0.39  Stroke 80 (0.7) 280 (1.0) 845 (0.9) 0.43  Death 272 (2.4) 679 (2.5) 2020 (2.2) 0.37 Admission characteristics, n (%)  Admission on weekend 89 (0.8) 205 (0.7) 899 (1.0) 0.28  Disposition <0.01∗  Routine 7027 (63.0) 16,679 (60.8) 55,793 (60.4)  Transfer to short-term hospital 94 (0.8) 147 (0.5) 380 (0.4)  Transfer to SNF, ICF, rehabilitation 1481 (13.3) 3492 (12.7) 9808 (10.6)  Home health care 2288 (20.5) 6435 (23.4) 24,285 (26.3)  Elective 10,162 (91.1) 24,819 (90.5) 84,688 (91.5) 0.24  LOS (d) 8.3 (6.8) 8.6 (6.6) 8.6 (7.5) <0.01∗  Cost (USD $, inflation adjusted) 33,813 (31,212) 33,538 (28,093) 34,839 (30,569) <0.01∗ ICF = intermediate care facility; SNF = Skilled nursing facility. ∗ P < 0.05. Fig. 3 Temporal trends in surgical volume and mortality for VATS lobectomy and open lobectomy by hospital size within the National Inpatient Sample from 2008 to 2014. (A) For open lobectomy, in-hospital mortality rates remained stable for medium and large hospitals. A statistical improvement in mortality was observed for small hospitals over time. Volume remained relatively stable. (B) A similar trend for VATS lobectomy was observed with in-hospital mortality rates remaining stable for medium and large hospitals. A statistical improvement in mortality was observed for small hospitals over time. Volume remained relatively stable. In multivariate analysis, hospital size was not a predictor of in-hospital mortality after open lobectomy (Table e3). Acute MI, AIDS, congestive heart failure, COPD, coagulopathy, liver disease, fluid and electrolyte disorders, metastatic cancer, weight loss, and history of paralysis were associated with increased odds of in-hospital mortality, whereas female gender, Medicaid or private insurance, drug abuse, dyslipidemia, hypertension (HTN), hypothyroidism, smoking, and obesity were protective (all P < 0.05). VATS lobectomy Regarding VATS lobectomy, comorbidities between hospital sizes differed in terms of age, history of COPD, chronic deficiency anemias, psychoses, pulmonary circulation disorders, HTN, MI, All Patient Refined Diagnosis-Related Group risk mortality and severity, Charlson Comorbidity Index, and Sum of Elixhauser Comorbidities (all P < 0.05). However, clinically, these differences were minor apart from a 6% higher rate of MI history at medium hospitals compared with small hospitals. Small hospitals were more commonly located in the Northeast compared with the South for medium and large hospitals (P = 0.04) and were least likely to be nonprofit (P = 0.04) and most likely to be urban teaching hospitals despite their size (P < 0.01; Table 3 ). Smaller hospitals compared with medium or large hospitals were associated with higher rates of major bleeding (3.6% versus 3.4% versus 2.6% respectively; P = 0.03), pulmonary embolism (0.9% versus 0.2% versus 0.5% respectively; P = 0.01), and death (2.1% versus 1.4% vs. 1.3% respectively; P < 0.01) but shorter LOS (6 versus 6.4 versus 6.2 d, respectively; P < 0.01) and increased overall and adjusted inflated costs (net difference $8122 between small and large hospitals for adjusted cost; P < 0.01) than in larger hospitals (P < 0.05; Table 4 ). In-hospital mortality rates remained stable for medium and large hospitals. However, again, a statistical improvement in mortality over time was observed for VATS lobectomy at small hospitals (Fig. 3B). In multivariate analysis, hospital size was not a predictor of in-hospital mortality after VATS (Table e4). Female gender, those with dyslipidemia, obesity, and smoking history were protective against in-hospital mortality (all adjusted P value <0.05), whereas fluid or electrolyte disorders, acute MI, AKI, cardiac arrest, development of major bleed, heart block, or stroke were associated with increased odds of in-hospital mortality (all adjusted P value <0.05).Table 3 Demographics, comorbidities, and hospital factors by hospital size for patients undergoing video-assisted thoracoscopic lobectomy from 2008 to 2014 in the National Inpatient Sample. Variable Small (n = 6140) Medium (n = 12,811) Large (n = 52,687) P value Demographics  Age, years, median (IQR) 68.0 (9.9) 67.8 (9.6) 67.8 (9.8) <0.01∗  Female, n (%) 3335 (54.3) 7272 (56.8) 29,443 (55.9) 0.41  Race, n (%)  White 4697 (83.4) 10,113 (83.7) 40,846 (82.6) 0.90  Black 456 (8.1) 941 (7.8) 3796 (7.7)  Hispanic 196 (3.5) 368 (3.0) 1779 (3.6)  Asian or Pacific Islander 180 (3.2) 365 (3.0) 1670 (3.4)  Payer, n (%) 0.14  Medicare 3645 (59.4) 8304 (64.9) 33,018 (62.8)  Medicaid 259 (4.2) 522 (4.1) 2324 (4.4)  Private 2092 (34.1) 3571 (27.9) 15,523 (29.5)  Self-pay 42 (0.7) 140 (1.1) 684 (1.3)  Median household income quartile per ZIP code 0.49  1 1099 (18.3) 2530 (20.1) 10,505 (20.3)  2 1591 (26.5) 2824 (22.4) 12,246 (23.6)  3 1544 (25.7) 3442 (27.3) 13,007 (25.1)  4 1770 (29.5) 3795 (30.1) 16,045 (31.0) Comorbidities, n (%)  Alcohol abuse 156 (2.5) 444 (3.5) 1477 (2.8) 0.16  Atrial fibrillation 981 (16.0) 2101 (16.4) 9166 (17.4) 0.32  Deficiency anemias 626 (10.2) 1552 (12.1) 4866 (9.2) <0.01∗  Congestive heart failure 198 (3.2) 461 (3.6) 1892 (3.6) 0.77  Chronic pulmonary disease 2678 (43.6) 6254 (48.8) 23,252 (44.1) <0.01∗  Coagulopathy 149 (2.4) 321 (2.5) 1040 (2.0) 0.20  Diabetes, uncomplicated 1106 (18.0) 2191 (17.1) 8655 (16.4) 0.34  Dyslipidemia 2373 (38.7) 5038 (39.3) 21,586 (41.0) 0.37  HTN 3527 (57.4) 7929 (61.9) 31,803 (60.4) 0.04∗  Liver disease 143 (2.3) 184 (1.4) 770 (1.5) 0.06  Fluid and electrolyte disorders 804 (13.1) 1855 (14.5) 7343 (13.9) 0.64  Metastatic cancer 744 (12.1) 1747 (13.6) 7436 (14.1) 0.23  Obesity 438 (7.1) 1001 (7.8) 4084 (7.8) 0.79  Peripheral vascular disorders 599 (9.8) 1245 (9.7) 4678 (8.9) 0.35  Psychoses 146 (2.4) 386 (3.0) 1122 (2.1) 0.04∗  Pulmonary circulation disorders 165 (2.7) 180 (1.4) 872 (1.7) 0.01∗  Renal failure 301 (4.9) 647 (5.1) 2766 (5.3) 0.85  Weight loss 186 (3.0) 327 (2.5) 1560 (3.0) 0.55  Smoking 3536 (57.6) 7691 (60.0) 30,551 (58.0) 0.43  Prior MI 1363 (22.2) 3592 (28.0) 12,995 (24.7) <0.01∗  Prior transient ischemic attack/stroke 1404 (22.9) 2918 (22.8) 11,561 (21.9) 0.57  APRDRG_Risk mortality 1.7 (0.8) 1.7 (0.8) 1.8 (0.8) <0.01∗  APRDRG_Severity 2.0 (0.8) 2.1 (0.8) 2.0 (0.8) <0.01∗  Charlson comorbidity index 3.7 (2.2) 3.8 (2.3) 3.8 (2.3) <0.01∗  Sum of Elixhauser comorbidities 3.5 (1.6) 3.5 (1.6) 3.4 (1.5) <0.01∗ Hospital factors, n (%)  Hospital region 0.04∗  Northeast 2330 (38.0) 3422 (26.7) 14,173 (26.9)  Midwest 939 (15.3) 1431 (11.2) 9354 (17.8)  South 1876 (30.6) 5696 (44.5) 20,002 (38.0)  West 994 (16.2) 2263 (17.7) 9159 (17.4)  Control/ownership of hospital 0.04∗  Government or private, collapsed category 2253 (36.7) 4524 (35.3) 19,894 (37.8)  Government, nonfederal 730 (11.9) 430 (3.4) 3835 (7.3)  Private, nonprofit 2713 (44.2) 6647 (51.9) 25,999 (49.4)  Private, invest own 444 (7.2) 1200 (9.4) 2867 (5.4)  Location/teaching status of hospital <0.01∗  Rural - 66 (0.5) 1779 (3.4)  Urban nonteaching 380 (6.2) 3869 (30.2) 14,397 (27.3)  Urban teaching 5755 (93.7) 8878 (69.3) 36,511 (69.3) APRDRG = All Patients Refined Diagnosis Related Groups. ∗ P < 0.05 = n < 11 per NIS guidelines. Table 4 Outcomes related to hospital size in patients undergoing ideo-assisted thoracoscopic lobectomy in the National Inpatient Sample from 2008 to 2014. Variable Small (n = 6140) Medium (n = 12,811) Large (n = 52,687) P value In-hospital outcomes, n (%)  Acute MI 73 (1.2) 85 (0.7) 392 (0.7) 0.2  AKI 244 (4.0) 560 (4.3) 2159 (4.1) 0.79  Major bleed 220 (3.6) 431 (3.4) 1380 (2.6) 0.03∗  Pulmonary embolism 56 (0.9) 30 (0.2) 241 (0.5) 0.01∗  Stroke 29 (0.5) 60 (0.5) 254 (0.5) 0.99  Death 126 (2.1) 177 (1.4) 681 (1.3) 0.23 Admission characteristics, n (%)  Admission on weekend 55 (0.9) 79 (0.7) 419 (0.8) 0.60  Disposition 0.23  Routine 4481 (73.0) 8727 (68.1) 35,728 (67.8)  Transfer to short-term hospital 47 (0.8) 55 (0.4) 132 (0.3)  Transfer to SNF, ICF, rehabilitation 383 (6.2) 1026 (8.0) 3723 (7.1)  Home health care 1097 (17.9) 2817 (22.0) 12,379 (23.5)  Elective 5837 (95.2) 11,925 (93.2) 49,877 (94.8) 0.17  LOS (d) 6.0 (7.2) 6.4 (5.9) 6.2 (5.7) <0.01∗  Cost (USD $, inflation adjusted) 33,314 (33,081) 30,853 (27,680) 29,984 (24,965) <0.01∗ ICF = intermediate care facility; SNF = skilled nursing facility. ∗ P < 0.05. Discussion This study takes an in-depth look at the impact of hospital size on national trends and in-hospital outcomes for open and VATs lobectomy in the United States. It serves as a snapshot from 2008 to 2014, a time of increasing expansion of VATS techniques into community hospitals. CALGB 39802, a prospective trial that established the safety and feasibility of VATS lobectomy, was published in 2007.22 Furthermore, the National Lung Screening Trial was published in 201123 and began to influence the willingness to obtain chest computed tomography scans as well as led to increased nodule identification and a stage shift with diagnosis of earlier stage lung cancer. This study had several important findings: First, it demonstrated a decreased volume of lobectomies being performed in the United States by ∼10%, which we hypothesize may be because of rise in sublobar resection over the same period,24 , 25 which has been shown in other national databases such as that of the Society of Thoracic Surgeons,26 as well as lower incidence of new lung cancers3 and/or in use of radiation therapy.27 Next, we identified an approximately twofold increase in the utilization of VATS for lobectomy (24%-47%) with a drop-in rate of open lobectomies (76%-53%). VATS lobectomy was most commonly performed in the Northeast and South followed by the West in urban teaching centers, which is consistent with early pioneering of this technique in these regions, whereas open lobectomy was more common in the South and Midwest with higher rates outside of teaching hospitals compared with VATS. Finally, we demonstrate an improving survival rate after lobectomy at smaller hospitals for both open and VATS lobectomy during our 7-y study period. This study provides useful benchmark information for thoracic surgeons in national trends not only about the use of minimally invasive surgery but also of outcomes and mortality. However, our results did not show clinically relevant differences in underlying patient demographics or patient comorbidities except for some differences in geographic apart from a slightly higher prevalence of prior MI in those patients at larger hospitals undergoing VATS. In terms of hospital characteristics between open and VATS lobectomy groups, smaller hospitals were more likely to be teaching hospitals compared with medium- or large-sized hospitals, which may contribute to more favorable outcomes over time for smaller hospitals. Meanwhile, geographic distribution did statistically vary by hospital size, but the authors do not believe; this led to clinically meaningful differences in outcomes. Although we did not identify clinically important differences in morbidity or mortality by hospital size, there were slightly higher rates of major bleeding (3.6% versus 2.6%; P = 0.03) and PE (0.9% versus 0.5%; P = 0.01) after VATS lobectomy at smaller hospitals compared with the largest hospital size. Nonetheless, after risk adjustment, our study failed to demonstrate hospital size as an independent risk factor for adverse outcomes, while unsurprisingly, certain comorbidities and development of postoperative complications were associated with increased mortality risk. Interestingly, Medicaid compared with Medicare was associated with lower mortality risk in open cases, which diverges from earlier findings within the NIS28 and may represent Medicaid expansion during the study period, which has been shown to be associated with improved mortality for lung cancer.29 However, the finding that obesity was protective was rather felt to be because of the lack of granularity within the coding of this variable in the database, rather than a true finding. Over the last decade, there has been an increased emphasis on surgeon/hospital volume effects on overall outcomes. Our study may support the prior studies, which suggest surgeon/and or hospital volume may impact outcomes8, 9, 10, 11 rather than hospital size alone or geographic region.30 Furthermore, our study did observe statistically different cost charges for open or for VATS surgery by hospital size (P < 0.01). In the case of open lobectomy, we believe the inflation-adjusted charges were clinically insignificant (cost difference of $643 dollars between large and small hospital size). However, for VATS lobectomy, the net difference in inflation-adjusted cost was $8349 between large and small hospital size, which may suggest despite improving outcomes at smaller hospitals, it may be overall cost-effective to perform VATS lobectomy at larger hospitals. Although we identified an annual improvement in outcomes for both open and VATS lobectomy in smaller hospitals, a similar significant trend was not identified for medium and larger hospitals. We hypothesize these different trends reflect major system-wide changes to smaller hospitals (rather than underlying differences in patient population), with significant advances in patient selection, surgical technique of lobectomies, increased cumulative experience of surgeons at small hospitals, and better perioperative care, as more thoracic surgery has moved out to the community, and with it, academic surgeons.31 The results of our study should be interpreted in light of both its strengths and limitations. The NIS is derived from hospital claims data without access to individual medical records and is subject to the shortcomings of administrative datasets. Inconsistent coding practices among institutions may have resulted in overestimation or underestimation of patient comorbidities and hospital outcomes, although HCUP quality control measures are in place to minimize these discrepancies. Sampling practices of the NIS also vary from year to year, as hospitals enter and leave the sampling frame, resulting in possible over- or under-sampling by study design. Despite our best efforts to use a validated coding scheme, residual confounding and misclassification may exist. Furthermore, we were unable to examine long-term outcomes beyond a single admission, which limited our ability to assess trends and the effect of hospital size on readmissions and aggregate costs after the index hospitalization or long-term cancer-free survival after lobectomy. In addition, charges were inflation adjusted and reported by hospital size but not region, which may account for some differences in aggregate hospital costs. Because of the nature of our database, frailty could not be measured directly. The NIS also does not contain details on patient presentation, cancer pathology, surgeon experience, and details of surgical procedure, which would have been important, as these have previously been identified as risk factors for adverse outcome. However, our dataset may provide more information about real-world practice as opposed to databases, such as the GTSD, which is less generalizable because of the voluntary participation of institutions included in it, making it prone to selection bias.16 , 17 Conclusions In this nationally representative, multi-institutional study based on insurance claims data, we demonstrate that utilization of VATS lobectomies has steadily increased over time, more so among small hospitals compared with large hospitals. Although overall mortality was stable during the study period, mortality was only shown to improve in smaller hospitals. Furthermore, mortality rates for VATS remained consistently lower than for open lobectomy. These findings are hypothesis generating and provide useful data for benchmarking hospital performance in the current era of value-based reimbursement. Author Contributions A.R.D., S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. all participated in inception and design of the study. A.R.D., S.H., R.S., A.A., and M.R. participated in acquisition of data. A.R.D., S.H., R.S., A.A., M.R., and G.N.M. participated in data analysis. A.R.D.,S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. participated in interpretation of data. A.R.D., S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. participated in drafting the article as well as final approval. Meeting Presentation The data have been previously presented at the American College of Surgeons meeting. Disclosure No financial disclosures or conflicts of interest. Supplementary Data Supplemental Material Supplemental Figure Acknowledgments This study was supported in part by the generous donation of the Jack Mitchell Thoracic Oncology Fellowship. The authors wish to acknowledge Cheryl Zogg, PhD, from the Center for Surgery and Public Health at Brigham and Womens Hospital for providing statistical support. ==== Refs References 1 Lung Cancer Facts: 29 Statistics and Facts | LCFA. Lung Cancer Foundation of America Available at: https://lcfamerica.org/lung-cancer-info/lung-cancer-facts/ 2 Cancer of the Lung and Bronchus - Cancer Stat Facts. SEER Available at: https://seer.cancer.gov/statfacts/html/lungb.html 3 USCS Data Visualizations Available at: https://gis.cdc.gov/grasp/USCS/DataViz.html 4 Hartwig M.G. D’Amico T.A. Thoracoscopic lobectomy: the gold standard for early-stage lung cancer? Ann Thorac Surg 89 2010 S2098 S2101 10.1016/j.athoracsur.2010.02.102 20493989 5 Long H. Tan Q. Luo Q. Thoracoscopic surgery versus thoracotomy for lung cancer: short-term outcomes of a randomized trial Ann Thorac Surg 105 2018 386 392 29198623 6 Bendixen M. Jørgensen O.D. Kronborg C. Andersen C. Licht P.B. Postoperative pain and quality of life after lobectomy via video-assisted thoracoscopic surgery or anterolateral thoracotomy for early stage lung cancer: a randomised controlled trial Lancet Oncol 17 2016 836 844 27160473 7 Zhang Z. Zhang Y. Feng H. Is video-assisted thoracic surgery lobectomy better than thoracotomy for early-stage non-small-cell lung cancer? A systematic review and meta-analysis Eur J Cardio-Thoracic Surg 44 2013 407 414 8 Al-Sahaf M. Lim E. The association between surgical volume, survival and quality of care J Thorac Dis 7 2015 S152 S155 25984361 9 Fuchs H.F. Harnsberger C.R. Broderick R.C. Mortality after esophagectomy is heavily impacted by center volume: retrospective analysis of the Nationwide Inpatient Sample Surg Endosc 31 2017 2491 2497 27660245 10 Mamidanna R. Ni Z. Anderson O. Surgeon volume and cancer esophagectomy, gastrectomy, and pancreatectomy: a population-based study in England Ann Surg 263 2016 727 732 26501701 11 Kennedy G.D. Tevis S.E. Kent K.C. Is there a relationship between patient satisfaction and favorable outcomes? Ann Surg 260 2014 592 598 discussion 598-600 25203875 12 Hannan E.L. Radzyner M. Rubin D. Dougherty J. Brennan M.F. The influence of hospital and surgeon volume on in-hospital mortality for colectomy, gastrectomy, and lung lobectomy in patients with cancer Surgery 131 2002 6 15 11812957 13 Lüchtenborg M. Riaz S.P. Coupland V.H. High procedure volume is strongly associated with improved survival after lung cancer surgery J Clin Oncol 31 2013 3141 3146 23897962 14 Falcoz P.-E. Puyraveau M. Rivera C. The impact of hospital and surgeon volume on the 30-day mortality of lung cancer surgery: a nation-based reappraisal J Thorac Cardiovasc Surg 148 2014 841 848 24534677 15 Broderick S.R. Grau-Sepulveda M. Kosinski A.S. The Society of thoracic surgeons composite score rating for pulmonary resection for lung cancer Ann Thorac Surg 109 2020 848 855 31689407 16 Lapar D.J. Stukenborg G.J. Lau C.L. Jones D.R. Kozower B.D. Differences in reported esophageal cancer resection outcomes between national clinical and administrative databases J Thorac Cardiovasc Surg 144 2012 1152 1157 22938777 17 Magee M.J. Wright C.D. McDonald D. Fernandez F.G. Kozower B.D. External validation of the Society of thoracic surgeons general thoracic surgery database Ann Thorac Surg 96 2013 1734 1739 discussion 1738-1739 23998406 18 Healthcare Cost and Utilization Project (HCUP) NIS Notes Available at: https://www.hcup-us.ahrq.gov/db/vars/h_contrl/nisnote.jsp 19 Healthcare Cost and Utilization Project (HCUP) http://www.ahrq.gov/data/hcup/index.html 20 HCUP-US NIS Overview Available at: https://www.hcup-us.ahrq.gov/nisoverview.jsp 21 HCUP Facts and Figures 2008: Statistics on Hospital-Based Care in the United States Available at: https://www.hcup-us.ahrq.gov/reports/factsandfigures/2008/definitions.jsp 22 Swanson S.J. Herndon J.E. D’Amico T.A. Video-assisted thoracic surgery lobectomy: report of CALGB 39802--a prospective, multi-institution feasibility study J Clin Oncol 25 2007 4993 4997 17971599 23 National Lung Screening Trial Research TeamAberle D.R. Adams A.M. Berg C.D. Reduced lung-cancer mortality with low-dose computed tomographic screening N Engl J Med 365 2011 395 409 21714641 24 Fernandez F.G. Kosinski A.S. Burfeind W. STS lung cancer resection risk model: higher quality data and superior outcomes Ann Thorac Surg 102 2016 370 377 27209606 25 Boffa D.J. Allen M.S. Grab J.D. Gaissert H.A. Harpole D.H. Wright C.D. Data from the society of thoracic surgeons general thoracic surgery database: the surgical management of primary lung tumors J Thorac Cardiovasc Surg 135 2008 247 254 18242243 26 Kneuertz P.J. Zhao J. D’Souza D.M. Abdel-Rasoul M. Merritt R.E. National trends and outcomes of segmentectomy in the Society of Thoracic Surgery database Ann Thorac Surg 2021 10.1016/j.athoracsur.2021.07.056 [Epub ahead of print] 27 Wegner R.E. Hasan S. Renz P. Colonias A. Turrisi A.T. Trends in intensity-modulated radiation therapy use for limited-stage small cell lung cancer: a National Cancer Database analysis Appl Rad Oncol 7 2018 26 33 28 LaPar D.J. Bhamidipati C.M. Mery C.M. Primary payer status affects mortality for major surgical operations Ann Surg 252 2010 544 551 20647910 29 Lam M.B. Phelan J. Orav E.J. Jha A.K. Keating N.L. Medicaid expansion and mortality among patients with breast, lung, and colorectal cancer JAMA Netw Open 3 2020 e2024366 33151317 30 Shroyer A.L. Quin J.A. Grau-Sepulveda M.V. Geographic variations in lung cancer lobectomy outcomes: the general thoracic surgery database Ann Thorac Surg 104 2017 1650 1655 28935347 31 Ducko C.T. Bravo-Iñiguez C.E. Jaklitsch M.T. Starfish model: recruiting academic surgeons to provide thoracic surgery in the community setting J Surg Oncol 115 2017 782 783 28464326

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==== Front J Surg Res J Surg Res The Journal of Surgical Research 0022-4804 1095-8673 Elsevier Inc. S0022-4804(22)00019-1 10.1016/j.jss.2021.12.047 Thoracic Surgery Pre-COVID-19 National Mortality Trends in Open and Video-Assisted Lobectomy for Non-Small Cell Lung Cancer Dezube Aaron R. MD a1∗ Hirji Sameer MD, MPH a1 Shah Rohan MD, MPH a Axtell Andrea MD, MPH b Rodriguez Maria MD c Swanson Scott J. MD a Jaklitsch Michael T. MD a Mody Gita N. MD, MPH d a Division of Thoracic and Cardiac Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts b Division of Thoracic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts c Department of Thoracic Surgery, Clinica Universidad de Navarra, Madrid, Spain d Division of Cardiothoracic Surgery, Department of Surgery, University of North Carolina, Chapel Hill, North Carolina ∗ Corresponding author. Division of Thoracic and Cardiac Surgery, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115. Tel.: +1 617-306-0961; fax: +1 617-730-2898. 1 These authors are co-first authors. 26 1 2022 6 2022 26 1 2022 274 213223 28 5 2021 4 11 2021 27 12 2021 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Introduction In the current era of episode-based hospital reimbursements, it is important to determine the impact of hospital size on contemporary national trends in surgical technique and outcomes of lobectomy. Methods Patients aged >18 y undergoing open and video-assisted thoracoscopic surgery (VATS) lobectomy from 2008 to 2014 were identified using insurance claims data from the National Inpatient Sample. The impact of hospital size on surgical approach and outcomes for both open and VATS lobectomy were analyzed. Results Over the 7-y period, 202,668 lobectomies were performed nationally, including 71,638 VATS and 131,030 open. Although the overall number of lobectomies decreased (30,058 in 2008 versus 27,340 in 2014, P < 0.01), the proportion of VATS lobectomies increased (24.0% versus 46.9%), and open lobectomies decreased (76.0% versus 53.0%, all P < 0.01). When stratified by hospital size, small hospitals had a significant increase in the proportion of open lobectomies (6.4%-12.2%; P = 0.01) and trend toward increased number of VATS lobectomies (2.7%-12.2%). Annual mortality rates for VATS (range: 1.0%-1.9%) and open (range: 1.9%-2.4%) lobectomy did not significantly differ over time (all P > 0.05) but did decrease among small hospitals (4.1%-1.3% and 5.1%-1.1% for VATS and open, respectively; both P < 0.05). After adjusting for confounders, hospital bed size was not a predictor of in-hospital mortality. Conclusions Utilization of VATS lobectomies has increased over time, more so among small hospitals. Mortality rates for open lobectomy remain consistently higher than VATS lobectomy (range 0.4%-1.4%) but did not significantly differ over time. This data can help benchmark hospital performance in the future. Keywords Lobectomy Lung cancer Outcomes Thoracotomy Utilization Video-assisted Thoracoscopic Surgery ==== Body pmcIntroduction Lung cancer is the number one cause of cancer-related mortality in the United States and worldwide, with an estimated 228,150 new cases in the United States in 2019 and 2.09 million new cases and 1.76 million deaths worldwide, despite a declining incidence over the last 2 decades from 70.2 cases per 100,000 people in 1999 to 55.2 per 100,000 people in 2017 within the United States.1, 2, 3 To date, lobectomy remains the gold standard for the management of early stage non–small cell lung cancer (NSCLC).4 Over the last decade, there has been a substantial growth in the use of video-assisted thoracoscopic surgery (VATS) in the management of patients, albeit with varying levels of uptake across different institutions. Recent studies have confirmed that VATS lobectomy is a safe and effective alternative to traditional lobectomy.5, 6, 7 In the contemporary era, there is growing literature demonstrating the impact of operative volume and/or hospital size on overall patient outcomes.8, 9, 10, 11 These studies have shown a strong correlation between lobectomy volume and patient outcomes, especially in patients treated at high-volume surgery centers.8 , 12 , 13 Although these findings are encouraging, volume alone has been shown to be an inadequate proxy of quality assessment after lobectomy.14 To address these inadequacies, the Society of Thoracic Surgeon's recently used the General Thoracic Surgery Database (GTSD) to develop a composite quality measure scores for measuring performance of hospitals performing lobectomy, which does not include hospital size.15 Although the utility and reliability of GTSD have been validated previously,16 , 17 the composite scores were not generalizable (as they only included voluntary institutions participating in the database) and were also prone to selection bias,16 , 17 leaving the question of how to predict and judge surgical quality for lobectomy patients at institutions with different characteristics. To our knowledge, there exists no study that has specially examined the impact of overall hospital size as a function of open or VATS lobectomies and outcomes (morbidity and mortality). We hypothesized that hospital size may play a significant role in terms of outcomes, in addition to known factors such as surgeon and hospital operative volume. This research may provide useful benchmarking data to guide resource utilization and provide a framework to improve quality and transparency at a national level. Thus, in this study, we sought to (1) describe contemporary national trends in open and VATS surgery volume and mortality, (2) examine the impact of hospital size on patient outcomes, and (3) develop a predictive model for in-hospital mortality after open and VATS surgery. Methods Data source The National Inpatient Sample (NIS) is the largest publicly available all-payer database of hospitalized patients in the United States and is sponsored by the Agency for Healthcare Research and Quality (AHRQ) as a part of the Healthcare Cost and Utilization Project (HCUP). It is composed of more than 45 state databases and includes anonymized data on discharge diagnoses and procedures on about eight million hospitalizations from about 1000 hospitals sampled annually.18 Although the NIS dataset constitutes a 20% stratified sample of US hospitals, it provides reliable sampling weights to calculate robust national estimates that represent more than 95% of the US population. This study adhered to best practices required by AHRQ for design and conduct of research using the NIS19 and followed recommendations for reporting statistics that are based on HCUP data. This study was considered exempt from institutional review board approval. Study population We identified hospitalizations in patients aged ≥18 y who underwent either open or VATS lobectomy between 2008 and 2014 for lung cancer. The 2014 endpoint was chosen, given the sampling design of the NIS, as assessment of studies with volume–outcome within the NIS is only valid until 2015.20 To isolate this cohort, we used the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), procedure codes highlighted in Table e1 in the Data Supplement. We then excluded patients who did not undergo a lobectomy to avoid both confounding and due to the heterogeneity of reporting of ICD-9-CM codes for sublobar resection within the NIS. We also excluded patients who underwent concurrent thoracic surgery procedures (i.e., additional procedures during the same operation) or those who underwent wedge resection or pneumonectomy. Because of the deidentified nature of hospitals in the NIS, we were unable to control for these coding practices at the hospital level. Finally, the possibility of variability in technique and volume across different hospitals cannot be adequately assessed, given the deidentified nature of the NIS. A consort flowchart is included as Supplemental Figure. Study outcomes Our primary outcomes of interest were national trends in procedures and all-cause, in-hospital mortality. Secondary outcomes of interest included specific adverse events such as acute myocardial infarction (MI), stroke, major bleed, acute kidney injury (AKI), hospital length of stay (LOS), inpatient cost, discharge disposition following surgery, and predictors of in-hospital outcomes. A list of ICD-9-CM codes used to define in-hospital complications is also included in Table e1 in the Data supplement. Statistical analysis Cochran-Armitage test for trend was conducted to determine significant differences in open and VATS volume and mortality over time. Open and VATS surgeries were then stratified by hospital bed size (small, medium, and large) as defined by the NIS.21 Briefly, bed size categories were based on the number of hospital beds and were specific to the hospital's region (Northeast, Midwest, South, and West) and teaching status (teaching versus nonteaching), as the NIS uses different cutoff points for rural, urban nonteaching, and urban teaching hospitals by region (see Data Supplement Table e2). Patient demographic information, clinically relevant diagnoses, surgical and in-hospital outcomes, hospital LOS and costs, and discharge disposition were extracted and compared by bed size. AHRQ comorbidities were used both as single categories and as Charlson comorbidity index to serve indirectly as an indicator for frailty. In addition to bed size, hospital location and teaching status were also examined. Normally distributed continuous variables were expressed as a mean with standard deviation and compared using one-way analysis of variance tests. Categorical variables were presented as number and percentages and compared using χ2 tests. We then adjusted outcomes for the small-sized hospitals using multivariable logistic and linear regression to control for patient demographics, comorbidities, admission, and hospital-level factors. Independent predictors for in-hospital mortality were determined by including all preoperative variables in a backward selection, parsimonious multivariable logistic regression model with P < 0.05 as the threshold for inclusion. The final model contained preoperative variables that met the threshold for inclusion along with hospital bed size. This second logistic regression model was performed similarly to the previous model. Missing data were rare (<1% for all variables). P value of <0.05 was the threshold criterion for statistical significance for all tests and models. Analysis was conducted using STATA version 13.1 (StataCorp LP, College Station, TX). Results Overall patient sample Over the 7-y period, 202,668 lobectomies were recorded nationally in the NIS, with 131,030 done open (65%) and 71,638 (35%) done via VATS. Overall number of annual lobectomies decreased from 30,058 in 2008 to 27,340 in 2014 (P < 0.01; Fig. 1 ). At the same time, the proportion of VATS lobectomies increased from 24% to 47%; meanwhile, open lobectomies decreased from 76% of all cases to 53% (P < 0.01). Furthermore, no statistical difference in overall in-hospital mortality for either VATS or open lobectomy was observed for all cases during the study period; however, mortality for VATS lobectomy remained consistently lower than open lobectomy (range 0.41% in 2010 to 1.42% in 2012; Fig. 2 ).Fig. 1 Temporal trend in open versus VATS lobectomy from 2008 to 2014 within the National Inpatient Sample. Figure 1 demonstrates decreased performance of overall lobectomies performed by open technique (n = 22,843 between 2008 and 2009 to n = 15,000 in 2013-2014) with a statistically significant increased utilization of video-assisted thoracoscopic lobectomy (n = 7215 in 2008-2009 to 12,015 in 2013-2014) over the same period from 2008 to 2014 (both P < 0.01). Fig. 2 Temporal trend in mortality between open versus VATS lobectomy from 2008 to 2014 within the National Inpatient Sample. Figure 2 depicts decreasing mortality rates for VATS and open lobectomy between 2008 and 2014; however, the temporal improvement in mortality for each method of resection did not achieve statistical significance (both P > 0.05). Open lobectomy Table 1 describes rates and demographics of patients undergoing open lobectomy. In terms of open lobectomy, rates were highest at small hospitals. Although the median age was statistically different, it was clinically similar (68 [IQR 10.2] versus 67 [IQR 9.9] versus 67 [IQR 9.8] y for small, medium, and large hospitals, respectively; P < 0.01). Patients at large hospitals had higher Charlson comorbidity indices, rates of metastatic cancer, and MI history but lower rates of chronic obstructive pulmonary disease (COPD) compared with smaller hospitals. No differences were found between hospital size with observed rates of in-hospital outcomes (AKI, MI, major bleeding, pulmonary embolism, stroke, and death) except for LOS (median 8.3 versus 8.6 versus 8.6 d for small, medium, and large hospitals, respectively; P < 0.01). Overall, patients at larger hospitals had more costly stays ($33,813 versus $33,538 versus $34,839 for small, medium, and large hospitals, respectively), but after adjustment for inflation, cost was highest at small hospitals (inflation-adjusted cost $31,212 versus $28,093 versus $30,569 for small, medium, and large hospitals, respectively; P < 0.01). However, patients at large hospitals were less likely to be discharged to a nonhome facility (P < 0.01; Table 2 ). Although annual mortality rates for open procedures did not statistically vary over time (1.9%-2.4%), a significant improvement in mortality for small hospitals did occur (P < 0.01; Fig. 3 A).Table 1 Demographics, comorbidities, and hospital factors by hospital size for patients undergoing open lobectomy from 2008 to 2014 in the National Inpatient Sample. Variable Small (n = 11,162) Medium (n = 27,461) Large (n = 92,407) P value Demographics, n (%)  Age (y), median (IQR) 68 (10.2) 67 (9.9) 67 (9.8) <0.01∗  Female 5769 (51.7) 13,824 (50.4) 45,886 (49.7) 0.20  Race 0.77  White 7924 (83.7) 20,402 (84.3) 67,472 (84.2)  Black 786 (8.3) 1891 (7.8) 6314 (7.9)  Hispanic 334 (3.5) 785 (3.2) 3017 (3.8)  Asian or Pacific Islander 197 (2.1) 550 (2.3) 1733 (2.2)  Payer 0.07  Medicare 7069 (63.6) 17,044 (62.2) 57,620 (62.4)  Medicaid 658 (5.9) 1503 (5.5) 5036 (5.5)  Private 3161 (28.4) 7916 (28.9) 26,161 (28.3)  Self-pay 115 (1.0) 336 (1.2) 1481 (1.6)  Median household income quartile per ZIP code 0.04∗  1 2386 (21.7) 7198 (26.8) 23,742 (26.2)  2 3470 (31.6) 7584 (28.2) 24,471 (27.0)  3 2827 (25.7) 6551 (24.3) 22,334 (24.7)  4 2308 (21.0) 5576 (20.7) 19,946 (22.0) Comorbidities, n (%)  Alcohol abuse 450 (4.0) 966 (3.5) 2853 (3.1) 0.03∗  Atrial fibrillation 2157 (19.3) 4971 (18.1) 17,707 (19.2) 0.22  Deficiency anemias 1476 (13.2) 3916 (14.3) 11,878 (12.9) 0.12  Congestive heart failure 598 (5.4) 1283 (4.7) 4063 (4.4) 0.13  Chronic pulmonary disease 5891 (52.8) 14,804 (53.9) 46,836 (50.7) <0.01∗  Coagulopathy 282 (2.5) 956 (3.5) 2575 (2.8) 0.03∗  Diabetes, uncomplicated 1961 (17.6) 5015 (18.3) 16,487 (17.8) 0.71  Dyslipidemia 3961 (35.5) 10,635 (38.7) 34,387 (37.2) 0.12  HTN 6749 (60.5) 16,787 (61.1) 54,877 (59.4) 0.09  Liver disease 155 (1.4) 370 (1.3) 1494 (1.6) 0.32  Fluid and electrolyte disorders 2231 (20.0) 5552 (20.2) 18,346 (19.9) 0.9  Metastatic cancer 2044 (18.3) 5044 (18.4) 18,532 (20.0) 0.04∗  Obesity 962 (8.6) 2320 (8.4) 7593 (8.2) 0.77  Peripheral vascular disorders 877 (7.9) 2699 (9.8) 7862 (8.5) <0.01∗  Psychoses 295 (2.6) 682 (2.5) 2489 (2.7) 0.71  Pulmonary circulation disorders 319 (2.9) 579 (2.1) 2085 (2.3) 0.14  Renal failure 614 (5.5) 1653 (6.0) 4916 (5.3) 0.15  Weight loss 570 (5.1) 1542 (5.6) 4299 (4.7) 6410 (4.9)  Smoking 6233 (55.8) 16,528 (60.2) 53,561 (58.0) 0.03∗  Prior MI 2996 (26.9) 8265 (30.1) 25,316 (27.4) <0.01∗  Prior transient ischemic attack/stroke 2698 (24.2) 6916 (25.2) 21,939 (23.7) 0.11  APRDRG_Risk mortality 2.0 (0.9) 1.9 (0.9) 1.9 (0.9) <0.01∗  APRDRG_Severity 2.2 (0.8) 2.3 (0.8) 2.2 (0.8) <0.01∗  Charlson comorbidity index 4.2 (2.6) 4.2 (2.5) 4.3 (2.6) <0.01∗  Sum of Elixhauser comorbidities 3.8 (1.6) 3.8 (1.6) 3.7 (1.6) <0.01∗ Hospital factors, n (%)  Hospital region <0.01∗  Northeast 1986 (17.8) 4689 (17.1) 16,572 (17.9)  Midwest 4279 (38.3) 7609 (27.7) 23,610 (25.6)  South 3722 (33.4) 10,643 (38.8) 37,454 (40.5)  West 1175 (10.5) 4521 (16.5) 14,772 (16.0)  Control/ownership of hospital  Government or private, collapsed category 5520 (49.5) 13,018 (47.4) 40,244 (43.6) <0.01  Government, nonfederal 527 (4.7) 763 (2.8) 5727 (6.2)  Private, nonprofit 3366 (30.2) 10,240 (37.3) 38, 645 (41.8)  Private, invest own 1725 (15.5) 3404 (12.4) 6225 (6.7)  Location/teaching status of hospital <0.01∗  Rural 56 (0.5) 318 (1.2) 6780 (7.3)  Urban nonteaching 2631 (23.6) 8910 (32.5) 34,646 (37.5)  Urban teaching 8475 (75.9) 18,233 (66.4) 50,981 (55.2) APRDRG = All Patients Refined Diagnosis Related Groups. ∗ P < 0.05. Table 2 Outcomes related to hospital size in patients undergoing open lobectomy in the National Inpatient Sample from 2008 to 2014. Variable Small (n = 11,162) Medium (n = 27,461) Large (n = 92,407) P value In-hospital outcomes, n (%)  Acute MI 153 (1.4) 301 (1.1) 872 (0.9) 0.13  AKI 627 (5.6) 1721 (6.3) 5472 (5.9) 0.51  Major bleed 492 (4.4) 1193 (4.3) 3913 (4.2) 0.89  Pulmonary embolism 104 (0.9) 176 (0.6) 697 (0.8) 0.39  Stroke 80 (0.7) 280 (1.0) 845 (0.9) 0.43  Death 272 (2.4) 679 (2.5) 2020 (2.2) 0.37 Admission characteristics, n (%)  Admission on weekend 89 (0.8) 205 (0.7) 899 (1.0) 0.28  Disposition <0.01∗  Routine 7027 (63.0) 16,679 (60.8) 55,793 (60.4)  Transfer to short-term hospital 94 (0.8) 147 (0.5) 380 (0.4)  Transfer to SNF, ICF, rehabilitation 1481 (13.3) 3492 (12.7) 9808 (10.6)  Home health care 2288 (20.5) 6435 (23.4) 24,285 (26.3)  Elective 10,162 (91.1) 24,819 (90.5) 84,688 (91.5) 0.24  LOS (d) 8.3 (6.8) 8.6 (6.6) 8.6 (7.5) <0.01∗  Cost (USD $, inflation adjusted) 33,813 (31,212) 33,538 (28,093) 34,839 (30,569) <0.01∗ ICF = intermediate care facility; SNF = Skilled nursing facility. ∗ P < 0.05. Fig. 3 Temporal trends in surgical volume and mortality for VATS lobectomy and open lobectomy by hospital size within the National Inpatient Sample from 2008 to 2014. (A) For open lobectomy, in-hospital mortality rates remained stable for medium and large hospitals. A statistical improvement in mortality was observed for small hospitals over time. Volume remained relatively stable. (B) A similar trend for VATS lobectomy was observed with in-hospital mortality rates remaining stable for medium and large hospitals. A statistical improvement in mortality was observed for small hospitals over time. Volume remained relatively stable. In multivariate analysis, hospital size was not a predictor of in-hospital mortality after open lobectomy (Table e3). Acute MI, AIDS, congestive heart failure, COPD, coagulopathy, liver disease, fluid and electrolyte disorders, metastatic cancer, weight loss, and history of paralysis were associated with increased odds of in-hospital mortality, whereas female gender, Medicaid or private insurance, drug abuse, dyslipidemia, hypertension (HTN), hypothyroidism, smoking, and obesity were protective (all P < 0.05). VATS lobectomy Regarding VATS lobectomy, comorbidities between hospital sizes differed in terms of age, history of COPD, chronic deficiency anemias, psychoses, pulmonary circulation disorders, HTN, MI, All Patient Refined Diagnosis-Related Group risk mortality and severity, Charlson Comorbidity Index, and Sum of Elixhauser Comorbidities (all P < 0.05). However, clinically, these differences were minor apart from a 6% higher rate of MI history at medium hospitals compared with small hospitals. Small hospitals were more commonly located in the Northeast compared with the South for medium and large hospitals (P = 0.04) and were least likely to be nonprofit (P = 0.04) and most likely to be urban teaching hospitals despite their size (P < 0.01; Table 3 ). Smaller hospitals compared with medium or large hospitals were associated with higher rates of major bleeding (3.6% versus 3.4% versus 2.6% respectively; P = 0.03), pulmonary embolism (0.9% versus 0.2% versus 0.5% respectively; P = 0.01), and death (2.1% versus 1.4% vs. 1.3% respectively; P < 0.01) but shorter LOS (6 versus 6.4 versus 6.2 d, respectively; P < 0.01) and increased overall and adjusted inflated costs (net difference $8122 between small and large hospitals for adjusted cost; P < 0.01) than in larger hospitals (P < 0.05; Table 4 ). In-hospital mortality rates remained stable for medium and large hospitals. However, again, a statistical improvement in mortality over time was observed for VATS lobectomy at small hospitals (Fig. 3B). In multivariate analysis, hospital size was not a predictor of in-hospital mortality after VATS (Table e4). Female gender, those with dyslipidemia, obesity, and smoking history were protective against in-hospital mortality (all adjusted P value <0.05), whereas fluid or electrolyte disorders, acute MI, AKI, cardiac arrest, development of major bleed, heart block, or stroke were associated with increased odds of in-hospital mortality (all adjusted P value <0.05).Table 3 Demographics, comorbidities, and hospital factors by hospital size for patients undergoing video-assisted thoracoscopic lobectomy from 2008 to 2014 in the National Inpatient Sample. Variable Small (n = 6140) Medium (n = 12,811) Large (n = 52,687) P value Demographics  Age, years, median (IQR) 68.0 (9.9) 67.8 (9.6) 67.8 (9.8) <0.01∗  Female, n (%) 3335 (54.3) 7272 (56.8) 29,443 (55.9) 0.41  Race, n (%)  White 4697 (83.4) 10,113 (83.7) 40,846 (82.6) 0.90  Black 456 (8.1) 941 (7.8) 3796 (7.7)  Hispanic 196 (3.5) 368 (3.0) 1779 (3.6)  Asian or Pacific Islander 180 (3.2) 365 (3.0) 1670 (3.4)  Payer, n (%) 0.14  Medicare 3645 (59.4) 8304 (64.9) 33,018 (62.8)  Medicaid 259 (4.2) 522 (4.1) 2324 (4.4)  Private 2092 (34.1) 3571 (27.9) 15,523 (29.5)  Self-pay 42 (0.7) 140 (1.1) 684 (1.3)  Median household income quartile per ZIP code 0.49  1 1099 (18.3) 2530 (20.1) 10,505 (20.3)  2 1591 (26.5) 2824 (22.4) 12,246 (23.6)  3 1544 (25.7) 3442 (27.3) 13,007 (25.1)  4 1770 (29.5) 3795 (30.1) 16,045 (31.0) Comorbidities, n (%)  Alcohol abuse 156 (2.5) 444 (3.5) 1477 (2.8) 0.16  Atrial fibrillation 981 (16.0) 2101 (16.4) 9166 (17.4) 0.32  Deficiency anemias 626 (10.2) 1552 (12.1) 4866 (9.2) <0.01∗  Congestive heart failure 198 (3.2) 461 (3.6) 1892 (3.6) 0.77  Chronic pulmonary disease 2678 (43.6) 6254 (48.8) 23,252 (44.1) <0.01∗  Coagulopathy 149 (2.4) 321 (2.5) 1040 (2.0) 0.20  Diabetes, uncomplicated 1106 (18.0) 2191 (17.1) 8655 (16.4) 0.34  Dyslipidemia 2373 (38.7) 5038 (39.3) 21,586 (41.0) 0.37  HTN 3527 (57.4) 7929 (61.9) 31,803 (60.4) 0.04∗  Liver disease 143 (2.3) 184 (1.4) 770 (1.5) 0.06  Fluid and electrolyte disorders 804 (13.1) 1855 (14.5) 7343 (13.9) 0.64  Metastatic cancer 744 (12.1) 1747 (13.6) 7436 (14.1) 0.23  Obesity 438 (7.1) 1001 (7.8) 4084 (7.8) 0.79  Peripheral vascular disorders 599 (9.8) 1245 (9.7) 4678 (8.9) 0.35  Psychoses 146 (2.4) 386 (3.0) 1122 (2.1) 0.04∗  Pulmonary circulation disorders 165 (2.7) 180 (1.4) 872 (1.7) 0.01∗  Renal failure 301 (4.9) 647 (5.1) 2766 (5.3) 0.85  Weight loss 186 (3.0) 327 (2.5) 1560 (3.0) 0.55  Smoking 3536 (57.6) 7691 (60.0) 30,551 (58.0) 0.43  Prior MI 1363 (22.2) 3592 (28.0) 12,995 (24.7) <0.01∗  Prior transient ischemic attack/stroke 1404 (22.9) 2918 (22.8) 11,561 (21.9) 0.57  APRDRG_Risk mortality 1.7 (0.8) 1.7 (0.8) 1.8 (0.8) <0.01∗  APRDRG_Severity 2.0 (0.8) 2.1 (0.8) 2.0 (0.8) <0.01∗  Charlson comorbidity index 3.7 (2.2) 3.8 (2.3) 3.8 (2.3) <0.01∗  Sum of Elixhauser comorbidities 3.5 (1.6) 3.5 (1.6) 3.4 (1.5) <0.01∗ Hospital factors, n (%)  Hospital region 0.04∗  Northeast 2330 (38.0) 3422 (26.7) 14,173 (26.9)  Midwest 939 (15.3) 1431 (11.2) 9354 (17.8)  South 1876 (30.6) 5696 (44.5) 20,002 (38.0)  West 994 (16.2) 2263 (17.7) 9159 (17.4)  Control/ownership of hospital 0.04∗  Government or private, collapsed category 2253 (36.7) 4524 (35.3) 19,894 (37.8)  Government, nonfederal 730 (11.9) 430 (3.4) 3835 (7.3)  Private, nonprofit 2713 (44.2) 6647 (51.9) 25,999 (49.4)  Private, invest own 444 (7.2) 1200 (9.4) 2867 (5.4)  Location/teaching status of hospital <0.01∗  Rural - 66 (0.5) 1779 (3.4)  Urban nonteaching 380 (6.2) 3869 (30.2) 14,397 (27.3)  Urban teaching 5755 (93.7) 8878 (69.3) 36,511 (69.3) APRDRG = All Patients Refined Diagnosis Related Groups. ∗ P < 0.05 = n < 11 per NIS guidelines. Table 4 Outcomes related to hospital size in patients undergoing ideo-assisted thoracoscopic lobectomy in the National Inpatient Sample from 2008 to 2014. Variable Small (n = 6140) Medium (n = 12,811) Large (n = 52,687) P value In-hospital outcomes, n (%)  Acute MI 73 (1.2) 85 (0.7) 392 (0.7) 0.2  AKI 244 (4.0) 560 (4.3) 2159 (4.1) 0.79  Major bleed 220 (3.6) 431 (3.4) 1380 (2.6) 0.03∗  Pulmonary embolism 56 (0.9) 30 (0.2) 241 (0.5) 0.01∗  Stroke 29 (0.5) 60 (0.5) 254 (0.5) 0.99  Death 126 (2.1) 177 (1.4) 681 (1.3) 0.23 Admission characteristics, n (%)  Admission on weekend 55 (0.9) 79 (0.7) 419 (0.8) 0.60  Disposition 0.23  Routine 4481 (73.0) 8727 (68.1) 35,728 (67.8)  Transfer to short-term hospital 47 (0.8) 55 (0.4) 132 (0.3)  Transfer to SNF, ICF, rehabilitation 383 (6.2) 1026 (8.0) 3723 (7.1)  Home health care 1097 (17.9) 2817 (22.0) 12,379 (23.5)  Elective 5837 (95.2) 11,925 (93.2) 49,877 (94.8) 0.17  LOS (d) 6.0 (7.2) 6.4 (5.9) 6.2 (5.7) <0.01∗  Cost (USD $, inflation adjusted) 33,314 (33,081) 30,853 (27,680) 29,984 (24,965) <0.01∗ ICF = intermediate care facility; SNF = skilled nursing facility. ∗ P < 0.05. Discussion This study takes an in-depth look at the impact of hospital size on national trends and in-hospital outcomes for open and VATs lobectomy in the United States. It serves as a snapshot from 2008 to 2014, a time of increasing expansion of VATS techniques into community hospitals. CALGB 39802, a prospective trial that established the safety and feasibility of VATS lobectomy, was published in 2007.22 Furthermore, the National Lung Screening Trial was published in 201123 and began to influence the willingness to obtain chest computed tomography scans as well as led to increased nodule identification and a stage shift with diagnosis of earlier stage lung cancer. This study had several important findings: First, it demonstrated a decreased volume of lobectomies being performed in the United States by ∼10%, which we hypothesize may be because of rise in sublobar resection over the same period,24 , 25 which has been shown in other national databases such as that of the Society of Thoracic Surgeons,26 as well as lower incidence of new lung cancers3 and/or in use of radiation therapy.27 Next, we identified an approximately twofold increase in the utilization of VATS for lobectomy (24%-47%) with a drop-in rate of open lobectomies (76%-53%). VATS lobectomy was most commonly performed in the Northeast and South followed by the West in urban teaching centers, which is consistent with early pioneering of this technique in these regions, whereas open lobectomy was more common in the South and Midwest with higher rates outside of teaching hospitals compared with VATS. Finally, we demonstrate an improving survival rate after lobectomy at smaller hospitals for both open and VATS lobectomy during our 7-y study period. This study provides useful benchmark information for thoracic surgeons in national trends not only about the use of minimally invasive surgery but also of outcomes and mortality. However, our results did not show clinically relevant differences in underlying patient demographics or patient comorbidities except for some differences in geographic apart from a slightly higher prevalence of prior MI in those patients at larger hospitals undergoing VATS. In terms of hospital characteristics between open and VATS lobectomy groups, smaller hospitals were more likely to be teaching hospitals compared with medium- or large-sized hospitals, which may contribute to more favorable outcomes over time for smaller hospitals. Meanwhile, geographic distribution did statistically vary by hospital size, but the authors do not believe; this led to clinically meaningful differences in outcomes. Although we did not identify clinically important differences in morbidity or mortality by hospital size, there were slightly higher rates of major bleeding (3.6% versus 2.6%; P = 0.03) and PE (0.9% versus 0.5%; P = 0.01) after VATS lobectomy at smaller hospitals compared with the largest hospital size. Nonetheless, after risk adjustment, our study failed to demonstrate hospital size as an independent risk factor for adverse outcomes, while unsurprisingly, certain comorbidities and development of postoperative complications were associated with increased mortality risk. Interestingly, Medicaid compared with Medicare was associated with lower mortality risk in open cases, which diverges from earlier findings within the NIS28 and may represent Medicaid expansion during the study period, which has been shown to be associated with improved mortality for lung cancer.29 However, the finding that obesity was protective was rather felt to be because of the lack of granularity within the coding of this variable in the database, rather than a true finding. Over the last decade, there has been an increased emphasis on surgeon/hospital volume effects on overall outcomes. Our study may support the prior studies, which suggest surgeon/and or hospital volume may impact outcomes8, 9, 10, 11 rather than hospital size alone or geographic region.30 Furthermore, our study did observe statistically different cost charges for open or for VATS surgery by hospital size (P < 0.01). In the case of open lobectomy, we believe the inflation-adjusted charges were clinically insignificant (cost difference of $643 dollars between large and small hospital size). However, for VATS lobectomy, the net difference in inflation-adjusted cost was $8349 between large and small hospital size, which may suggest despite improving outcomes at smaller hospitals, it may be overall cost-effective to perform VATS lobectomy at larger hospitals. Although we identified an annual improvement in outcomes for both open and VATS lobectomy in smaller hospitals, a similar significant trend was not identified for medium and larger hospitals. We hypothesize these different trends reflect major system-wide changes to smaller hospitals (rather than underlying differences in patient population), with significant advances in patient selection, surgical technique of lobectomies, increased cumulative experience of surgeons at small hospitals, and better perioperative care, as more thoracic surgery has moved out to the community, and with it, academic surgeons.31 The results of our study should be interpreted in light of both its strengths and limitations. The NIS is derived from hospital claims data without access to individual medical records and is subject to the shortcomings of administrative datasets. Inconsistent coding practices among institutions may have resulted in overestimation or underestimation of patient comorbidities and hospital outcomes, although HCUP quality control measures are in place to minimize these discrepancies. Sampling practices of the NIS also vary from year to year, as hospitals enter and leave the sampling frame, resulting in possible over- or under-sampling by study design. Despite our best efforts to use a validated coding scheme, residual confounding and misclassification may exist. Furthermore, we were unable to examine long-term outcomes beyond a single admission, which limited our ability to assess trends and the effect of hospital size on readmissions and aggregate costs after the index hospitalization or long-term cancer-free survival after lobectomy. In addition, charges were inflation adjusted and reported by hospital size but not region, which may account for some differences in aggregate hospital costs. Because of the nature of our database, frailty could not be measured directly. The NIS also does not contain details on patient presentation, cancer pathology, surgeon experience, and details of surgical procedure, which would have been important, as these have previously been identified as risk factors for adverse outcome. However, our dataset may provide more information about real-world practice as opposed to databases, such as the GTSD, which is less generalizable because of the voluntary participation of institutions included in it, making it prone to selection bias.16 , 17 Conclusions In this nationally representative, multi-institutional study based on insurance claims data, we demonstrate that utilization of VATS lobectomies has steadily increased over time, more so among small hospitals compared with large hospitals. Although overall mortality was stable during the study period, mortality was only shown to improve in smaller hospitals. Furthermore, mortality rates for VATS remained consistently lower than for open lobectomy. These findings are hypothesis generating and provide useful data for benchmarking hospital performance in the current era of value-based reimbursement. Author Contributions A.R.D., S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. all participated in inception and design of the study. A.R.D., S.H., R.S., A.A., and M.R. participated in acquisition of data. A.R.D., S.H., R.S., A.A., M.R., and G.N.M. participated in data analysis. A.R.D.,S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. participated in interpretation of data. A.R.D., S.H., R.S., A.A., M.R., S.S., M.T.J., and G.N.M. participated in drafting the article as well as final approval. Meeting Presentation The data have been previously presented at the American College of Surgeons meeting. Disclosure No financial disclosures or conflicts of interest. Supplementary Data Supplemental Material Supplemental Figure Acknowledgments This study was supported in part by the generous donation of the Jack Mitchell Thoracic Oncology Fellowship. The authors wish to acknowledge Cheryl Zogg, PhD, from the Center for Surgery and Public Health at Brigham and Womens Hospital for providing statistical support. ==== Refs References 1 Lung Cancer Facts: 29 Statistics and Facts | LCFA. Lung Cancer Foundation of America Available at: https://lcfamerica.org/lung-cancer-info/lung-cancer-facts/ 2 Cancer of the Lung and Bronchus - Cancer Stat Facts. SEER Available at: https://seer.cancer.gov/statfacts/html/lungb.html 3 USCS Data Visualizations Available at: https://gis.cdc.gov/grasp/USCS/DataViz.html 4 Hartwig M.G. D’Amico T.A. Thoracoscopic lobectomy: the gold standard for early-stage lung cancer? Ann Thorac Surg 89 2010 S2098 S2101 10.1016/j.athoracsur.2010.02.102 20493989 5 Long H. Tan Q. Luo Q. Thoracoscopic surgery versus thoracotomy for lung cancer: short-term outcomes of a randomized trial Ann Thorac Surg 105 2018 386 392 29198623 6 Bendixen M. Jørgensen O.D. Kronborg C. Andersen C. Licht P.B. Postoperative pain and quality of life after lobectomy via video-assisted thoracoscopic surgery or anterolateral thoracotomy for early stage lung cancer: a randomised controlled trial Lancet Oncol 17 2016 836 844 27160473 7 Zhang Z. Zhang Y. Feng H. Is video-assisted thoracic surgery lobectomy better than thoracotomy for early-stage non-small-cell lung cancer? A systematic review and meta-analysis Eur J Cardio-Thoracic Surg 44 2013 407 414 8 Al-Sahaf M. Lim E. The association between surgical volume, survival and quality of care J Thorac Dis 7 2015 S152 S155 25984361 9 Fuchs H.F. Harnsberger C.R. Broderick R.C. Mortality after esophagectomy is heavily impacted by center volume: retrospective analysis of the Nationwide Inpatient Sample Surg Endosc 31 2017 2491 2497 27660245 10 Mamidanna R. Ni Z. Anderson O. Surgeon volume and cancer esophagectomy, gastrectomy, and pancreatectomy: a population-based study in England Ann Surg 263 2016 727 732 26501701 11 Kennedy G.D. Tevis S.E. Kent K.C. Is there a relationship between patient satisfaction and favorable outcomes? Ann Surg 260 2014 592 598 discussion 598-600 25203875 12 Hannan E.L. Radzyner M. Rubin D. Dougherty J. Brennan M.F. The influence of hospital and surgeon volume on in-hospital mortality for colectomy, gastrectomy, and lung lobectomy in patients with cancer Surgery 131 2002 6 15 11812957 13 Lüchtenborg M. Riaz S.P. Coupland V.H. High procedure volume is strongly associated with improved survival after lung cancer surgery J Clin Oncol 31 2013 3141 3146 23897962 14 Falcoz P.-E. Puyraveau M. Rivera C. The impact of hospital and surgeon volume on the 30-day mortality of lung cancer surgery: a nation-based reappraisal J Thorac Cardiovasc Surg 148 2014 841 848 24534677 15 Broderick S.R. Grau-Sepulveda M. Kosinski A.S. The Society of thoracic surgeons composite score rating for pulmonary resection for lung cancer Ann Thorac Surg 109 2020 848 855 31689407 16 Lapar D.J. Stukenborg G.J. Lau C.L. Jones D.R. Kozower B.D. Differences in reported esophageal cancer resection outcomes between national clinical and administrative databases J Thorac Cardiovasc Surg 144 2012 1152 1157 22938777 17 Magee M.J. Wright C.D. McDonald D. Fernandez F.G. Kozower B.D. External validation of the Society of thoracic surgeons general thoracic surgery database Ann Thorac Surg 96 2013 1734 1739 discussion 1738-1739 23998406 18 Healthcare Cost and Utilization Project (HCUP) NIS Notes Available at: https://www.hcup-us.ahrq.gov/db/vars/h_contrl/nisnote.jsp 19 Healthcare Cost and Utilization Project (HCUP) http://www.ahrq.gov/data/hcup/index.html 20 HCUP-US NIS Overview Available at: https://www.hcup-us.ahrq.gov/nisoverview.jsp 21 HCUP Facts and Figures 2008: Statistics on Hospital-Based Care in the United States Available at: https://www.hcup-us.ahrq.gov/reports/factsandfigures/2008/definitions.jsp 22 Swanson S.J. Herndon J.E. D’Amico T.A. Video-assisted thoracic surgery lobectomy: report of CALGB 39802--a prospective, multi-institution feasibility study J Clin Oncol 25 2007 4993 4997 17971599 23 National Lung Screening Trial Research TeamAberle D.R. Adams A.M. Berg C.D. Reduced lung-cancer mortality with low-dose computed tomographic screening N Engl J Med 365 2011 395 409 21714641 24 Fernandez F.G. Kosinski A.S. Burfeind W. STS lung cancer resection risk model: higher quality data and superior outcomes Ann Thorac Surg 102 2016 370 377 27209606 25 Boffa D.J. Allen M.S. Grab J.D. Gaissert H.A. Harpole D.H. Wright C.D. Data from the society of thoracic surgeons general thoracic surgery database: the surgical management of primary lung tumors J Thorac Cardiovasc Surg 135 2008 247 254 18242243 26 Kneuertz P.J. Zhao J. D’Souza D.M. Abdel-Rasoul M. Merritt R.E. National trends and outcomes of segmentectomy in the Society of Thoracic Surgery database Ann Thorac Surg 2021 10.1016/j.athoracsur.2021.07.056 [Epub ahead of print] 27 Wegner R.E. Hasan S. Renz P. Colonias A. Turrisi A.T. Trends in intensity-modulated radiation therapy use for limited-stage small cell lung cancer: a National Cancer Database analysis Appl Rad Oncol 7 2018 26 33 28 LaPar D.J. Bhamidipati C.M. Mery C.M. Primary payer status affects mortality for major surgical operations Ann Surg 252 2010 544 551 20647910 29 Lam M.B. Phelan J. Orav E.J. Jha A.K. Keating N.L. Medicaid expansion and mortality among patients with breast, lung, and colorectal cancer JAMA Netw Open 3 2020 e2024366 33151317 30 Shroyer A.L. Quin J.A. Grau-Sepulveda M.V. Geographic variations in lung cancer lobectomy outcomes: the general thoracic surgery database Ann Thorac Surg 104 2017 1650 1655 28935347 31 Ducko C.T. Bravo-Iñiguez C.E. Jaklitsch M.T. Starfish model: recruiting academic surgeons to provide thoracic surgery in the community setting J Surg Oncol 115 2017 782 783 28464326

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10.1007/s11298-022-3084-5

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==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

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Innov Verwalt. 2022 Dec 15; 44(12):12-15

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==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750716

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):3

latin-1

CME (Berl)

10.1007/s11298-022-3046-y

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750718

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):42-43

latin-1

CME (Berl)

10.1007/s11298-022-3080-9

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750719

NO-CC CODE

2022-12-16 23:24:17

no

Pneumo News. 2022 Dec 15; 14(6):47

latin-1

Pneumo News

10.1007/s15033-022-3437-5

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750720

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):58

latin-1

CME (Berl)

10.1007/s11298-022-3088-1

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750721

NO-CC CODE

2022-12-16 23:24:17

no

HNO Nachr. 2022 Dec 15; 52(6):11

latin-1

HNO Nachr

10.1007/s00060-022-8473-6

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750722

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):62

latin-1

CME (Berl)

10.1007/s11298-022-3064-9

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750723

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):6-7

latin-1

CME (Berl)

10.1007/s11298-022-3074-7

oa_other

==== Front Innov Verwalt Innovative Verwaltung 1618-9876 2192-9068 Springer Fachmedien Wiesbaden Wiesbaden 1471 10.1007/s35114-022-1471-0 Titel Auf die Bindung der Mitarbeitenden kommt es an! Badura Bernhard Prof. Dr. Bernhard Badura studierte Soziologie, Philosophie und Politikwissenschaften in Tübingen, Freiburg, Konstanz und Harvard. Seit März 2008 ist er Emeritus der von ihm mitbegründeten Fakultät für Gesundheitswissenschaften der Universität Bielefeld sowie Geschäftsführer der Salubris UG (haftungsbeschränkt) & Co. KG mit dem Schwerpunkt Gesundheitsmanagement, Führung und Kultur. Munko Tobias Tobias Munko ist Berater für Betriebliches Gesundheitsmanagement bei Salubris und betreut systemische Gesundheitsmanagementprozesse sowohl in der Wirtschaft als auch im öffentlichen Dienst. Zuvor war er als wissenschaftlicher Mitarbeiter an der Fakultät für Gesundheitswissenschaften der Universität Bielefeld tätig. Salubris UG, Bielefeld, Germany 15 12 2022 2022 44 12 1215 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDas Führungsverhalten und die Kultur einer Organisation haben einen wesentlichen Einfluss auf Gesundheit und Energieeinsatz der Beschäftigten. Der Beitrag zeigt die Zusammenhänge und die wichtigsten Stellschrauben auf. Der öffentliche Dienst ist - wie viele andere Arbeitgeber - zunehmend mit einem "gedrehten Arbeitsmarkt" konfrontiert. Talentierte Arbeitssuchende können sich immer häufiger ihren Arbeitgeber aussuchen und nicht, wie bisher, Arbeitgeber ihre Beschäftigten. Ein erheblicher Anteil der knapp fünf Millionen Mitarbeiterinnen und Mitarbeiter des öffentlichen Dienstes wird in den kommenden Jahren in Rente gehen. In vielen Bereichen hat der öffentliche Dienst bereits heute schon Schwierigkeiten bei der Gewinnung neuer Arbeitskräfte: insbesondere in der Bildung, im Gesundheitswesen, auch bei der Polizei und im IT-Bereich. "Es geht um nicht weniger als die Frage, ob der öffentliche Sektor seine Kernaufgaben in Zukunft noch erfüllen kann", so die Aussage von Staatssekretär a. D. Volker Halsch (PWC-Studie 2022). Mangelhafte Attraktivität Das unter anderen vom Bundesinnenministerium unterstützte "Bleibebarometer öffentlicher Dienst" belegt zudem, dass die Attraktivität seiner einzelnen Behörden und Dienststellen entwicklungsbedürftig ist. Bemängelt werden von den knapp 7.000 Befragten insbesondere das Arbeitsklima, die Vereinbarkeit von Beruf und Familie, die Zufriedenheit mit den Vorgesetzten sowie die Entwicklungsmöglichkeiten in der eigenen Organisation (Next:Public 2022, S. 36) - auch ein mangelhaftes oder nicht vorhandenes Betriebliches Gesundheitsmanagement (BMG) (ebd. S. 19). Dabei kann zur Begegnung des drohenden Fachkräftemangels ein bedarfsgerechtes und wirksames BGM sehr wohl einen wichtigen Beitrag leisten: zur besseren Verfügbarkeit der vorhandenen Beschäftigten und zur Stärkung ihrer Bindung an die Ziele, Werte und Aufgaben des öffentlichen Dienstes. Was die Routinedaten der Sozialversicherung sagen Mit Blick auf die Gesundheit der Beschäftigten in Deutschland belegen die Routinedaten der gesetzlichen Unfallversicherung einen starken Rückgang der Arbeitsunfälle auf 806.217 im Jahr 2021 und - besonders erfreulich - einen starken Rückgang der tödlichen Arbeitsunfälle auf 399. Der sehr positiven Bilanz im Bereich der physischen Gesundheit gegenüber steht der starke Anstieg psychisch bedingter Arbeitsunfähigkeit in den Routinedaten der gesetzlichen Krankenversicherung. Die öffentliche Verwaltung nimmt hier, neben den Beschäftigten im Gesundheitswesen, einen Spitzenplatz ein (siehe Abbildung 1 und Abbildung 2). Die Beschäftigten der Bundesverwaltung zum Beispiel liegen bei der Abwesenheitsquote deutlich über den Versicherten der AOK (Jungmann et al. 2021). Der Bundesdurchschnitt aller AOK-Versicherten liegt bei 19 Tagen pro Jahr, der der Beschäftigten eines Bundeslandes bei 36 Tagen! Die Ursachenforschung belegt, dass Arbeitsunfähigkeit insbesondere mit "hausgemachten" Einflüssen zusammenhängt, neben Alter, Geschlecht, Bildungsgrad, jahreszeitlichen und konjunkturellen Einflüssen. Fehlzeiten werden mitbedingt durch:die Qualität der persönlichen Verbundenheit der Mitarbeitenden (verantwortliche Ebenen: Führungskräfte und Mitarbeitende) die Qualität der Ziele und Werte der Organisation ("Kultur") (verantwortlich: oberste Führungsebene) die Qualität der Führungskräfte (mitverantwortlich: oberste Führungsebene) Führungskräfte sehen die Arbeit im Homeoffice kritisch Eigene Analysen bestätigen immer wieder, dass es für die Attraktivität von Arbeit und Organisation eine besondere Rolle spielt, wie sinnhaft, transparent und beeinflussbar die Beschäftigten die Ziele und Aufgaben ihrer Organisation bewerten. Und dass dies keinesfalls in erster Linie in der Verantwortung der einzelnen Mitarbeitenden liegt, sondern zuallererst von der Qualität der vertikalen und horizontalen Beziehungen sowie der Bindekraft der jeweiligen Organisationskultur abhängt. Eine Studie der Universität Köln belegt, dass die Führungskräfte den Wunsch zahlreicher Beschäftigter nach mehr Flexibilität über Zeit und Ort ihrer Arbeit eher skeptisch beurteilen: Führungskräfte wurden als häufigster Grund genannt, weswegen bislang nicht im Homeoffice gearbeitet werden kann. Nur 15 Prozent der Befragten geben an, ihre Vorgesetzten seien geschult darin, ihre Mitarbeitenden im Homeoffice zu unterstützen. Über 60 Prozent glauben, Homeoffice könne ihre Karriere behindern. 75 Prozent behaupten, lange Anwesenheit würde als Zeichen besonderen Engagements gesehen (Neumann et al. 2020). Die damit offenkundige Misstrauenskultur gilt es zu überwinden, wenn es darum geht, den öffentlichen Dienst durch neue Arbeitsformen attraktiver zu machen. Bausteine einer bindungsorientierten Führungslehre Wir plädieren für eine Verwaltungsführung, die sich von zentralen Grundannahmen traditioneller Führungslehren verabschiedet, die wie folgt lauten: 1. Menschen strengen sich nur an, wenn sie laufend kontrolliert werden ("Mikromanagement"). 2. Materielle Vergütung ist der stärkste Motivator guter Arbeit ("Homo oeconomicus"). Wir definieren Mitarbeitendenbindung als Bereitschaft, sich aus eigener Überzeugung voll für die Ziele und Aufgaben ihres Arbeitgebers einzusetzen: kognitiv, emotional und physisch. Wer sich nicht mit der eigenen Arbeit/Organisation identifizieren kann: macht Dienst nach Vorschrift neigt zum Verlassen einer Organisation (Exit) resigniert ("innere Kündigung") arbeitet insgeheim gegen die Organisation fehlt häufiger ("Absentismus") hat ein höheres Risiko für psychische Probleme bei der Arbeit ("Präsentismus"). Innovationsbedarf bei Politik und Staat In einer von 64 Abgeordneten einer Bundestagsfraktion befürworteten Publikation stehen folgende, für eine neue Führungslehre überaus bedenkenswerte, Sätze: "Ohne eine Änderung der Kultur kann es keine grundlegende Reform des Staates geben." "Bei neuen Ideen und Vorschlägen geht der Blick meist nach oben […] was oben ankommt, hat schon fünfzig Prozent seines Innovationspotentials eingebüßt." "Für eine produktive und innovative Arbeitskultur ist diese Angst vor dem Unausgesprochenen jedoch Gift." "Die mittlere Führungsebene könnte dabei eine entscheidende Rolle spielen […] zu oft herrscht Verunsicherung unter der Annahme, dass die Förderung neuer Ideen nicht erwünscht sei" (Heilmann, Schöne 2020, S. 200). Beitrag des Behördlichen Gesundheitsmanagements In entwickelten Gesellschaften rückt neben Bildung die Gesundheit immer mehr ins Zentrum engagierter und innovativer Arbeit. In den zurückliegenden Jahren bekommt die Idee des Betrieblichen Gesundheitsmanagements immer mehr Zuspruch: auf den Führungsetagen der Unternehmen, bei den Gewerkschaften und den Beschäftigten des öffentlichen Dienstes (siehe z. B. Mindeststandards im Behördlichen Gesundheitsmanagement (BGM) der Landesverwaltung Nordrhein-Westfalen). BGM ist jedoch kein Selbstläufer. Ohne anhaltende und verbindliche Unterstützung durch Führungskräfte und ohne Qualifizierung der Expertinnen und Experten werden sich im BGM - wie auch bei anderen innovativen Ansätzen (z. B. dem Qualitätsmanagement) - kaum Erfolge zeigen. Ziel des BGM ist die Verbesserung der Zusammenarbeit - horizontal und vertikal - in Richtung gesunde Organisation, nicht nur das gesunde Verhalten ihrer Mitglieder. Das Interesse am Thema Gesundheit wird nur dann nachhaltig geweckt, wenn das BGM auch der Erreichung von Behördenzielen dient, die durch bessere Kooperation und gute Gesundheit erkennbar gefördert werden. Zur Erreichung dieser Ziele entscheidend sind konsequente Unterstützung durch die Führung und Orientierung an den wissenschaftlichen Grundlagen, insbesondere an folgenden allgemeinen Erkenntnissen:In einer Kopfarbeitenden-Wirtschaft ist die psychische Gesundheit von besonderer Bedeutung, aber auch besonders gefährdet. Die emotionale Bindung der Beschäftigten ist entscheidend für ihre intrinsische Motivation, ihre Gesundheit und ihr Arbeitsverhalten. Die Qualität der tagtäglichen Zusammenarbeit stärkt oder vermindert die emotionale Bindung. Zusammenarbeit wird vor allem anderen geprägt durch die Behördenkultur, ferner durch das Verhalten der Führungskräfte und durch die Beziehungsqualität im Team, für die Beschäftigte Mitverantwortung tragen. Behörden benötigen mehr und kontinuierlichere Information über ihre Mitarbeitenden: ihre Erwartungen, Bedingungen und ihr Wohlbefinden. Über die psychische Gesundheit entscheidet in der Arbeitswelt von heute, was sich an der Mensch-Mensch-Schnittstelle abspielt. Literatur DAK (2022): Psychreport 2021, https://www.dak.de/dak/download/report-2429408.pdf (Abruf am 05.10.2022). Heilmann, T., Schöne, N. (2020): NEUSTAAT. Politik und Staat müssen sich ändern, München. Neumann, J., Lindert, L., Seinsche, L., Zeike, S. J., Pfaff, H. (2020): Homeoffice- und Präsenzkultur im öffentlichen Dienst in Zeiten der Covid-19-Pandemie, Forschungs- oder Projektbericht. Next:Public (2022): Bleibebarometer Öffentlicher Dienst. Eine Befragung zu Bindungsfaktoren in der Verwaltung, https://nextpublic.de/wp-content/uploads/Studie_Bleibebarometer_Oeffentlicher_Dienst.pdf (Abruf am 05.10.2022). PwC-Studie (2022): Fachkräftemangel im öffentlichen Sektor, https://www.pwc.de/de/branchen-und-markte/oeffentlicher-sektor/fachkraeftemangel-im-oeffentlichen-sektor.html (Abruf am 05.10.2022). Jungmann, F., Schlipphak, A., Wegner, A. (2021): Ortsflexibles Arbeiten und krankheitsbedingte Fehlzeiten in der Bundesverwaltung. In: Badura, B. et al. (2020): Fehlzeiten-Report 2021, Betriebliche Prävention stärken - Lehren aus der Pandemie, Wiesbaden S. 801-814. Kompakt Verantwortlich dafür, dass Menschen sich mit ihrer Arbeit und Organisation identifizieren und somit verbunden fühlen, sind: das Gefühl der Zugehörigkeit und Einbindung in ein Kollektiv ("Wir-Gefühl"), das Gefühl, lernen, sich entwickeln und einbringen zu können ("Bildung", "Beteiligung"), die Anerkennung für erbrachte Beiträge und Leistungen ("Selbstwirksamkeit"), weniger Fremd- und mehr Selbstbestimmung ("Selbstorganisation"). Handlungsempfehlungen Datengestützte Organisationsdiagnose für bedarfsgerechte Ableitung von Prioritäten und Maßnahmen; Konkrete Zieldefinition bis hin zur Auswahl quantifizierbarer Zielparameter (Kennzahlen) zur Sicherung ihrer Ergebnisse; Ergebnissicherung als Grundlage für Lernprozesse im Gesundheitsmanagement; Lernprozesse und "Fehlerkultur" etablieren für kontinuierliche Verbesserung der Bedarfsgerechtigkeit, Wirksamkeit und Effizienz. Springer Professional Gesundheitsmanagement Badura, B., Munko, T. (2022): Gesundheitsmanagement: Der Weg zur gesunden Behörde, in: Handbuch Polizeimanagement, Wiesbaden, https://go.sn.pub/UupNVm

0

PMC9750724

NO-CC CODE

2022-12-16 23:24:17

no

CME (Berl). 2022 Dec 15; 19(12):62

latin-1

CME (Berl)

10.1007/s11298-022-3083-6

oa_other

==== Front CME (Berl) CME (Berl) CME (Berlin, Germany) 1614-371X 1614-3744 Springer Medizin Heidelberg 3034 10.1007/s11298-022-3034-2 CME Fortbildung Klimawandel und Allergien Luschkova Daria 1129846071001 Traidl-Hoffmann Claudia 1129846071002 Ludwig Alika 1129846071003 1129846071001 grid.7307.3 0000 0001 2108 9006 Lehrstuhl und Hochschulambulanz für Umweltmedizin, Medizinische Fakultät, Universität Augsburg, Neusässer Straße 47, 86156 Augsburg, Deutschland 1129846071002 grid.419801.5 0000 0000 9312 0220 Umweltmedizin, Universitätsklinikum Augsburg, Stenglinstr. 2, 86156 Augsburg, Deutschland 1129846071003 grid.7307.3 0000 0001 2108 9006 Umweltmedizin, Universität Augsburg, Augsburg, Deutschland 15 12 2022 2022 19 12 6574 © Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Die Klimakrise stellt eine große Herausforderung für die menschliche Gesundheit sowie das Gesundheitssystem dar und droht, den medizinischen Fortschritt der letzten Jahrzehnte zu gefährden. In der Bewältigung des Klimawandels könnte jedoch auch die größte Chance für die globale Gesundheit im 21. Jahrhundert liegen. Schlüsselwörter Planetare Gesundheit Klimawandel Hitze Schadstoffexposition Allergien Pollen Ambrosia Thunderstorm-Asthma COVID-19 Gesundheitssektor issue-copyright-statement© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDie Klimakrise und ihre Folgen, wie steigende Temperaturen, Waldbrände, Überschwemmungen, Dürren, Veränderungen in der Qualität und Quantität von Nahrungsmitteln und des Wassers wirken sich direkt und indirekt auf die physische und psychische Gesundheit des Menschen aus. Intensivere und häufigere Hitzewellen und die abnehmende Luftqualität erhöhen nachweislich die Gesamtmortalität, insbesondere bei den am meisten vulnerablen Personen. Die Klimaerwärmung verändert die bestehenden Ökosysteme und begünstigt biologische Invasionen von Arten, die Wärme und Trockenheit besser tolerieren. Die Erregerprofile verändern sich, die Übertragung und Verbreitung von durch Vektoren übertragene Krankheiten nehmen zu. Durch die Ausbreitung von Neophyten in Europa, wie beispielsweise der Ambrosiapflanze, entstehen neue Pollenquellen, die die Allergenexposition für Allergiker*innen erhöhen. Darüber hinaus verändert die insgesamt mildere Witterung, gerade in Kombination mit der Luftverschmutzung und einem erhöhten CO2-Gehalt, die Produktion und Allergenität von Pollen. Das Phänomen Thunderstorm-Asthma tritt zudem häufiger auf. Angesichts der durch den Klimawandel zunehmenden Prävalenz von allergischen Erkrankungen ist eine frühzeitige kausale immunmodulierende Therapie umso wichtiger. Intensivere und häufigere Hitzewellen und die abnehmende Luftqualität erhöhen nachweislich die Gesamtmortalität Das Phänomen Thunderstorm-Asthma tritt häufiger auf Im Rahmen einer Klimasprechstunde können Patient*innen individuell bezüglich einer Klimaadaption und -resilienz und der Vorteile einer CO2-Reduzierung beraten werden - für die eigene und die planetare Gesundheit. Fast 5% aller Treibhausgasemissionen in Europa stammen aus dem Gesundheitssektor. Er hat damit eine zentrale Verantwortung für eine klimaneutrale und nachhaltige Transformation. Bereits 2015 stellte die Lancet-Kommission für Gesundheit und Klimawandel fest, dass die Bewältigung des Klimawandels die größte Chance für die globale Gesundheit im 21. Jahrhundert sein könnte, und warnte zugleich, dass der Klimawandel alle Fortschritte zerstören könnte, die in den vergangenen Jahrzehnten im Bereich Gesundheit und Entwicklungszusammenarbeit erzielt wurden [1]. Der Generalsekretär der UN, Antonio Guterres, bemerkte 2020, dass menschliche Aktivitäten die Ursache für unseren Abstieg ins Chaos seien. Aber das bedeute auch, dass menschliches Handeln dazu beitragen könne, die Probleme zu lösen [2]. 1. Temperatur- und Schadstoffexposition und die Auswirkung auf die Gesundheit Der Weltklimarat der Vereinten Nationen IPCC (Intergovernmental Panel on Climate Change) hält in seinem Sachstandsbericht von 2021 fest, dass die zunehmende Häufigkeit und Intensität von Hitzeextremen, marinen Hitzewellen und Starkniederschlägen, landwirtschaftlichen und ökologischen Dürren sowie der Anteil heftiger tropischer Wirbelstürme mit der globalen Erwärmung unmittelbar zusammenhängt [3]. Der Juli 2021 war der weltweit heißeste Monat seit Beginn der Aufzeichnungen vor 142 Jahren [4]. Eine Exposition gegenüber Hitze führt neben mentaler wie physischer Leistungsminderung auch zu einer erhöhten Gesamtmortalität [5, 6]. Besonders vulnerabel sind Senioren, Kleinkinder und chronisch Kranke, insbesondere mit pulmonalen oder kardialen Vorerkrankungen, Niereninsuffizienz, Demenz oder Diabetes mellitus [7]. Zudem spielen sozioökologische Faktoren, wie das Leben in dicht bebauten Stadtgebieten, eine Rolle. In Deutschland leben 75% der Bevölkerung in Städten. In diesen kommt es zu einem Wärmeinseleffekt, synergetisch verstärkt durch erhöhte Ozon- und Feinstaubwerte. Der Temperaturanstieg begünstigt Waldbrände, die neben unmittelbaren Todesfällen auch zu posttraumatischen Belastungsstörungen sowie aufgrund der freigesetzten Luftschadstoffe zu erhöhter kardiovaskulärer und respiratorischer Mortalität führen können [6]. Neben Waldbränden sind anthropogene Emissionen für eine abnehmende Luftqualität verantwortlich. Die wichtigsten gesundheitsschädlichen Stoffe nach der WHO (World Health Organization) sind dabei Ozon, Stickstoff- und Schwefeldioxid und Feinstaub [8]. Weitere Luftschadstoffe umfassen Gase wie Benzol, Toluol, Xylol, flüssige Aerosole (Perchlorethylen, Methylenchlorid), inhalierbare an Partikel gebundene Schadstoffe wie -polyzyklische aromatische Kohlenwasserstoffe (PAK), Cadmium, Chrom, Blei und Quecksilber. Die Emissionen haben eine lange Verweildauer in der Atmosphäre und können lange Strecken über Kontinente und Ozeane überwinden. Kumulative Schäden, oxidativer Stress, proinflammatorische und inflammatorische Reaktionen sowie epigenetische Veränderungen sind mögliche Folgen [6]. Eine Ausweitung der Forschung in diesem Bereich ist essenziell [9]. Die wichtigsten gesundheitsschädlichen Stoffe nach der WHO sind dabei Ozon, Stickstoff- und Schwefeldioxid und Feinstaub Durch den Klimawandel nehmen mentale Erkrankungen wie Depressionen oder posttraumatische Belastungsstörungen zu [6], aber auch Herz-Kreislauf- und Stoffwechselerkrankungen, wie zum Beispiel Diabetes mellitus [10], Allergien und insbesondere Vektor-übertragene Infektionen [6, 11]. Faktoren, die den Klimawandel beschleunigen, wie die Vernichtung natürlicher Habitate und von Tier- und Pflanzenarten, erhöhen zudem das Risiko von Pandemien, wie zum Beispiel der aktuellen SARS-CoV-2-Pandemie [12]. Überschwemmungen können unter anderem zu vermehrter Belastung mit Schimmelpilzen und Hausstaubmilben führen und damit sekundär zu Asthma und allergischer Rhinitis [4]. 2. Zunahme allergischer Erkrankungen In den vergangenen Jahrzehnten haben sich allergische Erkrankungen epidemieartig ausgebreitet. Mehr als 128 Millionen Europäer sind betroffen. Vor allem in der jüngeren europäischen Bevölkerung sind Allergien mit über 30% weit verbreitet. Allergien sind durch Umweltfaktoren verursacht oder getriggert und werden in Prävalenz, phänotypischer Ausprägung und Schwere durch den Klimawandel und die Luftverschmutzung verstärkt [9]. Sie führen neben dem Verlust an Lebensqualität zu enormen Schäden sozioökonomischer Art. Luftschadstoffe wie zum Beispiel Ozon, Stickstoffoxide (NOx) und ultrafeine Partikel befeuern Entzündungsprozesse an den Schleimhäuten der oberen und unteren Atemwege. Hinzu kommt, dass Luftschadstoffe und Aeroallergene miteinander interagieren [9]. Die Urbanisierung trägt durch Luftschadstoffe und die Abnahme der Biodiversität auch zur Entstehung und Verschlechterung einer Neurodermitis bei [13]. 3. Änderungen der Aeroallergenexposition Der Klimawandel, die sich dabei einstellende mildere Witterung und die Luftverschmutzung wirken sich auf die Freisetzung und die atmosphärische Verbreitung der Pollen und dadurch auf allergische Erkrankungen aus (Tab. 1). • Verschiebung der Vegetationszonen in nördlichere Regionen • Veränderungen der Pollenflugsaison (Früherer Pollenflugbeginn, Ausdehnung der Pollenflugsaison) • Steigerung der Konzentration der Pollen in der Luft • Einwanderung und Verbreitung von Neophyten: z. B. Ambrosia artemisiifolia, Olivenbäume, Parietaria, Zypresse • Änderung der Pollenallergenität: Veränderungen der Allergenfreisetzung, Modulation des Profils an Begleitsubstanzen aus Pollen (z. B. PALMS, LPS) • Das Phänomen "Gewitterasthma": erhöhte Exposition gegen kleine Allergenfragmente nach Gewitter • Auswirkung auf Zeitspanne und Symptomstärke der Beschwerden bei Allergiker*innen 3.1. Veränderungen der Pollenflugsaison und der Pollenkonzentration in der Umgebungsluft Phänologische Merkmale der Pflanzen wie Blüte und Pollenproduktion reagieren sehr sensibel auf die Veränderungen der Umwelt. Die zunehmende Erwärmung, gerade in Kombination mit dem erhöhten CO2-Gehalt (ein natürlicher Düngeeffekt), verändert die Intensität und den Zeitpunkt der Blüte, bewirkt eine Verschiebung und eine Verlängerung der Vegetationsperiode und eine Vergrößerung der Biomasse. Dies resultiert in einem jahreszeitlich früheren Pollenflugbeginn (Hasel, Erle, Birke), der Ausdehnung der Pollenflugsaison (Spätblüher), dem Auftreten neuer allergener Pflanzen und deren Pollen in Europa (z. B. Ambrosia artemisiifolia, Olivenbäume, Parietaria, Zypresse) sowie in Verbindung mit Luftschadstoffen zu einer Steigerung der Pollenallergenität und Konzentration der Pollen in der Umgebungsluft [6, 9]. Dies wirkt sich sowohl auf die Zeitspanne als auch auf die Symptomstärke der Beschwerden der Allergiker aus. Durch die stärkere Allergenexposition steigt zudem die Möglichkeit einer Sensibilisierung. So blühten die ersten Haselsträucher des Bezugsjahres 2016 im Westen Deutschlands bereits Anfang Dezember. Somit fiel dort der Beginn des meteorologischen Winters mit dem Beginn des phänologischen Vorfrühlings zusammen [14]. Aufgrund der Kreuzreaktionen zu Hasel- und Erlenpollen können Birkenpollenallergiker auch bereits im Dezember Symptome zeigen. Eine möglichst genaue Vorhersage der lokalen Pollenbelastung (Pollenflug, Ferntransport) mithilfe des Pollenmonitorings ist wichtig, damit sich Betroffene rechtzeitig auf die Allergenexposition einstellen können. Dabei ist zu beachten, dass die Veränderungen im Pollenspektrum und der Pollenemission nicht nur von klimatischen Faktoren abhängen, sondern beispielsweise auch durch landwirtschaftliche Aktivitäten sowie Änderungen der Landnutzung und des Mikroklimas infolge zunehmender Urbanisierung beeinflusst werden [15]. Vor allem in der jüngeren europäischen Bevölkerung sind Allergien mit über 30% weit verbreitet 3.2. Neue Pollenquellen in Europa Die Rahmenbedingungen - zunehmend heißere und trockenere Sommer sowie mildere und niederschlagsreichere Winter - führen zu einer Veränderung der bestehenden Ökosysteme, des Artenspektrums und der Verschiebung der Vegetationszonen. Die Abundanz wärmeliebender und Trockenheit tolerierender Arten steigt. Parallel dazu werden kälte-angepasste Arten in nördlichere Regionen und höhere Lagen verdrängt. Die allergenarmen Rückzugsorte für Allergiker*innen, wie zum Beispiel die Alpen, gehen verloren. Zudem wird eine Invasion gebietsfremder Spezies begünstigt. Die Abundanz wärmeliebender und Trockenheit tolerierender Arten steigt Die Anzahl der invasiven Arten Deutschlands geht in die Tausende. Zuvor gebietsfremde Pflanzen, wie zum Beispiel Glaskraut, Olivenbäume und Zypressen, werden in Deutschland heimisch und stellen damit neue Pollenquellen dar [9]. Allergologisch gesehen verdient insbesondere die Ambrosia artemisiifolia (Beifußblättriges Traubenkraut) als Neophyt aus Nordamerika Beachtung. Die klimatischen Veränderungen der letzten Jahrzehnte begünstigen die Einbürgerung und Ausbreitung der gebietsfremden Pflanze. Seit den 1980er-Jahren wird eine rasche Expansion in Mitteleuropa verzeichnet [15]. Die Pflanze breitet sich vor allem an Ruderalstellen, Brachflächen und entlang von Verkehrswegen aus [15]. Das allergene Potenzial dieser Pflanze ist erheblich. Die Blütenstände produzieren mit circa einer Milliarde Pollen eine enorme Pollenmenge, die zudem kleiner als Graspollen sind und größere Distanzen zurücklegen können. Die späte Hauptblühzeit (August bis September) verlängert die Symptome der spezifisch sensibilisierten Personen bis in den Herbst. Bereits geringe Konzentrationen von fünf bis zehn Pollenkörnern pro Kubikmeter Luft reichen aus, um aller-gische und insbesondere asthmatische Symptome auszulösen [11]. Die Prävalenz der Sensibilisierungsrate für Ambrosia artemisiifolia steigt rasch an (aktuell über 8% der deutschen Erwachsenen) [16]. Zudem ist die Kreuzreaktivität zu Beifuß problematisch. Eine Neusensibilisierung gegen Ambrosiapollen ist nämlich oftmals nicht notwendig, um allergische Symptome hervorzurufen [9]. Ein komplettes Zurückdrängen des Neophyts wird inzwischen als nicht mehr realistisch angesehen (Abb. 1). In Studien wurde an Birkenpollen in Regionen mit einem hohen atmosphärischen Ozongehalt eine stärkere Allergenität nachgewiesen Neben Pollen kann die Allergenität auch bei Pilzsporen im Zuge des Klimawandels zunehmen 3.3. Steigerung der Allergenität und der Konzentration der Pollen in der Umgebungsluft Luftschadstoffe und Klimaszenarien wie ein hoher CO2-Gehalt oder Trockenheit wirken als zusätzliche Stressfaktoren auf die Pflanzen. Sie reagieren oftmals mit einer Veränderung der allergenkodierenden Transkripte, Protein- und Metabolitenprofile und steigern die Allergenität ihrer Pollen. Gerade in den urbanen Mikroklimaten oder an stark befahrenen Straßen steigert die schadstoffbelastete Umgebungsluft die Pollenkonzentration und die Allergenität der Pflanzen [18]. In Studien wurde zum Beispiel an Birkenpollen in Regionen mit einem hohen atmosphärischen Ozongehalt eine stärkere Allergenität nachgewiesen. In den Pollenproben waren nicht nur die Menge des Hauptallergens Bet v 1, sondern auch adjuvante Substanzen (wie pollenassoziierte Lipidmediatoren [PALMs], Lipopolysaccharide [LPS], Adenosin) moduliert [19, 20]. Diese wirken sowohl entzündungsfördernd als auch immunmodulierend und können eine Allergie fördern oder verstärken [18]. Sowohl Klimakammer- als auch Feldexperimente zeigen eine erhöhte Allergenität von Ambrosiapollen bei höherer Konzentration an Luftschadstoffen. Ein Anstieg des CO2-Gehaltes und Trockenstress führen zu einer deutlich vermehrten Produktion von Ambrosiapollen. Zudem wird das Ambrosia-Majorallergen Amb a 1 unter solchen Bedingungen verstärkt gebildet [21]. Höhere NO2-Konzentrationen können die Entstehung neuer Allergene in Pollen bewirken [22]. Die Pollen können sich zudem an Feinstaub- und Dieselrußpartikeln anlagern und bei Einatmung in dieser Kombination zu einer stärkeren allergischen Wirkung führen. Neben Pollen kann die Allergenität auch bei Pilzsporen im Zuge des Klimawandels zunehmen. 3.4. Das Phänomen "Gewitterasthma" Das Phänomen "gewitterbedingtes Asthma" oder "Thunderstorm Asthma" bezeichnet das gehäufte Auftreten teilweise schwerer Asthmaanfälle im zeitlichen und räumlichen Umfeld von Gewittern. Bei schweren Gewittern, deren Häufigkeit und Intensität im Zuge des Klimawandels zunehmen wird, und gleichzeitig hoher Pollenbelastung können Asthma-Exazerbationen oder starke Heuschnupfen-Symptomatik auftreten. Als Pathomechanismus wird vermutet, dass Pollen (insbesondere von Gräsern) und Pilzsporen (Alternaria und Cladosporium) im Vorfeld eines Gewitters verstärkt aufgewirbelt werden, durch die elektrostatische Ladung und die atmosphärische Luftfeuchtigkeit osmotisch bedingt aufquellen und bersten. In Pollenkörnern vorhandene zytoplasmatische Komponenten werden in die Umgebungsluft abgegeben. Dadurch entstehende kleinere, lungengängigere Pollenfragmente dringen tiefer in die Bronchien als gewöhnlich vor und können zu akuten Bronchospasmen führen [23, 24] (Abb. 2). Die Expositionssituation für auf das Allergen sensibilisierte Personen verschlechtert sich massiv. Auch Personen mit allergischer Rhinitis können plötzlich schwere Bronchialobstruktionen und Asthmaattacken erleiden [9]. Ein erhebliches Risiko besteht vor allem bei Patienten mit nicht adäquat behandeltem Asthma. Eines der größten gewitterbedingten Asthmaereignisse geschah in Melbourne, Australien. Dabei stieg die Anzahl der Notaufnahmen aufgrund von Atemproblemen nach einem Sturm am 21. November 2016 innerhalb weniger Stunden um 672% an (3.365 Fälle mehr als erwartet). Die Folge waren zahlreiche Behandlungen auf Intensivstationen und insgesamt zehn Todesfälle [23]. Auch in Europa wird das Phänomen "Gewitterasthma" immer häufiger beobachtet [25]. 4. Pollen und SARS-CoV-2-Viren Die Exposition gegenüber Pollen schwächt unabhängig vom Vorliegen einer Allergie die Immunabwehr gegenüber bestimmten Rhinoviren durch Verringerung der Interferonantwort. Dies trifft auch für SARS-CoV-2-Viren zu, wie eine weltweite epidemiologische Studie zeigt [26]. Ein Mund-Nasen-Schutz kann nicht nur die Übertragung von SARS-CoV-2-Viren, sondern auch die Aufnahme von Pollen verringern. Die Autoren empfehlen daher in Pandemiezeiten vor allem bei starkem Pollenflug das Tragen von FFP2-Masken auch im Freien [11, 26]. Ein Mund-Nasen-Schutz kann nicht nur die Übertragung von SARS-CoV-2-Viren, sondern auch die Aufnahme von Pollen verringern 5. Steigerung der Prävalenz des Eichenprozessionsspinners durch den Klimawandel Eichen können die aus allergologischer Sicht bedeutsamen Raupen des Eichenprozessionsspinners (Thaumetopoea processionea) beherbergen [11, 27]. Dieser Nachtfalter ist seit Jahren auf dem Vormarsch in Europa, auch in Deutschland. Die Häufigkeit des Vorkommens kann nur geschätzt werden. Für das menschliche Auge unsichtbare Brennhärchen der Raupen, die weit durch die Luft getragen werden können, lösen toxische und auch IgE-vermittelte Reaktionen aus. Zu den Symptomen gehören Urtikaria und andere Hautreaktionen, Reizung der Atemwege, Konjunktivitis, Atemnot bis hin zum anaphylaktischen Schock. Der Klimawandel begünstigt eine frühere Triebzeit und das Wachstum der Eichen. Aus Sicht des Gesundheitsschutzes sind eine Bekämpfung der Raupen oder Absperrungen rund um befallene Bäume in der Nähe von Kindergärten, Schulen oder Erholungsgebieten nötig. Eichen können die aus allergologischer Sicht bedeutsamen Raupen des Eichenprozessionsspinners beherbergen 6. Neue Nahrungsmittelallergene Der Klimawandel kann zum Beispiel durch Dürren oder Überschwemmungen die Nahrungsmittelproduktion bremsen - bis hin zu Hungersnöten [5]. Daher sind als neue Nahrungsmittel(quellen) beispielsweise Insekten interessant, die als proteinreiche Alternative für tierische Nahrungsmittel den Klimawandel wiederum bremsen können [5]. Die Larven des Mehlkäfers (Tenebrio molitor), Heimchen beziehungsweise Hausgrille (Acheta domesticus) und europäische Wanderheuschrecken (Locusta migratoria) wurden in der Europäischen Union bisher als "Novel Food" zugelassen [28]. Anaphylaktische Reaktionen beim Verzehr von Insekten wurden beobachtet, insbesondere infolge von Kreuzreaktionen zum Beispiel über die auch in anderen Arthropoden enthaltenen hitze- und verdauungsstabilen Panallergene Tropomyosin beziehungsweise Argininkinase bei Hausstaubmilben-, Küchenschaben- und oder Krustentierallergikern. Aber auch zum Beispiel (allergene) Algen, Säugetiere, Pflanzen und Pilze könnten mit Insekten kreuzreagieren [29]. 7. Fazit für den Gesundheitssektor Zahlreiche negative Auswirkungen der Klimakrise auf die Gesundheit sind erkannt. Allerdings stellte der Lancet Countdown on Health and Climate Change 2021 für Deutschland fest, dass in den Bereichen Hitzeschutz, Reduktion des CO2-Fußabdruckes des Gesundheitssektors und Integration des Themas in Aus-, Fort- und Weiterbildung in den letzten zwei Jahren bei der Umsetzung der Empfehlungen des Lancet Policy Briefs für Deutschland von 2019 "wesentliche Fortschritte jedoch ausblieben" [30]. Angesichts der durch den Klimawandel zunehmenden Häufigkeit von Atemwegsallergien ist eine frühzeitige kausale immunmodulierende Therapie umso wichtiger. Hierfür steht die AIT (allergenspezifische Immuntherapie, Hyposensibilisierung) in subkutaner oder sublingualer Form zur Verfügung und ist besonders mit Gräser- und Birkenpollen bei allergischer Rhinitis und/oder Konjunktivitis sehr erfolgversprechend, aber auch effektiv bei allergischem Asthma bronchiale. Allerdings gibt es für die AIT bereits jetzt eine erhebliche Unterversorgung der Betroffenen in Deutschland [31]. Dies hat vielfältige Ursachen, denen entgegengewirkt werden sollte (Tab. 3). Es besteht hoher Forschungsbedarf im Bereich der Immunologie und Allergologie. Beispielsweise könnten neue Erkenntnisse über die Wirkungsweise der AIT, deren Langzeiteffekte sowie geeignete Biomarker für die Auswahl der zu Behandelnden und auch für die Prognose des Therapieerfolges die Versorgung der Patient*innen effektiver und kosteneffizienter machen. Die Pathomechanismen und molekularen Prozesse der Allergieentwicklung sind noch teilweise ungeklärt [31] (Tab. 2).• Nationaler Aktionsplan Allergie [37] • Förderung der Forschung auf allergologischem Gebiet • Allergologie als verpflichtender Inhalt in Aus- und Weiterbildung aller Gesundheitsberufe und für erziehendes und lehrendes Personal • Schaffung von separaten und unabhängigen, vollwertigen Lehrstühlen für Allergologie und für Umweltmedizin • finanzielle Unterstützung von Informationskampagnen für die Bevölkerung z. B. zur allergenspezifischen Immuntherapie (AIT) • Erstattung der Pharmakotherapie der allergischen Rhinitis durch die gesetzliche Krankenversicherung • Erhalt der Verordnungs- und Erstattungsfähigkeit auch seltener Allergene für die AIT • Finanzielle Förderung der Standardisierung von Präparaten für AIT und Hauttestung • Sicherung der Finanzierung für Entwicklung, klinische Prüfung und Herstellung von Allergiediagnostika • Erstattung der Patientenschulungen für Neurodermitis, Anaphylaxie und Asthma durch die gesetzliche Krankenversicherung • Vorschreiben von Vorsorgeuntersuchungen bzw. Berufseingangsberatungen für Berufe mit besonderer Gefährdung [31, 38] Primärprävention • Förderung von Grundlagen-, angewandter oder klinischer Forschung im Bereich der Immunologie und Allergologie: Allergenforschung, Erforschung von Sensibilisierungswegen, Entstehung und Chronifizierung der allergischen Erkrankungen • Erforschung protektiver Umweltfaktoren in Hinsicht auf Allergien (Biodiversität, traditioneller Lebensstil) • Erforschung der Barrierefunktion und des Mikrobioms der Haut, des Darmes und der Atemwege • Bedeutung psychosozialer Faktoren auf Entstehung und Manifestation allergischer Erkrankungen • Veränderung atmosphärischer Zirkulationsmuster: Auswirkung auf Quantifizierung der Pollenkonzentration • Langzeittrends der Ambrosia-Allergie in Deutschland Sekundärprävention • Förderung von Studien zu Interaktion zwischen Exposom, Risiken von Umwelteinflüssen und allergischen Erkrankungen, bessere Verbreitung dieser Informationen • Verbesserung der molekularen Diagnostik, Entwicklung neuer Allergentherapeutika • Biomarker zur Auswahl geeigneter Patienten und Prognoseermittlung für die SIT • Ansprechen von Systemtherapien unter verschiedenen Umweltfaktoren wie Hitze, UV-Strahlung, Pollenexposition • Auswirkungen des Mikrobioms auf allergische Erkrankungen • Individuelles Pollenmonitoring • Personalisierte Frühwarnsysteme (z. B. Gewitter für Pollenallergiker) Eine gesetzliche Verankerung von gesundheitsbezogenem Hitzeschutz ist Voraussetzung, um Hitzeaktionspläne zu priorisieren [30]. Diese sollten auch Handlungsszenarien für außergewöhnlich extreme und komplexe Situationen beinhalten [32]. Durch entsprechende Stadtplanung, zum Beispiel dem Ausbau von Parks, Straßenbäumen und Dachbegrünung, kann dem Hitzeinseleffekt entgegengewirkt werden [11, 32]. Dabei ist es wichtig, von vornherein interdisziplinär und sektorübergreifend zusammenzuarbeiten unter Einbeziehung von unter anderem Stadtplanern, Architekten, Verkehrsexperten und Angehörigen der Gesundheitsberufe [33]. Gerade letztere wurden bisher vielfach nicht einbezogen. So erklärt sich etwa, dass am Potsdamer Platz in Berlin ausgerechnet Birkenalleen angelegt wurden. Die Bepflanzung sollte sich also (auch) nach dem Allergierisiko richten [11]. Eine Beratung der Patienten zu individueller Klimaadaptation und -resilienz kann zum Beispiel im Rahmen einer Klimasprechstunde erfolgen Eine Beratung der Patienten zu individueller Klimaadaptation und -resilienz kann zum Beispiel im Rahmen einer Klimasprechstunde erfolgen [11]. Lehrinhalte zum Thema Klimawandel und Gesundheit sowie zur planetaren Gesundheit ("Planetary Health") und Allergologie sollten in den Pflichtcurricula, Fort- und Weiter-bildungen aller Gesundheitsberufe verankert werden [32, 34]. Das Gesundheitswesen trägt mit 5,2% zu den deutschen Treibgasemissionen bei. Die WHO-Initiative "Healthy Hospitals, Healthy Planet, Healthy People" [35] unterscheidet sieben Bereiche für Klimaschutz im Krankenhaus: Energieeffizenz, Baudesign, alternative Energien, Verkehr (Anfahrt der Mitarbeiter*innen und Patient*innen), Essen, Abfall und Wasser. Klimaschutzmaßnahmen lassen sich auch in Praxen, Ärztekammern, auf Ärztetagen et cetera umsetzen. Hierzu gibt es bisher weder auf Landes-, Bundes- noch auf europäischer Ebene -Gesetzesvorschläge [32]. Der 125. Deutsche Ärztetag 2021 forderte Klimaneutralität im deutschen Gesundheitswesen bis zum Jahr 2030. Als Voraussetzungen wurden die Initiierung der erforderlichen rechtlichen Rahmenbedingungen, die Benennung von Klimabeauftragten und die Verabschiedung von Klimaschutzplänen genannt. In Deutschland existieren bereits einige Initiativen wie das Gütesiegel "Energiesparendes Krankenhaus", das Projekt KLIKgreen, das Fachkräfte in Kliniken zu Klimamanager*innen ausbildet, oder die Green Hospital Initiative Bayern. Wichtig sind Bereitstellung von Fördermitteln durch die Bundes- und Landesregierungen, Identifikation und Abbau rechtlicher Barrieren sowie Fortschrittskontrolle durch Bilanzierung und Reporting der Treibgasemissionen in den beteiligten Einrichtungen [32]. Das Gesundheitswesen trägt mit 5,2% zu den deutschen Treibhausgas-emissionen bei Im Sinne der Planetary Health ist die Erde ein medizinischer Notfall [36]. Es liegt an uns Leser*innen des CME-Journals, aufzustehen, aufzuklären, Bewusstsein zu schaffen und gerechte Lösungen für Klima und Gesundheitspolitik zu fordern [4]. Ärzt*innen können hierbei den Vertrauensvorschuss in der Bevölkerung nutzen, um Impulsgeber und Vorbilder zu sein. Die Entscheidungen, die wir heute treffen, einschließlich unseres individuellen Handelns oder Nichthandelns werden sich auch auf zukünftige Generationen auswirken. Wir haben die Verantwortung, die Auswirkungen des Klimawandels auf die öffentliche Gesundheit zu verringern. Herausgeber der Rubrik CME Zertifizierte Fortbildung: Prof. Dr. med. J. Bogner, München, Prof. Dr. med. H.J. Heppner, Schwelm, Prof. Dr. med. K. Parhofer, München Zitierweise: Luschkova D, Traidl-Hoffmann C, Ludwig A. Climate change and allergies. Allergo J Int 2022;31:114-20 https://doi.org/10.1007/s40629-022-00212-x Abkürzungen AIT allergenspezifische Immuntherapie LPS Lipopolysaccharide NOx Stickstoffoxide PAK polyzyklische aromatische Kohlenwasserstoffe PALM Pollenassoziierte Lipidmediator WHO World Health Organization Korrespondenzadresse Daria Luschkova Lehrstuhl und Hochschulambulanz für Umweltmedizin Medizinische Fakultät der Universität Augsburg Stenglinstraße 2 86156 Augsburg Deutschland [email protected] CME-Fragebogen Klimawandel und Allergien Teilnehmen und Punkte sammeln können Sie als e.Med-Abonnent*in von SpringerMedizin.de als registrierte*r Abonnent*in dieser Fachzeitschrift als Mitglied der Hausärztlich tätigen Internisten des Berufsverbandes Deutscher Internistinnen und Internisten e.V. Dieser CME-Kurs ist auf SpringerMedizin.de/CME zwölf Monate verfügbar. Sie finden ihn, wenn Sie den Titel in das Suchfeld eingeben. Alternativ können Sie auch mit der Option "Kurse nach Zeitschriften" zum Ziel navigieren oder den QR-Code links scannen. Dieser CME-Kurs wurde von der Bayerischen Landesärztekammer mit zwei Punkten in der Kategorie I (tutoriell unterstützte Online- Maßnahme) zur zertifizierten Fortbildung freigegeben und ist damit auch für andere Ärztekammern anerkennungsfähig. Für eine erfolgreiche Teilnahme müssen 70% der Fragen richtig beantwortet werden. Pro Frage ist jeweils nur eine Antwortmöglichkeit zutreffend. Bitte beachten Sie, dass Fragen wie auch Antwortoptionen online abweichend vom Heft in zufälliger Reihenfolge ausgespielt werden. Bei inhaltlichen Fragen erhalten Sie beim Kurs auf SpringerMedizin.de/CME tutorielle Unterstützung. Bei technischen Problemen erreichen Sie unseren Kundenservice kostenfrei unter der Nummer 0800 7780777 oder per Mail unter [email protected]. Welche der folgenden Aussagen rund um den Klimawandel ist richtig? Die zunehmende Häufigkeit und Intensität von Hitzeextremen, Starkniederschlägen, Dürren sowie der Anteil heftiger tropischer Wirbelstürme steht nicht im Zusammenhang mit der globalen Erwärmung. Die wichtigsten gesundheitsschädlichen Stoffe nach der WHO sind Ozon, Stickstoffoxid, Schwefeldioxid und Feinstaub. Viele anthropogene Emissionen von Schadstoffen haben zwar eine lange Verweildauer in der Atmosphäre, können aber keine langen Strecken über Kontinente und Ozeane überwinden. Die Häufigkeit von Erkrankungen wie Depressionen oder Diabetes mellitus wird durch den Klimawandel nicht beeinflusst. Die Vernichtung natürlicher Habitate sowie von Tier- und Pflanzenarten beschleunigt den Klimawandel, erhöht aber nicht das Risiko von Pandemien (wie zum Beispiel die durch SARS-CoV-2 verursachte Pandemie). Welche Personen gehören nicht zu den für Hitzewellen besonders vulnerablen Gruppen? Kleinkinder Demenzkranke Diabetiker Jugendliche Patienten mit Niereninsuffizienz Welche Aussage zum Klimawandel und dessen Auswirkung auf Pollen ist falsch? Der Klimawandel kann zu einem jahreszeitlich früheren Pollenflugbeginn und einer Ausdehnung der Pollenflugsaison führen. Der Klimawandel bewirkt ein Auftreten neuer allergener Pflanzen (z. B. Ambrosia artemisiifolia, Olivenbäume, Parietaria, Zypresse) und deren Pollen in Europa. Der Klimawandel kann eine Änderung der Pollenallergenität bewirken (Steigerung der Pollenallergenität, Modulation des Profils an Begleitsubstanzen aus Pollen [z. B. -pollenassoziierte Lipidmediatoren, Lipopolysaccharide]) und eine Veränderung der Allergenfreisetzung. Der Klimawandel kann sowohl die Zeitspanne als auch die Symptomstärke der Beschwerden der Allergiker beeinflussen. Aufgrund von Kreuzreaktionen mit Hasel- und Erlenpollen können Birkenpollenallergiker auch bereits im Juni Symptome zeigen. Welche Aussage zu Ambrosia artemisiifolia trifft zu? Das allergene Potenzial dieser Pflanze ist sehr gering, Konzentrationen von fünf bis zehn Pollenkörnern pro Kubikmeter Luft können allergische Symptome auslösen. Die Hauptblühzeit der Pflanze ist im Frühsommer und die Pollen sind genauso groß wie die Graspollen. Eine Neusensibilisierung gegen Ambrosiapollen ist wegen einer Kreuzreaktivität zu Lieschgras (Phl p 2) oftmals nicht notwendig, um allergische Symptome hervorzurufen. Die klimatischen Veränderungen der letzten Jahrzehnte begünstigen die Einbürgerung und Ausbreitung der gebietsfremden Pflanze - ein komplettes Zurückdrängen des Neophyten wird inzwischen als nicht mehr realistisch angesehen. Bei höheren atmosphärischen CO2- Gehalten wurde eine verringerte Pollenbildung der Ambrosia artemisiifolia nachgewiesen. Welche Aussage zu Klimawandel und Temperaturveränderungen ist richtig? Durch schwere Regenfällen, deren Häufigkeit und Intensität im Zuge des Klimawandels zunehmen wird, und gleichzeitig hoher Pollenbelastung können Asthma-Exazerbationen oder starke Heuschnupfen-Symptomatik auftreten (gewitterbedingtes Asthma). Die Populationsstärke, die geografische Reichweite, das Übertragungspotenzial vieler infektiöser Erreger auf den Menschen und die Vektorökologie werden durch den Klimawandel beeinflusst. Die Abundanz kälteliebender Arten kann bei uns durch den Klimawandel zunehmen. Der Temperaturanstieg bedingt eine Abnahme der weltweiten Gesamtbelastung durch Malaria. In Studien wurde an Birkenpollen in Regionen mit einem niedrigen atmosphärischen Ozongehalt und mit Luftverschmutzung, wie in den urbanen Mikroklimaten, eine stärkere Allergenität nachgewiesen. Personen, die Medikamente einnehmen, die sich auf den Elektrolythaushalt auswirken (wie -Diuretika, Psychopharmaka), haben ein höheres Risiko, während einer Hitzewelle zu sterben. Welche der folgenden Aussagen zu Pollen ist richtig? Die Pollenexposition schwächt die Immunabwehr gegenüber bestimmten Rhinoviren nur bei Allergikern. Es gibt in Studien keine Hinweise, dass eine gleichzeitige Pollenexposition die Immunabwehr gegen SARS-CoV-2-Viren schwächen kann. Das Tragen von FFP2-Masken kann vor der Exposition gegenüber Viren schützen, aber nicht gegenüber Pollen. Pollen können die Immunabwehr der Schleimhäute des Atemtraktes durch eine Verringerung der Interferon- Antwort beeinträchtigen. Die meisten ganzen Pollenkörner haben einen Durchmesser von < 30 µm. Welche Antwort zum Eichenprozessionsspinner ist richtig? Die Brennhärchen der Raupen können nur bei bereits sensibilisierten Personen Symptome an Haut, Augen oder Atemwegen auslösen. Die Brennhärchen der Raupen können weit durch die Luft getragen werden. Die Brennhärchen der Raupen sind für das menschliche Auge gerade noch sichtbar. Die Brennhärchen der Raupen können Symptome an Haut, Augen, Nase und Bronchien auslösen, aber keinen anaphylaktischen Schock. Wenn die Raupen als Prozession ihr Nest verlassen haben, ist es ungefährlich, sich diesem zu nähern. Welche Aussage zu Klimawandel und zur Nahrungsmittelallergien ist richtig? Der Verzehr durcherhitzter Insekten(bestandteile) kann keine allergischen Allgemeinreaktionen auslösen, weil die auslösenden Allergene wie zum Beispiel Tropomyosin oder Argininkinase hitzelabil sind. Patienten mit Allergien/Sensibilisierungen gegenüber Hausstaubmilben, Küchenschaben oder Krustentieren können bereits beim ersten Verzehr von Insekten(-bestandteilen) mit -allergischen Symptomen reagieren. Ein vermehrter Verzehr von Insekten (-bestandteilen) als proteinreiche Alternative zu Fleisch, Fisch, Milch oder Eiern hätte keinen Einfluss auf den Klimawandel. Kreuzsensibilisierungen mit Insekten konnten nur mit anderen Arthropoden wie Hausstaubmilben, Küchenschaben und Krustentieren, aber nicht mit Säugetieren, Algen, Pflanzen oder Pilzen nachgewiesen werden. In der EU wurden bisher keine Insekten(-bestandteile) über die Novel-Food-Verordnung zugelassen. Welche Aussage zum Einfluss des Gesundheitswesens auf Klimawandel und Gesundheit ist falsch? Deutschland hat nach dem Lancet Countdown on Health and Climate Change 2021 in den letzten zwei Jahren wesentliche Fortschritte gemacht, die von dort 2019 gegebenen Empfehlungen zur Reduktion des CO2-Fußabdruckes des Gesundheitssektors und zum -Hitzeschutz umzusetzen. Das Gesundheitswesen verursacht 5,2% der Treibgasemissionen in Deutschland. Eine WHO-Initiative unterscheidet beim Klimaschutz im Krankenhaus die sieben Bereiche Baudesign, Energieeffizienz, alternative Energien, Verkehr, Essen, Wasser und Abfall. Um die vom Deutschen Ärztetag 2021 geforderte Klimaneutralität des deutschen Gesundheitswesens bis 2030 zu erreichen, müssen unter anderem die rechtlichen Rahmen-bedingungen geschaffen, Klimabeauftragte in den einzelnen Einrichtungen ernannt und -Klimaschutzpläne verabschiedet werden. Die ärztliche Beratung im Rahmen von "Klimasprechstunden" kann Patienten helfen, Klimaresilienz zu entwickeln. Dies, sowie auf synergistische Effekte von gesundem und klimafreundlichem Verhalten (z. B. bei Fortbewegung, Ernährung) hinzuweisen, sollte aber auch in die normalen Sprechstunden mit einfließen. Zur Anpassung an den Klimawandel besteht im Bereich Allergologie und Umweltmedizin -besonderer Forschungsbedarf und es sind Forderungen an den Gesetzgeber zu stellen. Welche Antwort hierzu ist falsch? Entwicklung personalisierter Frühwarnsysteme für Pollenallergiker, zum Beispiel vor Gewitter Erforschung von protektiven Umweltfaktoren im Hinblick auf die Allergieentwicklung wie Biodiversität, "Bauernhofeffekt" Entwicklung von Biomarkern zur Auswahl besonders geeigneter Patienten für die spezifische Immuntherapie mit Allergenextrakten. Umsetzung/Neuauflage des "Nationalen Aktionsplan Allergie" in Deutschland. Erstattung der Bioresonanzmethode für die Diagnostik und Therapie von pollenassoziierten Nahrungsmittelallergien durch die gesetzliche Krankenversicherung. Interessenkonflikt Die Autorinnen geben an, dass keine Interessenkonflikte bestehen. Der Verlag erklärt, dass die inhaltliche Qualität des Beitrags durch zwei unabhängige Gutachten geprüft wurde. Werbung in dieser Zeitschriftenausgabe hat keinen Bezug zur CME-Fortbildung. Der Verlag garantiert, dass die CME-Fortbildung sowie die CME-Fragen frei sind von werblichen Aussagen und keinerlei Produktempfehlungen enthalten. Dies gilt insbesondere für Präparate, die zur Therapie des dargestellten Krankheitsbildes geeignet sind. ==== Refs Literatur 1. Watts N, Adger WN, Agnolucci P, Blackstock J, Byass P, Cai W et al. Health and climate change: policy responses to protect public health. Lancet 2015;386:1861-914 2. Harvey F. Humanity is waging war on nature. The Guardian, 2020 3. IPCC-Sachstandsbericht (AR6. Beitrag von Arbeitsgruppe I: Naturwissenschaftliche Grundlagen). 2021; ; accessed 22.1.2022 4. Pacheco SE, Guidos-Fogelbach G, Annesi-Maesano I, Pawankar R, D' Amato G, Latour-Staffeld P et al. Climate change and global issues in allergy and immunology. J Allergy Clin Immunol 2021;148:1366-77 5. Traidl-Hoffmann C. Klimaresilienz- Weg der Zukunft. Dtsch Arztebl 2020;117:B1332-1334 6. Luschkova D, Ludwig A, Traidl-Hoffmann C. Klimakrise und deren Auswirkungen auf die menschliche Gesundheit. Dtsch Med Wochenschr 2021;146:1636-41 7. Klauber H, Koch N. Individuelle und regionale Risikofaktoren für hitzebedingte Hospitalisierungen der über 65-Jährigen in Deutschland. In: Günster C et al. (eds.), Versorgungs-Report: Klima und Gesundheit. Berlin: Medizinisch Wissenschaftliche Verlagsgesellschaft; 2021 8. Agache I, Sampath V, Aguilera J, Akdis CA, Akdis M, Barry M et al. Climate Change and Global Health: A Call to more Research and more Action. Allergy 2022;77:1389-1407 9. Traidl-Hoffmann, C., Allergologie. In: Traidl-Hoffmann, C., Schulz, C. Herrmann, M. Simon, B. (Hrsg.). Planetary Health. Klima, Umwelt und Gesundheit im Anthropozän. Berlin: Medizinisch Wissenschaftliche Verlagsgesellschaft; 2021. p. 52-59. 10. Münzel T, Hahad O, Sørensen M, Lelieveld J, Duerr GD, Nieuwenhuijsen M et al., Environmental risk factors and cardiovascular diseases: a comprehensive expert review. Cardiovasc Res 2021;cvab316; 11. Traidl-Hoffmann C, Trippel K. Überhitzt. Dudenverlag; 2021 12. Atwoli L, Baqui AH, Benfield T, Bosurgi R, Godlee F, Hancocks S et al. Call for emergency action to limit global temperature increases, restore biodiversity and protect health: Wealthy nations must do much more, much faster. Allergy 2022;77:730-3 13. Luschkova D, Zeiser K, Ludwig A, Traidl-Hoffmann C. Neurodermitis ist eine Umwelterkrankung. Allergologie 2021;44:681-8 14. Endler C. Die Pollenflugvorhersage vom Deutschen Wetterdienst (DWD). Phänologie-Journal, Nr. 48; Juli 2017 15. Eis D, Helm D, Laußmann D, Stark K. Gesundheitliche Auswirkungen des Klimawandels durch UV-, Allergen- und Schadstoff-Exposition. In: Klimawandel und -Gesundheit-Ein Sachstandsbericht. 2011: Robert Koch-Institut 16. Thamm R, Hey I, Thamm M. Epidemiologie allergischer Erkrankungen: Prävalenzen und Trends in Deutschland. In: Klimek L, Vogelberg C, Werfel T. (Hrsg.). Weißbuch Allergie in Deutschland. München: Springer Medizin Verlag; 2018 17. Rasmussen K, Thyrring J, Muscarella R, Borchsenius F. Climate-change-induced range shifts of three allergenic ragweeds (Ambrosia L.) in Europe and their potential impact on human health. PeerJ 2017;5:e3104 18. Rauer D, Gilles S, Wimmer M, Frank U, Mueller C, Musiol S et al. Ragweed plants grown under elevated CO2 levels produce pollen which elicit stronger allergic lung -inflammation. Allergy 2021;76:1718-30 19. Beck I, Jochner S, Gilles S, McIntyre M, Buters JTM, Schmidt-Weber C et al. High environmental ozone levels lead to enhanced allergenicity of birch pollen. PloS One 2013;8:e80147 20. Gilles S, Fekete A, Zhang X, Beck I, Blume C, Ring J et al. Pollen metabolome analysis reveals adenosine as a major regulator of dendritic cell-primed T helper cell responses. J Allergy Clin Immunol 2011;127:454-61.e1-9 21. El Kelish A, Zhao F, Heller W, Durner J, Winkler JB, Behrendt H et al. Ragweed (Ambrosia artemisiifolia) pollen allergenicity: SuperSAGE transcriptomic analysis upon elevated CO2 and drought stress. BMC Plant Biol 2014;14:176 22. Zhao F, Elkelish A, Durner J, Lindermayr C, Winkler JB, Ruёff F et al. Common ragweed (Ambrosia artemisiifolia L.): allergenicity and molecular characterization of pollen after plant exposure to elevated NO2. Plant Cell Environ 2016;39:147-64 23. Chatelier J, Chan S, Tan JA, Stewart AG, Douglass JA. Managing Exacerbations in Thunderstorm Asthma: Current Insights. J Inflamm Res 2021;14:4537-50 24. D'Amato G, Annesi-Maesano I, Cecchi L, D'Amato M. Latest news on relationship between thunderstorms and respiratory allergy, severe asthma, and deaths for asthma. Allergy 2019; 4:9-11 25. Damialis A, Bayr D, Leier-Wirtz V, Kolek F, Plaza M, Kaschuba S et al. Thunderstorm Asthma: in search for relationships with airborne pollen and fungal spores from 23 sites in Bavaria, Germany. A rare incident or a common threat? J Allergy Clin Immunol 2020;145:AB336 26. Damialis A, Gilles S, Sofiev M, Sofieva V, Kolek F, Bayr D et al. Higher airborne pollen concentrations correlated with increased SARS-CoV-2 infection rates, as evidenced from 31 countries across the globe. Proc Natl Acad Sci U S A 2021;118:e2019034118 27. Rahlenbeck S, Utikal J. Eichenprozessionsspinner-Allergie: Raupen mit reizenden Brennhaaren. Dtsch Arztebl 2017;114:A-896 28. Europäische Kommission. Hausgrille: Kommission lässt drittes Insekt als Lebensmittelzutat für den EU-Markt zu. ; accessed 28.3.2022 29. de Gier S, Verhoeckx K. Insect (food) allergy and allergens. Mol Immunol 2018;100:82-106 30. Romanello M, McGushin A, Di Napoli C, Drummond P, Hughes N, Jamart L et al. The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. Lancet 2021;398:1619-62 31. Ludwig A, Bayr D, Pawlitzki M, Traidl-Hoffmann C. Der Einfluss des Klimawandels auf die Allergenexposition: Herausforderungen für die Versorgung von allergischen Erkrankungen. In: Günstner C, Klauber J, Robra BP, Schmuker C, Schneider A, eds. Versorgungs-Report Klima und Gesundheit. Berlin: Med. Wiss. Verlagsgesellsch; 2021. p. 133-43 32. Matthies-Wiesler F, Herrmann M, Schulz C, Gebb S, Jung L, Schneider A et al. The Lancet Countdown on Health and Climate Change. Policy Brief für Deutschland 2021. ; accessed 22.1.2022 33. Matthies-Wiesler F, Herrmann M, Von Philipsborn P, Wabnitz K, Geffert K, Schneider A et al. The Lancet Countdown on Health and Climate Change. Policy Brief für Deutschland 2020. ; accessed 22.1.2022 34. Lauletta M, Moisé E, La Grutta S, Cilluffo G, Piacentini G, Ferrante G et al. Climate advocacy among Italian pediatric pulmonologists: A national survey on the effects of climate change on respiratory allergies. Pediatr Pulmonol 2022;57:862-70 35. World Health Organization. Healthy Hospitals, Healthy Planet, Healthy People. Addressing climate change in health care settings. WHO Discussion Draft. ; accessed 22.1.2022 36. Traidl-Hoffmann C, Schulz C, Herrmann M, Simon B. Planetary Health. Klima, Umwelt und Gesundheit im Anthropozän. Berlin: Medizinisch Wissenschaftliche Verlagsgesellschaft; 2021 37. Schumacher B. AeDA/DGAKI informieren. Nationaler Aktionsplan Allergie. Fachgesellschaften wollen dem Versorgungsmangel nicht mehr länger zusehen. Allergo J 2014;23(8):81-2 38. Wagenmann M, Stenin I, Scheckenbach K. Qualität in der Allergologie. Laryngorhinootologie 2020;99:S272-S300

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==== Front Pneumo News Pneumo News Pneumo News 1865-5467 2199-3866 Springer Medizin Heidelberg 3438 10.1007/s15033-022-3438-4 CME Fortbildung Screening von Risikogruppen wird bald Realität Heußel Gudula [email protected] grid.5253.1 0000 0001 0328 4908 Thoraxklinik Heidelberg gGmbH, Universitätsklinikum Heidelberg, Röntgenstraße 1, 69126 Heidelberg, Deutschland 15 12 2022 2022 14 6 3038 © Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Medizin Verlag GmbH, ein Teil von Springer Nature 2022 ==== Body pmcDer Nutzen der Lungenkrebsfrüherkennung mit Niedrigdosis-Computertomografie überwiegt bei aktiven und ehemaligen Rauchern den Schaden. Vor Implementierung gilt es, eine qualitätsgesicherte Prozesskette vom Screening bis hin zur Operation zu definieren. Das Lungenkarzinom ist weltweit der häufigste zum Tod führende Tumor. Trotz aller Therapiefortschritte ist seine Prognose weiterhin sehr schlecht. Ursache hierfür ist die meist späte Erkennung des Tumors, oft über durch Metastasen vermittelte Symptome. Demgegenüber haben Patienten in früheren asymptomatischen Stadien eine deutlich bessere Prognose. Da das Lungenkarzinom überwiegend bei aktiven oder ehemaligen Rauchern und Passivrauchern auftritt, ist die Hauptrisikogruppe hierüber definiert. Der effektivste Ansatz, das Auftreten von Lungenkarzinom zu verhindern, wäre der Verzicht auf das Zigarettenrauchen. Leider führen politische, gesellschaftliche und ökonomische Rahmenbedingungen dazu, dass dieser Weg allenfalls halbherzig verfolgt wird. Lungenkrebsscreening Ein Screening soll Erkrankungen wie den Lungenkrebs in frühen, kurativ behandelbaren Stadien bei Risikogruppen erkennen. Dazu bietet sich die Computertomografie (CT) an, da sie in der Lage ist, kleinste Lungenläsionen zuverlässig zu erkennen. Die erste große randomisierte kontrollierte Studie (RCT) hierzu zeigte 2011 eine Mortalitätsreduktion mittels Niedrigdosis-CT um 20 % [1]. Damit ist die Niedrigdosis-CT zurzeit die einzige "evidente" Methode zur Erkennung von Lungenkarzinomen im Frühstadium. Das Screening mittels Niedrigdosis-CT ist in den USA inzwischen Realität und wird ausschließlich für Menschen mit hohen Lungenkrebsrisiko empfohlen. In Europa laufen mehrere Screeningstudien. Die größte, die niederländisch-belgische NELSON-Studie [2], zeigte einen ähnlichen Benefit wie die o. g. RCT. Screeningprogramme werden derzeit in verschiedenen Ländern implementiert. In Deutschland sollen laut den Experten Risikopatienten (z. B. starke Raucher) im Alter von 55 bis 74 Jahren in dafür zertifizierten, multidisziplinären medizinischen Zentren untersucht werden. Raucherentwöhnungsprogramme, spezielle CT-Niedrigdosistechnik, Rundherdvolumetrie speziell für solide und subsolide Herde, Klassifizierung gemäß Lung-RADS [3], spezialisierte Qualitätssicherung, interdisziplinäre Befundbesprechung und Strahlendosis sind zu berücksichtigen, da diese regelmäßig anders sind als in der üblichen Krankenversorgung (▶Abb. 1). Derzeit läuft in Norddeutschland die HANSE-Studie, die die Rekrutierung bereits beendet hat (www.hanse-lungencheck.de). In Essen und Heidelberg wird seit Mitte 2022 für die Europäische "4 in the Lung Run" Studie rekrutiert (4inthelungrun.com). Dabei steht seit den 1970er-Jahren mit der CT ein Verfahren zur Verfügung, welches das Bronchialkarzinom in einem operablen und damit oft heilbaren frühen Stadium erkennen kann. Im Jahr 1971 hatte der britische Ingenieur Godfrey Hounsfield seine seit 1949 andauernden Entwicklungen bei EMI (Electric and Musical Instruments) abgeschlossen und den ersten Menschen mittels CT untersucht [4]. Für seine Arbeiten hinsichtlich der Entwicklung dieser Methode erhielt Hounsfield zusammen mit dem südafrikanischen Physiker Allan McLeod Cormack, der wesentliche Studien über die Absorption von Röntgenstrahlen publiziert hatte, 1979 den Nobelpreis für Medizin. Sie wird in ihren modernen Ausführungen seit Jahren leitliniengerecht in Standarddosistechnik für die Diagnose von Lungenembolien (als Angio-CT mit Kontrastmittel), interstitiellen Lungenerkrankungen, zum Staging für die Früherkennung von Pneumonien bei immunkompromittierten Patienten usw. eingesetzt. Bei der Früherkennung des Lungenkarzinoms kann die CT sogar mit besonders niedriger Röntgenstrahlung ohne Kontrastmittel als Niedrigdosis-CT (LDCT) zur Anwendung kommen (▶Abb. 2). Wichtig ist dabei die Dünnschichttechnik (incl. Speicherung der Dünnschichtbilder), der direkte Vergleich mit Voraufnahmen durch erfahrene Thoraxradiologen ggf. mit Unterstützung von minimaler und maximaler Intensitätsprojektion (minIP und MIP) und spezialisierter Auswertesoftware zur Detektion und Volumenbestimmung von Rundherden [5] und die Anbindung an ein Lungenkrebszentrum (▶Abb. 3). Eine von Aussagekraft her ähnliche Alternative wäre die strahlenfreie Magnetresonanztomografie, die jedoch aufgrund der längeren Untersuchungszeit (> 30 min vs. < 5 min) für eine Anwendung in der Breite nicht geeignet ist. Low-dose-Computertomografie Die Niedrigdosis-CT wird mit der Idee durchgeführt, dass die Hochkontrastunterschiede zwischen Rundherden (ca. 50 HE) und umgebendem Lungengewebe (-800 HE) ausreichen, um diese Herde auch bei durch hohes Bildrauschen beeinträchtigter Bildqualität, wie sie bei deutlich reduzierter Strahlendosis bis hin zur Größenordnung der bei der Thoraxübersichtsaufnahme in zwei Ebenen anfallenden Dosis auftritt, zuverlässig zu erkennen. Dabei werden bewusst Nachteile wie die eingeschränkte Erkennbarkeit von Milchglasinfiltraten, Fibrose, Airtrapping, Kontrastmittelenhancement usw. (Niedrigkontrast) in Kauf genommen. Die Niedrigdosis-CT erfolgt daher stets ohne i.v.-Kontrastmittel, also in Nativtechnik (▶Abb. 2). Angio-Computertomografie Die Angio-CT (CTPE) ist beim klinisch relativ stabilen Patienten das Standardverfahren zur Diagnostik der Lungenarterienembolie [8]. Dabei wird über einen dicklumigen i.v. Zugang (grüne Braunüle) das Kontrastmittel mit einem Hochdruckinjektor injiziert und nach Bolustriggerung im Truncus pulmonalis ein Dünnschicht-CT der Lungenarterien in Standarddosistechnik angefertigt. Falls eine relative (z. B. Schilddrüsenautonomie mit dem Risiko der späteren thyreotoxischen Krise) oder schwerwiegende unüberwindbare Kontraindikation gegen Kontrastmittel vorliegt (z. B. GFR < 50 ml/min), ist eine gepaarte Lungenperfusions- und Ventilationsszintigrafie möglichst in SPECT-Technik möglich. Beide Verfahren gehen mit einer Strahlenbelastung um 5 mSv einher, sodass bei strahlenempfindlichen Patienten (z. B. Menschen < 40 Jahren mit normaler Lebenserwartung) eine strahlenfreie Magnetresonanztomografie erwogen werden sollte [9]. Diese kann auch in der Schwangerschaft und notfalls auch ohne i.v. Kontrastmittelgabe erfolgen. Interstitielle Lungenerkrankungen Die einzelnen interstitiellen Lungenerkankungen (ILD) sind zwar selten, es handelt sich jedoch um eine Vielzahl unterschiedlicher Erkrankungen, bei denen es gelegentlich zu einer pulmonalen Manifestation mit Retikulationen, Milchglasinfiltrate, Noduli, Konsolidierungen, Traktionsbronchiektasen oder Honigwaben kommt. Die Erkennung und Differenzierung ist klinisch bedeutsam, da die ILD häufig prognoseentscheidend ist [10]. Inzwischen sind unterschiedliche Medikamente zugelassen, eine unspezifische Immunsuppression für alle ILD wurde verlassen. Es gilt vielmehr, leitliniengerecht die unterschiedlichen Muster der ILD zu differenzieren und dabei die verschiedenen Formen inklusive ihrer jeweiligen Wahrscheinlichkeitslevel (typisch, wahrscheinlich, möglich, unvereinbar) zu differenzieren und anschließend im multidisziplinären Board zu diskutieren [11]. Die Thoraxübersicht kann diese Aufgabe nicht leisten, sondern dies ist Aufgabe der nativen Dünnschicht-CT in inspiratorischem Atemanhalt bei Standarddosis [12]. Die früher verwendete hochauflösende CT (HRCT) mit Lücken zwischen den Einzelschichten ist aus technischen und Strahlenschutzgründen sowie aufgrund ihrer Probleme bei der Verlaufskontrolle der ILD nicht mehr angezeigt. Früherkennung von Pneumonien bei immunkompromittierten Patienten Dass die CT ein schnelles und zuverlässiges Verfahren zur Erkennung von Pneumonien ist, hat sich einmal mehr während der Coronavirus-Pandemie gezeigt [13]. Vielerorts wurde die CT aus organisatorischen Gründen vorrangig zur Polymerase-Kettenreaktion (PCR) angewendet. Dieses Vorgehen wurde aufgrund der langjährigen Erfahrung der betreffenden Kliniken aufgrund des gut etablierten Diagnoseprozesses gewählt, da bereits seit Ende der 1990er-Jahre z. B. immunkompromittierte Patienten nach Transplantationen oder in Neutropenie frühzeitig und unkompliziert der nativen Dünnschicht-CT zugeführt werden. Neben der leichten und zuverlässigen Erkennung eines in der Thoraxübersicht noch okkulten Infiltrats leistet die CT dann bereits eine Differenzierung der mutmaßlich zugrundeliegenden Auslöser einer Dyspnoe. Sie ersetzt keine mikrobiologische Diagnostik, kann aber u. a. Lobärpneumonie, interstitielle Pneumonie, Lungenödem oder Pneumonitis besser als die Thoraxübersichtsaufnahme unterscheiden und Hinweise zum sofortigen Therapiebeginn geben [14]. Prospektive randomisierte Früherkennungsstudien zeigten, dass durch die Erkennung von Frühkarzinomen der Lunge mittels LDCT die lungenkrebsspezifische relative Mortalität um 20 % gesenkt werden kann. Aufgrund der publizierten Daten wurde in Deutschland ein Bewertungsprozess zur Prüfung der LDCT als Früherkennungsmaßnahme für Lungenkrebs eingeleitet (▶Abb. 3). Im Auftrag des Bundesministeriums hat das Bundesamt für Strahlenschutz (BfS) eine wissenschaftliche Bewertung gemäß §84 Absatz 3 Strahlenschutzgesetze vorgenommen und am 6. Dezember 2021 im Bundesanzeiger publiziert [15]. Darin wird ein Früherkennungsszenario vorgeschlagen, das leicht zugänglich ist und die erforderlich hohe Qualität des Gesamtprozesses ermöglicht (▶Abb. 4). Die Lungenkrebszentren haben die wichtige Aufgabe, unnötige Abklärungen zu verhindern und eine niedrige Komplikationsrate zu erreichen. Dieser Prozess ist nicht innerhalb der derzeitigen Erstattungssystems zu leisten, da Leistungen durch neuartige Strukturen erbracht werden müssen und invasive Maßnahmen z. B. durch Nutzung von Expertenwissen und Spezialsoftware (▶Abb. 1) sowie Verwertung von Voraufnahmen auch verhindert, statt erbracht werden sollen (▶Abb. 4). Es wird erwartet, dass das Bundesumweltministerium bis Ende 2022 eine Ausführungsbestimmung für eine Früherkennung von Lungenkrebs in Deutschland erlässt. Anschließend dürfte der gemeinsame Bundesausschuss (G-BA) seine Stellung zur Implementierung im deutschen Gesundheitssystem beraten und diese möglicherweise in 2024 veröffentlichen. Die Metaanalyse von 38 Publikationen zu RCT, in die Daten von 70.000 Personen einflossen, zeigte Hinweise auf einen Nutzen des Früherkennungsverfahrens für starke Raucherinnen und Raucher. In der Gruppe der mit LDCT untersuchten Teilnehmenden reduzierte sich die Lungenkrebsmortalität im Vergleich zur Kontrollgruppe um 15 %. Das bedeutet rechnerisch: Von 1.000 Teilnehmenden sterben in einem Zeitraum von etwa zehn Jahren nach Früherkennungsbeginn drei Menschen weniger an Lungenkrebs als ohne Früherkennung. Zusammenfassend wurde ein Nutzen der Früherkennung mit LDCT für (ehemalige) starke Raucherinnen und Raucher festgestellt, der den Schaden der Maßnahme überwiegt. Nutzen-Risiko-Awägung Für Deutschland liegt eine Kalkulation auf Basis der Daten des NLST (National Lung Cancer Screening Trial) vor [1]. Anhand von Befragungsdaten des Robert Koch-Instituts wurde die Zahl der möglichen Teilnehmer an einer Früherkennungsmaßnahme mittels LDCT hochgerechnet. Bei einer anzunehmenden Teilnahmebereitschaft von 50 % werden demnach zirka 1,4 Millionen der schweren Raucher in Deutschland im Alter zwischen 55 und 74 Jahre teilnehmen [16]. Realistisch ist allerdings mit einer niedrigeren Teilnahmebereitschaft und damit weniger CT-Untersuchungen zu rechnen, wenn die Einschlusskriterien korrekt angewendet werden. Bei einer jährlichen LDCT über drei Jahre wären dies ca. 3,8 Millionen Untersuchungen für die Früherkennung. Bei 50 Untersuchungen pro Arbeitstag pro CT-Gerät würde dies ca. 300 CT-Geräte auslasten. Angesichts von 2.600 CT-Geräten die 2009 in Deutschland installiert waren [17], könnte dies auch aufgrund der seitdem stattgehabten dramatischen Leistungssteigerung der Geräte im Wesentlichen mit der inzwischen vorhandenen CT-Infrastruktur abzubilden sein. Bei einem jährlichen CT-Intervall würden 916.918 LDCT-Untersuchungen mit kontrollbedürftigen Herden innerhalb von drei Jahren auftreten. Zusätzliche CT-Abklärung würde 456.167 Mal anfallen. Eine invasive Abklärung mittels Bronchoskopie oder Thoraxchirurgie würde in etwa 72.000 Fällen indiziert. Dabei würde bei rund 33.000 Probanden Lungenkrebs diagnostiziert. Es würden 4.155 Lungenkrebstodesfälle vermieden (Number Needed to Screen = 320). Bei diesem Hochrisikokollektiv würde das Risiko an Lungenkrebs zu sterben nach drei Jahren Früherkennung mit LDCT und 6,5 Jahren Nachbeobachtungszeit um 20 % sinken. Allerdings würden dabei auch mehr als 12.000 Personen mindestens eine Komplikation im Rahmen der invasiven Abklärung erleiden, davon würden ca. 30 % schwerwiegend verlaufen, einschließlich Todesfolge (jeweils etwa die Hälfte der Fälle bei bestätigtem und bei nicht bestätigtem Lungenkrebs). Kritische Punkte Vor Implementation der Früherkennung müssen einige kritische Punkte bedacht und diskutiert werden: Die derzeit in den großen Studien randomisierten Probanden wurden mit gezielten Rekrutierungsmaßnahmen (Anschreiben, Anrufe, Anzeigen) zur Studienteilnahme motiviert. Es bleibt daher offen, ob nicht nur eine etwas gesündere und regional in der Nähe der Studienzentren (in der Regel universitäre Ballungsgebiete) lebende Studienpopulation rekrutiert wurde. Auch der Raucherstatus war möglicherweise bereits überhäufig mit Exrauchern. Dies könnte zu erheblicher Beeinflussung der Ergebnisse geführt haben. Andererseits könnten bereits subklinische Beschwerden einer Tumorerkrankung vorgelegen haben, die zur Studienteilnahme an hochspezialisierten Zentren motivierten. Auch wurden neben auf Raucherstatus und Alter fokussierten Einschlusskriterien gezieltere Risikomodelle wie PLCOM2012 entwickelt, die weitere Parameter wie Bildung, Familienanamnese usw. einbeziehen. Algorithmen verfeinern die Abklärung In der neueren Studie wurden Algorithmen zur Abklärung von Lungenherden mit dem Ziel vorgegeben, die Anzahl unnötiger Verlaufs-CTs und unnötiger Biopsien zu senken. Die Ergebnisse der automatischen Analyse wurden vom Radiologen einzeln bestätigt oder verworfen. Die verbliebenen Herde wurden segmentiert und volumetriert (▶Abb. 1). Das Volumen und der Durchmesser sowie die deren Veränderung in der Verlaufskontrolle bestimmten dann aufgrund vorgegebener Entscheidungswege das weitere Vorgehen. Auf diese Weise konnte die Zahl von unnötigen Abklärungen deutlich gesenkt werden. Bei einer jährlichen Früherkennungs-LDCT im Alter von 50-75 Jahren läge das strahlenbedingte zusätzliche Lebenszeitrisiko für eine Krebserkrankung bei 0,25 % für Frauen und etwa 0,1 % für Männer. Angesichts eines Lungenkrebsrisikos von ca. 1,5 % in dem einzuschließenden Hochrisikokollektiv erscheint dieses Risiko gerechtfertigt. In der Routineversorgung wird es Risikoprobanden nach drei Jahren Teilnahme an einer Früherkennungsmaßnahme schwer zu vermitteln sein, warum die Untersuchung nicht weiter angeboten wird. Würde ein nationales Früherkennungsprogramm initiiert, müssten hier klare Vorgaben bezüglich Technik, Qualität, Qualifizierung, Befundung, Abklärung, Vorgehensweise bei Auffälligkeiten, Anbindung an Lungenkrebszentren usw. gemacht werden. Lungenkrebs häufiger bei niedrigem sozioökonomischen Status Die Empfehlungen des BfS skizzieren dazu ein realistisches Vorgehen, dessen angemessene Finanzierung eine Voraussetzung für einen Nutzen der Lungenkrebsfrüherkennung ist. Eine Unterfinanzierung würde u. U. zu Abstrichen bei der Prozessqualität führen, die z. B. die Anzahl unnötiger Eingriffe und unerkannter Tumoren steigert: Die Einschlusskriterien würden weniger streng angewendet, die Anzahl der betrachteten Voraufnahmen würde möglichst klein gehalten, außerhalb der eigenen Früherkennung eventuell vorhandene Voraufnahmen würden nicht erfragt, eingelesen und somit nicht mit einbezogen, usw. Schon der erforderliche Datenschutz stellt dabei ein Problem dar, der unter Erhalt des Früherkennungsziels nur mit erheblichem Personalaufwand und geeigneter Infrastruktur gewährleistet werden kann. Dies sind organisatorische und nicht technische Schwächen dieser Früherkennungsmaßnahme, die für den Kostenträger nicht oder nur schwer kontrollierbar sind, aber wesentlich zur Prozessqualität beitragen. Ein weiterer Punkt ergibt sich aus der unklaren Bereitschaft zu einer Teilnahme an einer Früherkennungsmaßnahme. Man muss davon ausgehen, dass sich wahrscheinlich weniger Risikoprobanden einer Früherkennung unterziehen werden als in der Mamma- oder Kolonkarzinomfrüherkennung. Das Lungenkarzinom tritt häufiger in niedrigen sozioökonomischen Gruppen auf als die zuvor genannten Karzinome, was sich sicher in der Akzeptanz und der Finanzierungsbereitschaft eines solchen Programmes bei einem selbst verursachten Risikofaktor niederschlägt. Schon deswegen muss auch ein Raucherentwöhnungsprogramm verpflichtend in ein mögliches Szenario eingebunden werden, denn dessen Nutzen ist bekannt und es bestehen keine Risiken. Die Früherkennung darf nicht zur Rechtfertigung des fortgesetzten Nikotinabusus führen. Früherkennung von Lungenkrebs durch spezialisierte Zentren Aus oben genannten Gründen und anlehnend an alle Fachgesellschaften, die eine CT-Früherkennung für das Lungenkarzinom empfehlen, ergeben sich daher sicher Empfehlungen an strukturelle Voraussetzungen zur Durchführung der Lungenkrebsfrüherkennung, vergleichbar den Empfehlungen des "American College of Chest Physicians" (ACCP). Der größte Nachteil der CT erwächst aus ihrer hohen Sensitivität: Gerade beim Risikokollektiv der Raucher werden zahlreiche Rundherde gefunden, deren invasive Abklärung zu so hohen Patientenbelastungen führen würde, dass deren Komplikationen den Vorteil der Lungenkrebsfrüherkennung bei weitem überwiegen würden. Es ist daher erforderlich, typischerweise nur solche Befunde näher zu untersuchen, die eine relevante Größenzunahme der soliden Anteile zeigen. Gerade vor dem Hintergrund vieler falsch positiver Rundherde und dem damit verbundenen Risiko unnötiger Eingriffe, welches in nicht spezialisierten Zentren höher ist, muss die Früherkennung von Lungenkrebs durch spezialisierte Zentren mit zertifizierten Partnern erfolgen. Der Nutzen der LDCT zur Früherkennung von Lungenkrebs dürfte sich daher nur bei Risikopersonen und vorwiegend in den Händen erfahrener multidisziplinärer Teams zeigen. Fazit Die Niedrigdosis-CT der Lunge ist ein sensitives Verfahren zur Früherkennung von Lungenkrebs. Im Risikokollektiv der aktiven und ehemaligen (bis 15 Jahre) Raucher (55-74 Jahre) ist die Früherkennung mit einem messbaren Überlebensvorteil verbunden. Die Qualität des Gesamtprozesses von der Zuweisung über die Durchführung und Befundung bis hin zur Einleitung von Konsequenzen und Abklärung ist kritisch für den tatsächlichen Nutzen der Früherkennung und gehört an spezialisierte Lungenkrebszentren. Die Niedrigdosis-CT zur Früherkennung von Lungenkrebs ist derzeit in Deutschland nur in Studien zulässig. Eine Genehmigung wird in wenigen Jahren erwarten. Dr. Gudula Heußel Thoraxklinik Heidelberg gGmbH am Universitätsklinikum Heidelberg Röntgenstraße 1 69126 Heidelberg [email protected] CME-Fragebogen Screening auf Lungenkrebs wird bald Realität Teilnehmen und Punkte sammeln können Sie als e.Med-Abonnent*in von SpringerMedizin.de als registrierte*r Abonnent*in dieser Fachzeitschrift Dieser CME-Kurs ist auf SpringerMedizin.de/CME zwölf Monate verfügbar. Sie finden ihn, wenn Sie den Titel in das Suchfeld eingeben. Alternativ können Sie auch mit der Option "Kurse nach Zeitschriften" zum Ziel navigieren oder den QR-Code links scannen. Dieser CME-Kurs wurde von der Bayerischen Landesärztekammer mit zwei Punkten in der Kategorie I (tutoriell unterstützte Online- Maßnahme) zur zertifizierten Fortbildung freigegeben und ist damit auch für andere Ärztekammern anerkennungsfähig. Für eine erfolgreiche Teilnahme müssen 70 % der Fragen richtig beantwortet werden. Pro Frage ist jeweils nur eine Antwortmöglichkeit zutreffend. Bitte beachten Sie, dass Fragen wie auch Antwortoptionen online abweichend vom Heft in zufälliger Reihenfolge ausgespielt werden. Bei inhaltlichen Fragen erhalten Sie beim Kurs auf SpringerMedizin.de/CME tutorielle Unterstützung. Bei technischen Problemen erreichen Sie unseren Kundenservice kostenfrei unter der Nummer 0800 7780777 oder per Mail unter [email protected]. Welche der folgenden Aussagen ist RICHTIG? Die Dosis ionisierender Strahlung ist bei der Magnetresonsanztomografie (MRT) niedrig. Die Hounsfield-Skala ersteckt sich ungefähr vom Bereich -1.000 HE bis +1.000 HE. Die Computertomografie (CT) erfordert zur Diagnostik einer interstitiellen Lungenparenchymerkrankung die Gabe von Kontrastmittel. Die Lungen-CT wird ausschließlich im Lungenfenster dargestellt. Die Röntgendosis von Low-dose-CT und Thoraxübersichtsaufnahme in zwei Ebenen sind vergleichbar. Welche der folgenden Aussagn ist FALSCH? Die Computertomografie (CT) wurde durch eine Abteilung der NASA entwickelt. Für die Entwicklung der CT wurde 1979 der Nobelpreises an Godfrey Hounsfield und Allan Cormack verliehen. Interstitielle Lungenparenchymerkrankung lassen sich mit der Magnetresonanztomografie (MRT) darstellen. Zur Diagnostik von Lungenembolien bei klinisch stabilen Patienten eignen sich Lungenperfusionsszintigrafie, MRT und CT. Die Kontrastmittelgabe bei einer Angio-CT erfolgt durch eine Hochdruckinjektion mit z. B 5 ml/s. Welche der folgenden Aussagen zur Technik der Computertomografie (CT) ist RICHTIG? Die Verwendung dicker Schichten in der CT ist in der Diagnostik interstitieller Lungenparenchymerkrankung angezeigt. Das heutige CT-Standardverfahren zur Untersuchung interstitieller Lungenparenchymerkrankung ist die HRCT mit 1 mm Schichtdicke alle 10 mm (sog. Inkremental-CT). Die typische transversale Bildebene zeigt den Patienten quasi von oben. MIP und minIP sind Reformatierungen, die bereits am Scanner erzeugt werden müssen; eine nachträgliche Berechnung ist aufgrund fehlender Projektionsdaten nicht mehr möglich. Die typische koronare Bildebene zeigt den Patienten quasi von vorne bzw. hinten. Welche Aussage zur Rundherddiagnostik ist RICHTIG? Die Quantifizierung von Lungenrundherden sollte durch eindimensionale Messung des größten Durchmessers erfolgen. Eine Tumorverdopplungszeit pulmonaler Rundherde zwischen 20 und 400 Tagen ist verdächtig auf das Vorliegen eines Malignoms. Die Quantifizierung von Lungenrundherden sollte durch Mittelung der Messung des größten und kleinsten Durchmessers erfolgen. Lungenherde mit Anteilen von Milchglasdichte sind typischerweise benigne. Bei symptomfreien Rauchern oder Exrauchern kann zur Früherkennung eines Bronchialkarzinoms eine Niedrigdosis-CT verordnet werden. Welche Aussage zur Computertomografie (CT) ist RICHTIG? Bei Vorliegen einer Niereninsuffizienz können eine CT oder Magnetresonsanztomografie nicht durchgeführt werden. Bei Vorliegen eines autonomen Adenoms der Schilddrüse können CT oder Magnetresonsanztomografie nicht durchgeführt werden. Alle Menschen ab 55 Jahren können eine Lungenkrebsfrüherkennung mittels Niedrigdosis-CT erhalten. Die Niedrigdosis-CT kann mit und ohne i.v. Kontrastmittel erfolgen. Die native CT der Lunge kann pulmonale Rundherde ausreichend darstellen. Welche Aussage zu interstitiellen Lungenparenchymerkrankungen ist RICHTIG? Die drei CT-Muster lauten in der aktuellen Leitlinie: 1. sicheres, 2. mögliches und 3. unvereinbar mit UIP-Muster. Bei Verdacht auf eine berufsbedingte Pneumokoniose muss vor einer Meldung an die Versicherung eine Vorstellung beim Arbeitsmediziner oder Pneumologen erfolgen. Bei Vorliegen von dreischichtigem Honeycombing im Subpleuralraum der basalen Lungenabschnitte ist eine idiopathische pulmonale Fbrose (IPF) diagnostiziert. Die Mikronoduli der Sarkoidose sind zentrilobulär verteilt. Eine Thoraxübersicht in zwei Ebenen im Stehen ist keine ausreichende radiologische Abklärung. Welche Aussage zur Kontrastmittelgabe ist RICHTIG? Eine thyreotoxische Krise tritt typischerweise sofort nach der Gabe jodhaltigen Kontrastmittels auf. Die i.v. Gabe von jodhaltigem Kontrastmittel ist bei einer glomerulären Filtrationsrate (GFR) < 50 ml/min kontraindiziert. Bei den vom Radiologen verwendeten Kontrastmitteln handelt es sich um dieselbe Stoffgruppe. Bei Vorliegen einer Niereninsuffizienz können eine Computertomografie oder Magnetresonsanztomografie nicht durchgeführt werden Bei Vorliegen eines autonomen Adenoms der Schilddrüse können eine Computertomografie oder Magnetresonsanztomografie nicht durchgeführt werden. Welche Aussage zur Teilnahme an einem Programm zur Früherkennung von Lungenkrebs ist RICHTIG? Es werden weniger als 20 % falsch positive Befunde erwartet. Es wurde kein Überlebensvorteil durch die Teilnahme gefunden. Die meisten gefundenen Lungenrundherde sind maligne. Bei bis zu 5 % der Teilnehmer werden Lungenrundherde gefunden. Die Inzidenz von Lungenkrebs liegt in der ersten Runde bei 1-3 %. Welche Aussage zum Lungenkrebs-Früherkennungsprogramm mittels Computertomografie (CT) ist FALSCH? Die Teilnahme ist in Deutschland derzeit nur in Studien zulässig. Ein Einschlusskriterium ist schwerer Raucher oder Ex-Raucher. Ein Einschlusskriterium ist ein Mindestalter von 50 oder 55 und ein Höchstalter von 74 Jahren. Die Untersuchung erfolgt mittels kontrastmittelverstärkter Niedrigdosis-CT. Bei Vorliegen von Rundherden werden diese zunächst im Verlauf mittels CT kontrolliert. Welche Aussage zum Lungenkrebs-Früherkennungsprogramm mittels Computertomografie (CT) ist RICHTIG? Die Teilnahme an routinemäßigen klinischen Programmen ist derzeit in Deutschland möglich. Die Auswertung der CTs erfolgt auf Bundesländerebene zentral. Die Möglichkeit zur Genehmigung eines Programms wird in Deutschland ab 2024 erwartet. Neben den Lungenrundherden werden weitere Zusatzbefunde aus den CTs nicht berichtet. Nicht nur schwere Raucher und Ex-Raucher profitieren nachweislich hinsichtlich ihres Überlebens von der Teilnahme. Interessenkonflikt Die Autorin erklärt, dass sie sich bei der Erstellung des Beitrages von keinen wirtschaftlichen Interessen leiten ließ. Sie legt folgende potenzielle Interessenkonflikte offen: keine. Der Verlag erklärt, dass die inhaltliche Qualität des Beitrags durch zwei unabhängige Gutachten bestätigt wurde. Werbung in dieser Zeitschriftenausgabe hat keinen Bezug zur CME-Fortbildung. Der Verlag garantiert, dass die CME-Fortbildung sowie die CME-Fragen frei sind von werblichen Aussagen und keinerlei Produktempfehlungen enthalten. Dies gilt insbesondere für Präparate, die zur Therapie des dargestellten Krankheitsbildes geeignet sind. ==== Refs Literatur 1. Aberle DR et al. N Eng J Med. 2011;365(5):395-409 2. de Koning HJ et al. N Engl J Med. 2020;382(6):503-13 3. Lung-RADS® Version 1.1, Assessment Categories Release date: 2019. [Online] [Zitat vom: 2.10.2022] 4. Hounsfield GN. Br J Radiol. 1973;46(552):1016-2 5. Kauczor HU et al. Eur Radiol. 2020;30(6):3277-294 6. Gould MK et al. Chest. 2013;143(5 Suppl):e93S-e120S 7. MeVis Lung-RADS. [Online] 2.10.2022. https://play.google.com/store/apps/details?id=de.mevis.lungRADS&hl=en. 8. Olschewski H. Pneumologe. 2020;17:365-75 9. Palm V et al. Rofo. 2020;192(1):38-49 10. Yaldizkaya MF et al. Med Klin Intensivmed Notfmed. 2022; https://doi.org/10.1007/s00063-022-00934-4 11. Raju S et al. Chest. 2017;151(6):1356-74 12. Lynch DA et al. Lancet Respir Med. 2018;6(2):138-53 13. Gaia C et al. Radiol Med. 2020;125(10):931-42 14. Solyanik O et al. Radiologe. 2017;57(1):22-28 15. Bericht Lungenkrebsfrüherkennung mittels Niedrigdosis-Computertomografie. [Hrsg.] Bundesamt für Strahlenschutz. August 2021, urn:nbn:de: 0221-2021082028027; 16. Stang A et al. Dtsch Arztebl Int. 2015;112(38):637-44 17. MRT: "Die Deutschen sind gut durchleuchtet". Ärztezeitung. 1.2.2011. [Zitat vom: 2.10.2022]

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==== Front Heart Lung Heart Lung Heart & Lung 0147-9563 1527-3288 Elsevier Inc. S0147-9563(22)00291-6 10.1016/j.hrtlng.2022.12.004 Article Interventions using digital technology to promote family engagement in the adult intensive care unit: An integrative review Shin Ji Won Ph.D, RN ab Choi JiYeon Ph.D, RN, ATSF cd⁎ Tate Judith Ph.D, RN, FAAN, ATSF b a University of California at Davis, Betty Irene Moore School of Nursing, Sacramento, CA, USA b The Ohio State University, College of Nursing, Columbus, OH, USA c Yonsei University College of Nursing, Mo-Im Kim Nursing Research Institute, Seoul, South Korea d Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea ⁎ Corresponding author at: Institute for Innovation in Digital Healthcare, Yonsei University College of Nursing Mo-Im Kim Nursing Research Institute, Yonsei University Institute for Innovation in Digital Healthcare, 50-1 Yonsei-Ro, Seodaemun-Gu, Seoul, South Korea. 15 12 2022 March-April 2023 15 12 2022 58 166178 7 7 2022 14 11 2022 4 12 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Family engagement is a key component of safe and effective care in the intensive care unit (ICU). As the COVID-19 pandemic has accelerated the adoption of digital technologies in healthcare settings, it is important to review the current science of family engagement interventions in the ICU using digital technology. Objectives This integrative review aimed to identify and evaluate studies that used digital technology to promote family engagement in adult ICUs and synthesize study findings. Methods Following the methodology of Whittemore and Knafl, PubMed, CINAHL, Web of Science, and Scopus were searched. We included studies conducted in the adult ICU setting; involved family engagement during ICU stay; and used digital technology to engage family members. We excluded studies that were not peer-reviewed or in English. Study findings were assessed using the model of family engagement in the ICU Results Of 2702 articles, 15 articles were analyzed. Various technologies (e.g., web-, tablet-, or SMS-based tools, video-conferencing, etc.) were used to provide information; augment the decision-making process; provide virtual access to family conferences or interdisciplinary rounds. While varying among interventions, “Information sharing” and “activation and participation” were most commonly addressed within the family engagement model. In studies that addressed the components of family engagement more comprehensively, interventions enabled tailoring of information with two-way communication and active family involvement in decision-making processes. Conclusions Future research should use more robust methods and develop interventions with close inputs from families. We recommend using conceptual components of family engagement to ensure comprehensiveness of the intervention. Keywords Critical care Family Family engagement Intensive care unit Digital technology ==== Body pmcIntroduction Family engagement is an important component of safe and effective person-centered care in the intensive care unit (ICU). Family engagement in the ICU is defined as an active partnership between health professionals and families to improve health outcomes, quality of care, and safety and delivery of healthcare.1 Although family caregiving or informal caregiving is a term that is used to describe unpaid care which goes beyond the care typically expected in a relationship,2 family engagement is different. This paper addresses family engagement in the ICU, which is not necessarily the same as family caregiving or informal caregiving. According to Brown et al., family engagement can include direct care activities, communication of values and goals of care, and methods to enhance respect and dignity.1 Of note, family engagement is not equivalent to family-centered care, which is “an approach to health care that is respectful of and responsive to individual families' needs and values."3 Instead, family engagement may be a component of family-centered care.4 Active family engagement in patient care, communicating the patient's and families' values and goals, and decision-making processes are recognized as invaluable aspects when providing healthcare.5 Over the last decades, studies have reported the potential benefits of family engagement on patient care in the ICU and post-ICU recovery.6 , 7 Several interventions such as family bedside visitation,8 family presence during resuscitation,9 ICU diaries,10 music or pet interventions,11 , 12 and patient/family advisory councils13 have been developed and tested to improve patient and family satisfaction, shorten length of ICU stay, or reduce adverse psychological outcomes for both patients and their families. Family engagement is also a core element of the ABCDEF bundle (Assess, Prevent, and Manage Pain; Both Spontaneous Awakening Trials and Spontaneous Breathing Trials; Choice of analgesia and sedation; Delirium: Assess, Prevent, and Manage; Early mobility and Exercise; and Family engagement and empowerment),14 an evidence-based care coordination and management strategy that aims to improve outcomes for critically ill patients. Digital technology has become more central in our lives, and the ICU has long been a technology-rich environment. Moreover, the coronavirus disease 2019 (COVID-19) pandemic has accelerated the adoption of digital technologies in healthcare settings. Owing to the COVID-19 restrictions on bedside family presence in the ICU, interventions using digital technologies were documented in the popular press as an alternative to in-person visitation.15 These interventions have allowed families to have limited views and communication with their critically ill family members.16 , 17 COVID-19 pandemic also prohibited other aspects of care from being used, such as family-clinician conferences, family presence at rounds, orientation guides, and ICU diaries. These restrictions are likely to be present in modified forms for the foreseeable future, preventing families from maintaining their roles in treatment decision-making processes as care partners and as voice of patients. Interventions using digital technology may also enable family engagement, regardless of the families’ presence at the bedside. Despite the increasing use of digital technology and the importance of family engagement in the ICU, to our knowledge, no studies to date has extensively examined or synthesized any findings to evaluate the current state of family engagement interventions in the ICU using digital technology. Considering the current gap in the literature, this integrative review aimed to (1) identify and evaluate studies that have used digital technology to promote family engagement and (2) assess the findings on how digital technology-based interventions involved family caregivers to promote family engagement in ICUs. Methods Study design This integrative review was conducted following the methodology of Whittemore and Knafl,18 which allows for the integration of both quantitative and qualitative research findings. This method was selected to include all quantitative and qualitative perspectives to describe and evaluate the studies that used digital technology to promote family engagement in the ICU. For this review, we included the following variables of interest: target population (family members of ICU patients), concept (digital technology-based intervention), and context (adult patients admitted to ICUs). Family members were defined as those who primarily provide physical, emotional, financial, or spiritual support to their loved one admitted to the ICU. Considering the rapidly expanding scope of digital technology, we set a quite broad definition which includes tools, systems, devices, or resources using a computer, Internet, or mobile devices to generate, store or process data, or communicate. Search strategies An initial search was conducted to determine if there were previous reviews on this topic to avoid duplication. We searched the International Prospective Register of Systematic Reviews (PROSPERO) and the Cochrane Library to ensure the absence of any similar reviews on the family engagement intervention in ICUs using digital technology. Three authors (JS, JC, and JT) designed the search strategies with assistance from a health science librarian. We searched four databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and Scopus. In Table 1 , we summarized the combination of search terms for each database. We matched search terms to database-specific indexing terms. To identify additional relevant papers, we manually searched reference lists of the retrieved papers that met the inclusion criteria.Table 1 Summary of database search terms. Table 1Databases Keywords PubMed (((((((((((((((((((tele*[Title/Abstract]) OR ("M-health"[Title/Abstract])) OR (mhealth[Title/Abstract])) OR ("M health"[Title/Abstract])) OR ("E-health"[Title/Abstract])) OR (Ehealth[Title/Abstract])) OR ("E health"[Title/Abstract])) OR (digital[Title/Abstract])) OR (web*[Title/Abstract])) OR (app[Title/Abstract])) OR (platform[Title/Abstract])) OR (video*[Title/Abstract])) OR (audiovisual[Title/Abstract])) OR (electronic[Title/Abstract])) OR (mobile[Title/Abstract])) OR (computer[Title/Abstract])) OR (virtual[Title/Abstract])) OR (application[Title/Abstract])) AND (((((((caregiv*[Title/Abstract]) OR (carer*[Title/Abstract])) OR (relative*[Title/Abstract])) OR (families[Title/Abstract])) OR (family[Title/Abstract])) OR (spouse*[Title/Abstract])) OR (partner*[Title/Abstract]))) AND ((((("intensive care"[Title/Abstract]) OR ("critical care"[Title/Abstract])) OR (ICU[Title/Abstract])) OR ("critical illness"[Title/Abstract])) OR ("critically ill"[Title/Abstract])) AND ((english[Filter]) AND (alladult[Filter]) AND (2000:2021[pdat])) Filters: English, Adult: 19+ years CINAHL AB (tele* OR "m-health" OR mhealth OR "m health" OR "e-health" OR ehealth OR "e health" OR digital OR web* OR app* OR platform OR electronic OR video* OR comput* OR mobile OR audiovisual OR virtual) AND (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND ("intensive care" OR "critical care" OR "critical illness" OR icu) OR TI (tele* OR "m-health" OR mhealth OR "m health" OR "e-health" OR ehealth OR "e health" OR digital OR web* OR app* OR platform OR electronic OR video* OR comput* OR mobile OR audiovisual OR virtual) AND (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND ("intensive care" OR "critical care" OR "critical illness" OR icu) Limiters - Published Date: 20000101-20211231; English Language; Age Groups: All Adult SCOPUS TITLE-ABS-KEY (tele* OR "M-health" OR mhealth OR "M health" OR "E-health" OR ehealth OR "E health" OR digital OR web* OR app OR application OR platform OR electronic OR video* OR computer* OR mobile OR audiovisual OR virtual) AND TITLE-ABS-KEY (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND TITLE-ABS-KEY ("intensive care" OR "critical care" OR "critical illness" OR icu OR "critically ill") AND NOT (neonat* OR adolescent* OR pediatric OR infant* OR child* OR teen*) AND (LIMIT-TO (PUBYEAR, 2021) OR LIMIT-TO (PUBYEAR, 2020) OR LIMIT-TO (PUBYEAR, 2019) OR LIMIT-TO (PUBYEAR, 2018) OR LIMIT-TO (PUBYEAR, 2017) OR LIMIT-TO (PUBYEAR, 2016) OR LIMIT-TO (PUBYEAR, 2015) OR LIMIT-TO (PUBYEAR, 2014) OR LIMIT-TO (PUBYEAR, 2013) OR LIMIT-TO (PUBYEAR, 2012) OR LIMIT-TO (PUBYEAR, 2011) OR LIMIT-TO (PUBYEAR, 2010) OR LIMIT-TO (PUBYEAR, 2009) OR LIMIT-TO (PUBYEAR, 2008) OR LIMIT-TO (PUBYEAR, 2007) OR LIMIT-TO (PUBYEAR, 2006) OR LIMIT-TO (PUBYEAR, 2005) OR LIMIT-TO (PUBYEAR, 2004) OR LIMIT-TO (PUBYEAR, 2003) OR LIMIT-TO (PUBYEAR, 2002) OR LIMIT-TO (PUBYEAR, 2001) OR LIMIT-TO (PUBYEAR, 2000)) AND (LIMIT-TO (LANGUAGE, "English")) Web of Science (TI = ((tele* OR "m-health" OR mhealth OR "m health" OR "e-health" OR ehealth OR "e health" OR digital OR web* OR app OR application OR platform OR electronic OR video* OR computer* OR mobile OR audiovisual OR virtual) AND (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND ("intensive care" OR "critical care" OR "critical illness" OR "critically ill" OR icu))) OR (AB = ((tele* OR "m-health" OR mhealth OR "m health" OR "e-health" OR ehealth OR "e health" OR digital OR web* OR app OR application OR platform OR electronic OR video* OR computer* OR mobile OR audiovisual OR virtual) AND (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND ("intensive care" OR "critical care" OR "critical illness" OR "critically ill" OR icu))) OR (AK = ((tele* OR "m-health" OR mhealth OR "m health" OR "e-health" OR ehealth OR "e health" OR digital OR web* OR app OR application OR platform OR electronic OR video* OR computer* OR mobile OR audiovisual OR virtual) AND (family OR caregiv* OR relative* OR spouse* OR partner* OR families) AND ("intensive care" OR "critical care" OR "critical illness" OR "critically ill" OR icu))) AND LANGUAGE: (English) Indexes=SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC Timespan=2000-2021 The inclusion criteria were studies that were (1) conducted in the adult ICU setting, (2) involved family engagement during the patient's ICU admission, (3) involved technology use as a way to engage and communicate with family members, (4) were published in English, and (5) published between January 2000 and January 2021. The exclusion criteria were as follows: (1) studies of case reports, reviews, editorials, theses, descriptive commentary, prototypes, conference abstracts, unpublished master's theses or doctoral dissertations, (2) studies that were conducted in non-adult ICU settings (e.g., neonatal or pediatric ICUs), (3) studies involved family engagement during post-ICU discharge and (4) studies did not include family members as the main target sample. We limited our scope to adult ICU settings because family engagement in pediatric or neonatal ICUs is different from adult ICUs, and providing care for a child undergoing ICU admission involves different challenges (e.g., altered parent-child bonding). Study screening and selection For screening and selection, all the studies were imported into Covidence, an online systematic review software (www.covidence.org). First, each author independently screened all the titles and abstracts using the eligibility criteria. After excluding the irrelevant papers, each author independently screened the full texts of the initially screened papers. For each step, following the independent screening process, all three authors discussed any discrepancies and reached a consensus on the eligibility of each study. The final sample consisted of 15 papers. Quality evaluation of the selected studies The three authors independently evaluated the quality of the selected studies using the Mixed-Methods Appraisal Tool (MMAT) version 2018,19 and discussed any discrepancies before reaching a consensus. The MMAT tool allows for the evaluation of multiple study designs, including quantitative, qualitative, and mixed-methods designs. Different quality criteria are applied for different study designs, which helps to consider the unique characteristics of each design. The MMAT comprises two sections. The screening section consists of two screening questions regarding the clarity of the research questions and the sufficiency of the collected data to address the research questions. The second section comprises five sets of quality criteria with three response options (yes, no, or can't tell) for each study design. An overall quality score was assigned to each study using asterisks that ranged from "none" (none of the quality criteria were met) to "*****" (all five criteria were met).19 We also reviewed the interventions described in these papers using the Template for Intervention Description and Replication (TIDieR) guidelines and checklist20 (Table S1, see Multimedia Appendix 1). The TIDieR guidelines were established to determine the quality of the intervention description in order to improve replicability. The checklist includes the areas that should be addressed in sufficient detail to evaluate intervention reporting by researchers. Data extraction and synthesis One author (JS) extracted data, and then two authors (JC and JAT) validated the extracted data. Fig. 1 presents an overview of the search results and selection process used in this study in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses diagram.21 Overall, 3979 possible citations were identified from the databases (PubMed, 670; CINAHL, 1030; Web of Science, 893; Scopus, 1386). Of these studies, 2702 remained after removing 1277 duplicate records. After title screening and abstract review, a further 2653 and 28 studies were excluded, respectively. Of the 21 papers assessed for eligibility, seven were excluded for the following reasons: not directed at family members (n = 3), not a full research paper (n = 2), lacking an exclusively technology-based strategy (n = 1), and conducted in a non-adult ICU setting (n = 1). An additional record was identified by citation searching. Finally, a total of 15 studies were included in the review.Fig. 1 PRISMA flowchart of the literature search and study selection process. Fig 1 For the final review, we extracted the following data from the selected articles: author, year of publication, region, design, sample, setting, measures and data collection methods, main findings, and study quality. We also extracted the characteristics of digital technology-based interventions in each study: purpose, main content, target users, types of technology, interaction, and personalization, if any. To determine the comprehensiveness of each intervention used in the selected studies, we used an analytical framework, the family engagement model introduced by Brown et al.1 The model, which was developed with the input of key stakeholders, including families, clinicians, researchers, and administrators, comprises five conceptual components of family engagement in the ICU: collaboration, respect and dignity, activation and participation, information sharing, and decision making.1 Collaboration includes the coordination of care by health professionals and active participation with patients and their families. By respecting the individuality of patients and their families and treating them with compassion, health professionals support their choices and individual needs. Additionally, by encouraging patients and their families to acquire skills and knowledge, family members can become active participants and provide “voice” to the care plans. Health professionals communicate essential information to patients and their families that can contribute to increased understanding, decreased uncertainty, and assist with decision making. Finally, health professionals further provide individualized information and encouragement to assist in treatment decision-making. Results Study characteristics The 15 selected studies include 8 non-experimental (3 quantitative descriptive, 3 qualitative, and 2 mixed-methods) and 7 experimental (3 quasi-experimental and 4 randomized controlled trials) studies. For non-experimental studies, the common aims were to investigate the participants' perceptions or user experiences with interventions using various technologies (e.g., text messages and interactive decision-making tools). Seven experimental studies tested the interventions to provide education,22, 23, 24 support decision making,25, 26, 27 and deliver family meetings.28 Characteristics of selected studies are summarized in Table 2 . The studies included varying sample groups. While seven studies included only family members, eight studies also included patients and/or clinicians in addition to family members. Most studies had not identified the family members' relationships with patients. In four studies that provided details about the family members' relationships with the patients, most of the family participants were patient spouses. Several of the family members were classified as surrogate decision-makers (n = 5). Most studies included the patient's clinical characteristics, such as ventilatory status or decisional capacity, as part of the family inclusion criteria. The sample sizes in each study ranged from 26 to 156 for quantitative descriptive, 19 to 230 for qualitative/mixed-methods, and 52 to 416 for experimental studies. The types of ICUs included medical,25 , 29, 30, 31, 32 surgical,33 cardiovascular,22 , 29 , 34 neuro,25 , 28 oncology,30 and COVID-1935 ICUs. Eight studies were conducted at a single site in a single unit,22 , 23 , 28 , 31, 32, 33, 34, 35 five at a single site in multiple units,25 , 27 , 29 , 30 , 36, and two in multiple centers.24 , 26 Most of the studies were conducted in the United States (10 out of 15), and the rest were conducted in other countries, including Italy (n = 2), Australia (n = 1), China (n = 1), and Iran (n = 1). The majority of the studies were conducted between 2015 and 2020 (n = 14).Table 2 Summary of selected studies. Table 2Author (year)/ Country Study design Purpose Sample/ Setting Measures and data collection Findings MMAT Quality appraisal(Max *****) Bastin et al. (2019) / USA Cross-sectional study To evaluate perceptions of television-based education among patients, caregivers, nurses, and other care providers in the ICU. *Providers (n = 114): Staff nurses (n = 97), other health care professionals (n = 17)*Patients and caregivers (n = 74)Medicine & cardiovascular ICUs at an academic medical center Likert scale survey of perceptions of the effects of television-based education on patient/family anxiety, satisfaction, knowledge, and health-related decision making.Patient and caregiver surveys were administered directly through the television screen in the patient's ICU room after they watched educational videos. 62% of patients and families strongly agreed that the videos increased satisfaction, 61% rated the quality of the videos highly, 71% strongly agreed that the videos were easy to understand, and 39% strongly agreed that they preferred television based education to traditional methods.Patients and caregivers are more optimistic than providers regarding the benefits of television-based education (P < .001). *** Carlucci et al. (2020) / Italy Qualitative To present analysis of family satisfaction during the pilot stage of remote family conference and? patient visits project Family members (n = 19)ICU of the COVID hospital Questionnaire assessing the quality of service perceived by the userQualitative evaluation of the project via telephone survey The information given to families were considered 100% excellent.Continuous contact with the patient and the physicians alleviated the suffering status of families.Qualitative evaluation showed that more frequent interviews with medical staff and news updates through SMS would be desirable. N/A (didn't pass the screening questions) Chiang et al. (2017)/ China A randomized controlled trial To determine whether 'education of families by tab' about the patient's condition was more associated with improved anxiety, stress, and depression levels than the 'education of families by routine'. Main family caregivers (n = 74): 'educationof families by tab' intervention (n = 39) vs. 'education of families by routine' control (n = 35)An ICU in a public district hospital Family anxiety and depression: Depression Anxiety Stress Scale – Chinese (C-DASS) was administered pre- and post-interventionFamily satisfaction: Society of Critical Care Medicine's Family Needs Assessment Questionnaire (SCCMFNA) - Communication and Physical Comfort Domain was completed post-intervention Intervention group did not show significantly lower stress and anxiety scores compared to control group.Significant group interaction effect was observed from the 2 depression subscale measurements (P < 0.01;P = 0.09) with a medium effect size.Information need satisfaction was not significantly different (intervention mean 26.53, SD 5.66; control mean 26.68, SD 4.95; P = 0.327). ***** Cox et al. (2019) /USA Multicenter, parallel, randomized clinical trial. To determine effects of a web-based decision aid about prolonged mechanical ventilation (PMV) on prognostic concordance between surrogate decision makers (SDMs) and clinicians. Patients on PMV (n = 277)Surrogates (n = 416)Clinicians (n = 427)Intervention (138 patients, 137 primary SDMs, 73 additional SDMs) vs. Control (139 patients, 138 primary SDMs, 68 additional SDMs)13 medical, surgical, trauma, cardia, and neurologic ICUs at 5 hospitals Concordance on 1-year survival estimates: Clinician-surrogate concordance scaleSurrogates experience-uncertainty in decision making: decisional conflict scale-satisfaction with clinician communication: quality of communication questionnaire-comprehension of diagnosis, treatment, and prognosis: medical comprehension scale-psychosocial distress: Hospital Anxiety and Depression Scale, posttraumatic stress symptom inventory Concordance improvement did not differ between intervention and control groups (P = 0.60).Intervention primary surrogates had greater reduction in decisional conflict than control surrogates (P = 0.041).No significant group differences on medical comprehension, communication, or surrogates' psychosocial distress were found. **** Dalal et al., (2016) / USA Mixed - methods To evaluate a web-based, patient-centered toolkit (PCTK) for enrollment strategy, use and usability of patient tools, and the content of patient-generated messages. Patients and family caregivers (n = 239):119 patients, 120 caregiversMICU (n = 103; 26 patients, 77 caregivers) and Oncology units (n = 136; 93 patients, 43 caregivers) Usage of the PCTK by participantsSystem Usability: System Usability Scale (SUS)Satisfaction: Likert scale satisfaction ratingQualitative analyses: message contents and all patient feedback Of 239, 200 patients (84%) used the PCTK for 1-4 days and 158 (66%) sent at least one message to providers.Use of educational content was highest for medications and test results.The mean SUS score was 74.0 (16.7) and 72% of respondents were satisfied or extremely satisfied with PCTK.The most common clinical theme identified in messages sent by patients and caregivers within the PCTK was health concerns, needs, preferences, or questions. *** de Havenon et al. (2015) / USA Prospective, nonrandomized, pilot study To test the effects of virtual family meeting with conference calling or Skype videoconferencing on family satisfaction and efficiency of decision making about patient care. Family members who opted for audiovisual intervention (n = 29) vs. control (n = 59)A neurocritical care unit at a large academic hospital Family meeting survey (6 items) assessing satisfaction with the meeting and decision-making processSurvey was administered after the standard family meetings (1st stage) and after the second meeting with videoconference option (2nd stage) whether or not they opted for the audiovisual intervention No significant group differences were found between groups on the family satisfaction with the decision-making process, makingdecisions that were reflective of the patient's wishes, unresolved issue, agreement on patient wishes, and overall satisfaction. N/A (didn't pass the screening questions) Ernecoff et al. (2016) / USA Qualitative To explore key stakeholders' perceptions of an interactive tablet-based and video-driven communication and decision support tool Surrogates and care providers (n = 58): 30 surrogates and 28 care providersMICU in a university hospital One-on-one in-depth, semi-structured interviews asking -perceptions about the acceptability and usefulness of the tool-design suggestions for refinements of such a toolInterviews were conducted by one trained researcher in a private room adjacent to the ICU or by telephone 95% (55/58) of participants perceived the proposed tool to be acceptableIdentified potential benefits include being helpful for families to prepare for the surrogate role and for family meetings, giving surrogates time and a framework to think about the patient's values and treatment options.Key design suggestions included: conceptualizing the tool as a supplement to rather than a substitute for communication; making access the tool flexible; incorporating interactive exercises; using video and narration to minimize the cognitive load; and building an extremely simple user interface ***** Gorman et al. (2020)/ Australia Prospective observational study To test 1) whether real-time SMS updates could be efficiently delivered to families and 2) these SMS updates would be accepted and welcomed by families Patients (n = 91) and family (n = 156)Cardiac surgery ICU Likert scale questionnaire asking participants' perceptions of the SMS serviceFamilies were followed up with a telephone call after the discharge and questionnaire was administered All five SMS messages were successfully sent for 91 patients to 114 participants (73%).Families perceived SMS service as reassuring, easy to follow, and helpful to keep participants informed. Almost all felt the SMS service did not increase anxiety.All disagreed with the SMS service being intrusive and would recommend the service to other families. **** Mistraletti et al. (2017)/ Italy Prospective multicenter before-and-after study To assess the effectiveness of information brochure and website on communication intended to improve the psychological outcome and family members' understanding of what is happening to long-stay ICU patients Relatives:Before (control, n = 144) vs. after (intervention, n = 179)Nine ICUs in both urban and rural hospitals, university and non-university hospitals) Family understanding of medical information: comprehension assessment interview (CAI)Family healthcare satisfaction: critical care family needs inventory (CCFNI)Family anxiety and depression: hospital anxiety and depression scale (HADS)Family post-traumatic symptoms: short screening scale for PTSDRelatives completed questionnaires after they attended an interdisciplinary family conference. Of the 179 relatives, 131 (73%) stated they had read the brochure and 34 (19%) reported viewing the website.The intervention was associated with an increased correct understanding of the prognosis (P = 0.04) and the therapeutic procedures (P = 0.03).The intervention was significantly associated with a lower incidence of post-traumatic stress symptoms (Poisson coefficient = −0.29, 95% CI −0.52-0.07).The intervention had no effect on the prevalence of symptoms of anxiety and depression. ***** Pignatiello et al. (2019) / USA Cross-sectional data analysis of a longitudinal randomized controlled trial To compare the differences in cognitive load reported by SDMs of the critically ill exposed to two different decision support interventions (video-based VS. avatar-based aids), while controlling for their age. Surrogate decision makers (n = 97)Video-based aid (n = 47)Avatar-based aid (n = 50)4 ICUs in a tertiary medical center Cognitive Load Scale (CLS): measuring intrinsic and extraneous cognitive loadCLS was administered immediately after the electronic decision aid Intrinsic and extraneous cognitive load of video-based decision support were lower than avatar-based decision support.While controlling for age, mean levels of intrinsic cognitive load were not significantly different from one another (P = .14), whereas extraneous cognitive load was significantly different from one another between the two study groups (P = .001). *** Sasangohar et al. (2020) / USA Qualitative To document family members' experience with virtual ICU visits during COVID-19 pandemic Family caregivers (n = 230)Virtual ICU A short interview designed to elicit- family members' feelings experienced during the virtual visit- barriers, challenges or concerns faced using virtual-ICU visit- opportunities for improvementsFamily members were interviewed post-visit via phone Over 86% of participants had positive sentiments and shared feelings of happiness, joy, gratitude and relief to be able to visit their family members.Reported barriers include inability to communicate due to patient status, technical difficulties, lack of touch and physical presence and frequency and clarity of communication with team.Suggested improvements included on-demand access, improved communication with a care team, improved scheduling process, and improved system feedback and technical capabilities. ***** Sucher et al. (2011) / USA Prospective observational study To assess how patients and families would perceive robotic telepresence aiding morning rounding process Patients (n = 24) and their families (n = 26)Surgical ICU in a tertiary hospital Likert scale survey asking opinions and attitudes toward the use of robotic telepresencePatients and families who had interaction with the robot completed survey anonymously 93% of participants were comfortable with the robot, and 84% reported that communication was easy.90% did not perceive the robot as annoying and 92% did not believe that the doctor cared less about them because of the robot. 92% supported the continued use of the robot. **** Suen et al. (2020a)/ USA Mixed-methods To develop and pilot-test the Family Support Tool, an interactive web-based tool to help individuals navigate the complexities of surrogate decision making in the ICU Phase 1: 30 surrogates and 28 ICU care providersPhase 2: 3 people with surrogate decision- making experience 1 intensivist, 1 palliative care physician, 1 ICU nurse, 1 social worker and 1 pastor.Phase 3: 6 surrogatesPhase 4: developmentPhase 5: 9 surrogates and 4 ICU physicians Phase 1: design of the preliminary schematic of the toolPhase 2: engage key stakeholders to refine the preliminary designPhase 3: user testing of low-fidelity prototypePhase 4: creation of a high-fidelity prototypePhase 5: user testing of high-fidelity prototype Technology development successful Surrogates judged the final tool as highly usable, acceptable and effective. The tool helped them to consider goals of care.Surrogates actively making decisions in the ICU judged the final tool to be highly usable, acceptable and effective.All surrogates reported the tool helped them consider goals of care and all indicated they would recommend the tool to a friend. ***** Suen et al. (2020b)/ USA Two-arm single blind patient level randomized trial To assess the feasibility, usability, acceptability, and perceived effectiveness of a communication intervention that pairs proactive family meetings with an interactive web-based tool to help surrogates prepare for clinician-family meetings. Patients and their primary surrogates (n = 57): intervention (n = 27) vs. control (n = 25)1 neuroscience ICU and 1 medical ICU in a tertiary hospital Usability and acceptability: System Usability Scale (SUS) and acceptability questionnaire were administered before the first family meetingPerceived effectiveness: internally generated Likert scale questionnaire was administered to intervention group immediately after the first and second family meetingQuality of communication questionnaire: Quality of Communication questionnaire was administered to both intervention and control group at 3-mos follow-up Surrogates reported that the tool was highly usable, acceptable, and effective.Compared to the control group, the intervention group reported higher overall quality of communication and higher quality in shared decision-making, but difference was not statistically significant ***** Ziyaefard et al. (2019) / Iran Quasi-experimental To evaluate the effects of virtual social media-based education on anxiety and satisfaction among family members of patients in the ICU following coronary artery bypass graft surgery (CABG). Family members of post-CABG patients in the ICU (n = 100)Intervention (n = 50) vs. control (n = 50)ICU of Cardiovascular, Medical, and Research Center Family anxiety: Spielberger State-Trait Anxiety Inventory (STAI)Family satisfaction with ICU: ICU family satisfaction questionnaire Intervention (virtual education) was effective in improving family satisfaction (P <0.001).Intervention group had lower degrees of anxiety than the control group (P < 0.001). *** Characteristics of interventions The various types, purposes, and features of each intervention were captured in the selected studies (Table 3 ). The interventions included web-, tablet-, SMS-, or video-based technologies to help families understand the ICU environment, illness, or treatment plan and augment participation and decision-making in family meetings. Telepresence using video conference, telephonic, or robotic technologies was also used for virtual access to the patient or to provide patient updates and information during multidisciplinary rounds or family conferences. Several interventions in the selected studies provided individualized information or tailored coaching based on the needs and input into the tools by the patients or their families.23 , 26 , 30 , 31 , 35 , 36 For example, if the family member indicated that the patient was receiving a particular therapy, information about this therapy could then be accessed.23 However, this information was not tailored or individualized. In the interventions that enabled two-way communication among users,25, 26, 27 , 30 , 31 , 33 , 35 , 36 the features enabled an exchange of questions and answers among families and clinicians or allowed the clinicians to view the families' input and prepare for the family conference in advance. In some studies, interventions involved virtual visits/telepresence using video calls, phones, or robots to enable collaboration between clinicians and families in real time to enhance treatment decision-making and facilitate family meetings.28 , 32 , 33 , 35 Table 3 Summary of family-engagement Interventions. Table 3Author (year) Intervention Purpose Main content Target users Interaction (two-way communication) Personalization (tailoring) TIDieR framework (1-12) Bastin et al. (2019) Video (television) -based education program To combine video instruction to patient/family education Introduction to the ICU; fall prevention; pain management; preventing health-acquired infections Patients and families Not described Not described 8 Carlucci et al. (2020) Remote family conference and patient visits via phone and video calls To enhance family members' participation and provide daily clinical updates Daily clinical updates; medical information pertinent to the patient's condition; Family, patient, clinicians Communicating families' concerns with clinicians Provision of medical information pertinent to the patient's condition 9 Chiang et al. (2017) Tablet-based family education package To educational and informative content by comprehensive and standardized information and systematic way of provision using audiovisual features Information about ICU care and specific information of patient's disease or specific treatment Family Not described Content was explained depending on the needs of individual patients 8 Cox et al. (2019) Interactive web-based decision aid To support the shared decision-making process for prolonged mechanical ventilation Definition of prolonged ventilation; decision options for goals of treatment; function of surrogates in decision making; family support needs Family Basis of an algorithm informed by responses within the tool Individualized prognosis 8 Dalal et al. (2016) Patient-centered toolkit: A web-based patient-facing and provider-facing tools To enhance collaborative decision making via education and patient-family-provider communication Navigate plan of care; establish recovery goals, input preferences and rate priorities; access medical records; educational content Family, patient, clinicians Exchange questions and answers among patients, families, and clinicians;Communication among clinicians Tailored safety tips and reminders 10 de Havenon et al. (2015) Conference calling or Skype teleconferencing before provider-family meeting To improve robust decisions about patient care by increasing families' participation in the decision-making process and enhancing the effectiveness of family meetings Not described Family, clinicians Not described Not described 8 Ernecoff et al. (2016) Interactive, tablet-based communication and decision support tool To prepare the family for conversations with clinicians; to give clinicians tailored information about the family and patient before the family meeting; to promote a personalized relationship between clinician and family Orientation to the ICU; principles of surrogate decision making; question prompt list and opportunity to write down questions; a values clarification exercise; education about treatment pathways; eliciting surrogates prognostic information; psychosocial resources. Family, clinicians The tool is programmed to interact with families;Clinicians can view families' input Tailored information was communicated with clinicians 9 Gorman et al. (2020) Real-time SMS updates To simply provide five pre-written messages related to key information about patient care to families during the ICU care Five specific clinical landmark events were sent at ICU admission; extubation; morning ward round; decision to discharge; and discharge from the ICU. Family Not described Not described 8 Mistraletti et al. (2017) Website information tool To provide families with knowledge of the ICU environment and patient's illness and enhance communication between providers and families Knowledge of ICU environment and treatment; family's role during the ICU care; illustration of post-ICU discharge expectations; emotional validation; stories of former ICU patientsor relatives Family Not described Not described 8 Pignatiello et al. (2019) Two electronic decision aid tools:video-basedavatar-based To support communication and decision-making between surrogates and clinicians Video-based decision aid: a short visual content discussing strategies for effective communication between providers and families, advocating for loved ones, and alternatives to consider.Avatar-based decision aid: an interactive virtual decision support led by an avatar decision coach; sharing patients’ story, patient's background, assessing their needs, discussing care plan Family, clinicians Video-based decision aid: Not applicableAvatar-based aid: two-way communication between family caregivers and a critical care team to discuss the care plan Video-based decision aid: Not applicableAvatar-based aid: to provide decision coaching based on family caregiver's input and needs 8 Sasangohar et al. (2020) Virtual ICU technology: Virtual visitation via smartphones, tablets, or computers To enable virtual family members in the ICU room Not applicable Family, patient Not applicable Not applicable 7 Sucher et al. (2011) Robotic telepresence multidisciplinary roundings To augment the multidisciplinary rounding process Not applicable Family, patient, clinicians Real-time two-way audio-video communication among patients, families, and clinicians Not applicable 9 Suen et al. (2020a,b) Interactive web-based family support tool To support communication and shared decision-making for surrogates Orientation to the ICU, emotional support, self-care tips, and families' expectations, questions, and understanding of patient's values and preferences, Family, clinicians Surrogates complete modules prior to a scheduled family meeting;Clinicians receive a one-page summary of surrogates' responses prior to family meeting surrogates' responses Not described 8,9 Ziyaefard et al. (2019) virtual social network-based education program via social media To alleviates family caregiver anxiety and satisfaction information about ICU conditions, hospital rules, and postoperative clinical situations, principles of taking care of patients at home and hospital, necessary diet and medications, level of patients’ activity at home, wound management, rehabilitation of patients Family caregivers Not described Not described 8 Findings of the selected studies Non-experimental studies Most non-experimental studies examined the participant's perceptions of the intervention. The acceptability and usability of the digital technology were assessed via qualitative interviews31 or using quantitative instrument, such as the System Usability Scale (SUS),30 and participants reported good acceptability and usability. Other outcomes reported include families' perceptions toward the intervention itself,33 service quality,34 , 35 effects of the digital technology on knowledge and decision-making,29 and barriers/concerns or suggestions to the digital technology.31 , 32 In two studies, satisfaction with the intervention was also reported as an outcome using investigator-developed Likert scale questionnaires29 , 30 and reported satisfaction in a majority of participants (62%29 and 72%30). Emotional responses of families were assessed in three studies using either qualitative interviews32 or internally developed surveys.29 , 34 One study reported that television-based education helped 63% of participants reduce their anxiety using a self-reported survey question.29 Another study assessed if the SMS messages of patient treatment and progress sent to family members were intrusive or made them feel anxious, and over 72% of participants responded the SMS did not induce negative emotions.34 Qualitative findings of another study reported that over 86% of participants had positive sentiments about being able to visit their loved one virtually, but some had mixed feelings since seeing the patient intubated contributed to sadness.32 In the same study, the technology provided a degree of closure for many who lost their loved ones. Experimental studies One study considered intervention usability measured using the SUS and perceived effectiveness using an internally generated Likert scale questionnaire.25 The family support communication tool was highly usable (mean SUS summary score 82.4) and effective (mean score 4.4 ± 0.2, where 1 ‘not at all’ to 5 ‘extremely well’). Satisfaction was considered as an outcome in four experimental studies, including satisfaction with the decision-making process (self-developed questionnaire),28 healthcare satisfaction (Critical Care Family Needs Inventory),24 family satisfaction with ICU (ICU family satisfaction questionnaire),22 and satisfaction with informational needs (communication and physical comfort domain of Society of Critical Care Medicine Family Needs Assessment).23 One study that tested a virtual social network-based education reported improved family satisfaction when the changes in the median of the satisfaction scores before and after the intervention were compared between groups (P < 0.001).22 Family members' psychological outcomes were measured in several experimental studies. Anxiety was considered as an outcome in four studies using well-validated measures, including Hospital Anxiety and Depression Scale (HADS),24 , 26 Spielberger State-Trait Anxiety Inventory,22 and Depression Anxiety Stress Scale (DASS).23 Among four studies that compared between-group outcomes, only one study that tested the virtual social network-based education reported significant reduction of anxiety in the intervention group (P < 0.001).22 Effects of the intervention on depression was examined in three studies using HADS24 , 26 and DASS.23 No significant effect on depression was found in all three studies, but one study found a significant group interaction effect.23 PTSD-related symptoms were reported in two studies using the Posttraumatic Stress Symptom Inventory26 and the short screening scale,24 respectively. One study found an association between the intervention and a lower incidence of PTSD-related symptoms in families during the ICU stay.24 Family involvement outcomes were also included as either primary or secondary outcomes in several experimental studies. Families’ understanding of medical information was measured in two studies using the Comprehension Assessment Interview24 and the medical comprehension scale,26 respectively. Communication with clinicians (Quality of Communication questionnaire),25 , 26 prognostic concordance between families and clinicians (Clinician-surrogate Concordance Scale),26 uncertainty in decision making (Decisional conflict scale).26 and family cognitive load (Cognitive load scale)36 were also reported in experimental studies. Additionally, studies also reported the intervention effect on improving correct understanding of the prognosis and the therapeutic procedures24 and a reduction in decisional conflict.26 Study evaluations Based on the MMAT study quality ratings, six of the studies met 100% of the criteria (*****), three met 80% (****), and four met 60% (***) (Table 2). Two of the studies did not pass the screening questions.28 , 35 None of the studies addressed all the items on the TIDieR checklist ( Table 3 ). All of the studies described the intervention and identified the problem. The studies included details about the digital technologies involved and the deliverers of a given intervention. Seven studies included interventions tailored to the specific needs of the family. Most studies included a single intervention session, although there were two studies that used iterative processes to refine the technology. Only one study included planned monitoring of intervention fidelity, but the results of the plan were not reported. Conceptual elements of ICU family engagement As summarized in Table 4 , we analyzed how the selected studies addressed each element of family engagement in their intervention using the framework by Brown et al.1 Interventions in most studies (9 of 15) addressed one or two elements.22, 23, 24 , 28 , 32, 33, 34, 35, 36 “Information sharing” 22, 23, 24, 25, 26, 27 , 29, 30, 31 , 33, 34, 35, 36 and “activation and participation.”22 , 25, 26, 27, 28, 29, 30, 31 , 35 were the most commonly addressed elements. Four studies addressed all five elements in their interventions which were on supporting shared the process of shared decision-making.25, 26, 27 , 30 The rest of the interventions involved only one or two engagement components.Table 4 Conceptual elements of the family engagement framework addressed by the selected studies. Table 4Author (year) Decision making Information sharing Collaboration Respect and dignity Activation and participation Bastin et al. (2019) V V V Carlucci et al. (2020) V V Chiang et al. (2017) V Cox et al. (2019) V V V V V Dalal et al. (2016) V V V V V de Havenon et al. (2015) V V Ernecoff et al. (2016) V V V V Gorman et al. (2020) V Mistraletti et al. (2017) V V Pignatiello et al. (2019) V Sasangohar et al. (2020) V Sucher et al. (2011) V V Suen et al. (2020a) V V V V V Suen et al. (2020b) V V V V V Ziyaefard et al. (2019) V V Discussion Since the first official guidelines of family support in the ICU were published in 2007,3 family members have been well acknowledged as essential care partners to improve outcomes for critically ill patients. Living in a time of digital transformation, which promises expanded potentials of better connection, communication, and customization using digital technologies, various efforts have been made to use digital technology to engage family members of the critically ill. In this integrative review, we identified and evaluated studies investigating digital technology-based interventions to promote family engagement in the ICU. Overall, the studies varied in their designs, objectives, and types of technology used. Our review is timely because this is one of the first integrative reviews that evaluated digital technology-based ICU family engagement interventions over the past 20 years based on an established conceptual framework. In our review, across the variety of digital technologies, most commonly involved engagement element was information sharing. Only four studies25, 26, 27 , 30 addressed all five family engagement components while the rest of the interventions involved only one or two of the engagement components. The study that introduced virtual visitations using smartphones, tablets, or computers during the COVID-19 pandemic32 addressed the respect and dignity family engagement component by providing patients and their families the opportunity to interact with one another and remain connected. The studies used tablet-based tools suggest that the same digital tool can either be simply designed for one-way information delivery23 or more complexly designed with extended features, such as video-driven communication or as decision-support that require collaborative communication and active partnerships between healthcare professionals and families.31 Although acceptability and usability were elicited from the families participating in these studies, only a few achieved constant family feedback during the process of designing the intervention.25 , 27 User-centered design, participatory technology development, or human-centered design, which consider the user an integral part of the design process, are commonly used terms in developing interventions using digital technology.37 Designers using such an approach actively seek input from users, both in controlled and natural settings, to determine which technological features are usable and attractive to the intervention. When introducing digital technology to improve family engagement, adopting user-centered design principles is essential for crafting an intervention that is reliable, less complicated, and, therefore suitable for families to use. The potential for digital technology to contribute to stress in ICU family members may be a legitimate concern. In this review, only a few studies mentioned family emotional reactions to the use of digital technology. One study assessed whether the intervention itself (SMS messages) was intrusive or made family caregivers feel anxious using survey questionnaire.34 Another study found that the technology (virtual ICU visitation) contributed family feelings of sadness while exploring their feelings by qualitative interviews.32 Moreover, most studies did not take into account individual characteristics such as education level, age or comfort with technology. Future studies need to include assessment of potential emotional distress while using technology and consider any facilitators or barriers that might influence the access or acceptance of digital technology. The effectiveness of digital technologies has been shown in informal caregivers of persons with dementia.38 Similar to the studies in our review, many of these interventions supported the information needs of informal caregivers and described important support services. However, unlike the studies in our review, the focus of interventions for dementia caregiver population has been to increase social support, deliver psychological therapy, and assist with the behavioral management of the care recipient.39 These studies targeted important caregiver outcomes such as social support, caregiver burden,40 and depression.41 In contrast, in our review, only a few studies22, 23, 24 , 26 examined the effects of the intervention on family members psychological outcomes such as anxiety, depression, and PTSD-related symptoms. This is likely due to the infancy of digital technology research in families of the critically ill. Although several experimental studies reported promising results in family satisfaction,22 anxiety,22 and family involvement outcomes,24 , 26 most studies included in this review were still at the early stage of intervention development or mainly focused on usability testing. Moreover, many studies used investigator-developed survey questions instead of validated measures, which limits our ability to compare and synthesize study findings. Given the current state of the evidence, more rigorous experimental studies are warranted. The COVID-19 pandemic revealed the critical importance of family presence on promoting the comfort of patients and families and reducing distress in ICU staff.42, 43, 44 During the pandemic, families were only able to contact their loved ones using digital technologies with phone- or tablet-based applications. Considering the time and traveling costs of making in-person visits that many families experienced prior to the pandemic-mandated visitation restriction, technological strategies were an attractive alternative to in-person family visits that can provide seamless connection among families, patients, and ICU staff. However, to our knowledge, no studies have engaged families in the design considerations for technological strategies for remote visitation. This review had several limitations. Although we included four major databases, we did not include all databases available for our search strategy. Therefore, we might have missed some studies that met our inclusion criteria. We limited our search to the use of digital technology-based strategies in only ICU inpatient settings and not during post-ICU periods. Considering the crucial role that families play during recovery from critical illness, there may be value in including studies conducted during post-ICU periods. The search was limited to only the inpatient phase of critical illness owing to the differences in the goals and contexts of care for family engagement after ICU discharge.36 Future reviews should focus on family engagement strategies for post-ICU recovery and outcomes. Since only studies written in English were included, this review was limited to studies from developed countries (the United States, Europe, Australia, China, and Iran). There has been an acceleration in the development of digital technologies, and the public is becoming more comfortable with technological solutions to common problems. Considering the pervasive use of technology in ICUs, this search may have missed relevant recent information because we limited our search to only research and excluded prototype papers, case reports, editorials, and descriptive commentaries. Moreover, the profound impact of the COVID-19 pandemic on clinical care in ICUs suggests that this topic will need to be revisited in the near future, as continued innovation of technologies to improve family engagement in ICUs is anticipated. Conclusions Although the importance of family engagement in the ICU setting is recognized, the evidence has not yet been fully established. Digital technology offers attractive solutions to overcome the challenges of engaging family members in the ICU. Findings from our review revealed that most digital technology-based interventions addressed the basic level of needs, such as simple one-way communication from clinician to family members. We recommend further testing of interventions using digital technology to address the collaboration and decision-making elements of family engagement. Funding sources This work was supported by Mo-Im Kim Nursing Research Institute, Yonsei University College of Nursing (JC), Institute for Innovation in Digital Healthcare, Yonsei University (JC) and the Heather M. Young Postdoctoral Fellowship, Gordon and Betty Moore Foundation (JS). Declaration of Competing Interest No conflict of interest has been declared by the authors. ==== Refs References 1 Brown S.M. Rozenblum R. Aboumatar H. 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Effects of a personalized web-based decision aid for surrogate decision makers of patients with prolonged mechanical ventilation: a randomized clinical trial Ann Intern Med 170 5 2019 285 297 10.7326/M18-2335 30690645 27 Suen A.O. Butler R.A. Arnold R. Developing the family support tool: an interactive, web-based tool to help families navigate the complexities of surrogate decision making in ICUs J Crit Care 56 2020 132 139 10.1016/j.jcrc.2019.12.002 31896447 28 de Havenon A. Petersen C. Tanana M. Wold J. Hoesch R. A pilot study of audiovisual family meetings in the intensive care unit J Crit Care 30 5 2015 881 883 10.1016/j.jcrc.2015.05.027 26100581 29 Thompson Bastin M.L. Short G.T. Cook A.M. Rust K. Flannery AH. Patients' and care providers' perceptions of television-based education in the intensive care unit Am J Crit Care 28 4 2019 307 315 10.4037/ajcc2019156 31263014 30 Dalal A.K. Dykes P.C. Collins S. A web-based, patient-centered toolkit to engage patients and caregivers in the acute care setting: a preliminary evaluation J Am Med Inform Assoc 23 1 2016 80 87 10.1093/jamia/ocv093 26239859 31 Ernecoff N.C. Witteman H.O. Chon K. Key stakeholders' perceptions of the acceptability and usefulness of a tablet-based tool to improve communication and shared decision making in ICUs J Crit Care 33 2016 19 25 10.1016/j.jcrc.2016.01.030 27037049 32 Sasangohar F. Dhala A. Zheng F. Ahmadi N. Kash B. Masud F. Use of telecritical care for family visitation to ICU during the COVID-19 pandemic: an interview study and sentiment analysis BMJ Qual Saf 30 9 2021 715 721 10.1136/bmjqs-2020-011604 33 Sucher J.F. Todd S.R. Jones S.L. Throckmorton T. Turner K.L. Moore F.A. Robotic telepresence: a helpful adjunct that is viewed favorably by critically ill surgical patients Am J Surg 202 6 2011 843 847 10.1016/j.amjsurg.2011.08.001 22137142 34 Gorman K. MacIsaac C. Presneill J. Hadley D. Nolte J. Bellomo R. Successful implementation of a short message service (SMS) as intensive care to family communication tool Crit Care Resusc 22 3 2020 221 226 32900328 35 Carlucci M. Carpagnano L.F. Dalfino L. Grasso S. Migliore G. Stand by me 2.0. Visits by family members at COVID-19 time Acta Biomed 91 2 2020 71 74 10.23750/abm.v91i2.9569 Published 2020 May 11 36 Pignatiello G.A. Daly B. Demaree H. Moore S. Hickman R.L. Jr. Comparing cognitive load levels among family members of the critically ill exposed to electronic decision aids Appl Nurs Res 50 2019 151192 10.1016/j.apnr.2019.151192 37 Gould J.D. Lewis C. Designing for usability: key principles and what designers think Commun ACM 28 3 1985 300 311 10.1145/3166.3170 38 Schulz R. Beach S.R. Czaja S.J. Martire L.M. Monin J.K. Family caregiving for older adults Annu Rev Psychol 71 2020 635 659 10.1146/annurev-psych-010419-050754 31905111 39 Godwin K.M. Mills W.L. Anderson J.A. Kunik M.E. Technology-driven interventions for caregivers of persons with dementia: a systematic review Am J Alzheimers Dis Other Demen 28 3 2013 216 222 10.1177/1533317513481091 23528881 40 Czaja S.J. Loewenstein D. Schulz R. Nair S.N. Perdomo D. A videophone psychosocial intervention for dementia caregivers Am J Geriatr Psychiatry 21 11 2013 1071 1081 10.1016/j.jagp.2013.02.019 23831174 41 Wasilewski M.B. Stinson J.N. Cameron JI. Web-based health interventions for family caregivers of elderly individuals: a scoping review Int J Med Inform 103 2017 109 138 10.1016/j.ijmedinf.2017.04.009 28550996 42 Cattelan J. Castellano S. Merdji H. Psychological effects of remote-only communication among reference persons of ICU patients during COVID-19 pandemic J Intensive Care 9 1 2021 5 10.1186/s40560-020-00520-w Published 2021 Jan 9 33422153 43 Tandon S. Medamana J. Roccaforte JD. Searching for humanity in the time of COVID Intensive Care Med 47 4 2021 500 502 10.1007/s00134-020-06231-y 32894335 44 Kanaris C. Moral distress in the intensive care unit during the pandemic: the burden of dying alone Intensive Care Med 47 1 2021 141 143 10.1007/s00134-020-06194-0 32789567

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==== Front Int J Surg Int J Surg International Journal of Surgery (London, England) 1743-9191 1743-9159 IJS Publishing Group Ltd. Published by Elsevier Ltd. S1743-9191(21)00121-7 10.1016/j.ijsu.2021.105987 105987 Review Crisis management for surgical teams and their leaders, lessons from the COVID-19 pandemic; A structured approach to developing resilience or natural organisational responses Pring Edward T. abc∗1 Malietzis George b1 Kendall Simon W.H. de Jenkins John T. ab Athanasiou Thanos b a Department of Surgery, St Mark!s Hospital, Watford Road, Harrow, HA1 3UJ, UK b Department of Surgery and Cancer, Imperial College London, Paddington, London, W2 1NY, UK c Chartered Management Institute, 77 Kingsway, London, WC2B 6SR, UK d Department of Cardiothoracic Surgery, The James Cook University Hospital, Marton Road, Middlesbrough, TS4 3BW, UK e Society for Cardiothoracic Surgery in Great Britain and Ireland & Hon President, Society Clinical Perfusion Scientists of Great Britain and Ireland, UK ∗ Corresponding author. Department of Surgery & Cancer, St Mary!s Hospital, Imperial College London, London, UK. 1 Both authors have contributed equally to this work and share first authorship. 4 6 2021 7 2021 4 6 2021 91 105987105987 1 3 2021 1 5 2021 25 5 2021 © 2021 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved. 2021 IJS Publishing Group Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Multiple industries and organisations are afflicted by and respond to institutional crises daily. As surgeons, we respond to crisis frequently and individually such as with critically unwell patients or in mass casualty scenarios; but rarely, do we encounter institutional or multi-institutional crisis with multiple actors as we have seen with the COVID-19 pan-demic. Businesses, private industry and the financial sector have been in a more precar-ious position regarding crisis and consequently have developed rapid response strate-gies employing foresight to reduce risk to assets and financial liquidity. Moreover, large nationalised governmental organisations such as the military have strategies in place ow-ing to a rapidly evolving geopolitical climate with the expectation of immediate new chal-lenges either in the negotiating room or indeed the field of conflict. Despite both nation-alised and privatised healthcare systems existing, both appeared ill-prepared for the COVID-19 global crisis. Methods A narrative review of the literature was undertaken exploring the approach to crisis man-agement and models used in organisations exposed to institutional crises outside the field of medicine. Results There are many parallels between the organisational management of private business institutions, large military organisations and surgical organisational management in healthcare. Models from management consultancies and the armed forces were ex-plored discussed and adapted for the surgical leader providing a framework through which the surgical leader can bring about an successful response to an institutional crisis and ensure future resilience. Conclusion We believe that healthcare, and surgeons (as leaders) in particular, can learn from these other organisations and industries to engage appropriate generic operational plans and contingencies in preparation for whatever further crises may arise in the future, both near and distant. As such, following a review of the literature, we have explored a number of models we believe are adaptable for the surgical community to ensure we remain a dy-namically responsive and ever prepared profession. Keywords Covid Crisis Management models Resilience Leadership Naturalisation Surgeon eader ==== Body pmc1 Introduction The sudden emergence of a crisis, such as COVID-19 has far reaching impacts within healthcare beyond the infectious disease aspects alone. The impact alone within surgery has been devastating both for surgeons and their patients. Certain surgical specialties such as head and neck surgeons, suffered early high exposure with a disproportionate incidence of illness [1] and surgeons overall accounted for 5 of the 18 deaths of all doc-tors reported by the April 22, 2020 in the UK (more than any other speciality) [2]. Elective operating lists were cancelled for both benign and malignant disease, screening and out-patient assessment all but stopped and emergency care had constraints placed upon it. As we pass through the second wave of COVID 19 infections it is important that we learn from the first wave and adapt our healthcare systems by adopting crisis management strategies. One key factor in any organisation response is the team. The surgical team is a hetero-genous entity comprising multiple disciplines, including surgeons, anaesthetists, inten-sivists, lead nurses, managers and allied health professionals, all working together to achieve a shared goal. Within each discipline there exists particular individuals who have the suitable qualities for leading a team through crisis. It is important to recognise that surgeons, though pivotal within the team are not necessarily best placed to lead the team. Recognising this and subsequently identifying leaders based on the qualities de-scribed throughout this manuscript is the first and most important step towards success-ful crisis navigation. It is also vital to recognise that although organisations are often hier-archical leaders and leadership roles occur and are adopted at any level throughout the hierarchy, from the Chief Executive Officer or Chief of Surgery through to and including ancillary or support staff. The requirements of leaders and leadership at each level are crucially different but as part of a whole are no less important or vital. Leaders at each strata of an organisations will have different logistical, theoretical and practical roles which are required for a natural organisational response. Such leadership and a willingness to speak up, highlight problems effectively and vitally ensures experts at each level can propose solutions and institute change. From a management standpoint we recognised that the level of preparedness and ap-proach to crisis management has been both disorganised and at times dysfunctional. In 2014, at the Whitehouse briefing on the Ebola epidemic response, Barrack Obama stated “There may and likely will come a time in which we have both an airborne disease that is deadly. And in order for us to deal with that effectively, we have to put in place an infrastructure - not just here at home, but globally - that allows us to see it quickly, isolate it quickly, respond to it quickly” [3]. Despite such warnings, the world appeared relatively unprepared for the crisis that was to unfold. France, for example, were inciner-ating their stockpile of FFP3 and surgical masks until March 2020 despite the presence of Sars-CoV-2 in the country [4]. Our aim was to identify models which were simple, re-producible and adaptable guiding the surgical leader through the management of insti-tutional crisis leading to a resilient, natural organisational response. 2 Method A narrative review was performed to identify management models from outside medicine applicable to healthcare structures. We applied these to key areas perceived to be in need of durable strategies that benefit our patients and healthcare organisations where uncertainty and change occur. We searched primarily for models that focussed upon sustainability and antifragility; concepts that must be the foundation of any successful strategy to manage healthcare crises. Models were discussed by the authors who are experienced in surgical leadership and organisational management and subsequent models were adapted to suit a surgical response. 3 Results From our review two approaches exemplified how a crisis can be managed successfully. The first approach is the Respond, Recover and Thrive model; based on how the busi-ness world is reacting and planning ahead by putting their customers and the prosperity of their organisations first. The second approach considered utilising a military model; a rapid response approach shapes the characteristics of a well-prepared army to fight the “invader”. 4 Respond, recover and thrive (RRT) business model The local, regional and global medical environment is challenging, complex, and unpre-dictable. The need for multilevel leadership has never been greater, and the demands on leaders are ever increasing. The “surgeon leader” needs to have an understanding of leadership at all levels of an organisation and the skills inherent to successful leadership. This crisis has afforded an opportunity for natural leaders and decision makers from within the surgical team, who could promote their skill set and develop expertise in this field. A “mega-crisis” such as the current pandemic demands effective surgical leader-ship today and requires different competencies to those of the past. One model that may be beneficial in this evolving crisis is that presented by the consulting firm Deloitte (Lon-don, UK) [5]. Deloitte, as experts in crisis response and management in business, and have published widely in this area. They state that a crisis, such as the COVID-19 pan-demic, develops over three-time frames or phases termed Respond, Recover and Thrive. Respond, in which an organisation deals with the present situation and manages conti-nuity; Recover, during which it learns and emerges stronger; and Thrive, where it pre-pares for and shapes the “next normal”. This model sets out the fundamental qualities of resilient leadership and defines the ba-sics by which one responds to a major threat and combats it with a well-orchestrated plan of action allowing a stronger recovery. This model is summarised in Fig. 1 .Fig. 1 A roadmap from Respond to Thrive – helping the resilient surgeon navigate through the Recover phase (5). Fig. 1 5 “The respond phase” The following five characteristics of resilient leadership will distinguish successful leaders as they guide their enterprises through and “respond” to the emergence of a new crisis. These are qualities that today#s surgeons need to have and base their response on to the COVID-19 crisis upon. 5.1 Design from the head and the heart In crisis, the hardest things can be the softest things. Resilient leaders are those who genuinely, sincerely, empathetically and compassionately walk in the shoes of col-leagues, patients, and those of their broader medical communities. Crisis managers must however, as resilient leaders, assume a hard, emotionally detached and rational line to protect both financial and clinical performance from the invariable emotionality that ac-companies such disruptions. Surgeons among healthcare leaders are experts in weigh-ing up risks against benefits and during crisis decisions should be made pragmatically based on the most up to date available information ensuring the balanced weighs in favour of the benefits. Uncertainty can paralyse decision-making; responding by the centralisation of decision-making into fewer nodes allows consistency, speed, and most importantly decisiveness. We have seen examples of this centralisation with positive feedback such as “cancer hubs” and other centralised modes of treatment delivery. Surgeons and leaders of all levels must have a catalogue of their resources and each site/department must have a clear and decisive plan on how to respond. Tools such as the remote readiness frame-work shown in Fig. 2 can help plan and allocate resources. Rapidly articulating data driven scenarios with modelling the projected clinical and financial impact must be readily available to help with the continuous and responsive counter planning. Each organisation needs to define “non-negotiables” and for us as medical professionals; patients and staff safety should take primacy. Finally, identifying the “levers” leadership has available and determining the precedence of actions to take, with firm agreement on the hierarchy of levers to be pulled as the severity of scenarios unfolds is also of paramount importance.Fig. 2 Remote readiness framework model for planning activities and services [6]. Fig. 2 5.2 Put the mission first Surgeons are trained throughout their career and clinical practice to be skilled in triage. This ability to prioritise means they can rapidly appraise the crisis at hand, seeking out opportunities amid difficult constraints and ultimately aim to stabilise their organisations. To achieve this, priorities should include launching and sustaining a crisis command cen-tre, supporting talent and strategy while maintaining business continuity of care within the financial constraints. Other major focuses within the respond phase that a surgeon ought to have are the strengthening of digital capabilities while staying engaged with patients, shoring up the supply chain and engaging with the ecosystem of their organi-sation. A surgeon who is able to achieve these mission objectives under the constraints of crisis could be termed a “Resilient Surgeon”. 5.3 Speed over elegance Resilient surgeons should take decisive action with courage and conviction based on imperfect information, knowing that expediency is essential. Within this they should ex-pect scrutiny and criticism that will arise following dissection of the crisis management plan after resolution. Surgeons are familiar with this concept; a catastrophic and critical situation in the operating theatre requires similar expediency, often with limited infor-mation and an expectation of detailed analysis and positive and negative criticism follow-ing the event either by the patient#s family or as part of existing governance structures. 5.4 Own the narrative Seizing and owning the narrative at the outset, being transparent about current realities, including what we do not know, whilst also painting a compelling picture of the future that inspires others to persevere, is another fundamental quality required by a surgeon as part of the respond phase. In a time of crisis, trust is paramount. This simple formula empha-sises the key elements of trust for individuals and for organisations: Trust = Transparency + Relationship + Experience 5.5 Embrace the long view Surgical leaders and their teams should maintain focus upon the horizon, anticipating new organisational models that will emerge whilst sparking the innovations that will define tomorrow. Resistance to change at times can be appropriate but acceptance that change is inevitable and pragmatism must be a key component of the resilient surgeon#s make-up. 6 “The Recover Phase” Resilient surgeons view recovery as a journey for their organisation, teams and stake-holders. There are five imperatives within the Recover Phase of the model to guide them from Respond to Thrive. 6.1 Mindset shift - understand the required Here the crisis-situation shifts from the unpredictability and frenetic activity of the early Respond period to a more settled, though still uncomfortable, sense of uncertainty (an “interim” normal). The focus of leadership expands from a very inward (and entirely ap-propriate) focus on employee safety and operational continuity to also include embracing a return to what is termed in business a “market-facing posture”. Organisations and sur-gical departments should be seen as being ready to return to business, available services should be made apparent to those feeding the market, be that hospital colleagues or primary healthcare. Patient fear and uncertainty must also be managed appropriately with the expectation that due process in place and their safety considered central to the newly evolved service. Management goals shift from managing the crisis; keeping the organisation functioning; to managing the transition back to a restored future. Planning shifts from short-term contingency planning to mid and long-term economic and sce-nario planning to understand the related impacts on clinical outcomes, operational pro-cedures, employees, finance, and so forth. Leadership attitude shifts from the primarily reactive mode described above to anticipating how to reinvent the organisation for this new era. 6.2 Identify and navigate the uncertainties and implications The substantial shifts in society, its institutions, and its individuals during the crisis have introduced major uncertainties into our once familiar structures. These shifts have re-sulted in macro-level changes with uncertainties about the underpinnings of healthcare, business and society that resilient leaders in any profession must navigate. Changes in the social contract and societal expectations of healthcare bodies and institutions are reframed to ensure the viability of all stakeholders. In business the implicit contract be-tween corporations and their stakeholders has always been based on accepted and generally unspoken assumptions about “the way things are” or status quo. But in a crisis the status quo has been forcibly changed, and that contract, as such, is rewritten requir-ing changes in the roles and rules for institutions. This parallels with the healthcare and surgical model, patient#s priorities are readdressed, their concept of acceptable risk changes and as such their relationship and expectation of their surgeon will change too. Managing the expectations of stakeholders associated with an institution from the Re- spond to the Thrive phase is paramount; Fig. 3 sets out a model through which this can be achieved.Fig. 3 Stakeholder management from Respond to Thrive (5). Fig. 3 The pandemic has precipitated financial discord through governments, economies, cor-porate sectors, financial institutions, treasurer#s offices, small businesses, non-profit or-ganisations and individual purses. The sources and uses of cash and the movement of liquidity during the crisis have been unpredictable; with resources poured into some sec-tors and other sectors squeezed financially. An expectation should also be present of the leaner times to come during the recovery phase where financial flows must be balanced. Leaders must plan for wide variations in their financial position and needs, all of which are dependent on the disease#s progression, the level of government stimulus, and the pace of economic recovery. Expectations are raised for physical, emotional, financial, and digital safety. Recovery will create anxiety among stakeholders as the post-COVID world takes shape. Understanding the fears of stakeholders and how their expectations for safety and security have changed, perhaps permanently, will be critical for surgical leaders as they seek to restore confidence and plan for the future. 6.3 Embed trust as the catalyst to recovery During the Recovery phase, resilient surgeons must inspire their teams to navigate through the significant COVID-related uncertainties. But great leadership requires even greater “followership” and “followership” is nurtured by trust. Many leaders have built a significant bank of trust from deftly navigating through the early frenzied unpredictable stages of the crisis and this should be encouraged and perpetuated. 6.4 Define the destination and launch the “recover playbook” Defining the destination first and then working backward is an approach that can help surgeons create more ambitious and creative plans. Envisioning the leadership team in a position of success is emotionally enabling, and it frees the team from some of the constraints of the present. It also disrupts incremental thinking, which often hampers creativity. Surgeons, as leaders of today, will need to ask key strategic questions when defining the destination e.g. what is most important in creating advantage: strategy, structure, or size? The answers can suggest a variety of tactics to pursue during recov-ery, such as accelerating implementation of pre-COVID-19 strategic options, scaling pi-lots in progress, developing novel organic models and approaches such as centralisation of services e.g. cancer hubs, public-private partnership and joint hospital networks [7]. With this comes the need of finding “deal opportunities” among struggling or failing units or hospitals. 6.5 Learn from the successes of other#s Recovery is uncharted territory and therefore observing and learning from others recovery strategies is critical. Other nations and other healthcare systems will be leading the curve and close observation and collaboration with colleagues at other centres is vital. Mistakes as well as successes will have been made by them and a pragmatic and flexible collegiate approach will help the resilient surgeon avoid erroneous approaches and capitalise upon successful models. 7 “The thrive phase” This crisis has unexpectedly redefined how surgeons interact with their patients. Every surgical unit needs to adapt, change and innovate their practices to remain viable and productive. There are no exceptions. Units and individual surgeons who fail to act will likely find it difficult to recover and thrive. 7.1 Understand the patients Every surgeon needs to understand what goes on in their patients# lives to have true empathy and compassion, although this has always been so. In-depth knowledge of what people have experienced in recent months forms the starting point for strong pa-tient relationships and all else can follow. Doctors and patients have navigated how to maintain the care delivered during this unprecedented medical crisis, as public health officials strongly urge them to avoid in-person visits to offices, clinics and hospitals. Tel-emedicine has been widely and discretely implemented. Video links are being used by doctors not only to treat COVID-19 patients who are not severe enough to require hos-pitalisation but also to assess anyone who needs routine care. Insurers and healthcare organisations have been challenged to embrace telemedicine along with doctors and patients. Obstacles that once may have prevented adoption have fallen away quickly. The surgical community are used to using technology and have readily embraced it, however, patients may not and we must not forget this and must ourselves be patient and willing to adapt to a better negotiated model for patients. While convenience yields to necessity during a lockdown, telemedicine may be a solu-tion, adopted out of duress, that will create significant post-crisis advantages for those who get it right. Cost and time efficiencies are being demonstrated that doctors and patients will likely remember beyond the pandemic. Beyond convenience, telemedicine may become a lasting solution, even after the COVID-19 threat recedes, to those who have long feared contracting an illness in a doctor#s office. It may also bring with it cost savings both to healthcare organisations and patients which will become even more im-portant in the recovery phase as global markets settle and state coffers refill. 7.2 Bring empathy and humanity The social distancing required to slow the spread of the coronavirus has refocused our attention toward more basic needs. Prestige and self-actualisation become less im-portant when people are locked down at home or worried about how to get to and from an essential job and stay safe at work. The priorities people have shifted toward family, food, learning, work, and money. This highlights the need for every patient experience strategy to be grounded in empathy and human needs. It shows why today#s surgeons need to sharply focus all patient interactions on building trust. 7.3 Embrace digital acceleration Most businesses and healthcare providers have taken to heart the necessity of digital transformation to stay current and competitive. But now it#s suddenly vital for every or-ganisation to accelerate their effort and boost their urgency. If digital tools are suddenly the only way to reach your patients, the need is obvious and existential. 7.4 Be open to collaboration With lockdowns and contagion concerns motivating new ways, some businesses are responding by collaborating with organisations that they would never have previously considered. This is a matter of business leaders looking beyond how things have always been done. The ecosystem of partnerships that makes business possible is extensive and varied. Rethinking how it $% put together can allow for greater creativity in response to a crisis and, in some cases, an opportunity for differentiation when the crisis ebbs. New public private partnerships will form; services may be better delivered in a certain manner in an environment different to that which we are traditionally used to. Private hospitals, for example, may be better placed to manage rapid throughput day case work-loads through profit-based delivery systems whilst larger, potentially state led, institutions may be better equipped to manage larger cases and therefore state and private providers both benefit from closer mergers and partnerships. An extension of this concept is the utilisation of the “expert stranger” when unfamiliar concepts and problems are encoun-tered by the team or the leadership. An expert stranger, as an individual with expertise in addressing a particular novel concept, enters an organisation and provides advice and input to allow an appropriate organisational response. Recognition and early employment of such individuals helps develop and train an adaptable and reactive team and mentor nascent and established leaders within the organisation broadening their experience. 7.5 Build for agility and adaptability Successful businesses have long recognised that being nimble, flexible and adaptable provides competitive advantages; the COVID-19 pandemic has reinforced that principle. Companies that pivoted quickly to fix deficiencies in their customer model exposed by new social distancing practices have been rewarded. Businesses that have adapted quickly have kept lines of communication open with customers and have benefitted from ongoing brand loyalty and this must be encouraged within the surgical community. When rebuilding our healthcare models, adaptability should be key, streamlining procurement and removing roadblocks associated with stale and fragmented state segments are vital. 7.6 The military model Militaries confront rapidly evolving crises almost daily. In the theatre of a combat situation there are logistical, personnel and time critical decisions that need to be made to attain the end goal. We have explored a number of approaches taken by military organisations globally and examine how the approaches may be utilised to aid surgical crisis manage-ment. As such, the militaries around the world have enshrined crisis management within their doctrine. One example being the UK Ministry of Defence Joint Doctrine Publication 01, UK Joint Operations Doctrine that devotes a significant proportion of its prose to Crisis Management [11]. Crisis management philosophy is interwoven throughout this doc-ument with Crisis response examined in relation to National Process including their stra-tegic structures and mechanisms for decision-making and crisis management, opera-tional level planning and latterly the command process. The military response to the Covid crisis in the UK perhaps demonstrates the efficacy in which the military can respond to a new and unforeseen problem by following set pro-cesses. This has been demonstrated by the logistical support provided by the military in the construction and opening of the NHS Nightingale hospital in London, a 4000-bed critical care unit – although the latter was not used significantly during the first wave of the crisis. Having a codified crisis management plan set out for a national organisation, which takes into account international actors allows a calm construction of a specific plan using a familiar set framework using universally understood language in the organisation setting. As surgeons and healthcare professionals we might learn from this example. National bodies and specialist societies could collaborate to produce a standardised surgical cri-sis management framework which could be manipulated and shaped to fit an individual crisis. 8 Operational planning – an integrated approach The military command structure is designed to handle issues that represent a very real danger and that escalate at an enormous and unpredictable pace, as such efficiency of decision making and communication are paramount. Hierarchal planning is seen as an important aspect within military organisational management. Bold leadership and deci-sive action are seen as vital attributes when time is short, pressure great and the stakes high. In military crisis management, hierarchy is designed with the operational level at its peak. The operational level is defined as the level of operations at which campaigns and major operations are planned, conducted and sustained to accomplish strategic objec-tives within theatres or areas of operations. The scale and level of command at the op-erational level is not pre-defined but assumes a size and shape that meets the demands of the specific operation. A set planning structure is key prior to and throughout a crisis, the military have three set questions which must be addressed during operational planning as part of crisis man-agement and resolution (Box 1 ).Box 1 Questions to be answered when planning for a crisis [8]. 1. What are the features of the current (crisis) situation? 2. What should the (more favourable) situation look like at the end? 3. How should the situation change or be changed? Alt-text: Box 1 This simple planning structure could be eminently transferable to the discipline of surgery and healthcare in general and takes into a count the dynamic nature of a crisis allowing development of scenarios which may or may not take place. 9 Multi-institutional working Fitting within other frameworks be they local, regional or national is vital during a crisis as it ensures everyone is speaking the same language, aids communication and allows con-tinuity of process. The UK military crisis management doctrine fits in with NATO opera-tional planning and crisis management doctrine, this allows a near seamless transition between smaller and larger institutions. This could be replicated between crisis manage-ment planning at individual surgical unit or regional level and national crisis management doctrine in healthcare. Hence the model can be scaled from single departmental units, to regional surgical groups to national healthcare policy groups. During crises operational planning becomes hugely important especially, when in the surgical situation, there are likely to be multi-institutional interests. The model in Fig. 4 is a modification of the models of integrated planning at operational level adapted from the UK JDP 01 [11]. Here we see how different units or organisations potentially interact and demonstrate how a coordinated approach across healthcare with a central crisis management plan can interact with smaller special interest and speciality units. This high-lights a need for a permanent framework in place from the top of the hierarchy. This model is also applicable to smaller departmental sub-units within a hospital with an over-arching crisis management policy devised and held by the hospital executive team.Fig. 4 Working within a multi-institutional hierarchical framework.[11]. Fig. 4 There are numerous other lessons we, as surgeons, could take from this military ap-proach especially military models which have already been adapted for the business market. A number of parallels have been drawn between military and business crisis management and this analogy may be taken one step further into the surgical field. Yuval Atsmon and his colleagues at McKinsey & company examine such concepts fur-ther in their article that promotes a military structured response to crisis in business [12]. There are many parallels to the surgical community and lessons can be learnt from this. They drew three main insights from military crisis management that would benefit the business community. These insights in business are also, we believe, pertinent in a resil-ient surgical response to crisis. Firstly, they identified that organisations should adopt a military-command structure with a view to help reduce confusion and enable faster, bet-ter decision making in an organisation. Secondly, management should be simultaneously across all “time horizons” based on an integrated, strategic crisis-action plan in order to reduce chaos and accelerate decision making [13]. Thirdly, that age-old principles of war can help keep an organisation focused and motivated, improving its chances of achieving objectives. 10 Multiple task specific teams We have discussed a military command structure in relation to the operational level and hierarchy above, Atsmon and colleagues explore this concept further and describe the use of multiple teams with specific designated tasks in relation to the crisis as opposed to all being handled by a single committee. These multiple teams feed into the hierarchy to operational level. Such teams will have specific purposes and during the crisis process new teams should form and others disband. Four essential areas are covered by these teams a simplified model of their roles and functions are shown in Fig. 5 .Fig. 5 Teams structure for crisis management [9]. Fig. 5 Although the military is a structure dependent on hierarchy, it does in fact have a deci-sion-making structure that is very flat. One example is that a commander sets a direction, expressing their intent to the organisation, they then rely upon junior colleagues to make reasoned and mature judgments based on the information they receive. Surgery, like the military has barriers associated with hierarchy however delegation of tasks to junior col-leagues not only will improve their professional development but also will facilitate a shared workload and may also allow an improved dialog to generate more radical ideas. New information in a crisis is often overwhelming, evolving and expansive. Communica-tion of such information traditionally occurs in a top-down fashion; this can lead to infor-mation overload and confusing and contradictory messages especially if the relaying of information is not timely. The model above allows information to be cascaded to the appropriate team to action and as such ensure the right information, goes to the right individuals in a timely and efficient manner enabling a focussed and expert response with appropriate feedback. 11 Development of plans over the “time horizon” Here we present a further example by Hirt, Atsmon and colleagues where a planning matrix was derived from the military format. Here we can see how crisis management planning should be multidimensional looking at concepts which have arisen from the crisis, how these concepts have developed, how data and feedback are extracted and what the outcomes may be. This matrix plots these events and triggers over a time period allowing a structured approach to the crisis management (see Table 1 ) [[9], [10]].Table 1 Hypothetical strategic management crisis management in surgery viewing events up to the “time horizon” based on the model presented by Atsmon and col-leagues [9,10]. Table 1Time Week 0 Week 2–4 3–6 months 1–2 years New Normal Starting position Baseline and crisis context Outpatient clinics and elective operat- ing stops Operating lists can- celled Outpatient clinics transition to virtual consultation environ- ment Prepare for staff re- deployment/absence Growing non- treated elective workload Progressing dis- ease Staff absence Assessment of circumstance – “clinical liquidity positiona” Return of capac- ity Firm establish- ment of protocols Development of facilities and sys- tems designed to deal with new working risk and conditions Current working paradigm chal- lenged post crisis – requiring per- manent change of clinical ap- proach More efficient work stream har- nessing new technology Scenarios (issues and opportunities) Ward closures Staff illness Anaesthetic machine and ITU support re- duced On-going emergency workload Streamlined vir- tual clinics Staff returning to work Multiple collabo- rating units – e.g. cancer Hubs Emergency work- load equipment shortages Collaborative/shared/merger of services be- tween units Centralisation of key disease spe- cific service – cancer hubs Closure of units/loss of ser- vice No vaccine and on-going pres- ence of re- strictions Financial re- striction High patient de- mand Fewer restrictions om institution of new technology Testing efficiency allows stream- lined service Isolation units al- low safe post- operative ITU care More efficient pa- tient pathways Posture and broad direction of travel 6 weeks to resume cancer services 8 weeks to resume all elective surgery 6 months return to independent function in home unit Pressure on cen- tral government regrading other maladies Utilising public support for re- starting services Pressure on re- gional commis- sioners to restart services Mothballing of units and Hub based specialist model Active develop- ment of improved patient experi- ence and out- come Establishment of efficient training pathways Return to a nor- malised environ- ment taking for- ward efficiencies developed during crisis A resilient model healthcare sys- tem, safe, effi- cient and timely access, best possible special- ist care, high vol- ume system Learning from both excellence and error Strategic moves (options, safety nets, and no-regrets moves) Stop all but emer- gency surgery Assess risk to surgi- cal patients Push for other Clean units to open for elective surgery Assess out- comes through audit of new ser- vice design Consolidate suc- cessful advances in care and the healthcare sys- tem Focus service shown to im- prove outcome (both clinical and PROM) Assess risk to surgi- cal staff Ensure adequate supplies of PPE are ordered ICT solutions to shutdown of services Workforce allocation Amalgamate re- sources from dif- ferent units Seek centralised funding for new system Implement ICT solutions – re- mote MDTs Efficient staff testing with ade- quate PPE for staff and patients available in all centre Abandon or modify practices where inefficiency or poor outcome prevail Prioritise healthcare evolu- tion and innova- tion ensuring it remains patient focussed Trigger points Fewer operative re- Analyse/monitor Analyse current Increase in ad- Higher patient sources non-treated dis- position and as- vanced disease workload but a ease (known and sess “liquidity” load and back more efficient Patient and staff risk unknown)b pro- challenges in log from crisis process of pa- gression to iden- multiple, scenar- tient manage- Progressive disease tify operative bur- ios ment den a In business the liquidity position describes the difference between the sum of both liquid assets and incoming cash flow on one side and outgoing cash flow from existing commitments. Clinically this could be metaphorically related to the difference between availability of resources (beds, staff, operative equipment and facilities) and current clinical burden e.g., surgical waiting list length, emergency workload and burden of disease progression. b We can class patients who have attended screening, are symptomatic or on the waiting list as “known”. There may be a significant number of patients who due to the crisis failed to attend screening, have not presented to hos-pital with symptoms etc. And these could be classed as unknown – uses of AI and data science may be able to pre-dict this population number. 12 Strategic principles in crisis management Finally, Atsmon discusses the age-old strategic principles which apply to modern day crisis management and remain important principles to adhere to (9):• Select and focus on the overarching goal • Maintain resilience as the crisis unfolds • Embrace offensive action • Inject surprise and innovation • Conserve effort to sustain Ultimately, the military crisis response model can be simplified to and summarised in an interlinked four-quadrant schematic, Fig. 6 , dependent on the following factors.1. Knowing the facts – fact finding and the response 2. Having a clear Direction – knowing the end point and prioritising to achieve this 3. Formulating educated assumptions – presaging what will happen and the subse-quent consequences 4. Communicating effectively – defining a clear overall message and a target group for whom the message should be directed. Fig. 6 A simplified four quadrant approach to crisis management. Fig. 6 Simplicity of approach facilitates reproducibility in the military, and as surgeons working in multidisciplinary and multiorganisational teams this too is key. Foremost this reduces uncertainty and the potential for subsequent error. It allows individuals with varied expo-sure to leadership and crisis management to understand a system, this can breed new talent and ensures efficient use and involvement of the whole team. 13 Discussion 13.1 Evaluating the models Two approaches to crisis management have been described; both have merits as well as disadvantages, Table 2 summarises the major strengths and weaknesses of each model described and provides a rating of suitability for use by the surgical leader.Table 2 Summary evaluation of strengths and weaknesses of the models evaluated in this study. Table 2Model Benefits Drawbacks Rat- ing Delloitte Model Renjen et al.[5,6,11] Clear roadmap Address human and institutional factors Provides a tangible timeframe with clear reference points Can be become overly complicated Danger of rigidity and protocolisation * UK Military Model JDP 01 (8) Conceptually easy to implement Engages multiple resources Well tested in real world scenarios Adaptive and reactive Robust ensuring antifragility and sustain- ability Limited focus on tech- nology and artificial in- telligence ** McKinsey Model Atsmon et al.(9) Hirt et al.(10) Explores the entire timeframe of crisis Builds on established successful models Simple and transferable concepts An exemplar of building upon earlier success by other organisations Difficult to enact on global level Does not exploit the benefits of advancing technology *** As shown above each model has certain benefits and drawbacks, however the models share a number of themes. The RRT model describes the time frames of crisis and like the military model takes the resilient surgeon leader from the point of crisis through to the time horizon. Both models provide an excellent generic structure from which a re-sponse to any potential crisis can be made. Both are relatively simplistic and reproducible and undoubtedly both are transferable to a multidisciplinary healthcare model. Psycho-logically the approach is not alien to surgeons whom by the very nature of their daily practice encounter crisis but both allow expansion on traditional concepts and facilitate the approach when a crisis extends beyond their usual bounds of practice. Both models share the concepts of agility, resilience and subsequent antifragility. Understanding these concepts is key. Crises often progress at a high tempo and the resilient surgical leader must be equipped with the skills to make difficult, contentious and controversial deci-sions. Team support and collective responsibility breed antifragility and subsequent sta-bility and these qualities must be fostered. This approach is exemplified by the crisis response of the Pan London Emergency Cardiac Surgery service [14], who's approach was more akin to the military model. Its command structure is exemplary with services coor-dinated from a lead clinical centre coordinating regional units and fits within the model of multi-institutional hierarchy. Two-way communication and continuous dialog up down and across the hierarchy have been lynch pins of success in this approach. The adoption and exploitation of new technology such as telemedicine is key to success and mainte-nance of the elective workload. As with the response of Evans et al. described earlier, we have seen collaboration between private and government partners to maintain service delivery [7,12]. Flexibility and pragmatism are key, when using models, despite in built elasticity within these models it is human nature to respond with rigidity. There is a danger in both these models of falling into the trap of protocolisation as there is no in-built safeguard against this. Both models perhaps fail to promote true innovation, interestingly the approach to hierarchy within the military model perhaps has advantages at this point as it promotes delegation to potential new talent, where decision making is centralised but ideas and innovations are developed at a “specialist junior” level and fed to the central leader. We feel a deficit lies in both models with the lack of inclusion of evolving technologies with neither model truly embracing the virtual environment. This is increasingly important and we will be more reliant on this as technology improves. Data are vital in reducing ambiguity, but mass data can also compound ambiguity too. We believe that an evolving crisis response model should include methods for filtering and assimilating data. Machine learning and artificial intelligence should be embraced as well-designed systems with validated algorithms will aid decision-making and help determine an action response whilst testing scenarios. Atsmon, in his teams’ approach to crisis management, demonstrates that models can be taken from other philosophically different organisations and utilised in new arenas. We therefore believe that aspects of both models can be applied in a crisis but flexibility, the unpredictability of clinical practice and restricted resources must be considered. A suc-cessful model will be pragmatic, adaptable and be self-defensible. Future models must embrace the virtual environment and utilise AI however we must also accept that this too comes with restrictions of domain requirements and reliance on other factors of function and capability which lie within the remit of other sectors. 14 Approaching surgical crisis management in the geopolitical environment We have described models from both industry and the military, both these sources share attributes whilst adding in unique perspectives on the approach to crisis manage-ment. One critical aspect of crises such as the Covid 19 pandemic is their global effect and implications. We must approach such problems in a three-tiered approach as iso-lationism does not breed resilience and will breed failure. Here we propose the follow-ing approach to the global crisis based on the models above. 15 International level response The onus lies with international organisations including the WHO Global Surgery with assistance from bodies such as the Lancet Commission on Global Surgery to help en-sure strategies are in place at a global level. Learned bodies with an international pres-ence such as the American college of Surgeons or various Royal Colleges of Surgeons in the UK and Ireland, all of which have a broad international membership, also have a responsibility to ensure strategies are in place to help their members and fellows coordi-nate responses to crisis on at the international/national interface. Such an example would be the International Cancer Benchmarking Partnership (ICBP), a global collab-oration seeking to compare and improve cancer survival across high-income countries, who's members issued a series of recommendations in response to an editorial published in the Lancet Oncology calling for cancer care to be safeguarded globally in a post COVID world [13,14]. A straightforward framework should be provided through which national bodies could cooperate and assist one another in times of crisis. The Model above describing the national crisis framework management could easily be applied on an international level with feedback between an international hub allowing resources to be sent where they are needed. Defining the phases of crisis in surgery as response, recovery and thrive phases will ensure the time horizon has been addressed in a generic, simple and clear manner. Data overload and too much instruction from on high can lead to an increase in red tape and confusion at national and local level and as such simplicity is key. 16 National level response More complicated models can be introduced at national level but of course there is a necessity to fit into the global picture as described above. National surgical organisations can adopt a more complicated strategy similar to what is described within the time hori-zon model. This may vary from speciality to specialty but should provide a clear and simple strategy which is both flexible, testable and reproducible. Management of hierar-chy will be key at national level and a centralised unit should be in place with a nominated quasi political figurehead, e.g., College President, chairing major decisions within a man-ageable yet diverse committee. Small sub committees must feed into this process acting as operations, insights, communication and plan ahead teams [13]. Needless to say, these teams should not solely consist of surgeons but colleagues from communication teams, nursing and paramedical teams and public involvement. Multiple committees are already in place for other purposes and a policy which allows immediate repurposing of such committees to new roles within the crisis hierarchy are paramount. 17 Regional and local level response We have seen how interaction is best served from communication both up and across the hierarchy. Following a set plan described above mean that a familiar and reproducible process is in place which organisations can follow. At regional and local level comes perhaps the hardest challenge as it is here where generalisability is lost and the need to address minutiae appears. However, the process and models remain the same and the same questions are asked. The Time Horizon concept allows a detailed plan to be drawn up and communicated up the line in a set format to those at national and international level. This allows exemplary plans to be shared and assistance offered those struggling to innovate. A local crisis hierarchy should be set up again with a committee structure described above. Training committees and regional MDTs may provide a prefabricated populated committee which could once again be repurposed at a moment's notice. Con-cerns could be fed into this by colleagues in de novo sub committees which wax and wane in size and existence depending on the crisis. 18 Managing escalation One concept of crisis management which is important but not addressed fully by these models is escalation. Both these models rely on institution in a timely manner. Change is always incremental and knowing when to intervene is both a complex, controversial and challenging decision. Early intervention may have a detrimental impact on patient care, financial through put and collegiate support conversely, late intervention could lead to complete collapse of service and may have catastrophic outcomes for the patient pop-ulation. A transferable graphical representation can be derived from Buron and Curtis# “anxiety escalation curve” (15), Fig. 7 contextualises this in the surgical crisis environ-ment.Fig. 7 The escalation curve (modified from Buron and Curtis) [15]. Fig. 7 This approach allows conceptualisation and communication of a controlled response, visualisation of escalation helps colleagues and managers to understand the decision process and defining a locus upon the scale realises the situation. The aim is to institute an early response and exit the crisis curve as shown in the figure. 19 Evaluating the crisis management response in surgery As we pass through from the second phase of the current international crisis we should prepare for the next. Not knowing what this will be we should set out a generic structure to support antifragility and resilience when such a crisis appears. Eventual warning to others and rehabilitation are the lessons for the next pandemic. A successful leader must both learn from both their successes and failures. New knowledge gained from a crisis should be applied scientifically and logically to help shape and adapt on going systems for the better. Re-evaluation and governance mechanisms should be formulated to audit and assess any adaptations and practices should evolve accordingly in an evidence-based manner. We have described models from two very different organisation types above, we have also seen how private industry has sought to learn lessons from military crisis manage-ment. These models are by no way a panacea to a crisis and each has its own inade-quacies however our approach demonstrates how a surgical leader must explore novel and established models selecting elements which augment their leadership approach and allow a pragmatic and evolving response. We too can learn lessons as surgeons from these models and we believe that by have a standardised structured response for crisis management in surgery at multiple levels within the organisations we can provide better care for our patients in future. 20 How to assess and appraise the situation We#ve demonstrated models and in part justified their applicability to surgery but we have not addressed appraisal and feedback fully. It is, in crisis, important to utilise resources which are already in place and here. It is vital in crisis management that managerial struc-tures are elastic and dynamic and can both identify and respond to the evolving situation. During a crisis we will see moments of failure, where we miss the mark and fail to achieve the goals we set, we will also however, see moments of excellence and take-home les-sons from both. Identify and learning from both excellence and failure are vital and use of online reporting tools feeding back centrally can be of assistance e.g. Learning from Excellence (LfE) (https://learningfromexcellence.com) providing both a structured and standardised feedback system and also a framework which is positive rather than puni-tive. 21 Limitations We have, in our opinion selected the models we think will fit the surgical environment best, we do however accept there are other models which are applicable. Our review here is narrative and not exhaustive in its nature and recommendations drawn from here are of course open to debate. Refinements of these tools are required to integrate them fully into the healthcare model but we regard this a starting point upon which to build. 22 Conclusion Crises are by their very nature unpredictable entities; despite warnings we never ex-pected an infectious pandemic to impact so greatly on our practice as surgeons. We were somewhat caught out by events but a response to the challenge has brought about more successes than failures. It is important to recognise that unlike war or aspects of a financial crisis; disease does not recognise constraints such as geopolitical borders. This leads to greater uncertainty as it spreads through differing cultural and economic spheres. As such, strategies to manage a surgical or public health crisis of this magnitude should be generic, affordable and adaptable providing a scaffold on which local institu-tions can build pragmatically. Crisis breeds innovation and advancement and provides impetus. The principles we propose are, universal and a promotion of a generic stand-ardisable format which surgeons can follow to avoid being unprepared when the next crisis ultimately appears, no matter what that may be. Ethical approval Not Applicable – Review Article. Source of funding None. Author contribution Edward T Pring – Concept development, Study Design; research of models and review of data, writing of manuscript. Georgios Malietzis – Concept development, Study Design; research of models and review of data, writing of manuscript. Simon Kendall – Review of manuscript, advice on models and applicability to surgeons, writing and editing. John Jenkins - Review of manuscript, advice on models and applicability to surgeons, writing and editing. Thanos Athanasiou - Concept development, Study Design, development of models, writing, editing, senior author. Trial registry number 1 Name of the registry: N/A. 2 Unique Identifying number or registration ID: N/A. 3 Hyperlink to your specific registration (must be publicly accessible and will be checked): N/A. Guarantor Thanos Athanasiou (senior Author). Edward Pring. Georgios Malietzis. Simon Kendall. John Jenkins. Data statement All data used in this review are readily available online and can be accessed by following the relevant references at the end of the article. “Provenance and peer review Not commissioned, externally peer-reviewed. Disclosures None. Declaration of competing interest None. Acknowledgements Permission to reference JDP01 and derived figures has been granted by kind permission of The Development, Concepts and Doctrine Centre, 10.13039/100009941 Ministry of Defence , 10.13039/100013986 HM Government, Shrivenham, UK . ==== Refs References 1 Meccariello G. Gallo O. What ENT doctors should know about COVID-19 contagion risks Head Neck April 2020 1248 1249 32329539 2 Cook T. Kursumovic E. Lennane S. Exclusive: deaths of NHS staff from covid-19 analysed [Internet] Health Serv. J. 2020 Apr Available from https://www.hsj.co.uk/exclusive-deaths-of-nhs-staff-from-covid-19-analysed/7027471.article 3 Obama B. Remarks by the President on Research for Potential Ebola Vaccines [Internet] 2014 The White House Office of the Press Secretary [cited 2020 May 28]. Available from https://obamawhitehouse.archives.gov/the-press-office/2014/12/02/remarks-president-research-potential-ebola-vaccines 4 Sage A. Coronavirus: France's Facemask Fiasco Burns Deep for Macron [Internet] 2020 May 27 The Times Available from https://www.thetimes.co.uk/article/coronavirus-frances-facemask-fiasco-burns-deep-for-macron-kfkbdsd57 5 Renjen P. The Heart of Resilient Leadership Responding to COVID-19 [Internet] 2020 Mar;1–24 Deloitte Insights Available from https://www2.deloitte.com/global/en/pages/about-deloitte/articles/the-heart-of-resilient-leadership.html 6 Renjen P. The Essence of Resilient Leadership: Business Recovery from COVID-19 vols. 1–18 2020 Deloitte Insights 7 Evans S. Taylor C. Antoniou A. Agarwal T. Burns E. Jenkins J.T. Implementation of a clinical pathway for the surgical treatment of colorectal cancer during the COVID-19 pandemic Colorectal Dis. 22 9 2020 1002 1005 32654417 8 [Internet]. JDP01 Developments Concepts and Doctrine Centre Ministry of Defense 2014 HM Government, UK . Joint Doctrine Publication 01 UK Joint Operations Doctrine Joint Doctrine Publication 01 (JDP 01) [cited 2020 Oct 14]. Available from https://assets.publishing.service.gov.uk/government/uploads/system/uploads/att achment_data/file/389775/20141209-JDP_01_UK_Joint_Operations_Doctrine.pdf 9 Atsmon Y. Chinn D. Hirt M. Smit S. Lessons from the Generals: Decisive Action amid the Chaos of Crisis 2020 McKinsey Co ;1(May) 10 Hirt M. Smit S. Bradley C. Uhlaner R. Mysore M. Atsmon Y. Getting Ahead of the Next Stage of the Coronavirus Crisis [Internet] 2020;(April):1–11 McKinsey Co Available from https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/getting-ahead-of-the-next-stage-of-the-coronavirus-crisis 11 Renjen P. The Essence of Resilient Leadership [Internet] 2020;maj:1–17 Deloitte Insights Available from https://www.forbes.com/sites/deloitte/2020/08/31/lessons-from-the-pandemic- on-how-to-break-down-organizational-silos-and-optimize-workforce- potential/#520b8b8e3c65 12 Hussain A. Balmforth D. Yates M. Lopez-Marco A. Rathwell C. Lambourne J. The Pan London emergency cardiac surgery service: coordinating a response to the COVID-19 pandemic J. Card. Surg. 35 7 2020 1563 1569 32598501 13 Butler J. Finley C. Norell C.H. Harrison S. Bryant H. Achiam M.P. New approaches to cancer care in a COVID-19 world Lancet Oncol. 21 July 2020 e339 e340 32615112 14 The Lancet Oncology Safeguarding cancer care in a post-COVID-19 world Lancet Oncol. [Internet] 21 5 2020 603 10.1016/S1470-2045(20)30243-6 Available from 32359483 15 Buron K.D. Curtis M. The Incredible 5-Point Scale: the Significantly Improved and Expanded [Internet] second ed. 2012 AAPC Publishing Swannee Available from www.aapcautismbooks.com

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==== Front Urban Clim Urban Clim Urban Climate 2212-0955 Elsevier B.V. S2212-0955(21)00118-8 10.1016/j.uclim.2021.100888 100888 Article The effect of COVID-19 pandemic on human mobility and ambient air quality around the world: A systematic review Faridi Sasan a1 Yousefian Fatemeh b1 Janjani Hosna c1 Niazi Sadegh d Azimi Faramarz e Naddafi Kazem ac Hassanvand Mohammad Sadegh ac⁎ a Centre for Air Pollution Research (CAPR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran b Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran c Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran d Queensland University of Technology (QUT), Faculty of Science, School of Earth and Atmospheric Siences, Brisbane 4001, Australia e Department of Environment Health Engineering, Environmental Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran ⁎ Corresponding author at: Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, 8th Floor, No. 1547, North Kargar Avenue, Tehran, Iran. 1 Contributed equally. 18 6 2021 7 2021 18 6 2021 38 100888100888 9 3 2021 18 5 2021 13 6 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. We conducted this systematic review to identify and appraise studies investigating the coronavirus disease 2019 (COVID-19) effect on ambient air pollution status worldwide. The review of studies was conducted using determined search terms via three major electronic databases (PubMed, Web of Science, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. A total of 26 full-text studies were included in our analysis. The lockdown measures related to COVID-19 pandemic caused significant decreases in the concentrations of PM2.5, NO2, PM10, SO2 and CO globally in the range of 2.9%–76.5%, 18.0%–96.0%, 6.0%–75.0%, 6.8%–49.0% and 6.2%–64.8%, respectively. However, O3 concentration increased in the range of 2.4%–252.3%. The highest decrease of PM2.5 was found in 16 states of Malaysia (76.5%), followed by Zaragoza (Spain) with 58.0% and Delhi (India) with 53.1%. The highest reduction of NO2 was found in Salé city (Morocco) with 96.0%, followed by Mumbai (India) with 75.0%, India with 70.0%, Valencia (Spain) with 69.0%, and São Paulo (Brazil) with 68.0%, respectively. The highest increase of O3 was recorded for Milan (Italy) with 252.3% and 169.9% during the first and third phases of lockdown measures, and for Kolkata (India) with 87% at the second phase of lockdown measures. Owing to the lockdown restrictions in the studied countries and cities, driving and public transit as a proxy of human mobilities and the factors affecting emission sources of ambient air pollution decreased in the ranges of 30–88% and 45–94%, respectively. There was a considerable variation in the reduction of ambient air pollutants in the countries and cities as the degree of lockdown measures had varied there. Our results illustrated that the COVID-19 pandemic had provided lessons and extra motivations for comprehensive implementing policies to reduce air pollution and its health effects in the future. Keywords Ambient air quality SARS-CoV-2 COVID-19 Lockdown measures ==== Body pmc1 Introduction The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged in Wuhan, China (Dacre et al., 2020; Niazi et al., 2020; Rugani and Caro, 2020), triggering a significant challenge for communities, healthcare organizations and economies worldwide (Faridi et al., 2020a; Wang and Su, 2020). To minimize close contact and virus transmission, lockdown restrictions were recommended by national and international public health bodies (Lancet, 2020; Petroni et al., 2020). Consequently, most countries across the world implemented lockdowns or quarantine/isolation measures to slow down the spread of the virus (Bao and Zhang, 2020; Gautam, 2020b; Sicard et al., 2020). As a consequence of these implementations, there were direct/indirect significant changes in the economic and environmental statuses (Dacre et al., 2020; Faridi et al., 2020c; Gautam, 2020a; Mahato et al., 2020). The COVID-19 pandemic has halted all human being's socio-economic activities, thereby the global oil demand plunged and as a result prices cut down sharply (Chauhan and Singh, 2020; Muhammad et al., 2020). Contrary to negative dramatic economic impacts due to SARS-CoV-2 spread (Agrawala et al., 2020; Collivignarelli et al., 2020; Dantas et al., 2020; Wang and Su, 2020), a considerable improvement in ambient air quality status was observed globally, particularly in the heavily polluted countries/cities (Agrawala et al., 2020; Dutheil et al., 2020; Gautam, 2020a; Lancet, 2020; Zambrano-Monserrate et al., 2020). The association between COVD-19 pandemic and ambient air quality status has been studied via various data analysis methodologies in different countries/cities. Therefore, it is fundamentally essential to combine and compare the results of these studies to support national and international policy-makers for adopting the most effective air pollution measures in the future. The present study aimed to summarize and assess the existing research findings of the impacts of the COVID-19 pandemic on air quality status globally. We also included the changes in meteorological parameters reported by conducted studies and human mobility index data to show significant ambient air quality status changes during the lockdown measures. 2 Methods 2.1 Search strategy and criteria for studies selection This systematic review aimed to review the studies investigating the relationship between SARS-CoV-2 and the status of ambient air quality worldwide. We explored the relevant studies published in three primary electronic databases of Scopus, Web of sciences, and PubMed from 1st January 2020 until 30th May 2020. The search strategy was developed using the following keywords: “Coronavirus”, “Corona”, “COVID”, “2019-nCoV acute respiratory disease”, “Novel coronavirus pneumonia”, “Severe pneumonia with novel pathogens”, “coronavirus 2”, “SARS-CoV-2”, “SARS virus”, “Covid pandemic”, “Covid lockdown”, “air quality”, “air pollution”, “environmental pollution”, “atmospheric pollution”, “air pollutants”, “particulate matter”, “PM2.5”, “PM10”, “NOX”, “NO2”, “nitrogen dioxide”, “nitrogen oxides”, “SO2”, “sulfur dioxide”, “carbon monoxide”, “CO”, “O3”, “ozone”, “tropospheric ozone”. Boolean operators such as “AND” and “OR” were used to combine the above-mentioned search key terms. Also, to increase the sensitivity and gather higher records (specifically for pre-proof manuscripts), additional documents were identified from hand-searching and reviewing the referenced list of retrieved papers. This systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline (Fig. 1 ). Studies were included if they met the following criteria: 1) published in a peer-review journal in English, 2) reported the quantitative and qualitative data for the association between air quality and COVID-19 pandemic. We excluded preprints, non-English language papers, conference abstracts, news articles, and posters. Two of the authors reviewed the titles and abstracts separately and selected the relevant studies. The final included studies were based on the full-text evaluation.Fig. 1 PRISMA flow diagram for selection of relevant studies. Fig. 1 3 Results 3.1 Search results As shown in Fig. 1, the initial searches provided 934 records (Scopus: 403, PubMed: 382, and Web of sciences: 149 documents). Also, we identified four records through other sources. In the screening step, 884 articles were unrelated to the purpose of our study and were excluded based on the title and abstract, and duplication. Finally, the remaining 54 studies were reviewed for eligibility evaluation, which by the end, 26 full-text articles met the inclusion criteria for the extraction of their findings. 3.2 Description of included studies Out of the 26 reviewed studies that met our quantitative and qualitative inclusion criteria, (Fig. S1) 15 were conducted in Asia (China, Malaysia, India, United Arab Emirates (UAE), Kazakhstan, Singapore, Thailand, Vietnam, Indonesia, Philippines, Cambodia, Laos, Myanmar), four in Southern America (Brazil), two in Europe (Italy, Spain, France) and one in Africa (Morocco). Another five studies were conducted in more than one continent (two in Asia and Europe, two in Asia, Europe, and Northern America (USA)). 3.3 The implemented lockdown restrictions to prevent the spread of COVID-19 around the world To better control the COVID-19 pandemic, the international public health bodies (e.g., World Health Organization and U.S. Centers for Disease Control and Prevention) have recommended that people stay at home (Lancet, 2020). As a result, the partial to complete lockdown restrictions were adopted and immediately implemented by countries worldwide. Table 1 gives detailed information concerning the countries and the type of lockdown actions, implementing to minimize the virus spread. The implemented lockdown measures consisted of closing down public transportation, religious public places, school/universities, businesses, shopping centers (except for essential services), industrial activates (except for crucial industries), the public entertainment places (Parks, Beaches, Restaurants, etc.) and the movements (traveling between states and abroad). We also reported the results of the reviewed studies based on the implemented stages in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7 . Table 2 gives detailed information on lockdown measures implemented in various phases and step by step in included countries. It should be noted that the lockdown measures had been implemented in multiple phases in the countries and cities under study (Chauhan and Singh, 2020; Collivignarelli et al., 2020; Kanniah et al., 2020). Some countries and cities implemented the complete lockdown, whereas others have applied partial measures. As a result, the reduction of ambient air pollutants can be varied there.Table 1 Scheme of activities allowed and prohibited during COVID-19 lockdown in the included studies. Table 1- Allowed (green), Prohibited (red), Not-reported (gray). aIn other countries (Singapore, Thailand, Vietnam, Indonesia, Philippine, Cambodia, Laos, Myanmar, and Morocco), lockdown measures without reporting any types of activities were reported. Fig. 2 PM2.5 changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 2 Fig. 3 NO2 changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 3 Fig. 4 PM10 changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 4 Fig. 5 SO2 changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 5 Fig. 6 CO changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 6 Fig. 7 O3 changes (%) due to COVID-19 pandemic lockdown bans over the world. Fig. 7 Table 2 Detail information on the included studies investigating the effect of COVID-19 on ambient air quality status. Table 2Study ID Country/Cities Methodology Sources of air quality data Air pollutants The changes of ambient air pollutants Concentration (percent changes) Concentration (percent changes) (Abdullah et al., 2020) Malaysia (16 states) Before MCO1 (14–17 March 2020) vs During Phase I MCO (18–31 March 2020) SAQMSs2 (68 stations: 1 background and 67 traffic and residential) PM2.5 (−42.6) During Phase I vs Phase II MCO (1–14 April 2020) (−76.5) (Wang et al., 2020) Northern China (30 Cities) Before lockdown: 1 to 23 January 2020 & During lockdown: 24 January to 9 February 2020 SAQMSs (366 sites: traffic, residential, industrial, and background) AQI, PM2.5, PM10, CO, SO2, NO2, and O3 O3: +20.1 μg m−3 (+51.0) AQI: −18.0 (−20.0), CO: −0.2 mg/m3 (−20.0), SO2: −2.2 μg m−3 (−16.0), NO2: −19.4 μg m−3 (−54), PM2.5: −13.6 μg m−3 (−21.0), PM10: −23 μg m−3 (−27.0) (Sicard et al., 2020) Nice Between two time periods in 2020 and the same periods averaged over 2017–2019: Before lockdown (from 1st January until the start date of the lockdown) and During the lockdown (from the start date of the lockdown until 8th April in Wuhan i.e. the end date of the lockdown, and until 18th April in Nice, Turin, Rome, and Valencia). SAQMSs (36 traffic, industrial and residential stations: 3 ones for Nice, 15 ones for Rome, 4 stations for Turin, 6 sites for Valencia, and 8 sites for Wuhan) NO, NO2, PM2.5 PM10 and O3 O3: (+24.0) PM10: (−5.9), PM2.5: (−2.9), NO2: (−62.8), NO: (−70.7) Rome O3: (+13.6), PM10: (+1.8), PM2.5: (+10.6) NO2: (−45.6), NO: (−68.5) Turin O3: (+7.0) PM10: (−8.9), PM2.5: (−12.6), NO2: (−30.4), NO: (−52.6) Valencia O3: (+2.4) PM10: (−32.1), PM2.5: (−12.6), NO2: (−69.0), NO: (−61.9) Wuhan O3: (+36.4) PM10: (−48.7), PM2.5: (−36.3), NO2: (−57.2) (Siciliano et al., 2020) Brazil (Rio de Janeiro) Partial lockdown (03/23/2020–04/05/2020) vs Before the partial lockdown (03/01/2020–03/22/2020) SAQMSs (2 stations: traffic and industrial) NOX, O3 and NMHC O3: (+ 6.3 to +12.9) NOX: (−24.4 to −48.1), NMHC: (−14.3 to −25.0) Relaxed partial lockdown (04/06/2020–04/16/2020) vsBefore the partial lockdown (03/01/2020–03/22/2020) O3: (+0.1 to +18.0) NOX: (−9.2 to −13.8), NMHC: (0.0 to −12.5) (Collivignarelli et al., 2020) Italy, Milan Phase I: Reference period (CTRL): February 7, 2020, to February 20, 2020, vs Partial Lockdown: 9th to 22nd of March 2020) SAQMSs PM10, PM2.5, BC, Benzene, SO2, CO, and NOX O3: (+169.9) PM10: (−39.5), PM2.5: (−37.1), BC: (−57.5), Benzene: (−49.6), CO: (−45.6), SO2: (−19.9), NO2: (−43.1), NOX: (−59.9) Phase II: Reference period (CTRL): February 7, 2020, to February 20, 2020, vs Total Lockdown: 23rd of March to 5th of April, O3: (+252.3) PM10: (−48.0), PM2.5: (−47.4), BC: (−71.0), Benzene: (−69.0) CO: (−57.6), SO2: (−25.4), NO2: (−61.4), NOX: (−74.5) Phase III: Partial Lockdown: 9th to 22nd of March 2020 vs Total Lockdown: 23rd of March to 5th of April, O3: (+30.5) PM10: (−14.1), PM2.5: (−16.3), BC: (−31.7), Benzene: (−38.4), CO: (−22.0), SO2: (−6.8), NO2: (−32.1), NOX: (−36.6) (Dantas et al., 2020) Brazil (Rio de Janeiro) During the lockdown: March 2–15, 2020 (First and Second weeks) vs Third (03/16–03/22), Fourth (03/23–03/29), Fifth (03/30–05/04), and Sixth (05/04–12/04) weeks SAQMSs (3 stations: industrial, traffic, and residential ones) PM10, NO2, CO, O3, and NMHC PM10: Fifth week (+2.1 to +3.6), Sixth (+3.6 to +25.5), O3: Third week (+31.1 to +63.0), Fourth week (−2.7 to +44.0), Fifth week (+17.0 to +67.1), Sixth (−2.7 to +34.0), NMHC: Third week (+21.4%) PM10: Third week (−15.1 to +10.7), Fourth week (−17.5 to −33.5), NO2: Third week (−1.8 to +28.8), Fourth week (−32.2 to −53.9), Fifth week (−19.7 to +32.1), Sixth (−16.8 to +1.4), CO: Third week (−15.2 to +12.0), Fourth week (−41.3 to −48.5), Fifth week (−30.3 to −30.4), Sixth week (−30.3 to −42.4), NMHC: Fourth and Fifth weeks (−28.4 and − 14.3) (Sharma and Zhang, 2020) India (22 cities) During lockdown: March 16th to April 14th from 2020 vs the same period of 2017, 2018 and 2019 SAQMSs (30 stations without reporting their types) AQI, PM10, PM2.5, CO,NO, NOX, NO2, O3 and SO2 O3: (+17.0) AQI: (−30.0), PM10: (−31.0), PM2.5: (−43.0), CO: (−10.0), NO2: (−18.0) (Burns et al., 2020) China (Yangtze River Delta such as Shanghai, Hangzhou, Nanjing, Hefei with 41 cities) Pre-lockdown: 1st January to 23rd January 2020 vs same periods in 2019 SAQMSs (41 stations without reporting their types) SO2, NO2, CO, O3, PM2.5 and PM10 O3: (+10.4) PM2.5: (−12.3), PM10: (−19.6), CO: (−7.8), NO2: (−18.5), SO2: (−29.3) Level I: roughly 24th January to 25th February 2020 vs same periods in 2019 O3: (+20.5) PM2.5: (−31.8), PM10: (−33.7), CO: (−20.9), NO2: (−45.1), SO2: (−20.4) Level II: roughly 26th February to 31st March 2020 vs same periods in 2019 – PM2.5: (−33.2), PM10: (−29.0), CO: (−14.7), NO2: (−25.9), SO2: (−27.2), O3: (−7.8) (Chauhan and Singh, 2020) USA (New York) Phase I: March 2020 vs March 2019 (covered Chinese Spring Festival) Phase II: March 2020 vs February 2020, Phase III: February 2020 vs January 2020) SAQMSs (NR) PM2.5 Phase I: (−32.0) and Phase II: (−20.0) USA (Los Angeles) Phase I: (−4.0) and Phase II: (−30.0) Spain (Zaragoza) Phase I: (−58.0) Italy (Rome) Phase I: (0.0), Phase II: (−24.0) and Phase III: (−47.1) UAE (Dubai) Phase I: (−11.0) and Phase II: (−6.0), India (Delhi) Phase I: (−35.0) India (Mumbai) Phase I: (−14.0) China (Beijing) Phase II: (−50.0) China (Shanghai) Phase I: (−50.0) (Krecl et al., 2020) Brazil (São Paulo) Before lockdown: March 2–20, During lockdown: March 24–April 3 SAQMSs (13 stations without reporting their types) NOX −34.0 to −68.0 (Xu et al., 2020b) China (Hubei province) January to March 2017–2020: February 2017–2019, February 2020 SAQMSs (NR) PM2.5, PM10, CO, NO2, SO2 and O3 O3: (+14.3) PM2.5: (−30.1), PM10: (−40.5), SO2: (−4.0), CO: (−27.9), NO2: (−61.4) (Mahato et al., 2020) India (Delhi) Before lockdown: 2nd March to 21st March) vsDuring lockdown: 25th March to 14th April 2020 SAQMSs (34 stations: traffic, industrial and residential) PM10, PM2.5, SO2, NO2, CO, O3, NH3, and AQI O3: (+0.78) PM2.5: (−53.1), PM10: (−51.9), SO2: (−18.0), NO2: (−52.7), CO: (−30.4), NH3: (−12.3), AQI: (−61.0) 24th March to 14th April during 2017 to 2020 PM10, PM2.5 PM10: (−56.6), PM2.5: (−32.6) (Xu et al., 2020a) China (Wuhan, Jingmen, Enshi) January 2017–2019 vs 2020 SAQMSs (NR) PM10, PM2.5, SO2, NO2, CO, O3 and AQI O3: (+12.7) PM10: (−37.6), PM2.5: (−36.2), SO2: (−45.0), NO2: (−35.6), CO: (−28.8) and AQI: (−32.2) February 2017–2019 vs 2020 O3: (+14.3) PM10: (−40.5), PM2.5: (−30.1), SO2: (−33.4), NO2: (−61.4), CO: (−27.9) and AQI: (−27.7) March 2017–2019 vs 2020 O3: (+11.6) PM10: (−22.5), PM2.5: (−15.8), SO2: (−29.7), NO2: (−56.6), CO: (−20.3), AQI: (−14.9) (Jain and Sharma, 2020) India (Delhi) Phase I: March–April 2019 and March–April 2020 SAQMSs (69 stations without reporting their types: 38 stations for Delhi, 10 ones for Mumbai, 4 ones for Chennai, 10 ones Bangalore, 7 ones for Kolkata) PM2.5, PM10, CO, NO2, and O3 O3: (+7.0) PM2.5: (−41.0), PM10: (−52.0), NO2: (−50.0), CO: (−29.0) Phase II: Before the lockdown (10th –20th March 2020) and (during lockdown (25th March to 6th April 2020) PM2.5: (−45.0), PM10: (−52.0), NO2: (−48.0), CO: (−41.0), O3: (−14.0) India (Mumbai) Phase I: March–April 2019 and March–April 2020 O3: (+8.0) PM2.5: (−33.0), PM10: NR (−47.0), NO2: (−75.0), CO: (−46.0) Phase II: Before the lockdown (10th –20th March 2020) and (during lockdown (25th March to 6th April 2020) NR NR India (Chennai) Phase I: March–April 2019 and March–April 2020 O3: (+3.0) PM2.5: (−14.0), PM10: NR, NO2: (−32.0), CO: (−35.0) Phase II: Before the lockdown (10th –20th March 2020) and (during lockdown (25th March to 6th April 2020) O3: (+73.0) PM2.5: (−39.0), PM10: NR, NO2: (−43.0), CO: (−23.0) India (Bangalore) Phase I: March–April 2019 and March–April 2020 PM2.5: (−22.0), PM10: (−34.0), NO2: (−60.0), CO: (−16.0), O3: (−11.0) Phase II: Before the lockdown (10th –20th March 2020) and (during lockdown (25th March to 6th April 2020) PM2.5: (−47.0), PM10: (−40.0), NO2: (−56.0), CO: (−15.0), O3: (−21.0) India (Kolkata) Phase I: March–April 2019 and March–April 2020 O3: (+17.0) PM2.5: (−22.0), PM10: (−34.0), NO2: (−60.0), CO: (−29.0) Phase II: Before the lockdown (10th –20th March 2020) and (during lockdown (25th March to 6th April 2020) O3: (+87.0) PM2.5: (−27.0), PM10: (−32.0), NO2: (−66.0), CO: (−16.0) (Isaifan, 2020) China (Wuhan) Before lockdown: from January 1st to 20th) and After quarantines: from February 10th to 25th Satellite data, NASA NO2, CO NO2: (−30.0), CO: (−25.0) (Chen et al., 2020) China (367 cities) January 1, 2016 vs March 14, 2020 Satellite data, Sentinel-5 PM2.5, NO2 NO2: −12.9 μg/m3 PM2.5: −18.9 μg/m3 China (Wuhan) NO2: −22.8 μg/m3 PM2.5: −1.4 μg/m3 (Gautam, 2020a) China Before and after COVID-19 (March 2019–March 2020) Satellite data, Sentinel–5P NO2 (−30.0) India (−70.0) Spain (−25.0) Italy (−30.0) France (−30.0) (Muhammad et al., 2020) China (Wuhan) 2019. January, February vs 2020. January, February Satellite data, Sentinel-5P and Aura NO2 (−30.0) China Before and after lockdown January, February 2020 (−20.0 to −30.0) Europe March 2019 vs March 2020 (−20.0 to −30.0) Italy March 2019 vs March 2020 (−20.0 to −30.0) France March 2019 vs March 2020 (−20.0 to −30.0) Spain March 2019 vs March 2020 (−20.0 to −30.0) USA March 2015–2019 vs March 2020 (−30.0) (Gautam, 2020a) India (Kerala state) March 31 to April 5 from 2016 to 2020 (average in 2020 compared to 2016–2019 (average) Satellite data, Sentinel–5P AOD (Aerosol Optical Depth) ֎ (Nakada and Urban, 2020) Brazil (São Paulo) Phase I: Five-year monthly (February, April, March) mean (2015–2019) vs Four-week before partial lockdown (February 25, 2020, to March 23, 2020) SAQMSs (4 stations: 3 traffic stations and one industrial) & Remote sensing (NO2)) Copernicus Sentinel-5 Precursor Tropospheric Monitoring Instrument (S5p/TROPOMI) PM2.5, PM10, CO, SO2, NO, NOX, NO2, and O3 PM10: (−12.7 to −22.8), PM2.5: (−29.8), NO: (−77.3 to +8.1), NO2: (−5.6 to −54.3), NOX: (−65.4 to +3.0), O3: (−4.3 to +31.5), SO2: (−18.1 to −32.7) and CO: (−36.1 to −64.8) Phase II: Five-year monthly mean (February, April, March 2015–2019) vs Four-week during partial lockdown (from March 24, 2020, to April 20, 2020) PM10: (+6.2 to +21.4), O3: (+2.9 to +13.4), SO2: (+6.2.1 to +8.0) PM2.5: (−0.3 to −3.6), NO: (−40.4 to +29.6), NO2: (−29.3 to +9.6), NOx: (−31.7 to +21.7), CO: (−15.8 to −29.8) (Tobías et al., 2020) Spain (Barcelona) Before lockdown: February 16th to March 13th, 2020 vs During lockdown: March 14th to 30th March 2020 SAQMSs (2 stations: one traffic and another urban background) PM10, NO2, O3, BC, SO2 (μg/m3) O3: +14.9 (+28.5) PM10: −6.2 (−27.8), BC: −0.5 (−45.4), NO2: −14.1 (−47.0), SO2:−0.2 (−19.4) O3: +24.1 (+57.7), SO2: +0.1 (+1.8.0) PM10: −9.1 (−31.0), NO2: −21.8 (−51.4), Phase I: Before lockdown > February 16th to March 13th, 2020 vs 16th to March 13th, 2019 and Phase II: During lockdown: March 14th to 30th March 2020 vs March 14th to 30th March 2019 Remote sensing data, Copernicus Sentinel-5 NO2 O3: +24.1 (57.7), SO2: −0.2 to +0.1 (−19.4 to +1.8) PM10: −6.2 to −9.1 (−27.8 to −31.0), NO2: −14.1 to −21.8 (−47 to −51.4), Phase I: (−22.0), Phase II: (−57.0) (Nadzir et al., 2020) Malaysia (Klang Valley) Before lockdown,:(28th November 2019 to 17th April 2020), During lock down:18th March–31st March 2020 (1st phase), 1st April–14th April 2020 (2nd phase), and 15th April–28th April 2020 (3rd phase) SAQMSs (5 stations: industrial, residential, and traffic), air sensor network AiRBOXSense CO, PM2.5, PM10 PM10: (+14.2 to −51.8), CO: (−40.5 to −47.5), PM2.5: (+41.2 to −58.9) (Le et al., 2020) China (Wuhan) 04/30/2018–04/29/2019 vs 04/30/2019–02/24/2020 Satellite data (Ozone monitoring instrument (OMI) on the launched Aura satellite ( NO2 (−50.0) China (337 major cities) First-quarter of 2020 (January, February, and March) vs the same period of the past year SAQMSs (NR) NO2, SO2, CO, O3, PM10, PM2.5 PM10: −66.0 (−20.5), PM2.5: −46.0 (−14.8), NO2: −24.0 (−25.0), CO: −1.5 (−6.2), SO2: −11.0 (−21.4) (Kerimray et al., 2020) Kazakhstan (Almaty) Before lockdown: 21 February to 18 March and During lockdown: March 19 to April 14, 2020 (27 days), compared to the same period of 2018 and 2019 SAQMSs (7 stations: industrial, traffic and residential) PM2.5 2018: (−28.0), 2019: (−29.0) and 2020: (−39.0) Three days of spring 2020 lockdown vs average concentrations detected in the same periods of 2015–2019 Sampling (6 locations: industrial, residential and traffic) BTEX Benzene (+199.0), Toluene (+110.0) Ethylbenzene (−72.0), o-Xylene (−61.0) Before the lockdown (March 2 – March 18, 2020) vs During lockdown (March 19 – April 14, 2020) SAQMSs (one traffic station) NO2, SO2, CO, O3 O3: +4.0 (+15.0), SO2: +3.0 (+7.0) NO2: −13.0 (−35.0), CO: −331.0 (−49.0) (Otmani et al., 2020) Morocco (Salé city) Before lockdown: (March 11th to 20th) and During the lockdown (March 21st to April 2nd) 2020 Sampling in an urban residential area (High volume for PM10 and electrochemical sensors for NO2 and SO2) PM10, NO2, and SO2 (μg/m3) PM10: −86.3 (−75.0), NO2: −5.4 (−96.0), SO2: −3.3 (−49.0) (Kanniah et al., 2020) Malaysia (Kuala Lumpur) Averaged over a window of 15-days on 1 March, 31 March, and 17 April 2020 vs compared to 5-years average values, Phase I: 1th March, Phase II: 31th March, and Phase III: 17th April Satellite data (Aura-OMI) NO2 Phase I: (−6.0), Phase II: (−33.0) and Phase III: (−27.0) Singapore (Singapore) Phase I: (−16.0), Phase II: (−27.0) and Phase III: (−30.0) Thailand (Bangkok) Phase I: (−1.0), Phase II: (−21.0) and Phase III: (−22.0) Vietnam (Hanoi) Phase I: (+25.0), Phase II: NR, and Phase III: NR Vietnam (Ho Chi Minh city) Phase I: (+3.0) and Phase III: (+1.0) Phase II: (−9.0) Indonesia (Jakarta) Phase I: (−13.0), Phase II: (−10.0) and Phase III: (−34.0) Philippine (Manila) Phase I: (+5.0) Phase II: (−31.0) and Phase III: (−34.0) Cambodia (Phnom Penh) Phase I: (+10.0) Phase II: (−4.0) and Phase III: (−6.0) Laos (Vientiane) Phase I: (−5.0), Phase II: (0.0) and Phase III: (−9.0) Myanmar (Yangon) Phase I: (+1.0) and Phase III: (+3.0) Phase II: (−4.0) Malaysia, 12 cites 18 March to 30 April of the years 2018, 2019 and 2020 SAQMSs (65 stations: residential, industrial, traffic, rural) PM10, PM2.5, CO and O3 O3: (+3.0 to +7.0) PM10: (−26.0 to −31.0), PM2.5: (−23.0 to −32.0), NO2: (−63.0 to −64.0), CO: (−25.0 to −32.0) and SO2: (−9.0 to −20.0) Malaysia 18 March to 30 April 2020 vs same periods of 2018–2019 Satellite data, Himawari-8 AOD (−40.0 to −60.0) Note: Due to the lack of uniformity in the presented results by the included studies, changes in ambient air pollutants are presented based on the concentration and percentage of changes; − and + show reduction and increasing, respectively. 1 Movement Control Order (MCO); 2 Stationary Ambient Air Quality Monitoring Stations; 3 Not reported. 3.4 The effects of COVID-19 pandemic on the ambient air quality around the world Table 2 summarizes the results of included studies investigating the indirect effects of COVID-19 pandemic lockdown measures on the ambient air quality status worldwide; a totally of 19 countries, mostly from Asia (China, India, Singapore, Kazakhstan, Thailand, Vietnam, Indonesia, Malaysia, Philippine, Cambodia, Laos, UAE, Myanmar), African countries (Morocco), Europe (Spain, Italy, France), Southern America (Brazil) and the USA. Besides, the applied methodology, the form of air quality data, the type of ambient air pollutants, and the relevant findings are outlined in Table 2. The changes of ambient air pollutants (PM2.5, PM10, NO2, NOX, NO, O3, SO2, CO, black carbon, BTEX (benzene, toluene, ethylbenzene, o-Xylene), NH3, and non-methane hydrocarbons (NMHC)) in the studies have been investigated in two ways: 1) more studies compared the average concentrations of ambient air pollutants during the lockdown measures in each country or city with those before lockdown bans in the same year, and 2) the rest of studies compared the average concentrations of ambient air pollutants during the lockdown measures with those during the same period of the previous year/years. In addition to ambient particulate matter and gaseous air pollutants, two studies reported the changes in Aerosol Optical Depth (AOD). Furthermore, a few studies investigated the air quality index (AQI). The reviewed studies have investigated the impact of lockdown measures on the anthropogenic and natural sources and their emissions using the data collected from stationary ground-based air quality monitoring stations (e.g., traffic, industrial, residential, and rural/background stations), satellite methods, and sampled air pollutants by samplers. As shown in Table 2, approximately all ambient air pollutants, except ambient O3, declined remarkably on a global scale. Therefore, the COVID-19 pandemic, as a new global challenge, caused a significant improvement in the ambient air quality status around the world. Graphs with the flags of different countries (Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7) were used to illustrate the changes of criteria air pollutants (PM2.5, PM10, NO2, O3, SO2, and CO) in the countries and cities under study. As shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, the lockdown measures related to COVID-19 pandemic decreased the levels of ambient PM2.5 (Fig. 2), NO2 (Fig. 3), PM10 (Fig. 4), SO2 (Fig. 5) and CO (Fig. 6) air pollutants in the range of 2.9%–76.5%, 18.0%–96.0%, 6.0%–75.0%, 6.8%–49.0% and 6.2%–64.8%, respectively. Compared to other air pollutants, O3 increased during the lockdown measures in the range of 2.4%–252.3% (Fig. 7). Regarding PM2.5 as the most notable marker of ambient air pollution, the highest reduction was found for Malaysia (76.5% and 58.9%), followed by Zaragoza (Spain) with 58.0%, Delhi (India) with 53.1% during the first phase of lockdown measures, Beijing and Shanghai (China) with 50.0% at the second and first phase of lockdown measures, and Milan with 47.0% (Italy). For PM2.5, the lowest reduction was recorded for Nice (France) with 2.9%, São Paulo with 3.6% (Brazil) during the second phase of lockdown measures, Los Angeles with 4.0% at the first phase of lockdown measures and Dubai (UAE) with 6.0% and 11.0% over the second and first phases of lockdown measures. Among the included studies, P. Sicard et al. (2020) stated that ambient PM2.5 increased equal to 10.6% compared to the same period averaged over the three previous years (2017–2019). In contrast, other studies reported decreases of 24.0% and 46.0% during the second and third phases of Rome's lockdown measures (Italy). For NO2, all countries and cities experienced a significant reduction compared to before lockdown or the same period of last year/years. The highest decline was found for Salé city (Morocco) with 96.0%, followed by Mumbai (India) with 75.0% during the first phase of lockdown measures, India with 70.0%, Valencia (Spain) with 69.0% and São Paulo (Brazil) with 68.0%, respectively. Furthermore, the lowest reduction of NO2 was recorded for 22 cities of India with an average of 18.0%, Yangtze River Delta region in China with 18.5%, São Paulo (Brazil) with 21.4% over the second phase of lockdown measures, and 337 major cities of China with 25.0%. Like ambient NO2, CO declined in all countries and cities compared to before lockdowns or the same period of last year/years. São Paulo (Brazil), Milan (Italy), and Almaty (Kazakhstan) with 64.8%, 57.6%, and 49.0% had the highest reduction of CO levels compared to other cities under study, In comparison, the lowest decrease of CO concentrations was recorded for 337 major cities in China (6.2%), the Yangtze River Delta region in China (7.8%) during the first phase of lockdown measures and 22 cities of India (10.0%). The highest reduction of ambient PM10 levels was recorded for Salé City (Morocco) with 75.0%, Delhi (India) in the range of 51.8%–56.5% and Klang Valley (Malaysia) with 51.8% during the lockdown restrictions in comparison to before lockdowns or the same period of last year/years, whereas the lowest was found for Nice (France) and Turin (Italy) with 5.9% and 8.9%, respectively. Among all countries and cities under investigation, Salé city (Morocco) with 49.0%, Wuhan, Jingmen, Enshi (China) with 45.0%, and Hubei Province (China) with 33.4% experienced the highest reduction of ambient SO2. For SO2, the lowest reduction was found in Milan (Italy) with 6.8% and 19.9% during the first and third phases of lockdown measures, and Delhi (India) with 18.0%. As described above, contrary to other criteria air pollutants, we observed an increase in the levels of ambient O3 in all countries and cities under study. The highest increase was recorded for Milan (Italy) with 252.3% and 169.9% during the first and third phase of lockdown measures, and Kolkata (India) with 87.0% at the second phase of lockdown measures. As discussed above, there was a variation in the reduction of ambient air pollutants in the countries and cities as the degree of lockdown measures had varied there. 4 Discussion 4.1 Reasons for improving ambient air quality reported by the studies during the lockdown restrictions and recommendations for the future researches Table S3 shows detailed information concerning sources of ambient air pollution in the reviewed countries and cities and their reasons for improving ambient air quality. The countries and cities under study adopted and implemented stringent restrictions on various sectors to better control the COVID-19 pandemic (Table 1 and Table S3). The major ambient air pollutants (PM2.5, PM10, NO2, SO2, and CO) in the countries and cities under investigation arise from a broad range of anthropogenic and natural sources such as vehicular emissions (as the most notable source of ambient air pollution globally), industrial units, power generation, residential heating, agricultural burning, wildfires, resuspended dust, and dust storm events. The ambient air quality data from stationary ground-based monitoring stations (e.g., traffic, industrial, residential, rural, and background stations) and satellite methods were used to show the effect of lockdown measures on aforementioned emission sources and therefore, the emissions of ambient air pollutants. Approximately all studies have reported that road and non-road transportation and commercial activities as the main contributors to ambient air pollution in urban areas declined drastically; thereby the level of all ambient air pollutants experienced a significant reduction in the countries and cities over the world. However, the emissions from residential heating and essential industry remained steady or slightly declined. All studies believe that the restrictions on various sectors are the main reasons to reduce all ambient air pollutants' levels, except for O3. As a secondary air pollutant, O3 is formed by photochemical reactions among its precursors, in particular, nitrogen oxides (NOx) and volatile organic compounds (VOCs). The increase in O3 concentration can be a consequence of the following possible causes. Firstly, the decline of ambient NOx in a VOC-limited urban environment might cause an increase in increase of ambient O3 (Siciliano et al., 2020; Tobías et al., 2020). Secondly, the decrease of titration of O3 by NOX due to the observed significant reduction in local NOX emissions' sources (Mahato et al., 2020; Nakada and Urban, 2020; Tobías et al., 2020; Yousefian et al., 2020). Thirdly, it may be related to the observed declines in PM2.5 leads to a rise in solar activity levels and an increase of O3 concentration. Finally, the lower ambient PM2.5 during the lockdown restrictions would be a less effective sink for the radicals of hydroperoxy (HO2) increasing the proxy radical-mediated O3 formation (Jafari et al., 2019; Kerimray et al., 2020; Wang et al., 2020). Source apportionment approach is capable of better identify local sources (on-road vehicles, industry, domestic, and others) of air pollution affected by the COVID-19 lockdowns and estimating their contributions during the COVID-19 lockdowns (Dai et al., 2020; Lv et al., 2020; Wang et al., 2021). Finally, we highlight future research needs for utilizing the positive matrix factorization (PMF) to better show ambient particulate matter sources and quantify the contributions of source affected by the COVID-19 lockdowns. According to the levels of economic development of countries, health-based priorities, the abatement policies of environmental risk factors, ambient air pollutant levels, the sources of ambient air pollution and their contributions in the cities around the world are differ (Hopke et al., 2020; Karagulian et al., 2015; Karagulian et al., 2017; Mukherjee and Agrawal, 2017), thereby the effect of implemented lockdown measures on them can be varied (Dai et al., 2020). A more recent recently study authored by (Dai et al., 2020) has provided insights into the significant changes in source contributions to PM2.5 and its chemical components during the COVID-19 pandemic in the Jinan district of Tianjin, China using dispersion normalized positive matrix factorization. Their results revealed significant changes in source contributions during the COVID-19 pandemic (Dai et al., 2020). Moreover, the source apportionment study of (Tian et al., 2021) and (Lin et al., 2021) highlighted that lockdown measures attributed to the COVID-19 pandemic affected the concentrations and relative contributions of primary emissions, secondary aerosol formation and carbonaceous aerosols compared to before lockdown. The most considerable fraction of the studies used ground-based measurements in their analysis that these data have immediately reflected the effect of human-being activities on ambient air quality. Around one-third of included studies used satellite observations, that 23% of the studies used Sentinel-5P Tropospheric Monitoring Instrument and the rest of them used AURA-OM, NASA, and Himawari-8. Only two studies have applied the sampling. About 65% of studies have conducted in Asia with highly polluted countries and the rest of ones related to the USA and Europe, and three studies considered multiple countries. Around 60% of studies compared the air quality during the lockdown with the same period in last years; however, the others considered the air quality during the lockdown (commonly in spring) versus before lockdown (winter) in a year. As shown in Table 2, the studies have used various platforms to measure ambient air pollutant concentrations and different methods to determine lockdown effects on ambient air pollutants. As a result, these differences could lead to considerable uncertainty for comparing their results. Additionally, given the speed with which manuscripts regarding the COVID-19 lockdown effects were prepared and published, it is possible that many are based on data with no final quality control. Consequently, we emphasize the need to control and validate air quality data prior to accounting the effect of COVID-19 lockdown on ambient air quality in the future researches. 4.2 Mobility index (MI) and meteorological parameters (MPs) during the lockdown restrictions In addition to reasons reported by the studies mentioned above (section 4.1 and Table S3), we examined the MPs (excluded from the reviewed articles) and the MI from both Apple (https://covid19.apple.com/mobility) and Google (https://www.google.com/covid19/mobility/) during the lockdown restrictions that better explain the results of reviewed studies. Both MI datasets reveal a relative trend of how people's movements changed within countries and cities during the lockdown restrictions related to the COVID-19 pandemic (Archer et al., 2020; Le et al., 2020). Google's and Apple's MI data can be considered as a proxy of human mobility affecting the ambient air pollutants' emissions (Archer et al., 2020; Bao and Zhang, 2020; Chen et al., 2020; Le et al., 2020; Muhammad et al., 2020). Google's MI dataset has been built from the data collected from people who allowed Google to access their location information. This MI dataset is classified into retail and recreation, grocery and pharmacy, parks and outings, transit stations, workplaces, and residential categories (details in Table 3 ). The data released from Jan 13, 2020, reflects how the lockdown restrictions imposed by the countries and cities impacted each category compared to the baseline (the median value, for the corresponding day of the week, during the five-week from Jan 3 to Feb 6, 2020). Furthermore, Apple's MI dataset is based on the direction requested by people in Apple Maps and classified into the following categories: driving by personal vehicles, public transit (such as subway, bus, train, and taxi stations), and walking (details in Fig. 8 , Fig. S2 and Table S1). The levels of all categories of Google's MI, except for people's residential movements, declined during the lockdown bans in the studied countries and cities (Table 3). The decrease of the latter sector confirms that intra city migration index as the main factor affecting the emission sources declined during the lockdown bans (Archer et al., 2020; Bao and Zhang, 2020; Le et al., 2020). Given Apple's MI dataset (Fig. 8 and Fig. S2), an interesting pattern was discovered for MI related to driving, transit and walking before and after lockdown restrictions. The data shows that MI has reduced in the range of approximately 30–88% for driving by personal vehicles, 45–94% for public transit, and 37–94% for walking. The highest reductions for the driving sector were observed in Spain, Italy, India, and France (Fig. 8), whereas the lowest declines were recorded in Singapore, Vietnam, the USA, Brazil, and their cities. Concerning public transit, Italy, Spain, and France experienced the highest reductions during the lockdown restrictions compared to other countries. Of 26 reviewed articles, only one study estimated the MI using a quantitatively developed model and reported the effect of lockdown measures on MI (Bao and Zhang, 2020). As expected, this study confirmed that the reduction in human mobility was significantly associated with the implemented bans and led to a reduction of the air pollution emissions (Bao and Zhang, 2020).Some studies have stated that the countries' restrictions to control the COVID-19 pandemic markedly reduced human mobility, as a result, led to decreasing the air pollution emissions (Archer et al., 2020; Chen et al., 2020; Le et al., 2020; Muhammad et al., 2020; Sicard et al., 2020). Moreover, we reviewed the results of MPs reported by the included studies (details in Table S2). Of 26 included articles, 12 studies reported the data of MPs including wind speed, wind direction, air temperature, relative humidity, precipitation, air pressure and the number of rainy days. As an important limitation, the impact of these parameters on ambient air quality status was not quantitatively evaluated by them; just the data have been used for a qualitative interpretation of ambient air pollutant concentrations. As can be seen from Table S2, six studies (out of 12 studies reported the MPs) have highlighted that implemented lockdown restrictions in response to the COVID-19 pandemic have improved the ambient air quality status, whereas the other studies have stated the effects of MPs on the improvement of ambient air quality status alongside the lockdown restrictions without any quantitative analysis. As previously conducted studies have reported (Hua et al., 2021; Jiang et al., 2020; Seinfeld and Pandis, 2016; Yousefian et al., 2020), meteorological factors, including wind speed, solar radiation, temperature, precipitation, relative humidity, nebulosity and planetary boundary layer or stability have an important effect on ambient air pollution levels. Wind speed, temperature stability, and turbulence affect significantly the dilution, transport, and dispersion of ambient air pollutants. Solar radiation that depends on nebulosity triggers the photochemical production of different oxidants that form smog, whereas precipitation has a scavenging effect that washes out particulate matter and some gaseous air pollutants from the atmosphere. As a result, the impact of meteorological parameters cannot be neglected and should be quantitatively investigated in the future studies. The meteorological factors and atmospheric chemistry can change with time (e.g. seasonally, daily and even hourly) and location (Hua et al., 2021), thus the concentrations of particulate matter and gaseous air pollutants can vary during the COVID-19 lockdown compared to reference period in various cities and countries under study. In the included studies, for instance, Otmani et al., 2020 confirmed that the wind speed (+24%), humidity (+2%), precipitation (+88%), and rainfall days (+69%) were increased during the lockdown period compared to the reference period (Otmani et al., 2020). These increases led to favorable meteorological conditions in order to better disperse air pollutants during the lockdown period. As mentioned previously, approximately all studies have not quantified the effect of meteorology on the decline of air pollutant levels during the COVID-19 lockdown measures. Due to the importance of meteorology, we emphasize the need to account for the effects of meteorology and atmospheric chemistry when determining the COVID-19 lockdown effects on ambient air pollutant concentrations in the future researches.Table 3 Mobility index report based on google tracking. Table 3Location Retail and recreation1 Grocery and pharmacy2 Parks and outing3 Public transit4 Workplaces Residential Brazil −51% −4% −53% −41% −3% +11% Cambodia −15% −10% −6% −38% −9% +4% France −8% +9% +82% −8% +9% −2% India −68% −23% −57% −48% −20% +14% Indonesia −20% −2% −14% −35% −2% +8% Italy −18% −17% +56% −12% +7% −6% Kazakhstan −43% −25% +1% −11% −16% +3% Laos +7% +9% +14% −19% +2% −1% Malaysia −23% −3% 0% −19% −15% +5% Morocco −22% −11% −3% −31% −9% +7% Myanmar (Burma) −11% −1% −13% −14% −2% +8% Philippines −57% −30% −38% −62% −20% +18% Singapore −29% −9% −25% −38% −13% +15% Spain −24% +2% +28% −30% +5% −3% Thailand +1% +14% +13% −26% −13% +2% UAE −24% −3% −45% −47% −24% +14% USA −22% −10% +53% −23% −19% +3% Vietnam −10% +5% −14% −4% −6% +6% 1 Places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters. 2 Places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies. 3 places like national parks, public beaches, marinas, dog parks, plazas, and public gardens. 4 Public transit hubs such as subway, bus, and train stations. Fig. 8 Extracted Mobility Index from the COVID-19 Community Mobility Report for the Spain (a), Italy (b), India (c) and France (d) before and during lockdown. Fig. 8 4.3 The threat of ambient air pollution to human health and lessons from COVID-19 pandemic to reduce it Short- and long-term exposure to ambient air pollutants is recognized as one of the most pressing topics in modern-day public health across developed and developing countries (Brook et al., 2017; Faridi et al., 2017; Faridi et al., 2019; Fazlzadeh et al., 2021; Lelieveld et al., 2015). Among ambient air pollutants, PM2.5 was identified as the fifth-ranked cause of global disease burden in 2015 (Al-Kindi et al., 2020; Burns et al., 2020; Janjani et al., 2020; Paunescu et al., 2019; Rajagopalan et al., 2018). Global premature deaths attributed to ambient PM2.5 rose considerably from 3.5 million in 1990 to 4.2 million in 2015 (Cohen et al., 2017). The loss of life expectancy due to long-term exposure to air pollution exceeds that of infectious diseases worldwide (Pozzer et al., 2020). It is also interesting to note that several studies show the association between exposure to air pollution and increased risk of COVID-19 lethality (Conticini et al., 2020; Contini and Costabile, 2020; Copat et al., 2020; Petroni et al., 2020; Yao et al., 2020; Yongjian et al., 2020). Air pollution affects the body's immunity, particularly the respiratory system, making people more vulnerable to COVID-19 infection (Conticini et al., 2020; Contini and Costabile, 2020; Copat et al., 2020; Petroni et al., 2020). More recently studies revealed that exposure to ambient PM2.5 air pollution is an essential cofactor increasing the risk of mortality from COVID-19 infection (Giani et al., 2020; Pozzer et al., 2020). It has been estimated that exposure to ambient PM2.5 air pollution from all anthropogenic and fossil fuel-related emissions contributed approximately 15% (95% confidence interval 7–33%) and 8% (4–25%) to COVID-19 infection mortality globally (Pozzer et al., 2020). As a driving force, COVID-19 pandemic revealed that air pollution is a controllable and modifiable risk factor because this pandemic as a global challenge has compelled the countries around the world to implement a part of societal and governmental interventions (as mentioned in Table 1) which had been effective in reducing air pollution emissions (Abdullah et al., 2020; Bao and Zhang, 2020; Burns et al., 2020; Chauhan and Singh, 2020). In reality, COVID-19 pandemic can be considered as a window of opportunity for accelerating in-depth and comprehensive implementation of all well-documented multisector policies and approaches (e.g., shifting to clean fuels, transportation reform, reduce traffic emissions, urban landscape reform, emission trading programs, redirection of science and funding, empowering civil society, Governmental and NGO-led publicity) to mitigate exposure to air pollution and its health effects at the local, regional, and global levels in the future (Bard et al., 2019; Burns et al., 2020; Faridi et al., 2020b; Giles et al., 2010; Hadley et al., 2018; Pozzer et al., 2020; Rajagopalan et al., 2018; Sanchez et al., 2020; van Dorn, 2017). In addition to previously air pollution mitigation policies documented, all nations learned that can adopt and continue to limit non-essential individual- and population-level travel by teleworking (Mannucci, 2020). Also, all developed and developing countries should avoid quickly forgetting the lessons learnt during the COVID-19 pandemic, because they experienced significant achievements in reducing air pollution (Mannucci, 2020). We all believe that continuous air pollution mitigation strategies at the local, regional, and global levels might help in decreasing the fatality rate not only over the ongoing COVID-19 pandemic but also in probable future pandemics related to respiratory diseases (Contini and Costabile, 2020; Copat et al., 2020; Giani et al., 2020). Finally, the COVID-19 pandemic will end with the population's vaccination or with herd immunity (Pozzer et al., 2020). However, there are no vaccines against air pollution and its health effects (Pozzer et al., 2020). The only remedy for declining air pollution and its health consequences is the strict implementation of societal and governmental interventions (Pozzer et al., 2020). 5 Conclusion Responding to the coronavirus disease 2019 (COVID-19) outbreak, countries mandatorily or inevitably implemented the lockdown measures, as a result, ambient air quality status markedly improved over the world. In the present study, we systematically reviewed the twenty-six included studies investigating the indirect effects of the COVID-19 pandemic on the ambient air quality status worldwide. The implemented lockdown measures related to the COVID-19 pandemic decreased ambient PM2.5, NO2, PM10, SO2 and CO air pollutants in the range of 2.9%–76.5%, 18.0%–96.0%, 6.0%–75.0%, 6.8%–49.0% and 6.2%–64.8% in the countries and cities throughout the world. O3 rose during the lockdown measures in the range of 2.4%–252.3%, in stark contrast to other air pollutants. We hope that the COVID-19 pandemic can be considered as a window of opportunity for accelerating the in-depth and comprehensive implementation of all well-documented multisector policies and approaches to mitigate exposure to air pollution and its health effects at the local, regional, and global levels in the future. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data Supplementary material Image 1 Acknowledgments This study was funded by the Institute for Environmental Research (IER), Tehran University of Medical Sciences (grant numbers 99-2-110-48679). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.uclim.2021.100888. ==== Refs References Abdullah S. Mansor A.A. Napi N.N.L.M. Mansor W.N.W. Ahmed A.N. Ismail M. Ramly Z.T.A. 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Van Dingenen R. Belis C. Janssens Maenhout G. Crippa M. Guizzardi D. Dentener F. Attribution of anthropogenic PM2. 5 to emission sources EUR 28510 2017 1 43 Kerimray A. Baimatova N. Ibragimova O.P. Bukenov B. Kenessov B. Plotitsyn P. Karaca F. Assessing air quality changes in large cities during COVID-19 lockdowns: the impacts of traffic-free urban conditions in Almaty, Kazakhstan Sci. Total Environ. 139179 2020 Krecl P. Targino A.C. Oukawa G.Y.C. Junior R.P.C. Drop in urban air pollution from COVID-19 pandemic: policy implications for the megacity of São Paulo Environ. Pollut. 114883 2020 Lancet T. COVID-19: protecting health-care workers Lancet (London, England) 395 2020 922 Le T. Wang Y. Liu L. Yang J. Yung Y.L. Li G. Seinfeld J.H. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China Science 369 2020 702 706 32554754 Lelieveld J. Evans J.S. Fnais M. Giannadaki D. Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale Nature 525 2015 367 371 26381985 Lin Y.-C. Zhang Y.-L. Xie F. Fan M.-Y. Xiaoyan L. Substantial decreases of light absorption, concentrations and relative contributions of fossil fuel to light-absorbing carbonaceous aerosols attributed to the COVID-19 lockdown in East China Environ. Pollut. 116615 2021 Lv Z. Wang X. Deng F. Ying Q. Archibald A.T. Jones R.L. Ding Y. Cheng Y. Fu M. Liu Y. Source–receptor relationship revealed by the halted traffic and aggravated haze in Beijing during the COVID-19 lockdown Environ. Sci. Technol. 54 2020 15660 15670 33225703 Mahato S. Pal S. Ghosh K.G. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India Sci. Total Environ. 139086 2020 Mannucci P.M. Traffic-Related Air Pollution and the Coronavirus Pandemia: Shadows and Lights 2020 SAGE Publications Sage UK London, England Muhammad S. Long X. Salman M. COVID-19 pandemic and environmental pollution: a blessing in disguise? Sci. Total Environ. 138820 2020 Mukherjee A. Agrawal M. World air particulate matter: sources, distribution and health effects Environ. Chem. Lett. 15 2017 283 309 Nadzir M.S.M. Ooi M.C.G. Alhasa K.M. Bakar M.A.A. Mohtar A.A.A. Nor M.F.F.M. Latif M.T. Abd Hamid H.H. Ali S.H.M. Ariff N.M. The impact of movement control order (MCO) during pandemic COVID-19 on local air quality in an Urban area of Klang Valley, Malaysia Aerosol Air Qual. Res. 20 2020 Nakada L.Y.K. Urban R.C. COVID-19 pandemic: impacts on the air quality during the partial lockdown in São Paulo state, Brazil Sci. Total Environ. 139087 2020 Niazi S. Groth R. Spann K. Johnson G.R. The role of respiratory droplet physicochemistry in limiting and promoting the airborne transmission of human coronaviruses: a critical review Environ. Pollut. 115767 2020 Otmani A. Benchrif A. Tahri M. Bounakhla M. Chakir E.M. El Bouch M. Krombi M. Impact of Covid-19 lockdown on PM(10), SO(2) and NO(2) concentrations in Salé City (Morocco) Sci. Total Environ. 735 2020 139541 32445829 Paunescu A.-C. Casas M. Ferrero A. Pañella P. Bougas N. Beydon N. Just J. Lezmi G. Sunyer J. Ballester F. Associations of black carbon with lung function and airway inflammation in schoolchildren Environ. Int. 131 2019 104984 31301585 Petroni M. Hill D. Younes L. Barkman L. Howard S. Howell I.B. Mirowsky J. Collins M.B. Hazardous air pollutant exposure as a contributing factor to COVID-19 mortality in the United States Environ. Res. Lett. 15 2020 0940a0949 Pozzer A. Dominici F. Haines A. Witt C. Münzel T. Lelieveld J. Regional and global contributions of air pollution to risk of death from COVID-19 Cardiovasc. Res. 116 2020 2247 2253 33236040 Rajagopalan S. Al-Kindi S.G. Brook R.D. Air pollution and cardiovascular disease: JACC state-of-the-art review J. Am. Coll. Cardiol. 72 2018 2054 2070 30336830 Rugani B. Caro D. Impact of COVID-19 outbreak measures of lockdown on the Italian carbon footprint Sci. Total Environ. 139806 2020 Sanchez K.A. Foster M. Nieuwenhuijsen M.J. May A.D. Ramani T. Zietsman J. Khreis H. Urban policy interventions to reduce traffic emissions and traffic-related air pollution: protocol for a systematic evidence map Environ. Int. 142 2020 105826 32505921 Seinfeld J.H. Pandis S.N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change 2016 John Wiley & Sons Sharma S. Zhang M. Anshika Gao J. Zhang H. Kota S.H. Effect of restricted emissions during COVID-19 on air quality in India Sci. Total Environ. 728 2020 138878 32335409 Sicard P. De Marco A. Agathokleous E. Feng Z. Xu X. Paoletti E. Rodriguez J.J.D. Calatayud V. Amplified ozone pollution in cities during the COVID-19 lockdown Sci. Total Environ. 139542 2020 Siciliano B. Dantas G. da Silva C.M. Arbilla G. Increased ozone levels during the COVID-19 lockdown: analysis for the city of Rio de Janeiro, Brazil Sci. Total Environ. 139765 2020 Tian J. Wang Q. Zhang Y. Yan M. Liu H. Zhang N. Ran W. Cao J. Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID-19 lockdown Environ. Int. 150 2021 106426 33578069 Tobías A. Carnerero C. Reche C. Massagué J. Via M. Minguillón M.C. Alastuey A. Querol X. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic Sci. Total Environ. 138540 2020 van Dorn A. Diesel and petrol cars to be banned by 2040 Lancet Respir. Med. 5 2017 684 Wang Q. Su M. A preliminary assessment of the impact of COVID-19 on environment–a case study of China Sci. Total Environ. 138915 2020 Wang Y. Yuan Y. Wang Q. Liu C. Zhi Q. Cao J. Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions Sci. Total Environ. 2020 139133 32402905 Wang H. Miao Q. Shen L. Yang Q. Wu Y. Wei H. Air pollutant variations in Suzhou during the 2019 novel coronavirus (COVID-19) lockdown of 2020: high time-resolution measurements of aerosol chemical compositions and source apportionment Environ. Pollut. 271 2021 116298 33373898 Xu K. Cui K. Young L.-H. Hsieh Y.-K. Wang Y.-F. Zhang J. Wan S. Impact of the COVID-19 event on air quality in Central China Aerosol Air Qual. Res. 20 2020 915 929 Xu K. Cui K. Young L.-H. Wang Y.-F. Hsieh Y.-K. Wan S. Zhang J. Air quality index, indicatory air pollutants and impact of COVID-19 event on the air quality near Central China Aerosol Air Qual. Res. 20 2020 Yao Y. Pan J. Liu Z. Meng X. Wang W. Kan H. Wang W. Temporal association between particulate matter pollution and case fatality rate of COVID-19 in Wuhan Environ. Res. 189 2020 109941 32678728 Yongjian Z. Jingu X. Fengming H. Liqing C. Association between short-term exposure to air pollution and COVID-19 infection: evidence from China Sci. Total Environ. 138704 2020 Yousefian F. Faridi S. Azimi F. Aghaei M. Shamsipour M. Yaghmaeian K. Hassanvand M.S. Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017 Sci. Rep. 10 2020 1 11 31913322 Zambrano-Monserrate M.A. Ruano M.A. Sanchez-Alcalde L. Indirect effects of COVID-19 on the environment Sci. Total Environ. 138813 2020

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Urban Clim. 2021 Jul 18; 38:100888

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10.1016/j.uclim.2021.100888

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==== Front Indian J Surg Indian J Surg The Indian Journal of Surgery 0972-2068 0973-9793 Springer India New Delhi 3642 10.1007/s12262-022-03642-7 Surgical Techniques and Innovations Laparoscopic Common Bile Duct Exploration Using a Disposable Bronchoscope http://orcid.org/0000-0002-8880-6484 Riojas-Garza Alberto [email protected] 12 Morales-Morales Carlos A. [email protected] 1 Leyva-Alvizo Adolfo [email protected] 1 Rodríguez Alejandro H. [email protected] 1 1 Departamento de Cirugía General, Escuela de Medicina y Ciencias de la Salud del Tecnológico y de Estudios Superiores de Monterrey Ignacio A. Santos, Monterrey, México 2 Av. Ignacio Morones Prieto 3000, Zona Los Callejones, N.L. 64718 Monterrey, México 15 12 2022 14 31 8 2022 5 12 2022 © Association of Surgeons of India 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Laparoscopic common bile duct exploration (LCBDE) remains underutilized in the management of common bile duct (CBD) stones. The exact cause of this under-utilization remains unclear; however, identified barriers to LCBDE implementation include lack of training and unavailability of dedicated instruments. LCBDE is an attractive alternative for stone retrieval in patients with Roux-en-Y gastric bypass given the anatomical difficulty in endoscopic retrograde cholangiopaneatography (ERCP). Direct visualization through choledochoscopy is the method of choice for LCBDE. However, dedicated choledoscopes are expensive and not widely available, which may lead surgeons to seek for alternatives at their particular environment. With the COVID-19 pandemic, disposable bronchoscopes have become widely accessible at our institution, raising the possibility of using one for direct vision of the biliary tract. We present the case of a 61-year-old male with past medical history of Roux-en-Y gastric bypass, who presented to the emergency department with a CBD stone. Successful LCBDE was achieved with the aid of a disposable bronchoscope for direct visualization of the biliary tract. Supplementary Information The online version contains supplementary material available at 10.1007/s12262-022-03642-7. Keywords Laparoscopic common bile duct exploration Common bile duct stones Bronchoscope ==== Body pmcIntroduction More than 30 years after its introduction, laparoscopic common bile duct exploration (LCBDE) remains an underutilized technique for the management of common bile duct (CBD) stones [1]. Barriers to its implementation include limited experience, lack of training, and unavailability of dedicated instruments for choledocoscopy. Boasting a minimally invasive nature and a CBD clearance rate of 77–98%, endoscopic retrograde cholangiopancreatography (ERCP) has become the preferred method for CBD extraction in many centers [2]. However, ERCP in patients with altered upper GI anatomy (such as post-Roux-en-Y gastric bypass) presents many challenges [3]. For its part, LCBDE has been reported to be a safe and effective procedure with the advantage of dealing with CBD stones and cholecystectomy in a single session. CBD clearance rates range from 87 to 100%, with morbidity reported at 5–19% [4]. LCBDE is an attractive alternative for stone retrieval in patients with Roux-en-Y gastric bypass given the anatomical difficulty in ERCP [5]. LCBDE can be done with “blind” techniques, where stones are extracted without direct visualization or with dedicated choledoscopes. Although no guidelines for a methodical implementation of these techniques exist, most literature concerning CBD clearance is based on choledochoscopy [6]. Direct visualization assures stone extraction during CBD manipulation eliminating fluoroscopy. Unfortunately, choledoscopes are expensive and not widely available, which may pressure surgeons to seek for alternatives at their particular environment [7]. Since the start of the COVID-19 pandemic, disposable bronchoscopes have become widely accessible at our institution, raising the possibility of using one for direct vision of the biliary tract. We present the case of a 61-year-old man with a past medical history of Roux-en-Y gastric bypass, who presented to the emergency department with a CBD stone. Successful LCBDE was achieved with the aid of a disposable bronchoscope for direct visualization of the biliary tract. Case A 61-year-old man with a past medical history of obesity and Roux-en-Y gastric bypass presented to the emergency department with jaundice. Laboratory analysis revealed an elevated total bilirubin (7.6 mg/dL). Contrast-enhanced abdominal CT scan reported a 10-mm stone in the distal portion of the CBD (Fig. 1). The patient was taken to the operating room for LCBDE and cholecystectomy.Fig. 1 Contrast-enhanced abdominal CT scan reported a dilatated CBD (19 mm) and a 10-mm stone in the distal portion A LCBDE and cholecystectomy were performed with 10-mm ports in the umbilicus and epigastric region and two 5-mm ports in the right upper quadrant. Dissection of the CBD was made, and a LCBDE was performed through a 1 cm choledochotomy. At this point, the idea of using a disposable bronchoscope was suggested by a surgical team member. An Ambu® aScope Broncho Slim bronchoscope (Ambu® Inc., Columbia, USA) was brought to the operating room. With a 3.8 mm diameter, the instrument was introduced through the 5-mm port at the midclavicular line and through the choledochotomy. The suction channel was used for irrigation for more visibility. An impacted stone was identified at the lower third of the CBD (Fig. 2). A Dakota 0.63-mm stone retrieval basket (Boston Scientific, Washington DC, USA) was introduced through a 1.2 mm working channel on the scope, and the stone was mobilized and extracted with laparoscopic instruments (Fig. 3). The gallbladder was dissected with no further complications. The operating time was 180 min. The patient was discharged home 3 days after surgery without complications.Fig. 2 Impacted stone at the distal CBD visualized with Ambu® aScope Broncho Slim bronchoscope Fig. 3 Stone extraction through choledochotomy with laparoscopic instruments after stone mobilization Discussion Currently, there is no consensus on the ideal method for stone extraction for CBD stones in patients with Roux-en-Y gastric bypass, and most centers rely on their own experience [5]. Although transgastric or doubled-ballon ERCP have been reported as successful procedures in gastric bypass patients with CBD stones, these are not frequently performed at our institution [8]. LCBDE is an effective method for CBD stone extraction. Choledochoscopy is the most popular technique in LCBDE because it provides direct visualization. Nevertheless, choledoscopes are expensive and not widely available. Ambu® aScope Broncho Slim bronchoscope is a disposable instrument widely available at our center with a cost of $539 USD per unit. It consists of a 3.8-mm flexible scope with a bidimensional high bending angle, a 1.2-mm working channel, and a suction channel (Fig. 4). Due to its slim caliber, the idea of using this instrument for a LCBDE seemed plausible. Limitations perceived using this instrument as compared to dedicated choledoscopes were (1) impaired visualization due to the inability to irrigate and vacuum at the same time through the bronchoscope. To improve visibility, the suction channel was used for irrigation; however, optimal view was compromised. (2) The bidirectional angles aided during the CBD canulation through the choledochotomy but limited the instrument manipulation inside the bile duct. Dedicated choledoscopes have a multi-direction drive which facilitates maneuvering and stone handling. (3) The absence of an integrated system of a guide wire, dilatator, and introducer was considered a drawback for duct cannulation. Introducers in some dedicated choledoscopes give direct guidance for instrument introduction to the biliary system. The lack of these features complicated the insertion of the bronchoscope. Despite these limitations, an impacted stone was mobilized with an extraction basket, a task hardly achievable with irrigation or Fogarty catheters. Operating time was 180 min, compared to a mean of 95–135 min in other studies where LCBDE was done with dedicated choledoscopes [9]. This difference could be explained by the limitations mentioned.Fig. 4 Ambu® aScope Broncho Slim bronchoscope. (1) Control lever. (2) Working channel 1.2 mm. (3) Suction connector. (4) Suction button. (5) Handle grip. (6) Bending capabilities. (7) Distal end. (8) View 2 advanced connector Laparoscopic common bile duct exploration is limited by sophisticated instruments not widely available; thus, alternatives should be sought. To our knowledge, this is the first case report of a laparoscopic common bile duct exploration with a disposable bronchoscope. Although not made for this procedure, it could be a feasible option for stone visualization and mobilization when dedicated choledoscopes lack. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 148 KB) Declarations Conflict of Interest The authors declare no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Cuschieri A, Croce E, Faggioni A, Jakimowicz J, Lacy A, Lezoche E, Morino M, Ribeiro VM, Toouli J, Visa J, Wayand W (1996) EAES ductal stone study. Preliminary findings of multi-center prospective randomized trial comparing two-stage vs single-stage management. Surg Endosc 10(12):1130–1135. 10.1007/s004649900264 2. Dasari BV, Tan CJ, Gurusamy KS, Martin DJ, Kirk G, McKie L, Diamond T, Taylor MA (2013) Surgical versus endoscopic treatment of bile duct stones. Cochrane Database Syst Rev 2013(12):CD003327. 10.1002/14651858.CD003327.pub4 3. Wang TJ Ryou M Evolving techniques for endoscopic retrograde cholangiopancreatography in gastric bypass patients Curr Opin Gastroenterol United States 2018 34 444 450 10.1097/MOG.0000000000000474 4. Fang L, Wang J, Dai WC, Liang B, Chen HM, Fu XW, et al (2018) Laparoscopic transcystic common bile duct exploration: surgical indications and procedure strategies. Surg Endosc [Internet]. Springer US; 32:4742–8. Available from: 10.1007/s00464-018-6195-z 5. Fuente I, Beskow A, Wright F, Uad P, de Santibañes M, Palavecino M, et al (2021) Laparoscopic transcystic common bile duct exploration as treatment for choledocholithiasis after Roux-en-Y gastric bypass. Surg Endosc [Internet]. Springer US; 35:6913–20. Available from: 10.1007/s00464-020-08201-3 6. Hajibandeh S, Hajibandeh S, Sarma DR, Balakrishnan S, Eltair M, Mankotia R, et al (2019) Laparoscopic transcystic versus transductal common bile duct exploration: a systematic review and meta-analysis. World J Surg [Internet]. Springer International Publishing; 43:1935–48. Available from: 10.1007/s00268-019-05005-y 7. ElGeidie A Atif E Naeem Y ElEbidy G Laparoscopic bile duct clearance without choledochoscopy Surg Laparosc Endosc Percutaneous Tech 2015 25 e152 e155 10.1097/SLE.0000000000000198 8. Cheng Q, Hort A, Yoon P, Loi K. (2021) Review of the endoscopic, surgical and radiological techniques of treating choledocholithiasis in bariatric Roux-en-Y gastric bypass patients and proposed management algorithm. Obes Surg [Internet]. Springer US; 31:4993–5004. Available from: 10.1007/s11695-021-05627-z 9. Kenny R, Richardson J, McGlone ER, Reddy M, Khan OA (2014) Laparoscopic common bile duct exploration versus pre or post-operative ERCP for common bile duct stones in patients undergoing cholecystectomy: is there any difference? Int J Surg [Internet]. Elsevier Ltd; 12:989–93. Available from: 10.1016/j.ijsu.2014.06.013

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Indian J Surg. 2022 Dec 15;:1-4

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==== Front Electron Commer Res Electronic Commerce Research 1389-5753 1572-9362 Springer US New York 9649 10.1007/s10660-022-09649-2 Article How to help teachers deal with students’ cheating in Online Examinations: Design and Implementation of International Chinese Online Teaching Test Anti-Cheating Monitoring System (OICIE-ACS) Pang Dikai [email protected] 1 http://orcid.org/0000-0003-3661-4365 Wang Tianyu [email protected] 2 Ge Dong [email protected] 3 Zhang Feipeng [email protected] 4 Chen Jian [email protected] 5 1 grid.7922.e 0000 0001 0244 7875 Department of Civil Engineering, Faculty of Engineering, Chulalongkorn University, 10330 Bangkok, Thailand 2 grid.410739.8 0000 0001 0723 6903 College of International Chinese Language Education, Yunnan Normal University, Juxian Street, Chenggong District, 650500 Kunming, Yunnan Province China 3 grid.443709.d 0000 0001 0048 9633 Global Innovation Academy, Siam University, 38 Petchkasem Road, Phasi Charoen, Bang Wa, 10160 Bangkok, Thailand 4 grid.410739.8 0000 0001 0723 6903 School of Physics and Electronic Information, Yunnan Normal University, Juxian Street, Chenggong District, 650500 Kunming City, Yunnan Province China 5 Nanjing Technical Vocational College, 58 Huangshan Road, Jianye District, 210019 Nanjing, Jiangsu Province China 15 12 2022 114 30 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The authenticity and effectiveness of teaching testing and evaluation is an important evaluation index for the development of online teaching, and how to combat cheating behavior has become a significant impediment in this process. This paper introduces a hybrid technology based on a fuzzy evaluation method that is used to judge and prompt (suspicious) cheating behavior in international Chinese online teaching tests or evaluations. The goal of this hybrid technology applied to international Chinese online teaching is to assist teachers in monitoring and judging international students’ cheating behavior in online testing or evaluation, and making sound judgments on it, to ensure the authenticity and effectiveness of the test or evaluation results. Based on the technical root of cheating behavior in the online testing process, as well as the technical flaws in various existing online testing systems, this system proposes the idea and scheme of online examination cheating detection using fuzzy cluster analysis, as well as the specific implementation steps. We applied the system to the small-scale practical teaching process to test its security and stability, and we got the expected positive results. Keywords Online teaching test Anti-cheating monitoring system International Chinese teaching ==== Body pmcIntroduction Online exams have been accepted and welcomed globally as an efficient and environmentally friendly way to take exams as online education technology has grown in popularity. Online teaching has gradually become the most effective teaching method solution since the global outbreak of the new coronavirus epidemic. Chinese universities have been promoting online exams as an effective exam solution, driven by both teaching trends and epidemic prevention and control, as the world’s most populous country with the greatest number of learners of all types. However, due to the uniqueness of the examination environment and the convenience of information technology, examination cheating has become a common problem in online teaching and learning evaluation. According to relevant research findings, the probability of cheating on online exams is more than four times that of traditional exams.[1]. The introduction of school-based online proctoring software integrated into existing learning management systems[2] has become the most common solution for schools worldwide; in China, previous researchers have begun to experiment with various strategies to monitor and contain it, including increasing the difficulty of exams[3, 4], adding process evaluative assessment[5], etc. There are also patents and articles published based on various technologies, such as those based on identifying specific information[6, 7], camera technology [8], facial expression collection [9, 10], etc. Data access and retrieval processes for higher education institutions are offered utilizing the Dynamic Student Data Management System (DSDM-AICV). The movement of dynamic data makes it easier to investigate the connections between student data and better data management. [11]. In addition to the summative assessment test represented by the final exam, there are also researchers who focus on the cheating mechanism of process assessment for online learning and propose solutions [12], etc. In addition to the summative assessment test represented by the final exam. These studies and solutions have begun to have a positive impact on Chinese learners, assisting teachers to effectively supervise (particularly those who take tests in specific locations, such as computer classrooms, using a prescribed platform) and to some extent ensure the authenticity of the tests; however, for international students who are unable to obtain visas to complete their studies in China due to the epidemic, these studies have operational limitations in many ways. According to a research study conducted on the online learning of international students studying in Chinese universities, students not only cheat on final tests, but it is even a collective behavior[13]. Because international students who participate in online teaching and testing are located all over the world, and the real conditions and network environment vary greatly, how to effectively prevent and control international students’ cheating in online tests has become an important issue in current international Chinese language education with great practical significance. The author team of this paper includes teachers who have long been involved in international Chinese teaching and research, and international students’ cheating behavior has been troubling us since the large-scale online teaching and testing work began in 2020. The research team identified the following research work through repeated exchanges with technical experts: (i) design a fuzzy clustering algorithm-based online test cheating detection model based on the current network conditions of online teaching and testing in international Chinese education; (ii) conduct experiments to verify the feasibility and effectiveness of the method; and (iii) discuss the factors influencing the occurrence of online test cheating behavior and its consequences. OICIE-ACS technology design strategy OICIE-ACS system architecture Online International Chinese language education ANTI-CHEAT System (OICIE-ACS) is an anti-cheating system developed for international Chinese learners’ online test cheating behavior, which can provide anti-cheating security access solutions for various international Chinese online test systems under the Windows platform system. Fig. 1 Functional Structure of OICIE-ACS System Concerning the relevant research design [14, 15], OICIE-ACS mainly consists of four parts, including (i)login module, (ii)registration module, (3) examinee identification and authentication module, and(iii) web page control module. The functional structure of the system is shown in Fig. 1. Microsoft SQLServe is a relational database management system. It has the advantages of ease of use, scalability, and a high degree of integration with related software, and it can meet the database requirements of this system. Given that the teaching and research assistance systems widely used in Chinese universities, such as the examination system, the book management system, the student management system, and so on, are all developed based on Microsoft products, this system can quickly connect the above systems by using the SQL Server database to reduce the trouble in all kinds of connection woes. As a result, as a Chinese online exam system built on ASP NET and SQL Server databases, OICIE-ACS can effectively solve all kinds of (suspected) cheating problems in previous Chinese online examinations. The system’s overall architecture includes system structure, functional module division, data flow, methods, and so on. The system employs a B/S architecture [15, 16] (as illustrated in Fig. 2), with the client side written in Java and the server side employing a SQL database management system. The user layer is in charge of logging in users, registering users, and managing user names, passwords, and other information; the server layer is in charge of back-end maintenance, processing user requests, and receiving user feedback; and the system layer is in charge of running the preceding layers from the bottom up. Fig. 2 OICIE-ACS system structure diagram OICIE-ACS core module design Anti-cut screen module Based on our exam management experience, some widely used software, such as Microsoft Office software, can be compatible in the exam system, which means that students can use Office software to record and save exam-related books and materials, which they can then access via screen switching during the online exam. Cheating is defined as such behavior in the online closed book exam. The anti-screen-cutting module is intended to prevent test takers from using their answering device to switch interfaces in order to cheat [17, 18]. The administrator can set a buffer time to prevent screen-cutting actions due to misuse, and the act of returning to the test system within the specified buffer time will not be judged as cheating and will not execute the turn-in operation. Cut off the remote assistance module In this study, remote assistance refers to the operation behavior in which students who take the exam employ others to help control the exam host and complete the answer via computer remote control. As a result, turning off the system’s remote assistance function can effectively prevent such behavior. The system is configured to disable remote assistance for specific test modules[19]. This design eliminates the possibility of test takers using the remote assistance function to remotely operate the tester’s computer and complete the testing process instead. OICIE-ACS can scan the running environment of various types of computer platforms and ensure the operation of computer networks during the start-up process for overseas users. Video surveillance prevention and control The main rationale behind enabling video surveillance, which requires the prior installation of a camera on the computer, is that the test taker can be monitored during the test to prevent behaviors such as changing test takers or consulting reference materials for cheating during the test[20].Although turning on the camera can monitor the test taker’s behavior, due to the limited monitoring range, there are visual blind spots, which cannot effectively prevent cheating from occurring. We try to enable camera surveillance to observe test takers’ test movements and record videos or take photos to rectify them. The results will be uploaded to the administration side of the online examination system. OICIE-ACS startup environment OICIE-ACS can scan the running environment of various types of computer platforms and ensure the operation of computer networks during the start-up process for overseas users,and ensures the operation of computer networks by establishing good security policies and perfect security mechanisms to ensure that the OICIE-ACS system works best.[16]. The research team can negotiate the communication protocol with other platforms that carry out operational activities for the same purpose and call relevant data through the interface based on the common goal of serving the teaching and management of the school. In terms of security, given that the students taking the exam are most likely ordinary students, the likelihood of professional hackers participating in the exam is extremely low, and the exam is mostly held in a relatively fixed time and secure IP domain. Windows development tools also provide reasonably professional security. Referring to relevant research reports [21], we believe the system is relatively safe, as it has not been attacked by professional hackers. In terms of stability, because the design function of the system is relatively simple and is developed by professional engineers with rich work experience, the stability of the system is ensured to the maximum extent that can be considered. In addition, we can also use public service systems such as “Alibaba Cloud” to help the system allocate relevant resources efficiently and dynamically to ensure the stability of the system operation. At the beginning of the test, the common IM chat software process is monitored, and if a tester using IM is found during the test, the system will collect and record logs such as the corresponding time in the background and computer IP address MAC machine code data. After the statistics are completed, they are submitted to the management module for the administrator to judge whether there is cheating behavior after the test is completed by integrating manual experience. Figure 3 shows some Windows system processes of WeChat, a famous IM software with the largest number of users in China. We only need to monitor the background process similar to other IM software, find the process in the list in time and feed it back to the system, so that it can be properly handled. Fig. 3 Some Windows system processes of WeChat OICIE-ACS question bank and determination mechanism OICIE-ACS random question bank setting OICIE-ACS uses random question bank technology [22] to issue randomly generated test papers to different test takers at the same test time. There are differences in the test papers used by each test taker, but the number of questions, difficulty, and differentiation of the test papers are in line with the same test standard of the current test. Based on the test objectives, the test designer can establish a question bank before the exam begins and use an access database to achieve automatic paper formation and intelligent scoring, which effectively avoids the possibility of test takers communicating with each other when answering questions or directly copying others’ answers. Creating OICIE-ACS question bank Question generation The question bank is managed according to multi-level catalog management, and the information of the question bank can be added, deleted, bulk deleted, modified, previewed, imported, exported, etc. The question bank is built based on the reliability, validity, difficulty, differentiation, and other indicators of the test questions. Teachers only need to select the difficulty and length of the test when forming the test questions, and the system will automatically research the corresponding questions according to the ratios and automatically generate test papers with the presentation of differentiation. For effective learning in higher education, Sentimental Analysis Assist Student-Teacher Communication (SAA-STC) has been recommended. The suggested methodology oversees a variety of e-learning areas to monitor student-teacher interaction for efficient learning. [30]. It is frequently emphasized to students to participate actively in class discussions. In order to evaluate ITF-HMI for online education in Higher Education Systems, Interactive Teaching Framework is examined in this research. The students were able to monitor their development across all instructional activities because to the established online tasks. [31]. Question bank test management This system support manual input and administrators can use Word and Excel programs in the question bank system to achieve a key import also; Current system version includes seven kinds of international Chinese teaching test with common basic question types: single-choice, multiple-choice, indefinite choice, fill-in-the-blank, judgment, short answer, expository questions, but also according to the test subjects and content needs, set case study, complete fill-in-the-blank, reading comprehension. It can also set case study, completion, reading comprehension, listening comprehension, video answering, and other questions according to the test subjects and content needs; among them, formula questions, audio answering questions, and video answering questions can be quickly imported with one key. Automatic scoring Administrators can access the test data analysis in the background to understand the test pass rate, wrong question ranking, score segmentation analysis, etc. No need for manual statistics, the system can automatically analyze, each test paper can form an independent data analysis, forming an independent test report; test records support export and printing, including test papers, answer keys, test papers, test records, candidate reports and other types of data within the test records can be downloaded or printed online through the system printing function, which effectively guarantees the data security of the test. OICIE-ACS consists of foreground display and background management. The pass rate and wrong question ranking are recorded by the back desk data. After the examination, professional teachers and management personnel shall conduct at least two rounds of verification and review before publishing to the front page for students to query. The work experience in the past few years shows that, under normal circumstances, it takes 1–2 days for teachers to check the scores after the examination, and 1–2 days for managers to review, which means that the system can provide students with the examination pass rate and wrong question ranking within 2 working days as soon as possible after the examination. OICIE-ACS topic picture setting To avoid participants searching for answers to test questions through Internet sites during online examinations, this system enhances the confidentiality of the test question text to prevent test takers from copying the test question text during the test [23]. The administrator (examiner) has the option of converting the test questions into image format for visual recognition by the test taker before releasing the questions, but he or she cannot copy and paste the test question information for recognition. At the same time, when test takers use the mouse and keyboard, they are prohibited from using the right-click selection and paste functions, the keyboard functions such as shift, ctrl, and alt, and the real-time use of Chinese and English subtitles to indicate that “selection and copying of test content is prohibited during the test”. OICIE-ACS WEB startup and server sending response process are shown in Fig. 4.The process includes: Call the OICIE-ACS question bank questions–>extract the question text–>put the question text into the program word–>copy the relevant question text from the word–>paste it in the program “drawing” to generate the question picture–>then take the picture from the clipboard Take out the image ---> publish the image test questions. The image mode generation method of the test question is as follows: use a similar opencv tool [24] to calculate the width value and height value of the intercepted area based on the coordinates of the upper left corner and the lower right corner of the screenshot area, call the opencv Rect function to achieve area framing, and then extract the area and put it in a new Mat variable. Finally, the system also provides the function that the administrator can unlock the copy text by entering the permission password. Fig. 4 OICIE-ACS WEB startup and server sending response process. Note: [Wordg] program is relatively easy to control, but the [drawing] program does not have a ready-made interface, it is more difficult to control some, administrators are recommended to use the windows API function to send a message to the [drawing] program to control the purpose OICIE-ACS integrated technical judging mechanism In addition, to meet the diversified needs of international Chinese learning tests, we also provide a multidimensional comprehensive decision evaluation module for OICIE-ACS, which can be set up by the examiner according to the test needs and environmental conditions. Fuzzy clustering analysis can help the system complete the feature clustering of detected cheating samples, help to extract features, and provide anti-cheating strategies for features. Additionally, the calculation method based on fuzzy matrix can effectively and flexibly configure the system to better serve the online examination needs of different subjects. The systematic introduction of fuzzy evaluation model is not only to improve the granularity and accuracy, but also because online international Chinese teaching has opened many courses of different types, which means different examination evaluation methods and evaluation accuracy. By introducing the hybrid technology based on fuzzy evaluation method, it can flexibly and effectively meet the needs of examination evaluation of different courses. Referring to some comprehensive judgment studies on judgment matrix [25, 26], we designed the judgment matrix synthesis algorithm in this study. The sample setup form and algorithm are shown in the Table 1: Table 1 OICIE-ACS Sample Comprehensive Examination Cheating Determination Form Lever1 Indicators Lever2 Iindicators Evaluation Probability N SN SS C Behavioral Status (A1) look aroun (A11) Body off the screen (A12) There are other peoplearoun (A13) [0.8A1j,A1j] [0.6A1j,0.8A1j) [0.3A1j,0.6A1j) [0,0.3A1j) Computer process exceptions (A2) IM process is not closed (A21) Enable Virtual Machine or Remote Assistance (A22) MAC machine code and IP address exceptions (A23) [0.8A2k,A2k] [0.6A2k,0.8A2k) [0.3A2k,0.6A2k) [0,0.3A2k) Data Anomalies (A3) Speed of completion of short answer questions (A31) Multiple use of the copy key (A32) Switching screens multiple times (A33 ) [0.8A3i,A3i] [0.6A3i,0.8A3i) [0.3A3i,0.6A3i) [0,0.3A3i) Other (A4 ) Internet disconnection (A4) [0.8A4n,A4n] [0.6A4n,0.8A4n) [0.3A4n,0.6A4n) [0,0.3A4n) Overall Rating Probability [0.8,1] [0.6,0.8) [0.3,0.6) [0,0.3) Note: N: Normal level, SN: Suspected Normal, SS: Seriously Suspected, C : Cheating The algorithm is as follows.1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{gathered} \operatorname{B} =\left( {\begin{array}{*{20}{c}} {{\operatorname{A} _1}} \\ {{\operatorname{A} _2}} \\ {{\operatorname{A} _3}} \\ \vdots \\ {{\operatorname{A} _m}} \end{array}} \right)\left( {\begin{array}{*{20}{c}} {{\operatorname{A} _{11}}}&{{\operatorname{A} _{21}}}&{{\operatorname{A} _{31}}}& \cdots &{{\operatorname{A} _{m1}}} \\ {{\operatorname{A} _{12}}}&{{\operatorname{A} _{22}}}&{{\operatorname{A} _{32}}}& \cdots &{{\operatorname{A} _{m2}}} \\ {{\operatorname{A} _{13}}}&{{\operatorname{A} _{23}}}&{{\operatorname{A} _{33}}}& \cdots &{{\operatorname{A} _{m3}}} \\ \vdots & \vdots & \vdots & \cdots & \vdots \\ {{\operatorname{A} _{1j}}}&{{\operatorname{A} _{2k}}}&{{\operatorname{A} _{3l}}}& \cdots &{{\operatorname{A} _{mn}}} \end{array}} \right)= \hfill \\ {\operatorname{A} _1} \cdot \left( {{\operatorname{A} _{11}}+{\operatorname{A} _{12}}+{\operatorname{A} _{13}}+ \cdots {\operatorname{A} _{1j}}} \right)+{\operatorname{A} _2} \cdot ({\operatorname{A} _{21}}+{\operatorname{A} _{22}}+{\operatorname{A} _{23}}+ \cdots {\operatorname{A} _{2k}})+ \cdots {\operatorname{A} _m} \cdot ({\operatorname{A} _{m1}}+{\operatorname{A} _{m2}}+{\operatorname{A} _{m3}}+ \cdots {\operatorname{A} _{mn}}) \hfill \\ \end{gathered}$$\end{document} The Lever1 Indicators is “A1-Am”, and the Lever2 Indicators is “Amn” of the corresponding Level1 Indicators. According to the above formula, we can calculate the corresponding comprehensive examination judgment table “B”, and can flexibly configure it with the actual teaching situation. Effectiveness of OICIE-ACS integrated technology application In June 2021, we invited three teachers who were offering the same course in two parallel classes (with similar number of students) at the same time to participate in the small testing of the application effect of this system. All classes used the same teaching management system platform with the function of disrupting the order of questions only. The test papers used by each instructor in both parallel classes were automatically generated through the test system and were identical; the test times were identical. One of the classes was the experimental class and used the OICIE-ACS system for anti-cheating monitoring; the other class was the control class and did not use any anti-cheating monitoring measures. The results of the experiment are shown in the Table 2 and Fig. 5. Table 2 Experimental results of anti-cheating monitoring by OICIE-ACS system Teacher Number Experimental class (N = 25) Control class (N = 27) Average score (out of 100) Suspected cheating behavior frequency Average score (out of 100) Suspected cheating behavior frequency 1 81 5 87 23 2 78 9 81 18 3 83 8 85 12 Fig. 5 Comparison of the frequency of cheating in the test between the experimental class and the control class The above experimental data showed that the average scores of the same tests in the experimental class were all lower than those in the control class by different degrees, and the frequency of suspicious cheating behaviors was also lower than that in the control class. However, unfortunately, due to the limited number of international students in each class, and small number of teachers teaching the same courses in parallel classes at the same time, it is impossible to obtain more data for more in-depth statistical analysis. For all that, the above cases show that the use of OICIE-ACS comprehensive fuzzy judgment and multi-level comprehensive fuzzy judgment system can effectively solve the problem of comprehensive determination of cheating in international Chinese teaching online tests to a certain extent. Compared with the classical mathematical calculation method, the fuzzy judging method can well combine qualitative and quantitative analysis, which improves the granularity and precision in the assessment calculation; at the same time, the system can provide a refined tool for different subject tests of international Chinese teaching, and also provides a new idea for the quantification of OICIE-ACS in online tests. Finally, to improve the accuracy of judging cheating behaviors, the system can be set to add another manual audit function in the final part of the determination process to monitor the final results more effectively. The manual auditing process is divided into two parts: firstly, to confirm the new cheating means features extracted automatically to further reduce false positives; secondly, to do a screening of the data that fail to remove cheating features but have a high degree of suspicion, extract the cheating behaviors present in them, and submit the relevant data to the analysis background to improve the accuracy of subsequent automation. In addition, IM software can generate many new processes through real-time online iterative versions [27–29]. If no relevant information is entered in the original process library, a manual audit can be used to guide the system to stack information as required, to more accurately determine the new elements of cheating; In addition, due to the limitation of the nature of the test papers prepared by teachers themselves, there is a high probability that the standards of test questions (such as reliability, validity, difficulty and discrimination) are inconsistent, which leads to inconsistent standards of randomly generated test questions. Such situations can also be detected through manual audit, and the proportion of relevant scores can be adjusted later. Conclusion The OICIE-ACS anti-cheating system helps prevent cheating by international students in international Chinese online teaching tests and helps improve the quality of international Chinese teaching. Since the system is still in the experimental operation and debugging stage, we propose the following improvement measures and suggestions: (1) the current online testing system uses a public-based program that is not reliable enough; (2) the online testing system should choose the appropriate question bank and test question generation method according to the actual needs of teachers and students (such as test types and test purposes); (3) OICIE-ACS should also establish a more effective anti-cheating mechanism system to adapt to different subjects and different types of exams; (4) OICIE-ACS should pay attention to the actual feedback from instructors and iteratively update the anti-cheating mechanism and version to facilitate the monitoring and prevention of cheating behaviors with the times. We also put forward the following suggestions on how teachers and administrators can reduce students’ online exam cheating: (1) Before the online examination begins, the students will be given a detailed description of the seriousness of cheating and the regulations governing punishment; (2) Demonstrate the existing functions and technologies of the anti-cheating system to warn students; (3) Register and configure the relevant network information of the students taking the online exam in advance, such as computer IP, mac address, etc; (4) Conduct online invigilation during the online examination, and collect data on new (suspected) abnormal cheating behaviors for subsequent research and analysis. It is hoped that the discussion in this paper can provide some reference basis for future research on anti-cheating technology of international online Chinese teaching tests, thus promoting the sound and rapid development of the field. 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