Healthcare costs associated with treating and preventing antibiotic resistant staphylococcal infections have continued to rise in the US and worldwide. Health disparities have been widely reported for infections caused by invasive forms of these antibiotic resistant strains known as methicillin resistant Staphylococcus aureus (MRSA) 5. Little is known to explain these health disparities observed among those who are MRSA carriers6,7 or those who go on to develop infection5,8. Household contacts, for example, have been cited as a risk for infection13 14. Overall, there are very few studies that look at why invasive MRSA infections occur more frequently among US blacks compared to whites. Paramount to improving the healthcare delivery to those who are infected with MRSA is efficient identification of those who might be at highest risk for infection so that correct and appropriate antibiotic therapy can be given when they present with symptoms of infection. We propose to use geographic information system tools (GIS) and geo-spatial statistical modeling to identify specific socio -environmental and -economic conditions, which may be associated with MRSA infections. Identifying those children who have risk factors for MRSA infection based on characteristics found in a particular geographic community (e.g., median household income, housing conditions, built environments, density of neighborhood, access to healthcare facilities, etc.) can improve the delivery of preventive or empiric treatment modalities. Using geo-spatial modeling and GIS tools will allow more rapid and accurate targeting of appropriate intervention and treatment. This study addresses the high rates of infection by the antibiotic resistant bacteria, MRSA, in children who live in urban areas. We determine both antibiotic resistant (cases) and non-antibiotic resistant (control group 1) Staphylococcus aureus during a ten year span 2002-2012 based on microbiological cultures. We will apply an eco-social model combined with principles of spatial diffusion to analyze our dataset. Our study focuses on income, housing conditions and household crowding, and built environments. By using GIS, we link individual patient profiles, based on their specific place of residence and patient health information records, to US Census data variables related to income, crowding and housing conditions to get a better sense of a patient's community area. Combining both individual and community area variables will allow us to better understand what role a patient's socio environmental conditions play in his/her risk for infection. For example, 'clustering' of patients infected by MRSA will be identified based on geo mapping these patients' residences, which then provides us with information on the community characteristics beyond what is available from patient health record information. The results generated by this research will indirectly impact the delivery of healthcare and assist with the development of health policy guidelines as it pertains to infection control, quality of healthcare delivery for those who may be at increased risk for MRSA and save healthcare dollars by improving the efficiency of diagnosing and managing those infected.
NIH ExPorter
NIH ExPorter