Correcting Misinformation on Firearms Injuries

Accurate data on the nature of firearm injuries are essential for crafting effective policies for prevention but are currently lacking. It has been established that medical record coders often misclassify assault cases as “unintentional,” with the result that publicly available statistics on nonfatal firearms injuries are heavily biased with respect to the distribution of intents. The study by Miller et al1 investigates causes of misclassification, using patient case-level data from 3 level I US trauma centers. The authors found that 28% of assaults (234 of 837) were misclassified as accidents by medical record coders. Almost half (114) of these errors involved cases in which the medical record included a description of the circumstances that unmistakably indicated an assault; in the other cases, “assault” was the only reasonable supposition (eg, if the patient sustained multiple gunshot injuries). Although it is now normal for medical records coding to include an external cause of injury, it remains true that the primary purpose of coding is for billing and that payments are not affected by the choice of external-cause code. Hence, there is no financial incentive for providers to code the external cause accurately. In the 3 trauma centers in the study by Miller et al,1 the trauma registrars, using the same medical-record information as the medical-record coders, accurately coded intent of firearms injuries, with no bias against assault. It appears, then, that medical record coders could do much better. Since 2010, more than 800 journal articles have examined firearm injuries using hospital data sources according to a Google Scholar search performed on October 25, 2022, using the following terms: National Emergency Department Sample or emergency department data or State Emergency Department Data or emergency department database or ER data or trauma center or National Emergency Department Database and firearm injury. The systematic error in intent classification is not widely known or acknowledged by researchers in this field.2 The national scope of the problem is indicated by a recent analysis of data in the National Emergency Department Sample (NEDS), which is constructed by the Healthcare Cost and Utilization Project from a representative sample of 990 hospitals in 37 US states.3 The NEDS firearms injury estimates for 2016 indicated that 50% of all firearms injuries nationwide were unintentional. Other more reliable estimates place that proportion at 16% or less.3 What can be done to improve the accuracy of intent coding? Solutions depend on the sources of the miscoded injury intent. Miller et al1 suggest that coders need to be incentivized, which could be accomplished if their supervisors (and their organizations) believe it is important. Biased conceptions about the nature of firearm injuries may be another source of inaccurate intent coding. Miller et al document that misclassification increases the number of Black individuals with unintentional injuries by a factor of 6 compared with an increase of just 1.6 for White individuals with unintentional injuries. This result is an especially distorted picture of the mix of firearms injuries among Black individuals. Miller et al1 also suggest that it would be helpful for International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10) coding instructions to be changed. Currently, the suggested default in an ambiguous case is “unintentional” and it arguably should be “assault,” given that the overwhelming majority of nonfatal injuries are assaults. While it has not been conclusively demonstrated that this coding instruction plays a large role in the observed classification bias, the contrast with the coding mix under the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9), in which the default was “undetermined” rather than “unintentional,” suggests the possibility of a large effect.4 In 2014, unintentional injuries constituted 38% (31 508 of 82 092) of + Related article