Medical care solicitation by criminals with gunshot wound injuries: a survey of Washington, DC, jail detainees

Machine learning classification of new firearm injury encounters in the St Louis region: 2010-2020

OBJECTIVES

To improve firearm injury encounter classification (new vs follow-up) using machine learning (ML) and compare our ML model to other common approaches.

MATERIALS AND METHODS

This retrospective study used data from the St Louis region-wide hospital-based violence intervention program data repository (2010-2020). We randomly selected 500 patients with a firearm injury diagnosis for inclusion, with 808 total firearm injury encounters split (70/30) for training and testing. We trained a least absolute shrinkage and selection operator (LASSO) regression model with the following predictors: admission type, time between firearm injury visits, number of prior firearm injury emergency department (ED) visits, encounter type (ED or other), and diagnostic codes. Our gold standard for new firearm injury encounter classification was manual chart review. We then used our test data to compare the performance of our ML model to other commonly used approaches (proxy measures of ED visits and time between firearm injury encounters, and diagnostic code encounter type designation [initial vs subsequent or sequela]). Performance metrics included area under the curve (AUC), sensitivity, and specificity with 95% confidence intervals (CIs).

RESULTS

The ML model had excellent discrimination (0.92, 0.88-0.96) with high sensitivity (0.95, 0.90-0.98) and specificity (0.89, 0.81-0.95). AUC was significantly higher than time-based outcomes, sensitivity was slightly (but not significantly) lower than other approaches, and specificity was higher than all other methods.

DISCUSSION

ML successfully delineated new firearm injury encounters, outperforming other approaches in ruling out encounters for follow-up.

CONCLUSION

ML can be used to identify new firearm injury encounters and may be particularly useful in studies assessing re-injuries.

Novel Linkage of Individual and Geographic Data to Study Firearm Violence

Firearm violence is the end result of a causative web of individual-level and geographic risk factors. Few, if any, studies of firearm violence have been able to simultaneously determine the population-based relative risks that individuals experience as a result of what they were doing at a specific point in time and where they were, geographically, at a specific point in time. This paper describes the linkage of individual and geographic data that was undertaken as part of a population-based case-control study of firearm violence in Philadelphia. New methods and applications of these linked data relevant to researchers and policymakers interested in firearm violence are also discussed.