Predictors of firearm violence in urban communities: A machine-learning approach

Interpersonal firearm violence is a leading cause of death and injuries in the United States. Identifying community characteristics associated with firearm violence is important to improve confounder selection and control in health research, to better understand community-level factors that are associated with firearm violence, and to enhance community surveillance and control of firearm violence. The objective of this research was to use machine learning to identify an optimal set of predictors for urban interpersonal firearm violence rates using a broad set of community characteristics. The final list of 18 predictive covariates explain 77.8% of the variance in firearm violence rates, and are publicly available, facilitating their inclusion in analyses relating violence and health. This list includes the black isolation and segregation indices, rates of educational attainment, marital status, indicators of wealth and poverty, longitude, latitude, and temperature. •Used machine-learning to identify an optimal set of predictors of firearm violence.•Considered over 300 community characteristics from publically available sources.•Identified an optimal set of 18 variables that explained 77.8% of variation.•Poverty, wealth, and marital status were part of the set of optimal predictors.•Housing segregation, education, and temperature also predicted violence rates.