Predicting Firearm Suicide among US Army Veterans Transitioning from Active Service

United States (US) Veterans are significantly more likely to die by suicide than civilians. Machine learning (ML) models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce Veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most Veteran suicides, and firearm-specific preventions exist. We used data from US Army Veterans from 2010-2019 (N = 800,579) to develop and compare firearm-specific ML models with a method-agnostic model to predict firearm suicides among transitioning Army Veterans up to 10 years after discharge. The models performed comparably overall (AU-ROC=0.710-0.708; ICI=0.0003-0.0005% for firearm-specific and method-agnostic models, respectively), with the best model depending on the intervention threshold. Results from the current study show the method-agnostic model was better at predicting firearm suicides at the highest intervention threshold, whereas the firearm-specific model was better at lower thresholds. When considering fairness with respect to sex and race/ethnicity, the firearm-specific model was best across all thresholds. Thus, model choice depends on weighing numerous factors and optimal thresholds might differ for coordinated firearm-specific and method-agnostic interventions.