Predicting Homelessness Among Transitioning U.S. Army Soldiers

Abstract

Introduction: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.

Methods: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service.

Results: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs).

Conclusions: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions.

Figure 1.. SHAP values and bee swarm plots from the Stage-1 Random Forest model predicting homelessness

Note: The mean absolute value SHAP for all predictors was 0.058531. SHAP values were derived from a Random Forest prediction model. Abbreviations: SHAP, Shapley Additive Explanations ^aThe predictors include 6 geospatial variables and 4 VADIR (Veterans Affairs/Department of Defense Identity Repository) variables.