Jack Tsai, Dorota Szymkowiak, Dina Hooshyar, Sarah M Gildea, Irving Hwang, Chris J Kennedy, Andrew J King, Katherine A Koh, Alex Luedtke, Brian P Marx, Ann E Montgomery, Robert W O'Brien, Maria V Petukhova, Nancy A Sampson, Murray B Stein, Robert J Ursano, Ronald C Kessler
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...
February 3, 2024: American Journal of Preventive Medicine