• 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, and Ronald C Kessler.
• National Center on Homelessness among Veterans, VA Homeless Programs Office, Washington, District of Columbia; School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas. Electronic address: Jack.Tsai@uth.tmc.edu.
• Am J Prev Med. 2024 Jun 1; 66 (6): 9991007999-1007.

IntroductionThis study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.MethodsThe 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.ResultsTwelve-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).ConclusionsMachine learning can help target outreach and assessment of TSMs for homeless prevention interventions.Published by Elsevier Inc.

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