Predicting Homelessness Among Transitioning U.S. Army Soldiers

• Published in: American journal of preventive medicine Vol. 66; no. 6; pp. 999 - 1007
• Main Authors: Tsai, Jack, Szymkowiak, Dorota, Hooshyar, Dina, Gildea, Sarah M., Hwang, Irving, Kennedy, Chris J., King, Andrew J., Koh, Katherine A., Luedtke, Alex, Marx, Brian P., Montgomery, Ann E., O'Brien, Robert W., Petukhova, Maria V., Sampson, Nancy A., Stein, Murray B., Ursano, Robert J., Kessler, Ronald C.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01-06-2024
• Subjects: Adult; Female; Humans; Ill-Housed Persons - statistics & numerical data; Longitudinal Studies; Machine Learning; Male; Military Personnel - statistics & numerical data; Prevalence; Surveys and Questionnaires; United States; Young Adult; United States
• ISSN: 0749-3797; 1873-2607; 1873-2607
• DOI: 10.1016/j.amepre.2024.01.018

This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention.
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.
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).
Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions.