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.
Original language | English (US) |
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Pages (from-to) | 999-1007 |
Number of pages | 9 |
Journal | American Journal of Preventive Medicine |
Volume | 66 |
Issue number | 6 |
DOIs | |
State | Accepted/In press - 2024 |
ASJC Scopus subject areas
- Epidemiology
- Public Health, Environmental and Occupational Health