ArticleAmerican journal of preventive medicine2024
Predicting Homelessness Among Transitioning U.S. Army Soldiers.
Article in American journal of preventive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- When military-to-civilian transition support falls short: A qualitative assessment of transition needs among U.S. Veterans.Military psychology : the official journal of the Division of Military Psychology, American Psychological Association · 2026Article
- Joint models targeting U.S. Army soldiers at high-risk of post-separation unemployment, homelessness, and suicide-related behaviors.Npj mental health research · 2026Article
- Retrospective Study of Homelessness among Transitioning Service Members Within Two Years after Military Service.Administration and policy in mental health · 2026Article
- Homelessness prediction models in high-income countries: a scoping review.BMC public health · 2025Article
- Improving explainability of post-separation suicide attempt prediction models for transitioning service members: insights from the Army Study to Assess Risk and Resilience in Servicemembers - Longitudinal Study.Translational psychiatry · 2025Article
- Identifying Prevention Targets for Homelessness Among Recently Discharged U.S. Veterans Across Systems.Health services insights · 2025Article
- Differentiating Individual Characteristics Associated with Suicidal Ideations, Plans, and Attempts among low-Income Veterans.Chronic stress (Thousand Oaks, Calif.)Article
Corrections and comments
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Authors and funding
17 authors at 9 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
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