Predicting Homelessness Among Transitioning U.S. Army Soldiers.
Am J Prev Med
; 66(6): 999-1007, 2024 06.
Article
em En
| MEDLINE
| ID: mdl-38311192
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pessoas Mal Alojadas
/
Aprendizado de Máquina
/
Militares
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Aspecto:
Determinantes_sociais_saude
Limite:
Adult
/
Female
/
Humans
/
Male
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Am J Prev Med
/
Am. j. prev. med
/
American journal of preventive medicine
Ano de publicação:
2024
Tipo de documento:
Article