%0 Journal Article %T Predicting homelessness among U.S. Army soldiers no longer on active duty %J American journal of preventive medicine %D 2022 %A Koh, Katherine A. %A Montgomery, Ann Elizabeth %A O'Brien, Robert W. %A Kennedy, Chris J. %A Luedtke, Alex %A Sampson, Nancy A. %A Gildea, Sarah M. %A Hwang, Irving %A King, Andrew J. %A Petriceks, Aldis H. %A Petukhova, Maria V. %A Stein, Murray B. %A Ursano, Robert J. %A Kessler, Ronald C. %V 63 %N 1 %P 13-23 %X INTRODUCTION: The ability to predict and prevent homelessness has been an elusive goal. The purpose of this study was to develop a prediction model that identified U.S. Army soldiers at high risk of becoming homeless after transitioning to civilian life based on information available before the time of this transition.

METHODS: The prospective cohort study consisted of observations from 16,589 soldiers who were separated or deactivated from service and who had previously participated in 1 of 3 baseline surveys of the Army Study to Assess Risk and Resilience in Servicemembers in 2011-2014. A machine learning model was developed in a 70% training sample and evaluated in the remaining 30% test sample to predict self-reported homelessness in 1 of 2 Longitudinal Study surveys administered in 2016-2018 and 2018-2019. Predictors included survey, administrative, and geospatial variables available before separation/deactivation. Analysis was conducted in November 2020-May 2021.

RESULTS: The 12-month prevalence of homelessness was 2.9% (SE=0.2%) in the total Longitudinal Study sample. The area under the receiver operating characteristic curve in the test sample was 0.78 (SE=0.02) for homelessness. The 4 highest ventiles (top 20%) of predicted risk included 61% of respondents with homelessness. Self-reported lifetime histories of depression, trauma of having a loved one murdered, and post-traumatic stress disorder were the 3 strongest predictors of homelessness.

CONCLUSIONS: A prediction model for homelessness can accurately target soldiers for preventive intervention before transition to civilian life.

Language: en

%G en %I Elsevier Publishing %@ 0749-3797 %U http://dx.doi.org/10.1016/j.amepre.2021.12.028