Predicting Homelessness Among U.S. Army Soldiers No Longer on Active Duty

Academic Article


  • 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.
  • Authors

    Digital Object Identifier (doi)

    Author List

  • Koh KA; Montgomery AE; O'Brien RW; Kennedy CJ; Luedtke A; Sampson NA; Gildea SM; Hwang I; King AJ; Petriceks AH
  • Start Page

  • 13
  • End Page

  • 23
  • Volume

  • 63
  • Issue

  • 1