Katherine A Koh1, Ann Elizabeth Montgomery2, Robert W O'Brien3, Chris J Kennedy4, Alex Luedtke5, Nancy A Sampson6, Sarah M Gildea6, Irving Hwang6, Andrew J King6, Aldis H Petriceks7, Maria V Petukhova6, Murray B Stein8, Robert J Ursano9, Ronald C Kessler6. 1. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts; Boston Health Care for the Homeless Program, Boston, Massachusetts. Electronic address: kkoh@partners.org. 2. Department of Health Behavior, School of Public Health, The University of Alabama at Birmingham, Birmingham, Alabama; VA Health Care System, Birmingham, U.S. Department of Veteran Affairs, Birmingham, Alabama. 3. VA Health Services Research and Development Service, Washington, District of Columbia. 4. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts. 5. Department of Statistics, University of Washington, Seattle, Washington; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 6. Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts. 7. Harvard Medical School, Boston, Massachusetts. 8. Department of Psychiatry, University of California San Diego, San Diego, California; Department of Family Medicine & Public Health, University of California San Diego, San Diego, California. 9. Department of Psychiatry, Center for the Study of Traumatic Stress, Uniformed Services University of the Health Sciences, Bethesda, Maryland.
Abstract
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.
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: Katherine A Koh; Melanie Racine; Jessie M Gaeta; John Goldie; Daniel P Martin; Barry Bock; Mary Takach; James J O'Connell; Zirui Song Journal: Health Aff (Millwood) Date: 2020-02 Impact factor: 6.301