Predicting Homelessness Among Transitioning U.S. Army Soldiers.

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.

How well can U.S. military veterans' suicidal ideation be predicted from static and change-based indicators of their psychosocial well-being as they adapt to civilian life?

BACKGROUND: Identifying predictors of suicidal ideation (SI) is important to inform suicide prevention efforts, particularly among high-risk populations like military veterans. Although many studies have examined the contribution of psychopathology to veterans' SI, fewer studies have examined whether experiencing good psychosocial well-being with regard to multiple aspects of life can protect veterans from SI or evaluated whether SI risk prediction can be enhanced by considering change in life circumstances along with static factors. METHODS: The study drew from a longitudinal population-based sample of 7141 U.S. veterans assessed throughout the first three years after leaving military service. Machine learning methods (cross-validated random forests) were applied to examine the predictive utility of static and change-based well-being indicators to veterans' SI, as compared to psychopathology predictors. RESULTS: Although psychopathology models performed better, the full set of well-being predictors demonstrated acceptable discrimination in predicting new-onset SI and accounted for approximately two-thirds of cases of SI in the top strata (quintile) of predicted risk. Greater engagement in health promoting behavior and social well-being were most important in predicting reduced SI risk, with several change-based predictors of SI identified but stronger associations observed for static as compared to change-based indicator sets as a whole. CONCLUSIONS: Findings support the value of considering veterans' broader well-being in identifying individuals at risk for suicidal ideation and suggest the possibility that well-being promotion efforts may be useful in reducing suicide risk. Findings also highlight the need for additional attention to change-based predictors to better understand their potential value in identifying individuals at risk for SI.

Prospective Associations of Military Discharge Characterization with Post-active Duty Suicide Attempts and Homelessness: Results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

INTRODUCTION: Active duty service members transitioning to civilian life can experience significant readjustment stressors. Over the past two decades of the United States' longest sustained conflict, reducing transitioning veterans' suicidal behavior and homelessness became national priorities. However, it remains a significant challenge to identify which service members are at greatest risk of these post-active duty outcomes. Discharge characterization, which indicates the quality of an individual's military service and affects eligibility for benefits and services at the Department of Veterans Affairs, is a potentially important indicator of risk. MATERIALS AND METHODS: This study used data from two self-report panel surveys of the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) (LS1: 2016-2018, n = 14,508; and LS2: 2018-2019, n = 12,156), which were administered to respondents who previously participated while on active duty in one of the three Army STARRS baseline self-report surveys (2011-2014): the New Soldier Study (NSS), a survey of soldiers entering basic training; All Army Study, a survey of active duty soldiers around the world; and the Pre-Post Deployment Study, a survey of soldiers before and after combat deployment. Human Subjects Committees of the participating institutions approved all recruitment, informed consent, and data collection protocols. We used modified Poisson regression models to prospectively examine the association of discharge characterization (honorable, general, "bad paper" [other than honorable, bad conduct, dishonorable], and uncharacterized [due to separation within the first 180 days of service]) with suicide attempt (subsample of n = 4334 observations) and homelessness (subsample of n = 6837 observations) among those no longer on active duty (i.e., separated or deactivated). Analyses controlled for other suicide attempt and homelessness risk factors using standardized risk indices that were previously developed using the LS survey data. RESULTS: Twelve-month prevalence rates of self-reported suicide attempts and homelessness in the total pooled LS sample were 1.0% and 2.9%, respectively. While not associated with suicide attempt risk, discharge characterization was associated with homelessness after controlling for other risk factors. Compared to soldiers with an honorable discharge, those with a bad paper discharge had an increased risk of homelessness in the total sample (relative risk [RR] = 4.4 [95% CI = 2.3-8.4]), as well as within subsamples defined by which baseline survey respondents completed (NSS vs. All Army Study/Pre-Post Deployment Study), whether respondents had been separated (vs. deactivated), and how much time had elapsed since respondents were last on active duty. CONCLUSIONS: There is a robust association between receiving a bad paper discharge and post-separation/deactivation homelessness. Policies that enhance transition assistance and access to mental healthcare for high-risk soldiers may aid in reducing post-separation/deactivation homelessness among those who do not receive an honorable discharge.

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

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.

Measuring and improving the quality of mental health care: a global perspective.

Mental disorders are common worldwide, yet the quality of care for these disorders has not increased to the same extent as that for physical conditions. In this paper, we present a framework for promoting quality measurement as a tool for improving quality of mental health care. We identify key barriers to this effort, including lack of standardized information technology-based data sources, limited scientific evidence for mental health quality measures, lack of provider training and support, and cultural barriers to integrating mental health care within general health environments. We describe several innovations that are underway worldwide which can mitigate these barriers. Based on these experiences, we offer several recommendations for improving quality of mental health care. Health care payers and providers will need a portfolio of validated measures of patient-centered outcomes across a spectrum of conditions. Common data elements will have to be developed and embedded within existing electronic health records and other information technology tools. Mental health outcomes will need to be assessed more routinely, and measurement-based care should become part of the overall culture of the mental health care system. Health care systems will need a valid way to stratify quality measures, in order to address potential gaps among subpopulations and identify groups in most need of quality improvement. Much more attention should be devoted to workforce training in and capacity for quality improvement. The field of mental health quality improvement is a team sport, requiring coordination across different providers, involvement of consumer advocates, and leveraging of resources and incentives from health care payers and systems.

Supportive housing approaches in the Collaborative Initiative to Help End Chronic Homelessness (CICH).

The Federal Collaborative Initiative to Help End Chronic Homelessness funded 11 sites to expand permanent housing and offer supportive services to persons experiencing chronic homelessness and suffering from mental and substance use disorders. This study examines qualitative data on how the projects used US Department of Housing and Urban Development funding and three housing approaches (scattered units, congregate/clustered, or a combination) for rapid placement of clients. Each housing approach called for adaptations by the services teams and property personnel in order to support clients with independent living skills, prevent housing loss, and promote their overall health in line with Initiative goals. Property personnel reported taking on new roles with clients and forming new collaborative arrangements with services teams. The authors discuss the lessons reported by sites that were associated with housing configuration, type of lease, and role of property personnel.

Neighborhoods as a developmental context: a multilevel analysis of neighborhood effects on Head Start families and children.

Neighborhoods have been recognized in theory and research as an important context for child development. This study used data from the Head Start Family and Child Experiences Survey (FACES) and Census 2000 to assess the underlying factor structure and impact of neighborhood factors on child cognitive and behavioral outcomes, including the critical family and social factors that may mediate and/or moderate these relationships. Factor analyses found five factors described Head Start neighborhoods. After controlling for family and child factors, multilevel analyses found significant direct effects of neighborhood factors on Head Start children's cognitive and behavioral outcomes. There were no mediation effects found for family or social variables between neighborhood factors and child outcomes. A large number of moderation effects were found although there was not a clear pattern to the results. Future research, policy, and practice implications are discussed.