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

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.

Associations of active-duty mental health trajectories with post-military adjustment: Results from the STARRS Longitudinal Study

BACKGROUND

Many servicemembers experience difficulties transitioning from military to civilian life. We examined whether changes in mental health observed during active duty were associated with indices of post-military adjustment.

METHODS

Survey data from the multi-wave Army STARRS Pre/Post Deployment Study (PPDS; conducted 2012-2014) were linked to follow-up data from wave 1 of the STARRS Longitudinal Study (STARRS-LS1; conducted 2016-2018). Empirical Bayes estimates of intercepts and slopes of posttraumatic stress, problematic anger, and depressive symptoms during the PPDS were extracted from mixed-effects growth models and evaluated as predictors of life stress among 1080 participants who had separated or retired from the Army at STARRS-LS1; and of job satisfaction among 586 veterans who were employed at STARRS-LS1.

RESULTS

Higher average levels and larger increases in posttraumatic stress, anger, and depression over the deployment period were each associated with increased stress and (in the case of anger and depression) reduced job satisfaction. Posttraumatic stress and anger slopes were associated with overall stress (b = 5.60, p < 0.01 and b = 15.64, p = 0.04, respectively) and relationship stress (b = 5.50, p = 0.01 and b = 22.86, p = 0.01, respectively) beyond the average levels of those symptoms.

LIMITATIONS

Some transition-related difficulties may have resolved before outcome assessment; some measures were not previously validated.

CONCLUSIONS

Larger increases in posttraumatic stress and anger over a deployment period were associated with increased stress after leaving the Army, even after controlling for average symptom levels during the same period. Monitoring changes in mental health during active duty may help identify personnel who need additional support to facilitate the military-to-civilian transition.

Stressful Life Events and Risk of Homelessness After Active Duty: An Assessment of Risk and Resilience Among Servicemembers

OBJECTIVES

The transition from military to civilian life may present increased exposure to various stressful life events (SLEs) that can increase the risk of homelessness (eg, loss of employment, dissolution of romantic relationships). We assessed the extent to which exposure to SLEs occurring proximal to US Army soldier transitions out of active duty was associated with risk of homelessness.

METHODS

A total of 16 589 respondents who were no longer on active duty but participated while on active duty during 2011-2014 baseline surveys completed follow-up surveys during 2016-2018 and 2018-2019. The follow-up surveys assessed SLEs and homelessness occurring in the past 12 months. We used modified Poisson regression models to evaluate how much differential SLE exposure and effects explained the aggregate association of a risk index with homelessness among a sample of 6837 respondents, weighted to represent the full sample.

RESULTS

More than half (n = 3510, 52.8%) of respondents reported experiencing any SLEs in the past 12 months. Most (60.5%) of the difference in prevalence of homelessness among respondents defined as being at high risk of homelessness (vs lower risk) was explained by differential exposure to, and/or effects of, these SLEs. Personal betrayal by a loved one and economic problems played the largest roles in adjusted risk differences (0.045 and 0.074, respectively).

CONCLUSIONS

Homelessness might be reduced by gearing interventions toward soldiers at high risk of homelessness who are transitioning out of active duty to reduce exposure to and effects of modifiable SLEs on experiencing homelessness.

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.

Supporting servicemembers and veterans during their transition to civilian life using certified sponsors: A three-arm randomized controlled trial

Transitioning servicemembers and veterans (TSMVs) face difficulties throughout their reintegration to civilian life, including challenges with employment, poor social connection, and elevated risk for suicide. To meet the needs of this high-risk population, national initiatives have leveraged community-based interventions. Authors conducted a three-arm randomized controlled trial (n = 200) to evaluate two community-based interventions. The first, Team Red, White, and Blue (RWB), connects TSMVs to their community through physical/social activities. The second, Expiration Term of Service Sponsorship Program (ETS-SP) provides one-on-one certified sponsors to TSMVs who provide support during the reintegration process. TSMVs were assessed at baseline, 3, 6, and 12 months. The primary hypothesis was not supported as reintegration difficulties and social support were not significantly different for participants randomly assigned to the two community-based interventions (Arm-2/RWB and Arm-3/RWB + ETS-SP), when the data from the separate arms were collapsed and combined, compared to the waitlist. The results did support the secondary hypothesis as Arm-3/RWB + ETS-SP had less reintegration difficulties over 12 months and initially had more social support compared to Arm-2/RWB, which suggest that augmenting interventions with sponsors outperforms participation in community-based interventions alone. Overall, the results show some limitations of the studied community-based interventions, as implemented and researched within this study. The authors identified factors that may have contributed to the null findings for the primary hypothesis, which can be addressed in future studies, such as addressing the unique needs of TSMVs, enrolling TSMVs into interventions prior to military discharge, measuring and improving participation levels, and providing stepped-care interventions based on risk levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Actionable Predictive Factors of Homelessness in a Psychiatric Population: Results from the REHABase Cohort Using a Machine Learning Approach

BACKGROUND

There is a lack of knowledge regarding the actionable key predictive factors of homelessness in psychiatric populations. Therefore, we used a machine learning model to explore the REHABase database (for rehabilitation database-n = 3416), which is a cohort of users referred to French psychosocial rehabilitation centers in France.

METHODS

First, we analyzed whether the different risk factors previously associated with homelessness in mental health were also significant risk factors in the REHABase. In the second step, we used unbiased classification and regression trees to determine the key predictors of homelessness. Post hoc analyses were performed to examine the importance of the predictors and to explore the impact of cognitive factors among the participants.

RESULTS

&nbsp;First, risk factors that were previously found to be associated with homelessness were also significant risk factors in the REHABase. Among all the variables studied with a machine learning approach, the most robust variable in terms of predictive value was the nature of the psychotropic medication (sex/sex relative mean predictor importance: 22.8, σ = 3.4). Post hoc analyses revealed that first-generation antipsychotics (15.61%; p < 0.05 FDR corrected), loxapine (16.57%; p < 0.05 FWER corrected) and hypnotics (17.56%; p < 0.05 FWER corrected) were significantly associated with homelessness. Antidepressant medication was associated with a protective effect against housing deprivation (9.21%; p < 0.05 FWER corrected).

CONCLUSIONS

Psychotropic medication was found to be an important predictor of homelessness in our REHABase cohort, particularly loxapine and hypnotics. On the other hand, the putative protective effect of antidepressants confirms the need for systematic screening of depression and anxiety in the homeless population.