Addressing racial and ethnic disparities in premature exits from permanent supportive housing among residents with substance use disorders

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Panadero, Sonya Gabrielian, Marissa J. Seamans, Lillian Gelberg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4442590/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted 4 You are reading this latest preprint version Abstract Background. Permanent supportive housing (PSH) is an evidence-based practice for reducing homelessness that subsidizes permanent, independent housing and provides case management—including linkages to health services. Substance use disorders (SUDs) are common contributing factors towards premature, unwanted (“negative”) PSH exits; little is known about racial/ethnic differences in negative PSH exits among residents with SUDs. Within the nation’s largest PSH program at the Department of Veterans Affairs (VA), we examined relationships among SUDs and negative PSH exits (for up to five years post-PSH move-in) across racial/ethnic subgroups. Methods. We used VA administrative data to identify a cohort of homeless-experienced Veterans (HEVs) (n = 2,712) who were housed through VA Greater Los Angeles’ PSH program from 2016–2019. We analyzed negative PSH exits by HEVs with and without SUDs across racial/ethnic subgroups (i.e., African American/Black, Non-Hispanic White, Hispanic/Latino, and Other/Mixed [Asian, American Indian or Alaskan Native, and Native Hawaiian or Other Pacific Islander, and multi-race]) in controlled models and accounting for competing risk of death. Results. In competing risk models, HEVs with at least one SUD had 1.3 times the hazard of negative PSH exits compared to those without SUDs (95% CI: 1.00, 1.61). When stratifying by race/ethnicity, Other/Mixed race residents with at least one SUD had 6.4 times the hazard of negative PSH exits compared to their peers without SUDs (95% CI: 1.61–25.50). Hispanic/Latino residents with at least one SUD had 1.9 times the hazard compared to those without SUDs, also indicating a strong relationship with negative PSH exits; however, this association was not statistically significant (95% CI: 0.85–4.37). Black residents with at least one SUD had 1.2 times the hazard compared to those without SUDs (95% CI: 0.85–1.64), indicating no evidence of an association with negative PSH exits. Similarly, Non-Hispanic White residents with at least one SUD had 1.1 times the hazard compared to those without SUDs (95% CI: 0.75–1.66). Conclusions. These findings suggest relationships between SUDs and negative PSH exits differ between race/ethnic groups and suggest there may be value in culturally specific tailoring and implementation of SUD services for these subgroups. Permanent supportive housing substance use disorder homelessness health disparities Figures Figure 1 Figure 2 BACKGROUND Permanent supportive housing (PSH), which combines subsidies for permanent and independent housing with field-based supportive services, is an evidence-based practice that addresses homelessness and its profound associated health and social disparities. PSH has demonstrated success in retaining homeless-experienced residents for up to two years, including those with substance use disorders (SUDs) ( 1 – 3 ); however, SUDs are also one of the most significant contributing factors towards premature, unwanted (“negative”) PSH exits (e.g., eviction) and returns to homelessness ( 4 – 6 ). The prevalence of SUDs varies across racial/ethnic subgroups, with increased prevalence among PSH residents who self-identify as racial/ethnic minorities compared to Non-Hispanic White residents who have experienced homelessness ( 7 ). As such, there is a need to identify subpopulations of homeless-experienced residents with heightened vulnerabilities towards negative PSH exits and to provide these groups with supports that enhance equity in housing stabilization interventions. Developed in the early 1990s, PSH draws upon principles of “Housing First,” providing affordable, low-barrier housing options to individuals experiencing homelessness, and accompanied by linkages to medical and mental health services. PSH case management and other field-based supportive services are guided by a harm-reduction approach, and do not mandate SUD treatment and/or sobriety ( 8 – 10 ). There is substantial evidence that PSH reduces homelessness and increases housing stability for residents with SUDs ( 1 , 2 ). However, despite the effectiveness of the PSH model for residents with SUDs, substance use remains one of the most significant contributing factors towards housing instability ( 11 ), including within PSH programs ( 4 , 6 ). In partnership with the Department of Housing and Urban Development (HUD), the Department of Veterans Affairs’ (VA) Supportive Housing (HUD-VASH) program is the nation’s largest PSH initiative and a useful setting to examine disparities in PSH outcomes and inform improvement efforts. SUDs are highly prevalent among homeless-experienced Veterans (HEVs), estimated to have 60–76% prevalence ( 12 , 13 ) compared to 11–18% among the general Veteran population ( 14 , 15 ). Moreover, relative to the general Veteran population, HEVs have greater racial/ethnic diversity ( 16 , 17 ) and diversity among Veterans is only projected to increase in coming years ( 18 ). As such, there is a need to assess racial/ethnic disparities in PSH outcomes to inform tailored and targeted strategies for mitigating these disparities and to ensure the provision of equitable VA medical care and social services across subgroups of HEV residents. Existing literature has identified disparities in SUD diagnoses among Veterans who self-identify as racial/ethnic minoritized groups, including the underdiagnosis of SUDs among Hispanic/Latino Veterans ( 19 ). Existing literature has also identified higher odds of housing instability among Veterans who self-identify as racial/ethnic minoritized groups ( 17 ). However, we know little about the relationships between SUDs and PSH outcomes across racial/ethnic subgroups of Veterans. An analysis of PSH outcomes from the first wave of HUD-VASH voucher administration found HUD-VASH to be more effective in improving housing retention outcomes among Non-Hispanic White HEVs with SUDs compared to African American/Black HEVs with SUDs; this analysis specifically noted that interactions between SUDs and race/ethnicity were deserving of future study ( 20 ). The interactions among SUDs, race/ethnicity, and housing outcomes in this context remain understudied. To fill this gap, among a cohort of HEVs housed through HUD-VASH in Los Angeles, we used administrative data to examine the relationships between SUDs and negative PSH exits, overall and by race/ethnicity, for up to five years post-PSH entry. METHODS Sample and procedures We used VA administrative data (from the Corporate Data Warehouse, CDW) and VA’s homeless registry (the Homeless Operations Management and Evaluation System, HOMES) to identify a cohort of HEVs (n = 2,933) housed through HUD-VASH at VA Greater Los Angeles between 2016–2019. VA Greater Los Angeles’ HUD-VASH program is the largest of any VA facility in the nation. In addition to financial subsidies for permanent housing, HUD-VASH provides field-based case management that includes linkages to medical and behavioral health services within and outside VA, including SUD treatment. We retrospectively captured housing information up to five-years post HEVs’ move-in date to PSH (i.e., through December 31, 2021). To identify our analytic sample, we abstracted residents’ HUD-VASH records from HOMES, including information from case managers about move-in dates, retention in PSH, and HUD-VASH exits, when applicable. Though some residents exit HUD-VASH for positive reasons (e.g., income increases typically attributed to employment or disability claim attainment, relocation to other permanent housing) most residents who exit HUD-VASH case management do so for negative reasons (e.g., eviction, incarceration, or returns to homelessness). From the 2,933 HEVs who moved into Los-Angeles-based HUD-VASH in 2016–2019, we used CDW to exclude persons with missing data on key variables of interest, including race/ethnicity (n = 177) and marital status (n = 18). Those with “Other” marked as their reason for PSH exit (n = 26) were also treated as missing. Our final analytic sample included 2,712 residents. We retrospectively captured time between each resident’s PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]). These data were originally abstracted for a project examining smoking behavior and housing outcomes among this cohort of HEVs. All study procedures were reviewed and approved by VA Greater Los Angeles’ Institutional Review Board as constituting quality improvement. Measures Conceptual framework Measure selection and analyses were guided by the Behavioral Model for Vulnerable Populations ( 21 ) which describes person-level factors that predispose residents to health and housing outcomes (including age, gender, race/ethnicity, and marital status), which interact with characteristics that enable health access (e.g., primary care empanelment), needs (here, evaluated need for medical and mental health care), and health behaviors (e.g., primary care utilization) to influence HUD-VASH outcomes (retention or positive exits versus negative exits) (Fig. 1 ). Predisposing factors Demographic variables included age (modeled as a continuous variable at the time of move-in); gender (men and women); and marital status (stratified as married, previously married, or never married at the time of move-in) ( 11 , 14 ). For our key predisposing factor of interest, used to stratify the sample, we drew from VA administrative data which captures race across the following categories: African American or Black; White; Asian; American Indian or Alaskan Native (AIAN); Native Hawaiian or Other Pacific Islander (NHPI); Other Race; or Unknown. Ethnicity, a separate measure, captured Veterans identified as “Hispanic or Latino” versus “Not Hispanic or Latino”. To create a combined measure of race and ethnicity , we identified White and African American/Black patients who were not of Hispanic or Latino ethnicity, labelling these residents as Non-Hispanic White and African American/Black, respectively. We collapsed residents of White race and Hispanic/Latino ethnicity into a “Hispanic/Latino” category. Residents who identified as a race other than White but with Hispanic ethnicity (e.g., African American/Black race and Hispanic/Latino ethnicity) were coded as “Other/Mixed.” Asian, AIAN, and NHPI, and multi-racial Veterans were combined into the Other/Mixed category due to small sample sizes. Enabling factors We drew from the administrative data to determine Veterans empaneled to primary care, coined “Patient Aligned Care Teams” (PACTs), the VA’s patient-centered medical home model. We included Veterans assigned to specialty PACTS (e.g., Homeless-PACT [H-PACT] with providers and services tailored to HEVs) as empaneled. Primary care empanelment was modeled as a binary variable at the time of PSH move-in. Need factors Need factors were determined using diagnoses captured by primary or secondary International Classification of Disease, Tenth Revision (ICD-10) codes associated with VA outpatient or inpatient encounters in the administrative data over the two years prior to PSH move-in. ICD-10 codes associated with diagnoses are available in the supplemental materials. Mental health diagnoses included in these analyses included schizophrenia and other psychotic disorders, bipolar disorders, post-traumatic stress disorder (PTSD), depressive disorders (e.g., major depression, dysthymia), and anxiety disorders (e.g., panic disorder, generalized anxiety disorder, social anxiety). Binary indicators for each mental health diagnosis reflect the presence of a visit for the given diagnosis versus the absence of a visit for the diagnosis. For mental health diagnoses, we modeled diagnoses separately due to their distinct relationships with housing retention as identified in prior literature ( 4 , 22 , 23 ). Physical health diagnoses were ascertained via the Elixhauser Comorbidity Index Score ( 24 ), altered to exclude diagnoses already adjusted for in the study model (i.e., mental health diagnoses and substance use disorders). For our key predictor variable of interest, we were focused on the presence or absence of SUD diagnoses , which we defined to encompass alcohol use disorder or any drug use disorder (including opioids, cannabis, sedatives/hypnotics or anxiolytics, cocaine, other stimulants, hallucinogens, inhalants, and other psychoactive substances). Health Behaviors Using administrative data, we characterized primary care utilization as the health behavior of interest. We captured primary care engagement in one-year post-PSH move-in, modeled as a binary variable (at least one primary care visit, yes or no). Housing outcomes Our outcome of interest was housing retention, which was captured through retention or exit from HUD-VASH PSH. Among residents who exited housing, their exit date was recorded along with a reason for exit in HOMES by case managers. We note that some residents in HUD-VASH exit rental units but remain enrolled in the program; we did not obtain that data, which is not available within VA’s homeless registry. Residents who were deemed to have negatively exited were confirmed by housing arrangement information (i.e., place not meant for habitation, transitional housing, shelter, treatment facility, or other temporary tenure), upon exit entered by case managers with the corresponding exit date. In the case of residents whose housing arrangement information was unknown, they were considered to have exited housing and presumed to have returned to homeless as the case manager could not locate them to determine their housing arrangement. We stratified housing retention as: 1) retained (i.e., still housed at end of observation period; this included Veterans who exited the HUD-VASH program due to accomplishment of case management goals and/or no longer had need for case management and supportive services but remained housed; 2) positive or neutral exits; and 3) negative exits. We classified HUD-VASH exits as positive or neutral if they were associated with the following exit reasons: Veteran found/chose other housing; was no longer financially eligible for housing voucher (i.e., income was higher than eligible income rates); was escalated to a higher level of care; or was transferred to another HUD-VASH unit, e.g., in a different city or state. We classified negative exits as those attributed to other exit reasons, including: the Veteran cannot be located; did not comply with case management; was incarcerated; was no longer interested in participating in HUD-VASH; was unhappy with HUD-VASH housing; or was evicted and/or had other housing related issues or problems. The outcome of interest was dichotomized (“Yes” or No”) as experienced a negative PSH exit versus the absence of a negative exit (i.e., a positive or neutral PSH exit or retained housing). Of note, we also used VA administrative data to identify residents who became deceased over the study period as opposed to exiting for other reasons, as this is a competing event (i.e., precludes the resident from exiting PSH during the study period). Time-to-event Each HEV was retrospectively followed beginning with their PSH move-in date and ending with either of the following events: the outcome of interest (i.e., PSH exit), a competing event (i.e., death), or the end of the study follow-up period (i.e., December 31, 2021)—whichever occurred first. We then calculated the time (in days) between each resident’s PSH move-in date and their respective event. Analyses We characterized predisposing, enabling, and need factors among HEVs with and without SUDs. We did not compare exposed and unexposed groups or include inferential statistics (e.g., p-values) per the STROBE guidelines ( 25 ). We retrospectively captured time between each resident’s PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]). Time-to-event (with PSH exit serving as “event”) data were used to calculate incidence rates. This was followed by survival analyses, using hazard functions, to compare occurrence of negative PSH exits among HEVs with SUDs versus those with no SUDs. The proportional hazards assumption (i.e., that the relative hazards remain constant over time), which is the fundamental assumption for hazard regressions, was tested to examine if the effects of SUDs on negative PSH housing exits varied over time. In addition, we further tested the proportional hazards assumption to examine if the effects of SUDs on negative PSH housing exits varied over time within each racial/ethnic group. The proportional hazards assumption was not violated in any racial/ethnic group. The reported incidence rates do not account for the competing risk of death. Therefore, to account for the competing risk of death in survival analyses, we fit Fine-Gray subdistribution hazard models, as this approach estimates hazards over time in the presence of competing events ( 26 ). We estimated hazard ratios and 95% confidence intervals (95% CIs) for negative PSH exits comparing HEVs with SUDs to those without SUDs, accounting for the competing risk of death in multivariable models, and controlling for all other predisposing (age, gender, marital status), need (mental health diagnoses and Elixhauser score), enabling (primary care empanelment), and health behavior (primary care engagement) factors. These models were stratified across the four racial/ethnic subgroups to examine if the relationship between SUDs and negative PSH exits varied by racial/ethnicity. All analyses were conducted using Base SAS 9.4 ©. RESULTS Sample characteristics Table 1 describes the analytic sample (n = 2,712). Of the sample, 50% were Black, 33% were Non-Hispanic White, 12% were Hispanic/Latino, 4% were Other/Mixed, and 40% had at least one SUD (Table 1 ). A majority (90%) of HEVs in the cohort were male and 88% were not married. The mean age at program entry was 53.4 ± 13.6 years. The average follow-up time (i.e., the average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]) was 3.0 years among HEVs with SUDs and 3.1 years among HEVs without SUDs. A minority (n = 397, 15%) of HEVs experienced a negative PSH exit; 225 (8%) died while in housing; most 2,090 (77%) were retained or experienced a positive/neutral PSH exit. Table 1 Characteristics and negative permanent supportive housing (PSH) exits of HUD-VASH Veterans who entered PSH in 2016–2019 by having a substance use disorder (SUD) (n = 2712) Sample Characteristics by Domains Analytic Sample N = 2712 Analytic Sample by SUDs Substance Use Disorder n = 1077 (40%) No Substance Use Disorder n = 1635 (60%) Average follow-up time in years (mean ± sd) a (3.1 ± 1.6) (3.0 ± 1.6) (3.1 ± 1.6) Outcome n % n % n % PSH Retention or Positive Exit 2090 77 787 73 1303 80 Negative PSH Exit 397 15 183 17 214 13 Deceased 225 8 107 10 118 7 Predisposing Factors Gender Male 2436 90 1022 95 1414 867 Female 276 10 55 5 221 14 Age (mean ± sd) (53.4 ± 13.6) (53.8 ± 11.9) (53.0 ± 14.5) Race/Ethnicity African American/Black 1369 50 530 49 839 51 White, Non-Hispanic 908 33 371 34 537 33 Hispanic/Latino 316 12 145 13 171 10 Other/Mixed b 119 4 31 3 88 5 Marital Status Married 333 12 106 10 227 14 Not Married 2,379 88 971 91 1,408 86 Enabling Factors PACT Empanelment 1,361 50 618 57 743 45 Need Factors Elixhauser c (mean ± sd) (2.1 ± 2.3) (2.7 ± 2.4) (1.8 ± 2.1) Mental Health Diagnoses PTSD 803 30 514 48 289 18 Schizophrenia 343 13 234 22 109 7 Bipolar 207 8 147 14 60 4 Depression 959 35 611 57 348 21 Anxiety 529 20 320 30 209 13 Health Behavior At least one primary care visit 1-year post PSH move-in 2,025 75 925 86 1,110 67 a Average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]) b Includes Asian, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, Other Race, and Multi-racial/ethnic c Elixhauser Comorbidity Index Score is a measure of overall severity of comorbidities. The higher the score, the higher the comorbidities. In this analysis, the substance use and mental health diagnoses were removed from Elixhauser calculations to avoid over adjusting for SUDs and mental health diagnoses. Table 1 displays differences in needs and housing outcomes between HEVs with at least one SUD and HEVs without SUDs. HEVs with SUDs were more likely to be diagnosed with PTSD (48%), schizophrenia or other psychotic disorders (22%), bipolar disorders (14%), depressive disorders (57%), and anxiety disorders (30%) compared to HEVs without SUDs (18%; 7%; 4%; 21%; and 13% respectively). In addition, HEVs with SUDs had a higher mean number of physical health comorbidities compared to HEVs without SUDs (average Elixhauser score of 2.7 versus 1.8). HEVs with SUDs were also more likely to have at least one primary care visit within one year of PSH move-in date (86%) compared to HEVs without SUDs (67%). HEVs with SUDs also had a higher proportion of negative PSH exits (17% versus 13%) and a higher proportion of deaths (10% versus 7%), compared to those without SUDs. Associations between substance use disorders and negative PSH exits The incidence of negative housing exits was slightly higher among HEVs with SUDs than in the group with no SUDs (incidence per 1,000 person-years = 56.6 vs. 42.2). HEVs with at least one SUD had 1.26 times the hazard of negative PSH exits compared to those without SUDs (cHR Overall = 1.29; 95% CI = 1.06, 1.57). After controlling for predisposing, need, and enabling factors, the hazard ratio did not change materially (aHR Overall =1.27; 95% CI = 1.00, 1.61; see Table 2 ). Table 2 Hazard ratio of negative permanent supportive housing (PSH) exits according to substance use disorder and race/ethnicity (N = 2712) Race/Ethnicity Substance Use Disorder Total N Total Person-Years Negative PSH Exits a Adjusted Hazard Ratio (95% CI) b, c Total Complete-Case Population No 1635 5077 42.2 Reference Yes 1077 3232 56.6 1.27 (1.00, 1.61) African American / Black No 839 2659 43.6 Reference Yes 530 1659 54.2 1.18 (0.85, 1.64) White, Non-Hispanic No 537 1590 45.3 Reference Yes 371 1071 54.2 1.12 (0.75, 1.66) Hispanic / Latino No 171 534 31.8 Reference Yes 145 428 58.4 1.92 (0.85, 4.37) Other / Mixed d No 88 295 30.5 Reference Yes 31 79 126.6 6.41 (1.61, 25.50) a Per 1,000 Person-Years b Using a Fine and Gray competing risk analysis c Adjusting for gender, age, marital status, physical health diagnoses (Elixhauser Comorbidity Index Score), mental health diagnoses (PTSD, schizophrenia, bipolar, depression, and anxiety disorder) d Includes Asian, AIAN, NHPI, Other Race, and Multi-Racial/Ethnic Stratification by race/ethnicity Table 2 also presents hazard ratios of negative PSH exits by SUD status, stratified by race/ethnicity. Other/Mixed race HEVs with at least one SUD had 6.4 times the risk of negative PSH exits compared to their peers without SUDs (aHR Other/Mixed = 6.41, 95% CI: 1.61–25.50), whereas associations between SUDs and negative PSH exits were not statistically significant among Black and Non-Hispanic White HEVs (aHR Black =1.18, 95% CI: 0.85–1.64; aHR White =1.12, 95% CI: 0.75–1.66) (Fig. 2 ). Hispanic/Latino HEVs with at least one SUD had 1.9 the hazard compared to those without SUD; however, this association was not statistically significant (aHR Hisp/Latino =1.92, 95% CI: 0.85–4.37). DISCUSSION We examined the relationships between SUDs and housing outcomes across racial/ethnic subgroups in a cohort of Veterans housed in HUD-VASH in Los Angeles. We identified an overall association between SUDs and negative PSH exits. However, in analyses stratified by race and ethnicity, we found this association varied by race/ethnic group. There was no statistically significant association between SUDs and negative PSH exits for Black, Non-Hispanic White, and Hispanic/Latino residents. Though it did not reach statistical significance, for residents of Hispanic/Latino ethnicity, the effect of presence of SUDs on negative PSH exits was nearly double that of White and Black subgroups. We observed a statistically significant positive association for Other/Mixed race HEVs. Notably, the relationships between SUDs and negative PSH exits were much stronger among Other/Mixed HEVs compared to other racial/ethnic groups, although this group comprises a small subset of HEVs (4%). Our findings differ from prior studies that broadly examined SUDs as associated with increased rates of premature or unwanted exits from PSH but did not focus on race/ethnic differences ( 6 ). In these data, among most racial/ethnic subgroups, the effects of SUDs on negative PSH exits were not significant, which suggests that current strategies to retain residents with SUDs in PSH, (e.g., improving timely access to supportive services, including behavioral health care) may be effective among these subgroups ( 4 ). However, despite these efforts however, our analyses highlight potentially important disparities in PSH housing outcomes among Hispanic/Latino and Other/Mixed race PSH residents with SUDs. Among Hispanic/Latino and Other/Mixed race residents, disparities in health behaviors, including SUD service utilization, may contribute to the increased effect of SUDs on negative PSH exits. In prior literature, Veterans of Hispanic/Latino and Other/Mixed race/ethnicity were found to have SUD prevalence rates nearly two times that of clinically documented SUD ( 19 ). Further indicating a gap in VA treatment receipt for SUD among these minoritized groups, White Veterans diagnosed with SUDs were found to be much more likely to receive treatment for SUD diagnoses as compared to Hispanic/Latino Veterans diagnosed with SUDs ( 27 ). This trend is also seen among Asian and NHPI populations. Across the general population, outside of Veteran-specific literature, minoritized communities have been shown to severely underutilize SUD treatment. Underutilization among these populations is often attributed to barriers to access including stigma, cost, lack of knowledge, and cultural attitudes ( 28 ). We suspect that tailored implementation approaches designed to increase adoption of evidence-based SUD treatments dissemination within VA (e.g., using peers to activate HEVs from racial/ethnic minoritized groups) may address these disparities and increase health equity within the PSH program. Prior research has found that other potentially relevant factors in examining relationships between SUDs and negative PSH exits include socioeconomic disparities associated with developing SUDs ( 29 ), differential stigma associated with specific substance use ( 30 ), and other social factors associated with SUDs (e.g., disparate marketing for substances in low-income and minority communities [31]). Racial/ethnic minority Veterans are also noted to have an increased risk of adverse SUD and psychiatric treatment outcomes (e.g., involuntary hospitalizations, shorter treatment duration) compared to their Non-Hispanic White peers ( 32 ). In general, researchers have attributed increased risk of SUDs among racial/ethnic minority populations to differential access to health services, social supports, and other healthy coping mechanisms (e.g., professional/clinic services, social service resources, community infrastructure). We note that, in this study, these disparities may be mitigated in part by the VA infrastructure; during the study period, all HUD-VASH residents were eligible for VA healthcare which awarded them equitable potential access to all health services, including SUD treatment. Strengths and limitations The primary strength of this study is its ability to examine longitudinal data for a large subset of PSH enrollees in a system that integrates housing and health services. VA administrative and homeless registry data provides robust information related to diagnoses, date of housing move-in, exits from PSH enrollment, and the competing risk of death. This study also had limitations. First, misclassification of PSH exits (i.e., negative, positive, neutral) may have occurred. Each exit is categorized using standardized reasons for exit which omit granular details about factors contributing to each participant’s PSH exit. Second, while there a large sample size for the entire cohort, when stratifying by race/ethnicity, small proportions in some subgroups (i.e., Asian, AIAN, NHPI, Other, and Mixed) necessitated collapsing of these subgroups into one category (“Other/Mixed”) which comprised 4% of HEVs. Future studies with larger samples sizes and/or utilizing qualitative methods could help provide greater insights into the potential vulnerabilities of racial/ethnic subgroups with smaller populations. Third, these analyses were based on diagnosed and documented SUDs, which may vary by race/ethnicity. In addition, in this study, we combined all diagnoses of substance use disorders within the relevant time frame (two years prior to housing move-in). Future research would benefit from examining differences in housing retention associated with specific substances used. We note specific complexities in data interpretation related to persons who only had cannabis use disorder to classify them as having a SUD; cannabis was legalized in the state of California in 2016, including at the study site ( 33 ). Fourth, it is possible that the high rates of comorbid mental health disorders and SUDs among this population overshadowed effects of SUDs on negative PSH exits. Future studies may benefit from assessing the relationships between comorbid mental health and SUD diagnoses on housing outcomes. Last, as a study conducted with one large and urban VA, it is unclear how much our findings extrapolate to a national HUD-VASH sample, or to homeless-experienced consumers who receive PSH services or health services outside the VA. CONCLUSIONS This study suggests that specific race/ethnicity groups largely explain the associations between SUDs and negative PSH exits, with the relationship between SUDs and negative PSH exits being much stronger among Other/Mixed HEVs and trending towards significance among Hispanic/Latino HEVs as compared to PSH residents of other race/ethnicity groups. PSH programs and providers should consider potential heightened vulnerabilities for negative housing outcomes among minoritized residents, particularly those of Hispanic/Latino, Asian, NHPI, AIAN, and Other/Mixed race and ethnicity. These findings would benefit from integration with qualitative data that explores potential reasons for differential rates for negative exits among PSH residents of different race/ethnicity groups. Such research could inform culturally-specific tailoring of SUD services and implementation strategies that support equitable use of SUD services within these subgroups, which ultimately have potential to reduce Veteran homelessness and increase health equity. Abbreviations AIAN American Indian or Alaskan Native CDW Corporate Data Warehouse CI confidence interval HEV homeless-experienced Veteran HOMES Homeless Operations Management and Evaluation System HR = hazard ratio HUD Department of Housing and Development ICD-10 International Classification of Disease, Tenth Revision NHPI Native Hawaiian or Other Pacific Islander (NHPI) PACT Patient Aligned Care Teams PSH permanent supportive housing sd standard deviation SUD substance use disorder VA Department of Veteran Affairs VASH Department of Veterans Affairs’ Supportive Housing Declarations Ethics approval and consent to participate All study procedures were reviewed and approved by VA Greater Los Angeles’ Institutional Review Board as constituting quality improvement. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. A Limited Dataset (LDS) will be created and shared pursuant to a Data Use Agreement (DUA) appropriately limiting use of the dataset and prohibiting the recipient from identifying or re-identifying (or taking steps to identify or re-identify) any individual whose data are included in the dataset. Competing interests The authors declare that they have no competing interests. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: TJP was supported by a VA Quality Enhancement Research Initiative (QUERI) Advance Diversity in Implementation Leadership (ADIL) award. TH was supported by a pilot grant (PI: Harris) awarded by the Department of Veteran Affairs Office of Rehabilitation Research & Development (RR&D) Center on Enhancing Community Integration for Homeless Veterans (MPIs: Green, Marder, Gabrielian); MJS was supported by a grant from the National Institute on Drug Abuse (K01DA054359). The funders had no role in study design; collection, analysis, and interpretation of data; writing the manuscript; or the decision to submit the manuscript for publication. Authors’ contributions TJP : Conceptualization, Methodology, Formal Analysis, Writing – Original Draft, Writing – Review & Editing, Visualization. SG : Conceptualization, Supervision, Writing – Original Draft, Writing – Review & Editing. MJS : Conceptualization, Methodology Writing – Review & Editing, Supervision. LG : Writing – Review & Editing. JT : Data Curation, Writing – Review & Editing. TH : Conceptualization, Methodology, Data Curation, Writing – Original Draft, Writing – Review & Editing, Supervision, Funding Acquisition. Acknowledgements The authors are grateful to VA QUERI ADIL for the opportunity for continued training that supported this study. We also appreciate Stephanie Chassman and Alec Chapman for their mentorship and guidance that also supported this study. The authors would also like to acknowledge the VA staff and Veterans who made this work possible. References Rog DJ, Marshall T, Dougherty RH, George P, Daniels AS, Ghose SS, et al. Permanent Supportive Housing: Assessing the Evidence. Psychiatric Serv. 2014;65(3):287–94. Collins SE, Malone DK, Clifasefi SL. Housing Retention in Single-Site Housing First for Chronically Homeless Individuals With Severe Alcohol Problems. Am J Public Health. 2013;103(S2):S269–74. Aubry T, Bloch G, Brcic V, Saad A, Magwood O, Abdalla T, et al. Effectiveness of permanent supportive housing and income assistance interventions for homeless individuals in high-income countries: a systematic review. Lancet Public Health. 2020;5(6):e342–60. Gabrielian S, Burns AV, Nanda N, Hellemann G, Kane V, Young AS. Factors Associated With Premature Exits From Supported Housing. Psychiatric Serv. 2016;67(1):86–93. Montgomery AE, Cusack MC, Gabrielian S. Supporting veterans’ transitions from permanent supportive housing. Psychiatr Rehabil J. 2017;40(4):371–9. Montgomery AE, Cusack M, Szymkowiak D, Fargo J, O’Toole T. Factors contributing to eviction from permanent supportive housing: Lessons from HUD-VASH. Eval Program Plann. 2017;61:55–63. Hoggatt KJ, Harris AHS, Washington DL, Williams EC. Prevalence of substance use and substance-related disorders among US Veterans Health Administration patients. Drug Alcohol Depend. 2021;225:108791. Tsemberis S, Gulcur L, Nakae M, Housing, First. Consumer Choice, and Harm Reduction for Homeless Individuals With a Dual Diagnosis. Am J Public Health. 2004;94(4):651–6. Tsemberis S, Eisenberg RF. Pathways to Housing: Supported Housing for Street-Dwelling Homeless Individuals With Psychiatric Disabilities. Psychiatric Serv. 2000;51(4):487–93. Stefancic A, Tsemberis S. Housing First for Long-Term Shelter Dwellers with Psychiatric Disabilities in a Suburban County: A Four-Year Study of Housing Access and Retention. J Prim Prev. 2007;28(3–4):265–79. Tsai J, Rosenheck RA. Risk Factors for Homelessness Among US Veterans. Epidemiol Rev. 2015;37(1):177–95. Tsai J, Kasprow WJ, Rosenheck RA. Alcohol and drug use disorders among homeless veterans: Prevalence and association with supported housing outcomes. Addict Behav. 2014;39(2):455–60. National Survey of. Homeless Veterans in 100,000 Homes Campaign Communities (100,000 Homes). 2011 Nov. Teeters J, Lancaster C, Brown D, Back S. Substance use disorders in military veterans: prevalence and treatment challenges. Subst Abuse Rehabil. 2017;8:69–77. Hoggatt KJ, Chawla N, Washington DL, Yano EM. Trends in substance use disorder diagnoses among Veterans, 2009–2019. Am J Addict. 2023;32(4):393–401. Nichter B, Tsai J, Pietrzak RH. Prevalence, correlates, and mental health burden associated with homelessness in U.S. military veterans. Psychol Med. 2023;53(9):3952–62. Montgomery AE, Szymkowiak D, Tsai J. Housing Instability and Homeless Program Use Among Veterans: The Intersection of Race, Sex, and Homelessness. Hous Policy Debate. 2020;30(3):396–408. National Center for Veterans Analysis and Statistics [Internet]. 2023. The Changing Characteristics of the Veteran Population: Veteran Population Projection Model 2020. Williams EC, Fletcher OV, Frost MC, Harris AHS, Washington DL, Hoggatt KJ. Comparison of Substance Use Disorder Diagnosis Rates From Electronic Health Record Data With Substance Use Disorder Prevalence Rates Reported in Surveys Across Sociodemographic Groups in the Veterans Health Administration. JAMA Netw Open. 2022;5(6):e2219651. O’Connell MJ, Kasprow WJ, Rosenheck RA. Differential Impact of Supported Housing on Selected Subgroups of Homeless Veterans With Substance Abuse Histories. Psychiatric Serv. 2012;63(12):1195–205. Gelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273–302. Wong MS, Clair K, Stigers PJ, Montgomery AE, Kern RS, Gabrielian S. Housing outcomes among homeless-experienced veterans engaged in vocational services. Am J Orthopsychiatry. 2022;92(6):741–7. Greenberg GA, Rosenheck RA. Correlates of Past Homelessness in the National Epidemiological Survey on Alcohol and Related Conditions. Adm Policy Mental Health Mental Health Serv Res. 2010;37(4):357–66. Austin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL. Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work. Med Care. 2015;53(9):e65–72. Hayes-Larson E, Kezios KL, Mooney SJ, Lovasi G. Who is in this study, anyway? Guidelines for a useful Table 1. J Clin Epidemiol. 2019;114:125–32. Fine J, Gray R. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 2012;94(446):496–509. Golub A, Vazan P, Bennett AS, Liberty HJ. Unmet Need for Treatment of Substance Use Disorders and Serious Psychological Distress Among Veterans: A Nationwide Analysis Using the NSDUH. Mil Med. 2013;178(1):107–14. Wu LT, Blazer DG. Substance use disorders and co-morbidities among Asian Americans and Native Hawaiians/Pacific Islanders. Psychol Med. 2015;45(3):481–94. Molina KM, Alegría M, Chen CN. Neighborhood context and substance use disorders: A comparative analysis of racial and ethnic groups in the United States. Drug Alcohol Depend. 2012;125:S35–43. Kulesza M. Substance Use Related Stigma: What we Know and the Way Forward. J Addict Behav Ther Rehabil. 2013;02(02). Sudhinaraset M, Wigglesworth C, Takeuchi DT. Social and Cultural Contexts of Alcohol Use: Influences in a Social-Ecological Framework. Alcohol Res. 2016;38(1):35–45. Kilbourne AM, Bauer MS, Pincus H, Williford WO, Kirk GF, Beresford T. Clinical, psychosocial, and treatment differences in minority patients with bipolar disorder. Bipolar Disord. 2005;7(1):89–97. Proposition 64: The Adult Use of Marijuana Act. 2024 Judicial Council of California United States of America: https://www.courts.ca.gov/prop64.htm ; 2016. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 27 May, 2024 Editor assigned by journal 27 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 18 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4442590","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307222873,"identity":"3e1fbffd-d842-4e3b-b486-aa7810ca1735","order_by":0,"name":"Talia J. Panadero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3PMWvCQBTA8RcCZjlwNSD2K5wIuqj5Kj0CmdyyZHxZ4iK4tt/iHN1eONAlNHQTuhiETg66tVCkSUVw8dJuUu4PD+7gfjwOwGS6wzyykQBO1ZmXMxRJHWmAVRF1IYGY/4bAFVHiGeuIk8b0Aeph4KjFLoryHifndcd0hAlMZ3DqLmdB2M2yt5KwsKclIJAYkCVp0nfj5IcE7pOONAtMv4A8me8Hn3Hy0vFqSUugKrcIuZn0rTihTvmXVeugJQWqNidfbt5DFzOfccVsriVNXx33EY1k7i+OGI0ZX0+L7aOGnOPXF5vxW+9u5mz/TEwmk+lf9w0m11hRPgIAVQAAAABJRU5ErkJggg==","orcid":"","institution":"Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), Department of Veteran Affairs (VA) Greater Los Angeles","correspondingAuthor":true,"prefix":"","firstName":"Talia","middleName":"J.","lastName":"Panadero","suffix":""},{"id":307222874,"identity":"a0ef9539-403b-477b-9c34-aa5f795c5bcd","order_by":1,"name":"Sonya Gabrielian","email":"","orcid":"","institution":"Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), Department of Veteran Affairs (VA) Greater Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Sonya","middleName":"","lastName":"Gabrielian","suffix":""},{"id":307222875,"identity":"f39e741b-ef4a-44b4-829e-0e05dc25596c","order_by":2,"name":"Marissa J. Seamans","email":"","orcid":"","institution":"Department of Epidemiology, UCLA Fielding School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Marissa","middleName":"J.","lastName":"Seamans","suffix":""},{"id":307222876,"identity":"047ea8f5-f3f8-4e58-9148-e2c4333f9663","order_by":3,"name":"Lillian Gelberg","email":"","orcid":"","institution":"Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), Department of Veteran Affairs (VA) Greater Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Lillian","middleName":"","lastName":"Gelberg","suffix":""},{"id":307222877,"identity":"edb97b24-ba7b-4e68-aae1-f7ec7f0665fe","order_by":4,"name":"Jack Tsai","email":"","orcid":"","institution":"National Center on Homelessness among Veterans, Department of Veteran Affairs Central Office","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"","lastName":"Tsai","suffix":""},{"id":307222878,"identity":"6378b9be-ed0c-425f-83cd-e5a23f6577e3","order_by":5,"name":"Taylor Harris","email":"","orcid":"","institution":"Center for the Study of Healthcare Innovation, Implementation, and Policy (CSHIIP), Department of Veteran Affairs (VA) Greater Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Taylor","middleName":"","lastName":"Harris","suffix":""}],"badges":[],"createdAt":"2024-05-19 00:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4442590/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4442590/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-21169-2","type":"published","date":"2025-01-28T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57943399,"identity":"a433635f-20b3-4153-a3b6-32c6e4e4134d","added_by":"auto","created_at":"2024-06-07 19:05:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework adapted from the Behavioral Model for Vulnerable Populations (Gelberg, et al., 2000).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4442590/v1/c22543c0bc13a6f0d2403de0.png"},{"id":57941541,"identity":"739bf9c1-a5cd-4c49-9e1a-6abf4bc52081","added_by":"auto","created_at":"2024-06-07 18:57:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of substance use disorder diagnoses on negative permanent supportive housing exits: log(adjusted hazard ratio) and 95% confidence intervals stratified by race/ethnicity.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4442590/v1/a1ea5a49a030cca5fb0f3e31.png"},{"id":75351928,"identity":"8c2548ea-dcc2-4770-bae6-792bb0b2ff74","added_by":"auto","created_at":"2025-02-03 16:12:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1225149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4442590/v1/6f51d2f5-9f5b-4822-a728-11e87403e08d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing racial and ethnic disparities in premature exits from permanent supportive housing among residents with substance use disorders","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003ePermanent supportive housing (PSH), which combines subsidies for permanent and independent housing with field-based supportive services, is an evidence-based practice that addresses homelessness and its profound associated health and social disparities. PSH has demonstrated success in retaining homeless-experienced residents for up to two years, including those with substance use disorders (SUDs) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e); however, SUDs are also one of the most significant contributing factors towards premature, unwanted (\u0026ldquo;negative\u0026rdquo;) PSH exits (e.g., eviction) and returns to homelessness (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The prevalence of SUDs varies across racial/ethnic subgroups, with increased prevalence among PSH residents who self-identify as racial/ethnic minorities compared to Non-Hispanic White residents who have experienced homelessness (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). As such, there is a need to identify subpopulations of homeless-experienced residents with heightened vulnerabilities towards negative PSH exits and to provide these groups with supports that enhance equity in housing stabilization interventions.\u003c/p\u003e \u003cp\u003eDeveloped in the early 1990s, PSH draws upon principles of \u0026ldquo;Housing First,\u0026rdquo; providing affordable, low-barrier housing options to individuals experiencing homelessness, and accompanied by linkages to medical and mental health services. PSH case management and other field-based supportive services are guided by a harm-reduction approach, and do not mandate SUD treatment and/or sobriety (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). There is substantial evidence that PSH reduces homelessness and increases housing stability for residents with SUDs (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, despite the effectiveness of the PSH model for residents with SUDs, substance use remains one of the most significant contributing factors towards housing instability (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), including within PSH programs (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn partnership with the Department of Housing and Urban Development (HUD), the Department of Veterans Affairs\u0026rsquo; (VA) Supportive Housing (HUD-VASH) program is the nation\u0026rsquo;s largest PSH initiative and a useful setting to examine disparities in PSH outcomes and inform improvement efforts. SUDs are highly prevalent among homeless-experienced Veterans (HEVs), estimated to have 60\u0026ndash;76% prevalence (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) compared to 11\u0026ndash;18% among the general Veteran population (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Moreover, relative to the general Veteran population, HEVs have greater racial/ethnic diversity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) and diversity among Veterans is only projected to increase in coming years (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). As such, there is a need to assess racial/ethnic disparities in PSH outcomes to inform tailored and targeted strategies for mitigating these disparities and to ensure the provision of equitable VA medical care and social services across subgroups of HEV residents.\u003c/p\u003e \u003cp\u003eExisting literature has identified disparities in SUD diagnoses among Veterans who self-identify as racial/ethnic minoritized groups, including the underdiagnosis of SUDs among Hispanic/Latino Veterans (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Existing literature has also identified higher odds of housing instability among Veterans who self-identify as racial/ethnic minoritized groups (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, we know little about the relationships between SUDs and PSH outcomes across racial/ethnic subgroups of Veterans. An analysis of PSH outcomes from the first wave of HUD-VASH voucher administration found HUD-VASH to be more effective in improving housing retention outcomes among Non-Hispanic White HEVs with SUDs compared to African American/Black HEVs with SUDs; this analysis specifically noted that interactions between SUDs and race/ethnicity were deserving of future study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The interactions among SUDs, race/ethnicity, and housing outcomes in this context remain understudied. To fill this gap, among a cohort of HEVs housed through HUD-VASH in Los Angeles, we used administrative data to examine the relationships between SUDs and negative PSH exits, overall and by race/ethnicity, for up to five years post-PSH entry.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample and procedures\u003c/h2\u003e \u003cp\u003eWe used VA administrative data (from the Corporate Data Warehouse, CDW) and VA\u0026rsquo;s homeless registry (the Homeless Operations Management and Evaluation System, HOMES) to identify a cohort of HEVs (n\u0026thinsp;=\u0026thinsp;2,933) housed through HUD-VASH at VA Greater Los Angeles between 2016\u0026ndash;2019. VA Greater Los Angeles\u0026rsquo; HUD-VASH program is the largest of any VA facility in the nation. In addition to financial subsidies for permanent housing, HUD-VASH provides field-based case management that includes linkages to medical and behavioral health services within and outside VA, including SUD treatment. We retrospectively captured housing information up to five-years post HEVs\u0026rsquo; move-in date to PSH (i.e., through December 31, 2021).\u003c/p\u003e \u003cp\u003eTo identify our analytic sample, we abstracted residents\u0026rsquo; HUD-VASH records from HOMES, including information from case managers about move-in dates, retention in PSH, and HUD-VASH exits, when applicable. Though some residents exit HUD-VASH for positive reasons (e.g., income increases typically attributed to employment or disability claim attainment, relocation to other permanent housing) most residents who exit HUD-VASH case management do so for negative reasons (e.g., eviction, incarceration, or returns to homelessness). From the 2,933 HEVs who moved into Los-Angeles-based HUD-VASH in 2016\u0026ndash;2019, we used CDW to exclude persons with missing data on key variables of interest, including race/ethnicity (n\u0026thinsp;=\u0026thinsp;177) and marital status (n\u0026thinsp;=\u0026thinsp;18). Those with \u0026ldquo;Other\u0026rdquo; marked as their reason for PSH exit (n\u0026thinsp;=\u0026thinsp;26) were also treated as missing. Our final analytic sample included 2,712 residents. We retrospectively captured time between each resident\u0026rsquo;s PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]).\u003c/p\u003e \u003cp\u003eThese data were originally abstracted for a project examining smoking behavior and housing outcomes among this cohort of HEVs. All study procedures were reviewed and approved by VA Greater Los Angeles\u0026rsquo; Institutional Review Board as constituting quality improvement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eConceptual framework\u003c/h2\u003e \u003cp\u003eMeasure selection and analyses were guided by the Behavioral Model for Vulnerable Populations (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) which describes person-level factors that predispose residents to health and housing outcomes (including age, gender, race/ethnicity, and marital status), which interact with characteristics that enable health access (e.g., primary care empanelment), needs (here, evaluated need for medical and mental health care), and health behaviors (e.g., primary care utilization) to influence HUD-VASH outcomes (retention or positive exits versus negative exits) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePredisposing factors\u003c/h2\u003e \u003cp\u003eDemographic variables included \u003cem\u003eage\u003c/em\u003e (modeled as a continuous variable at the time of move-in); \u003cem\u003egender\u003c/em\u003e (men and women); and \u003cem\u003emarital status\u003c/em\u003e (stratified as married, previously married, or never married at the time of move-in) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor our key predisposing factor of interest, used to stratify the sample, we drew from VA administrative data which captures race across the following categories: African American or Black; White; Asian; American Indian or Alaskan Native (AIAN); Native Hawaiian or Other Pacific Islander (NHPI); Other Race; or Unknown. Ethnicity, a separate measure, captured Veterans identified as \u0026ldquo;Hispanic or Latino\u0026rdquo; versus \u0026ldquo;Not Hispanic or Latino\u0026rdquo;. To create a combined measure of \u003cem\u003erace and ethnicity\u003c/em\u003e, we identified White and African American/Black patients who were not of Hispanic or Latino ethnicity, labelling these residents as Non-Hispanic White and African American/Black, respectively. We collapsed residents of White race and Hispanic/Latino ethnicity into a \u0026ldquo;Hispanic/Latino\u0026rdquo; category. Residents who identified as a race other than White but with Hispanic ethnicity (e.g., African American/Black race and Hispanic/Latino ethnicity) were coded as \u0026ldquo;Other/Mixed.\u0026rdquo; Asian, AIAN, and NHPI, and multi-racial Veterans were combined into the Other/Mixed category due to small sample sizes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEnabling factors\u003c/h2\u003e \u003cp\u003eWe drew from the administrative data to determine Veterans empaneled to primary care, coined \u0026ldquo;Patient Aligned Care Teams\u0026rdquo; (PACTs), the VA\u0026rsquo;s patient-centered medical home model. We included Veterans assigned to specialty PACTS (e.g., Homeless-PACT [H-PACT] with providers and services tailored to HEVs) as empaneled. \u003cem\u003ePrimary care empanelment\u003c/em\u003e was modeled as a binary variable at the time of PSH move-in.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eNeed factors\u003c/h2\u003e \u003cp\u003eNeed factors were determined using diagnoses captured by primary or secondary International Classification of Disease, Tenth Revision (ICD-10) codes associated with VA outpatient or inpatient encounters in the administrative data over the two years prior to PSH move-in. ICD-10 codes associated with diagnoses are available in the supplemental materials.\u003c/p\u003e \u003cp\u003eMental health diagnoses included in these analyses included schizophrenia and other psychotic disorders, bipolar disorders, post-traumatic stress disorder (PTSD), depressive disorders (e.g., major depression, dysthymia), and anxiety disorders (e.g., panic disorder, generalized anxiety disorder, social anxiety). Binary indicators for each mental health diagnosis reflect the presence of a visit for the given diagnosis versus the absence of a visit for the diagnosis. For mental health diagnoses, we modeled diagnoses separately due to their distinct relationships with housing retention as identified in prior literature (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Physical health diagnoses were ascertained via the Elixhauser Comorbidity Index Score (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), altered to exclude diagnoses already adjusted for in the study model (i.e., mental health diagnoses and substance use disorders).\u003c/p\u003e \u003cp\u003eFor our key predictor variable of interest, we were focused on the presence or absence of \u003cem\u003eSUD diagnoses\u003c/em\u003e, which we defined to encompass alcohol use disorder or any drug use disorder (including opioids, cannabis, sedatives/hypnotics or anxiolytics, cocaine, other stimulants, hallucinogens, inhalants, and other psychoactive substances).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eHealth Behaviors\u003c/h2\u003e \u003cp\u003eUsing administrative data, we characterized primary care utilization as the health behavior of interest. We captured primary care engagement in one-year post-PSH move-in, modeled as a binary variable (at least one primary care visit, yes or no).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eHousing outcomes\u003c/h2\u003e \u003cp\u003eOur outcome of interest was housing retention, which was captured through retention or exit from HUD-VASH PSH. Among residents who exited housing, their exit date was recorded along with a reason for exit in HOMES by case managers. We note that some residents in HUD-VASH exit rental units but remain enrolled in the program; we did not obtain that data, which is not available within VA\u0026rsquo;s homeless registry. Residents who were deemed to have negatively exited were confirmed by housing arrangement information (i.e., place not meant for habitation, transitional housing, shelter, treatment facility, or other temporary tenure), upon exit entered by case managers with the corresponding exit date. In the case of residents whose housing arrangement information was unknown, they were considered to have exited housing and presumed to have returned to homeless as the case manager could not locate them to determine their housing arrangement.\u003c/p\u003e \u003cp\u003eWe stratified housing retention as: 1) retained (i.e., still housed at end of observation period; this included Veterans who exited the HUD-VASH program due to accomplishment of case management goals and/or no longer had need for case management and supportive services but remained housed; 2) positive or neutral exits; and 3) negative exits. We classified HUD-VASH exits as positive or neutral if they were associated with the following exit reasons: Veteran found/chose other housing; was no longer financially eligible for housing voucher (i.e., income was higher than eligible income rates); was escalated to a higher level of care; or was transferred to another HUD-VASH unit, e.g., in a different city or state. We classified negative exits as those attributed to other exit reasons, including: the Veteran cannot be located; did not comply with case management; was incarcerated; was no longer interested in participating in HUD-VASH; was unhappy with HUD-VASH housing; or was evicted and/or had other housing related issues or problems.\u003c/p\u003e \u003cp\u003eThe outcome of interest was dichotomized (\u0026ldquo;Yes\u0026rdquo; or No\u0026rdquo;) as experienced a negative PSH exit versus the absence of a negative exit (i.e., a positive or neutral PSH exit or retained housing). Of note, we also used VA administrative data to identify residents who became deceased over the study period as opposed to exiting for other reasons, as this is a competing event (i.e., precludes the resident from exiting PSH during the study period).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTime-to-event\u003c/h2\u003e \u003cp\u003eEach HEV was retrospectively followed beginning with their PSH move-in date and ending with either of the following events: the outcome of interest (i.e., PSH exit), a competing event (i.e., death), or the end of the study follow-up period (i.e., December 31, 2021)\u0026mdash;whichever occurred first. We then calculated the time (in days) between each resident\u0026rsquo;s PSH move-in date and their respective event.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalyses\u003c/h2\u003e \u003cp\u003eWe characterized predisposing, enabling, and need factors among HEVs with and without SUDs. We did not compare exposed and unexposed groups or include inferential statistics (e.g., p-values) per the STROBE guidelines (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We retrospectively captured time between each resident\u0026rsquo;s PSH move-in date and the event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]). Time-to-event (with PSH exit serving as \u0026ldquo;event\u0026rdquo;) data were used to calculate incidence rates. This was followed by survival analyses, using hazard functions, to compare occurrence of negative PSH exits among HEVs with SUDs versus those with no SUDs.\u003c/p\u003e \u003cp\u003eThe proportional hazards assumption (i.e., that the relative hazards remain constant over time), which is the fundamental assumption for hazard regressions, was tested to examine if the effects of SUDs on negative PSH housing exits varied over time. In addition, we further tested the proportional hazards assumption to examine if the effects of SUDs on negative PSH housing exits varied over time within each racial/ethnic group. The proportional hazards assumption was not violated in any racial/ethnic group.\u003c/p\u003e \u003cp\u003eThe reported incidence rates do not account for the competing risk of death. Therefore, to account for the competing risk of death in survival analyses, we fit Fine-Gray subdistribution hazard models, as this approach estimates hazards over time in the presence of competing events (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). We estimated hazard ratios and 95% confidence intervals (95% CIs) for negative PSH exits comparing HEVs with SUDs to those without SUDs, accounting for the competing risk of death in multivariable models, and controlling for all other predisposing (age, gender, marital status), need (mental health diagnoses and Elixhauser score), enabling (primary care empanelment), and health behavior (primary care engagement) factors. These models were stratified across the four racial/ethnic subgroups to examine if the relationship between SUDs and negative PSH exits varied by racial/ethnicity. All analyses were conducted using Base SAS 9.4 \u0026copy;.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the analytic sample (n\u0026thinsp;=\u0026thinsp;2,712). Of the sample, 50% were Black, 33% were Non-Hispanic White, 12% were Hispanic/Latino, 4% were Other/Mixed, and 40% had at least one SUD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A majority (90%) of HEVs in the cohort were male and 88% were not married. The mean age at program entry was 53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6 years. The average follow-up time (i.e., the average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021]) was 3.0 years among HEVs with SUDs and 3.1 years among HEVs without SUDs. A minority (n\u0026thinsp;=\u0026thinsp;397, 15%) of HEVs experienced a negative PSH exit; 225 (8%) died while in housing; most 2,090 (77%) were retained or experienced a positive/neutral PSH exit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and negative permanent supportive housing (PSH) exits of HUD-VASH Veterans who entered PSH in 2016\u0026ndash;2019 by having a substance use disorder (SUD) (n\u0026thinsp;=\u0026thinsp;2712)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSample Characteristics by Domains\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e \u003cp\u003eAnalytic Sample\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2712\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eAnalytic Sample by SUDs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSubstance Use Disorder\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;1077 (40%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003eNo Substance Use Disorder\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en\u0026thinsp;=\u0026thinsp;1635 (60%)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAverage follow-up time in years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(3.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003en\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e%\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003en\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e%\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003en\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e%\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePSH Retention or Positive Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNegative PSH Exit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDeceased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePredisposing Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(53.0\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican American/Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhite, Non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHispanic/Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther/Mixed\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEnabling Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePACT Empanelment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeed Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eElixhauser\u003csup\u003ec\u003c/sup\u003e (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e(2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e(1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMental Health Diagnoses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBipolar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth Behavior\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAt least one primary care visit 1-year post PSH move-in\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1,110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Average time between PSH move-in date and event of interest (i.e., PSH exit), competing event (i.e., death), or administrative censor (i.e., end of study follow-up [December 31, 2021])\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Includes Asian, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, Other Race, and Multi-racial/ethnic\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Elixhauser Comorbidity Index Score is a measure of overall severity of comorbidities. The higher the score, the higher the comorbidities. In this analysis, the substance use and mental health diagnoses were removed from Elixhauser calculations to avoid over adjusting for SUDs and mental health diagnoses.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays differences in needs and housing outcomes between HEVs with at least one SUD and HEVs without SUDs. HEVs with SUDs were more likely to be diagnosed with PTSD (48%), schizophrenia or other psychotic disorders (22%), bipolar disorders (14%), depressive disorders (57%), and anxiety disorders (30%) compared to HEVs without SUDs (18%; 7%; 4%; 21%; and 13% respectively). In addition, HEVs with SUDs had a higher mean number of physical health comorbidities compared to HEVs without SUDs (average Elixhauser score of 2.7 versus 1.8). HEVs with SUDs were also more likely to have at least one primary care visit within one year of PSH move-in date (86%) compared to HEVs without SUDs (67%). HEVs with SUDs also had a higher proportion of negative PSH exits (17% versus 13%) and a higher proportion of deaths (10% versus 7%), compared to those without SUDs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between substance use disorders and negative PSH exits\u003c/h2\u003e \u003cp\u003eThe incidence of negative housing exits was slightly higher among HEVs with SUDs than in the group with no SUDs (incidence per 1,000 person-years\u0026thinsp;=\u0026thinsp;56.6 vs. 42.2). HEVs with at least one SUD had 1.26 times the hazard of negative PSH exits compared to those without SUDs (cHR\u003csub\u003eOverall\u003c/sub\u003e = 1.29; 95% CI\u0026thinsp;=\u0026thinsp;1.06, 1.57). After controlling for predisposing, need, and enabling factors, the hazard ratio did not change materially (aHR\u003csub\u003eOverall\u003c/sub\u003e=1.27; 95% CI\u0026thinsp;=\u0026thinsp;1.00, 1.61; see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratio of negative permanent supportive housing (PSH) exits according to substance use disorder and race/ethnicity (N\u0026thinsp;=\u0026thinsp;2712)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubstance Use Disorder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Person-Years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNegative PSH Exits\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted Hazard Ratio (95% CI)\u003csup\u003eb, c\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal Complete-Case Population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.27 (1.00, 1.61)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAfrican American / Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.18 (0.85, 1.64)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWhite, Non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.12 (0.75, 1.66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHispanic / Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.92 (0.85, 4.37)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOther / Mixed\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.41 (1.61, 25.50)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Per 1,000 Person-Years\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Using a Fine and Gray competing risk analysis\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Adjusting for gender, age, marital status, physical health diagnoses (Elixhauser Comorbidity Index Score), mental health diagnoses (PTSD, schizophrenia, bipolar, depression, and anxiety disorder)\u003c/p\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Includes Asian, AIAN, NHPI, Other Race, and Multi-Racial/Ethnic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStratification by race/ethnicity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also presents hazard ratios of negative PSH exits by SUD status, stratified by race/ethnicity. Other/Mixed race HEVs with at least one SUD had 6.4 times the risk of negative PSH exits compared to their peers without SUDs (aHR\u003csub\u003eOther/Mixed\u003c/sub\u003e= 6.41, 95% CI: 1.61\u0026ndash;25.50), whereas associations between SUDs and negative PSH exits were not statistically significant among Black and Non-Hispanic White HEVs (aHR\u003csub\u003eBlack\u003c/sub\u003e=1.18, 95% CI: 0.85\u0026ndash;1.64; aHR\u003csub\u003eWhite\u003c/sub\u003e=1.12, 95% CI: 0.75\u0026ndash;1.66) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hispanic/Latino HEVs with at least one SUD had 1.9 the hazard compared to those without SUD; however, this association was not statistically significant (aHR\u003csub\u003eHisp/Latino\u003c/sub\u003e=1.92, 95% CI: 0.85\u0026ndash;4.37).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe examined the relationships between SUDs and housing outcomes across racial/ethnic subgroups in a cohort of Veterans housed in HUD-VASH in Los Angeles. We identified an overall association between SUDs and negative PSH exits. However, in analyses stratified by race and ethnicity, we found this association varied by race/ethnic group. There was no statistically significant association between SUDs and negative PSH exits for Black, Non-Hispanic White, and Hispanic/Latino residents. Though it did not reach statistical significance, for residents of Hispanic/Latino ethnicity, the effect of presence of SUDs on negative PSH exits was nearly double that of White and Black subgroups. We observed a statistically significant positive association for Other/Mixed race HEVs. Notably, the relationships between SUDs and negative PSH exits were much stronger among Other/Mixed HEVs compared to other racial/ethnic groups, although this group comprises a small subset of HEVs (4%).\u003c/p\u003e \u003cp\u003eOur findings differ from prior studies that broadly examined SUDs as associated with increased rates of premature or unwanted exits from PSH but did not focus on race/ethnic differences (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In these data, among most racial/ethnic subgroups, the effects of SUDs on negative PSH exits were not significant, which suggests that current strategies to retain residents with SUDs in PSH, (e.g., improving timely access to supportive services, including behavioral health care) may be effective among these subgroups (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, despite these efforts however, our analyses highlight potentially important disparities in PSH housing outcomes among Hispanic/Latino and Other/Mixed race PSH residents with SUDs.\u003c/p\u003e \u003cp\u003eAmong Hispanic/Latino and Other/Mixed race residents, disparities in health behaviors, including SUD service utilization, may contribute to the increased effect of SUDs on negative PSH exits. In prior literature, Veterans of Hispanic/Latino and Other/Mixed race/ethnicity were found to have SUD prevalence rates nearly two times that of clinically documented SUD (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Further indicating a gap in VA treatment receipt for SUD among these minoritized groups, White Veterans diagnosed with SUDs were found to be much more likely to receive treatment for SUD diagnoses as compared to Hispanic/Latino Veterans diagnosed with SUDs (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This trend is also seen among Asian and NHPI populations. Across the general population, outside of Veteran-specific literature, minoritized communities have been shown to severely underutilize SUD treatment. Underutilization among these populations is often attributed to barriers to access including stigma, cost, lack of knowledge, and cultural attitudes (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We suspect that tailored implementation approaches designed to increase adoption of evidence-based SUD treatments dissemination within VA (e.g., using peers to activate HEVs from racial/ethnic minoritized groups) may address these disparities and increase health equity within the PSH program.\u003c/p\u003e \u003cp\u003ePrior research has found that other potentially relevant factors in examining relationships between SUDs and negative PSH exits include socioeconomic disparities associated with developing SUDs (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), differential stigma associated with specific substance use (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), and other social factors associated with SUDs (e.g., disparate marketing for substances in low-income and minority communities [31]). Racial/ethnic minority Veterans are also noted to have an increased risk of adverse SUD and psychiatric treatment outcomes (e.g., involuntary hospitalizations, shorter treatment duration) compared to their Non-Hispanic White peers (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). In general, researchers have attributed increased risk of SUDs among racial/ethnic minority populations to differential access to health services, social supports, and other healthy coping mechanisms (e.g., professional/clinic services, social service resources, community infrastructure). We note that, in this study, these disparities may be mitigated in part by the VA infrastructure; during the study period, all HUD-VASH residents were eligible for VA healthcare which awarded them equitable potential access to all health services, including SUD treatment.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe primary strength of this study is its ability to examine longitudinal data for a large subset of PSH enrollees in a system that integrates housing and health services. VA administrative and homeless registry data provides robust information related to diagnoses, date of housing move-in, exits from PSH enrollment, and the competing risk of death.\u003c/p\u003e \u003cp\u003eThis study also had limitations. First, misclassification of PSH exits (i.e., negative, positive, neutral) may have occurred. Each exit is categorized using standardized reasons for exit which omit granular details about factors contributing to each participant\u0026rsquo;s PSH exit. Second, while there a large sample size for the entire cohort, when stratifying by race/ethnicity, small proportions in some subgroups (i.e., Asian, AIAN, NHPI, Other, and Mixed) necessitated collapsing of these subgroups into one category (\u0026ldquo;Other/Mixed\u0026rdquo;) which comprised 4% of HEVs. Future studies with larger samples sizes and/or utilizing qualitative methods could help provide greater insights into the potential vulnerabilities of racial/ethnic subgroups with smaller populations. Third, these analyses were based on diagnosed and documented SUDs, which may vary by race/ethnicity. In addition, in this study, we combined all diagnoses of substance use disorders within the relevant time frame (two years prior to housing move-in). Future research would benefit from examining differences in housing retention associated with specific substances used. We note specific complexities in data interpretation related to persons who only had cannabis use disorder to classify them as having a SUD; cannabis was legalized in the state of California in 2016, including at the study site (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Fourth, it is possible that the high rates of comorbid mental health disorders and SUDs among this population overshadowed effects of SUDs on negative PSH exits. Future studies may benefit from assessing the relationships between comorbid mental health and SUD diagnoses on housing outcomes. Last, as a study conducted with one large and urban VA, it is unclear how much our findings extrapolate to a national HUD-VASH sample, or to homeless-experienced consumers who receive PSH services or health services outside the VA.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study suggests that specific race/ethnicity groups largely explain the associations between SUDs and negative PSH exits, with the relationship between SUDs and negative PSH exits being much stronger among Other/Mixed HEVs and trending towards significance among Hispanic/Latino HEVs as compared to PSH residents of other race/ethnicity groups. PSH programs and providers should consider potential heightened vulnerabilities for negative housing outcomes among minoritized residents, particularly those of Hispanic/Latino, Asian, NHPI, AIAN, and Other/Mixed race and ethnicity. These findings would benefit from integration with qualitative data that explores potential reasons for differential rates for negative exits among PSH residents of different race/ethnicity groups. Such research could inform culturally-specific tailoring of SUD services and implementation strategies that support equitable use of SUD services within these subgroups, which ultimately have potential to reduce Veteran homelessness and increase health equity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmerican Indian or Alaskan Native\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCorporate Data Warehouse\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehomeless-experienced Veteran\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHOMES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHomeless Operations Management and Evaluation System HR\u0026thinsp;=\u0026thinsp;hazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHUD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepartment of Housing and Development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Disease, Tenth Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNative Hawaiian or Other Pacific Islander (NHPI)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePACT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePatient Aligned Care Teams\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epermanent supportive housing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esubstance use disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepartment of Veteran Affairs\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVASH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepartment of Veterans Affairs\u0026rsquo; Supportive Housing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll study procedures were reviewed and approved by VA Greater Los Angeles\u0026rsquo; Institutional Review Board as constituting quality improvement.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. A Limited Dataset (LDS) will be created and shared pursuant to a Data Use Agreement (DUA) appropriately limiting use of the dataset and prohibiting the recipient from identifying or re-identifying (or taking steps to identify or re-identify) any individual whose data are included in the dataset.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: TJP was supported by a VA Quality Enhancement Research Initiative (QUERI) Advance Diversity in Implementation Leadership (ADIL) award. TH was supported by a pilot grant (PI: Harris) awarded by the Department of Veteran Affairs Office of Rehabilitation Research \u0026amp; Development (RR\u0026amp;D) Center on Enhancing Community Integration for Homeless Veterans (MPIs: Green, Marder, Gabrielian); MJS was supported by a grant from the National Institute on Drug Abuse (K01DA054359). The funders had no role in study design; collection, analysis, and interpretation of data; writing the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTJP\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization. \u003cstrong\u003eSG\u003c/strong\u003e: Conceptualization, Supervision, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eMJS\u003c/strong\u003e: Conceptualization, Methodology Writing \u0026ndash; Review \u0026amp; Editing, Supervision. \u003cstrong\u003eLG\u003c/strong\u003e: Writing \u0026ndash; Review \u0026amp; Editing.\u003cstrong\u003e\u0026nbsp;JT\u003c/strong\u003e: Data Curation, Writing \u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eTH\u003c/strong\u003e: Conceptualization, Methodology, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Supervision, Funding Acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to VA QUERI ADIL for the opportunity for continued training that supported this study. We also appreciate Stephanie Chassman and Alec Chapman for their mentorship and guidance that also supported this study. The authors would also like to acknowledge the VA staff and Veterans who made this work possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRog DJ, Marshall T, Dougherty RH, George P, Daniels AS, Ghose SS, et al. Permanent Supportive Housing: Assessing the Evidence. Psychiatric Serv. 2014;65(3):287\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins SE, Malone DK, Clifasefi SL. Housing Retention in Single-Site Housing First for Chronically Homeless Individuals With Severe Alcohol Problems. Am J Public Health. 2013;103(S2):S269\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAubry T, Bloch G, Brcic V, Saad A, Magwood O, Abdalla T, et al. Effectiveness of permanent supportive housing and income assistance interventions for homeless individuals in high-income countries: a systematic review. Lancet Public Health. 2020;5(6):e342\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabrielian S, Burns AV, Nanda N, Hellemann G, Kane V, Young AS. Factors Associated With Premature Exits From Supported Housing. Psychiatric Serv. 2016;67(1):86\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontgomery AE, Cusack MC, Gabrielian S. Supporting veterans\u0026rsquo; transitions from permanent supportive housing. Psychiatr Rehabil J. 2017;40(4):371\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontgomery AE, Cusack M, Szymkowiak D, Fargo J, O\u0026rsquo;Toole T. Factors contributing to eviction from permanent supportive housing: Lessons from HUD-VASH. Eval Program Plann. 2017;61:55\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoggatt KJ, Harris AHS, Washington DL, Williams EC. Prevalence of substance use and substance-related disorders among US Veterans Health Administration patients. Drug Alcohol Depend. 2021;225:108791.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsemberis S, Gulcur L, Nakae M, Housing, First. Consumer Choice, and Harm Reduction for Homeless Individuals With a Dual Diagnosis. Am J Public Health. 2004;94(4):651\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsemberis S, Eisenberg RF. Pathways to Housing: Supported Housing for Street-Dwelling Homeless Individuals With Psychiatric Disabilities. Psychiatric Serv. 2000;51(4):487\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStefancic A, Tsemberis S. Housing First for Long-Term Shelter Dwellers with Psychiatric Disabilities in a Suburban County: A Four-Year Study of Housing Access and Retention. J Prim Prev. 2007;28(3\u0026ndash;4):265\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai J, Rosenheck RA. Risk Factors for Homelessness Among US Veterans. Epidemiol Rev. 2015;37(1):177\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai J, Kasprow WJ, Rosenheck RA. Alcohol and drug use disorders among homeless veterans: Prevalence and association with supported housing outcomes. Addict Behav. 2014;39(2):455\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Survey of. Homeless Veterans in 100,000 Homes Campaign Communities (100,000 Homes). 2011 Nov.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeeters J, Lancaster C, Brown D, Back S. Substance use disorders in military veterans: prevalence and treatment challenges. Subst Abuse Rehabil. 2017;8:69\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoggatt KJ, Chawla N, Washington DL, Yano EM. Trends in substance use disorder diagnoses among Veterans, 2009\u0026ndash;2019. Am J Addict. 2023;32(4):393\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichter B, Tsai J, Pietrzak RH. Prevalence, correlates, and mental health burden associated with homelessness in U.S. military veterans. Psychol Med. 2023;53(9):3952\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontgomery AE, Szymkowiak D, Tsai J. Housing Instability and Homeless Program Use Among Veterans: The Intersection of Race, Sex, and Homelessness. Hous Policy Debate. 2020;30(3):396\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Center for Veterans Analysis and Statistics [Internet]. 2023. The Changing Characteristics of the Veteran Population: Veteran Population Projection Model 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams EC, Fletcher OV, Frost MC, Harris AHS, Washington DL, Hoggatt KJ. Comparison of Substance Use Disorder Diagnosis Rates From Electronic Health Record Data With Substance Use Disorder Prevalence Rates Reported in Surveys Across Sociodemographic Groups in the Veterans Health Administration. JAMA Netw Open. 2022;5(6):e2219651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Connell MJ, Kasprow WJ, Rosenheck RA. Differential Impact of Supported Housing on Selected Subgroups of Homeless Veterans With Substance Abuse Histories. Psychiatric Serv. 2012;63(12):1195\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelberg L, Andersen RM, Leake BD. The Behavioral Model for Vulnerable Populations: application to medical care use and outcomes for homeless people. Health Serv Res. 2000;34(6):1273\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong MS, Clair K, Stigers PJ, Montgomery AE, Kern RS, Gabrielian S. Housing outcomes among homeless-experienced veterans engaged in vocational services. Am J Orthopsychiatry. 2022;92(6):741\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenberg GA, Rosenheck RA. Correlates of Past Homelessness in the National Epidemiological Survey on Alcohol and Related Conditions. Adm Policy Mental Health Mental Health Serv Res. 2010;37(4):357\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAustin SR, Wong YN, Uzzo RG, Beck JR, Egleston BL. Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work. Med Care. 2015;53(9):e65\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayes-Larson E, Kezios KL, Mooney SJ, Lovasi G. Who is in this study, anyway? Guidelines for a useful Table 1. J Clin Epidemiol. 2019;114:125\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine J, Gray R. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 2012;94(446):496\u0026ndash;509.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolub A, Vazan P, Bennett AS, Liberty HJ. Unmet Need for Treatment of Substance Use Disorders and Serious Psychological Distress Among Veterans: A Nationwide Analysis Using the NSDUH. Mil Med. 2013;178(1):107\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu LT, Blazer DG. Substance use disorders and co-morbidities among Asian Americans and Native Hawaiians/Pacific Islanders. Psychol Med. 2015;45(3):481\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina KM, Alegr\u0026iacute;a M, Chen CN. Neighborhood context and substance use disorders: A comparative analysis of racial and ethnic groups in the United States. Drug Alcohol Depend. 2012;125:S35\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulesza M. Substance Use Related Stigma: What we Know and the Way Forward. J Addict Behav Ther Rehabil. 2013;02(02).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudhinaraset M, Wigglesworth C, Takeuchi DT. Social and Cultural Contexts of Alcohol Use: Influences in a Social-Ecological Framework. Alcohol Res. 2016;38(1):35\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKilbourne AM, Bauer MS, Pincus H, Williford WO, Kirk GF, Beresford T. Clinical, psychosocial, and treatment differences in minority patients with bipolar disorder. Bipolar Disord. 2005;7(1):89\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProposition 64: The Adult Use of Marijuana Act. 2024 Judicial Council of California United States of America: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.courts.ca.gov/prop64.htm\u003c/span\u003e\u003cspan address=\"https://www.courts.ca.gov/prop64.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; 2016.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Permanent supportive housing, substance use disorder, homelessness, health disparities","lastPublishedDoi":"10.21203/rs.3.rs-4442590/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4442590/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003ePermanent supportive housing (PSH) is an evidence-based practice for reducing homelessness that subsidizes permanent, independent housing and provides case management\u0026mdash;including linkages to health services. Substance use disorders (SUDs) are common contributing factors towards premature, unwanted (\u0026ldquo;negative\u0026rdquo;) PSH exits; little is known about racial/ethnic differences in negative PSH exits among residents with SUDs. Within the nation\u0026rsquo;s largest PSH program at the Department of Veterans Affairs (VA), we examined relationships among SUDs and negative PSH exits (for up to five years post-PSH move-in) across racial/ethnic subgroups.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eWe used VA administrative data to identify a cohort of homeless-experienced Veterans (HEVs) (n\u0026thinsp;=\u0026thinsp;2,712) who were housed through VA Greater Los Angeles\u0026rsquo; PSH program from 2016\u0026ndash;2019. We analyzed negative PSH exits by HEVs with and without SUDs across racial/ethnic subgroups (i.e., African American/Black, Non-Hispanic White, Hispanic/Latino, and Other/Mixed [Asian, American Indian or Alaskan Native, and Native Hawaiian or Other Pacific Islander, and multi-race]) in controlled models and accounting for competing risk of death.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eIn competing risk models, HEVs with at least one SUD had 1.3 times the hazard of negative PSH exits compared to those without SUDs (95% CI: 1.00, 1.61). When stratifying by race/ethnicity, Other/Mixed race residents with at least one SUD had 6.4 times the hazard of negative PSH exits compared to their peers without SUDs (95% CI: 1.61\u0026ndash;25.50). Hispanic/Latino residents with at least one SUD had 1.9 times the hazard compared to those without SUDs, also indicating a strong relationship with negative PSH exits; however, this association was not statistically significant (95% CI: 0.85\u0026ndash;4.37). Black residents with at least one SUD had 1.2 times the hazard compared to those without SUDs (95% CI: 0.85\u0026ndash;1.64), indicating no evidence of an association with negative PSH exits. Similarly, Non-Hispanic White residents with at least one SUD had 1.1 times the hazard compared to those without SUDs (95% CI: 0.75\u0026ndash;1.66).\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eThese findings suggest relationships between SUDs and negative PSH exits differ between race/ethnic groups and suggest there may be value in culturally specific tailoring and implementation of SUD services for these subgroups.\u003c/p\u003e","manuscriptTitle":"Addressing racial and ethnic disparities in premature exits from permanent supportive housing among residents with substance use disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:57:43","doi":"10.21203/rs.3.rs-4442590/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-27T11:11:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-27T10:44:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T12:14:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-19T00:35:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d703b78-06d7-46ec-85ec-61e3019671f1","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:09:09+00:00","versionOfRecord":{"articleIdentity":"rs-4442590","link":"https://doi.org/10.1186/s12889-024-21169-2","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-01-28 15:57:32","publishedOnDateReadable":"January 28th, 2025"},"versionCreatedAt":"2024-06-07 18:57:43","video":"","vorDoi":"10.1186/s12889-024-21169-2","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21169-2","workflowStages":[]},"version":"v1","identity":"rs-4442590","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4442590","identity":"rs-4442590","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}