Research Article

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Quality of Life Research (2020) 29:2051–2061 https://doi.org/10.1007/s11136-020-02482-w

Health‑related quality of life and related factors among chronically homeless adults living in different permanent supportive housing models: a cross‑sectional study

Antoinette L. Spector1  · Katherine G. Quinn2 · Timothy L. McAuliffe2 · Wayne DiFranceisco2 · Arturo Bendixen3 · Julia Dickson‑Gomez1,2

Accepted: 12 March 2020 / Published online: 28 March 2020 © Springer Nature Switzerland AG 2020

Abstract Purpose Permanent supportive housing (PSH) is an effective intervention to improve residential stability and reduce the utilization of costlier healthcare services for the chronically homeless. However, there has been little focus on health-related quality of life (HRQL) once they enter PSH, and the potential influence of other factors including the PSH model. Study results can shed light on the HRQL of the PSH population and inform strategies to improve PSH program effectiveness in this area. Methods In this cross-sectional study, survey methods were used to assess the HRQL of PSH residents in the Chicago metropolitan area. The survey also included questions on socio-demographics, health behaviors, housing and neighborhood characteristics, and housing satisfaction. The SF-36 was used to obtain physical (PCS) and mental component summary (MCS) scores for HRQL. Other variables were selected using the Wilson and Cleary HRQL model. Statistical analyses included summary statistics, bivariate analyses, and fully adjusted linear regression models. Results The study sample included 855 adults currently in PSH. The sample was predominantly African American men with an average age of 53 years. Mean scores for PCS and MCS were 39.4 and 46.1, respectively, (out of 100). In adjusted analyses, older age and being on disability were associated with worse PCS. Having HIV was associated with better PCS. Being non-Hispanic Black, living in fixed-sited housing, and being in PSH for longer durations were associated with better MCS. More depressive symptoms was associated with worse PCS and MCS. Conclusion While both aspects of the PSH model (housing configuration and service provision) were initially associated with HRQL in unadjusted analyses, housing configuration was the only PSH model variable that remained significant once accounting for other factors. Depressive symptomology and the social environment also appear to be important correlates of HRQL and are potential areas to target in PSH programs.

Keywords Health-related quality of life · Supportive housing · Homelessness · Well-being

Abbreviations ACT Assertive community treatment HIV Human immunodeficiency virus

Electronic supplementary material The online version of this article (https ://doi.org/10.1007/s1113 6-020-02482 -w) contains supplementary material, which is available to authorized users.

* Antoinette L. Spector [email protected]

Katherine G. Quinn [email protected]

Timothy L. McAuliffe [email protected]

Wayne DiFranceisco [email protected]

Julia Dickson-Gomez [email protected]

1 Department of Epidemiology, Institute for Health and Equity, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA

2 Department of Psychiatry and Behavioral Medicine, Center for AIDS Intervention Research, Medical College of Wisconsin, 2701 N. Summit Ave., Milwaukee, WI 53202, USA

3 Chicago, USA

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HUD Department of Housing and Urban Development HRQL Health-related quality of life MCS Mental component summary PCS Physical component summary PLWH People living with HIV PSH Permanent supportive housing SF-36 36-Item Short Form survey SMI Severe mental illness SUD Substance use disorders VA Veterans Administration

Introduction

In the United States (US), there are estimated to be 2.5 to 3.5 million people who experience homelessness annually [1]. Among them, nearly one in five experience chronic homelessness—defined as being continuously homeless for at least one year, or four or more episodes of homelessness in the past three years, while also living with a disabling physical or mental condition [2]. Chronic homelessness is associated with an increased risk for early mortality and a greater likelihood of having a mental illness, infectious and non-infectious diseases compared to the general population [1–4]. Moreover, the context of being homeless also makes it more difficult to treat medical conditions and many homeless individuals lack access to primary health care or preventive services [5–7].

In response, the US Department of Housing and Urban Development (HUD) and Veterans Administration (VA) have advocated for permanent supportive housing (PSH) to end chronic homelessness and funded PSH programs that target people who are considered the most medically vulner- able [3]. PSH programs provide affordable, permanent hous- ing in combination with supportive services, which could include case management, employment services, legal ser- vices, health clinics, and behavioral health specialists. Most programs also follow a Housing First approach wherein indi- viduals are not required to demonstrate sobriety or stable employment, for example, prior to receiving housing [8].

PSH programs have been shown to improve residen- tial stability of formerly homeless individuals, as well as to reduce utilization of costlier types of healthcare such as emergency department visits and inpatient stays [4, 9–11]. For people living with HIV (PLWH), supportive housing is associated with significant improvements in viral suppres- sion, medication adherence, and a decreased risk for forward transmission [12, 13]. Among formerly homeless individu- als with severe mental illness (SMI), supportive housing is associated with reduced rates and length of hospitalizations [10]. Supportive services, particularly case management, are considered critical to PSH programs achieving effec- tive outcomes by facilitating access to medical and social

services, as well as working with clients—and sometimes their landlords—through challenges that could undermine their housing stability [14].

There is some evidence that PSH also has a positive impact on quality of life, specifically in the areas of social well-being and sleep quality [3]. However, health-related quality of life (HRQL) is an understudied area. One longitu- dinal study did examine HRQL among individuals entering a supportive housing program but found it did not change significantly over an 18-month period or relative to controls [15]. Additional studies examining HRQL are considered critical to research on the effectiveness of PSH because HRQL encompasses an individual’s perceptions of their mental and physical well-being [3, 16]. Wilson and Cleary [17] theorized that HRQL is composed of five domains (bio- logical and physiological variables, symptom status, func- tional status, general health perceptions, and overall quality of life) and influenced by several interconnected factors that can be organized according to individual and environmen- tal characteristics [17]. Individual characteristics of HRQL include perceptions of physical and mental health, as well as elements like socio-demographics, medical conditions, and functional status [17, 18]. Environmental characteris- tics are the structural and process elements of a person’s environment and include such factors as psychological and social supports [17]. Zubritsky et al. [18] further adapted the Wilson and Cleary model to examine the HRQL of older adults receiving long-term services and supports [18]. Their expanded model was put forth to address the unique cog- nitive and organizational elements that can influence the HRQL of adults in community-based and facility-based care settings and has also been used in research on homeless individuals with SMI and substance use disorders (SUD) [18, 19]. Several individual characteristics—including gen- der, race and ethnicity, depression, social support, and drug use—have been associated with single or multiple domains of HRQL among homeless and marginally housed people living with HIV [20–22]. Yet, the HRQL correlates among the PSH population are unclear.

Despite numerous documented benefits of PSH, consid- erable variation exists across programs, which may play an important role in health outcomes like HRQL. The qualita- tive phase of the current study identified two types of hous- ing configurations and three types of service provision in PSH programs [8]. People lived in either congregate housing (also called fixed-site) or were given vouchers to cover a large portion of the cost for free-market rental housing (also called scattered-site) [8]. In addition, individuals were either linked with supportive services and mental health treatment via an Assertive Community Treatment (ACT) team—who provides proactive support to people with SMI and SUD— or, individuals worked with a case manager that connected them to needed resources within the PSH program and local

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community [8]. Case management was generally provided at a lower (approximately 50:1 client/case manager ratio) or higher (15:1 ratio) intensity [8]. Ideally, individuals were matched to PSH programs based on their service needs. Yet, the qualitative phase also revealed that people might often be placed in whichever program had available space and could lead to a mismatch in what a program offered compared to an individual’s need or preference [8, 14]. Furthermore, factors, such as a criminal record, can restrict an individual from living in their desired scattered-site housing location; some programs may only accept individuals with specific diagnoses, like SMI, based on their funding sources [8, 14]. Considering this additional context, the extent to which cer- tain PSH models may have a greater or lesser impact on health outcomes, like HRQL, is less apparent.

To better understand the factors that are associated with the HRQL of individuals in PSH, we conducted a cross- sectional study that included three specific aims. The first aim was to describe the HRQL of chronically homeless indi- viduals who were currently in a PSH program. The second aim was to identify how HRQL varies by PSH model. While we expected that individuals who were receiving less intense services would have better physical and mental well-being on average, this hypothesis has not yet been empirically tested and service needs do not exclusively drive program placement. The final aim of our study was to identify other correlates of HRQL in the PSH population and to deter- mine if the PSH model would be a significant factor when accounting for other variables. Study results can shed light on the HRQL of individuals in PSH and help inform strate- gies to improve PSH program effectiveness in this area.

Methods

Study population and data collection

The current study is part of a larger study that included a qualitative phase with PSH program administrators and a longitudinal study of PSH residents, who had been housed in their respective programs for various lengths of time when the baseline assessments were completed. Residents were subsequently assessed at 6, 12, and 18 months. We used a stratified sampling strategy to recruit 888 residents from the five different supportive housing models that we identi- fied during the qualitative phase of our study [8]. Housing configuration included fixed-site, in which residents live in a single building, or scattered-site, in which residents receive vouchers that they could use to rent their own unit. The dif- ferent housing service provision models included: low-inten- sity case management, which has a case manager to client ratio of approximately 50:1; intensive case management with a case manager to client ratio of approximately 15:1; and

a behavioral health model which used approaches such as Assertive Community Treatment (or Community Treatment Services), where a team of specialists coordinates clients’ care. We then looked at the number of programs and clients that fell under each type to construct a targeted sampling plan. Target numbers varied depending on the number of potential participants in each category so that no agency or program was overrepresented but also so that every housing category had representation to allow for meaningful compar- isons. No recruitment target was set at over 50% of potential residents. There were fewer than 30 participants housed in scattered-site, low-intensity case management programs, so we did not recruit any participants from that housing type.

Recruitment occurred in stages. First, we contacted hous- ing agencies and programs and meetings were arranged with case managers so that the purpose of the study could be explained, and any questions answered. Case managers were given recruitment materials including posters with tear off phone numbers to place in common areas in fixed-site hous- ing programs, to pass out to clients during regular visits, or to mail to clients in scattered site apartments. In addition, we arranged meetings with clients to explain the project and give them contact information. Participants called a toll-free number, where they were given more information about the study and screened for eligibility by research staff members.

Eligibility criteria included being 18 years or older, living in one of the participating supportive housing programs, and not having a cognitive impairment that would preclude their participation. If eligible, participants were given an appoint- ment in one of the fixed-site supportive housing agencies, or a public library near where they lived. All participants were asked to provide written informed consent prior to surveys. Informed consent included permission to contact their sup- portive housing agency to verify that they were housed in a supportive housing program. After collecting informed consent, participants completed an audio-assisted computer self-interview of approximately 90 min in duration. Partici- pants received $40 cash for their time and two transporta- tions vouchers for their travel at each assessment. Participant recruitment, data collection and analysis were conducted by teams at the Medical College of Wisconsin. All study proto- cols were approved by the Institutional Review Board of the Medical College of Wisconsin.

Measures

Health‑related quality of life

The outcome for this study was HRQL and was measured using the 36-item Short Form health survey (SF-36). The SF-36 measures health status, physical functioning, role limitations due to physical and emotional problems, bodily pain, general health, vitality, social functioning and mental

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health across eight domains [23]. The SF-36 also yields physical component summary (PCS) and mental component summary (MCS) scores that are normalized and range from 0–100, with the higher score meaning better HRQL. All subscale and summary scores were calculated in IBM SPSS Statistics version 24 using the standard scoring manual [24].

Individual characteristics

We obtained self-reported information on participants’ socio-demographic characteristics. These included age, gen- der, race and ethnicity, educational attainment, and current employment status. Age was divided into four categories: (1) 18–35 years, (2) 36–45 years, (3) 46–54 years, and (4) 55 years and older. Gender identity was dichotomized into men or women. Race and ethnicity were also dichotomized into Non-Hispanic Black versus those in any other racial or ethnic category. Educational attainment was divided into three categories: (1) did not graduate from high school (HS), (2) HS graduate or equivalent, and (3) at least some post- secondary education (including junior college or a technical/ trade school). Employment status was categorized as either employed (full- or part-time), unemployed, or disabled. We also collected self-reported information on their HIV status, mental health symptoms, and substance use. We measured depressive symptoms using the 10-item Center for Epide- miological Studies of Depression (CES-D 10), and anxiety symptoms using the Generalized Anxiety Disorder Assess- ment (GAD-7) [25, 26]. To measure alcohol use, we used the Alcohol Use Disorders Identification Test (AUDIT) to con- struct a dichotomous variable (yes/no) on any problematic alcohol use behaviors [27, 28]. Any response that indicated a problematic drinking behavior (e.g., any instance of not being able to stop drinking once starting) within the previous six months, regardless of behavior frequency, was coded as problematic alcohol use. Drug use was measured with the Addiction Severity Index (ASI) [29, 30]. The problematic drug use variable was similarly dichotomized (yes/no) in response to questions on experiencing problems or negative consequences resulting from drug use in the previous six months. Any “yes” response to these questions was coded as problematic drug use.

Environmental characteristics

From the survey, we obtained information on the name and location of each participant’s supportive housing program to identify the PSH model. From the name of the sup- portive housing program, we added a variable for housing configuration (fixed-site, scattered-site) and service provi- sion model (low-intensity case management, intensive case management, or behavioral health model). We also collected information on participants’ self-reported longest episode of

homelessness and the length of time they had been housed in their current housing program. Length of time in their current housing program was verified by the agency for most participants and any missing values were imputed with self- reported information when available. Additional variables included participants’ housing satisfaction and access to social support. Housing satisfaction was measured using a modified version of the SAMSHA Housing Satisfaction Scale. The scale consists of 19 items with the following anchor points, disagree strongly, somewhat disagree, some- what agree, agree strongly. We also obtained information on participants’ perceptions of social support with five items asking participants to rate how often “you have someone to love and make you feel wanted, to help with daily chores, buy medicines, transportation, or to give you money if you needed it” [31, 32]. We also measured their perceived access to health care using the Access to Care scale, which consists of six items on a five-point Likert scale (strongly agree to strongly disagree) [33, 34]. We summed responses to the six items (e.g., “If I need medical care, I can get admitted without any trouble”; or “It is hard for me to get medical care in an emergency”) to create a continuous variable with higher scores indicating worse access [33, 34].

Statistical analysis

We performed descriptive analyses for socio-demographic characteristics, PSH model distribution, and SF-36 sum- mary scores (PCS and MCS) using frequencies, percentages, means, and standard deviations (SD). We then conducted bivariate analyses to determine the associations between the SF-36 summary scores and the PSH model components using t-test and one-way analysis of variance (ANOVA) with a Bonferroni adjustment at a significance level of p < 0.10. Next, we used t-test, ANOVA, and pairwise correlation at a significance level of p < 0.10 to determine the associations between the SF-36 summary scores and each of the covari- ates identified using the expanded version of the Wilson and Cleary model of HRQL [17, 18]. Lastly, we performed multivariate linear regression analyses. The SF-36 summary scores (PCS and MCS) were the dependent variables and we regressed each with the individual and environmental char- acteristic variables that were significant in bivariate analy- ses. The level of significance for the multivariate analyses was set at p < 0.05. All statistical analyses were performed using STATA 15 [35].

Results

Table 1 shows the demographic characteristics of the sam- ple. The majority were over the age of 45 (83%), Non-His- panic black (80%) and men (57%). Most were high school

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graduates (30%) or had some post-secondary education (36%), yet the majority were either not working or retired (41%), or self-reported that they were disabled (42%). A large proportion of the sample resided in fixed-site hous- ing (58%) and received intensive case management services (63%).

Table 2 shows the unadjusted mean values of the SF-36 summary scores. The average PCS score was 39.4 (out of a possible 100) and the average MCS score was 46.1 (out of a possible 100), with higher scores indicating better physical and mental HRQL, respectively. After stratifying the sample by housing configuration, we found that there was a difference in unadjusted MCS scores with those in fixed-site housing reporting significantly higher mental well- being than those in scattered site (47.5 vs. 44.2; p < 0.001). There was no difference in unadjusted PCS scores by hous- ing configuration. Regarding the service model, there were significant differences in both the unadjusted PCS and MCS scores (p < 0.001 for both). For PCS, physical well-being

was highest among people receiving low-intensity case man- agement, followed by those in the behavioral health model and receiving intensive case management (42.4 vs. 40.0 vs. 38.3). Regarding MCS, people receiving low-intensity case management services had the highest mental well-being, fol- lowed by intensive case management and behavioral health (mean MCS score of 49.8 vs. 46.2 vs. 42.5).

Table 3 shows the variables that were associated with PCS scores after accounting for all individual and environmental characteristics that were included based on bivariate analy- ses (Supplemental Table S5). In the fully adjusted model, we found that several individual characteristics were signifi- cantly associated with the physical well-being of our sample. Being 55 years or older (p = 0.041) and not working due to disability (p < 0.001) were associated with worse physi- cal well-being compared to those who were 18–35 years or employed full- or part-time. Conversely, fewer depression symptoms (p = 0.034) and being HIV-positive (p = 0.023) were associated with better physical well-being. Number of years in PSH and longest episode of homelessness were the only significant environmental characteristics. Being in PSH for less than a year or having one’s single longest episode of homelessness of less than a year was associated with significantly better physical well-being than those in PSH between three to five years (p = 0.025) or whose single longest episode of homelessness was two years or more but less than four years (p = 0.011).

Table 4 shows the variables that were associated with MCS scores after accounting for all individual and environ- mental characteristics included after conducting bivariate analyses (Supplemental Table S5). With respect to individual characteristics, Non-Hispanic Black participants (p = 0.016), relative to those in other racial and ethnic groups, and those

Table 1 Summary statistics (n = 855)

Variables n %

Age (years)  18–35 54 6  36–45 89 10  46–54 293 34  55+ 419 49

Gender  Men 491 57  Women 364 43

Race/ethnicity  Non-Hispanic Black 685 80  Other race or ethnicity 162 19  Unknown or missing 8 1

Education level  Any post-secondary education 307 36  High school graduate or general education diploma 256 30  Did not graduate from high school 286 33  Unknown or missing 6 1

Employment status  Full- or part-time 117 14  Not working or retired 347 41  Disabled 363 42  Unknown or missing 28 3

Permanent supportive housing configuration  Fixed-site 499 58  Scattered-site 356 42

Permanent supportive housing service model  Low-intensity case management 151 18  Intensive case management 537 63  Behavior health/medical model 167 19

Table 2 Unadjusted comparisons of health-related quality of life scores among adults in permanent supportive housing by supportive housing model type (n = 849)

PCS physical component summary, MCS mental component sum- mary, SD standard deviation ***p < 0.001 a t-test analysis b one-way analysis of variance

PCS score mean (SD) MCS score mean (SD)

Overall 39.4 (11.5) 46.1 (12.4) Housing configurationa

 Fixed-site 39.80 (11.5) 47.50 (12.3)***  Scattered-site 38.73 (11.5) 44.15 (12.4)

Service modelb

 Low-intensity 42.36 (11.3)*** 49.77 (11.8)***  Intensive 38.31 (11.2) 46.21 (12.5)  Behavioral health

model 39.98 (11.9) 42.47 (11.8)

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with fewer anxiety (p < 0.001) and depression (p < 0.001) symptoms reported better mental well-being. Individuals with any problematic drug use behaviors had worse mental well-being on average compared to individuals with none (p = 0.048). For environmental characteristics, living in scat- tered-site housing was associated with lower mental well- being compared to living in fixed-site housing (p = 0.010). Additionally, being in PSH for one to two years (0.043) or more than five years (p = 0.007) was associated with better mental well-being compared to those in PSH less than a year.

Discussion

The purpose of this study was to examine the HRQL of chronically homeless individuals who were currently in a PSH program, and to determine associated factors. Our results have implications for PSH programs and efforts to improve HRQL among residents. Our unadjusted analyses initially supported our hypothesis that individuals receiving

less intense services would have better HRQL, as many behavioral health (or ACT) programs are intended for indi- viduals with SMI, who are likely to have poorer overall health. However, after accounting for other factors in our adjusted model—and contrary to our hypothesis—PSH ser- vice intensity was not significantly associated with physical or mental HRQL. Rather, PSH housing configuration was independently associated with HRQL, with individuals liv- ing in scattered-site housing having worse mental well-being on average compared to those in fixed-site. PSH providers have previously identified the potential relationship between housing configuration and social well-being, indicating the fixed-site environment as an easier place to foster a sense of community among residents [3, 8]. Taken together, these findings suggest that increased efforts to promote commu- nity integration among scattered-site residents, such as peer support programs, could have a positive impact on their social and mental well-being [8]. Also—and despite some tension around the “permanence” of PSH for both philo- sophical and practical reasons [14]—residing in PSH for longer periods of time may be critical to improving mental

Table 3 Fully adjusted linear regression model of physical component summary scores among adults in permanent supportive housing (n = 679)

PSH permanent supportive housing, CM case management *p < 0.05, **p < 0.01, ***p < 0.001

Variable β 95% CI p value

PSH model  Service provision  Intensive CM (vs low-intensity CM) − 2.06 − 4.28, 0.16 0.069  Behavioral health (vs low-intensity CM) 0.59 − 2.15, 3.32 0.674

Individual characteristics Age  36–45 years (vs 18–35) − 1.31 − 5.26, 2.64 0.515  46–54 years (vs 18–35) − 3.29 − 6.76, 0.17 0.062  55 years or older (vs 18–35) − 3.56* − 6.97, − 0.15 0.041

Women − 1.05 − 2.75, 0.65 0.228 Unemployed or not currently working (vs employed) − 2.40 − 4.82, 0.03 0.052 Disabled (vs employed) − 8.11*** − 10.57, − 5.65 < 0.001 HIV+ 2.66* 0.36, 4.95 0.023 Depression scale − 0.13* − 0.24, − 0.10 0.034 Anxiety scale − 0.09 − 0.32, 0.15 0.465 Environmental characteristics Number of years in PSH  1 to 2 years (vs < 1 year) − 1.27 − 4.00, 1.47 0.363  3 to 5 years (vs < 1 year) − 3.25* − 6.09, − 0.42 0.025  More than 5 years (vs < 1 year) − 2.42 − 5.18, 0.34 0.085

Longest episode of homelessness  1 year or more but less than 2 years (vs < 1 year) − 0.52 − 2.96, 1.91 0.672  2 years or more but less than 4 years (vs < 1 year) − 2.97* − 5.27, − 0.68 0.011  4 years or more (vs < 1 year) − 1.04 − 3.23, 1.16 0.354

Housing satisfaction scale 0.06 − 0.02, 0.14 0.168 Healthcare access scale 0.00 − 0.19, − 0.19 0.993

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HRQL, as we found individuals in PSH for five years or more had the highest mental well-being score and was the most significant compared to those housed less than a year. Housing stability, afforded by PSH programs, provide indi- viduals with greater opportunities to engage in physical and mental health treatment and may support engagement and retention in medical care. Furthermore, detecting signifi- cant improvements in HRQL for individuals with a history of chronic homelessness may take substantially longer than previously considered, highlighting the ongoing need for prospective studies to examine housing stability and health outcomes in this population.

Several individual-level characteristics were also associ- ated with the HRQL of PSH residents. For example, greater depressive symptomology was associated with worse mental

and physical well-being. This finding is consistent with prior research in both the general population and among people who are homeless or marginally housed [22, 36]. Strine et al. [36] found that greater depression symptoms were related to worse mental and physical well-being in their nation- ally representative sample of community-dwelling adults in the United States [36]. Among homeless and marginally housed adults living with HIV, Riley et al. [22] similarly found increased depression symptoms to be associated with worse HRQL across all domains [22]. In sum, these findings suggest a need to develop interventions that routinely screen for and treat depression among PSH residents, regardless of service intensity, housing configuration, or health condition.

Another individual characteristic related to the HRQL of the PSH population was race and ethnicity. We found

Table 4 Fully adjusted linear regression model of mental component summary scores among adults in permanent supportive housing (n = 681)

PSH permanent supportive housing, CM case management, HS high school, GED ssgeneral education diploma *p < 0.05, **p < 0.01, ***p < 0.001

Variable β 95% CI p value

PSH model Housing configuration  Scattered-site (vs fixed-site) − 1.70* − 2.99, − 0.40 0.010

Service provision  Intensive CM (vs low-intensity CM) 0.99 − 0.72, 2.71 0.257  Behavioral health (vs low-intensity CM) − 0.61 − 2.69, 1.48 0.566

Individual characteristics Age  36–45 years (vs 18–35) − 0.79 − 3.66, 2.07 0.587  46–54 years (vs 18–35) 0.03 − 2.44, 2.50 0.982  55 years or older (vs 18–35) − 0.65 − 3.08, 1.78 0.599

Non-Hispanic black (vs all other groups) 1.82* 0.34, 3.30 0.016 Educational attainment  HS or GED graduate (vs did not graduate HS) 1.20 − 0.27, 2.67 0.111  Some postsecondary education (vs did not graduate HS) − 0.49 − 1.92, 0.95 0.506

Depression scale − 0.54*** − 0.62, − 0.45 < 0.001 Anxiety scale − 0.63*** − 0.80, − 0.47 < 0.001 Had problematic alcohol use − 0.89 − 2.21, 0.43 0.187 Had problematic drug use − 1.74* − 3.47, − 0.02 0.048 Environmental characteristics Number of years in PSH  1 to 2 years (vs < 1 year) 2.02* 0.07, 3.97 0.043  3 to 5 years (vs < 1 year) 1.91 − 0.12, 3.94 0.066

More than 5 years (vs < 1 year) 2.75** 0.76, 4.74 0.007 Longest episode of homelessness  1 year or more but less than 2 years (vs < 1 year) − 0.51 − 2.26, 1.23 0.563  2 years or more but less than 4 years (vs < 1 year) − 1.59 − 3.23, 0.05 0.058  4 years or more (vs < 1 year) − 0.79 − 2.37, 0.78 0.317

Social support scale 0.06 − 0.06, 0.18 0.238 Housing satisfaction scale 0.03 − 0.03, 0.09 0.378 Healthcare access scale 0.03 − 0.10, 0.16 0.664

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that Non-Hispanic Black participants reported better mental well-being than those from other racial and ethnic groups. While Caucasian race has been associated with worse phys- ical well-being among homeless and marginally housed PLWH, to our knowledge, this is the first study to identify a relationship between race and ethnicity and HRQL in the PSH population [21]. Considering that most of our sample was Non-Hispanic Black (80%), which is consistent with Non-Hispanic Blacks being largely overrepresented among people experiencing homelessness in the US population, our findings suggest that the racial and ethnic environ- ment within PSH may be an important aspect to consider in understanding the mental well-being of this population [37]. Previous research has explored the relationship between the racial environment and mental well-being among African- Americans and has found that social processes, like identi- fying with and feeling supported within their racial group, play an important role in bolstering their mental well-being [38, 39]. For example, Hughes et al. found that increased racial identification, such as feeling a sense of closeness to your racial group, was associated with fewer depressive symptoms in their national study of community-dwelling African-Americans [39]. PSH programs have a potentially important role in fostering a sense of community as a strat- egy to improve the mental well-being of this population, and especially for racial or ethnic minority residents. Additional research is warranted to explore the role of social factors, like racial identity, in the relationship between race and eth- nicity and mental well-being among adults in PSH.

Not surprisingly, both the mental and physical well-being scores of PSH residents were lower, on average, than the general population—estimated at 53.8 and 49.2, respectively (compared to our averages of MCS = 46.1 and PCS = 39.4) [40]. We also found that the mental well-being of our sample was better than the physical well-being. This finding runs counter to previous work by Hwang et al. [15], who found physical well-being (PCS ranged from 43.5–46.9) to be bet- ter than mental well-being (MCS ranged from 39.2–41.1) in their sample of supportive housing residents [15]. One possible reason for this discrepancy may be differences in the study samples. We recruited our sample from several dif- ferent PSH programs that provided services to a population of chronically homeless individuals, who may have more physical disabilities than those entering the housing program for the Hwang et al. study. In contrast, the Hwang et al. study recruited individuals from a single supportive housing pro- gram, who reserves several units for individuals with severe and persistent mental illness and may further explain why their sample had worse mental well-being, relative to physi- cal well-being, compared to our sample [15].

Surprisingly, our analyses did not find social support to be independently associated with mental or physical well-being within the PSH population and runs contrary to research on

the health of homeless and unstably housed PLWH [21, 41]. While we were able to account for some aspects of social support in our analysis, it is possible that our measure did not adequately capture all the relevant dimensions of social support within the PSH population. Our measure empha- sized receiving tangible assistance from others, which may not be readily accessible or most influential within a for- merly homeless population. Given our previous findings indicating the importance of social factors on mental well- being, additional research is necessary to better understand the specific role that social support plays and the best ways to measure its impact.

Another unexpected finding of our study was that PLWH reported higher physical well-being relative to their coun- terparts without HIV. Supportive housing programs may be better equipped to address the medical needs of PLWH by providing an environment that facilitates linkages and adher- ence to HIV medications. Previous studies have found sup- portive housing to be effective in improving health outcomes in PLWH and improvements in HIV measures are associated with better physical well-being in this population [12, 20, 42, 43]. In our study, 92% of the participants living with HIV reported that they were currently taking HIV medica- tions and 72% reported that they were adherent at least 90% of the time (data not reported). This lends support to the effectiveness of supportive housing in improving the health outcomes of PLWH. Yet, it also raises questions regarding the physical well-being of people in PSH with other chronic conditions, as we found no association between a longer length of time in PSH being associated with better physical well-being. Comorbidities and functional impairments are prevalent among PSH residents, are associated with worse self-rated physical health, and housing providers may not be comfortable addressing the physical health needs of PSH residents [44–48]. Furthermore, reductions in funding have made it more difficult for programs to provide support- ive services, often leading to increased caseloads for case managers [14]. With the supportive services component of PSH being considered essential to its success, policies that constrain funding for PSH programs could hinder progress toward the expressed goal of improving health outcomes. Future research should explore the capacity and effective- ness of PSH programs in facilitating healthcare engagement among individuals with chronic health conditions and how this relates to the HRQL of the PSH population.

Our study has several potential limitations. First, data pre- sented here are cross-sectional and non-experimental in nature, so we were not able to determine causation as it relates to the HRQL of PSH residents. Second, while we used a theory- driven approach to identify the most appropriate variables for our analyses, it is possible that we may have missed other important variables that should have been included. Addition- ally, except for HIV, we were limited in our ability to account

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for other medical conditions that are an important part of understanding HRQL. We were also limited by a large num- ber of people who did not report on their longest period of homelessness and their data were not included in the fully adjusted models. While we felt this variable was still impor- tant to include, it is possible that this created a biased sample. Notwithstanding these limitations, our findings provide new insights regarding the HRQL of formerly homeless individuals who are living in PSH.

Conclusion

Our study adds to the limited research on the well-being of people in PSH. We found many factors were associated with the HRQL of the PSH population, both individual and envi- ronmental in nature. While both aspects of the PSH model were initially associated with HRQL in unadjusted analyses, housing configuration was the only PSH model variable that remained significant once accounting for other factors. Depres- sive symptomology and the social environment also appear to be important correlates of HRQL and are potential areas to target in PSH programs.

Acknowledgements The authors sincerely appreciate the assistance of the many permanent supportive housing programs and residents who cooperated with our research. We would also like to thank all Supportive Housing: Optimizing Placement team members for their contributions in making this research study possible.

Author contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by KGQ, TLM, AB, and JD-G. Data analysis was performed by ALS and WD. The first draft of the manuscript was written by ALS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding This study was supported by the National Institute on Drug Abuse (R01DA038085, PI: Dickson-Gomez).

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict of interest.

Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institu- tional and/or national research committee (Institutional Review Board at the Medical College of Wisconsin) and with the 1964 Helsinki Dec- laration and its later amendments or comparable ethical standards.

Informed consent Informed consent was obtained from all individual participants included in the study.

References

1. Fazel, S., Geddes, J. R., & Kushel, M. (2014). The health of homeless people in high-income countries: descriptive epide- miology, health consequences, and clinical and policy recom- mendations. The Lancet, 384(9953), 1529–1540. https ://doi. org/10.1016/S0140 -6736(14)61132 -6.

2. National Alliance to End Homelessness. (2019). “Chronically Homeless”. National Alliance to End Homelessness. Retrieved May 13, 2019, from https ://endho meles sness .org/homel essne ss- in-ameri ca/who-exper ience s-homel essne ss/chron icall y-homel ess/.

3. National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Population Health and Public Health Practice, Policy and Global Affairs, Science and Technology for Sustainability Program, and Committee on an Evaluation of Permanent Supportive Housing Programs for Homeless Individuals. (2018). Permanent Supportive Housing: Evaluating the Evidence for Improving Health Outcomes Among People Experiencing Chronic Homelessness. Washington, DC: National Academies Press.

4. Aubry, T., Nelson, G., & Tsemberis, S. (2015). Housing first for people with severe mental illness who are homeless: A review of the research and findings from the at home–chez soi dem- onstration project. Canadian Journal of Psychiatry, 60(11), 467–474. https ://doi.org/10.1177/07067 43715 06001 102.

5. Baggett, T. P., O’Connell, J. J., Singer, D. E., & Rigotti, N. A. (2010). The unmet health care needs of homeless adults: A national study. American Journal of Public Health, 100(7), 1326–1333. https ://doi.org/10.2105/AJPH.2009.18010 9.

6. Flick, U. (2007). Homelessness and health: Challenges for health psychology. Journal of Health Psychology, 12(5), 691– 695. https ://doi.org/10.1177/13591 05307 08057 6.

7. Schanzer, B., Dominguez, B., Shrout, P. E., & Caton, C. L. M. (2007). Homelessness, health status, and health care use. American Journal of Public Health, 97(3), 464–469. https :// doi.org/10.2105/AJPH.2005.07619 0.

8. Dickson-Gomez, J., et  al. (2017). Identifying variability in permanent supportive housing: A comparative effectiveness approach to measuring health outcomes. American Journal of Orthopsychiatry, 87(4), 414–424. https ://doi.org/10.1037/ort00 00232 .

9. Fitzpatrick-Lewis, D., Ganann, R., Krishnaratne, S., Ciliska, D., Kouyoumdjian, F., & Hwang, S. W. (2011). Effectiveness of interventions to improve the health and housing status of home- less people: A rapid systematic review. BMC Public Health. https ://doi.org/10.1186/1471-2458-11-638.

10. Kyle, T., & Dunn, J. R. (2008). Effects of housing circumstances on health, quality of life and healthcare use for people with severe mental illness: A review. Health & Social Care in the Community, 16(1), 1–15. https ://doi.org/10.1111/j.1365-2524.2007.00723 .x.

11. Rog, D. J., et al. (2014). Permanent supportive housing: Assess- ing the evidence. Psychiatric Services, 65(3), 287–294. https :// doi.org/10.1176/appi.ps.20130 0261.

12. Aidala, A. A., et al. (2016). Housing status, medical care, and health outcomes among people living with HIV/AIDS: A sys- tematic review. American Journal of Public Health, 106(1), e1–e23. https ://doi.org/10.2105/AJPH.2015.30290 5.

13. Bowen, E. A., Canfield, J., Moore, S., Hines, M., Hartke, B., & Rademacher, C. (2017). Predictors of CD4 health and viral sup- pression outcomes for formerly homeless people living with HIV/ AIDS in scattered site supportive housing. AIDS Care, 29(11), 1458–1462. https ://doi.org/10.1080/09540 121.2017.13079 20.

14. Quinn, K., Dickson-Gomez, J., Nowicki, K., Johnson, A. K., & Bendixen, A. V. (2018). Supportive housing for chronically

2060 Quality of Life Research (2020) 29:2051–2061

1 3

homeless individuals: Challenges and opportunities for provid- ers in Chicago, USA. Health & Social Care in the Community, 26(1), e31–e38. https ://doi.org/10.1111/hsc.12467 .

15. Hwang, S. W., Gogosis, E., Chambers, C., Dunn, J. R., Hoch, J. S., & Aubry, T. (2011). Health status, quality of life, residen- tial stability, substance use, and health care utilization among adults applying to a supportive housing program. Journal of Urban Health, 88(6), 1076–1090. https ://doi.org/10.1007/s1152 4-011-9592-3.

16. Fayers, P. M., & Machin, D. (2016). Quality of life: The assess- ment, analysis, and reporting of patient-reported outcomes (3rd ed.). Hoboken, NJ: Wiley.

17. Wilson, I., & Cleary, P. (1995). Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. JAMA, 273(1), 59–65. https ://doi.org/10.1001/ jama.273.1.59.

18. Zubritsky, C., Abbott, K. M., Hirschman, K. B., Bowles, K. H., Foust, J. B., & Naylor, M. D. (2013). Health-related quality of life: Expanding a conceptual framework to include older adults who receive long-term services and supports. Gerontologist, 53(2), 205–210. https ://doi.org/10.1093/geron t/gns09 3.

19. Gabrielian, S., et al. (2015). Factors affecting exits from homeless- ness among persons with serious mental illness and substance use disorders. Journal of Clinical Psychiatry, 76(4), e469–e476. https ://doi.org/10.4088/JCP.14m09 229.

20. Riley, E. D., Bangsberg, D. R., Perry, S., Clark, R. A., Moss, A. R., & Wu, A. W. (2003). Reliability and validity of the SF-36 in HIV- infected homeless and marginally housed individuals. Quality of Life Research, 12(8), 1051–1058. https ://doi.org/10.1023/A:10261 66021 386.

21. Riley, E. D., Neilands, T. B., Moore, K., Cohen, J., Bangsberg, D. R., & Havlir, D. (2012). Social, structural and behavioral determi- nants of overall health status in a cohort of homeless and unstably housed HIV-infected men. PLoS ONE, 7(4), e35207. https ://doi. org/10.1371/journ al.pone.00352 07.

22. Riley, E. D., et  al. (2003). Depression and drug use impact health status among marginally housed HIV-infected individu- als. AIDS Patient Care and STDs, 17(8), 401–406. https ://doi. org/10.1089/10872 91033 22277 411.

23. McHorney, C. A., Ware, J. E., Lu, J. F., & Sherbourne, C. D. (1994). The MOS 36-item Short-Form Health Survey (SF-36): III. Tests of data quality, scaling assumptions, and reliability across diverse patient groups. Medical Care, 32(1), 40–66.

24. Ware, J. E., Snow, K. K., Kosinski, M., & Gandek, B. (1993). SF-36 health survey: Manual and Interpretation guide. Boston: The Health Institute New England Medical Center.

25. Andresen, E. M., Malmgren, J. A., Carter, W. B., & Patrick, D. L. (1994). Screening for depression in well older adults: Evalu- ation of a short form of the CES-D. American Journal of Pre- ventive Medicine, 10(2), 77–84. https ://doi.org/10.1016/S0749 -3797(18)30622 -6.

26. Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097. https ://doi.org/10.1001/archi nte.166.10.1092.

27. Saunders, J. M., Aasland, O. G., Babor, T. F., de la Fuente, J. R., & Grant, M. E. (1993). Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption–II. Addic- tion, 88(6), 791–804. https ://doi.org/10.1111/j.1360-0443.1993. tb020 93.x.

28. Allen, J. P., Litten, R. Z., Fertig, J. B., & Babor, T. (1997). A review of research on the alcohol use disorders identifica- tion test (AUDIT). Alcoholism, 21(4), 613–619. https ://doi. org/10.1111/j.1530-0277.1997.tb038 11.x.

29. Cacciola, J. S., Alterman, A. I., Habing, B., & McLellan, A. T. (2011). Recent status scores for version 6 of the Addiction Severity Index (ASI-6): Recent status scores for ASI-6. Addiction, 106(9), 1588–1602. https ://doi.org/10.1111/j.1360-0443.2011.03482 .x.

30. McLellan, A. T., Luborsky, L., Woody, G. E., & O’Brien, C. P. (1980). An improved diagnostic evaluation instrument for substance abuse patients: The Addiction Severity Index. Jour- nal of Nervous and Mental Disease, 168(1), 26–33. https ://doi. org/10.1097/00005 053-19800 1000-00006 .

31. Hays, R. D., Cunningham, W. E., Ettl, M. K., Beck, C. K., & Sha- piro, M. F. (1995). Health related quality of life in HIV disease. Assessment, 2(4), 363–380. https ://doi.org/10.1177/10731 91195 00200 4006.

32. Fleishman, J. A., et al. (2000). Coping, conflictual social inter- actions, social support, and mood among HIV-infected persons. American Journal of Community Psychology, 28(4), 421–453. https ://doi.org/10.1023/A:10051 32430 171.

33. Cunningham, W. E., Hays, R. D., Williams, K. W., Beck, K. C., Dixon, W. J., & Shapiro, M. F. (1995). Access to medical care and health-related quality of life for low-income persons with symp- tomatic human immunodeficiency virus. Medical Care, 33(7), 739–754.

34. Cunningham, W. E., et al. (1999). The impact of competing sub- sistence needs and barriers on access to medical care for persons with human immunodeficiency virus receiving care in the United States. Medical Care, 37(12), 1270–1281.

35. Stata: Software for Statistics and Data Science. Retrieved Decem- ber 11, 2018, from https ://www.stata .com/.

36. Strine, T. W., Chapman, D. P., Kobau, R., Balluz, L., & Mokdad, A. H. (2004). Depression, anxiety, and physical impairments and quality of life in the US noninstitutionalized population. Psychi- atric Services, 55(12), 1408–1413. https ://doi.org/10.1176/appi. ps.55.12.1408.

37. U.S. Department of Housing and Urban Development. (2018). The 2018 Annual Homeless Assessment Report (AHAR) to Congress, Part 1: Point-in-time estimates of homelessness.

38. Postmes, T., & Branscombe, N. R. (2002). Influence of long-term racial environmental composition on subjective well-being in African Americans. Journal of Personality and Social Psychol- ogy, 83(3), 735–751. https ://doi.org/10.1037/0022-3514.83.3.735.

39. Hughes, M., Kiecolt, K. J., Keith, V. M., & Demo, D. H. (2015). Racial identity and well-being among African Ameri- cans. Social Psychology Quarterly, 78(1), 25–48. https ://doi. org/10.1177/01902 72514 55404 3.

40. Maglinte, G. A., Hays, R. D., & Kaplan, R. M. (2012). US general population norms for telephone administration of the SF-36v2. Journal of Clinical Epidemiology, 65(5), 497–502. https ://doi. org/10.1016/j.jclin epi.2011.09.008.

41. Riley, E. D., Moore, K., Sorensen, J. L., Tulsky, J. P., Bangsberg, D. R., & Neilands, T. B. (2011). Basic subsistence needs and overall health among human immunodeficiency virus-infected homeless and unstably housed women. American Journal of Epi- demiology, 174(5), 515–522. https ://doi.org/10.1093/aje/kwr20 9.

42. Buchanan, D., Kee, R., Sadowski, L. S., & Garcia, D. (2009). The health impact of supportive housing for HIV-positive home- less patients: A randomized controlled trial. American Journal of Public Health, 99(S3), S675–S680. https ://doi.org/10.2105/ AJPH.2008.13781 0.

43. Leaver, C. A., Bargh, G., Dunn, J. R., & Hwang, S. W. (2007). The effects of housing status on health-related outcomes in peo- ple living with HIV: A systematic review of the literature. AIDS and Behavior, 11(2), 85–100. https ://doi.org/10.1007/s1046 1-007-9246-3.

44. Weinstein, L. C., Henwood, B. F., Matejkowski, J., & Santana, A. J. (2011). Moving from street to home: Health status of entrants to

2061Quality of Life Research (2020) 29:2051–2061

1 3

a housing first program. Journal of Primary Care & Community Health, 2(1), 11–15. https ://doi.org/10.1177/21501 31910 38358 0.

45. Henwood, B. F., Lahey, J., Rhoades, H., Pitts, D. B., Pynoos, J., & Brown, R. T. (2019). Geriatric conditions among formerly homeless older adults living in permanent supportive housing. Journal of General Internal Medicine. https ://doi.org/10.1007/ s1160 6-018-4793-z.

46. Henwood, B. F., et al. (2013). Addressing chronic disease within supportive housing programs. Prog Community Health Partnersh, 7(1), 67–75. https ://doi.org/10.1353/cpr.2013.0005.

47. Henwood, B. F., Katz, M. L., & Gilmer, T. P. (2015). Aging in place within permanent supportive housing. International Journal

of Geriatric Psychiatry, 30(1), 80–87. https ://doi.org/10.1002/ gps.4120.

48. Rhoades, H., Wenzel, S. L., & Henwood, B. F. (2019). Changes in self-rated physical health after moving into permanent sup- portive housing. American Journal of Health Promotion, 33(7), 1073–1076. https ://doi.org/10.1177/08901 17119 84900 4.

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