Predictors of self-reported general health status in people experiencing homelessness with serious mental illness

Abstract

Purpose

The growing homeless population in the U.S.A. is disproportionately impacted by poor mental and physical health status, including a higher incidence of acute and chronic health problems, increased hospitalizations, and premature mortality compared to the general population. This study examined the association between demographic, social, and clinical factors and perceptions of general health status among the homeless population during admission to an integrated behavioral health treatment program.

Methods

The study sample included 331 adults experiencing homelessness with a serious mental illness or co-occurring disorder. Participants were enrolled in services at a day program for unsheltered homeless adults, a residential substance use treatment program for males experiencing homelessness, a psychiatric step-down respite program for those experiencing homelessness following psychiatric hospitalization, permanent supportive housing for formerly chronically homeless adults, a faith-based food distribution program, and homeless encampment sites in a large urban area. Participants were interviewed using The Substance Abuse and Mental Health Services Administration’s National Outcome Measures tool and a validated health-related quality of life measurement tool, SF-36. Data were examined using in elastic net regression.

Results

The study found seven factors to be particularly strong predictors of SF-36 general health scores. Male gender, “other” sexual identity, stimulant use, and Asian race were all associated with better perceptions of health status, while transgender status, inhalant use, and number of times arrested were associated with poorer perceptions.

Conclusion

This study suggests targeted areas for health screening within the homeless population; however, more studies are necessary to demonstrate generalizability of the results.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11136-023-03370-9.

Introduction

The growing homeless population in the U.S.A. [1] is disproportionately impacted by poor mental and physical health status [2–5], including a higher incidence of acute and chronic health problems, increased hospitalizations, and premature mortality compared to the general population [6–13] as well as higher healthcare costs [14, 15]. Further, studies have shown that past experiences of trauma are highly prevalent among the homeless [16, 17], resulting in a higher prevalence of post-traumatic stress disorder (PTSD) compared to the population at large [18]. Thus, people experiencing homelessness face major vulnerabilities likely to adversely impact their health and well-being, including physical and sexual violence and other traumas [19].

Recent policy research has elucidated stark differences between people who are unsheltered and people who are sheltered [20]. Findings indicate that those who are unsheltered are more likely to stay homeless longer and have more significant health challenges [20]. Further, research points to gender differences in exposure to violence and trauma, levels of social support, diagnoses of chronic illnesses, and past month illicit drug use among persons who are experiencing unsheltered homelessness with serious mental illness [20, 21]. Further, the more significant health issues and vulnerabilities in this population have been shown to be present prior to housing loss as well as during the early experiences of homelessness [20].

Additionally, poor mental health status among homeless women in particular has been associated with lower self-reported social support, physical or sexual violence in the previous year, more chronic health conditions, and illicit drug use during the previous month [21]. Furthermore, a lack of social connectedness among the homeless, including repeated social exclusion, social isolation [22, 23], and low levels of social support and social functioning, have been shown to contribute to poor health [23]. Relationships with family have also been shown to be significant predictors of lower self-reported health status among those experiencing homelessness [24], and one study found family support to be a key contributor to positive views of recovery from mental health illness among homeless youth [25].

Together, these findings point to the possibility of increased needs and vulnerabilities among people experiencing homelessness. Yet, few studies have examined self-reported risk factors for perceptions of low health status among the homeless population, especially those with serious mental illness. Accordingly, the purpose of this study was to examine the association between demographic, social, and clinical factors and perceptions of general health status among a sample of people experiencing homelessness with serious mental illness during admission to an integrated behavioral health treatment program in a large urban area.

Methods

Participants

The study sample included 331 adults (18 years and older) experiencing homelessness with serious mental illness (SMI) or a coexisting SMI and substance use disorder (COD) who were continuously admitted to an integrated behavioral health treatment program. All persons enrolled in the treatment program were included in this analysis. Enrollment at the time of this study ran from January 2019 to October 2021. The Mini-International Neuropsychiatric Interview (M.I.N.I.) was utilized to identify individuals with SMI. The M.I.N.I. was administered by licensed masters-level therapists who participated in an orientation to the structure of the M.I.N.I and observed a clinical supervisor administer the M.I.N.I with 5 patients. The masters-level therapists were then observed by their clinical supervisor administering the M.I.N.I. with three or more patients. All M.I.N.I results were reviewed by the principal investigator who served as the clinical supervisor to interpret results and compare M.I.N.I findings with the findings from other clinical assessments collected routinely as part of the intake process. These included the Patient Health Questionnaire-9 (PHQ9), PTSD Checklist for DSMV (PCL-5), Generalized Anxiety Disorder 7-item (GAD-7), Young Mania Rating Scale (YMRS), and Brief Psychiatric Rating Scale (BPRS). Services were provided at multiple homeless services locations and patients were self-referred or referred by other services providers at these programs and no patients were turned away if they met enrollment criteria. Accordingly, patients were not approached specifically for participation in this data analysis as this study was an analysis of data collected during program intake. Recruitment sites included a day program for unsheltered homeless, a residential substance use treatment program for males experiencing homelessness, a psychiatric step-down respite program for those experiencing homelessness following psychiatric hospitalization, permanent supportive housing for formerly chronically homeless adults, a faith-based food distribution program, and homeless encampment sites in a large urban area. All participants were homeless as detailed by federal definitions [26]. Structured clinical interviews by trained staff were used to collect data from participants. This study was reviewed by the University of Texas Health Science Center at Houston Institutional Review Board.

Measures

The Substance Abuse and Mental Health Services Administration (SAMHSA) Center for Mental Health Services (CMHS) National Outcome Measures (NOMs) Client-Level Measures for Discretionary Programs Providing Direct Services: Services Tool for Adult Programs; SAMHSA’s Performance Accountability and Reporting System (SPARS) November 2021 [27] was utilized to obtain patient self-reported information across the following eight domains: demographics, military history, drug and alcohol use, family and living conditions, education, employment and income, crime and criminal justice status, mental and physical health problems and treatment/recovery/functioning, violence and trauma, and social connectedness. The NOMS Government Performance and Results Act (GPRA) interview tool is required by SAMHSA CMHS funded programs to be administered at program intake, every 6 months and at discharge in order to collect outcome data that “embody meaningful, real-life outcomes for people who are striving to attain and sustain recovery, build resilience, and work, learn, live, and participate fully in their communities'' [28, 29]. As such, NOMS/GPRA data provide an important source of patient-reported information to inform patient-centered, recovery-oriented treatment for vulnerable populations served by SAMHSA-funded programs. Items from all patient self-report domains, 63 items in total, were included in the analysis.

Outcome measure

The 36-Item Short Form Survey (SF-36) is a set of generic, coherent, and easily administered health-related quality of life (HR-QOL) measures tool drawn from the RAND Corporation’s Medical Outcomes Study that provides a broad measure of health status rather than focusing on specific groups (such as age, disease, or treatment) [30]. The SF-36 is free for use as long as RAND’s terms and conditions for use are adhered to [30]. Accordingly, this tool has been widely used in health research to obtain patient-reported information on physical and mental health status, with more than ten thousand publications using the SF-36 to date [30–34] and assess the impact of various diseases, interventions, and characteristics on quality of life (QOL) outcomes in various populations [31, 34]. The SF-36 tool comprises scales of eight separate health domains: physical functioning, role limitations due to physical health, emotional well-being, role limitations due to emotional problems, energy/fatigue, social functioning, bodily pain, and general health. The SF-36 Scoring Manual and tool developers do not provide support to calculate a single measure of health-related quality of life, such as a “SF-36 Total/Global/Overall Score.” Additionally, the tool developers do not recommend combining SF-36 summary measures to produce an overall score of health-related quality of life [32]. Because the focus of this study is on the examination of patient perspectives on health status [34], this study utilized the general health domain (benchmark reliability α = 0.78, mean = 56.99, standard deviation = 21.11) derived from the RAND Corporation’s Medical Outcomes Study [30], as its primary outcome variable, which consisted of five items:

1. “In general, would you say your health is:”

2. “I seem to get sick a little easier than others.”

3. “I am as healthy as anybody I know.”

4. “I expect my health to get worse.”

5. “My health is excellent.”

Each item has a five-point Likert scale response range. The first is scored from “Excellent” to “Poor,” while the last four are scored from “Very True” to “Very False.” The scale drew from equally weighted survey questions scored from 0 to 100, with lower scores indicating poorer outcomes [30].

Data analysis

Elastic net regression modeled the SF-36 domain general health a function of a large set of dichotomous and continuous predictors; after one-hot encoding (i.e., converting categorical predictors into a set of binary 0/1 indicators), 63 predictors were included in the current analysis. Of these, 51 predictors were treated as continuous, including a set of variables with a native ordinal scale (e.g., Likert-type items using a 5-point scale from “strongly disagree” to “strongly agree”) and the remainder were treated as dichotomous. The full set of predictors is described in Table 1.

Table 1.

Sample characteristics

Characteristic	N (%)
NOMS: What is your gender? (Gender*)	–
NOMS: Male	268 (81%)
NOMS: Female	60 (18%)
NOMS: Transgender	3 (0.9%)
NOMS: Are you Hispanic or Latino? (Ethnicity*)	–
NOMS: Not Hispanic or Latino	273 (83%)
NOMS: Hispanic or Latino	57 (17%)
NOMS: What Race do you consider yourself? (Race*)	–
NOMS: Black	182 (55%)
NOMS: Asian	7 (2.1%)
NOMS: Native Hawaiian	3 (0.9%)
NOMS: Alaska Native	3 (0.9%)
NOMS: White	123 (37%)
NOMS: American Indian	30 (9.1%)
NOMS: Which of the following do you consider yourself to be? (Sexual Identity*)	–
NOMS: Heterosexual	301 (91%)
NOMS: Homosexual	16 (4.8%)
NOMS: Bisexual	10 (3.0%)
NOMS: Other	3 (0.9%)
NOMS: Have you ever served in the Armed Forces, the Reserves, or the National Guard? (Ever Served*)	–
NOMS: No	292 (90%)
NOMS: Yes	31 (9.6%)
NOMS: Have you ever experienced violence or trauma in any setting (including community or school violence; domestic violence; physical, psychological, or sexual maltreatment/assault within or outside of the family; natural disaster; terrorism; neglect; or traumatic grief)? (Violence Trauma*)	–
NOMS: No	24 (7.4%)
NOMS: Yes	301 (93%)

Characteristic	Mean (SD)
SF-36 Total Score	51.34 (26.11)
Age	45.39 (12.19)
NOMS: I am getting along with my family. (Gets Along With Family*)	3.01 (1.37)
NOMS: I do well in social situations. (Social Situations*)	3.29 (1.19)
NOMS: I do well in school and/or work. (SchoolOrWork*)	3.46 (1.26)
NOMS: In the last 4 weeks, how satisfied are you with your personal relationships? (Relationship Satisfaction*)	2.57 (1.30)
NOMS: I am happy with the friendships I have. (Friendships*)	2.84 (1.31)
NOMS: I have people with whom I can do enjoyable things. (Enjoy People*)	2.95 (1.30)
NOMS: I feel I belong in my community. (Belong In Community*)	2.89 (1.28)
NOMS: In a crisis, I would have the support I need from family or friends. (Support From Family*)	2.84 (1.32)
NOMS: I have family or friends that are supportive of my recovery. (Supportive Family Friends*)	3.18 (1.33)
NOMS: What is the highest level of education you have finished, whether or not you received a degree? (Education*)	12.38 (1.31)
NOMS: Are you currently employed? (Employment*)	4.59 (1.81)
NOMS: In the last 4 weeks, have you enough money to meet your needs? (Enough Money For Needs*)	1.67 (1.21)
NOMS: In the past 30 days, how many nights have you been homeless? (Nights Homeless*)	14.11 (13.27)
NOMS: In the past 30 days how many nights have you spent in a hospital for mental health care? (Nights Hospital MHC*)	4.80 (8.62)
NOMS: In the past 30 days how many nights have you spent in a correctional facility including jail or prison? (Nights Jail*)	0.26 (1.68)
NOMS: In the past 30 days, how often have you used… tobacco products (cigarettes, chewing tobacco, cigars, etc.)? (Substance Use—Tobacco*)	2.71 (1.38)
NOMS: In the past 30 days, how often have you used alcoholic beverages (beer, wine, liquor, etc.)? (Substance Use—Alcohol*)	1.76 (1.09)
NOMS: In the past 30 days, how often have you used cannabis (marijuana, pot, grass, hash, etc.)? (Substance Use—Cannabis*)	1.52 (0.98)
NOMS: In the past 30 days, how often have you used cocaine (coke, crack, etc.)? (Substance Use—Cocaine*)	1.37 (0.85)
NOMS: In the past 30 days, how often have you used prescription stimulants (Ritalin, Concerta, Dexedrine, Adderall, diet pills, etc.)? (Substance Use—Stimulants*)	1.04 (0.28)
NOMS: In the past 30 days, how often have you used… methamphetamine (speed, crystal meth, ice, etc.)? (Substance Use—Methamphetamine*)	1.08 (0.40)
NOMS: In the past 30 days, how often have you used inhalants (nitrous oxide, glue, gas, paint thinner, etc.)? (Substance Use—Inhalants*)	1.01 (0.17)
NOMS: In the past 30 days, how often have you used sedatives or sleeping pills (Valium, Serepax, Ativan, Librium, Xanax, Rohypnol, GHB, etc.)? (Substance Use—Sedatives*)	1.10 (0.48)
NOMS: In the past 30 days, how often have you used hallucinogens (LSD, acid, mushrooms, PCP, Special K, ecstasy, etc.)? (Substance Use—Hallucinogens*)	1.08 (0.39)
NOMS: In the past 30 days, how often have you used street opioids (heroin, opium, etc.)? (Substance Use—Street Opioids*)	1.03 (0.25)
NOMS: In the past 30 days, how often have you used…prescription opioids (fentanyl, oxycodone [OxyContin, Percocet], hydrocodone [Vicodin], methadone, buprenorphine, etc.)? (Substance Use—Prescription Opioids*)	1.04 (0.31)
NOMS: In the past 30 days, how often have you used other—specify (e-cigarettes, etc.)? (Substance Use—Other*)	1.14 (0.55)
NOMS: I feel capable of managing my healthcare needs. (Capable Managing Health Care Needs*)	2.00 (0.88)
NOMS: I deal effectively with daily problems. (Handling Daily Life*)	3.40 (1.20)
NOMS: I am able to control my life. (Control Life*)	3.05 (1.21)
NOMS: I am able to deal with crisis. (Deal With Crisis*)	3.11 (1.15)
NOMS: My housing situation is satisfactory. (Functioning Housing*)	1.76 (1.15)
NOMS: My symptoms are not bothering me. (Symptoms*)	2.11 (1.18)
NOMS: During the past 30 days, about how often did you feel nervous? (Nervous*)	2.50 (1.27)
NOMS: During the past 30 days, about how often did you feel hopeless? (Hopeless*)	2.21 (1.29)
NOMS: During the past 30 days, about how often did you feel restless or fidgety? (Restless*)	2.50 (1.28)
NOMS: During the past 30 days, about how often did you feel so depressed that nothing could cheer you up? (Depressed*)	1.95 (1.29)
NOMS: During the past 30 days, about how often did you feel that everything was an effort? (Everything Effort*)	2.59 (1.29)
NOMS: During the past 30 days, about how often did you feel worthless? (Worthless*)	1.96 (1.43)
NOMS: During the past 30 days, how much have you been bothered by these psychological or emotional problems? (Psychological Emotional Problems*)	3.80 (1.21)
NOMS: In the last 4 weeks, how would you rate your quality of life? (Life Quality*)	2.67 (1.21)
NOMS: In the last 4 weeks, do you have enough energy for everyday life? (Enough Energy For Everyday Life*)	2.92 (1.30)
NOMS: In the last 4 weeks, how satisfied are you with your ability to perform your daily living activities? (Perform Daily Activities Satisfaction*)	3.04 (1.23)
NOMS: In the last 4 weeks, how satisfied are you with yourself? (Self-Satisfaction*)	2.80 (1.28)
NOMS: In the past 30 days, how many times have you been arrested? (Num Times Arrested*)	0.08 (0.31)
NOMS: I generally accomplish what I set out to do. (Generally, Accomplish Goal*)	3.45 (1.16)

*National Outcome Measures (NOMs) variables

All analyses were conducted using the R Statistical Computing Environment [35] using tidymodels [36], and glmnet [37]. Prior to analyses, the sample was assessed for data entry mistakes and missing data. Missing data (i.e., participants responded “refused,” “don’t know,” or “not applicable” in response to a question or certain questions/sections were deferred) comprised less than 10% of all variables. All missing observations were imputed via the bagImpute function from the tidymodels package in R [38].

Elastic net regression [39] was used to model the general health domain outcome as a function of all 63 predictors at the same time [40]. The elastic net penalizes (i.e., shrinks) regression coefficients to prevent inflated estimates that may arise due to multicollinearity; further, the method may reduce coefficients all the way to zero, providing de facto variable selection. The model utilized tenfold cross-validation across a Latin hypercube grid search to identify optimized values of two tuning parameters, lambda and alpha, where the former describes the magnitude of the penalty placed on model coefficients and the latter describes the blend of the penalty between two related methods (ridge regression; LASSO). Optimized values for alpha and lambda were chosen by root mean squared error (RMSE). The elastic net model provided penalized coefficients that may be interpreted in the traditional way as relating the magnitude and direction of the effect of a predictor on the outcome variable. Model assumptions (e.g., homogeneity of variance; normality of residuals) were tested via visual examination of graphical plots.

Results

Sample characteristics

Measures of central tendency, dispersion, and frequency are described in Table 1. Of the n = 331 person sample, 81% were male and 91% were heterosexual. The population was predominantly African American (55%) or White (37%) and about 17% were Hispanic/Latino. The average age of the sample was 45.4 (sd = 12.2), with a range of 19 to 73 years old. Forty-seven percent of the sample was recruited from a homeless day program serving the unsheltered population or homeless encampment site, indicating they were unsheltered. Overall, 93% of the sample indicated they had a past experience of trauma or violence against them, 10% had served in the military at some point in their life, and 96% were currently unemployed. In the 30 days prior to enrollment, 35% had spent at least one night in a hospital for mental health reasons, 6% had spent at least one night in jail, and 41% had used illicit drugs. The average score for the SF-36 domain general health was 51.3 (sd = 26.1).

Elastic net

Tenfold cross-validation identified optimized values for lambda = 0.997 and alpha (0.887). The model retained 63 predictors; a full table of penalized regression coefficient has been included in an online electronic supplement. Strong predictors of lower outcome values were higher levels of inhalant use (b = − 12.90), transgender status (b = − 12.52), and number of times arrested (b = − 7.10). Conversely, strong predictors of higher outcome values included “other” sexual identity (b = 10.16), higher levels of stimulant use (b = 9.33), male gender (b = 8.35), and Asian race (b = 7.90). However, several of these should be interpreted with caution: with the penalized regression algorithm, categorical predictors (e.g., gender) are interpreted as a given category (e.g., male) relative to the other options of that category (i.e., the penalization requires including all categories in the model, as opposed to traditional methods that leave out one reference category). Further, some of these higher-magnitude coefficients may reflect overfitting, despite the use of cross-validation in the training data set and a held-out test data set. For example, only 2% of the sample reported inhalant use greater than the lowest NOMS response option. Findings should be considered preliminary until generalizability may be evaluated with additional data from disparate sources.

Discussion

This study had a large sample of participants experiencing homelessness with SMI or COD and examined the effects of various demographic, social, and clinical factors on the general health status domain in the SF-36. Even though those experiencing homelessness are a diverse population with various demographic, housing, financial, and social characteristics, this study identified multiple significant predictors of both lower and higher self-reported general health status.

Inhalant use, transgender status, and number of times arrested were identified as the strongest predictors of lower self-reported general health status. Chronic exposure to inhalants can produce significant damage to the heart, lungs, liver, and kidneys [41]. While the impact of inhalant use on health status has been established, it has historically been the least studied form of substance abuse [42]. A higher prevalence of inhalant use has been found among homeless youth [43] and individuals reporting a sexual minority status [44]. A recent international study examining health status among a homeless population linked inhalant use of glue in particular with higher rates of negative states of health and disability, worse than those observed in other socially excluded groups [45]. Accordingly, this finding warrants further exploration to better understand the impacts of inhalant use among homeless populations to inform interventional research.

The finding that persons reporting transgender status were more likely to self-report lower general health status is consistent with prior research [46]. Because transgender men experiencing homelessness have been found to be particularly at risk for physical health problems [46], this study finding provides more evidence of the need to explore this understudied area.

The inverse relationship between number of times arrested and self-reported general health status is consistent with prior research on health and interactions with the criminal justice system. For example, history of imprisonment has been found to be associated with greater levels of health risks (including infection with HIV, heroin and cocaine use, and mental illness) within the homeless population [47].

Several predictors including male gender, “other” sexual identity, stimulant use, and Asian race were identified as the strongest predictors of higher self-reported general health status. Our finding that homeless cisgender females having poorer health than homeless males is supported by previous findings showing females to be more vulnerable to the health effects of homelessness compared to males [48]. Further, research has revealed a greater number of chronic physical conditions among females experiencing homelessness [48], with reports of females being frailer and having more physical health problems than homeless men [49, 50].

The finding that persons reporting “other” sexual identify is a strong predictor of greater self-reported general health status should be explored in future research. Sexual minorities who are also homeless have been found to be at greater risk for physical health problems [46]; however, current research on sexual identity and homelessness has been limited to persons who report lesbian, gay, bisexual, or heterosexual identity [46].

Additionally, the finding that higher levels of stimulant use was associated with a higher self-reported general health status should be interpreted with caution. Within the NOMS data, certain stimulant use including methamphetamine and cocaine were assessed separately and both associated with lower self-reported general health status. While a common cause of mortality among homeless and unstably housed women is acute intoxication where cocaine is present [51], research examining prescription stimulant misuse among people experiencing homelessness is needed. Further, prescription stimulants act on the central nervous system to increase alertness, attention, and energy and are prescribed off-label among older adults to treat depression, poststroke recovery, motor function, and fatigue [52]. Additionally, research has shown that the most fundamental perceived benefits of the alertness resulting from methamphetamine use is the ability to cope with a multiplicity of vulnerabilities directly tied to homelessness or housing insecurity [53]. Accordingly, self-reports of improved general health status with stimulants other than methamphetamine and cocaine may point to a perceived beneficial impact of prescription stimulants among persons who are experiencing homelessness.

Finally, the finding that Asian homeless individuals are more likely to endorse higher general health status also needs further examination in future studies. Although prior research has demonstrated that Asian individuals were more likely to self-report positive health status (excellent, very good, or good) than their Hispanic, White, and Black counterparts in the general population, we are not aware of any studies examining this question in homeless individuals [54]. Further, because they account for a relatively small percentage of the population in the U.S.A. and other western nations, Asians are often included in an “other” racial category in studies, making comparison of self-reported health status with other racial groups challenging [54].

The limitations of this study include the use of a self-reported measure administered with an interviewer present. While the interviewers received extensive training with the homeless population, the possibility of some degree of response bias from participants either under- or over-reporting answers should be considered. This research was also conducted solely in a large urban center in the southern U.S.A., which limits the generalizability of the study to other geographic regions in the U.S.A. and internationally. Furthermore, one residential recruitment site in this study served an exclusively male homeless population, which could have biased the sample away from other genders in the homeless population. A potential source of sampling bias also stemmed from the recruitment process as the social workers tasked with enrolling the participants may have been less likely to interact with or locate those with more significant mental illness (as they may have been less approachable), biasing the sample toward those with less severe symptoms. In addition, as previously noted, many categorical variable levels demonstrated low representation, and several continuous variables reflected means closer to their lowest potential value; as such, results should be considered preliminary until replicated to provide evidence supporting generalizability. Finally, much of the recruitment period coincided with the COVID-19 pandemic, which at times resulted in services, recruitment sites, and staff being unavailable.

One of the strengths of this study is the sample, which was relatively large and diverse and drew from a variety of different recruitment sites across a large urban area. All of these characteristics might help generalize the findings to the homeless population at large. Further, very few of the individuals approached by staff refused to participate, which reduces the non-response bias in the study. Finally, this study used the SF-36 general health score as the outcome, which helps give a more global perspective on how participant demographics, social interactions, and clinical factors contribute to overall QOL as it pertains to health. Future research can draw and expand on these findings to determine specific interactions of these characteristics with various aspects of health.

Conclusion

People experiencing homelessness face considerable physical, mental, social, and financial challenges that adversely affect their health. This study found that characteristics and patient-reported factors such as male gender, “other” sexual identity, stimulant use, and Asian race were associated with positive perceptions of general health status, while transgender status, inhalant use, and number of times arrested were associated with negative perceptions of general health status. These findings suggest potential areas to target for screening within the homeless population; however, further studies are necessary to demonstrate generalizability of these results.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Each of the authors made contributions to the study design and preparation of the manuscript. Material and data collection were performed by JH’s staff, including LP and GH. Data analysis for the revised draft was performed by RS. The first draft was written by LP, GH, and JH, and all authors (including Robert Suchting, who was hired to assist with methodology and analysis revisions) collaborated with each other in making revisions for future versions. The final manuscript was read and approved by each of the authors.

Funding

This research was supported by the Substance Abuse and Mental Health Services Administration (Grant #SM80721, Grant ID: SP082128-01—awarded to Dr. Jane Hamilton, PhD, MPH).

Data availability

The data that support the findings of this study are available from the corresponding author, LP, upon reasonable request.

Declarations

Competing interests

The authors have no competing interests (financial or otherwise) to disclose that are relevant to this research.

Ethical approval

This study was reviewed and approved by the University of Texas Health Science Center at Houston Institutional Review Board.

Consent to participate

All participants enrolled in this study gave written informed consent and verbally acknowledged understanding and willingness to participate.

Consent to publish

There were no identifiable figures, data, or descriptions of the participants in this study.