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. Author manuscript; available in PMC: 2025 Jan 1.
Published in final edited form as: J Soc Work Pract Addict. 2023 Jan 22;24(3):297–308. doi: 10.1080/1533256x.2023.2170592

Predictors of Health in Substance Use Disorder Recovery: Economic Stability in Residential Aftercare Environments

JUSTIN S BELL 1, ANN KANG 1, SAGE BENNER 1, SHAUN BHATIA 1, LEONARD A JASON 1
PMCID: PMC11390098  NIHMSID: NIHMS1872006  PMID: 39268410

Abstract

The exit from active substance use presents barriers to achieving and maintaining health, especially as individuals lack the economic resources to afford healthcare access. Treatment settings that strengthen resources may support stability in recovery and influence health. Analyzing a sample of recovery home residents over two years (N = 494), the current study assessed individually held resources (e.g., wages, employment) and the average economic conditions of a resident’s house (e.g., house employment rate) to understand their association with self-reported health status. Employment status, but not wages or transportation access, was associated with reported health scores. The average employment rate of a recovery home was also positively correlated with the health of its residents. Results indicate the need to address employment and other economic issues which plague recovering individuals. Community aftercare settings may offer such a pathway through affordable housing, employment opportunities, and supportive relationships.

Keywords: health, recovery, substance use disorder, substance use, recovery homes, employment

Substance use is a known risk factor for many physical and mental disorders (Ross & Peselow, 2012; Schulte & Hser, 2014; Wu et al., 2018). Use and misuse of various drugs - including alcohol, cocaine, heroin, prescription drugs, and methamphetamine - is associated with a variety of negative health outcomes: diseases of the heart, cancer, diabetes, as well as suicidal ideation, mood disorders, and anxiety (Schulte & Hser, 2014). The causes of these associations are various and often bidirectional. While substance use has harmful effects on the body that contribute to the development of physical disorders, individuals may also seek out substances to self-medicate their physical conditions (e.g., taking opioids for chronic pain; Garland et al., 2013).

The use of substances and diagnosis of a substance use disorder (SUD) presents unique challenges to properly treating co-occurring medical conditions. Those with SUDs are less likely to adhere to treatment recommendations, follow medication regimens, and manage their condition properly (Magura et al., 2012). Substance use decreases the effectiveness of medications meant to treat medical conditions, potentially worsening the course of illness (Schulte & Hser, 2014). These complications contribute to increased medical care usage. Individuals with chronic medical conditions and co-occurring SUDs are more likely to be hospitalized than those without SUDs (Wu et al., 2018).

Economic Stability & Health in Recovery

Health has more recently been understood holistically, comprising contextual and social factors. The World Health Organization’s Social Determinants of Health (SDH; WHO, 2007) model suggests economic and social conditions drive health and health behaviors. The SDH model groups factors into five general domains: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context. These non-medical factors have been demonstrated to be responsible for many health disparities, with economic stability playing a large role in driving disparate health outcomes. For example, a study of counties in 45 states found that socioeconomic conditions accounted for 47% of county-level variation in health outcomes, compared to a 16% impact by clinical care (Hood et al., 2016). Other evidence suggests employment is strongly linked with health in the United States. While most working adults have employer-sponsored insurance, the unemployed face challenges accessing healthcare and adverse health outcomes tend to increase with greater lengths of unemployment (Silver et al., 2021). Tackling comorbid SUD and medical issuesmakes addressing treatment especially burdensome for individuals who experience instability due to a lack of economic resources.Unemployed and/or uninsured individuals with SUD are often unable to afford even maintenance medical care (Ponitz et al., 2006). Many state-level Medicaid programs do not cover all levels of care needed to effectively treat SUDs, causing further barriers to access for vulnerable populations (Grogan et al., 2016). Even when properly insured, insurance policies emphasize treating physical or mental health conditions rather than SUD-related issues, so issues related to substance use often go underdiagnosed (Priester et al., 2016).

Various destabilizing factors prevent individuals with low socioeconomic status and SUDs from preventing or treating co-occurring medical conditions. Besides lack of insurance access, these individuals often lack necessities that aid in maintaining health, like proper diet and housing (Galea & Vhalov, 2002). Exacerbating these resource issues, individuals with SUDs often have trouble gaining employment (McCabe & Whaler, 2016). Employment promotes financial stability that aids in maintaining health by providing income and insurance for medical care (McCabe & Whaler, 2016). In interviews with individuals recovering from SUD and co-occurring medical conditions, employment helped take their minds off their medical conditions. Employment provided a purpose and something to look forward to even when agitated by their symptoms. A daily routine provided stability in their lives, while a new employment identity provided a sense of pride in their work (Dunn et al., 2008). The unemployed, therefore, may be especially vulnerable when experiencing co-occurring SUD and medical conditions.

Recovery Homes

Community-based residential aftercare programs, also known as recovery homes, may help recovering individuals accumulate the resources necessary to improve their health. Recovery homes provide a low-cost, abstinent living environment for individuals recovering from SUDs (Jason & Ferrari, 2010). Living with fellow former substance users provides continuous social support following treatment to protect against relapse (Jason et al., 2007; Majer et al., 2020). However, these environments also encourage stability and reintegration back into communities by providing affordable housing, promoting employment, and reducing criminal recidivism (Jason et al., 2015; Jason, et al., 2007).

Oxford Houses (OH), a large network of recovery homes, comprise a distinct model of abstinence-based, communitysubstance use aftercare in which each house is entirely self-governedJason & Ferrari, 2010; OH Annual Report, 2020). Following principles of democracy and self-reliance, almost all aspects of OH operations are carried out under democratic procedures. For example, an 80% majority is needed to elect a new member into the house and residents. However, residents are evicted if they do not contribute to house expenses, relapse on alcohol or drugs, or participate in disruptive behavior (Jason & Ferrari, 2010).These houses (typically 7-12 individuals) are gender-segregated, rented, and utilize self-support to pay all household expenses (OH Annual Report, 2020). This makes OHs especially affordable for individuals exiting treatment (OH Annual Report, 2020). Unlike other models of recovery housing (e.g., sober living homes), OH does not employ professional staff in their homes.

Economic stability factors at both the individual level and within the house environment may influence the health of OH residents. By encouraging residents to be self-reliant, , residents of OH are more likely to be employed or attend vocational training than individuals in usual treatment environments (Gomez et al., 2014). Employment, in turn, may help support health for recovery home residents.. A cross-sectional investigation of the association between employment and medical care among residents of OHs found employment was associated with less medical care need and utilization (Ponitz et al., 2006).

The housing environment, comprised of the individuals who share a living space, may also contribute to health. Previous research has explored house-level effects, relationships derived from looking at the house and its members as whole. Evidence suggests, for example, that residing in houses which are more experienced in recovery, or have greater lengths of average sobriety, is associated with a greater chance of staying abstinent (Beasley et al., 2013). Research also suggests that residing in houses which are more stable, or experience less turnover, is associated with greater increases in recovery capital. These findings extend to house financial stability. Houses with less financial challenges (i.e., paying rent) tend to report residents with greater recovery capital (Jason et al., 2022). Little is known about these house-level factors and their association with health. The economic stability of a house’s residents, as a social determinant of health, may act beyond the individual level, ecologically influencing the health and health behaviors of OH residents.

Current Study

The current study assessed several factors related to economic stability for residents of a community aftercare program (OH) and their relationship to reported medical problems and medical care utilization. Following the SDH model, self-reported employment resources (i.e., wages, transportation, employment status) and length of stay in the house were assessed to determine individual-level relationships between economic stability and health. Extending prior investigations of ‘house-level’ variables in recovery home environments, the relationship between average house employment rate, average length of stay of house members, and health was also assessed. While a cross-sectional study has investigated factors related to medical care need and emergency care utilization among residents of OH (Ponitz et al., 2006), this is the first longitudinal study to examine these relationships. As health develops across the lifespan and contexts, the current study’s longitudinal analysis strategy allowed for the exploration of within participant changes in economic stability and their association with health. .

We hypothesized that factors related to resource and economic stabilization (i.e., greater time in a recovery home, employment, wages, transportation,) would be significantly associated with reported medical problems and hospitalization. As the risk for health problems, disability, and decreased physical quality of life generally increases with age (McGrath et al., 2019), we also hypothesized age would be associated with these health outcomes, with older residents reporting a greater number of days experiencing medical problems and being hospitalized than younger residents. Finally, as previous studies of the health of OH residents have found associations between mental stressors and medical care need (Ponitz et al., 2006), we hypothesized increased stress would be associated with decreased health.

Materials and Methods

Participants and procedure

The current study investigated the medical status of individuals residing in OH (N = 494). Data was collected from a larger longitudinal study involving OH residents of homes located in North Carolina, Texas, and Oregon.Resident-elected presidents of OHs were asked to introduce the study to their fellow members by reading of summary of the study from a researcher-provided script. Houses were accepted if the president and all members, or all but one member, agreed to take part. The first 13 consenting homes were selected from each state, with 3 more added for a total of 42 OHs.

Face-to-face interviews were conducted by field research staff with participants every four months over two years, and participants were awarded $20 for their completion of each time point. Residents in participating OHs could join the study at any time during the 2-year data collection period. There were 714 residents in the selected houses during this time, of which 666 (93%) originally agreed to participate.

Participants were included for analysis in the current study if they reported currently residing in an OH recovery home. Of the 633 participants eligible, 140 participants were removed for reporting less than two waves on key variables. Permission to do this study was obtained from DePaul University Institutional Review Board.

Measures

Sociodemographic variables.

We assessed age, gender, employment, education, and race with items from the Addiction Severity Index Lite (ASI-Lite; McLellan et al., 1980). We evaluated and dummy-coded residents’ employment status in 2 ordered categories of 1) unemployed or other forms of income (disability, student, military service, or retirement), or 2) part-time or full-time employment.

Length of Stay in Oxford House.

A participant’s length of stay in Oxford House was estimated by having participants provide the date of their entry into the house at their first wave. The date was then converted into months by subtracting this date from each subsequent wave’s date of completion. Descriptive statistics determined the variable was highly skewed/non-normally distributed, and therefore the variable was log-transformed (Mean score = 2.05 [13.77 months], SD = 1.14). Average reported length of stay at baseline was 6.52 months (SD = 9.71).

Addiction Severity Index (ASI) Employment Subscale.

To calculate an individual’s resources achieved from employment and resources to achieve access to employment, an employment resource composite score was derived from the ASI- Lite. The composite score consisted of the following items: “Do you have a valid driver’s license?”; “Do you have an automobile available for your use?”; “How many days were you paid for working in the past 30?”; “How much did you receive from employment (new income) in the past 30 days?”. Scores on these items were transformed and summed to form a medical status composite score according to recommendations by McGahan et al. (1986). A higher score is indicative of greater employment resources, including transportation and wages. For this sample, α = .62 (Mean score = 0.34, SD = 0.25). Average reported score at baseline was .37 (SD = 0.24).

ASI Medical Status.

To assess health and the degree to which participants experienced medical problems, a medical status composite score was derived from the ASI-Lite. The composite score consisted of three items on the original measure: “How many days have you experienced medical problems in the last 30?”; “How troubled or bothered have you been by these medical problems in the past 30 days?”; and “How important to you now is treatment for these medical problems?” Scores on these items were transformed and summed to form a medical status composite score according to recommendations by McGahan et al. (1986). A higher score is indicative of greater medical problems and severity of problems. The ASI Medical Status subscale has demonstrated high concurrent validity with other measures of physical health and is a robust predictor of quality of life and health status (Calsyn et al., 2004). For this sample, α = .71 (Mean score = .05, SD = 0.17). Average reported medical score at baseline was .06 (SD = 0.19).

Hospitalization.

To assess emergency medical care utilization, a single item was derived from Form 90, a structured interview form adapted from Project MATCH (Tonigan et al., 1997): “Total number of days [of the last 90] in hospital for medical problems?” (Mean score = 0.55; SD = 4.97). The original Project MATCH study assessed the degree that patient characteristics determined the effectiveness of several alcohol use disorder treatments. Form 90, utilized in the MATCH study, captures a variety of substance use and treatment domains and has demonstrated high reliability and internal consistency (Tonigan et al., 1997). The Form 90 hospitalization item was significantly correlated with the World Health Organization Physical Quality of Life subscale in this sample (r = −.06, p < .05), an indicator of concurrent validity. Average hospitalization time at baseline was .24 (SD = 2.97).

Perceived Stress.

Perception of stress was captured using the Perceived Stress Scale (PSS; Cohen et al., 1983). Respondents rated their response to questions along a 5-point Likert Scale (1 = never; 5 = very often). Sample items included “How often have you felt difficulties were piling up so high that you could not overcome them?” and “How often have you felt confident about your ability to handle your personal problems?” The four items on the stress scale were averaged, and for this sample, α = .75 (Mean score = 2.30, SD = 0.75). Average reported stress at baseline was 2.49 (SD = 0.74).

House Employment Rate.

The rate of employment for an individual’s house was calculated by dividing the number of employed in each house by the total number of participants at that wave. The rate was then imputed for each resident, based on their wave of participation. For example, since rate of employment for house 10 at wave 1 was 71.4%, every participant in house received a score of 0.714 on that wave (Mean score = .89, SD = .16).

Average House Length of Stay.

The average length of stay of residents within each house was calculated by adding the total length of stay for the house at each wave and dividing it by the number of residents in the house at that wave. The log-transformed length of stay variable was used to calculate this variable (Mean score = 2.01, SD = .79).

Statistical analysis

Descriptive statistics were conducted to investigate socio-demographic and health-related variables. There were a large number of zero-count observations regarding ASI Medical Status (89.7% of the observations) and hospitalization (97.3% of the observations). Negative binomial modeling was selected to handle the presence of over-dispersed count data in these outcome variables.

To account for the nested structure of the data (time points nested within individuals), two-level negative binomial models were fitted in R (R Core Team, 2020) with the glmmTMB R package (Brooks et al., 2017). Main predictors of interest included time (T0-T6), perceived stress, and dichotomized employment status (1 = employed; 0 = not in employment). The model included age in years as a covariate and participant was included as a random effect to account for repeated measures. Separate models were estimated for each outcome variable (ASI Medical Status score and last 90 days reporting hospitalization).

Results

Demographics

The final sample (N = 494) was 53.9% male. The ethnicity of the sample was 77.4% Caucasian; 22.6% reported other ethnicities. The average age of participants was 37.7 years (range: 18 - 82 years). 47.2% of the sample reported possessing a high school diploma or general education development (GED) certificate. 38.2% reported attending ‘some college’ and 12.0% reported possessing a college degree or greater. 2.5% of the sample reported possessing a training or technical degree.

ASI Medical Status

Individual-Level Economic Stability & Covariates.

Length of stay in the housewas not associated with reported medical issues (as derived from their ASI Medical Status score;β = 0.001, SE = 0.054, p > .05). Employment resources (i.e., wages, transportation access) were not associated with health (β = 0.034, SE = 0.023, p > .05). However, dummy-coded employment status was significantly associated with ASI Medical Status score, with the employed reporting less medical issues scores as compared to unemployed residents (β = −0.082, SE = 0.226, p < .001). Stress was significantly associated with ASI Medical Status; reporting higher stress was indicative of a more medical issues as compared to reporting lower stress (β = 0.029, SE = 0.008, p < .001). Agewas not significantly associated with Medical Status, but the relationship approached significance (β = 0.001, SE = 0.000, p = .055). .

House-Level Economic Stability.

The average employment rate an individual resided in was significantly associated with reporting less self-reported medical problems (β = −0.155, SE = 0.037, p < .001). The average length of stay in an individual’s residence was not significantly associated with ASI Medical Status (β = −.003, SE = 0.008, p > .05).

Experiences of Hospitalization in the Last 90 Days

Individual-Level Economic Stability & Covariates.

The number of days spent in the hospital in the last 30 days was not significantly associated with an individual’s length of stay at their OH (β = −0.523, SE = 0.484, p > 0.05). Self-reported employment resources were not associated with hospitalization (β = 0.330, SE = 2.592, p > .05). Employment status was also not significantly associated with hospitalization (β = −1.112, SE = 2.592, p > 0.05). Hospitalization was significantly associated with stress, with a one-unit increase in perceived stress associated with an approximately 1.71 increase in days hospitalized (SE = 0.812, p < .05). Age, however, was not significantly associated with the number of days hospitalized (β = 0.010, SE = 0.060, p > 0.05).

House-Level Economic Stability.

The average employment rate an individual resided in was not associated with hospitalization (β = −0.040, SE = 2.888, p > .05). The average length of stay in an individual’s residence was not significantly associated with hospitalization (β = 0.461, SE = 0.691, p > .05).

Discussion

Our study examined the relationship between economic stability factors in recovery (i.e.,housing, employment resources, employment) and medical status and emergency care utilization among 494 residents of an Oxford House (OH) recovery home. Variables were examined at both the individual and the house-level in order to explore the potential influence of a recovery house environment on resident’s health, i.e., are the resources of a resident’s peers associated with that resident’s health. Findings from this study demonstrated that, at the individual level, employment and reduced stress were associated with associated with decreased medical problems. Reduced stress was also associated with increased recent hospitalization. House-level variables indicated that average house employment rate was associated with health. Specifically, the greater that one’s house reported employment (full or part time), the greater one’s health tended to be. Results of this study support a social determinants model of health. Economic stability, the resources essential to strengthening one’s well-being, can be just as essential for individuals in recovery. According to the results of the current study, employment status, but not other economic resources (i.e., transportation wages), was related to health for people in recovery.

Better attention to one’s health may be gained by accessing employment in recovery from substance and alcohol use, thought the directionality is currently unclear. . Maintenance of chronic health conditions may become challenging as an individual pursues substance use. Lacking financial resources, some substance users only find adequate healthcare through emergency departments (Hawk & D’Onofrio, 2018). Employment, in this case, becomes attractive for people in recovery; so-called “good jobs” – jobs which provide health insurance and other benefits – become desirable to lift one’s status and physical well-being (Sinakhone et al., 2017). In a meta-analysis specifically targeting studies exploring causal relationships between employment and health in the general population, employment was found to be predictive of better health (Hergenrather et al., 2015). However, research has also looked at the employment-health relationship the other way. According to Laudet (2012), having a comorbid physical and/or mental health condition in recovery reduced the chance of employment by half. From this perspective, health is a barrier to accessing employment. Those who are employed may be generally healthier, and therefore more able to meet their job’s requirements or may potentially face less discrimination as they reveal their health conditions.

Results of the current study seem to link the economic health of a house and the health of its residents. The health of a resident was influenced by the employment of their house, above and beyond their own employment status. Results add to a body of literature demonstrating the role of the recovery house environment to influence its residents (e.g., Beasley et al., 2013; Jason et al., 2022). In the current study, this relationship was extended to the health of residents as average house employment was found to be positively associated with residents’ health. Once again, the exact nature of this relationship is unclear. It is possible that areas with high employment generally have citizens with better health, as is demonstrated by research linking community economic depression to poor health outcomes (Broman et al., 1995). On the other hand, there may be some forms of support received from having residents around who are employed. Residents with chronic health conditions frequently report receiving support for their condition from their fellow residents, including encouragement to continue medical treatment (Contreras & Jason, 2013). More exploration is needed to understand the exact role of employment in the house, and its influence on resident health.

Factors related to instability, like low socioeconomic status, criminal involvement, and poverty-related stress burden individuals’ mental and physical health, (Santiago, 2011). Likewise, our study finds that stress is positively associated with medical problems and longer hospitalizations. Previous studies have shown that stress heavily burdens/impacts mental and physical health; reduced stress is associated with less somatic symptoms and alleviation of burnout/exhaustion and mental problems (Glise et al, 2014). Chronic stress is related to instability and occurs at higher rates among the financially strained, the unemployed, and those from low socio-economic status backgrounds (Baum et al., 1999; Sumner et al., 2016). Substance users face a variety of resource-related stressors as a result of their use, including unemployment, homelessness, and nutrition access (Henkel, 2011; National Coalition for the Homeless, 2017). This economic-related stress can compete against health as a priority for recovering people (Dong et al., 2018). Therefore, while stress reduction techniques encountered in treatment or therapy may help alleviate stress on an individual level, they will not address the environmental roots of stress.

While the exact nature of the relationships of the variables in this study are unclear due to its correlational nature, the study has potential implications for the care of recovering people. Results of this study join with literature demonstrating the importance of employment in recovery (e.g., Gomez et al., 2014, Gunn et al., 2008, Henkel, 2011, Ponitz et al., 2006). Not only can employment benefit one’s identity transformation and self-esteem in the transition out of substance use, there may also be a benefit to one’s physical well-being. More support is needed for people in recovery to access vocational resources, especially those with comorbid physical health conditions. New to the literature is the observed relationship between a recovery house’s employment rate and the health of its residents. Once again, the relationship is correlational, but these environments, as well as other treatment environments, should continue to push for resources that aid employment access. Residents with comorbid health conditions may require extra assistance to navigate the challenges associated with finding a job.

Limitations & Future Directions

A limitation of this study was that the directionality of variable relationships could not be determined. Specifically, we were unable to make causal inferences due to a lack of comparison group and randomized design which would have allowed for a clearer understanding of the role of recovering housing and stability in health. For example, while employment was associated with decreased medical problems and emergency care utilization, it could be that healthier individuals are more likely to seek and maintain employment. However, a previous meta-analysis of studies investigating causal relationships between employment and health concluded that employment was associated with better physical health (Hergenrather et al., 2015). The role of stress is also unclear. It is unknown how living in an OH impacts stress, or what factors allow for the alleviation of stress during SUD recovery. Changes in health could be considered a natural consequence of time. However, age was controlled in our models to parse out the specific effects of time in the house. Further research is needed to directly compare residents of Ohs with residents of other abstinent-supportive living environments (i.e., SLHs), and individuals who pursue usual aftercare environments (i.e., returning home, living in shelters) to determine the specific effects of OH. Another limitation was the inability to assess all resources or stability factors that might be accessible to residents in our sample. For example, employment status was divided into two categories: 1) unemployed or other forms of income (disability, student, military service, or retirement), and 2) part-time or full-time employment. What was unknown was the exact degree these individuals were receiving support from these other specified sources, which may have also helped support their health.

More exploration is needed to understand the specific supports recovery residents use to strengthen and maintain their health. While the current study supported a relationship between economic stability factors in OH residents and improved health, it is not clear what factors are influencing this relationship. Future investigations might track the relationship between gaining health insurance benefits and health among residents or look at medication adherence as a form of stabilization. The health-strengthening activities residents participate in might also be studied. It is unknown, for example, what the role of activities like exercise or meditation is in the house, whether these activities are encouraged within residential communities, and how they ultimately impact health. Understanding these factors could be valuable knowledge for OH communities and aftercare residential communities at large. By targeting key barriers to health, clinicians, researchers, and communities themselves could learn to create environments that support the health needs of an extremely vulnerable population.

Funding Acknowledgement:

This work was supported by the National Institute on Alcohol Abuse and Alcoholism [grant number AA022763]. The authors report there are no competing interests to declare.

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