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. Author manuscript; available in PMC: 2022 Jun 30.
Published before final editing as: J Ethn Subst Abuse. 2020 Dec 30:1–17. doi: 10.1080/15332640.2020.1861497

Reducing Health Disparities among Black Individuals in the Post-Treatment Environment

Leonard A Jason 1, Mayra Guerrero 2, Ted Bobak 3, John M Light 4, Mike Stoolmiller 5
PMCID: PMC8649697  NIHMSID: NIHMS1757619  PMID: 33380259

Abstract

An important step in reducing health disparities among racial and ethnic minorities with substance use disorders involves identifying interventions that lead to successful recovery outcomes for this population. The current study evaluated outcomes of a community-based recovery support program for those with substance use disorders. Participants included 632 residents of recovery homes in three states in the US. A multi-item recovery factor was found to increase over time for these residents. However, rates of improvement among Black individuals were higher than for other racial/ethnic groups. Black Americans perhaps place a higher value on communal relationships relative to all other racial/ethnic groups, and by adopting such a communitarian perspective, they might be even more receptive to living in a house that values participation and involvement. The implications of these findings for health disparities research are discussed.

Keywords: Ethnic/Racial Health Disparities, Recovery Homes, Oxford Houses, Substance Use Disorders

Racial and ethnic minorities systematically experience more barriers to obtaining substance abuse services than their white counterparts (Wells et al., 2001). Such obstacles deter entry into treatment and treatment completion across treatment types (Acevedo et al., 2012; Davis & Ancis, 2012; Lappan, Brown, & Hendricks, 2020; Saloner & Le Cook, 2013). Even when racial and ethnic minorities enter into treatment, the care they typically receive is comparatively lower quality than that received by their White counterparts (Algeria et al., 2004). For example, Black and Latinx individuals report experiencing discrimination in mental health and substance use treatment settings (Mays, Jones, Delany-Brumsey, Coles, & Cochran, 2018), which are, in turn, associated with lower treatment satisfaction and early treatment exit (Mayset al., 2018).

While racial and ethnic differences across rates of substance use disorders (SUDs) are modest (SAMHSA, 2018), these groups have been disproportionally more impacted by the negative consequences of substance use compared to Whites (Bluthenthal, Jacobson, & Robinson, 2007). For example, Blacks compared to Whites after incarceration return to communities with a higher concentration of available illicit drugs (Knighton et al., 2018). Racial/ethnic minorities have higher burdens of substance use-related impairments and mortality rates (Montgomery, Burlew, Haeny, & Jones, 2020). Black men particularly have a higher chance of overdosing accidentally (Centers for Disease Control and Prevention, 2017a), contracting debilitating diseases (i.e., HIV and Hepatitis) (Centers for Disease Control and Prevention, 2017b), and committing suicide (Ashrafioun et al., 2017).

Given the health disparities that exist between Black and Latinx individuals versus White individuals with SUDs, one route to intervention is to identify and examine settings and resources that might provide these groups with community-based opportunities and resources that help lead to more equitable health outcomes. One such resource, recovery homes, currently constitute the largest available recovery-specific, community-based, post-treatment support option (Polcin et al., 2010). The largest network of recovery homes in the US are Oxford Houses. Self-run Oxford Houses have residents enforce rules, make decisions regarding eviction, collect weekly rent, and oversee the houses’ general operation (Jason, Olson, & Foli, 2008). There are over 2,000 Oxford Houses across the United States, comprising over 20,000 residents (Jason, Wiedbusch, Bobak, & Taullahu, 2020).

Previous studies have found positive recovery outcomes among Black and Latinx residents of Oxford House (Bishop, Jason, & Ferrari, 1998; Jason et al., 2013; Walt, Stevens, Jason, & Ferrari, 2012). Additionally, recovery improvements have been greater for Black individuals living in Oxford House compared to White individuals. For example, Blacks reside longer (Bishop et al., 1998) and relapse at lower rates than Non-Hispanic White residents (Harvey, 2014), perhaps because Black residents gain more resources than White residents when living in recovery homes (Brown, Davis, Jason, & Ferrari, 2006). Moreover, these resources need not be physical: Black women with more arrests and longer incarceration periods living in recovery homes report less psychological distress over resource loss compared to White women with similar criminal histories (Walt et al.,, 2012). We take these findings to suggest that Black women in recovery homes may be less affected by structural hardships than White women.

Beyond the social capital benefits attained from living in Oxford Houses, Black and Latinx recovery home residents experience positive employment and financial outcome in Oxford House.. More specifically, Black residents report significantly more employment in the past 30 days while living in a recovery home than White residents (Belyaev-Glantsman, Jason, & Ferrari, 2009). Jason et al. (2013) found higher employment income among Latinx individuals in Oxford Houses, compared to Latinx individuals not provided Oxford Houses.

While previous research has examined several recovery outcomes among Black and Latinx Oxford House residents, these outcomes have been examined separately rather than as a unified, holistic construct measuring different areas of functioning. A recent multi-level confirmatory factor analysis found that “recovery” can be treated as a single latent variable at both the individual and house-levels (Jason, Guerrero, Salomon-Amend, Stevens, Light, & Stoolmiller, 2020a). This unidimensional recovery latent factor was defined by the following indicators: 1) employment wages, 2) abstinence self-efficacy, 3) stress, 4) self-esteem, 5) social support, 6) hope, 7) psychological sense of community, and 8) quality of life. This recovery factor had face validity, internal and external validity, and reasonable reliability. Notably, the recovery factor successfully predicted relapse after residents left these homes. Specifically, relapse rates were higher for younger individuals who had involuntarily left the house, and who were less “recovered,” indicated by lower scores on the latent recovery factor (Jason, Guerrero, Salomon-Amend, Stevens, Light, & Stoolmiller, 2020b). The participant’s race/ethnicity was not a significant predictor of their baseline recovery factor and relapse in either study. However, these studies only examined the baseline recovery factor scores rather than changes in the recovery factor scores over time.

The current longitudinal study used a growth model to examine race/ethnic differences in changes in the recovery factor over time. The participants were 632 residents of recovery homes in three states of the United States. Our first exploratory question was whether or not recovery measured by a latent variable increased, was stable, or decreased over time. Once this was determined, we were interested in assessing the effects of resident-level variables such as gender, age, and ethnicity, as well as the effects of organizational variables such as house income levels. This is an exploratory study, as we were not certain about how each of these variables might affect outcomes over time.

Method

Settings

The study was conducted in a set of self-governed Oxford Houses, which are rented and gender-segregated, housing about 6 to 12 individuals in recovery. All residents are required to follow three primary rules; paying their fair share of the rent (which is usually from $100 to $125 per week), contributing to the maintenance of the home, and abstaining from using alcohol and other drugs.

Data were collected from Oxford Houses located in North Carolina, Texas, and Oregon. Member-elected house presidents were asked to introduce the study to residents by reading a project-provided script about the study; houses were accepted into the study if the house president and all or all but one member agreed to participate. The first thirteen consenting houses from each state were accepted, and three more houses were added for a total of 42. One house dropped out of the study, replaced by another house after wave 1 brings the total to 43 houses. However, only 42 houses had 2 or more waves of information available on their residents.

This longitudinal study was designed to collect information from participating house members every four months over a 2-year period for a total of 7 waves. This research design involved a) new residents entering the recovery homes and participating in the study after the initial wave 1 assessment, b) residents who declined to participate at wave 1 but joined the study at a later wave and c) residents that left over the course of the study. Details on how this design affected statistical models are presented below (see the analytic approach section). A small number of participants left the Oxford House and later rejoined either the same or another Oxford House. The analysis only used data from the first residency period for each resident.

Participants were recruited and interviewed by field research staff in face-to-face meetings. Participants were compensated $20 for completing each assessment. Permission was obtained through the DePaul University Institutional Review Board.

Measures

Resident demographic information included age, sex, and race/ethnicity. Race/ethnicity was broken down in to 4 categories, White (78.8%), Black (8.6%), Latinx (10.1%) and all other (2.5%). In preliminary analyses, using White as the reference group, the only significant contrast was for Blacks; the contrasts for Latinx and all others were negligible. Accordingly, ethnic contrasts were simplified to not-Black (reference group including White, Latinx, and all other) vs Black in all analyses. Prior time in Oxford House residence was assessed when participants first entered the study. Oxford Houses are gendered, and accordingly, gender was included as a house level predictor (see below).

The recovery factor (RF) was an estimated factor score based on a confirmatory factor analysis of all recovery capital indicators (see Jason et al., 2020a for details). Factor score determinacies across all missing data patterns were uniformly high, .88 to .90, indicating that the estimated factor score computed from the individual indicators was a reliable observed indicator of the underlying latent factor. This analysis includes all recovery factor scores available for each resident during their first residency period up to wave 6 of the study (comparable to that reported in Jason et al. 2020a,b). Of the 632 total residents, 382, 127, 43, 38, 16, and 26 residents had, respectively, 1, 2, 3, 4, 5, and 6 recovery factor scores. Since only 80 subjects had 4 or more repeated assessments, the power to detect individual differences and their predictors in curvilinear trajectories is low.

The recovery factor was calculated from the following measures as indicators:

Wages. The square root of the wages for the last 30 days was computed and used as a continuous variable.

Quality of Life. The World Health Organization Quality of Life Assessment-Brief (WHOQOL Group, 1998) is a 24-item questionnaire that assesses quality of life across four dimensions: social relationships, environment, physical, and psychosocial. This scale has been validated in substance using populations (Garcia-Rea & LePage, 2010). The subscales varied in their reliability (αs = .89 for social relationships, .84 for environment, .83 for physical, and .83 for psychological). The alpha for the measure for our sample was .89.

Self-efficacy. The Drug Taking Confidence Questionnaire (Sklar et al., 1999) is an 8-item survey measuring self-efficacy in terms of abstinence. Participants are asked to consider themselves in 8 theoretical high-risk situations and indicate how confident they are that they could resist the urge to use a substance given the theoretical circumstances. This measure for our sample has good reliability (α =.95).

Self-esteem. The Rosenberg’s Self-Esteem Scale (Rosenberg, 1965) was utilized to measure the participant’s positive and negative feelings about the self. SES is a widely used 10-item, global self-esteem scale measured on a 4-point Likert Scale ranging from “strongly agree” to “strongly disagree”. Examples of items include “I think I have a number of good qualities,” “I take a positive attitude towards myself,” and “I feel I do not have much to be proud of”. The internal reliability of the SES scale for our sample was .92.

Stress. The Perceived Stress Scale (Cohen et al., 1983) measured the degree to which situations in participants lives are appraised as stressful. PSS consists of 4-items measured on a 5-point Likert scale ranging from “never” to “very often.” Examples of items include “how often have you felt that you were unable to control the important things in your life?” and “how often have you felt difficulties were piling up so high that you could not overcome them?” The internal reliability of the perceived stress scale for our sample was .73.

Social support. The Interpersonal Support Evaluation List (Cohen & Wills, 1985; Cohen, Mermelstein et al., 1985) was utilized to measure three types of perceived social support (tangible, appraisal, and belonging). Tangible support refers to instrumental aid and monetary assistance; appraisal support refers to having someone to talk to about one’s problems; and belonging support refers to the availability of people one can do activities with. The Interpersonal Support Evaluation List consists of 12-items measured on a 4-point Likert scale ranging from definitely false to definitely true. The internal reliability of the support scale for our sample was .88.

Sense of Community. The Psychological Sense of Community (Jason et al., 2015) is a 9-item scale utilized to measure participant’s sense of community at their Oxford House. Examples of items include “This Oxford House in important to me” and “For me, this Oxford House is a good fit”. The three subscales are Entity, Membership, and Self, and for our sample, they have Cronbach alphas of .67, .92, and .91, respectively. The SOC scale was used as a whole measure (α = .91).

Hope. The State Hope Scale (Snyder et al., 1996) consists of 6 items that measure participants’ current state of hope. The Hope measure contains two sub-scales Agency (α = .94) and Pathways (α = .81). We included a 3-item subscale of hope that measures Environmental Context (Stevens et al., 2014) (α = .97). This 9-item hope scale was analyzed as a whole measure, and for our sample the α = .90.

Recovery Home Processes Questionnaire. The house presidents at study wave 1 filled out the Recovery Home Processes Questionnaire, providing data on 5 variables: the house new applicant acceptance rate (Out of every 10 applicants, how many does your house accept?), house savings [How would you describe the financial condition of the house: significant debt (>$5,000);some debt ($1–5,000); neither debt or savings; some savings ($1–5,000); significant savings(>$5,000)], house average resident poverty (on average, how many residents have difficulty meeting their monthly financial obligations?), house involvement in chapter activities [How involved is the house in chapter activities: Not at all involved, somewhat involved, much involved, a great deal involved]. A final house level variable was the sex of the house (female or male).

Analytic Approach

We specified a 2-level growth curve model for the 6 repeated recovery factor measures across 6 waves with residents (level 1) nested within recovery homes (level 2), using Mplus (version 8.3, Muthen & Muthen, 2018). For simplicity, we started with the standard assumptions for growth curve analysis, multivariate normality for latent variables and constant time-specific residual variance at levels 1 and 2. We used Bayesian estimation with the Mplus default non-informative priors to cope with the modest sample size at level 2. Significance for a parameter was judged based on whether the 95% credibility interval (highest posterior density) included zero.

Because the research design involved residents starting their participation at later waves or leaving before the final wave, or both, the number of repeated measures available for growth curve analysis varied from 1 to 6 (see settings subsection above for details). All residents with at least one recovery factor score were included in all models to minimize bias and maximize power under the standard assumption that missing recovery factor scores are missing at random (MAR) conditional on observed variables included in the model. In addition, residents varied considerably in how long they had resided in the house regardless of when they started the study, so all models also included prior time in residence (log-transformed) as a control variable.

Descriptive statistics and preliminary growth curve analyses without predictors suggested a curvilinear mean trajectory, so the growth curve model we used to evaluate predictor effects included random linear and quadratic trends at the resident level.1,2

When a level 1 predictor varied significantly across level 2 units (i.e., had a significant intra-class correlation), the level 2 effect of the level 1 predictor was included in the model but as a latent variable rather than the level 2 mean of the level 1 predictor, which helps eliminate any potential attenuation bias of the level 2 effects (Asparouhov & Muthen, 2019). Preliminary analyses indicated that of the 3 resident level predictors (race/ethnicity, age at wave 1, and prior time in residence at wave 1), only prior time in Oxford House residence and age at wave 1 varied significantly across homes, so we included both level 1 and level 2 effects for both of these level 1 predictors. We also considered 5 house level predictors (e.g., house savings), described in the Recovery Home Process measure, which were entered in the model as observed variables in the level 2 equation.3

Results

Descriptive Statistics

Table 1 shows descriptive statistics of residents by wave. There was a small but steady increase in the recovery factor means across each additional wave in residence until wave 6, when the mean drops slightly compared to wave 5 (the recovery factor was centered at 0 for wave 1).

Table 1.

Descriptive statistics by resident wave for resident level variables.

Variable Wave Mean Sd Skew Kurtosis Min Max N
Recovery Factor Score 1 0.00a 0.79 0.05 −0.20 −2.19 2.05 632
2 0.28 0.77 −0.34 −0.33 −1.79 2.09 240
3 0.39 0.72 −0.39 −0.07 −1.97 1.76 125
4 0.52 0.72 −0.17 −0.31 −1.21 2.09 79
5 0.64 0.75 −0.54 −0.33 −1.21 1.80 45
6 0.55 0.72 −0.81 0.25 −1.44 1.63 32
Log Prior Time in Residence at Wave 1 1 1.13 1.18 −0.08 0.34 −2.05 4.39 623
2 1.44 1.27 −0.28 0.32 −2.05 4.39 236
3 1.72 1.28 −0.38 0.47 −2.05 4.39 124
4 1.93 1.26 −0.29 0.33 −2.05 4.39 79
5 2.26 1.38 −0.80 1.02 −2.05 4.39 45
6 2.53 1.14 −0.32 −0.42 0.00 4.39 32
Age at Wave 1 1 37.12 10.56 0.58 −0.34 18 70 602
2 38.22 11.04 0.56 −0.41 18 70 228
3 39.95 11.16 0.29 −0.78 20 68 121
4 39.51 11.19 0.17 −1.03 20 63 78
5 40.33 10.79 0.08 −0.92 22 62 45
6 39.91 12.23 0.18 −1.29 22 62 32
Black 1 0.09 631
2 0.10 240
3 0.12 125
4 0.15 79
5 0.18 45
6 0.16 32
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Note:

a

The recovery factor was centered at 0 based on values for wave 1.

Single Predictor Models4

Table 2 shows results from the recovery factor single predictor models.5 None of the age effects were significant, and age was dropped from further consideration. Both prior time in residence (negative effect) and Black ethnicity (positive effect) were significant predictors of the initial slope. In addition, prior time in residence had significant positive effects on the recovery factor’s initial status at both resident and house levels.

Table 2.

Recovery Factor single predictor model results.

Resident Level House Level
Predictor Initial Status Initial Slope Quadratic Initial Status
Resident Log Prior Time In Residencea 0.130* −0.075* 0.015 0.515*
Resident Agea 0.001 0.002 −0.001 −0.010
Resident Blackc 0.027 0.275* −0.056
House Female −0.225*
House Acceptance Rateb −0.044
House Savingsb 0.194*
House Average Resident Povertyb −0.129*
Involvement in Chapter Activitiesb 0.107
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Notes:

a

= resident wave 1

b

= study wave 1

c

= Black does not have a house level effect because preliminary analyses indicated it did not vary significantly across houses (i.e., the intra-class correlation was small and non-significant).

*

= significant at p < .05, 95% credibility interval does not include 0.

Three of the five house level predictors, female gender (negative effect, female houses lower than male houses), house savings (positive effect, affluent houses with higher savings than poorer houses), and house average resident poverty (negative effect, houses serving poorer residents lower than houses serving more affluent residents) had significant effects on the house level initial recovery factor status. House level involvement in chapter activities and house level acceptance rates were dropped from further consideration.

Multi-Predictor Models6

Table 3 shows house- and resident-level predictors in the final multi-predictor model. House level recovery factor initial status was significantly related to house level prior time in residence and house level poverty. Resident level prior time in residence predicted resident level recovery factor initial status. In addition, resident level prior time in residence and Black were significant predictors of the resident level recovery factor initial slope.

Table 3.

Parameter estimates for regression effects for final 2-level model.

Level Outcome-Predictor Estimate 95% CI lo 95% CI hi Sig p < .05
House Initial Status On Prior Residence 0.484 0.242 0.766 *
House Initial Status On Poverty −0.098 −0.196 −0.001 *
Resident Initial Status On Black 0.020 −0.182 0.222
Resident Initial Status On Prior Residence 0.127 0.076 0.179 *
Resident Initial Slope On Black 0.278 0.029 0.536 *
Resident Initial Slope On Prior Residence −0.075 −0.145 −0.004 *
Resident Quadratic On Black −0.054 −0.117 0.010
Resident Quadratic On Prior Residence 0.013 −0.005 0.033
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The significant positive effect for Blacks on the resident level recovery factor initial slope indicates a greater initial increase in the recovery factor compared to other racial/ethnic groups. The effect is medium in size by most standard effect size metrics (e.g. Cohen, 1988). The standardized regression coefficient is 0.28 (not shown in Table 3), Black race uniquely accounts for about 8% of the initial slope variance (squared semi-partial correlation), and the mean initial slope for Blacks is about 1 initial slope standard deviation higher compared to the other racial/ethnic groups. Figure 1 shows the Black vs non-Black recovery factor score difference at each wave, corresponding to the gap at each wave between the two fitted growth curves (fitted growth curves not shown). The 95% confidence intervals for the differences indicate that the difference is significant (at p < .05) for waves 2, 3, and 4. There were no significant Black versus non-Black recovery factor score differences at waves 1, 5, and 6 (but only 8 and 5 Black residents had data at, respectively, waves 5 and 6, so sample sizes were too small for meaningful comparisons of those waves).

Figure 1.

Figure 1

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shows model fitted difference in the recovery factor scores for Black vs not-Black residents at each wave with 95% confidence intervals. Difference is significant (at p < .05) at waves 2, 3 and 4.

The significant negative effect of house level poverty on the house level recovery factor initial status indicates that residents in houses that serve less affluent residents tend to have lower initial starting points on the recovery factor at their initial assessment. The effect is medium in size with a standardized regression coefficient of −0.29 (not shown in Table 3), and house level poverty uniquely accounts for about 9% of the house initial status variance (squared semi-partial correlation). However, it is worth noting that despite the lower starting point, the model still implies that residents in these houses would still experience positive growth in the recovery factor.

Discussion

The study’s major finding was that while overall participants evidenced positive changes in their recovery scores, improvements were fastest for Black residents compared to all other racial/ethnic groups. This increase in the recovery factor protects residents from a premature exit from the Oxford House during the high-risk period when most negative exits occur. The findings provide a successful health disparities outcome, as the recovery homes were successful in enhancing a protective, social capital factor among two under-resourced groups, Black and low-income individuals. These are encouraging findings as recovery homes already provide positive environmental support to other under-resourced groups, such as the estimated 63% of residents who have experienced homelessness (Jason, Ferrari, Dvorchak, Groessl, & Malloy, 1997) due to past substance use, criminal involvement, or psychiatric comorbidity. The downturn for Black individuals after wave 4 is probably due to the very small absolute number of them who had as many as 5 or 6 waves of data. Regardless, the downturn after 4 waves is probably inconsequential to success and sobriety in the Oxford House because there’s very little relapse after spending 12 months in the home (Jason et al., 2008).

A key question that emerged from our findings is why those who are Black might have better access to the recovery capital available in recovery homes, compared to other racial and ethnic groups? Black Americans perhaps place a higher value on social relationships, and by adopting such a communitarian perspective, they might be even more receptive to living in a house that values participation and involvement, with each member having an equal value in decisions (Flynn et al., 2006; Molefe, 2017). Communalism is an important value orientation among African Americans, Caribbean Blacks, and those that emigrated from African countries (Schwartz et al., 2010; Wallace & Constantine, 2005). Communalism recognizes the interdependence of people and the importance of social relationships (Boykin et al., 1997). Additionally, Black Americans in our study may have experienced communal living styles previously, and may be more equipped to adapt to a new communal living style. Given that communalism emphasizes social relationships and placing the needs of the group over personal needs, communalism may help Black individuals adapt and thrive in the Oxford House setting.

Oxford Houses might therefore be highly compatible with Black Americans, thus facilitating their access to recovery capital. This is important given the recent calls for recovery support services to be culturally-congruent with the values, traditions, and belief systems of individuals, particularly among Black Americans (James & Jordan, 2018; SAMHSA, 2012).

Table 2 indicates that house level factors such as female houses, lower house savings, and higher house average resident poverty had negative effects on the initial house level recovery factor. However, Table 3 indicates that among these three variables, only the average initial house level of poverty was significantly related to lower average house levels of the recovery factor at the multi-predictor level. Despite the lower initial status, residents in these houses still show positive growth in their recovery factor scores over time.

One possible framework for understanding the study’s findings is to examine community adaptations of “recovery capital” (Lyons & Lurigio, 2010). Recovery capital is thought to enhance recovery by providing supportive intrapersonal and environmental resources needed to initiate and sustain recovery from SUD (White & Cloud, 2008). The intrapersonal component includes endowments such as self-efficacy, knowledge, personal health, education, hope, employment, financial assets, and transport. In contrast, the environmental component can be further subdivided into a social branch (supportive, pro-recovery relationships with family and significant others, peer-mentors, recovery, and support groups), and a community branch (treatment resources and support services, social acceptance and lack of stigma, continuum of care resources, non-SUD support services for mental and physical health). Community capital may also include attitudes and recovery processes consistent with the cultural norms of specific subgroups.

While recovery capital may explain how recovery homes protect residents and improve recovery rates, little is known about how recovery capital in recovery home environments is structured and accessed. Recovery homes may increase social capital by encouraging social bonds through friendships, lending money, and advice-seeking (Jason, Guerrero, Lynch, Stevens, Salomon-Amend, & Light, 2020). Social capital, in turn, could lead to improvements in resident recovery-related beliefs and behaviors such as abstinence related self-efficacy, coping with life stress, self-esteem, and hope that increase the chances of abstinence, social integration, and success.

Although an Oxford House is not a drug treatment program, it is therapeutic and contributes to recovery capital by providing sober housing as a self-financed, self-governing social structure. Ideally, an Oxford House should allow members to accumulate recovery capital based on their specific needs, and these include friendships among Oxford House residents (personal recovery capital), social support from Oxford House residents (social recovery capital), and the social-reintegration that results from Oxford Houses being located in mainstream communities (community and cultural recovery capital). Additionally, Oxford House can help residents build on their intrapersonal capital through their relationships with other residents (Jason et al., in press).

Our findings must be considered in light of several limitations. The size of our sample may not be representative of other Oxford Houses or other traditional recovery homes. Further, the sample is demographically homogenous, with the vast majority of participants being White. Future studies should use a more racially diverse sample. However, the large race effects found in our study despite the homogeneous sample is noteworthy. Additionally, the homes included in the study were located in three states, and it is unclear whether these findings can generalize to recovery homes in other regions of the US. It is also unclear what occurred for residents who exited the Oxford Houses in terms of whether they maintained their abstinence as well as retained employment.

Our findings have relevant research and policy implications for reducing health disparities among Black individuals with SUDs. The low rates of treatment entry among Black and Latinx populations with SUDs are partly due to socioeconomic status (Le Cook & Alegria, 2011; Daughter et al., 2018; Salomer & Le Cook, 2013), thus highlighting the need to identify low-cost and effective interventions that reduce any financial barriers to receiving addiction support services. Greater rates of involvement with the criminal justice system also contribute to the disparities in treatment utilization among racial/ethnic minorities compared to Whites (Le Cook et al. 2011; Salomer et al., 2013). Interventions that can be widely implemented and made accessible to populations most at-risk, i.e., individuals with criminal justice involvement, are paramount for reducing health disparities for those with SUDs. Oxford Houses are economical and proven effective, particularly among Black individuals, making Oxford House a viable community-based intervention that can help reduce the disparities among racial/ethnic minorities with SUDs, and ultimately lead to more equitable health outcomes.

Acknowledgments

The authors appreciate the financial support from the National Institute on Alcohol Abuse and Alcoholism (grant number AA022763). The authors appreciate the social network help of Ed Stevens and the editorial work of Meghan Salomon-Amend. We also acknowledge the help of several members of the Oxford House organization, and in particular Paul Molloy, Alex Snowden, Casey Longan, and Howard Wilkins.

Footnotes

Declaration of interest statement

The authors have no conflicts of interest.

1

Although resident level variation in quadratic trends appeared inconsequential (not unexpected given the limited number of residents with 4 or more recovery factor scores), we retained the random quadratic effect to avoid convergence problems (divergence of the independent simulations) using the Bayesian estimation algorithm. Preliminary analyses also indicated that house level differences in linear and quadratic trends were absent, most likely due to the modest number of recovery homes, so only a random intercept was included at the house level.

2

In a quadratic growth model, the choice of contrasts for the time metric affects the interpretation of both the intercept and the linear growth component. This can be easily visualized by imagining the level and slope one would observe while traversing from left to right all points on a U shaped (quadratic) curve. The slope starts out negative on the left leg but eventually flips and becomes positive on the right leg. We suspect that the recovery factor changes more quickly immediately following house entry, than the change that takes place after a longer period of residence, and this initial change is important for resident success. Accordingly, contrasts for resident wave, the time metric, were specified so that the intercept represented initial status at wave 1 and the linear component represented initial slope at wave 1.

3

Each potential predictor of growth was initially tested singly and included effects on all the growth components at level 1 (initial status, initial slope and quadratic trend) and level 2 (initial status). Predictors with no significant effects were not considered further. For the predictors with at least one significant effect, we tested a set of models that combined all the significant resident level predictors with one significant house level predictor at a time because of the modest number of recovery homes in the study.

4

No Predictor Model: The main findings from the 2-level growth curve model before any predictors were entered included the significant positive initial slope mean and significant negative quadratic mean for the recovery factor. Resident level variation was only substantial for the initial status and initial slope. House level variation was only substantial for the initial status. The random growth effects accounted for between 58–70% of the variance in the repeated measures of the recovery factor. The Bayesian posterior predictive p value was 0.48 indicating the 2-level model fit well (i.e., lack of fit was non-significant).

5

Two of the 3 resident-level predictors, age at wave 1 and prior time in residence at wave 1, had significant intra-class correlations and so had effects at both resident and house levels.

6

For house gender and house savings, the effects were not significant, mostly because of correlation with the competing predictor, house level prior time in residence. In other words, residents in female houses tended to have lower average prior time in residence compared to residents in male houses (r = −0.31, p = 0.14) and residents of more affluent houses had longer average prior time in residence compared to residents of poorer houses (r = 0.40 p = 0.06). House average resident poverty, however, was not as highly correlated with house level prior time in residence (r = −0.12, p = 0.56) and the effect on the house level recovery factor initial status was negative and significant (the correlations involve latent variables so the df is not available or relevant for computing p-values). Because the recovery factor includes monthly wages, this relation could be due primarily to lower wages. Accordingly, gender and house level savings were not considered further.

Contributor Information

Leonard A. Jason, DePaul University

Mayra Guerrero, DePaul University.

Ted Bobak, DePaul University.

John M. Light, Oregon Research Institute

Mike Stoolmiller, Oregon Research Institute.

References

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