Understanding Length of Stay in Recovery Homes

Abstract

Abstinence-specific social support within dyadic relationships is one of the best post-treatment prognostic indicators of recovery and is probably responsible for much of the effectiveness of self-help group participation. Acute treatment after-care in the form of sober-living environments—i.e., recovery houses—provides many opportunities for recovering individuals to acquire such support. However, like most recovery settings, recovery homes do not work for everyone. The current study was based on a longitudinal study of 602 Oxford House residents of which this study focused on 155 who at their first assessment, or baseline, had been in residence for 2 months or less, and we tracked them over time to see how long they remained in the recovery homes. For new residents who had only been in the recovery homes for 1/2 weeks, the ultimate rate of departure was about 40%. However, for residents with 2 weeks of time during the first assessment, the rate of departure fell to about 31%. By the time a resident had 6 weeks of residence in the recovery homes, the hazard for leaving the homes had dropped to about 25%. In conclusion, the hazard of leaving the home over time dropped off rapidly as time in residence accumulated. In addition, having an important person from the recovery home in one’s social network predicted lower hazard rates, given accumulated time in residence. The study indicates that the first few weeks in recovery homes are particularly vulnerable times for residents to leave prematurely, so more efforts are needed to better understand why some residents are able to maintain residency during these critical first weeks in these settings.

Substance use disorders (SUD) are among the most expensive health problems in the U.S. (Hedegaard et al., 2020). Among people aged 12 or older in 2021, 47.5% who drank alcohol and 14.3% who used an illicit drug; the percentage with a SUD is highest among young adults aged 18 to 25 (25.6%), followed by adults aged 26 or older (16.1%) (SAMHSA’s National Survey on Drug Use and Health, 2021). Moreover, even individuals who complete treatment have high relapse rates, with some end up homeless or in jail, thus adding to a growing humanitarian and public health crisis for communities (Jason, Wiedbusch, Bobak, & Taullahu, 2020). Effective methods for reducing the risk of relapse thus remains a major unmet treatment goal.

Lack of social support is a key barrier to sustaining recovery from addiction. Studies of both therapist-supervised and self-help groups have documented the benefits of such associations in maintaining abstinence at all stages of recovery (Islam et al., 2023). Nevertheless, knowledge is still limited regarding the process of social transition from active substance use to recovery, in which the recovering individual must in many cases build a recovery-supportive social network nearly from scratch (Hard et al., 2022).

Recovery homes might in part reduce relapse by supporting changes in the social context. For example, follow-up stays in supportive, cohesive post-treatment settings have been shown to encourage personal transformation and substantially reduce relapse rates following release from a few weeks of acute treatment (Mericle et al., 2019 ). Sober-living homes are currently the largest recovery-specific, community-based support options in the post-treatment period (Jason et al., 2020). Oxford Houses (OHs) are the largest single network of such recovery houses in the U.S., with over 25,000 individuals in some 3,000 residences at any given time. OHs are rented, single-family homes with a gender-segregated capacity for 6 to12 individuals.

Although recovery homes have a well-established track record in helping substance-dependent residents sustain recovery (Jason & Bobak, 2022). ), early dropout remains a problem. There is evidence that about 50% of residents leave the home in less than six months, even though six months of residence appears necessary to solidify a positive recovery trajectory (Jason et al., 2007). On the other hand, that 50% represents a significant opportunity, in that better understanding of premature dropout leading to even modest improvement in this rate would benefit a large absolute number of individuals.

Social support specifically oriented to recovery is known to be a key ingredient to successful recovery (e.g., Islam et al., 2023). Theoretical discussions of social support for recovery have typically addressed self-help groups such as Alcoholics Anonymous (e.g. Moos, 2007), but apply as well to other kinds of recovery-oriented social settings such as OH recovery homes, which maintain a similar philosophy of mutual support. Indeed, there is evidence that resource sharing or seeking advice from other more recovery-wise residents improves one’s many indicators of recovery (Jason, et al., 2021).

There is evidence that certain types of relationships are particularly strong predictors of successful recovery (Jason, Bobak, et al., 2022). For instance, access to instrumental support in the form of financial help as well as access to advice from more highly-recovered peers has been shown to lower the risk of premature departure from a recovery home (Jason, Lynch, Bobak, Light, & Doogan, 2022). However, there is a critical period during which a newly-recovering individual must be able to form such positive, supportive relationships. For recovery home residents, after moving into their new home, if things go well, the newcomer begins a process of social integration into the house social system consisting of these positive, supportive relationships. However, in many cases, things do not go well; if the newcomer has difficulty following the rules and norms associated with house residents, he or she may be sanctioned, and a process of mutual rejection ensues. Because extant studies have not focused on this period, little is known about why some individuals integrate well, while others do not. Our study examined early stays in recovery homes. We had two hypotheses: 1). the hazard of leaving the homes would be greatest during the first few weeks of residence in recovery homes, and 2). having an important person in a social network would lead to longer periods of residency in recovery homes.

Method

Settings

The study was conducted in a set of self-run OHs, 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.

Traditional recovery homes are low-cost, community-based residential programs for people with substance use disorders. Typically, residents of traditional recovery homes have no restrictions on their length of stay but are required to abstain from substance misuse and pay a modest rent to the recovery homeowner. In these settings, the staff and/or owner determine who can enter the recovery homes and whether residents need to leave if they relapse or violate house rules. In contrast with traditional recovery homes, OH comprise a distinct model of recovery housing, as there is no professional staff associated with OHs. The central difference between the two recovery home models is that TRHs employ house managers (paid staff) to run house meetings, enforce rules, make decisions regarding eviction, collect weekly rent, and oversee the general operation of the houses. These TRH criteria might add supportive factors that would aid in the oversight of vaccination in their facilities, which are not available in OHs. The OHs are either for males or females, and costs are reasonable, at about $110 to $150 per week. Residents are encouraged to seek treatments for different disorders outside the house, such as medication assisted therapies, and are required to regularly attend self-help meetings of their choice. If residents use alcohol or any illegal substances, they are required to leave these recovery houses. However, once they have entered treatment and ceased using, they can request moving back into the recovery home.

Data were collected from OHs 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. Once a house was recruited, all residents of those houses were invited to participate in the study by one of three recruiters who visited houses to explain the study to possible participants. This longitudinal study was designed to collect information from participating house members every four months over a 2-year period. 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). More details of this recruitment process are available elsewhere (Jason et al., 2022, 2023).

Participants

Participants were recruited and interviewed by field research staff in face-to-face meetings. Participants completed measures (see Jason et al., 2022, 2023). They were compensated $20 for completing their assessment. All participants provided written consent after being given an explanation of the study by one of the three recruiters. Permissions for this study were granted by the DePaul University Institutional Review Board.

Residents of participating homes were able to enter the study at any point during 2 years, and data were collected every 4 months over 2 years. For each wave of data collection, participants were classified as either evicted from the OH for any reason, left voluntarily, or still in residence. With 6 survey waves, a maximum of 5 follow-up time intervals were available for observing and predicting potential exits. Of course, participants entering OHs after wave 1 had fewer follow up intervals available than those in the study at wave 1, but the analysis included all such participants having at least one follow-up interval—that is, at least one period after their first wave of study participation for which we could determine definitively whether they had exited from the house or not and the reason for the exit (whether they were evicted or not).

There were 714 residents of the OHs during this period of time, of which 666 (93%) agreed to participate in this investigation. Of the 666 who agreed to participate, 64 were excluded for the following reasons: 34 entered an OH for the first time at wave 7 and we were not able to determine whether they relapsed given that the last data collection occurred during wave 7; 15 were forced out because their OH closed before wave 7 and before they filled out a second survey; 15 were missing their reason for leaving and had only filled out 1 survey before leaving. The final study/analysis sample was 602 - (84%) of the population. However, this study focused on those 155 residents who were recruited to participate at wave 1 (their baseline) when they were in their first two months of residency in the OHs.

Resident demographic information included age, sex, race/ethnicity, education, employment and time in residence prior to joining the study. Race/ethnicity was broken down in to 4 categories, White, Black, Latinx, and all other. The analysis sample of 602 was 51% male, with a mean age of 37.0 years (SD = 10.5). Participants identified as White (78.8%), Black (8.5%), Latinx (10.0%), with all other ethnicities accounting for 2.7% (Asian American, Alaskan Native, American Indian, and Pacific Islander). Missing data on exits and predictors were minimal, never exceeding 5% of the 602 residents for any variable (more information on demographic data is available at Jason et al., 2022, 2023).

Measure

The Important People Inventory (IP, Clifford et al. 1992), a measure revised from the Important People and Activities Inventory (Clifford & Longabaugh 1991) was used to assess abstinence social support. Participants described important persons of their social network (within the past 6 months), rating them on a 5-point Likert scale that distinguished nonusers from substance users, and this resulted in computing a percentage of the four most important persons (dividing the number of persons identified as being abstinent or in recovery by the sum total of persons identified) consistent with previous investigations (Zywiak et al. 2002, 2009; Epstein et al. 2018). The internal reliability of the IP in the present study was good (Cronbach’s alpha = .79).

Statistical Analyses

All models were estimated using Mplus (version 8.421) with corrections to standard errors for correlation of subjects within OHs. Our regression approach to predictors and outcomes necessarily involves many individual models each with a large amount of primary and auxiliary output so to facilitate the processing of all the Mplus output. Mplus Automation (version 0.8.122) was used for running related sets of models and identifying relevant results

Results

The sample size was based on 155 subjects that had been in OH residence for 2 months or less during the first time we surveyed them. We could then examine what occurred with them over the next waves of data collection. We examined hazard models looking at prior time in residence as a predictor on top of the baseline hazard. The observed empirical hazard of involuntary exit after 1 wave (4 months) was 25%. In other words, about 25% were evicted before they could fill out a second survey about 4 months later. We then modeled prior time as a categorical predictor of the hazard of involuntary exit, either from 0–2 months, or more than 2 months. The model suggested that the newer arrivals (0–2 months) had an initial hazard rate of 29%, slightly higher than the overall initial hazard of 25% for the first 4 months.

Figure 1 is a plot of the fitted hazard using prior time as a continuous predictor, net of the baseline hazard. The baseline hazard has been fixed at the value for the first discrete time interval so prior time is modifying the hazard of an eviction in the first 4 months of observation for each subject. The prior times range from 0 to 81 months in our data set. We used a log transformation on prior time to reduce the extreme skewness. The plot, however, converts log units back to weeks, and shows the hazard from 0 to 25 weeks. For the residents with 1/2 week of prior time, the hazard is about 40%. For residents with 2 weeks of prior time, the hazard is about 31%. By the time a resident has 6 weeks of prior time, the hazard has dropped to about 25%. The hazard drops rapidly as prior time accumulates, even in the first discrete time interval (4 months) of observation. If one extends prior time, the hazard of leaving the OH keeps dropping slowly and at 81 months for the long timers the hazard is about 10%.

Figure 1.

Fitted hazard using prior time as a continuous predictor

To summarize, the hazard ratio for 1/2 week vs 6 weeks is 40%/25% = 1.6 meaning that there’s a 60% increase in risk of eviction for the brand new recruit compared to a veteran of 6 weeks in the house. The long timers seem to have a risk of about 10% meaning that there’s a 300% decrease in risk compared to the brand new recruit. When we tried to create a more fine-grained categorical variable with prior time to contrast 2 weeks or less vs more, the results were not significant, probably because only 41 residents had 2 weeks or less of prior time. When we set the cutoff at 4 weeks or less vs more, the number of residents with 4 or fewer accumulated weeks of residence increased to 123, and the results were significant. The 4 weeks or less group has an estimated hazard of 32% vs 19% for the more than 4 weeks group, which is a hazard ratio of 1.68 or an increase of 68% in the hazard of eviction. This is fairly similar to treating prior time as a continuous predictor and contrasting 1/2 weeks vs 6 weeks.

Besides the much smaller number of qualifying residents, the shorter accumulated-time cutoff of 2 or fewer weeks might have failed to show significance because other effects might be operating that could be somewhat protective, like a grace period or honeymoon effect where the newcomers get some slack in the first 2 weeks, or some time is required for residents to get to know the newcomer (for better or for worse), and vice versa. We note trend (but non-significant) indications that the peak hazard is between 2 and 4 weeks, not in the first 2 weeks. Future studies with more cases of brand-new residents should investigate this question further. But in the present data, the linear effect is the simplest, most parsimonious description of the hazard.

Finally, for those who had an important person that was living in their OH, these 52 residents had a significantly longer number of days living in OH (M = 309.0, SD= 277.2) than those 70 who did not ((M=199.7, SD = 170.6)(t(79) = −2.51, p = .01).

Discussion

Our study found that those who are relatively new entrants into OH recovery homes are at later risk of being evicted, but that risk of eviction does decrease over time. It does appear that having another resident who is a most important person does enhance length of stay in the recovery homes. The findings suggest that there may be a critical period during the first few weeks of residence in OHs where a newly-recovering individual is at risk of leaving and this hazard can be reduced positive, supportive relationships. Because extant studies have not focused on this period, little is known about why some individuals integrate well, while others do not, or about the specific challenges faced by new residents as they begin the process of social integration and are, or are not, able to acclimate to the norms and rules of the house.

Our study points to the important role of social connections in maintaining recovery in recovery homes and reduce risk of relapse and leaving the homes. Social support is known to be a key ingredient to successful recovery (Islam et al., 2022). Theoretical discussions of social support for recovery have typically addressed self-help groups such as Alcoholics Anonymous (Moos, 2007), but apply as well to other kinds of recovery-oriented social settings such as OH recovery homes, which maintain a similar philosophy of mutual support. While the effects of affiliation with a recovery group (e.g., becoming an OH resident) are well described by extant studies, the process of such affiliation is much less well-understood. Although Humphreys et al. (1994) noted several decades ago that “…the process by which persons become affiliated with mutual-help groups is a topic of interest,” very little empirical research has addressed this fundamental question, particularly during the period of greatest risk, which is during the first few weeks of residence in recovery homes. However, interest in this transitional process continues. For instance, the “Social Identity Model of Recovery” (SIMOR; Best et al., 2016) has identified identity transition as an essential ingredient of successful recovery, in which the “addict identity” operative during active substance dependence is replaced by a new “recovery identity.” The theoretical perspective explaining identity transition is expressed in terms of a change in the reference group(s). This theoretical perspective maintains that humans construct an identity (“who I am”) from the reflected image they receive from others in their social environment. The SIMOR model is based on a conception of recovery that includes not only abstinence but also changes in lifestyle and worldview that support abstinence in the recovering individual’s social settings. The focus on social identity explicitly directs attention to factors that support the individual’s progress in constructing this new identity. But this takes time to occur, and for those that leave the sober living home after a few weeks, it is less likely that these residents will be able to maintain their recovery. Best and colleagues (2016) go on to argue that social identity has long been recognized as being strongly linked to the lifestyles and worldviews of one’s social relationships, that is, of whatever reference groups one is part of. Although they identify transition to new reference others as the lynchpin of recovery, Best et al. (2016) nevertheless are only able to outline some possible predictors of this transition due to a lack of relevant empirical studies. In particular, we know virtually nothing about why this transition seems to be more effective for recovery home residents or what is the period of greatest risk to the resident.

Our findings suggest that some types of dyadic relationships may be especially important to remain in good standing with fellow residents. The recovery influence transmitted by specific fellow residents or the house culture may depend on the quality of a resident’s extra-house support network as well as other individual-level factors such as recovery status, and other important individual characteristics. It is possible that some residents are more skilled at developing recovery-supportive relationships than others, but little is known about what skills are involved and how they translate to improved outcomes. There is a need to better understand the types of relationships and relationship-building cultures that most facilitate recovery, under what circumstances, and for what classes of individuals. To observe these dynamics, there is a need to focus on the critical period during which a new resident must be able to form such positive, supportive relationships, and contribute to the house-wide recovery culture. For recovery home residents, this period appears to be within the first few weeks of moving in.

Those settings that are culturally supportive may provide more congruent experiences, such as welcoming the involvement of extended family members and the use of more culturally congruent communication styles, characterized by an emphasis on relationships, downplaying direct conflict in relationships in order to preserve harmony, and respect. They might be more able to access friendships, advice, and resources from other residents. Such environments may allow residents to experience a greater sense of comfort and affiliation or sense of community and may be more likely to abstain from alcohol, secure income employment, and reduce involvement with the criminal justice system.

There is a need to learn more about those factors that might have influenced decisions to stay or leave the house, and how these choices may relate to residents’ satisfaction with the house experience. Recovery homes are not closed social networks and many residents interact with peers on social media, family members, and others, which may influence residents’ beliefs and behaviors. It is of importance to explore whether remaining in recovery homes was influenced by being welcomed by residents, having reduced exposure to community violence, the ethnic/racial mixture of the recovery homes, micro-aggressions directed towards residents, what other housing choices were available, etc.

One limitation of our study is that our findings are based on a presumed linear effect of prior time and the data are quite sparse for really low values of prior time (i.e. the first few weeks to a month in residence). There also might be a difference between individuals who leave voluntarily or are evicted, and this needs to be explored in future data analysis. Also, because these homes are rented, finances/economic support could be a major consideration. Even if there are important people within the home, an inability to pay for services would be more likely to predict length of stay. Finally, we only have length of time in residence as our outcome variable, and other outcomes need to be assessed such as recovery processes as well as long term outcomes after leaving the recovery homes.

Our study did find that the first few weeks does appear to pose some risk for residents staying within an Oxford House. It is still unclear what the factors might be for staying a longer period of time. Clearly, having a person in the house that one considers a most important person does contribute to longer stays in these recover homes. There is much to be learned from a network perspective treats the process of forming new, recovery-supportive relationships as essentially dyadic, it also takes account of the pre-existing structure of relationships, which constrain both how these relationships change over time, and how new relationships with new individuals may form (Marin & Wellman, 2011). Thus, to understand the development of dyadic relationships over time among residents of recovery homes, it is necessary to consider how the existing structure at any given time may promote or inhibit changes in relevant relationships like friendship.