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. Author manuscript; available in PMC: 2025 Nov 1.
Published in final edited form as: Drug Alcohol Depend. 2024 Sep 26;264:112456. doi: 10.1016/j.drugalcdep.2024.112456

Buprenorphine Use Among Non-Hospital Residential Programs

Cynthia Nichols 1, Daniel Baslock 2, Margaret Lloyd Sieger 3
PMCID: PMC11527563  NIHMSID: NIHMS2027848  PMID: 39369475

Abstract

Background:

The purpose of this study is to investigate the use of buprenorphine within non-hospital residential programs. We hypothesize that programs offering long-term treatment will be less likely to accept or prescribe buprenorphine, but those that accept public insurance will demonstrate relative increased likelihood of buprenorphine availability.

Method:

This study analyzed data from the 2021 National Substance Use and Mental Health Services Survey. The analytic sample (n=3,654) included a subset of facilities that reported providing only substance use treatment, including three non-mutually exclusive service types: detox, short-term, and long-term. A logistic regression examined the association between buprenorphine availability and residential service type, holding constant characteristics associated with the outcome of interest. We then tested an interaction between public insurance and long-term service type on the outcome of interest.

Results:

While long-term service type was associated with reduced odds of buprenorphine availability (OR=.288, p < .05), programs that both offered long-term residential programs and accepted public health insurance had 3.5 higher odds of accepting or prescribing buprenorphine (OR=4.586, p<.01) compared to long-term programs without public insurance.

Implications:

Patients who require treatment of longer duration may face barriers to buprenorphine availability; however, public insurance acceptance may increase odds of availability of buprenorphine among long-term programs.

Keywords: Medications for opioid use disorder, residential treatment, opioid use disorder

Introduction

Opioid overdose is a major public health issue and a leading cause of accidental death. According to the Centers for Disease Control and Prevention (CDC) (2023) the age adjusted rate of overdose deaths increased by 14% from 2020 to 2021. Overdose deaths involving synthetic opioids, specifically fentanyl, rose over 22% in the same year (CDC, 2023). Presently, data suggests we are entering the fourth wave of the opioid epidemic with increases in overdose deaths involving stimulants and synthetic opioids (Ciccarone, 2021; Jenkins, 2021). These changes speak to the importance of accessible treatment options for individuals with opioid use disorders (OUD).

FDA approved medications for opioid use disorder (MOUD), delivered in conjunction with other recovery-oriented support services, constitute the gold standard of evidence-based treatment for OUD (Kampman & Jarvis, 2015; Mattick et al., 2009; Volkow et al., 2019). Use of MOUD has been shown to improve treatment retention, social functioning, and reduce non-prescribed opioid use, criminal justice involvement, infection disease transmission, and fatal overdose (Mancher et al., 2019; Stahler & Mennis, 2020; Volkow et al., 2019). MOUD comes in different forms that address varied neurochemical and neurobehavioral sequelae of OUD, such as craving and withdrawal symptoms. One of the most widely prescribed forms of MOUD is buprenorphine, a partial opioid receptor agonist that suppresses and reduces cravings. Particularly since the X-waiver elimination, benefits of buprenorphine compared to older MOUD options (i.e., methadone) include that it can be prescribed in any clinical setting. Buprenorphine is also associated with the lowest rates of opioid-related emergency department visits compared to other types of opioids (National Institute on Drug Abuse (NIDA), 2021). In 2022, over 16 million buprenorphine prescriptions were dispensed nationally (CDC, 2023b).

Although buprenorphine has demonstrated effectiveness, vulnerable transitions exist in an individual’s course of treatment. Unlike methadone, buprenorphine induction often requires the individual to discontinue opioid use and, inevitably, experience withdrawal symptoms, which may result in a relapse to illicit substances (Adams et al., 2021). Furthermore, risk of relapse increases following short-term inpatient treatment, immediately after MOUD initiation, and after MOUD discontinuation (Andersson et al., 2019; Sordo et al., 2017; Tkacz et al., 2012). Thus, long-term recovery supports are essential, especially for those using buprenorphine. Research has shown negative outcomes when long-term services are not provided, including fewer connections to recovery communities, and increased risk of relapse and readmission (Kelly & White, 2010; White, 2016).

In addition to programmatic duration, treatment setting is an important characteristic that shapes recovery outcomes. Buprenorphine is typically administered in outpatient settings; however, residential treatment programs remain a crucial support service providing care for individuals with OUD in all stages of recovery. Studies have shown residential programs are effective in improving substance use, mental health, and social outcomes (de Andrade et al., 2019), including for patients using MOUD (Stahler & Mennis, 2020). While the use of MOUD has shown to be effective in improving recovery outcomes, residential programs have been slower to adopt the use of MOUD for multiple reasons, including ongoing stigma of MOUD use, ideological differences, lack of insurance reimbursement, questionable standards of care, and limited treatment availability in rural areas (Huhn et al., 2020; O’Brien et al., 2022; Stopka et al., 2024; Volkow et al., 2019).

However, the relationship between MOUD, programmatic duration (i.e., long-versus short-term), and setting (i.e., residential versus outpatient) is complex, at least according to one earlier study. In the first paper to examine the association between MOUD use and treatment completion in both short- and long-term residential settings with a national sample, Stahler and Mennis (2020) observed that any MOUD use (buprenorphine, methadone, or naltrexone) was associated with a 34% increase in retention and 40% increase in likelihood of treatment completion in short-term residential programs (Stahler & Mennis, 2020). Conversely, in long-term residential programs, MOUD prescriptions were associated with no effect on retention and a 26% reduced likelihood of treatment completion (Stahler & Mennis, 2020). These findings may point to challenges integrating this particular evidence-based intervention (EBI) in existing long-term residential service systems. A potential limitation of this study is that there is no way of determining actual MOUD use by clients using the TEDS-D (Treatment Episode Dataset-Discharge), only availability of MOUD or provision of treatment plans.

In an effort to understand the programmatic and contextual factors that contribute to successful integration of MOUD in residential treatment, one prior study explored the relationship between characteristics of residential programs and the availability and use of MOUD. Huhn et al. (2020) used data from the 2017 National Survey of Substance Abuse Treatment Services (N-SSATS), the 2017 Treatment Episode Data Set-Admissions (TEDS-A), state-level opioid overdose mortality rates, and state level information on Medicaid policy and coverage to investigate the availably and use of MOUD. It was found that 17.7% of admissions to residential programs used MOUD in states that expanded Medicaid, but only 1.9% of facilities offered MOUD in states that did not expand Medicaid (Huhn et al., 2020). In terms of types of MOUD offered, 29.8% offered extended-release naltrexone, 33.3% offered buprenorphine, and 2.1% methadone (Huhn et al., 2020). Only 1.3% of facilities offered all types of MOUD and 60% offered none (Huhn et al., 2020). Furthermore, programs that offered MOUD had lower odds of offering psychiatric medications, being licensed by state or hospital authority, or being accredited by a health organization. Lastly, residential programs that did not offer any MOUD had higher odds of accepting cash-only payments than those that offered MOUD (Huhn et al., 2020).

Huhn et al.’s (2020) study emphasizes how contextual factors—at both extrinsic and programmatic levels—can support or challenge the implementation of MOUD in residential programs. Its primary limitations for informing current policy and practice include that it did not differentiate type of MOUD and relied on data collected prior to the X-Waiver elimination, a former policy requiring an extensive training and registration process by providers wanting to prescribe MOUD. Additionally, the study investigated whether a facility offered MOUD (presumably measuring whether the facility employed a prescriber). Although an important question, the integration of MOUD in long-term residential treatment settings does not require a prescriber. Instead, it requires that the facility either prescribe directly or connect patients to community-based prescribers. This latter question provides a broader picture of the state of treatment for OUD. Lastly, this previous study did not address whether any of the hypothesized contextual factors differentially impacted MOUD uptake depending on type of residential services. Given Stahler and Mennis’ findings about different levels of MOUD integration for short-versus long-term residential treatment, research that explores interactions between context, especially public insurance, and MOUD is needed. Thus, the purpose of this current study is to build on Huhn et al.’s (2020) and Stahler and Mennis’ (2020) earlier work to investigate facility-level factors associated with availability of buprenorphine in non-hospital residential programs and to test an interaction term between public insurance and service type on the outcome of interest.

Theoretical Framework

To guide our inquiry, we turned to the consolidated framework for implementation research (CFIR), a frequently used framework in the implementation science field that arranges multiple constructs impacting implementation across five domains: (a) intervention characteristics, (b) outer setting, (c) inner setting, (d) characteristics of individuals, and (e) the implementation process (Damschroder & Hagedorn, 2011). CFIR has been used to assess barriers and facilitators to EBI implementation in general health and substance use treatment settings (Finlay et al., 2020; Fockele et al., 2021; Guerrero et al., 2020; Louie et al., 2021; Patel et al., 2022).

Based on existing literature, the most pertinent CFIR domains for the current study are inner and outer setting (Hoeppner et al., 2024; Huhn et al., 2020; O’Brien et al., 2022; Taylor et al., 2021). Outer setting factors refer to the economic, political and social context surrounding an intervention, such as external policies, networks, and communications (Damschroder et al., 2009). Inner setting factors refer to the characteristics of the organization, including work culture (Damschroder et al., 2009). Specific inner setting characteristics that increase buprenorphine uptake can include organizational culture supportive of MOUD (Damschroder et al., 2022). Types of services that may indicate a more accepting culture of buprenorphine use include other recovery support services, such as recovery coaches, self-help groups, mentoring support, and use of naloxone. Of note, literature has discussed ideological differences between MOUD use and the recovery community, including self-help groups; hence it is important to better understand the association between self-help offerings and MOUD availability in residential programs (Hoeppner et al., 2024). Research has shown that specific outer setting factors that increase EBI uptake, including MOUD, are increased funding, public accreditation, and insurance acceptance (Allen et al., 2019; Heffernan et al., 2023; Huhn et al., 2020; Wood et al., 2022).

While the other domains of intervention characteristics, characteristics of the individual, and process may also be important factors in MOUD integration, these domains were excluded from the following study due to lack of data in the sample used (Huhn et al., 2020).

Study Hypotheses

Given existing literature regarding differences in MOUD integration in long-term residential settings, we hypothesize that buprenorphine availability will be significantly reduced in residential programs that offer long-term services compared to programs that only offer short-term and detox services.

Regarding the impact of inner setting factors, we hypothesize that buprenorphine availability will be significantly increased in facilities with organizational cultures that support MOUD.

Regarding the impact of outer setting factors, we further hypothesize that buprenorphine availability will be significantly increased in facilities that accept public funding, accept public insurance, and/or are publicly accredited.

Finally, based on Huhn et al.’s (2020) and Stahler and Mennis’ (2020) previous studies, we hypothesize that accepting public insurance will moderate the relationship between long-term residential services and buprenorphine availability, such that buprenorphine availability will be significantly increased in long-term treatment facilities which accept public insurance compared to long-term treatment facilities which do not accept public insurance.

Methods

Sample

Data for this study come from the 2021 National Substance Use and Mental Health Services Survey (N-SUMHSS). The N-SUMHSS is a national survey of mental health and substance use treatment facilities in 50 states, seven territories, and D.C. The N-SUMHSS is a combination of its predecessors, the National Survey of Substance Abuse Treatment (N-SSATS) and National Mental Health Services Survey (N-MHSS) (Substance Abuse and Mental Health Services Administration (SAMHSA), 2023). Complete information on the N-SUMHSS, including research design and methods, can be found here: https://www.datafiles.samhsa.gov/dataset/national-substance-use-and-mental-health-services-survey-2021-n-sumhss-2021-ds0001.

Our study samples a subset of all respondents. Figure 1 depicts our sampling strategy. The survey was distributed to 32,371 facilities resulting in a 69.2% response rate. Information on non-responses was limited to state location. Response rates ranged from 47% to 80% across states. Of the facilities that responded to the survey, 17,752 (79.2% of respondents) reported providing substance use treatment (with or without mental health treatment). For our study, we further narrowed the sample with a subset of non-hospital residential facilities (including programs offering detoxification, short-term, and long-term services) which 1) reported providing substance use treatment only (n=3,654; 21% of facilities providing any substance use treatment), and 2) were not missing data on our dependent variable (n = 39; 0.2% of facilities providing any substance use treatment). This resulted in an analytic sample of n = 3,615 facilities. For our multivariable model, missingness on control variables was no higher than 2% and addressed with listwise deletion.

Figure 1: Analytic Sample Flow Chart.

Figure 1:

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Variables

Dependent Variables

Our primary dependent variable was buprenorphine availability (1=yes, 0 =no). The N-SUMHSS asked substance use facilities how the facility treats OUD. Respondents were able to mark all that applied among seven answer choices. Two choices operationalized our dependent variable: 1) this facility accepts clients using MOUD, but the medications originate from or are prescribed by another entity and/or 2) this facility utilizes prescribers of buprenorphine to treat OUD. MOUD was defined as the use of methadone, buprenorphine products, and/or naltrexone for OUD treatment. Of note, we chose to include the first criteria due to literature thus far suggesting that buprenorphine tends to be the most common type of MOUD accepted among programs accepting MOUD (Huhn et al., 2020; Presnall et al., 2022)

Facilities were coded as “yes, buprenorphine available” if they checked either or both of the above answer choices. Facilities were coded as “no, buprenorphine not available” if they did not check either of these answer choices.

Independent Variables

Three items were used to assess residential services type. The N-SUMHSS asked non-hospital residential facilities if they offered 1) residential detoxification (medical withdrawal, clinically managed residential detoxification, or social detoxification), 2) residential short-term treatment (clinically managed high-intensity residential treatment, typically 30 days or less), and/or 3) residential long-term treatment (clinically managed medium- or low-intensity residential treatment). Sixty percent of respondents offer more than one type of service setting and there were also no clear patterns of combinations of service offerings. We chose to dummy code these variables to understand the unique effect of each service type on likelihood of the outcome.

Control Variables

Several variables associated with the outcome of interest were included as controls. To account for the effect of outer setting factors, we held constant the facility’s status as a MOUD provider, funding sources, payment type, and certifying organizations. Facilities’ federal opioid treatment program (OTP) status was coded as 1=yes, 0=no. Whether a facility accepted federal, state, or local funds was coded as 1=yes, 0=no. Payment type was recoded as 1=accepts public insurance, 0=does not accept public insurance. Public insurance types included Medicare, Medicaid, State-financed health insurance plan other than Medicaid, Federal military insurance, SAMHSA funding/block grants, and IHS/Tribal/Urban (ITU) funds. Certifying organizations for each facility was recoded as 1=certified by public organizations and 0=not certified by public organizations. Public certifying organizations, included hospital licensing state substance use treatment agency, state mental health department, state department of health, SAMHSA certification for OTPs, and drug enforcement agency (DEA).

To account for inner setting factors, specifically organizational culture that supports MOUD, we controlled for harm reduction and other recovery support services offered at the facility. Four transitional services were included as dummy variables: Naloxone and overdose education, discharge planning, aftercare/continuing care, and outcome follow-up after discharge. Five recovery support services were also included as dummy variables: mentoring/peer support, self-help groups, assistance in locating housing, employment counseling, assistance with social services, and recovery coach.

Interaction Term

To test our fourth hypothesis, we created an interaction term by multiplying the dummy variable indicating long-term services by the dummy variable indicating public insurance acceptance.

Cluster Variable

To account for similarities in policies and practices across state, state was included in the model as a cluster variable.

Data Analysis

Univariate and bivariate statistics were used to describe the sample and compare programs with and without buprenorphine availability.

To test our question of interest, we developed a logistic regression to examine the factors associated with buprenorphine availability. Logistic regression is appropriate with cross-sectional data to measure associations between independent variables and a binary outcome variable. Model assumptions (i.e., multicollinearity) were examined using Spearman’s rho correlations. Correlations between all model variables were under ρ=.7 with the highest correlation being ρ=.48. Our logistic regression included all independent and control variables discussed above, and findings were reported with robust standard errors to account for clustering at the state level.

Results

Table 1 presents sample characteristics. Sixty percent of the sample offered more than one non-hospital residential service type, with 77% offering long-term residential, 65% offering short-term residential, and 39% offering detoxification. Over half of the programs accepted federal, state, or local funds (54%) and very few programs reported being a federal OTP (5%). Most programs accepted public insurance types (78%) and were accredited by public certifying organizations (89%). Of the four types of transitional services offered, the most common was discharge planning (99% of programs), followed by aftercare (86%), outcome follow-up after discharge (82%), and naloxone and overdose education (74%). Of the six types of recovery support services, the most offered were self-help groups, assistance in locating housing, and mentoring/peer support (83–84%). There were also high percentages of programs that provided assistance with accessing social programs (81%). The least common services provided were employment counseling (63%) and recovery coaching services (39%).

Table 1: Descriptive Characteristics of Non-Hospital Residential Programs that Do and Do Not Accept or Prescribe Buprenorphine (n=3,615).

 Total Sample (n = 3,615)
 Accepts or Prescribes Buprenorphine (n = 2,929)
 Does Not Accept or Prescribe Buprenorphine (n =686)
 Test Statistic
Mean or N SD or %a Mean or N SD or %a Mean or N SD or %a
Service type b
 Offers detoxification 1,345 37.2% 1,248 42.6% 97 14.1% Χ2 (2) = 193.25, p<.001
 Offers short-term residential 2,287 63.5% 1,990 67.9% 307 44.8% Χ2 (2) = 131.17, p<.001
 Offers long-term residential 2,786 77.1% 2,186 74.6% 600 87.5% Χ2 (2) = 51.91, p<.001
Federal OTP 187 5.2% 169 5.8% 18 2.6% Χ2 (2) = 53.60, p<.001
Accepts federal, state, or local funds 1,934 53.5% 1,653 56.44% 281 41.0% Χ2 (2) = 59.66, p<.001
Payment Type
 Public insurance 2,802 77.5% 2,421 82.7% 381 55.5% Χ2 (1) = 234.46, p<.001
 Other insurance/payment type 3,335 92.3% 2,708 92.5% 627 91.4% Χ2 (1) =0.87, p=.352
Certifying Organization
 Public certifying organization 3,224 89.2% 2,698 92.1% 526 76.7% Χ2 (1 = 137.31, p<.001
 Other certifying organization 2,453 67.9% 2,000 68.3% 453 66.0% Χ2 (1) = 1.29, p=.257
Transitional services
 Discharge planning 3,572 98.8% 2,903 99.1% 669 97.5% Χ2 (2) = 19.36, p<.001
Aftercare/continuing care 3,119 86.3% 2,570 87.7% 549 80.0% Χ2 (2) = 30.45, p<.001
Naloxone and overdose education 2,678 74.1% 2,433 83.1% 245 35.7% Χ2 (2) = 659.48, p<.001
Outcome follow-up after discharge 2,960 81.9% 2,468 84.3% 492 71.7% Χ2 (2) = 61.46, p<.001
Recovery support services
 Mentoring/peer support 2,014 83.4% 2,519 86.0% 495 72.2% Χ2 (2) = 78.33, p<.001
 Self-help groups 3,019 83.5% 2,567 87.6% 452 65.9% Χ2 (2) = 195.99, p<.001
 Assistance in locating housing 3,028 83.8% 2,597 88.7% 431 62.8% Χ2 (2) = 280.20, p<.001
 Employment counseling 2,268 62.7% 1,914 65.4% 354 51.6% Χ2 (2) = 45.19, p<.001
 Assistance with social services 2,909 80.5% 2,493 85.1% 352 62.4% Χ2 (2) = 216.28, p<.001
 Recovery coach 1,417 39.2% 1,230 42.0% 187 27.3% Χ2 (2) = 50.812, p<.001
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Notes. OTP=opioid treatment program. Bivariate Χ2 tests compared percentages across programs that accept or prescribe buprenorphine and those that do not. For example, cross tabulations found that significantly more facilities that accept or prescribe buprenorphine offer detoxification services (42.6%) compared to programs that do not accept or prescribe buprenorphine (14.1%).

a

Percentages are column percents.

b

60% of respondents offer more than one service type; therefore, service type is not mutually exclusive.

Table 1 presents column percentages and bivariate comparisons for all independent and control variables across both categories of the dependent variable. Because the number of buprenorphine-available facilities is larger than the number of non-available facilities, column percentages are presented to permit comparison of proportions. For example, we see that 42% of facilities that accept or prescribe buprenorphine offer detoxification, while only 14% of facilities that do not accept or prescribe buprenorphine offer detoxification. This comparison is significant at p < .001. Thus, significantly more buprenorphine-available facilities offer detoxification compared to non-available facilities. Significant bivariate associations were observed between all covariates and buprenorphine status. All factors were relatively more common in buprenorphine-available settings, except for long-term residential services. Eighty-eight percent of facilities that do not accept/prescribe buprenorphine offered long-term residential services compared to 75% of facilities that accept/prescribe buprenorphine (p < .001).

Factors Associated with Buprenorphine Availability

Table 2 presents two nested multivariable logistic regression models, the second of which includes the interaction term between long-term residential programs and public insurance. Since the interaction is significant, the main effects for the two composite variables should not be interpreted directly. Thus, we will focus on results from model two and interpretation of the interaction term.

Table 2: Residential Type Associated with Buprenorphine Availability (n =3,524).
Model 1
Model 2
OR 95% CI
 OR 95% CI
LL UL LL UL
Service type
 Offers detoxification 3.085*** 1.851 5.142 2.968*** 1.789 4.923
 Offers short term residential 1.545*** 1.188 2.009 1.583*** 1.214 2.065
 Offers long term residential .988 .538 1.814 .288* .097 .855
Outer setting factors
 Public insurance 2.455*** 1.468 4.107 .639 .819 2.256
 Public accreditation 1.418 .872 2.307 1.360 1.070 2.431
 Accepts federal, state, or local funds 1.598* .365 1.998 1.613* .332 2.080
 OTP .854 .365 1.998 .830 .255 1.600
Inner setting factors
 Discharge planning .786 .264 2.340 .819 .264 2.547
 Aftercare/Continuing care .756 .486 1.175 .783 .493 1.242
 Naloxone and overdose education 5.032*** 3.594 7.045 4.993*** 3.573 6.978
 Outcome follow-up after discharge .906 .604 1.360 .883 .581 1.342
 Mentoring/Peer support 1.220 .888 1.675 1.246 .912 1.703
 Self-help groups 1.682 .902 3.135 1.669 .911 3.056
 Assistance in locating housing 1.911*** 1.447 2.522 1.899*** 1.426 2.523
 Employment counseling 1.122 .867 1.451 1.099 .850 1.420
 Assistance with social services 1.529 .979 2.388 1.592 1.017 2.492
 Recovery coach .960 .704 1.308 .972 .716 1.319
Long term residential × Public insurance 4.586** 1.692 12.430
Constant
 .114
 .036
 .358
 .332
 .088
 1.255
Log pseudolikelihood −1224.152 −1215.378
Prob > chi2 0.000 0.000
Psuedo R2 0.274 0.279
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Notes. OTP=opioid treatment program. OR = odds ratio, CI = confidence interval, LL = lower limit, UL = upper limit,

Interpret OR values as follows: (OR-1)*100 = % change in odds of accepting or prescribing any buprenorphine for every unit increase in predictor variable, e.g., (.854)*100 = 14.6% decrease in odds of buprenorphine use for programs that are OTP.

*

p < .05

**

p < .01

***

p < .001

We observed that facilities with long-term residential and that accepted public insurance were three times more likely to accept/prescribe buprenorphine (OR = 4.586, 95% CI=1.692–12.430, p<.001) compared to long-term settings without public insurance. Programs that offered detox had a 197% increased odds of buprenorphine availability (OR = 2.968, 95% CI=1.789–4.923, p < .001) and short-term residential treatment had 58% higher odds of buprenorphine availability (OR=1.583, 95% CI=1.214–2.065, p<.001) compared to programs that did not offer detox or short-term residential, respectively.

Some control variables were also significantly associated with buprenorphine availability. Programs that accepted federal, state, or local funding had 61% higher odds of accepting or prescribing buprenorphine (OR=1.613, 95% CI=.332–2.080, p<.05) compared to programs that did not accept funding. Furthermore, programs that offered naloxone and overdose education had 400% higher odds of accepting or prescribing buprenorphine (OR=4.993, 95% CI=3.573–6.978, p<.001) compared to programs that did not offer naloxone or overdose education. Lastly, programs that offered assistance with locating housing had 90% higher odds of accepting or prescribing buprenorphine (OR=1.899, 95% CI=1.426–2.523, p<.001) compared to programs that did not offer these services.

Discussion

The use of buprenorphine is an effective intervention that can reduce cravings for opioids, increase retention to treatment, as well as reduce opioid use and mortality (Dong et al., 2021; D’Onofrio et al., 2015; Koehl et al., 2019; Mattick et al., 2009). As mentioned previously, buprenorphine is most effective when used in conjunction with other forms of treatment, including behavioral therapies and other supportive services, such as residential treatment (Koehl et al., 2019). Despite the many benefits of buprenorphine, it is still underutilized due to various factors including lack of prescribers, insurance restrictions, and the ongoing stigma associated with the use of MOUD (Volkow, 2018). Our results indicated 43% of detoxification, 68% of short-term programs, and 75% of long-term programs accepted or prescribed buprenorphine. While our results are assessing availability at the program level, meaning acceptance and prescribing, comparatively, Huhn et al. (2020) found 33% of residential programs offered buprenorphine and Stahler and Mennis (2020) found that 18% of short term and 17% of long-term program clients were receiving MOUD. As is the limitation with many prior studies on the topic, we were unable to measure MOUD use as the patient-level.

This study adds to the growing body of literature addressing the use of buprenorphine within residential programs and substantiates prior research revealing the underutilization of buprenorphine in long-term residential settings (Stahler & Mennis, 2020). Our findings reveal that buprenorphine was more likely to be accepted or prescribed among programs offering detoxification or short-term residential treatment (Olfson et al., 2021). Detoxification and short-term residential programs may be better able to accommodate the use of buprenorphine due to these types of medications often being used for detoxification and withdrawal management. While detoxification is usually short-term and not necessarily providing long-term treatment besides withdrawal management, detox is an important access point for buprenorphine being initiated and our findings highlight detox facilities being better equipped to provide and accept use of buprenorphine. According to the American Society for Addiction Medicine (ASAM), higher levels of care including detoxification and short-term residential programs are more likely to offer medication services, including MOUD (Polites et al., 2024). Additionally, research has shown that underutilization of buprenorphine can be related to a lack of buprenorphine prescribers (Madras et al., 2020); hence, long-term residential programs may lack the capabilities to offer buprenorphine without sufficient medical supports, such as prescribers within the residential program or the surrounding area (Andraka-Christou et al., 2023; Duncan et al., 2015). However, of importance, we also found long term residential programs that accepted public insurance had significantly and substantially increased odds of buprenorphine availability, suggesting the importance of public insurance acceptance as a facilitator to buprenorphine availability in long-term service settings.

Our findings suggest that public insurance may overcome specific barriers to buprenorphine availability in facilities with longer duration services. One such barrier is the type of buprenorphine covered by insurance. Research has shown that since 2017 more insurance types cover immediate-release buprenorphine, with fewer insurance types covering extended-release buprenorphine (injectable type), an often more desirable medication for some residential programs that prefer clients to not bring medications into the program (Andraka-Christou et al., 2023). Public insurance may facilitate access to extended-release buprenorphine specifically. Another barrier to buprenorphine availability in long-term treatment stems from the managed care axiom “medical necessity” (Dickson-Gomez et al., 2022). Although ASAM recommends four levels of care for OUD treatment, including long-term residential, long-term treatment may not be considered medically necessary, especially when compared to a lower resource option (i.e., a take home prescription like buprenorphine). Public insurance may consider a wider variety of services “medically necessary” compared to private insurance, and therefore facilitate programs with long-term services to make buprenorphine available to patients. Finally, we suspect that public regulations related to patient care embedded in Medicaid policy ensure the programs offering long-term services also afford their clients access to buprenorphine (and vice versa). For example, several states’ Medicaid programs require evidence that buprenorphine patients are referred for or receiving psychosocial treatment in conjunction with MOUD before reimbursing for the medication (SAMHSA, 2018).

As the CFIR framework suggests, other inner and outer setting factors can act as facilitators or barriers to implementation. For example, we found that programs receiving federal, state, or local funding had higher odds of accepting or prescribing buprenorphine. This corroborates earlier research that funding can facilitate implementation of EBIs by providing additional support to organizations’ infrastructure, including training (Bach-Mortensen et al., 2018; L. J. Damschroder et al., 2022; D’Ippolito et al., 2015). Literature has discussed the requirement of programs to accept buprenorphine if they use federal, state, or local funding; however, this can vary by state (Wood et al., 2022). Most funding provided by SAMHSA and federal programs requires organizations to implement EBI, such as MOUD (D’Ippolito et al., 2015).

Additionally, we found that programs accredited by public entities, such as the state department of public health, had higher odds of buprenorphine availability compared to other accrediting bodies (i.e., JCO, CARF). This contrasts with Huhn et al.’s (2020) study finding that facilities that offered MOUD had lower odds of being licensed by a state authority or being accredited by a health organization. Differences in findings between our study and Huhn et al.’s (2020) study likely reflect differences in sample characteristics between the two datasets, including that Huhn et al. used the 2017 N-SSATs and TEDS-A datasets. Significant policy activity expanding MOUD availability occurred between 2017 and 2021, likely influencing the relationship between state licensure and buprenorphine availability in residential programs. More broadly, recent studies demonstrate the importance of health department accreditation in fostering the use of evidence based decision making (EBDM) (Allen et al., 2019). Another study also found that accreditation led to improvements in capacity to provide high-quality treatment and improvements in relationships with partners (Heffernan et al., 2023). These results suggest residential programs that are accredited by a public entity (i.e., state mental health department, state department of public health) potentially have more resources and supports in place to support implementation of buprenorphine.

Our findings also reveal inner setting factors that are associated with increased odds of buprenorphine availability. Both transitional and recovery service factors increased availability of buprenorphine in our model, arguably reflecting an inner setting culture that is more supportive of patients using MOUD (Damschroder et al., 2022; Tracy & Wallace, 2016). Our findings further suggest programs that offer other supportive services, specifically assistance with locating housing, may be more inclusive of the use of buprenorphine. Although limitations of the data prevent us from understanding what these housing services entailed, this may reflect the harm reduction philosophy exemplified in Housing First (Kerman et al., 2021). Furthermore, while our results suggest an association between self-help groups and buprenorphine availability in residential programs, there has historically been tension between MOUD use and self-help groups; hence, this finding may be related to self-help groups being commonly offered in residential programs. Some self-help groups, such as Narcotics Anonymous (NA), have maintained a strict opposition to the use of MOUD in achieving abstinence with individuals sometimes not being allowed to count their days of sobriety if using MOUD or hold service positions in meetings (Hoeppner et al., 2024; Monico et al., 2015). Further research is needed to better understand the impact of self-help offerings and MOUD availability in residential programs.

Expanding further on the role of ideology in inner setting culture and buprenorphine availability, we found programs that accepted or prescribed buprenorphine to have significantly higher odds of offering naloxone and overdose education. The use of MOUD has historically conflicted with abstinence-based ideologies, which has been the traditional model of treatment especially in long-term residential settings (Lee & O’Malley, 2018). Harm reduction models, which aim to reduce the negative consequences of substance use tend to support the use of MOUD and other interventions, including the use of naloxone (Corrigan et al., 2019). Our results suggest that facilities offering overdose and naloxone education may be more likely to share similar favorable attitudes and perspectives about the use of buprenorphine, or vice versa. This may be related to education and encouragement from leadership to adopt harm reduction and MOUD interventions, as other studies have found staff trainings and education can increase willingness to provide buprenorphine treatment (Fenstemaker et al., 2024; Netherland et al., 2009; Wyse et al., 2022). Findings from Finlay et al. (2020) further demonstrate the importance of leadership in the implementation of buprenorphine in residential programs. They note that supportive leadership can facilitate the implementation of buprenorphine, but only if that support is consistent, as inconsistent support was found in their study to be an implementation barrier (Finlay et al., 2020)

While the CFIR framework is helpful in guiding our understanding of what types of modifiable factors are useful in implementation of interventions, it does not specify which factors are the most important in the facilitation of implementation. We found that public insurance acceptance in long-term settings and offering naloxone and overdose education had the largest effects on buprenorphine availability. This suggests that more federal and state resources should be devoted to increasing public insurance acceptance among residential program settings, particularly long-term programs, and creating a supportive environment towards harm reductions strategies, such as the use of naloxone, in treatment programs. Furthermore, we posit that increasing Medicaid funding and public accreditation standards are policy changes that are likely to impact buprenorphine practices even if inner setting factors related to ideology about the use of MOUD are slower to progress.

Limitations

Our study is limited in several ways. As such, results must be interpreted cautiously. First, this is cross-sectional data and we do not know details about how long buprenorphine was available and if it continued to be available following survey collection. Some of the questions were unclear about what the specific practice was involved regarding the availability of buprenorphine. We also cannot understand causal relationships or presume these factors are contributing to an organizational culture that accepts buprenorphine. We could not account for all confounding factors potentially impacting buprenorphine availability. There is selection bias, as the survey was sent electronically, and participation was voluntarily. We have no way of knowing whether any of these providers that reported accepting methadone, buprenorphine, or naltrexone from other prescribers allow buprenorphine. That said, we know from earlier studies that buprenorphine is the most accepted type of MOUD. If a program accepts clients using MOUD, they very likely accepting buprenorphine. Lastly, we have no way of determining offering or uptake of buprenorphine among residential clients and were only able to assess availability as reported by responses on the N-SUMHSS.

Conclusion

Ensuring continuity in care for patients using MOUD is critical to sustained positive outcomes. Public insurance acceptance among long-term residential programs is associated with increased buprenorphine availability. While the direction of effect cannot be clarified with the current research design, our results do suggest that public insurance is implicated in the availability of buprenorphine in long-term treatment. Efforts to ensure that long-term treatment facilities accept public insurance may improve buprenorphine availability. Similarly, Naloxone education in residential programs is also associated with increased buprenorphine availability; thus, additional efforts to expand naloxone and overdose education may allow for more supportive environments of buprenorphine use. Future research is needed to establish causality.

Highlights.

  • Public insurance acceptance among long-term residential programs increases buprenorphine availability.

  • More residential programs with buprenorphine availability offer detox and short-term services.

  • Residential programs offering naloxone and overdose education have increased odds of buprenorphine availability.

  • Efforts to expand public insurance and naloxone/overdose education in long-term residential programs may improve buprenorphine availability.

Funding Information:

Funding for the research and manuscript writing was also provided by NIDA (T32 DA007233, Nichols; T32 DA007233, Baslock; K01 DA058060, Lloyd Sieger).

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declaration of Competing Interest

None

Ethical Approval: This article contains research using administrative data.

Informed Consent: N/A

References

  1. Adams KK, Machnicz M, & Sobieraj DM (2021). Initiating buprenorphine to treat opioid use disorder without prerequisite withdrawal: A systematic review. Addiction Science & Clinical Practice, 16(1), 36. 10.1186/s13722-021-00244-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Allen P, Mazzucca S, Parks RG, Robinson M, Tabak RG, & Brownson R (2019). Local Health department accreditation is associated with organizational supports for evidence-based decision making. Frontiers in Public Health, 7, 374. 10.3389/fpubh.2019.00374 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andersson HW, Wenaas M, & Nordfjærn T (2019). Relapse after inpatient substance use treatment: A prospective cohort study among users of illicit substances. Addictive Behaviors, 90, 222–228. 10.1016/j.addbeh.2018.11.008 [DOI] [PubMed] [Google Scholar]
  4. Andraka-Christou B, Simon KI, Bradford WD, & Nguyen T (2023). Buprenorphine treatment for opioid use disorder: Comparison of insurance restrictions, 2017–21. Health Affairs, 42(5), 658–664. 10.1377/hlthaff.2022.01513 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bach-Mortensen AM, Lange BCL, & Montgomery P (2018). Barriers and facilitators to implementing evidence-based interventions among third sector organisations: A systematic review. Implementation Science, 13(1), 103. 10.1186/s13012-018-0789-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Centers for Disease Control and Prevention (CDC). (2023a, August 22). Drug overdose deaths | Drug overdose | CDC injury center. Centers for Disease Control and Prevention. https://www.cdc.gov/drugoverdose/deaths/index.html [Google Scholar]
  7. Centers for Disease Control and Prevention (CDC). (2023b, December 12). United States Dispensing rate maps | Drug overdose | CDC injury center. https://www.cdc.gov/drugoverdose/rxrate-maps/index.html [Google Scholar]
  8. Ciccarone D (2021). The rise of illicit fentanyls, stimulants and the fourth wave of the opioid overdose crisis. Current Opinion in Psychiatry, 34(4), 344–350. 10.1097/YCO.0000000000000717 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Corrigan PW, Qin S, Davidson L, Schomerus G, Shuman V, & Smelson D (2019). How does the public understand recovery from severe mental illness versus substance use disorder? Psychiatric Rehabilitation Journal, 42(4), 341–349. 10.1037/prj0000380 [DOI] [PubMed] [Google Scholar]
  10. Damschroder L, & Hagedorn H (2011). A Guiding Framework and Approach for Implementation research in substance use disorders treatment. Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors, 25, 194–205. 10.1037/a0022284 [DOI] [PubMed] [Google Scholar]
  11. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, & Lowery JC (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4(1), 50. 10.1186/1748-5908-4-50 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Damschroder LJ, Reardon CM, Widerquist MAO, & Lowery J (2022). The updated Consolidated Framework for Implementation Research based on user feedback. Implementation Science, 17(1), 75. 10.1186/s13012-022-01245-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. de Andrade D, Elphinston RA, Quinn C, Allan J, & Hides L (2019). The effectiveness of residential treatment services for individuals with substance use disorders: A systematic review. Drug and Alcohol Dependence, 201, 227–235. 10.1016/j.drugalcdep.2019.03.031 [DOI] [PubMed] [Google Scholar]
  14. Dickson-Gomez J, Weeks M, Green D, Boutouis S, Galletly C, & Christenson E (2022). Insurance barriers to substance use disorder treatment after passage of mental health and addiction parity laws and the affordable care act: A qualitative analysis. Drug and Alcohol Dependence Reports, 3, 100051. 10.1016/j.dadr.2022.100051 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. D’Ippolito M, Lundgren L, Amodeo M, Beltrame C, Lim L, & Chassler D (2015). Addiction treatment staff perceptions of training as a facilitator or barrier to implementing evidence-based practices: A national qualitative research study. Substance Abuse, 36(1), 42–50. 10.1080/08897077.2013.849646 [DOI] [PubMed] [Google Scholar]
  16. Dong KA, Lavergne KJ, Salvalaggio G, Weber SM, Xue CJ, Kestler A, Kaczorowski J, Orkin AM, Pugh A, & Hyshka E (2021). Emergency physician perspectives on initiating buprenorphine/naloxone in the emergency department: A qualitative study. Journal of the American College of Emergency Physicians Open, 2(2), e12409. 10.1002/emp2.12409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. D’Onofrio G, O’Connor PG, Pantalon MV, Chawarski MC, Busch SH, Owens PH, Bernstein SL, & Fiellin DA (2015). Emergency department–initiated buprenorphine/naloxone treatment for opioid dependence: A Randomized clinical trial. JAMA : The Journal of the American Medical Association, 313(16), 1636–1644. 10.1001/jama.2015.3474 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Duncan LG, Mendoza S, & Hansen H (2015). Buprenorphine maintenance for opioid dependence in public sector healthcare: Benefits and barriers. Journal of Addiction Medicine and Therapeutic Science, 1(2), 31–36. 10.17352/2455-3484.000008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fenstemaker C, Abrams EA, Obringer B, King K, Dhanani LY, & Franz B (2024). Primary care professionals’ perspectives on tailoring buprenorphine training for rural practice. The Journal of Rural Health, n/a(n/a). 10.1111/jrh.12832 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Finlay AK, Morse E, Stimmel M, Taylor E, Timko C, Harris AHS, Smelson D, Yu M, Blue-Howells J, & Binswanger IA (2020). Barriers to medications for opioid use disorder among veterans involved in the legal system: A qualitative study. Journal of General Internal Medicine, 35(9), 2529–2536. 10.1007/s11606-020-05944-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fockele CE, Duber HC, Finegood B, Morse SC, & Whiteside LK (2021). Improving transitions of care for patients initiated on buprenorphine for opioid use disorder from the emergency departments in King County, Washington. Journal of the American College of Emergency Physicians Open, 2(2), e12408. 10.1002/emp2.12408 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Guerrero E, Ober AJ, Howard DL, Khachikian T, Kong Y, van Deen WK, Valdez A, Trotzky-Sirr R, & Menchine M (2020). Organizational factors associated with practitioners’ support for treatment of opioid use disorder in the emergency department. Addictive Behaviors, 102, 106197. 10.1016/j.addbeh.2019.106197 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Heffernan M, Melnick M, Siegfried AL, & Papanikolaou M (2023). Benefits and impacts of public health accreditation for small local health departments. Journal of Public Health Management and Practice, 29(3), E108. 10.1097/PHH.0000000000001678 [DOI] [PubMed] [Google Scholar]
  24. Hoeppner BB, Simpson HV, Weerts C, Riggs MJ, Williamson AC, Finley-Abboud D, Hoffman LA, Rutherford PX, McCarthy P, Ojeda J, Mericle AA, Rao V, Bergman BG, Dankwah AB, & Kelly JF (2024). A nationwide survey study of recovery community centers supporting people in recovery from substance use disorder. Journal of Addiction Medicine, 18(3), 274. 10.1097/ADM.0000000000001285 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Huhn AS, Hobelmann JG, Strickland JC, Oyler GA, Bergeria CL, Umbricht A, & Dunn KE (2020). Differences in Availability and use of medications for opioid use disorder in residential treatment settings in the United States. JAMA Network Open, 3(2), e1920843. 10.1001/jamanetworkopen.2019.20843 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jenkins RA (2021). The fourth wave of the US opioid epidemic and its implications for the rural US: A federal perspective. Preventive Medicine, 152(Pt 2), 106541. 10.1016/j.ypmed.2021.106541 [DOI] [PubMed] [Google Scholar]
  27. Kampman K, & Jarvis M (2015). American Society of Addiction Medicine (ASAM) National practice guideline for the use of medications in the treatment of addiction involving opioid use. Journal of Addiction Medicine, 9(5), 358. 10.1097/ADM.0000000000000166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kelly JF, & White WL (2010). Addiction recovery management: Theory, research and practice. Springer Science & Business Media. [Google Scholar]
  29. Kerman N, Polillo A, Bardwell G, Gran-Ruaz S, Savage C, Felteau C, & Tsemberis S (2021). Harm reduction outcomes and practices in Housing First: A mixed-methods systematic review. Drug and Alcohol Dependence, 228, 109052. 10.1016/j.drugalcdep.2021.109052 [DOI] [PubMed] [Google Scholar]
  30. Koehl JL, Zimmerman DE, & Bridgeman PJ (2019). Medications for management of opioid use disorder. American Journal of Health-System Pharmacy, 76(15), 1097–1103. 10.1093/ajhp/zxz105 [DOI] [PubMed] [Google Scholar]
  31. Lee S, & O’Malley D (2018). Abstinence-only: Are You not working the program or is the program not working for you? Journal of Social Work Practice in the Addictions, 18, 289–304. 10.1080/1533256X.2018.1489259 [DOI] [Google Scholar]
  32. Louie E, Barrett EL, Baillie A, Haber P, & Morley KC (2021). A systematic review of evidence-based practice implementation in drug and alcohol settings: Applying the consolidated framework for implementation research framework. Implementation Science, 16(1), 22. 10.1186/s13012-021-01090-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Madras BK, Ahmad NJ, Wen J, & Sharfstein JS (2020). Improving access to evidence-based medical treatment for opioid use disorder: Strategies to address key barriers within the treatment system. NAM Perspectives, 2020, 10.31478/202004b. 10.31478/202004b [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mancher M, Leshner AI, & National Academies of Sciences, E. (2019). Medications for opioid use disorder save lives. National Academies Press. [PubMed] [Google Scholar]
  35. Mattick RP, Breen C, Kimber J, & Davoli M (2009). Methadone maintenance therapy versus no opioid replacement therapy for opioid dependence. The Cochrane Database of Systematic Reviews, 2009(3), CD002209. 10.1002/14651858.CD002209.pub2 [DOI] [PubMed] [Google Scholar]
  36. Monico LB, Gryczynski J, Mitchell SG, Schwartz RP, O’Grady KE, & Jaffe JH (2015). Buprenorphine treatment and 12-step meeting attendance: Conflicts, compatibilities, and patient outcomes. Journal of Substance Abuse Treatment, 57, 89–95. 10.1016/j.jsat.2015.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. National Institute on Drug Abuse (NIDA). (2021, April 13). What is the treatment need versus the diversion risk for opioid use disorder treatment? https://nida.nih.gov/publications/research-reports/medications-to-treat-opioid-addiction/what-treatment-need-versus-diversion-risk-opioid-use-disorder-treatment [Google Scholar]
  38. Netherland J, Botsko M, Egan JE, Saxon AJ, Cunningham CO, Finkelstein R, Gourevitch MN, Renner JA, Sohler N, Sullivan LE, Weiss L, & Fiellin DA (2009). Factors affecting willingness to provide buprenorphine treatment. Journal of Substance Abuse Treatment, 36(3), 244–251. 10.1016/j.jsat.2008.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. O’Brien PL, Stewart MT, Shields MC, White M, Dubenitz J, Dey J, & Mulvaney-Day N (2022). Residential treatment and medication treatment for opioid use disorder: The role of state Medicaid innovations in advancing the field. Drug and Alcohol Dependence Reports, 4, 100087. 10.1016/j.dadr.2022.100087 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Olfson M, Zhang V. (Shu), King M, & Mojtabai R (2021). Changes in buprenorphine treatment after Medicaid expansion. Psychiatric Services (Washington, D.C.), 72(6), 633–640. 10.1176/appi.ps.202000491 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Patel E, Solomon K, Saleem H, Saloner B, Pugh T, Hulsey E, & Leontsini E (2022). Implementation of buprenorphine initiation and warm handoff protocols in emergency departments: A qualitative study of Pennsylvania hospitals. Journal of Substance Abuse Treatment, 136, 108658. 10.1016/j.jsat.2021.108658 [DOI] [PubMed] [Google Scholar]
  42. Polites A, Sewick B, Florin J, & Trytek J (2024). ASAM dimensions and levels of care. Addictions Counseling Essentials. https://cod.pressbooks.pub/addictionscounseling/chapter/chapter-1/ [Google Scholar]
  43. Presnall NJ, Butler GC, & Grucza RA (2022). Consumer access to buprenorphine and methadone in certified community behavioral health centers: A secret shopper study. Journal of Substance Abuse Treatment, 139. 10.1016/j.jsat.2022.108788 [DOI] [PubMed] [Google Scholar]
  44. Sordo L, Barrio G, Bravo MJ, Indave BI, Degenhardt L, Wiessing L, Ferri M, & Pastor-Barriuso R (2017). Mortality risk during and after opioid substitution treatment: Systematic review and meta-analysis of cohort studies. BMJ, 357, j1550–j1550. 10.1136/bmj.j1550 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Stahler GJ, & Mennis J (2020). The effect of medications for opioid use disorder (MOUD) on residential treatment completion and retention in the US. Drug and Alcohol Dependence, 212, 108067. 10.1016/j.drugalcdep.2020.108067 [DOI] [PubMed] [Google Scholar]
  46. Stopka TJ, Estadt AT, Leichtling G, Schleicher JC, Mixson LS, Bresett J, Romo E, Dowd P, Walters SM, Young AM, Zule W, Friedmann PD, Go VF, Baker R, & Fredericksen RJ (2024). Barriers to opioid use disorder treatment among people who use drugs in the rural United States: A qualitative, multi-site study. Social Science & Medicine, 346, 116660. 10.1016/j.socscimed.2024.116660 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Substance Abuse and Mental Health Services Administration (SAMHSA). (2023). N-SUMHSS 2021: Data on substance use and mental health treatment facilities. https://store.samhsa.gov/sites/default/files/pep23-07-00-001.pdf [Google Scholar]
  48. Taylor JL, Johnson S, Cruz R, Gray JR, Schiff D, & Bagley SM (2021). Integrating harm reduction into outpatient opioid use disorder treatment settings. Journal of General Internal Medicine, 36(12), 3810–3819. 10.1007/s11606-021-06904-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Tkacz J, Severt J, Cacciola J, & Ruetsch C (2012). Compliance with buprenorphine medication-assisted treatment and relapse to opioid use. The American Journal on Addictions, 21(1), 55–62. 10.1111/j.1521-0391.2011.00186.x [DOI] [PubMed] [Google Scholar]
  50. Volkow ND (2018). Medications for opioid use disorder: Bridging the gap in care. The Lancet, 391(10118), 285–287. 10.1016/S0140-6736(17)32893-3 [DOI] [PubMed] [Google Scholar]
  51. Volkow ND, Jones EB, Einstein EB, & Wargo EM (2019). Prevention and treatment of opioid misuse and addiction: A review. JAMA Psychiatry, 76(2), 208–216. 10.1001/jamapsychiatry.2018.3126 [DOI] [PubMed] [Google Scholar]
  52. White W (2016). Recovery Management and recovery-oriented systems of care. Journal of Addictions Nursing, 27(2), 151–153. 10.1097/JAN.0000000000000127 [DOI] [PubMed] [Google Scholar]
  53. Wood CA, Duello A, Miles J, Lohmann B, Gochez-Kerr T, Richardson K, Anderson-Harper R, & Winograd RP (2022). Acceptance of medications for opioid use disorder in recovery housing programs in Missouri. Journal of Substance Abuse Treatment, 138, 108747. 10.1016/j.jsat.2022.108747 [DOI] [PubMed] [Google Scholar]
  54. Wyse JJ, Mackey K, Lovejoy TI, Kansagara D, Tuepker A, Gordon AJ, Todd Korthuis P, Herreid-O’Neill A, Williams B, & Morasco BJ (2022). Expanding access to medications for opioid use disorder through locally-initiated implementation. Addiction Science & Clinical Practice, 17(1), 32. 10.1186/s13722-022-00312-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

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