Outcomes from the Medication Assisted Treatment Pilot Program for Adults With Opioid Use Disorders in Rural Colorado

doi:10.21203/rs.3.rs-769124/v1

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Research Article

Outcomes from the Medication Assisted Treatment Pilot Program for Adults With Opioid Use Disorders in Rural Colorado

https://doi.org/10.21203/rs.3.rs-769124/v1

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published 03 Jan, 2022

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Background. As Colorado ranked among the top nationally in non-medical use of opioids, a pilot medication for opioid use disorder (MOUD) program was developed to increase the number of NPs and PAs providing MOUD in order to bring this evidence based treatment 2 counties showing disproportionally high opioid overdose deaths. Over the first 18 months, the MOUD Pilot Program led to 15 new health care providers receiving MOUD waiver training and 1,005 patients receiving MOUD from the 3 participating organizations. Here we evaluate the impact of the pilot MOUD program implemented in 2 rural counties severely affected by the opioid crisis on patient centered clinical and functional outcomes.

Methods. Under state-funded law, three rural agencies submitted patient-level data at baseline (N = 1005) and after 6 months of treatment (N = 190, 25%) between December 2017 and January 2020. The Addiction Severity Index with McNemar-Bowker and t tests were used to measure program impact.

Results. Patients in treatment reported using less heroin (13.0 vs. 3.7 days), opioids (3.7 vs. 1.9 days), and alcohol (3.2 vs 0.7 days, all P < 0.01). Patients reported improved health (53.4% vs. 68.2%, P = 0.04), less days of disability (8.69 vs. 6.51, P = 0.02), symptoms (29.8% vs 21.3%), pain (67.5 % to 53.6), worry (45.3% vs 62.3%), anxiety (49.7% vs 23.2%), depression (54.1% vs 23.3%, all P < 0.02) after treatment.

Conclusions. This study shows decreased substance use, improved physical and mental health, and reduced symptoms after 6 months of MOUD. Although more research on retention and long-term effects is needed, data shows improved health outcomes after 6 months of MOUD. Lessons learned from implementing this pilot program informed program expansion into other rural areas in need to address some of Colorado’ major public health crises.

Health Policy

Psychiatry

Medication-Assisted Treatment for Opioid Use Disorder

Pain

Rural

Community Settings

Patient Centered Outcomes

Opioid use disorders (OUD) remain a national epidemic due to the rise in both illicit and prescribed opioid use, as well as opioid-related mortality and morbidity [1]. There were over 67,000 opioid overdose deaths in 2018 [2]. Opioid use can lead to many additional problems, including comorbid mental health issues, risk behaviors, and economic instability [3, 4], posing a huge burden for people with addictions, their families, and their communities.

While OUD impacts all demographic groups, it has more profound impacts on those who lack access to care. Rural and frontier counties have the highest overdose death rates, possibly due to service gaps. Although 25% of Coloradans reported using either prescription pain medications or non-prescribed opioids, only 33% had access to treatment and only 20% had services available in their county [2, 5]. This underscores the urgency of bringing OUD treatment to remote underserved areas.

Medication for Opioid Use Disorder (MOUD) is an evidence-based practice that combines medication and behavioral interventions. Despite demonstrated efficacy in survival and treatment retention, MOUD services are rarely available in rural areas, leading to inconsistent access to care [5, 6].

Nurses, as one of the largest groups of health service providers in the United States, are in a unique position to care for people with OUD in underserved rural areas [6]. After nurse practitioners became eligible in 2016 to prescribe opioid-based buprenorphine for the treatment of OUD, Colorado Senate Bill 17–074 was introduced as a community-based effort to fund a pilot program bringing Nurse Practitioner (NP) and Physician Assistant (PA)-led MOUD to rural Colorado communities. Between December 2017 and June 2019, the University of Colorado College of Nursing (CU Nursing) worked to engage rural clinics, train providers, and implement a MOUD pilot program in Pueblo and Routt counties. The program added 15 nurse practitioners and physician assistants to the MOUD workforce and served 1005 new patients during the first 18 months [7]. Although the legislation was designed primarily to increase access, CU Nursing and community stakeholders also wanted to know to what extent the program was helping patient’s lives, including mental and physical health, social functioning, employment, disability, and legal system involvement. Here we report the impact of participating in the MOUD program in one of three rural clinics for at least 6 months on a variety of patient-centered outcomes. Lessons learned from this pilot informed further development of MOUD services in rural Colorado.

Settings and Program Implementation

The pilot program was designed to implement MOUD at health care sites in two rural counties, Pueblo and Routt, which had OUD-related overdose and death rates higher than state and national averages. Three community clinics received funding between 2017 and 2019: a methadone clinic that added buprenorphine to its services, a community mental health center that expanded its existing MOUD services, and a startup clinic that provided MOUD as part of a multidisciplinary pain services [7]. Service delivery began on December 1, 2017.

Participants

The clinics in this study serve a racially and ethnically diverse patient population with a mix of health insurance and living experiences. As reported elsewhere [7], patients seeking MOUD services were over 18 years old and met OUD clinical criteria. Prior to starting MOUD, patients underwent a clinical interview by trained staff and consented to receive treatment and to have their clinical information aggregated as part of program evaluation. Patients could refuse providing some information without consequences to their treatment. All procedures were designed to comply with ethical standards for human subject’s studies, although this study was determined by the Colorado Multiple Institutional Review Board to be non-human subject research (protocol #19-2217).

Procedure and Instruments

As part of the program evaluation, MOUD service data were collected between 2017 and 2020. Process data were collected monthly from each of the program sites during the duration of the pilot and for up to 6 additional months for patient follow up. Aggregated agency-level data were analyzed based on monthly reports about services provided, successes, and barriers for implementation and patient retention, and used to answer process evaluation questions [7].

De-identified patient data collected at study entry from 1005 clients included self-reported demographics, medical history, and substance use information. Assessment of treatment outcomes was conducted 6 months after treatment initiation using the validated Addiction Severity Index (ASI), 5th edition [8] to capture past-month drug use, overall health, social functioning, and physical and psychological symptoms. Additional items from the ASI 6th edition [9] were used to evaluate coexisting medical and behavioral health conditions, pain, recent emergency department use or inpatient hospitalization, and other social determinants of health including employment and legal problems. Patients also completed the Patient Health Questionnaire (PHQ-9) to measure depressive symptoms [10] and the Generalized Anxiety Disorder scale (GAD-7) for anxiety [11].

Data were collected at each site by a case manager or intake worker and entered into the REDCap electronic data management system [12] hosted at the University of Colorado Anschutz campus. Aggregated, de-identified data were extracted for analysis and assessed for data integrity at the end of data collection by CU Nursing researchers. The patient-level REDCap data set had a high rate of missing data, which reflects the voluntary nature of the collection and the clinics’ focus on delivering services rather than on data collection, as well as patient loss to follow up.

Statistical Analysis

All statistical tests were conducted on de-identified aggregated data using SPSS version 26 (IBM Corporation). Demographic characteristics were summarized using descriptive statistics, and outcome variables were checked for normal distribution. For continuous variables, mean values and standard errors are presented. Percentages were tabulated for categorical data. Pre-Post changes in client-centered outcome variables were evaluated using McNemar-Bowker tests for nominal variables and t tests for interval-level variables. Wilcoxon rant sum tests were performed for confirmatory sensitivity analysis.

Patient Characteristics

Of 1005 inductions, 288 (28.7%) were still in treatment 6 months after the start of MOUD, and 177 had 6-month substance use data with various degrees of completeness by variable. Complete data on substance use on evaluable subjects (those who started treatment and completed both the baseline and follow-up assessments, max n = 169) were used for the MOUD effectiveness analysis. Baseline patient characteristics in this study are presented in Table 1. Overall, demographics were similar between patients who remained in treatment for at least 6 months (evaluable patients) and those lost to follow-up (non-evaluable, also comparable to the whole population) [7]. On average, evaluablepatients were adults 25 to 44 years old (61.6%), not married (78.9%), White (57.0%) or Hispanic (36.7%), had completed approximately 12.5 years of education (48.5% had completed high school), and had Medicaid insurance coverage (86.9%).

About half of the patients had been unemployed during the last 3 years (52.6%), with no differences between evaluable and non-evaluable patients. About a third of patients presented to the clinic based on a judicial system referral or were worried about legal problems (30%). About 1 in 3 patients reported having had some outpatient (32.0%) or inpatient detoxification (27.3%) treatment previously, and 2.37% had not attempted any other treatment methods in the past, with no differences between evaluable or non-evaluable patients. Most patients (83.1%) initially reported that their current decision to get treatment was self-prompted. However, compared to patients who started but were lost to follow up, evaluable patients were less likely to be court-ordered (14.1% vs 7.1%, P = 0.01) or to be on parole or probation (25.2% vs 11.1%, P = 0.01). All patients had a history of use opioids and/or heroin, and many also used other substances (32-38%); most were in poor to moderate health (80.2%) and suffered moderate to extreme pain (65%, see Figure 1).

Changes in Clinical Outcomes after MOUD treatment

Pre–post treatment changes on measures regarding substance use were assessed among the 169 evaluable patients with complete baseline and 6-month data on substance use (Figure 1). Those who were still in treatment reported less heroin use in the past month than at start, with an average of 13.0 vs 3.7 days in the last month, with 52.1% vs 20.4% of patients reporting any heroin use, and 37.1% vs 5.4% using heroin daily (Supplemental Table 1), all P < .01. They also reported less use of prescription opioids (22.3% vs 11.0% of patients reported any opioid use, with 11.7 % vs 3.5 using opioids daily with an average of 3.7 vs 1.9 days, P < .01 for all comparisons). After 6 months, patients also reported significantly less alcohol use (28.6% vs 13.1% of patients reported any drinking), with 4.40% of participants who drank daily at baseline vs 0.6% post treatment. Patients also reported drinking less days per month on average (3.2 vs 0.7 days, P < .001). Interestingly, use of cannabis increased significantly (19.1% to 30.2% of daily use, P < .02), although the percent of people reporting any usedid not change (49.1% vs 43.0%). On average the number of days used in the last month increased slightly (8.5 vs 10.5 days, P < .01), which could indicate a compensatory pain management strategy given that 41% of patients at baseline had indicated marijuana use for pain management. The sensitivity analysis for substance use (Supplemental Figure 1) yielded similar trends in terms of both significance and directionality. Patients reported less sedatives (2.6 vs 1.1 days, P < .01), but there were no differences in methamphetamine, barbiturate, or cocaine use after treatment.

Health changes after 6 months of treatment are shown in Figure 2. The percent of patients reporting moderate to severe pain or discomfort diminished significantly after treatment (67.5 % to 53.6 %, P < .01). Patients also showed significant drops in anxiety (49.7 % at baseline vs 23.2% post treatment showed moderate to high anxiety), based on GAD-7 average scores decreasing significantly after treatment (10.5 ± 0.5 vs 6.1 ± 0.4, P = .00), which corresponds to a shift in clinical interpretation from “moderate anxiety” to “mild anxiety.” Major depressive levels paralleled this trend (54.1% vs 23.3% of patients scored moderate to high depression, P < .01), with an average clinical shift from “moderate depression” to “mild depression” (scores: 10.8 ± 0.6 vs 6.2 ± 0.5, P < .01). Although the average days with symptoms did not change significantly (8.87 vs 6.28 days, P = .47), the frequency of symptoms slightly but significantly changed (daily: 29.8% vs 21.3%; no symptoms: 35.4% vs 44.8%; overall change P = .03). There were also significantly fewer days in which they felt unable to carry out normal activities because of physical or medical problems after treatment (8.68 vs 6.51 days, P = .02), and fewer patients reported having this issue for most parts of the month (26.9 % vs. 22.7%, P = .04) (Figure 2 and Supplemental Figure 2). Changes in scores were confirmed with sensitivity analyses (P < .01 for the nonparametric tests).

MOUD Treatment and Changes in Social Determinants of Health

Baseline data on factors associated with OUD, including employment, health care utilization, and legal system involvement, demonstrated that the program reached a patient population with substantial life problems related to OUD. After 6 months of MAT, patients had slightly but not significantly fewer emergency room visits in the prior month, P = .10 (Supplemental Figure 2). Likewise, there were no changes for the number of days in jail, P = .89, although those numbers were very low at the start of treatment, leaving limited room for improvement. The number of days patients worked slightly but not significantly increased (7.58 vs 8.53, P = .40).

Figure 3 shows patients’ perceptions of their health and social issues from baseline to 6 months after treatment start. After 6 months, patient concerns regarding their overall health increased after treatment, going from 45.3% to 62.3%, moderately to extremely concerned (P = .02); however, their perception of the importance of continuing treatment did not see a significant change (61.1% vs 68.1% reporting at least moderate importance, P = .12). On the other hand, patients seemed less concerned about work issues after treatment (overall 43.6% vs 29.0% were concerned, P = .06) or the need for work-related counseling (drop from 20.2 to 12.9% in moderately to highly concerned, P = .02). There were no significant changes in concerns about legal issues.

Retention in Care

Monthly reporting and community of practice debriefs revealed that despite outreach efforts (partnership with jails, hospitals, churches, local health care providers), stigma regarding substance use and associated mental health issues remains a major treatment barrier in rural areas and requires strategies to build support for treating patients. Retaining patients was also a major problem, despite education efforts to lower stigma, having an open door policy for returning patients, and using multi-modal treatment with behavioral health modalities.

There is an urgent need to expand access to OUD treatment in rural areas. This program provided access to MOUD treatment to 1005 patients in two rural counties. Our analysis showed that patients who were still in treatment after 6 months had significant reductions in opioid use, alcohol use, and other drug use, as well as improvements in health and employment indicators and clinically meaningful decreases in both anxiety and depression symptoms. These changes have real-life consequences for patients, families, and communities.

Social Determinants of Health and OUD

Our data showed that the MOUD services reached a rural patient population with significant life problems related to OUD. A high rate of unemployment among MOUD treatment recipients, ~25-fold higher than the average state population, is consistent with the rural-urban divide in Colorado [12]. Unemployment has been associated with poorer health outcomes, including greater psychological distress [13] and food insecurity [14], which in turn is linked to adverse mental health outcomes, including suicide, [15, 16]. People seeking OUD services presented with moderate mental health issues, physical symptoms, and rates of ER utilization, which is characteristic for this population. OUD is also associated with high rates of medical comorbidities, including HIV risk and Hepatitis C infection [17, 18]. Because comorbidities can contribute to early death in individuals with OUD [18], it is imperative to address them through comprehensive patient care approaches.

Impact of Treatment on Opioid and Other Substance Use

Our findings add to previous data showing the efficacy of MOUD, which involves the combination of behavioral therapies with FDA-approved medications as the standard of care for OUD. Treatment offered by clinic sites included methadone delivered by a regulated opioid treatment program as well as buprenorphine and naltrexone used by other outpatient clinics [7]. Consistent with the literature [5], patients in our study who completed > 6 months of MOUD treatment used opioids and other substances on fewer days, had less health care utilization, and had improved self-reported health compared to their own pre-treatment levels. Alcohol use was also prevalent in this population, especially in combination with other substances. MAT is also a valid treatment for alcohol use disorder [19], and MOUD treatment also had a positive impact on the use of alcohol and sedative analgesics. In contrast, daily usage of marijuana seemed to have increased after 6 months of MOUD treatment. Although outside the scope of this study, patients may use marijuana to cope with pain, anxiety, or other concerns. This finding suggests the importance of screening for cannabis use and underlying reasons for use [20]. A shift in marijuana use during OUD treatment might reveal new challenges due to Colorado’s legalization of marijuana [21].

Impact of Treatment on Health and Other Opioid Related Crises.

Patients’ health status improved after 6 months of MOUD treatment. Overall, patients reported less concern about their health, but also placed more importance on receiving treatment for health concerns, which might suggest increased motivation for self-management and health maintenance. There also was a significant reduction in patients’ concerns about pain, which is a very positive sign. Our findings align with the National Academy of Medicine’s recommendations regarding the need for rigorous pain management strategies and tapering doses for enhanced outcomes [22, 23]. Treatment with MOUD drugs has been also shown to decrease OUD-related mortality [24]. Although out of the scope of this study, reductions in substance use may lead to fewer opioid overdoses, long-term enhanced quality of life, and reduced mortality among patients who remain in treatment for at least 6 months [25]. While small numbers preclude reaching major conclusions, opioid-related deaths in these two counties indeed dropped from 2017 to 2019 (18.0 vs 14.8 and 20.6 vs no cases per 100000 people in Pueblo and Routt, respectively) [26].

Chronic pain affects many aspects of a person’s daily life, including physical and mental health, family, work, and social relationships [27]. Opioid use can also cause trouble concentrating, maintaining sleep, and depression [28] and MOUD patients reported high levels of anxiety and depression at the start of treatment. Similar to previous studies, our data demonstrated significant decreases in both anxiety and depression after MOUD treatment. Additionally, because an estimated 20% of opioid overdose deaths are related to suicide, our results underscore the importance of thorough screening and prompt treatment for mental health concerns [29]. Study sites offered a range of behavioral health treatments [7] which are also linked to improved patient outcomes independent of the type of psychosocial modality offered.

Impaired social functioning and inability to successfully fulfill personal or societal roles are debilitating features of OUD and hallmark criteria for diagnosis [30]. Notably, patients who completed 6 months of MOUD treatment also reported an improved ability to function during the prior month. While there were no significant changes in the number of days of paid work, which could reflect in loss of productivity resulting from pain and OUD, patients reported fewer concerns with work related-problems.

Access versus Retention in Care

Pilot program grantees advertised their services to prospective OUD patients through partnerships with medical providers in the community, law enforcement agencies, local organizations, churches, and community groups [7]. Engagement in MOUD treatment has been associated with better patient retention than other OUD treatments [24], superior outcomes, and less relapse [18,31]. Consistent with the literature, the MOUD program nevertheless had low retention, mostly due to patients “feeling cured” or having opioid cravings or ongoing pain [31, 32]. The behavioral health component of MOUD is key for retention because it helps patients actively manage their OUD [33]. Equipping providers with modalities like motivational interviewing or mindfulness has proven successful for a variety of conditions ranging from chronic pain management to mental health [34] and has the potential to enhance patient retention.

Societal influences are another potential cause of patient dropout. Despite available treatment, people with mental disorders commonly fail to seek help due to the stigma associated with these services [35]. Our results showed that more clients who dropped out were court-ordered to start treatment. Although there were no differences in self-reported motivation at the start of treatment, patients who continued might have a combination of true self-determination, commitment to change, and external support. Further studies should explore motivation and the role of social determinants of health and support systems in predicting whether OUD patents remain in treatment. Despite community outreach efforts, opioid-related stigma and negative attitudes are still prevalent, and some still favor withdrawal and detox modalities despite the detrimental effects of these treatment approaches on care retention. Our stakeholder-informed evaluation confirmed that people with OUD are widely stigmatized and stereotyped (unpublished work) which can affect patient functioning [36] and whether patients seek and continue treatment [6, 7]. These results emphasize a need for ongoing efforts to lower stigma, maintain open door policies for returning to treatment, and resources to maintain a patient registry and to track individuals who fall out of care, given the myriad of factors that contribute to patient success.

Study Limitations

Several limitations are noted. First, we used subjective assessments, which may not be reliable if patients were under detox and having withdrawal symptoms. However, the measures used are similar to accepted processes used in other studies. Second, results are limited to patients still in treatment and providing data. Not all patients lost to follow-up were necessarily treatment failures; some simply may be missing data, which could be partly attributed to voluntary data collection (i.e., clinics not tracking their patients well over time vs patients not returning for care). Because the evaluable patients were demographically and clinically similar to the non-evaluable ones at the start of treatment, the risk of biased results is minimized. Third, the evaluation focused on data from only two rural counties that may not represent other parts of Colorado or of the country. Finally, this study lacked a comparison group, so competing explanations like history or maturation effects cannot be ruled out. Our results demonstrate effectiveness for those patients remaining in treatment. As public health impact is limited by retention in care, this should be a primary focus of future studies.

The ongoing pain and opioid epidemic necessitate heightened efforts to implement evidence-based care across the United States. Health care providers and patients in rural and small towns have limited resources, and both behavioral and social barriers can impede treatment success. The results of this study were part of a multi-pronged statewide effort to address the opioid crisis in both metropolitan and rural, underserved areas. Results also informed expansion efforts to improve the lives of Coloradans with OUD. While more research is needed regarding retention and long-term effects, this study highlights the effectiveness of MOUD as one strategy to address these major public health crises.

ASI:

Addiction Severity Index (ASI)

CU Nursing:

University of Colorado College of Nursing

OUD:

Opioid Use Disorders

MOUD:

Medication for substance use disorders

MAT:

Medication Assisted Treatment

PHQ-9:

Patient Health Questionnaire (depressive symptoms)

GAD-7:

The Generalized Anxiety Disorder scale

SPSS:

Statistical Package for the Social Sciences

Ethics approval and consent to participate

As an evaluation of pilot program implementation, this study was regarded as non-human subjects research by the Colorado IRB (protocol #19-2217). In compliance with ethical standards for human subject’s studies, however, all individual-level data was collected at clinical sites to guide treatment; only aggregated, de-identified was used in this study. Patients had the right to refuse providing data for evaluation. Administrative, site-level data about characteristics of opioid treatment programs was also used as part of the evaluation.

Consent for publication

Not applicable

Availability of data and materials

Due to the nature of sensitive information, the datasets used in this study are available from the corresponding author and with stakeholder’s permission upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

The program described in this manuscript was funded by the Colorado Senate Bill (SB17-074) Pilot MOUD program from 2017-2020, with additional infrastructure support from the Colorado Clinical and Translational Research Center, National Institutes of Health/National Center for Research Resources grant #UL1 RR025780. The content is solely the responsibility of the authors and does not necessarily reflect the views of the Colorado Government.

Author’s Contributions

CRA – Conceptualization; Data curation; Methodology; Formal analysis; Validation; Visualization; Writing – original draft Writing – review & editing; TS – Conceptualization; Funding acquisition; Investigation; Project administration; Validation; Writing – review & editing; MW – Conceptualization; Funding acquisition; Investigation; Project administration; Supervision; Validation; Writing – review & editing; AA, NB, UH – Investigation; Project administration; Resources; Writing – review & editing; PFC – Conceptualization; Validation; Methodology; Resources; Supervision Software; Validation; Visualization; Writing – original draft; Writing – review & editing. Each author made substantial contributions to the conception of the work, analysis, or interpretation of data. All authors have reviewed and approved the submitted version and agree to be personally accountable for the integrity and accuracy of the work.

Acknowledgements

We want to acknowledge all the providers, and staff who greatly contribute to this program, especially Erica Whitney, Bella Vigil, for their diligent clinical service, care coordination and data entry, and Samantha Mehring for data coordination. We also want to acknowledge the Colorado Senate for the funding which made possible this project, and the Colorado Consortium of Substance Use Prevention for their ongoing support. Last, we also wish to acknowledge the participants in this study, for their immense courage and resiliency.

1. Han B, Compton WM, Blanco C, Crane E, Lee J, Jones CM. Prescription opioid use, misuse, and use disorders in U.S. adults: 2015 National Survey on Drug Use and Health. Ann Intern Med. 2017;167(5):293-301.

2. Centers for Disease Control and Prevention. Drug overdose deaths. https://www.cdc.gov/drugoverdose/data/statedeaths.html 2019. Updated March 19, 2020. Accessed August 2020.

3. Hagemeier NE. Introduction to the opioid epidemic: the economic burden on the healthcare system and impact on quality of life. Am J Manag Care. 2018;24(10 Suppl):S200-s206.

4. US Department of Health and Human Services. Facing Addiction in America: The Surgeon General's Report on Alcohol, Drugs, and Health. Washington, DC; 2016.

5. Colorado Health Institute. The Colorado Health Access Survey: Progress in Peril. 2019.

6. Andrilla CHA, Moore TE, Patterson DG, Larson EH. Geographic distribution of providers with a DEA waiver to prescribe buprenorphine for the treatment of Opioid Use Disorder: a 5-year update. J Rural Health. 2019;35(1):108-112.

7. Sorrell TR, Weber M, Alvarez A, et al. From policy to practice: pilot program increases access to medication for opioid use disorder in rural Colorado. J Subst Abuse Treat. 2020;114:108027.

8. McLellan AT, Kushner H, Metzger D, et al. The Fifth Edition of the Addiction Severity Index. J Subst Abuse Treat. 1992;9(3):199-213.

9. Cacciola JS, Alterman AI, Habing B, McLellan AT. Recent status scores for version 6 of the Addiction Severity Index (ASI-6). Addiction. 2011;106(9):1588-1602.

10. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613.

11. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-1097.

12. U.S. Bureau of Labor Statistics. 2019. https://data.bls.gov/timeseries/LASST080000000000004?amp%253bdata_tool=XGtable&output_view=data&include_graphs=true. Accessed September, 2020.

13. Brydsten A, Hammarström A, San Sebastian M. Health inequalities between employed and unemployed in northern Sweden: a decomposition analysis of social determinants for mental health. I J Equity Health. 2018;17(1):59.

14. Huang J, Kim Y, Birkenmaier J. Unemployment and household food hardship in the economic recession. Public Health Nutr. 2016;19(3):511-519.

15. Milner A, Page A, LaMontagne AD. Cause and effect in studies on unemployment, mental health and suicide: a meta-analytic and conceptual review. Psychol Med. 2014;44(5):909-917.

16. Swendsen J, Conway KP, Degenhardt L, et al. Mental disorders as risk factors for substance use, abuse and dependence: results from the 10-year follow-up of the National Comorbidity Survey. Addiction. 2010;105(6):1117-1128.

17. Unick GJ, Rosenblum D, Mars S, Ciccarone D. Intertwined epidemics: national demographic trends in hospitalizations for heroin- and opioid-related overdoses, 1993-2009. PLoS One. 2013;8(2):e54496.

18. National Academies of Sciences, Engineering, and Medicine. Medications for Opioid Use Disorder save lives: consensus study report. Washington, DC: National Academies Press; 2019. https://doi.org/10.17226/25310.

19. Robertson AG, Easter MM, Lin H, Frisman LK, Swanson JW, Swartz MS. Medication-assisted treatment for alcohol-dependent adults with serious mental illness and criminal justice involvement: effects on treatment utilization and outcomes. Am J Psychiatry. 2018;175(7):665-673.

20. Shams I, Sanger N, Bhatt M, et al. The association between health conditions and cannabis use in patients with opioid use disorder receiving methadone maintenance treatment. BJ Psych Open. 2019;5(6):e91.

21. Brooks-Russell A, Ma M, Levinson AH, et al. Adolescent marijuana use, marijuana-related perceptions, and use of other substances before and after initiation of retail marijuana sales in Colorado (2013-2015). Prev Sci. 2019;20(2):185-193.

22. Rich RJ, Chou R, Mariano ER, et al. Best Practices, Research Gaps, and Future Priorities to Support Tapering Patients on Long-Term Opioid Therapy for Chronic Non-Cancer Pain in Outpatient Settings. National Academy of Medicine; August 10, 2020.

23. National Academies of Sciences Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Committee on Pain Management and Regulatory Strategies to Address Prescription Opioid Abuse. Evidence on strategies for addressing the opioid epidemic. In: Phillips JK, Ford MA, Bonnie RJ, eds. Pain Management and the Opioid Epidemic: Balancing Societal and Individual Benefits and Risks of Prescription Opioid Use. Vol 13. 2017.

24. Sordo L, Barrio G, Bravo MJ, et al. Mortality risk during and after opioid substitution treatment: systematic review and meta-analysis of cohort studies. BMJ. 2017;357:j1550.

25. Nosyk B, Bray JW, Wittenberg E, et al. Short term health-related quality of life improvement during opioid agonist treatment. Drug Alcohol Depend. 2015;157:121-128.

26. Prescription Drug Data Profiles. Colorado Department of Public Health and Environment. https://cdphe.colorado.gov/prevention-and-wellness/injury-prevention/opioid-overdose-prevention/prescription-drug-data. Accessed January 2, 2021.

27. Dueñas M, Ojeda B, Salazar A, Mico JA, Failde I. A review of chronic pain impact on patients, their social environment and the health care system. J Pain Res. 2016;9:457-467.

28. Hartwell EE, Pfeifer JG, McCauley JL, Moran-Santa Maria M, Back SE. Sleep disturbances and pain among individuals with prescription opioid dependence. Addict Behav. 2014;39(10):1537-1542.

29. Oquendo MA, Volkow ND. Suicide: a silent contributor to opioid-overdose deaths. N Engl J Med. 2018;378(17):1567-1569.

30. American Psychiatric Association. Criteria from American Psychiatric Association. In. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition: Washington, DC; 2013:541.

31. Williams AR, Barbieri V, Mishlen K, et al. Long-term follow-up study of community-based patients receiving XR-NTX for opioid use disorders. Am J Addict. 2017;26(4):319-325.

32. Damian AJ, Mendelson T, Agus D. Predictors of buprenorphine treatment success of opioid dependence in two Baltimore City grassroots recovery programs. Addict Behav. 2017;73:129-132.

33. Carroll KM, Weiss RD. The role of behavioral interventions in buprenorphine maintenance treatment: A review. Am J Psychiatry. 2019;17(2):183-192.

34. Blanco C, Volkow ND. Management of opioid use disorder in the USA: present status and future directions. Lancet. 2019;393(10182):1760-1772.

35. Henderson C, Evans-Lacko S, Thornicroft G. Mental illness stigma, help seeking, and public health programs. Am J Public Health. 2013;103(5):777-780.

36. von Hippel C, Henry JD, Terrett G, Mercuri K, McAlear K, Rendell PG. Stereotype threat and social function in opioid substitution therapy patients. Br J Clin Psychol. 2017;56(2):160-171.

Table 1. Baseline demographics of patients in the Colorado MOUD Pilot Program

Demographics describe patient characteristics at the start of treatment (n=190) collected by three participating rural clinical sites. Missing data due to lack of self-reporting varied by item, from ~ 6% for age and race/ethnicity to >50% for employment or substance use specification; actual counts per variable are noted.

^aEvaluable patients are those who started the MOUD treatment and completed both baseline and follow-up surveys, as contrast with non-evaluable patients had an induction but withdrew or were lost to follow-up and thus did not have follow-up data regarding substance use.^bReasons for start treatment are all independent variables and do not add up to 100%.

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published 03 Jan, 2022

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Editorial decision: Major Revision
07 Sep, 2021
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Editor invited by journal
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Outcomes from the Medication Assisted Treatment Pilot Program for Adults With Opioid Use Disorders in Rural Colorado

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