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Background: Rural communities face disproportionate rates of opioid use disorder (OUD) and overdose mortality but continue to be underrepresented in clinical research and underserved in access to medications for opioid use disorder (MOUD). Structural barriers including shortages of qualified providers, transportation challenges, and stigma limit uptake of evidence-based treatment. To address these gaps, the National Drug Abuse Treatment Clinical Trials Network (CTN) launched two pragmatic trials focused exclusively on rural populations: CTN-0102, a telemedicine (TM) feasibility study connecting rural primary care patients to external MOUD providers, and CTN-0102XR (RXR), a pilot randomized trial evaluating extended-release buprenorphine (Brixadi®) compared to sublingual buprenorphine-naloxone.

Objective: The article aims to describe the implementation of these two rural pragmatic trials, identify challenges encountered in study implementation, and present lessons learned. We applied frameworks from implementation science, including the Consolidated Framework for Implementation Research (CFIR) and the Expert Recommendations for Implementing Change (ERIC), to demonstrate how pragmatic trial implementation mirrors implementation of evidence-based programs and practices, and can benefit from established implementation frameworks and strategies.

Results: Across 13 rural clinics in 10 states, both trials demonstrated the feasibility of integrating MOUD into primary care settings through pragmatic study designs closely aligned with routine clinical workflows. Principal challenges included limited clinic research capacity, staff stigma toward OUD treatment, communication barriers between local clinics and external TM vendors, and variable digital access. Solutions included engaging local champions, co-developing workflows tailored to each clinic’s operations, simplifying technology requirements, and emphasizing bi-directional communication among clinic, research, and vendor teams. Continuous technical assistance and protocol flexibility and adaptation were crucial for sustaining engagement and aligning study procedures with clinical realities. Findings demonstrated that rural clinics can feasibly implement TM-based MOUD coordination and extended-release buprenorphine with adequate support and contextual adaptation.

Conclusions: Lessons from CTN-0102 and CTN-0102XR underscore that pragmatic trials in rural settings benefit from early contextual assessment, engagement of community stakeholders, adaptable protocols, and strong implementation support. Applying implementation science frameworks facilitates solutions to real-world barriers and enhances study sustainability. Future CTN efforts should continue to prioritize rural site inclusion, capacity building, and equitable access to evidence-based OUD treatment.

Related protocols: CTN-0102, CTN-0102-XR

Background: The National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) has supported clinical trials of substance use disorder (SUD) interventions for 25 years. This review describes the use of implementation outcomes across CTN trials, characterizes outcomes included, and identifies gaps and potential opportunities to strengthen implementation research within the CTN and the field of SUD treatment.

Methods: This systematic review included active or completed studies listed on the CTN Dissemination Library webpage as of August 18, 2021, and approved by the CTN for development by January 1, 2022. Study summaries and protocols were reviewed if they: 1) measured at least one implementation outcome and 2) examined a practice change, intervention, or process. Extracted data elements included trial design characteristics, implementation frameworks, and outcome assessment domains informed by the RE-AIM and Proctor Implementation Outcomes Frameworks.

Results: 114 protocols were considered, 42 full-text protocols were screened, and 25 were included for data extraction. Start dates of trials spanned a 20-year period (2004–2024) with latter studies including more implementation outcomes. Fidelity (n = 29) and reach/penetration (n = 26) were the most included implementation outcomes. Equity was not identified in any protocols. Methods of defining, capturing, and evaluating outcomes data varied across trials and outcomes.

Conclusions: The inclusion of implementation outcomes increased over time, perhaps reflecting a growing emphasis on implementation research. Incorporating measures of equity could advance knowledge about differential receipt or effectiveness of SUD interventions. Future research should seek to improve the consistency and comprehensiveness in descriptions of implementation science elements.

Related protocols: CTN-0016, CTN-0056, CTN-0062-Ot, CTN-0064, CTN-0065, CTN-0069, CTN-0074, CTN-0074-A-1, CTN-0075, CTN-0076-Ot, CTN-0079, CTN-0079-A-1, CTN-0088, CTN-0090, CTN-0091, CTN-0095, CTN-0096, CTN-0097, CTN-0098, CTN-0099, CTN-0102, CTN-0103, CTN-0107, CTN-0116, CTN-0121

Rural primary care clinics can expand their medication treatment for opioid use disorder (MOUD) capacity by coordinating care with external telemedicine (TM) vendors specializing in addiction medicine. This study used mixed methods to identify factors that influence patient referrals from rural primary care clinics to TM vendors for MOUD.

Between July/August 2020 and January/February 2021, 582 patients with OUD were identified across six primary care sites; that included 68 referred to an external TM vendor to receive MOUD. Mixed effects logistic regression identified individual and site-level factors associated with being referred to the TM vendor. Clinic providers and staff participated in in-depth interviews and focus groups to discuss their considerations for referring patients to the TM vendor.

Patient referrals were positively associated with local household broadband coverage (OR=2.55, p<0.001) and negatively associated with local population density (OR=0.01, p=0.003) and the number of buprenorphine prescribers in the county (OR=0.85, p<0.001). Clinic personnel expressed appreciation for psychiatric expertise and the flexibility to access MOUD brought by the TM vendor. Perceived concerns about TM referral included a lack of trust with external providers, uncertainty about TM service quality, workflow delays, and patients’ technological and insurance challenges.

Conclusions: This study revealed several clinic-level factors that may potentially influence patient referral to TM vendor services for MOUD. To facilitate the referral process and utilization of TM vendors, efforts should be made to foster open communication and trust between clinic providers and TM vendors, streamline workflows, and improve Internet access for patients.

Related protocols: CTN-0102

There is limited research examining factors impacting MOUD retention in rural settings, especially within the context of the COVID-19 pandemic. Using electronic health records data collected as part of a NIDA Clinical Trials Network study (CTN-0102), this study explored how the onset of the COVID-19 pandemic may have impacted MOUD retention in a sample of 563 rural primary care patients.

Cox regression model was applied to examine if COVID-19 was related to treatment retention, controlling for demographics, clinic, insurance type, and other diagnoses. The independent variable was the number of days between the patient’s first MOUD prescription date during the pre-COVID observation period (10/1/2019–3/13/2020) and the start of the COVID-19 pandemic. The dependent variable was retention on MOUD, defined as the time from the first MOUD prescription documented during the pre-COVID observation period to the first break in consecutive MOUD prescriptions (right censored at 180 days).

The findings demonstrated that there was a reduced risk of a prescription break for every 10-day increase in the time from the first documented MOUD prescription to the onset of the COVID-19 pandemic (HR = 0.96, 95% CI = 0.92–0.99; p = 0.011).

Conclusions: While the data did not include complete treatment histories to determine who was new to MOUD treatment, the findings suggest that patients whose first documented MOUD prescription in the dataset was closer to the onset of the pandemic had a greater likelihood of experiencing retention challenges. This underscores the importance for clinics to establish comprehensive contingency plans for future emergencies to ensure uninterrupted MOUD treatment and support, particularly for individuals in the early stabilization phase of their recovery.

Related protocols: CTN-0102

Opioid and alcohol use disorders are increasingly being addressed in primary care, yet how medications to treat these disorders are prescribed in rural regions is unknown.

For this study, a secondary analysis of EHR data gathered as part of CTN-0102, researchers determined prevalence, types, and duration of medication prescription for opioid and/or alcohol use disorder among adult patients in rural primary clinics. The sample included 1874 adult patients who visited one of six rural primary care sites in the Northeastern and Northwestern United States at least once from October 2019 to January 2021 and had a diagnosis code for opioid use disorder (OUD), alcohol use disorder (AUD), or co-occurring opioid and alcohol use disorder (OUD + AUD) during that time.

Patients with OUD + AUD were more likely to be prescribed medication for at least one of these disorders (85.3 %) than patients with OUD only (63.7 %) or AUD only (10.3 %). Further, the OUD + AUD group had the highest number of days on medication (M = 264.7), followed by OUD only (M = 220.5), then the AUD only group (M = 62.5). Only 8.8 % of patients with OUD + AUD were prescribed naltrexone or medication for OUD + AUD to treat both substance use disorders.

Conclusions: Medications for treating AUD as well as OUD are available, but few patients with OUD + AUD and even fewer with AUD received pharmacological treatment for AUD. The current work highlights the need for rural clinicians to consider medications for AUD as an important treatment method for patients with AUD only or OUD + AUD.

Related protocols: CTN-0102

Transition Age Youth (TAY), aged 18-25 years, face barriers to medication treatment for opioid use disorder (MOUD) resulting in lower retention. We evaluated OUD prevalence and MOUD receipt comparing TAY to adults aged 26 or older residing in rural settings.

Electronic Health Records (October 2019 – January 2021) for 36,762 patients across six primary care clinics involved in a large feasibility trial (CTN-0102) in US rural communities were analyzed. All clinics implemented a standardized intervention. Mixed effects logistic/linear regression estimated odds of OUD diagnosis among all patients; and among those with OUD, the odds of receiving MOUD and days prescribed MOUD during the 15-month study period, comparing age categories (TAY=18-25 years vs. adults 26 or older). Covariates included gender, race, ethnicity, mental health comorbidities, and insurance status.

OUD prevalence was 2.82% among TAY (n=3,122) and 3.24% among adults aged 26 or older (n=33,208). After adjusting for covariates and clustering, TAY had significantly lower odds of OUD diagnosis compared to adults 26 years or older (OR=0.58, 95% CI 0.45 to 0.73). There were no significant differences in MOUD receipt between age groups. Compared to adults 26 or older, TAY with OUD had significantly fewer MOUD days during the study, -43.81 days (-76.85 to -10.77).

Conclusions: While no differences were observed in MOUD prescription receipt between TAY and adults 26 or older, TAY with OUD had fewer total days prescribed MOUD indicating lower retention. Further research generalizable to rural communities is needed to assess retention among rural TAY with OUD.

Related protocols: CTN-0102

The purpose of this study, part of CTN-0102, was to investigate the prevalence of opioid use disorder (OUD) and medication treatment for OUD (MOUD) receipt in rural primary care settings and identify characteristics associated with MOUD among patients with OUD.

Researchers performed secondary analyses based on electronic health records of all adult patients who visited 1 of the 6 rural primary care clinic sites from October 2019 to January 2021. Mixed effects logistic regression was conducted to assess MOUD receipt (Y/N) in relation to patient characteristics (eg, demographics, other substance use disorders [SUDs], mental health disorders, and chronic pain) and the number of MOUD prescribers per clinic.

The prevalence of OUD varied from 0.7% to 8.2% (Mean [SD] = 3.3% [95% CI: 0.4, 6.1]) among 36,762 primary care patients across 6 clinic sites. Among 1,164 patients with OUD, on average 50.1% received MOUD (95% CI: 28.0, 72.3). Patients in clinics with more than 3 MOUD prescribers had more than 3 times the odds of receiving MOUD (OR = 3.42; 95% CI, 1.22-9.62) as those in clinics with fewer than 3 prescribers. MOUD was positively associated with younger age (18-30 [OR = 6.97; 95% CI, 3.37-14.42], 31-64 [OR = 5.03; 95% CI, 2.64-9.57], relative to those 65 and older), having other co-occurring SUDs (OR = 3.77; 95% CI, 2.57-5.52), being male (OR = 1.50; 95% CI, 1.12-2.01), and negatively associated with having chronic pain disorders (OR = 0.69; 95% CI, 0.50-0.94).

Conclusions: The prevalence of OUD and MOUD are high but vary considerably across rural primary care clinics; primary care MOUD prescribers play a key role on MOUD access in rural settings.

Related protocols: CTN-0102

Co-occurring substance use disorders (SUDs) among individuals with opioid use disorder (OUD) are associated with additional impairment, overdose, and death. This study, a secondary analysis of data from CTN-0102, examined characteristics of patients who have OUD with and without co-occurring SUDs in rural primary care clinics.

Researchers analyzed data from six rural primary care clinics, including demographics, diagnoses, encounters, and prescriptions of medication for OUD (MOUD), as well as EHR data from an external telemedicine vendor that provided MOUD to some clinic patients. The study population included all adult patients who had a visit to the participating clinics from October 2019 to January 2021.

The authors identified 1164 patients with OUD; 72.6 % had OUD only, 11.5 % had OUD and stimulant use disorder (OUD + StUD), and 15.9 % had OUD and other non-stimulant substance use disorder (OUD + Other). The OUD + StUD group had the highest rates of hepatitis C virus (25.4 % for OUD + StUD, 17.8 % for OUD + Other, and 7.5 % for OUD Only; p < 0.001) and the highest rates of mental health disorders (78.4 %, 69.7 %, and 59.9 %, respectively; p < 0.001). Compared to the OUD Only group, patients in the OUD + StUD and OUD + Other groups were more likely to receive telehealth services provided by clinic staff, in-clinic behavioral health services, and in-clinic MOUD. The OUD + StUD group had the highest proportion of referrals to the external telemedicine vendor.

Conclusions: More than 27 % of patients with OUD in rural primary care clinics had other co-occurring SUDs, and these patients received more healthcare services than those with OUD only. Future studies should examine variations in outcomes associated with these other services among patients with OUD and co-occurring SUDs.

Related protocols: CTN-0102

International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD-10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics’ OUD patient registries and clinician/staff EHR entries.

The study, a secondary analysis of data gathered as part of CTN-0102, a feasibility study about the expansion of medication treatment for OUD in rural communities, used three data sources from each of 4 rural primary care clinics in Washington and Idaho: (1) a limited dataset extracted from the EHR, (2) a clinic-based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD-10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid-induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain.

Analysis found that F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently.

Conclusions: This study found three ICD-10 diagnosis codes (F11.10 [opioid abuse, uncomplicated], F11.21 [opioid dependence, in remission], F11.99 [opioid use, unspecified with unspecified opioid-induced disorder]) that were used more consistently for patients with OUD and others (F11.20 [opioid dependence, uncomplicated], F11.23 [opioid dependence with withdrawal], F11.90 [opioid use, unspecified, uncomplicated]) that were applied to a mix of patients with OUD and patients with chronic pain and no evidence of OUD. Lack of uniform application of ICD diagnosis codes for patients with OUD makes it challenging to use diagnosis code data from the EHR to identify a research population of persons with OUD. Given the richness of the EHR data, it is important to develop new approaches so that researchers can confidently incorporate ICD diagnosis codes in accurately identifying patients with OUD and characterizing their clinical care in their studies.

Related protocols: CTN-0102

The use of telemedicine (TM) has accelerated in recent years, yet research on the implementation and effectiveness of TM-delivered medication treatment for opioid use disorder (MOUD) has been limited. This study investigated the feasibility of implementing a care coordination model involving MOUD delivered via an external TM provider for the purpose of expanding access to MOUD for patients in rural settings.

This study, National Drug Abuse Treatment Clinical Trials Network protocol CTN-0102, tested a care coordination model in 6 rural primary care sites by establishing referral and coordination between the clinic and a TM company for MOUD. The intervention spanned approximately 6 months from July/August 2020 to January 2021, coinciding with the peak of the COVID-19 pandemic. Each clinic tracked patients with OUD in a registry during the intervention period. A pre-/post-intervention design (N = 6) was used to assess the clinic-level outcome as patient-days on MOUD based on patient electronic health records.

All clinics implemented critical components of the intervention, with an overall TM referral rate of 11.7% among patients in the registry. Five of the 6 sites showed an increase in patient-days on MOUD during the intervention period compared to the 6-month period before the intervention (mean increase per 1,000 patients: 132 days, P = .08, Cohen’s d = 0.55). The largest increases occurred in clinics that lacked MOUD capacity or had a greater number of patients initiating MOUD during the intervention period.

Conclusions: To expand access to MOUD in rural settings, the care coordination model is most effective when implemented in clinics that have negligible or limited MOUD capacity.

Related protocols: CTN-0102

Telemedicine (TM) enabled by digital health technologies to provide medical services has been considered a key solution to increasing health care access in rural communities. With the immediate need for remote care due to the COVID-19 pandemic, many health care systems have rapidly incorporated digital technologies to support the delivery of remote care options, including medication treatment for individuals with opioid use disorder (OUD). In responding to the opioid crisis and the COVID-19 pandemic, public health officials and scientific communities appear to strongly support and advocate for greater use of TM-based medication treatment for opioid use disorder (MOUD) to improve access to care and have suggested that broad use of TM during the pandemic should be sustained. Nevertheless, research on the implementation and effectiveness of TM-based MOUD has been limited.

To address this knowledge gap, the National Drug Abuse Treatment Clinical Trials Network (CTN) funded (via the NIH HEAL Initiative) a study on Rural Expansion of Medication Treatment for Opioid Use Disorder (Rural MOUD; CTN-0102) to investigate the implementation and effectiveness of adding TM-based MOUD to rural primary care for expanding access to MOUD. In preparation for this large-scale, randomized controlled trial incorporating TM in rural primary care, a feasibility study is being conducted to develop and pilot test implementation procedures.

In this commentary, the authors share some of their experiences, which include several challenges, during the initial two-month period of the feasibility study phase. While these challenges could be due, at least in part, to adjusting to the COVID-19 pandemic and new workflows to accommodate the study, they are notable and could have a substantial impact on the larger, planned pragmatic trial and on TM-based MOUD more broadly. Challenges include low rates of identification of risk for OUD from screening, low rates of referral to TM, digital device and internet access issues, workflow and capacity barriers, and insurance coverage. These challenges also highlight the lack of empirical guidance for best TM practice and quality remote care models. With TM expanding rapidly, understanding implementation and demonstrating what TM approaches are effective are critical for ensuring the best care for persons with OUD.

Related protocols: CTN-0102

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs.

This article provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has included the CTN’s efforts to systematically embed digital screeners for SUDs into general medical settings to impact care models across the nation. This work has also included a pivotal multi-site clinical trial conducted on the CTN platform, whose data led to the very first “prescription digital therapeutic” authorized by the U.S. Food and Drug Administration (FDA) for the treatment of SUDs. Further CTN research includes the study of telehealth to increase capacity for science-based SUD treatment in rural and under-resourced communities. In addition, the CTN has supported an assessment of the feasibility of detecting cocaine-taking behavior via smartwatch sensing. And, the CTN has supported the conduct of clinical trials entirely online (including the recruitment of national and hard-to-reach/under-served participant samples online, with remote intervention delivery and data collection). Further, the CTN is supporting innovative work focused on the use of digital health technologies and data analytics to identify digital biomarkers and understand the clinical trajectories of individuals receiving medications for opioid use disorder (OUD).

This paper concludes by outlining the many potential future opportunities to leverage the unique national CTN research network to scale-up the science on digital health to examine optimal strategies to increase the reach of science-based SUD service delivery models both within and outside of healthcare.

Related protocols: CTN-0044, CTN-0059, CTN-0073-Ot, CTN-0076, CTN-0083, CTN-0084-A-2, CTN-0090, CTN-0095, CTN-0101, CTN-0102