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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

Despite comprising one-fifth of the population, individuals living in rural areas are underrepresented in clinical trials on substance use disorders (SUD). The inclusion of rural residents is critical to reducing disparities in health outcomes and improving adoption of evidence-based interventions for SUDs. The current commentary provides five guiding principles to assist researchers in including rural populations in trials. These principles emphasize including rural communities in the study design phase and throughout the research process, understanding the local rural context, consideration of operationalization of rural in research, suggested adaptions for research, and embracing opportunities for bi-directional and sustainable partnerships. When approached thoughtfully, research funding can catalyze rural capacity, foster durable partnerships, and enrich scientific inference to assist in establishing the evidence-base for SUD research.

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

This is the primary outcomes paper for CTN-0105.

Background: Pharmacists play a key role in combating the opioid-related overdose epidemic in the United States (US), but little is known about their experience and willingness to deliver preventive services for opioid use disorder (OUD).

Aims: This study seeks to identify correlates of pharmacists’ concerns about drug use problems (prescription drug misuse/use disorder and illicit drug use/use disorder) as well as their practice experience delivering preventive services for OUD (e.g., asked about opioid use, provided advice, made a referral) and willingness to provide services to patients with drug use problems.

Design: An online survey of licensed US pharmacists was conducted. Participants were recruited from Community Pharmacy Enhanced Services Networks (CPESN) and state pharmacist associations (N=1146).

Findings: Overall, 75% of surveyed pharmacists indicated having concerns about opioid use problems, and 62% had concerns about non-opioid drug use problems at their pharmacies. Pharmacists who were White, practiced at a rural location, worked at a chain pharmacy, had not received opioid-related training in the past year, or practiced screening patients for opioid use had elevated odds of perceiving concerns about opioid use problems in their practice settings. Pharmacists who were White, practiced at a rural location, or had not received opioid-related training in the past year had elevated odds of perceiving concerns about non-opioid (illicit) drug use problems. Being male, being White, or having received opioid-related training were associated with increased odds of screening patients for opioid use problems. Being White, having practiced at a rural location (vs. an urban location), being a pharmacy owner/manager, or having received opioid-related training were associated with increased odds of delivering opioid-related advice/intervention. Being male or having received opioid-related training were associated with increased odds of making a referral to OUD treatment. Finally, being male, being White, having practiced pharmacy services for under 6 years, having received opioid-related training for 2 h in the past year, or having performed OUD-related preventive services (asked about opioid use, provided advice, or made a referral) were associated with increased levels of commitment/readiness for providing care to patients with drug use problems.

Conclusions: The overall findings highlight pharmacists’ involvement with OUD preventive services. It is critical to promote opioid-related preventive service training for pharmacists and provide incentives/tools to help initiate a structured practice of delivering such preventive services.

Related protocols: CTN-0105

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

This is the primary outcomes paper for CTN-0124.

This paper aims to address the challenges in providing adequate access to Medications for Opioid Use Disorder (MOUDs) within underserved areas by examining the potential role of pharmacies and proposing actionable strategies for improvement. The health system faces increasing criticism for failure to provide adequate access to MOUDs. Pharmacies are being promoted as an asset to increase MOUD access in rural and underserved areas.

Method: An engineering systems analysis approach was employed to investigate ways to enhance pharmacy roles in delivering MOUDs in underserved regions. Interviews were conducted with community pharmacists, pharmacy associations, and addiction treatment experts. Innovation development techniques with experts from inside and outside the field were used to create recommendations.

Results: The findings underscore the importance of embracing technological advancements to overcome resource limitations and expand MOUD access in underserved areas. When our project began, we believed pharmacy roles related to MOUD distribution could expand through traditional means. We now feel that expansion is practical only through adoption of recent developments in information and communication technology. Our recommendations offer actionable strategies to expand MOUD availability in underserved areas. Technological changes can address stigma and mindsets, workflow simplification, family engagement, integration with other parts of the health system, reimbursement practice, and regulation.

Conclusions: Implementing pertinent technological innovations could augment MOUD availability within the pharmacy sector, thus addressing the pressing need for improved access to treatment in underserved communities. These technological changes would expand MOUD access within a field constrained by limited and diminishing resources.

Related protocols: CTN-0124

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

Screening for substance use in rural primary care clinics faces unique challenges due to limited resources, high patient volumes, and multiple demands on providers. To explore the potential for electronic health record (EHR)-integrated screening in this context, researchers conducted an implementation feasibility study with a rural federally-qualified health center (FQHC) in Maine. This was an ancillary study to a NIDA Clinical Trials Network study of screening in urban primary care clinics (CTN-0062).

Researchers worked with stakeholders from three FQHC clinics to define and implement their optimal screening approach. Clinics used the Tobacco, Alcohol, Prescription Medication, and Other Substance (TAPS) Tool, completed on tablet computers in the waiting room, and results were immediately recorded in the EHR. Adult patients presenting for annual preventive care visits, but not those with other visit types, were eligible for screening. Data were analyzed for the first 12 months following implementation at each clinic to assess screening rates and prevalence of reported unhealthy substance use, and documentation of counseling using an EHR-integrated clinical decision support tool, for patients screening positive for moderate-high risk alcohol or drug use.

Screening was completed by 3749 patients, representing 93.4% of those with screening-eligible annual preventive care visits, and 18.5% of adult patients presenting for any type of primary care visit. Screening was self-administered in 92.9% of cases. The prevalence of moderate-high risk substance use detected on screening was 14.6% for tobacco, 30.4% for alcohol, 10.8% for cannabis, 0.3% for illicit drugs, and 0.6% for non-medical use of prescription drugs. Brief substance use counseling was documented for 17.4% of patients with any moderate-high risk alcohol or drug use.

Conclusions: Self-administered EHR-integrated screening was feasible to implement, and detected substantial alcohol, cannabis, and tobacco use in rural FQHC clinics. Counseling was documented for a minority of patients with moderate-high risk use, possibly indicating a need for better support of primary care providers in addressing substance use. There is potential to broaden the reach of screening by offering it at routine medical visits rather than restricting to annual preventive care visits, within these and other rural primary care clinics.

Related protocols: CTN-0062-Ot

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

This is the Primary Outcomes Article for CTN-0103.

National opioid-related overdose fatalities totaled 650,000 from 1999 to 2021. Some of the highest rates occurred in New Hampshire, where 40% of the population lives rurally. Medications for opioid use disorder (MOUD; methadone, buprenorphine, and naltrexone) have demonstrated effectiveness in reducing opioid overdose and mortality. Methadone access barriers disproportionally impact rural areas and naltrexone uptake has been limited. Buprenorphine availability has increased and relaxed regulations reduces barriers in general medical settings common in rural areas. Barriers to prescribing buprenorphine include lack of confidence, inadequate training, and lack of access to experts. To address these barriers, learning collaboratives have trained clinics on best-practice performance data collection to inform quality improvement (QI). This project sought to explore the feasibility of training clinics to collect performance data and initiate QI alongside clinics’ participation in a Project ECHO virtual collaborative for buprenorphine providers.

Eighteen New Hampshire clinics participating in a Project ECHO were offered a supplemental project exploring the feasibility of performance data collection to inform QI targeting increased alignment with best practice. Feasibility was assessed descriptively, through each clinic’s participation in training sessions, data collection, and QI initiatives. An end-of-project survey was conducted to understand clinic staff perceptions of how useful and acceptable they found the program.

Five of the eighteen health care clinics that participated in the Project ECHO joined the training project, four of which served rural communities in New Hampshire. All five clinics met the criteria for engagement, as each clinic attended at least one training session, submitted at least one month of performance data, and completed at least one QI initiative. Survey results showed that while clinic staff perceived the training and data collection to be useful, there were several barriers to collecting the data, including lack of staff time, and difficulty standardizing documentation within the clinic electronic health record.

Conclusions: Results suggest that training clinics to monitor their performance and base QI initiatives on data has potential to impact clinical best practice. While data collection was inconsistent, clinics completed several data-informed QI initiatives, indicating that smaller scale data collection might be more attainable.

Related protocols: CTN-0103

Both COVID-19 deaths and opioid overdose deaths continue to increase in the United States. Little is known about the characteristics of counties with high rates of mortality for both.

In this study supported by the CTN Greater Southern California Node, researchers analyzed county-level data on COVID-19 mortality from January 1 to May 31, 2020, and on opioid overdose mortality during 2014-2018. The outcome variable, “high-risk county” was a binary indicator of high mortality rates (above 75% quartile) for both COVID-19 and opioid overdose. Geospatial logistic regression models were conducted separately for urban and rural counties to identify social determinants of health associated with being a high-risk county.

After adjusting for other covariates, researchers found that the overall mortality rate of COVID-19 is higher in counties with larger population size and a higher proportion of racial/ethnic minorities, although counties with high rates of opioid overdose mortality have lower proportions of racial/ethnic minorities, a higher proportion of females, and are more economically disadvantaged. Significant predictors of rural counties with high mortality rates for both COVID-19 and opioid overdose include higher proportions of Blacks (Adjusted odds ratio [aOR], 1.04; 95%CI, 1.01-1.07), American Indians and Alaska Natives (aOR, 1.07; 95%CI, 1.02-1.13), and two or more races (aOR, 1.34; 95%CI, 1.13-1.60). Additional predictors for high-risk urban counties include population density (aOR, 1.12; 95%CI, 1.04-1.22) and higher unemployment rates during the COVID-19 pandemic (aOR, 1.23; 95%CI, 1.07-1.41).

Conclusions: Rural counties with high proportions of racial/ethnic minorities and urban counties with high unemployment rates are at high mortality risk for COVID-19 and opioid overdose.