Risk of diabetes and hypertension in a population with alcohol use disorders.

BACKGROUND: A population-based follow-up study assessing the risk of developing hypertension and diabetes associated with alcohol use disorder (AUD) is crucial. We investigated this relationship by using insurance claims data from Taiwan. METHODS: From the claims data, an AUD cohort (N = 60,590) diagnosed between 2000 and 2006 and a non-AUD comparison cohort (N = 60,590) without the diagnosis of hypertension or diabetes at baseline were established and matched by propensity scores estimated by baseline demographic status and the Charlson comorbidity index (CCI). We assessed the incidence rates of hypertension and/or diabetes at the end of 2016 and used Cox's method to estimate the related hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Relative to the comparison cohort, the AUD cohort had an approximately 1.70-fold higher incidence of hypertension (35.1 vs. 20.7 per 1,000 person-years), with an adjusted HR (aHR) of 1.72 (95% CI: 1.68-1.76), 2.16-fold higher incidence of diabetes (20.2 vs. 9.36 per 1,000 person-years), with an aHR of 2.18 (95% CI: 2.11-2.24), and 1.91-fold higher incidence of both diabetes and hypertension (10.3 vs. 5.38 per 1,000 person-years) with an aHR of 2.02 (95% CI: 1.94-2.10). The incidence rates of all outcomes were greater in men than in women, whereas the HRs were greater for AUD in women than for AUD in men relative to the respective comparison patients. The risk increased further for subjects with CCI ≥ 1, which was higher in the AUD cohort. CONCLUSIONS: The increased risk of developing diabetes and hypertension in patients with AUD, especially the differences noted according to gender, indicates that clinicians should address potential comorbidities in these patients.

Medication-based treatment among rural, primary care patients diagnosed with opioid use disorder and alcohol use disorder.

INTRODUCTION: 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. METHODS: We 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. RESULTS: 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.

Social Determinants of Health and Continuity of Medications for Opioid Use Disorder Among Patients Receiving Treatment in Rural Primary Care Settings.

OBJECTIVES: Factors associated with treatment retention on medications for opioid use disorder (MOUD) in rural settings are poorly understood. This study examines associations between social determinants of health (SDoH) and MOUD retention among patients with opioid use disorder (OUD) in rural primary care settings. METHODS: We analyzed patient electronic health records from 6 rural clinics. Participants (N = 575) were adult patients with OUD and had any prescription for MOUD from October 2019 to April 2020. MOUD retention was measured by MOUD days and continuity defined as continuous 180 MOUD days with no more than a 7-day gap. Mixed-effect regressions assessed associations between the outcomes and SDoH (Medicaid insurance, social deprivation index [SDI], driving time from home to the clinic), telehealth use, and other covariates. RESULTS: Mean patient MOUD days were 127 days (SD = 50.7 days). Living in more disadvantaged areas (based on SDI) (adjusted relative risk [aRR]: 0.98; 95% confidence interval [CI], 0.98-0.99) and having more than an hour (compared with an hour or less) driving time from home to clinic (aRR: 0.95; 95% CI, 0.93-0.97) were associated with fewer MOUD days. Using telehealth was associated with more MOUD days (aRR: 1.23; 95% CI, 1.21-1.26). In this cohort, 21.7% of the participants were retained on MOUD for at least 180 days. SDoH and use of telehealth were not associated with having continuity of MOUD. CONCLUSIONS: Addressing SDoH (eg, SDI) and providing telehealth (eg, improvements in public transportation, internet access) may improve MOUD days in rural settings.

Referral of patients from rural primary care clinics to telemedicine vendors for opioid use disorder treatment: A mixed-methods study.

INTRODUCTION: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Individual-Level Risk Prediction of Return to Use During Opioid Use Disorder Treatment.

Importance: No existing model allows clinicians to predict whether patients might return to opioid use in the early stages of treatment for opioid use disorder. Objective: To develop an individual-level prediction tool for risk of return to use in opioid use disorder. Design, Setting, and Participants: This decision analytical model used predictive modeling with individual-level data harmonized in June 1, 2019, to October 1, 2022, from 3 multicenter, pragmatic, randomized clinical trials of at least 12 weeks' duration within the National Institute on Drug Abuse Clinical Trials Network (CTN) performed between 2006 and 2016. The clinical trials covered a variety of treatment settings, including federally licensed treatment sites, physician practices, and inpatient treatment facilities. All 3 trials enrolled adult participants older than 18 years, with broad pragmatic inclusion and few exclusion criteria except for major medical and unstable psychiatric comorbidities. Intervention: All participants received 1 of 3 medications for opioid use disorder: methadone, buprenorphine, or extended-release naltrexone. Main Outcomes and Measures: Predictive models were developed for return to use, which was defined as 4 consecutive weeks of urine drug screen (UDS) results either missing or positive for nonprescribed opioids by week 12 of treatment. Results: The overall sample included 2199 trial participants (mean [SD] age, 35.3 [10.7] years; 728 women [33.1%] and 1471 men [66.9%]). The final model based on 4 predictors at treatment entry (heroin use days, morphine- and cocaine-positive UDS results, and heroin injection in the past 30 days) yielded an area under the receiver operating characteristic curve (AUROC) of 0.67 (95% CI, 0.62-0.71). Adding UDS in the first 3 treatment weeks improved model performance (AUROC, 0.82; 95% CI, 0.78-0.85). A simplified score (CTN-0094 OUD Return-to-Use Risk Score) provided good clinical risk stratification wherein patients with weekly opioid-negative UDS results in the 3 weeks after treatment initiation had a 13% risk of return to use compared with 85% for those with 3 weeks of opioid-positive or missing UDS results (AUROC, 0.80; 95% CI, 0.76-0.84). Conclusions and Relevance: The prediction model described in this study may be a universal risk measure for return to opioid use by treatment week 3. Interventions to prevent return to regular use should focus on this critical early treatment period.

Medication treatment for opioid use disorder among rural primary care patients.

PURPOSE: 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. METHODS: 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. FINDINGS: 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.

Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities.

BACKGROUND AND AIMS: 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. DESIGN: Cross-sectional observational study. SETTING: Four rural primary care clinics in Washington and Idaho, USA. PARTICIPANTS: 307 patients. MEASUREMENTS: This study used three data sources from each clinic: (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. FINDINGS: 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: Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.

Stakeholder perspectives on a telemedicine referral and coordination model to expand medication treatment for opioid use disorder in rural primary care clinics.

INTRODUCTION: Opioid overdose deaths are increasing rapidly in the United States. Medications for opioid use disorder (MOUD) are effective and can be delivered in primary care, but uptake has been limited in rural communities. Referral to and coordination with an external telemedicine (TM) vendor by rural primary care clinics for MOUD (TM-MOUD) may increase MOUD access for rural patients, but we know little about perspectives on this model among key stakeholders. As part of a TM-MOUD feasibility study, we explored TM-MOUD acceptability and feasibility among personnel and patients from seven rural primary care clinics and a TM-MOUD vendor. METHODS: We conducted virtual interviews or focus groups with clinic administrators (n = 7 interviews), clinic primary care and behavioral health providers (8 groups, n = 30), other clinic staff (9 groups, n = 37), patients receiving MOUD (n = 16 interviews), TM-MOUD vendor staff (n = 4 interviews), and vendor-affiliated behavioral health and prescribing providers (n = 17 interviews). We asked about experiences with and acceptability of MOUD (primarily buprenorphine) and telemedicine (TM) and a TM-MOUD referral and coordination model. We conducted content analysis to identify themes and participants quantitatively rated acceptability of TM-MOUD elements on a 4-item scale. RESULTS: Perceived benefits of vendor-based TM-MOUD included reduced logistical barriers, more privacy and less stigma, and access to services not available locally (e.g., counseling, pain management). Barriers included lack of internet or poor connectivity in patients' homes, limited communication and trust between TM-MOUD and clinic providers, and questions about the value to the clinic of TM-MOUD referral to external vendor. Acceptability ratings for TM-MOUD were generally high; they were lowest among frontline staff. CONCLUSIONS: Rural primary care clinic personnel, TM-MOUD vendor personnel, and patients generally perceived referral from primary care to a TM-MOUD vendor to hold potential for increasing access to MOUD in rural communities. Increasing TM-MOUD uptake requires buy-in and understanding among staff of the TM-MOUD workflow, TM services offered, requirements for patients, advantages over clinic-based or TM services from clinic providers, and identification of appropriate patients. Poverty, along with patient hesitation to initiate treatment, creates substantial barriers to MOUD treatment generally; insufficient internet availability creates a substantial barrier to TM-MOUD.

Co-occurring substance use disorders among patients with opioid use disorder in rural primary care clinics.

BACKGROUND: Co-occurring substance use disorders (SUDs) among individuals with opioid use disorder (OUD) are associated with additional impairment, overdose, and death. This study examined characteristics of patients who have OUD with and without co-occurring SUDs in rural primary care clinics. METHODS: Secondary analysis used electronic health record (EHR) 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. RESULTS: We 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.

Telemedicine-delivered treatment for substance use disorder: A scoping review.

INTRODUCTION: The COVID pandemic prompted a significant increase in the utilization of telemedicine (TM) for substance use disorder (SUD) treatment. As we transition towards a "new normal" policy, it is crucial to comprehensively understand the evidence of TM in SUD treatment. This scoping review aims to summarize existing evidence regarding TM's acceptability, quality, effectiveness, access/utilization, and cost in the context of SUD treatment in order to identify knowledge gaps and inform policy decisions regarding TM for SUDs. METHOD: We searched studies published in 2012-2022 from PubMed, Cochrane Library, Embase, Web of Science, and other sources. Findings were synthesized using thematic analysis. RESULTS: A total of 856 relevant articles were screened, with a final total of 42 articles included in the review. TM in SUD treatment was perceived to be generally beneficial and acceptable. TM was as effective as in-person SUD care in terms of substance use reduction and treatment retention; however, most studies lacked rigorous designs and follow-up durations were brief (≤3 months). Telephone-based TM platforms (vs video) were positively associated with older age, lower education, and no prior overdose. Providers generally consider TM to be affordable for patients, but no relevant studies were available from patient perspectives. CONCLUSIONS: TM in SUD treatment is generally perceived to be beneficial and acceptable and as effective as in-person care, although more rigorously designed studies on effectiveness are still lacking. Access and utilization of TM may vary by platform. TM service quality and costs are the least studied and warrant further investigations.

Care coordination between rural primary care and telemedicine to expand medication treatment for opioid use disorder: Results from a single-arm, multisite feasibility study.

PURPOSE: 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. METHODS: The study 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. FINDINGS: 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.

Perceptions of COVID-19 risk during the pandemic: perspectives from people seeking medication for opioid use disorder.

INTRODUCTION: The Coronavirus Disease 2019 (COVID-19) pandemic has had devastating consequences for persons with opioid use disorder (OUD). Yet, little is known about how people seeking treatment for OUD perceive the risks of COVID-19 and how their perception interplays with their health behaviours. METHODS: In-depth interviews were conducted from September 2021 to March 2022 with 32 patients seeking medication treatment for OUD (MOUD) in Southern California. All interviews were conducted virtually and lasted between one and two hours. Interviews were recorded and transcribed verbatim. Two qualitative researchers independently conducted a content analysis of the transcripts to identify themes. RESULTS: Three primary themes were identified: (1) perceptions and beliefs about COVID-19 susceptibility and severity; (2) perceptions of COVID-19 risk compared to substance use behaviours; and (3) vaccine hesitancy. Participants were mixed in their beliefs of susceptibility to contracting COVID-19 and the severity of the disease if contracted. Some participants reported taking precautions to mitigate their chances of acquiring COVID-19, and other participants reported that COVID was not a big concern as substance use took priority. For many of the participants, COVID-19 concerns were overshadowed by the risk of overdosing on substances and other risky substance use behaviour. Most of the participants (n = 23; 72%) had received at least one COVID-19 vaccine by the time of the interview, but over half (n = 19; 59%) expressed vaccine hesitancy. Vaccine hesitancy was driven by concerns about the unknown long-term side effects and potential interactions of the vaccine with MOUD. CONCLUSIONS: Our study provides insight into COVID-19 prevention measures as well as vaccination perceptions and hesitancy among people who received treatment for OUD.Key messagesParticipants expressed diverse perceptions of the seriousness of COVID-19, with some taking precautions to mitigate their chances of acquiring COVID-19 and others perceiving that the risk of contracting COVID-19 was less than the risk of overdosing.Substance use, social isolation, vaccine hesitancy and COVID-19 risk behaviours should be studied as co-occurring phenomena that have potentially overlapping relationships that can influence behaviours that impact health and well-being.

Mental Health Service Utilization and Disparities in the U.S: Observation of the First Year into the COVID Pandemic.

This study examined mental health service utilization and disparities during the first year of COVID. We analyzed data from all adult respondents with any mental illness in the past year (n = 6967) in the 2020 National Survey on Drug Use and Health to evaluate if mental health service utilization differed by geographic areas, race/ethnicity, and age groups. Only 46% of individuals with any mental illness had received mental health treatment. Compared to non-Hispanic Whites, Asian and Hispanics were less likely to receive outpatient services and prescription medicine. Rural residents received less outpatient treatment compared to large metropolitan residents. No difference was found in telemedicine utilization across area types and race/ethnicity groups. Older individuals were less likely to utilize telemedicine services. Our findings highlighted continued mental health treatment disparities among race/ethnic minorities and other sub-populations during COVID. Targeted strategies are warranted to allow older populations to benefit from telemedicine.

Telemedicine along the cascade of care for substance use disorders during the COVID-19 pandemic in the United States.

BACKGROUND: The COVID-19 pandemic has changed the landscape of healthcare service delivery. This review aims to describe telemedicine-delivered substance use disorder (SUD) treatments and services along the cascade of care in the U.S. after the start of the COVID-19 pandemic. METHODS: A literature review was conducted on PubMed, Embase, Web of Science, and Cochrane Library (Wiley). English-language articles that describe any healthcare services for patients with SUDs using telemedicine in the U.S. since the onset of the COVID-19 pandemic were identified (N = 33). We narratively summarized telemedicine-based service provision along the cascade of SUD care, such as screening/assessment, prescription, monitoring, recovery support, and other services. RESULTS: Soon after the onset of COVID-19 and mandated restrictions, cadres of healthcare providers from different specialties mobilized to ramp up video- and audio-based services to remotely treat patients with SUDs. Medication prescription (48.5%) and individual counseling (39.4%) were the most frequently reported services delivered via telemedicine. Other steps of SUD care delivered by telemedicine characterized in our review included SUD screening and assessment (30.3%), induction (21.2%), medication management (27.3%), monitoring (27.3%), recovery support (15.2%), and referral (24.2%). Feasibility issues and challenges to implementing telemedicine included patients' lack of access to technology and health insurance coverage, providers' capacity limits and concerns, and clinics' financial and office-space constraints. CONCLUSION: The COVID-19 pandemic has offered a window of opportunity to advance telemedicine expertise by formalizing clinical guidance and routinizing provider in-service training in virtual SUD treatment. Findings suggest enhanced efforts to reduce disparities in telemedicine-based services.

The opioid use disorder core outcomes set (OUD-COS) for treatment research: findings from a Delphi consensus study.

BACKGROUND AND AIM: There is no gold-standard and considerable heterogeneity in outcome measures used to evaluate treatments for opioid use disorder (OUD) along the opioid treatment cascade. The aim of this study was to develop the US National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network (CTN) opioid use disorder core outcomes set (OUD-COS). DESIGN: Four-round, e-Delphi expert panel consensus study and plenary research group discussion and targeted consultation. SETTING: United States. PARTICIPANTS: A panel of 25 members including clinical practitioners, clinical researchers and administrative staff from the CTN, the network's affiliated clinical and community sites and the NIDA Centre for the CTN. MEASUREMENTS: From a pool of 24 candidate items in four domains (biomedical/disease status; behaviors, symptoms and functioning; opioid treatment cascade; and morbidity and mortality), the panel completed an on-line questionnaire to rank items with defined specification on a 9-point scale for importance, with a standard 70% consensus criterion. FINDINGS: After the fourth round of the questionnaire and subsequent discussion, consensus was reached for five outcomes: two patient-reported (global impression of improvement and incident non-fatal overdose); one clinician-reported (illicit/non-medical drug toxicology); and two from administrative records (duration of treatment and fatal opioid poisoning). CONCLUSIONS: An e-Delphi consensus study has produced the US National Institute on Drug Abuse (NIDA) National Drug Abuse Treatment Clinical Trials Network opioid use disorder core outcomes set (version 1) for opioid use disorder treatment efficacy and effectiveness research.

Association Between Benzodiazepine and Opioid Prescription and Mortality Among Patients in a Large Healthcare System.

OBJECTIVE: Coprescription of opioids and benzodiazepines (BDZ) is associated with adverse outcomes, including greater healthcare utilization and overdose risk. This study aims to examine opioid and BDZ coprescription, dosing, and mortality among patients with and without opioid use disorder (OUD) in a large healthcare system. METHODS: Using data from the California state Prescription Drug Monitoring Program during 2010 to 2014 linked with a large healthcare system electronic health record database and mortality records from the Centers for Disease Control National Death Index, this study examined 5202 patients (1978 with OUD, 3224 controls). Multiple logistic regression analyses were conducted to examine relationships between most recent BDZ and opioid prescription, and their interaction with respect to mortality. RESULTS: About 10.5% of the sample died on or before December 31, 2014. About 17.7% were prescribed BDZ during the final month of observation. Individuals with OUD were prescribed higher average BDZ and opioid doses than those without OUD. After adjusting for covariates, increased prescribed doses of BDZ (odds ratio [OR]=1.34, 95%CI: 1.15-1.55 per 10 mg/d increment) and opioids (OR = 1.04, 95%CI: 1.02-1.05 per 10 mg/d increment) were positively associated with mortality. Non-OUD patients who received both BDZ and opioid prescriptions had a higher mortality than those who received only BDZ or opioids (The ratio of odds ratio (ROR) = 3.83, 95%CI: 1.78-8.21). CONCLUSIONS: Study findings highlight significant mortality associated with the coprescription of opioids and BDZ in a general healthcare setting. Further research is needed to elucidate factors associated with mortality among non-OUD patients who are co-prescribed opioids and BDZ.

Social Determinants of Mortality of COVID-19 and Opioid Overdose in American Rural and Urban Counties.

OBJECTIVES: 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. METHODS: We 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. We conducted geospatial logistic regression models separately for urban and rural counties to identify social determinants of health associated with being a high-risk county. RESULTS: After adjusting for other covariates, 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.

The impact of COVID-19 on substance use disorder treatment in California: Service providers' perspectives.

BACKGROUND: The COVID-19 pandemic has had a profound impact on the U.S. health care system, including addiction treatment. The objective of this study is to describe the impact of COVID on the delivery of treatment for substance use disorders (SUDs) from the perspectives of service providers. METHODS: Between May and September 2020, 61 service providers from 16 SUD treatment sites in California participated in virtual focus groups that lasted about an hour. We recorded the discussions and transcribed them verbatim. Two qualitative analysts independently conducted content analysis to identify themes from the transcripts. RESULTS: At the beginning of the pandemic, service providers observed a slight decrease in patient admissions, followed by an uptick in patient flow due to increased mental health issues, alcohol use, and relapse. Many of the clinics adopted flexible service delivery modes, such as curbside dosing and extended take-home medication, to enable social distancing in clinic settings. Approximately half of the clinic encounters offered telemedicime, and a considerable proportion of patients preferred to use telephone-based services rather than video-based services. Internet instability and technical difficulties limited the use of telemedicine among their patients. CONCLUSION: COVID has been challenging for SUD treatment, but health care systems rapidly reacted with adjustments that may result in long-term changes in SUD service delivery. Telemedicine-based services have played a major role in ensuring uninterrupted patient care. Providers need organizational, technical, and logistical support to improve and sustain telemedicine services that increase access to quality care for their patients.

Long-term follow-up assessment of opioid use outcomes among individuals with comorbid mental disorders and opioid use disorder treated with buprenorphine or methadone in a randomized clinical trial.

AIMS: To investigate whether reduction in opioid use differs when treated by either buprenorphine-naloxone (BUP) or methadone (MET) among adults with comorbid opioid use disorder (OUD) and mental disorders. DESIGN, SETTING AND PARTICIPANTS: In a randomized controlled trial, adults with OUD were randomized to 24 weeks of either BUP or MET treatment and were followed up in 3-yearly assessments. The present secondary analyses were based on 597 participants who completed all assessments. MEASUREMENTS: The outcome measure was the number of days of using opioids per month during the follow-up period. The Mini-International Neuropsychiatric Interview (MINI) was used to classify participants into three groups: life-time mood disorder (n = 302), life-time mental disorder other than mood disorder (n = 114) and no mental disorder (n = 181). Medication treatment (BUP, MET, no treatment) during the follow-up period was a time-varying predictor. FINDINGS: Based on zero-inflated Poisson (ZIP) mixed regression analysis, it was found that relative to no treatment, opioid use during the follow-up was significantly reduced by BUP [odds ratio (OR) = 0.12, 95% confidence interval (CI) = 0.07-0.21 for any use; risk ratio (RR) = 0.77, 95% CI =0.66-0.89 for days of use] and by MET [OR = 0.33, 95% CI = 0.25-0.45 for any use; RR = 0.78, 95% CI = 0.72-0.84 for days of use]. Relative to MET, BUP was associated with a lower likelihood of any opioid use among participants with mood disorders (OR = 0.52, 95% CI = 0.36-0.74) and for participants without mental disorder (OR = 0.37, 95% CI = 0.21-0.66) and fewer number of days using opioids (RR = 0.37, 95% CI = 0.25-0.56) among participants with other mental disorders. CONCLUSIONS: Among adults with comorbid opioid use disorder and mental disorders, treatment with buprenorphine-naloxone produced greater reductions in opioid use than treatment with methadone.

Disparities in digital access among American rural and urban households and implications for telemedicine-based services.

PURPOSE: To examine characteristics associated with disparities in digital access (i.e., access to high-speed Internet via a computer or smartphone) in American rural and urban households given that digital access has a direct impact on access to telemedicine-based services. METHODS: Using the 2019 American Community Survey, we analyzed the proportions of geographic area, race/ethnicity, and socioeconomic status according to device and high-speed Internet access. Maximum likelihood logit estimators estimated how these factors influenced device and high-speed Internet access. FINDINGS: Of 105,312,959 households, 32.29% were without a desktop or laptop computer with high-speed Internet (WDW), 21.51% were without a smartphone with a data plan for wireless Internet (WSW), and 14.02% were without any digital access (WDA). Nonmetropolitan households were significantly more likely to be WDA than metropolitan households (odds ratio [OR] = 1.87; 95% confidence interval [CI]: 1.83-1.91). Relative to non-Hispanic Whites, non-Hispanic Blacks (OR = 1.60; 95% CI: 1.56-1.64), American Indian or Alaska natives (OR = 2.00; 95% CI: 1.82-2.19), or Hispanics (OR = 1.70; 95% CI: 1.66-1.74) were significantly more likely to be WDA. When compared to households with private health insurance coverage, households WDA were significantly more likely to have no insurance (OR = 2.44; 95% CI: 2.36-2.53) or public insurance coverage (OR = 3.78; 95% CI: 3.70-3.86). Households with any digital access reported higher income and more family members living at home. Using the same predictors, similar findings were reported for households WDW or WSW. CONCLUSIONS: Significant disparities in digital access exist among nonmetropolitan households, racial/ethnic minority households, and lower-income households. The lack of digital access has implications for the accessibility of health care services via telemedicine and thus could exacerbate health disparities.