Nurse Care Management for Opioid Use Disorder Treatment: The PROUD Cluster Randomized Clinical Trial.

Importance: Few primary care (PC) practices treat patients with medications for opioid use disorder (OUD) despite availability of effective treatments. Objective: To assess whether implementation of the Massachusetts model of nurse care management for OUD in PC increases OUD treatment with buprenorphine or extended-release injectable naltrexone and secondarily decreases acute care utilization. Design, Setting, and Participants: The Primary Care Opioid Use Disorders Treatment (PROUD) trial was a mixed-methods, implementation-effectiveness cluster randomized clinical trial conducted in 6 diverse health systems across 5 US states (New York, Florida, Michigan, Texas, and Washington). Two PC clinics in each system were randomized to intervention or usual care (UC) stratified by system (5 systems were notified on February 28, 2018, and 1 system with delayed data use agreement on August 31, 2018). Data were obtained from electronic health records and insurance claims. An implementation monitoring team collected qualitative data. Primary care patients were included if they were 16 to 90 years old and visited a participating clinic from up to 3 years before a system's randomization date through 2 years after. Intervention: The PROUD intervention included 3 components: (1) salary for a full-time OUD nurse care manager; (2) training and technical assistance for nurse care managers; and (3) 3 or more PC clinicians agreeing to prescribe buprenorphine. Main Outcomes and Measures: The primary outcome was a clinic-level measure of patient-years of OUD treatment (buprenorphine or extended-release injectable naltrexone) per 10 000 PC patients during the 2 years postrandomization (follow-up). The secondary outcome, among patients with OUD prerandomization, was a patient-level measure of the number of days of acute care utilization during follow-up. Results: During the baseline period, a total of 130 623 patients were seen in intervention clinics (mean [SD] age, 48.6 [17.7] years; 59.7% female), and 159 459 patients were seen in UC clinics (mean [SD] age, 47.2 [17.5] years; 63.0% female). Intervention clinics provided 8.2 (95% CI, 5.4-∞) more patient-years of OUD treatment per 10 000 PC patients compared with UC clinics (P = .002). Most of the benefit accrued in 2 health systems and in patients new to clinics (5.8 [95% CI, 1.3-∞] more patient-years) or newly treated for OUD postrandomization (8.3 [95% CI, 4.3-∞] more patient-years). Qualitative data indicated that keys to successful implementation included broad commitment to treat OUD in PC from system leaders and PC teams, full financial coverage for OUD treatment, and straightforward pathways for patients to access nurse care managers. Acute care utilization did not differ between intervention and UC clinics (relative rate, 1.16; 95% CI, 0.47-2.92; P = .70). Conclusions and Relevance: The PROUD cluster randomized clinical trial intervention meaningfully increased PC OUD treatment, albeit unevenly across health systems; however, it did not decrease acute care utilization among patients with OUD. Trial Registration: ClinicalTrials.gov Identifier: NCT03407638.

Inequalities in oral health: Understanding the contributions of education and income.

OBJECTIVE: To quantify the extent to which income and education explain gradients in oral health outcomes. METHODS: Using data from the Canadian Community Health Survey (CCHS 2003), binary logistic regression models were constructed to examine the relationship between income and education on self-reported oral health (SROH) and chewing difficulties (CD) while controlling for age, sex, ethnicity, employment status and dental insurance coverage. The relative index of inequality (RII) was utilized to quantify the extent to which income and education explain gradients in poor SROH and CD. RESULTS: Income and education gradients were present for SROH and CD. From fully adjusted models, income inequalities were greater for CD (RIIinc = 2.85) than for SROH (RIIinc = 2.75), with no substantial difference in education inequalities between the two. Income explained 37.4% and 42.4% of the education gradient in SROH and CD respectively, whereas education explained 45.2% and 6.1% of income gradients in SROH and CD respectively. Education appears to play a larger role than income when explaining inequalities in SROH; however, it is the opposite for CD. CONCLUSION: In this sample of the Canadian adult population, income explained over one third of the education gradient in SROH and CDs, whereas the contribution of education to income gradients varied by choice of self-reported outcome. Results call for stakeholders to improve affordability of dental care in order to reduce inequalities in the Canadian population.