Impact of overdose on health plan disenrollment among patients prescribed long-term opioids: Retrospective cohort study.

CONTEXT: Health plan disenrollment may disrupt chronic or preventive care for patients prescribed long-term opioid therapy (LTOT). PURPOSE: To assess whether overdose events in patients prescribed LTOT are associated with subsequent health plan disenrollment. DESIGN: Retrospective cohort study. SETTING AND DATASET: Data from the Optum Labs Data Warehouse which includes de-identified medical and pharmacy claims and enrollment records for commercial and Medicare Advantage enrollees. The database contains longitudinal health information on patients, representing a mixture of ages and geographical regions across the United States. PATIENTS: Adults prescribed stable opioid therapy (≥10 morphine milligram equivalents/day) for a 6-month baseline period prior to an index opioid prescription from January 1, 2018 to December 31, 2018. MAIN MEASURES: Health plan disenrollment during follow-up. RESULTS: The cohort comprised 404,151 patients who were followed up after 800,250 baseline periods of stable opioid dosing. During a mean follow-up of 9.1 months, unadjusted disenrollment rates among primary commercial beneficiaries and Medicare Advantage enrollees were 37.2 and 13.9 per 100 person-years, respectively. Incident overdoses were associated with subsequent health plan disenrollment with a statistically significantly stronger association among primary commercial insurance beneficiaries [adjusted incidence rate ratio (aIRR) 1.48 (95% CI: 1.33-1.64)] as compared to Medicare Advantage enrollees [aIRR 1.15 (95% CI: 1.07-1.23)]. CONCLUSIONS: Among patients prescribed long-term opioids, overdose events were strongly associated with subsequent health plan disenrollment, especially among primary commercial insurance beneficiaries. These findings raise concerns about the social consequences of overdose, including potential health insurance loss, which may limit patient access to care at a time of heightened vulnerability.

Comparison of record linkage software for deduplicating patient identities in California's Prescription Drug Monitoring Program.

BACKGROUND: To help prevent overdose deaths involving prescription drugs, accurate linkage of prescription drug monitoring program (PDMP) records for individual patients is essential. OBJECTIVES: To compare the accuracy of the linkage program used by California's PDMP against various record linkage programs with respect to accuracy in deduplicating patient identities in the PDMP, with implications for identifying high-risk opioid use and outlier behaviors. RESEARCH DESIGN: We evaluated California's program, Link Plus, LinkSolv, and The Link King on 557 861 PDMP identity records with addresses in two 3-digit zip code areas for patients who filled a controlled substance prescription in 2013. Manual review was performed on a stratified sample of 720 paired records identified as matches by at least one program. MEASURES: We estimated sensitivity and positive predictive value, and computed PDMP patient alerts for the patient entities identified by each program. RESULTS: Sensitivity was 95% for LinkSolv and The Link King, 84% for Link Plus, and 73% for California's program; positive predictive value was ≥93% for all programs. The number of patient entities prompting a PDMP alert was similar among the programs for all alerts except multiple provider episodes (obtaining prescriptions from ≥6 prescribers or ≥6 pharmacies in the last 6 months), which were 10.9%, 26.6%, and 16.9% greater using The Link King, Link Plus, and LinkSolv, respectively, compared to California's program. CONCLUSIONS: PDMPs should assess the accuracy of record linkage algorithms and the impacts of these algorithms on patient safety alerts and develop national best practices for PDMP record linkage.

Using Prescription Drug Monitoring Program Data to Assess Likelihood of Incident Long-Term Opioid Use: a Statewide Cohort Study.

BACKGROUND: Limiting the incidence of opioid-naïve patients who transition to long-term opioid use (i.e., continual use for > 90 days) is a key strategy for reducing opioid-related harms. OBJECTIVE: To identify variables constructed from data routinely collected by prescription drug monitoring programs that are associated with opioid-naïve patients' likelihood of transitioning to long-term use after an initial opioid prescription. DESIGN: Statewide cohort study using prescription drug monitoring program data PARTICIPANTS: All opioid-naïve patients in California (no opioid prescriptions within the prior 2 years) age ≥ 12 years prescribed an initial oral opioid analgesic from 2010 to 2017. METHODS AND MAIN MEASURES: Multiple logistic regression models using variables constructed from prescription drug monitoring program data through the day of each patient's initial opioid prescription, and, alternatively, data available up to 30 and 60 days after the initial prescription were constructed to identify probability of transition to long-term use. Model fit was determined by the area under the receiver operating characteristic curve (C-statistic). KEY RESULTS: Among 30,569,125 episodes of patients receiving new opioid prescriptions, 1,809,750 (5.9%) resulted in long-term use. Variables with the highest adjusted odds ratios included concurrent benzodiazepine use, ≥ 2 unique prescribers, and receipt of non-pill, non-liquid formulations. C-statistics for the day 0, day 30, and day 60 models were 0.81, 0.88, and 0.94, respectively. Models assessing opioid dose using the number of pills prescribed had greater discriminative capacity than those using milligram morphine equivalents. CONCLUSIONS: Data routinely collected by prescription drug monitoring programs can be used to identify patients who are likely to develop long-term use. Guidelines for new opioid prescriptions based on pill counts may be simpler and more clinically useful than guidelines based on days' supply or milligram morphine equivalents.

Trends in Buprenorphine to Treat Opioid Use Disorder in California, 2012 to 2018: Medicaid Outpaces the Rest of the State.

OBJECTIVES: Opioid use disorder remains undertreated in the United States. One of the primary mechanisms for expanding access to treatment has been the use of buprenorphine. In this study, we compare prescribing trends of buprenorphine paid through Medicaid versus other payer sources. METHODS: Combined data from California's prescription drug monitoring program and California's Department of Health Care Services was used to obtain statewide quarterly prescription rates for buprenorphine, indicated for the treatment of opioid use disorder, from 2012 to 2018. RESULTS: From 2012 to 2018, the rate of individuals treated with buprenorphine in Medicaid increased by 657% (1.39-10.5 Medicaid beneficiaries per 10,000) with increases beginning in 2014 and continuing through 2018. Rate of individual prescribing among non-Medicaid sources increased by 93.7% (6.54-12.7 non-Medicaid individuals per 10,000) with most increases occurring before 2014. CONCLUSIONS: California Medicaid has made considerable gains in buprenorphine access, with access growing steadily even after expansions through the Affordable Care Act plateaued. In contrast, recent gains in buprenorphine access for individuals without Medicaid are uninspiring, indicating that initiatives to improve buprenorphine access to patients without Medicaid are urgently needed.