• Stephen G Henry, Susan L Stewart, Eryn Murphy, Iraklis Erik Tseregounis, Andrew J Crawford, Aaron B Shev, James J Gasper, Daniel J Tancredi, Magdalena Cerdá, MarshallBrandon D LBDLDepartment of Epidemiology, Brown University, Providence, RI, USA., and Garen J Wintemute.
• Department of Internal Medicine and Center for Healthcare Policy and Research, University of California, Davis, Sacramento, CA, USA. sghenry@ucdavis.edu.
• J Gen Intern Med. 2021 Dec 1; 36 (12): 367236793672-3679.

BackgroundLimiting 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.ObjectiveTo 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.DesignStatewide 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 MeasuresMultiple 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 ResultsAmong 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.ConclusionsData 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.© 2021. Society of General Internal Medicine.

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