NCJ Number
255784
Journal
American Journal of Preventive Medicine Volume: 57 Issue: 6 Dated: December 2019 Pages: E211-E217
Date Published
December 2019
Length
7 pages
Annotation
Since clinicians and public health officials lack indicators to identify individuals at highest risk for opioid overdose, the current project developed and validated a predictive model, using prescription histories from Prescription Drug Monitoring Programs to identify those at risk for fatal overdose because of any opioid or illicit opioids.
Abstract
From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents ages 18-80 years with a filled opioid prescription (n=565,175) from January to June 2016. Fatal opioid overdoses were identified from the Office of the Chief Medical Examiner and were linked at the person-level with Prescription Drug Monitoring Program data. Split-half technique was used to develop and validate a multivariate logistic regression with a 6-month lookback period and assessed model calibration and discrimination. Predictors for any opioid-related fatal overdose included male sex; ages 65-80 years old; under Medicaid or Medicare, one or more long-acting opioid fills, one or more buprenorphine fills, two to three and four or more short-acting schedule II opioid fills, opioid days' supply of about 91 days, average morphine milligram equivalent daily dose, two or more benzodiazepine fills, and one or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). Overall, a model for predicting fatal opioid overdoses was developed using Prescription Drug Monitoring Program data. Given the recent national epidemic of deaths involving heroin and fentanyl, it is noteworthy that the model performed equally well in identifying those at risk for overdose deaths from both illicit and prescription opioids. (publisher abstract modified)