Predicting Opioid Overdose Deaths Using Prescription Drug Monitoring Program Data

Lindsey M. Ferris, Brendan Saloner, Noa Krawczyk, Kristen E. Schneider, Molly P. Jarman, Kate Jackson, B. Casey Lyons, Matthew D. Eisenberg, Tom M. Richards, Klaus W. Lemke, Jonathan P. Weiner

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Introduction: Prescription Drug Monitoring Program data can provide insights into a patient's likelihood of an opioid overdose, yet clinicians and public health officials lack indicators to identify individuals at highest risk accurately. A predictive model was developed and validated using Prescription Drug Monitoring Program prescription histories to identify those at risk for fatal overdose because of any opioid or illicit opioids. Methods: From December 2018 to July 2019, a retrospective cohort analysis was performed on Maryland residents aged 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. Results: Predictors of any opioid-related fatal overdose included male sex, age 65–80 years, Medicaid, Medicare, 1 or more long-acting opioid fills, 1 or more buprenorphine fills, 2 to 3 and 4 or more short-acting schedule II opioid fills, opioid days’ supply ≥91 days, average morphine milligram equivalent daily dose, 2 or more benzodiazepine fills, and 1 or more muscle relaxant fills. Model discrimination for the validation cohort was good (area under the curve: any, 0.81; illicit, 0.77). Conclusions: 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.

Original languageEnglish (US)
Pages (from-to)e211-e217
JournalAmerican journal of preventive medicine
Issue number6
StatePublished - Dec 2019

ASJC Scopus subject areas

  • Epidemiology
  • Public Health, Environmental and Occupational Health


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