Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms

Pharmacoepidemiol Drug Saf. 2019 Jan;28(1):62-69. doi: 10.1002/pds.4443. Epub 2018 Apr 24.


Objective: Compare and validate 5 algorithms to detect aberrant behavior with opioids: Opioid Misuse Score, Controlled Substance-Patterns of Utilization Requiring Evaluation (CS-PURE), Overutilization Monitoring System, Katz, and Cepeda algorithms.

Study design and setting: We identified new prescription opioid users from 2 insurance databases: Medicaid (2000-2006) and Clinformatics Data Mart (CDM; 2004-2013). Patients were followed 1 year, and aberrant opioid behavior was defined according to each algorithm, using Cohen's kappa to assess agreement. Risk differences were calculated comparing risk of opioid-related adverse events for identified aberrant and nonaberrant users.

Results: About 3.8 million Medicaid and 4.3 million CDM patients initiated prescription opioid use. Algorithms flagged potential aberrant behavior in 0.02% to 12.8% of initiators in Medicaid and 0.01% to 7.9% of initiators in CDM. Cohen's kappa values were poor to moderate (0.00 to 0.50 in Medicaid; 0.00 to 0.30 in CDM). Algorithms varied substantially in their ability to predict opioid-related adverse events; the Overutilization Monitoring System had the highest risk differences between aberrant and nonaberrant users (14.0% in Medicaid; 13.4% in CDM), and the Katz algorithm had the lowest (0.96% in Medicaid; 0.47% in CDM).

Conclusions: In 2 large databases, algorithms applied to prescription data had varying accuracy in identifying increased risk of adverse opioid-related events.

Keywords: abuse; claims data; misuse; opioid; pharmacoepidemiology; prescription opioid; validation.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Algorithms
  • Analgesics, Opioid / adverse effects*
  • Databases, Factual / statistics & numerical data
  • Drug Prescriptions / statistics & numerical data
  • Drug Utilization Review / methods*
  • Drug and Narcotic Control / methods
  • Female
  • Humans
  • Male
  • Medicaid / statistics & numerical data
  • Middle Aged
  • Opioid Epidemic / prevention & control*
  • Opioid-Related Disorders / etiology
  • Opioid-Related Disorders / prevention & control*
  • Prescription Drug Misuse / prevention & control
  • Prescription Drugs / adverse effects*
  • Risk Assessment / methods
  • United States / epidemiology
  • Young Adult


  • Analgesics, Opioid
  • Prescription Drugs