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Using prescription claims to detect aberrant behaviors with opioids: comparison and validation of 5 algorithms
Kathryn Rough1,2, Krista F Huybrechts1, Sonia Hernandez-Diaz2
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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.
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