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Pharmacoepidemiol Drug Saf. 2018 Apr 24. doi: 10.1002/pds.4443. [Epub ahead of print]

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

Author information

1
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
2
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
3
Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

Abstract

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

PMID:
29687539
PMCID:
PMC6200661
[Available on 2019-10-24]
DOI:
10.1002/pds.4443

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