Analytic models to identify patients at risk for prescription opioid abuse.

OBJECTIVE: To assess the feasibility of using medical and prescription drug claims data to develop models that identify patients at risk for prescription opioid abuse or misuse. STUDY
DESIGN: Deidentified prescription drug and medical claims for approximately 632,000 privately insured patients in Maine from 2005 to 2006 were used. Patients receiving prescription opioids were divided into 2 mutually exclusive groups, namely, prescription opioid abusers and nonabusers.
METHODS: Potential risk factors for prescription opioid abuse were incorporated into logistic models to identify their effects on the probability that a prescription opioid user was diagnosed as having prescription opioid abuse. Different models were based on data available to prescription monitoring programs and managed care organizations. Best-fitting models were identified based on statistical significance (P <or=.05), parsimony, clinical relevance, and area under the receiver operating characteristic curve.
RESULTS: The drug claims models found that the following factors (measured over a 3-month period) were associated with risk for prescription opioid abuse: age 18 to 34 years, male sex, 4 or more opioid prescriptions, opioid prescriptions from 2 or more pharmacies, early prescription opioid refills, escalating morphine sulfate dosages, and opioid prescriptions from 2 or more physicians. The model integrating drug and medical claims found that the following factors (measured over a 12-month period) were associated with risk for prescription opioid abuse or misuse: age 18 to 24 years, male sex, 12 or more opioid prescriptions, opioid prescriptions from 3 or more pharmacies, early prescription opioid refills, escalating morphine dosages, psychiatric outpatient visits, hospital visits, and diagnoses of nonopioid substance abuse, depression, posttraumatic stress disorder, and hepatitis.
CONCLUSION: Using drug and medical claims data, it is feasible to develop models that could assist prescription-monitoring programs, payers, and healthcare providers in evaluating patient characteristics associated with elevated risk for prescription opioid abuse.