title: Analytic models to identify patients at risk for prescription opioid abuse.
abstract: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Analysis Group, Inc, 111 Huntington Ave, 10th Fl, Boston, MA 02199, USA. awhite@analysisgroup.com
authors.forename: Alan G
authors.initials: AG
authors.lastname: White
authors.forename: Howard G
authors.initials: HG
authors.lastname: Birnbaum
authors.forename: Matt
authors.initials: M
authors.lastname: Schiller
authors.forename: Jackson
authors.initials: J
authors.lastname: Tang
authors.forename: Nathaniel P
authors.initials: NP
authors.lastname: Katz
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D000701:Analgesics, Opioid; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016017:Odds Ratio; D018570:Risk Assessment / Q000379:methods; D019966:Substance-Related Disorders / Q000209:etiology*; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid