title: Cost drivers of prescription opioid abuse in commercial and Medicare populations.
abstract: OBJECTIVE
  Growth in the number of patients with pain conditions, and the subsequent rise in prescription opioid use for treatment, has been accompanied by an increase in diagnosed opioid abuse. Understanding what drives the incremental healthcare costs of members diagnosed with prescription opioid abuse may assist in developing better screening techniques for abuse.
  
  
  METHODS
  This retrospective analysis examined costs, resource use, and comorbidities 365 days pre- and postdiagnosis in prescription opioid users diagnosed with abuse (cases) vs. their matched nondiagnosed controls. Inclusion criteria for cases were diagnosis of opioid abuse (ICD-9-CM: 304.0x, 304.7x, 305.5x, 965.0x). Multivariate analysis used generalized linear modeling with log-transformed cost as dependent variable, controlling for comorbidities.
  
  
  RESULTS
  Final sample sizes were 8,390 cases and 16,780 matched controls. Postindex abuse-related costs were $2,099 for commercial members, $539 for Medicare members aged < 65, and $170 for Medicare members aged ≥ 65. A higher percentage of cases had pain conditions (82.0% vs. 57.4% commercial, 95.9% vs. 87.5% Medicare members aged < 65, 92.9% vs. 82.4% Medicare members aged ≥ 65, P < 0.0001), and a higher numbers of cases had multiple opioid prescribers (3.7 vs. 1.4 commercial, 3.3 vs. 2.2 Medicare < 65, 2.2 vs. 1.6 Medicare ≥ 65, P < 0.0001) than controls preindex. Cases had higher rates of substance abuse and psychiatric diagnoses pre- and postindex (P < 0.0001, all comparisons). Adjusted costs were 28% higher for cases than for controls (P < 0.0001).
  
  
  CONCLUSIONS
  Costs of members diagnosed with prescription opioid abuse are driven by higher pain and psychiatric comorbidities relative to nonabuse controls.
authors.affiliation: Comprehensive Health Insights, Inc., Louisville, Kentucky, U.S.A.
authors.forename: Margaret K
authors.initials: MK
authors.lastname: Pasquale
authors.forename: Ashish V
authors.initials: AV
authors.lastname: Joshi
authors.forename: Robert
authors.initials: R
authors.lastname: Dufour
authors.forename: David
authors.initials: D
authors.lastname: Schaaf
authors.forename: Jack
authors.initials: J
authors.lastname: Mardekian
authors.forename: George A
authors.initials: GA
authors.lastname: Andrews
authors.forename: Nick C
authors.initials: NC
authors.lastname: Patel
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000701:Analgesics, Opioid / Q000191:economics*; D002648:Child; D005260:Female; D017048:Health Care Costs; D006801:Humans; D008297:Male; D006278:Medicare / Q000191:economics*; D008875:Middle Aged; D009293:Opioid-Related Disorders / Q000191:economics*; D012189:Retrospective Studies; D014481:United States; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid
keywords: healthcare costs; opioid abuse; pharmacoeconomics; prescription drug abuse