title: An Examination of Claims-based Predictors of Overdose from a Large Medicaid Program.
abstract: Health systems may play an important role in identification of patients at-risk of opioid medication overdose. However, standard measures for identifying overdose risk in administrative data do not exist.
  
  
  
  Examine the association between opioid medication overdose and 2 validated measures of nonmedical use of prescription opioids within claims data.
  
  
  
  A longitudinal retrospective cohort study that estimated associations between overdose and nonmedical use.
  
  
  
  Adult Pennsylvania Medicaid program 2007-2012 patients initiating opioid treatment who were: nondual eligible, without cancer diagnosis, and not in long-term care facilities or receiving hospice.
  
  
  
  Overdose (International Classification of Disease, ninth edition, prescription opioid poisonings codes), opioid abuse (opioid use disorder diagnosis while possessing an opioid prescription), opioid misuse (a composite indicator of number of opioid prescribers, number of pharmacies, and days supplied), and dose exposure during opioid treatment episodes.
  
  
  
  A total of 372,347 Medicaid enrollees with 583,013 new opioid treatment episodes were included in the cohort. Opioid overdose was higher among those with abuse (1.5%) compared with those without (0.2%, P<0.001). Overdose was higher among those with probable (1.8%) and possible (0.9%) misuse compared with those without (0.2%, P<0.001). Abuse [adjusted rate ratio (ARR), 1.52; 95% confidence interval (CI), 1.10-2.10), probable misuse (ARR, 1.98; 95% CI, 1.46-2.67), and possible misuse (ARR, 1.76; 95% CI, 1.48-2.09) were associated with significantly more events of opioid medication overdose compared with those without.
  
  
  
  Claims-based measures can be used by health systems to identify individuals at-risk of overdose who can be targeted for restrictions on opioid prescribing, dispensing, or referral to treatment.
authors.affiliation: *School of Social Work †Department of Psychiatry, School of Medicine ‡Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh §VA Pittsburgh Healthcare System ∥Division of General Internal Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA ¶University of Arizona College of Pharmacy, Tucson, AZ #University of Pittsburgh Graduate School of Public Health **University of Pittsburgh School of Medicine, Pittsburgh, PA.
authors.forename: Gerald
authors.initials: G
authors.lastname: Cochran
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Gordon
authors.forename: Wei-Hsuan
authors.initials: WH
authors.lastname: Lo-Ciganic
authors.forename: Walid F
authors.initials: WF
authors.lastname: Gellad
authors.forename: Winfred
authors.initials: W
authors.lastname: Frazier
authors.forename: Carroline
authors.initials: C
authors.lastname: Lobo
authors.forename: Chung-Chou H
authors.initials: CH
authors.lastname: Chang
authors.forename: Ping
authors.initials: P
authors.lastname: Zheng
authors.forename: Julie M
authors.initials: JM
authors.lastname: Donohue
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000701:Analgesics, Opioid / Q000506:poisoning*; D015897:Comorbidity; D062787:Drug Overdose / Q000453:epidemiology*; D005260:Female; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data*; D008137:Longitudinal Studies; D008297:Male; D008484:Medicaid / Q000706:statistics & numerical data*; D008875:Middle Aged; D009293:Opioid-Related Disorders / Q000453:epidemiology*; D010414:Pennsylvania / Q000453:epidemiology; D010594:Pharmacies / Q000706:statistics & numerical data; D010818:Practice Patterns, Physicians' / Q000706:statistics & numerical data; D012111:Residence Characteristics / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012737:Sex Factors; D012959:Socioeconomic Factors; D014481:United States; D055815:Young Adult
chemical_list: D000701:Analgesics, Opioid