title: A Tool to Assess Risk of De Novo Opioid Abuse or Dependence.
abstract: Determining risk factors for opioid abuse or dependence will help clinicians practice informed prescribing and may help mitigate opioid abuse or dependence. The purpose of this study is to identify variables predicting opioid abuse or dependence.
  
  
  
  A retrospective cohort study using de-identified integrated pharmacy and medical claims was performed between October 2009 and September 2013. Patients with at least 1 opioid prescription claim during the index period (index claim) were identified. We ascertained risk factors using data from 12 months before the index claim (pre-period) and captured abuse or dependency diagnosis using data from 12 months after the index claim (postperiod). We included continuously eligible (pre- and postperiod) commercially insured patients aged 18 years or older. We excluded patients with cancer, residence in a long-term care facility, or a previous diagnosis of opioid abuse or dependence (identified by International Classification of Diseases 9th revision code or buprenorphine/naloxone claim in the pre-period). The outcome was a diagnosis of opioid abuse (International Classification of Diseases 9th revision code 304.0x) or dependence (305.5).
  
  
  
  The final sample consisted of 694,851 patients. Opioid abuse or dependence was observed in 2067 patients (0.3%). Several factors predicted opioid abuse or dependence: younger age (per decade [older] odds ratio [OR], 0.68); being a chronic opioid user (OR, 4.39); history of mental illness (OR, 3.45); nonopioid substance abuse (OR, 2.82); alcohol abuse (OR, 2.37); high morphine equivalent dose per day user (OR, 1.98); tobacco use (OR, 1.80); obtaining opioids from multiple prescribers (OR, 1.71); residing in the South (OR, 1.65), West (OR, 1.49), or Midwest (OR, 1.24); using multiple pharmacies (OR, 1.59); male gender (OR, 1.43); and increased 30-day adjusted opioid prescriptions (OR, 1.05).
  
  
  
  Readily available demographic, clinical, behavioral, pharmacy, and geographic information can be used to predict the likelihood of opioid abuse or dependence.
authors.affiliation: Division of Medical Education, Department of Internal Medicine, Washington University School of Medicine, St Louis, Mo. Electronic address: t.ciesielski@wustl.edu.
authors.forename: Thomas
authors.initials: T
authors.lastname: Ciesielski
authors.affiliation: Express Scripts, St Louis, Mo.
authors.forename: Reethi
authors.initials: R
authors.lastname: Iyengar
authors.affiliation: Express Scripts, St Louis, Mo.
authors.forename: Amit
authors.initials: A
authors.lastname: Bothra
authors.affiliation: Express Scripts, St Louis, Mo.
authors.forename: Dave
authors.initials: D
authors.lastname: Tomala
authors.affiliation: Division of Medical Education, Department of Internal Medicine, Washington University School of Medicine, St Louis, Mo.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Cislo
authors.affiliation: Division of General Medical Sciences, Department of Internal Medicine, Washington University School of Medicine, St Louis, Mo.
authors.forename: Brian F
authors.initials: BF
authors.lastname: Gage
mesh_terms: D000328:Adult; D000367:Age Factors; D000437:Alcoholism / Q000453:epidemiology*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D001523:Mental Disorders / Q000453:epidemiology*; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D009293:Opioid-Related Disorders / Q000453:epidemiology*; D010594:Pharmacies / Q000706:statistics & numerical data; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D019966:Substance-Related Disorders / Q000453:epidemiology; D014481:United States / Q000453:epidemiology
keywords: Demographic factors; Opioid abuse; Opioid dependence; Pharmacy claims-based factors; Predictive model; Prescription drug monitoring program