title: Cost-effectiveness of distributing naloxone to heroin users for lay overdose reversal in Russian cities.
abstract: OBJECTIVE
  To evaluate the cost-effectiveness of distributing naloxone to illicit opioid users for lay overdose reversal in Russian cities.
  
  
  METHODS
  This study adapted an integrated Markov and decision analytic model to Russian cities. The model took a lifetime, societal perspective, relied on published literature, and was calibrated to epidemiologic findings.
  
  
  RESULTS
  For each 20% of heroin users reached with naloxone distribution, the model predicted a 13.4% reduction in overdose deaths in the first 5 years and 7.6% over a lifetime; on probabilistic analysis, one death would be prevented for every 89 naloxone kits distributed (95% CI = 32-260). Naloxone distribution was cost-effective in all deterministic and probabilistic sensitivity analyses and cost-saving if resulting in a reduction in overdose events. Naloxone distribution increased costs by US$13 (95% CI = US$3-US$32) and QALYs by 0.137 (95% CI = 0.022-0.389) for an incremental cost of US$94 per QALY gained (95% CI = US$40-US$325). In a worst-case scenario where overdose was rarely witnessed and naloxone was rarely used, minimally effective, and expensive, the incremental cost was US$1987 per QALY gained. If national expenditures on drug-related HIV, tuberculosis, and criminal justice were applied to heroin users, the incremental cost was US$928 per QALY gained.
  
  
  CONCLUSIONS
  Naloxone distribution to heroin users for lay overdose reversal is highly likely to reduce overdose deaths in target communities and is robustly cost-effective, even within the constraints of this conservative model.
authors.affiliation: Substance Use Research Unit, San Francisco Department of Public Health, San Francisco, CA, USA.
authors.forename: Phillip O
authors.initials: PO
authors.lastname: Coffin
authors.forename: Sean D
authors.initials: SD
authors.lastname: Sullivan
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D003362:Cost-Benefit Analysis; D062787:Drug Overdose / Q000188:drug therapy* / Q000191:economics / Q000401:mortality; D005260:Female; D006556:Heroin Dependence / Q000188:drug therapy* / Q000191:economics / Q000401:mortality; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D018803:Models, Economic; D009270:Naloxone / Q000008:administration & dosage / Q000191:economics*; D009292:Narcotic Antagonists / Q000008:administration & dosage / Q000191:economics*; D019057:Quality-Adjusted Life Years; D012307:Risk Factors; D012426:Russia / Q000453:epidemiology; D013997:Time Factors; D014505:Urban Population / Q000706:statistics & numerical data*; D055815:Young Adult
chemical_list: D009292:Narcotic Antagonists; D009270:Naloxone