BACKGROUND: Opioid overdose is a leading cause of accidental death in the United States. OBJECTIVE: To estimate the cost-effectiveness of distributing naloxone, an opioid antagonist, to heroin users for use at witnessed overdoses. DESIGN: Integrated Markov and decision analytic model using deterministic and probabilistic analyses and incorporating recurrent overdoses and a secondary analysis assuming heroin users are a net cost to society. DATA SOURCES: Published literature calibrated to epidemiologic data. TARGET POPULATION: Hypothetical 21-year-old novice U.S. heroin user and more experienced users with scenario analyses. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTION: Naloxone distribution for lay administration. OUTCOME MEASURES: Overdose deaths prevented and incremental cost-effectiveness ratio (ICER). RESULTS OF BASE-CASE ANALYSIS: In the probabilistic analysis, 6% of overdose deaths were prevented with naloxone distribution; 1 death was prevented for every 227 naloxone kits distributed (95% CI, 71 to 716). Naloxone distribution increased costs by $53 (CI, $3 to $156) and quality-adjusted life-years by 0.119 (CI, 0.017 to 0.378) for an ICER of $438 (CI, $48 to $1706). RESULTS OF SENSITIVITY ANALYSIS: Naloxone distribution was cost-effective in all deterministic and probabilistic sensitivity and scenario analyses, and it was cost-saving if it resulted in fewer overdoses or emergency medical service activations. In a "worst-case scenario" where overdose was rarely witnessed and naloxone was rarely used, minimally effective, and expensive, the ICER was $14 000. If national drug-related expenditures were applied to heroin users, the ICER was $2429. LIMITATION: Limited sources of controlled data resulted in wide CIs. CONCLUSION: Naloxone distribution to heroin users is likely to reduce overdose deaths and is cost-effective, even under markedly conservative assumptions. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.
BACKGROUND: Opioid overdose is a leading cause of accidental death in the United States. OBJECTIVE: To estimate the cost-effectiveness of distributing naloxone, an opioid antagonist, to heroin users for use at witnessed overdoses. DESIGN: Integrated Markov and decision analytic model using deterministic and probabilistic analyses and incorporating recurrent overdoses and a secondary analysis assuming heroin users are a net cost to society. DATA SOURCES: Published literature calibrated to epidemiologic data. TARGET POPULATION: Hypothetical 21-year-old novice U.S. heroin user and more experienced users with scenario analyses. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTION: Naloxone distribution for lay administration. OUTCOME MEASURES: Overdose deaths prevented and incremental cost-effectiveness ratio (ICER). RESULTS OF BASE-CASE ANALYSIS: In the probabilistic analysis, 6% of overdose deaths were prevented with naloxone distribution; 1 death was prevented for every 227 naloxone kits distributed (95% CI, 71 to 716). Naloxone distribution increased costs by $53 (CI, $3 to $156) and quality-adjusted life-years by 0.119 (CI, 0.017 to 0.378) for an ICER of $438 (CI, $48 to $1706). RESULTS OF SENSITIVITY ANALYSIS: Naloxone distribution was cost-effective in all deterministic and probabilistic sensitivity and scenario analyses, and it was cost-saving if it resulted in fewer overdoses or emergency medical service activations. In a "worst-case scenario" where overdose was rarely witnessed and naloxone was rarely used, minimally effective, and expensive, the ICER was $14 000. If national drug-related expenditures were applied to heroin users, the ICER was $2429. LIMITATION: Limited sources of controlled data resulted in wide CIs. CONCLUSION:Naloxone distribution to heroin users is likely to reduce overdose deaths and is cost-effective, even under markedly conservative assumptions. PRIMARY FUNDING SOURCE: National Institute of Allergy and Infectious Diseases.
Authors: Michael A Irvine; Margot Kuo; Jane A Buxton; Robert Balshaw; Michael Otterstatter; Laura Macdougall; M-J Milloy; Aamir Bharmal; Bonnie Henry; Mark Tyndall; Daniel Coombs; Mark Gilbert Journal: Addiction Date: 2019-06-28 Impact factor: 6.526
Authors: Traci C Green; Corey Davis; Ziming Xuan; Alexander Y Walley; Jeffrey Bratberg Journal: Am J Public Health Date: 2020-04-16 Impact factor: 9.308
Authors: Christopher Rowe; Glenn-Milo Santos; Eric Vittinghoff; Eliza Wheeler; Peter Davidson; Philip O Coffin Journal: Addiction Date: 2015-08 Impact factor: 6.526