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Original Research |

Cost-Effectiveness of Distributing Naloxone to Heroin Users for Lay Overdose Reversal

Phillip O. Coffin, MD; and Sean D. Sullivan, PhD
[+] Article and Author Information

From the San Francisco Department of Public Health, San Francisco, California, and University of Washington, Seattle, Washington.

Disclaimer: The authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Grant Support: By grant 5T32AI007140-33 from the National Institute of Allergy and Infectious Diseases at the National Institutes of Health.

Potential Conflicts of Interest: None disclosed. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M12-1737.

Reproducible Research Statement: Study protocol: Not applicable. Statistical code: Mathematical operations available from Dr. Coffin (e-mail, pcoffin@gmail.com). Data set: Input parameters and sources provided in the text and Appendix.

Requests for Single Reprints: Phillip O. Coffin, MD, San Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, CA 94102; e-mail, pcoffin@gmail.com.

Current Author Addresses: Dr. Coffin: San Francisco Department of Public Health, 25 Van Ness Avenue, Suite 500, San Francisco, CA 94102.

Dr. Sullivan: Pharmaceutical Outcomes Research and Policy Program, University of Washington, 1959 NE Pacific Street, Box 357630, Seattle, WA 98195-7630.

Author Contributions: Conception and design: P.O. Coffin, S.D. Sullivan.

Analysis and interpretation of the data: P.O. Coffin, S.D. Sullivan.

Drafting of the article: P.O. Coffin.

Critical revision of the article for important intellectual content: P.O. Coffin, S.D. Sullivan.

Final approval of the article: P.O. Coffin, S.D. Sullivan.

Provision of study materials or patients: P.O. Coffin.

Statistical expertise: P.O. Coffin, S.D. Sullivan.

Obtaining of funding: P.O. Coffin.

Administrative, technical, or logistic support: P.O. Coffin.

Collection and assembly of data: P.O. Coffin.


Ann Intern Med. 2013;158(1):1-9. doi:10.7326/0003-4819-158-1-201301010-00003
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Chinese translation

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.

Figures

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Figure 1.

Markov model of heroin use, overdose, discontinuation, and death.

Shapes and lines represent health states and transitions, respectively. At each overdose “tunnel state,” which individuals pass through in a set sequence akin to passing through a tunnel, a decision analytic model generated the probability of survival or death.

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Appendix Figure 1.

Decision analytic model of an overdose in the setting of naloxone distribution to heroin users.

EMS = emergency medical services.

* Modified by the likelihood of naloxone being present at an overdose.

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Appendix Figure 2.

Relative (top) and absolute (bottom) reduction in overdose death rate among active heroin users from naloxone distribution for lay overdose reversal.

Solid lines represent the results of the deterministic model from baseline parameters; other lines represent the results of the probabilistic analysis, including the mean (dotted line) and 95% CI (dashed lines). Absolute rates were adjusted by the number of active heroin users to represent the effect in a community of heroin users at various stages of drug use.

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Appendix Figure 3.

One-way sensitivity analyses of naloxone distribution to heroin users for lay overdose reversal.

Analyses of all model parameters based on maximum predicted ranges. EMS = emergency medical services; QALY = quality-adjusted life-year.

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Figure 2.

Cost-effectiveness acceptability curve for naloxone distribution under traditional assumptions and applying national drug-related expenditures to heroin users.

The y-axis represents the probability that naloxone distribution is preferred at a given willingness to pay and includes a secondary analysis assuming heroin users are a net cost to society. QALY = quality-adjusted life-year.

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Summary for Patients

Naloxone for Heroin Overdose Reversal

The full report is titled “Cost-Effectiveness of Distributing Naloxone to Heroin Users for Lay Overdose Reversal.” It is in the 1 January 2013 issue of Annals of Internal Medicine (volume 158, pages 1-9). The authors are P.O. Coffin and S.D. Sullivan.

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