Economic Evaluation in Opioid Modeling: Systematic Review.


Journal

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
ISSN: 1524-4733
Titre abrégé: Value Health
Pays: United States
ID NLM: 100883818

Informations de publication

Date de publication:
02 2021
Historique:
received: 01 03 2020
revised: 29 06 2020
accepted: 25 07 2020
entrez: 1 2 2021
pubmed: 2 2 2021
medline: 7 4 2021
Statut: ppublish

Résumé

The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

Identifiants

pubmed: 33518022
pii: S1098-3015(20)34399-0
doi: 10.1016/j.jval.2020.07.013
pmc: PMC7864393
mid: NIHMS1629473
pii:
doi:

Substances chimiques

Analgesics, Opioid 0
Narcotic Antagonists 0
Naloxone 36B82AMQ7N
Methadone UC6VBE7V1Z

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, P.H.S. Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

158-173

Subventions

Organisme : FDA HHS
ID : U01 FD006868
Pays : United States
Organisme : NIDA NIH HHS
ID : UM1 DA049415
Pays : United States

Informations de copyright

Copyright © 2020 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.

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Auteurs

Elizabeth Beaulieu (E)

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA.

Catherine DiGennaro (C)

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA.

Erin Stringfellow (E)

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA.

Ava Connolly (A)

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA.

Ava Hamilton (A)

Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.

Ayaz Hyder (A)

Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, USA.

Magdalena Cerdá (M)

Center for Opioid Epidemiology and Policy, Department of Population Health, New York University School of Medicine, New York, NY, USA.

Katherine M Keyes (KM)

Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.

Mohammad S Jalali (MS)

MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, USA; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: msjalali@mgh.harvard.edu.

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Classifications MeSH