Public Health Interventions and Overdose-Related Outcomes Among Persons With Opioid Use Disorder.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 Apr 2024
Historique:
medline: 3 4 2024
pubmed: 3 4 2024
entrez: 3 4 2024
Statut: epublish

Résumé

Given the high number of opioid overdose deaths in the US and the complex epidemiology of opioid use disorder (OUD), systems models can serve as a tool to identify opportunities for public health interventions. To estimate the projected 3-year association between public health interventions and opioid overdose-related outcomes among persons with OUD. This decision analytical model used a simulation model of the estimated US population aged 12 years and older with OUD that was developed and analyzed between January 2019 and December 2023. The model was parameterized and calibrated using 2019 to 2020 data and used to estimate the relative change in outcomes associated with simulated public health interventions implemented between 2021 and 2023. Projected OUD and medications for OUD (MOUD) prevalence in 2023 and number of nonfatal and fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. In a baseline scenario assuming parameters calibrated using 2019 to 2020 data remained constant, the model projected more than 16 million persons with OUD not receiving MOUD treatment and nearly 1.7 million persons receiving MOUD treatment in 2023. Additionally, the model projected over 5 million nonfatal and over 145 000 fatal opioid-involved overdoses among persons with OUD between 2021 and 2023. When simulating combinations of interventions that involved reducing overdose rates by 50%, the model projected decreases of up to 35.2% in nonfatal and 36.6% in fatal opioid-involved overdoses among persons with OUD. Interventions specific to persons with OUD not currently receiving MOUD treatment demonstrated the greatest reduction in numbers of nonfatal and fatal overdoses. Combinations of interventions that increased MOUD initiation and decreased OUD recurrence were projected to reduce OUD prevalence by up to 23.4%, increase MOUD prevalence by up to 137.1%, and reduce nonfatal and fatal opioid-involved overdoses among persons with OUD by 6.7% and 3.5%, respectively. In this decision analytical model study of persons with OUD, findings suggested that expansion of evidence-based interventions that directly reduce the risk of overdose fatality among persons with OUD, such as through harm reduction efforts, could engender the highest reductions in fatal overdoses in the short-term. Interventions aimed at increasing MOUD initiation and retention of persons in treatment projected considerable improvement in MOUD and OUD prevalence but could require a longer time horizon for substantial reductions in opioid-involved overdoses.

Identifiants

pubmed: 38568691
pii: 2817002
doi: 10.1001/jamanetworkopen.2024.4617
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e244617

Auteurs

Nisha Nataraj (N)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

S Michaela Rikard (SM)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Kun Zhang (K)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Xinyi Jiang (X)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Gery P Guy (GP)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Ketra Rice (K)

Division of Injury Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Christine L Mattson (CL)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

R Matthew Gladden (RM)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Desiree M Mustaquim (DM)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Zachary N Illg (ZN)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.
Department of Emergency Medicine, Emory University School of Medicine, Atlanta, Georgia.

Puja Seth (P)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Rita K Noonan (RK)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Jan L Losby (JL)

Division of Overdose Prevention, National Center for Injury Prevention and Control, US Centers for Disease Control and Prevention, Atlanta, Georgia.

Classifications MeSH