An individual-based dynamic model to assess interventions to mitigate opioid overdose risk.

ABSTRACT:

Background

Illicit opioid overdose continues to rise in North America and is a leading cause of death. Mathematical modeling is a valuable tool to investigate the epidemiology of this public health issue, as it can characterize key features of population outcomes and quantify the broader effect of structural and interventional changes on overdose mortality. The aim of this study is to quantify and predict the impact of key harm reduction strategies at differing levels of scale-up on fatal and nonfatal overdose among a population of people engaging in unregulated opioid use in Toronto.

Methods

An individual-based model for opioid overdose was built featuring demographic and behavioural variation among members of the population. Key individual attributes known to scale the ris

SUBMITTER: Gallant K 

PROVIDER: S-EPMC11321061 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature