Evidence map›Paper›PMID 39135022›Full record

ArticleHarm reduction journal2024

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

Kirsten Gallant, Ryan Lukeman

Abstract read
In one paragraph

Article in Harm reduction journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Kirsten GallantDepartment of Mathematics, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada.
Ryan LukemanDepartment of Mathematics, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada. rlukeman@stfx.ca.

Funding

Natural Sciences and Engineering Research Council of Canada RGPIN 2017-06094
6 · The paper itself

Abstract

backgroundIllicit 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.

methodsAn 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 risk of fatal and nonfatal overdose were identified and incorporated into a dynamic modeling framework, wherein every member of the simulated population encompasses a set of distinct characteristics that govern demographics, intervention usage, and overdose incidence. The model was parametrized to fatal and nonfatal overdose events reported in Toronto in 2019. The interventions considered were opioid agonist therapy (OAT), supervised consumption sites (SCS), take-home naloxone (THN), drug-checking, and reducing fentanyl in the drug supply. Harm reduction scenarios were explored relative to a baseline model to examine the impact of each intervention being scaled from 0% use to 100% use on overdose events.

resultsModel simulations resulted in 3690.6 nonfatal and 295.4 fatal overdoses, coinciding with 2019 data from Toronto. From this baseline, at full scale-up, 290 deaths were averted by THN, 248 from eliminating fentanyl from the drug supply, 124 from SCS use, 173 from OAT, and 100 by drug-checking services. Drug-checking and reducing fentanyl in the drug supply were the only harm reduction strategies that reduced the number of nonfatal overdoses.

conclusionsWithin a multi-faceted harm reduction approach, scaling up take-home naloxone, and reducing fentanyl in the drug supply led to the largest reduction in opioid overdose fatality in Toronto. Detailed model simulation studies provide an additional tool to assess and inform public health policy on harm reduction.

Indexed as

Harm ReductionNaloxoneNarcotic AntagonistsOpiate OverdoseOpioid-Related DisordersAdolescentAdultAnalgesics, OpioidDrug OverdoseFemaleFentanylHumansMaleMiddle AgedModels, TheoreticalOntarioAnalgesics, OpioidFentanylNaloxoneNarcotic AntagonistsDrug checkingHarm reductionIndividual-based modelInterventionsMathematical modelMicrosimulation modelOpioidsOpioid use disorderOverdose riskPWIDTake-home naloxone

Identifiers

PMID39135022
PMCPMC11321061

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.