Department of Mathematics, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada.
Harm Reduct J. 2024 Aug 13;21(1):146. doi: 10.1186/s12954-024-01069-9.
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.
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 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.
Model 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.
Within 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.
在北美，非法阿片类药物过量的情况持续上升，是导致死亡的主要原因之一。数学建模是研究这一公共卫生问题的一种有价值的工具，因为它可以描述人口结果的关键特征，并量化结构和干预变化对过量死亡率的更广泛影响。本研究的目的是量化和预测在多伦多不受监管的阿片类药物使用者人群中，不同规模扩大的关键减少伤害策略对致命和非致命药物过量的影响。
建立了一种基于个体的阿片类药物过量模型，其中包括人群中个体的人口统计学和行为变化。确定了已知会扩大致命和非致命药物过量风险的关键个体属性，并将其纳入动态建模框架中，其中模拟人群中的每个成员都具有一组独特的特征，这些特征可用于管理人口统计学、干预措施的使用和药物过量的发生率。该模型根据 2019 年多伦多报告的致命和非致命药物过量事件进行了参数化。考虑的干预措施包括阿片类激动剂治疗（OAT）、监督消费场所（SCS）、纳洛酮带回家（THN）、药物检测以及减少药物供应中的芬太尼。相对于基线模型，探讨了减少伤害方案，以检查每种干预措施从 0%使用到 100%使用对药物过量事件的影响。
模型模拟导致 3690.6 例非致命和 295.4 例致命药物过量，与 2019 年多伦多的数据相符。从这个基线开始，在全面推广的情况下，THN 可避免 290 人死亡，消除药物供应中的芬太尼可避免 248 人死亡，SCS 使用可避免 124 人死亡，OAT 可避免 173 人死亡，药物检测服务可避免 100 人死亡。药物检测和减少药物供应中的芬太尼是唯一减少非致命药物过量的减少伤害策略。
在多方面减少伤害的方法中，扩大纳洛酮带回家和减少药物供应中的芬太尼可导致多伦多阿片类药物过量死亡率的最大减少。详细的模型模拟研究为评估和为减少伤害提供公共卫生政策提供了额外的工具。
