Cadet Kechna, Desjardins Michael R, Morrison Christopher, Martins Silvia S
Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, United States of America.
Department of Epidemiology and Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Prev Med Rep. 2025 Feb 19;51:103012. doi: 10.1016/j.pmedr.2025.103012. eCollection 2025 Mar.
In the current wave of the opioid epidemic, the prevalence of polysubstance use continues to complicate drug-related deaths. Most studies to date use non-spatial statistical approaches to examine the association between polysubstance use and overdose risk, without considering the spatial distribution of these latent sub-patterns of use. This paper describes the utility and potential impact of using disease mapping and Bayesian spatiotemporal approaches for analyzing and monitoring polysubstance use and overdose risk to better respond to the ongoing opioid epidemic. We discuss the application of Bayesian spatiotemporal approaches in analyzing polysubstance use among people who use drugs. Bayesian spatiotemporal analyses offer a salient approach to detecting localized distributions of overdose events and tailor local interventions to community needs in order to reduce polysubstance use and related adverse health among people who use drugs. This can help improve precision and efficacy response in reducing polysubstance use adverse outcomes and optimize resource allocation.
在当前一波阿片类药物流行浪潮中，多种物质使用的普遍性继续使与药物相关的死亡情况变得复杂。迄今为止，大多数研究使用非空间统计方法来检验多种物质使用与过量用药风险之间的关联，而没有考虑这些潜在使用子模式的空间分布。本文描述了使用疾病地图绘制和贝叶斯时空方法来分析和监测多种物质使用及过量用药风险，以便更好地应对当前阿片类药物流行的效用和潜在影响。我们讨论了贝叶斯时空方法在分析吸毒者多种物质使用情况中的应用。贝叶斯时空分析为检测过量用药事件的局部分布并根据社区需求定制局部干预措施提供了一种显著方法，以减少吸毒者中的多种物质使用及相关不良健康状况。这有助于提高减少多种物质使用不良后果方面的应对精准度和效果，并优化资源分配。
