A Dynamic Spatial Factor Model to Describe the Opioid Syndemic in Ohio.
A Dynamic Spatial Factor Model to Describe the Opioid Syndemic in Ohio.
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描述俄亥俄州阿片类药物流行病的动态空间因素模型。
DOI：
10.1097/ede.0000000000001617
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Hepler,StaciA
中科院分区：
文献类型：
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作者：
Kline,David;Waller,LanceA;McKnight,Erin;Bonny,Andrea;Miller,WilliamC;Hepler,StaciA
背景：阿片类药物在美国的流行已经持续了20多年。随着阿片类药物的滥用越来越多地转向注射非法生产的阿片类药物，它与艾滋病毒和丙型肝炎的传播有关。方法：获取2014~2019年年度县级阿片类药物过量死亡人数、阿片类药物滥用治疗入院情况、新诊断的急、慢性丙型肝炎和新诊断的HIV病例。结合集合点的概念框架，我们建立了一个动态的空间因素模型来描述俄亥俄州各县的阿片类药物集合点，并估计了各流行病之间的复杂协同效应。结果：我们估计了表征集合点在空间和时间上变化的三个潜在因素。第一个因素反映了总体负担，在俄亥俄州南部最大。第二个因素描述了危害，在城市县是最严重的。第三个因素突出了丙型肝炎感染率高于预期而艾滋病毒感染率低于预期的县，这表明未来艾滋病毒暴发的局部风险增加。结论：通过动态空间因素的估计，我们能够估计复杂的相关性，并表征构成总体的结果之间的协同作用。潜在因素总结了跨多个空间时间序列的共同变化，并提供了新的洞察，以了解流行病之间的关系。我们的框架为综合复杂的相互作用和估计潜在的变异来源提供了一种连贯的方法，可以应用于其他合成词。
Background:The opioid epidemic has been ongoing for over 20 years in the United States. As opioid misuse has shifted increasingly toward injection of illicitly produced opioids, it has been associated with HIV and hepatitis C transmission. These epidemics interact to form the opioid syndemic.Methods:We obtain annual county-level counts of opioid overdose deaths, treatment admissions for opioid misuse, and newly diagnosed cases of acute and chronic hepatitis C and newly diagnosed HIV from 2014 to 2019. Aligned with the conceptual framework of syndemics, we develop a dynamic spatial factor model to describe the opioid syndemic for counties in Ohio and estimate the complex synergies between each of the epidemics.Results:We estimate three latent factors characterizing variation of the syndemic across space and time. The first factor reflects overall burden and is greatest in southern Ohio. The second factor describes harms and is greatest in urban counties. The third factor highlights counties with higher than expected hepatitis C rates and lower than expected HIV rates, which suggests elevated localized risk for future HIV outbreaks.Conclusions:Through the estimation of dynamic spatial factors, we are able to estimate the complex dependencies and characterize the synergy across outcomes that underlie the syndemic. The latent factors summarize shared variation across multiple spatial time series and provide new insights into the relationships between the epidemics within the syndemic. Our framework provides a coherent approach for synthesizing complex interactions and estimating underlying sources of variation that can be applied to other syndemics.