A Bayesian Spatio-Temporal Approach for Estimating County-Level Opioid Misuse Rates in Ohio

摘要 阿片类药物滥用是一种全国性的流行病，是与毒品有关的对美国的重大威胁。而天平 尽管阿片类药物滥用问题的严重性是不可否认的，但缺乏对当地阿片类药物滥用流行率的估计。 这种当地估计对于优化定向预防的资源分配至关重要。 和治疗计划，以遏制这种流行病的浪潮。这项提议的目标是开发一种新的空间- 估计县级阿片类药物滥用发生率的时间证据合成法。要做到这一点，原则 丰度建模和证据合成将用于创建新的建模框架 贝叶斯范式。该模型的基础将基于丰度建模，该模型允许 纳入县级社会环境协变量信息，同时考虑空间和 时间依赖性。来自证据合成的想法将被用于合成常规监测数据 提供有关县级流行率的间接信息和基于专家的先前信息 意见和外部来源。通过采用这种方法，监视数据，如个人计数 进入阿片类药物滥用和阿片类药物滥用死亡的治疗，可以被用来提供估计 阿片类药物滥用的实际利率、县级流行率及其与社会的关系 环境因素。

Abstract Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the opioid misuse problem is undeniable, estimates of the local prevalence of opioid misuse are lacking. Such local estimates are of the utmost importance for optimizing resource allocation for targeted prevention and treatment programs to stem the tide of this epidemic. The goal of this proposal is to develop a new spatio- temporal evidence synthesis approach to estimate county-level rates of opioid misuse. To do so, principles of abundance modeling and evidence synthesis will be used to create a new modeling framework within the Bayesian paradigm. The foundation of the model will be based on abundance modeling which allows the incorporation of county-level social environmental covariate information while accounting for spatial and temporal dependence. Ideas from evidence synthesis will be used to synthesize routine surveillance data which provide indirect information about county-level prevalence and prior information based on expert opinion and external sources. By taking this approach, surveillance data, such as counts of individuals entering treatment for opioid misuse and deaths from opioid misuse, can be leveraged to inform estimation of the actual rates of interest, county-level prevalence of opioid misuse and their association with social environmental factors.