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，也是对美国的一个重大毒品相关威胁。同时规模 虽然类阿片滥用问题的严重性是不可否认的，但缺乏对当地类阿片滥用流行率的估计。 这种地方估计对于优化有针对性的预防资源分配至关重要 和治疗方案来阻止这一流行病的蔓延。该项目的目标是开发一个新的空间- 时间证据综合方法估计县级阿片类药物滥用率。为此， 丰度建模和证据合成将用于在 贝叶斯范式。该模型的基础将基于丰度建模， 纳入县级社会环境协变量信息，同时考虑空间和 时间依赖性从证据合成的想法将用于合成常规监测数据 提供了关于县级患病率的间接信息和基于专家的先验信息， 的观点和外部来源。通过采用这种方法，监测数据，如个人计数， 因阿片类药物滥用而进入治疗和因阿片类药物滥用而死亡，可以用来为估计 实际利率，阿片类药物滥用的县级流行率及其与社会 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.