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

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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.