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

• Activity Number: 12 - High-Dimensional Parameter Learning on Spatio-Temporal Hidden Markov Models and Its Applications in Epidemiology
• Type: Invited
• Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
• Sponsor: IMS
• Author(s): Staci Hepler* and David Kline and Lance Waller
• Companies: Wake Forest University and The Ohio State University and Emory University
• Keywords: spatial; abundance; opioid; epidemiology

Abstract

Opioid misuse is a national epidemic and a significant drug related threat to the United States. While the scale of the problem is undeniable, estimates of the local prevalence of opioid misuse are lacking, despite their importance to policy-making and resource allocation. This is due, in part, to the challenge of directly measuring opioid misuse at a local level. In this talk, we develop a spatio-temporal abundance model that integrates indirect county-level data on opioid overdose deaths and treatment admissions with state-level survey estimates on rates of opioid misuse to estimate the latent county-level rates of misuse. We investigate the performance of the proposed model via simulation and apply it to data from the state of Ohio.