The Bayesian multivariate spatiotemporal approach of Drug Overdose Surveillance in Ohio

Music City Center 

Geospatial analysis of the substance use disorder (SUD) population has provided various insights for the surveillance of the SUD population. Numerous data sources have been investigated but the chronic challenge regarding delayed reporting and the scarcity of the data still remains. To overcome this challenge, we conducted the Bayesian multivariate spatiotemporal modeling analysis using the real-time Urine drug test results for diverse sets of drugs (e.g. Fentanyl, Cocaine, Heroine and Methamphetamine). We use the multivariate Bayesian spatiotemporal approach to investigate the shared geospatial pattern of the substance use population. By looking at their shared components, we can investigate the co-evolving pattern of the drug substance use population in each county from 2013 to 2023. With this effort, we can confirm the existing belief about polysubstance use, and identify new shared patterns with newly emerged substances. We also expect information sharing of multiple drugs can help improve the estimation results of small areas. This talk will discuss the analysis results for various sets of drugs and how the map of substance use population changes in the 10-year period in Ohio.

opioid overdose

Bayesian spatiotemporal modeling

substance use disorder

public health surveillance 

Section on Statistics in Epidemiology