Spatio-Temporal Causal Intervention Effects for Opiate Overdose Incidents in Cincinnati, Ohio

• Activity Number: 573 - Simulation and Stochastic Bayesian Modeling
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Zehang Richard Li* and Forrest W Crawford and Joshua Warren and Katie McConnell and Gregg Gonsalves
• Companies: Yale University and Yale School of Public Health and Yale University and Yale University and Yale School of Public Health
• Keywords: spatial model; Bayesian hierarchical model; causal inference; spillover effects; opioid overdose

Abstract

Opioid use and overdose have become an important public health issues in the United States. However, understanding the spatial and temporal dynamics of opioid overdose incidents and effects of public health interventions and policy changes can be challenging. Effects may be heterogeneous across space and time, and may exhibit spillover into regions in which the intervention did not take place. Using a publicly available dataset consisting of the time/date, location, and nature of heroin-related emergency calls in the city of Cincinnati, Ohio, we use a Bayesian hierarchical model to characterize and predict the risks of overdose incidents in small areas over time, incorporating geographic, social, and demographic covariates. We characterize the predictive performance of this model, and outline a framework for estimating causal impacts of public health interventions.