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A multivariate spatio-temporal model of the opioid epidemic in Ohio: A factor model approach (306605)

Keywords: opioid, spatio-temporal, Bayesian, multivariate

Opioid misuse is a significant public health issue and a national epidemic with a high prevalence of associated morbidity and mortality. The epidemic is particularly severe in Ohio which has some of the highest overdose rates in the country. It is important to understand spatial and temporal trends of the opioid epidemic to learn more about areas that are most affected and to inform potential community interventions. We propose a multivariate spatio-temporal model to leverage existing surveillance measures, opioid-associated deaths and treatment admissions, to learn about the underlying epidemic for counties in Ohio. We do this using a temporally varying spatial factor that synthesizes information from both counts to estimate common underlying risk. We demonstrate the use of this model with county-level data from 2007-2017 in Ohio. We also estimate the effects of spatially-varying covariates to further explain heterogeneity in risk across the state.