Abstract
Ohio is one of the states most impacted by the opioid epidemic and experienced the second highest age-adjusted fatal drug overdose rate in 2017. Initially it was believed prescription opioids were driving the opioid crisis in Ohio. However, as the epidemic evolved, opioid overdose deaths due to fentanyl have drastically increased. In this work we develop a Bayesian multivariate spatiotemporal model for Ohio county overdose death rates from 2007 to 2018 due to different types of opioids. The log-odds are assumed to follow a spatially varying change point regression model. By assuming the regression coefficients are a multivariate conditional autoregressive process, we capture spatial dependence within each drug type and also dependence across drug types. The proposed model allows us to not only study spatiotemporal trends in overdose death rates but also to detect county-level shifts in these trends over time for various types of opioids.
Funding Statement
Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number R21DA045236.
Acknowledgments
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
These data were provided by the Ohio Department of Health. The Department specifically disclaims responsibility for any analyses, interpretations or conclusions.
Citation
Staci A. Hepler. Lance A. Waller. David M. Kline. "A multivariate spatiotemporal change-point model of opioid overdose deaths in Ohio." Ann. Appl. Stat. 15 (3) 1329 - 1342, September 2021. https://doi.org/10.1214/20-AOAS1415
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