Using synthetic populations to understand geospatial patterns in opioid related overdose and predicted opioid misuse
Savannah Bates (),
James Rineer and
Georgiy Bobashev ()
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Savannah Bates: North Carolina State University
Vasiliy Leonenko: ITMO University
James Rineer: RTI International
Georgiy Bobashev: RTI International
Computational and Mathematical Organization Theory, 2019, vol. 25, issue 1, No 4, 36-47
Abstract Ohio is leading the nation in an epidemic of overdose deaths, most of which are caused by opioids. Through this study we estimate associations between opioid drug overdoses measured as EMS calls and model-predicted drug misuse. The RTI-developed synthetic population statistically represents every household in Cincinnati and allows one to develop a geographically explicit model that links Cincinnati EMS data, and other datasets. From the publicly available National Survey on Drug Use and Health (NSDUH), we developed a model of opioid misuse and assigned probability of misuse to each synthetic individual. We then analyzed EMS overdose data in the context of local level misuse and demographic characteristics. The main results show locations where there is a dramatic variation in ratio values between overdose events and the number of misusers. We concluded that, for optimal efficacy, intervention strategies should consider the existence of exceptional geographic locations with extremely high or low values of this ratio.
Keywords: Opioids; Synthetic populations; Data linkage; Overdose (search for similar items in EconPapers)
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