Exploring County-level Spatio-temporal Patterns in Opioid Overdose related Emergency Department Visits

Abstract

Opioid overdoses within the United States are rising at alarming rates. This has been negatively impacting the social and economic well-being of the country. Due to the differences in the socio-ecological factors, the geographical distribution of opioid overdose rates differ across various regions of the United States. Thus, a thorough analysis of the opioid overdose trends at the county-level is crucial in understanding the resource allocation needs and for solving the crisis swiftly. In this study, we analyzed the county-level spatio-temporal variations in opioid overdose rates by combining techniques from spatial statistics, geographical information system (GIS), and data mining. Furthermore, we implemented multilevel modeling to model the temporal changes in the overdose rates and to determine the associated risk factors. Data on emergency department opioid overdose (EDOOD) visits and socio-ecological factors from the state of Virginia was utilized for the analyses. The same approach can be applied for other states without big modification, given similar data. The study identified that the counties within the state of Virginia had notable differences in their EDOOD visit rates with many counties in the southwestern region being consistently identified as the hotspots (areas with higher concentration of  EDOOD visits). Similarly, it was noted that most counties had decreasing EDOOD visit rates from 2016 to 2018 which seemed to increase by 2019. The changes in the visit rates were found to be significantly associated with the changes in socioeconomic factors and access to clinical care. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This research was supported in part by the NSF grants DGE: 1922598 (PI: Sikdar) and 1945764 (PI: Sikdar), and the NIH grant F31-DA051154 (PI: Gonçalves). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Data Availability

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