A spatiotemporal analysis of the social determinants of health for COVID-19

Submitted: 18 September 2022
Accepted: 24 January 2023
Published: 25 May 2023
Abstract Views: 901
PDF: 377
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This research aims to uncover how the association between social determinants of health and COVID-19 cases and fatality rate have changed across time and space. To begin to understand these associations and show the benefits of analysing temporal and spatial variations in COVID-19, we utilized Geographically Weighted Regression (GWR). The results emphasize the advantages for using GWR in data with a spatial component, while showing the changing spatiotemporal magnitude of association between a given social determinant and cases or fatalities. While previous research has demonstrated the merits of GWR for spatial epidemiology, our study fills a gap in the literature, by examining a suite of variables across time to reveal how the pandemic unfolded across the US at a county-level spatial scale. The results speak to the importance of understanding the local effects that a social determinant may have on populations at the county level. From a public health perspective, these results can be used for an understanding of the disproportionate disease burden felt by different populations, while upholding and building upon trends observed in epidemiological literature.



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How to Cite

Bonzani, C., Scull, P., & Yamamoto, D. (2023). A spatiotemporal analysis of the social determinants of health for COVID-19. Geospatial Health, 18(1). https://doi.org/10.4081/gh.2023.1153

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