The impact of health inequity on spatial variation of COVID-19 transmission in England
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Considerable spatial heterogeneity has been observed in COVID-19 transmission across administrative areas of England throughout the pandemic. This study investigates what drives these differences. We constructed a probabilistic case count model for 306 administrative areas of England across 95 weeks, fit using a Bayesian evidence synthesis framework. We incorporate the impact of acquired immunity, of spatial exportation of cases, and 16 spatially-varying socio-economic, socio-demographic, health, and mobility variables. Model comparison assesses the relative contributions of these respective mechanisms. We find that spatially-varying and time-varying differences in week-to-week transmission were definitively associated with differences in: time spent at home, variant-of-concern proportion, and adult social care funding. However, model comparison demonstrates that the impact of these terms is negligible compared to the role of spatial exportation between administrative areas. While these results confirm the impact of some, but not all, static measures of spatially-varying inequity in England, our work corroborates the finding that observed differences in disease transmission during the pandemic were predominantly driven by underlying epidemiological factors rather than aggregated metrics of demography and health inequity between areas. Further work is required to assess how health inequity more broadly contributes to these epidemiological factors.
Date Issued
2024-05
Date Acceptance
2024-05-07
Citation
PLoS Computational Biology, 2024, 20 (5)
ISSN
1553-734X
Publisher
Public Library of Science (PLoS)
Journal / Book Title
PLoS Computational Biology
Volume
20
Issue
5
Copyright Statement
© 2024 Rawson et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38805483
Subjects
Bayes Theorem
COVID-19
England
Health Status Disparities
Humans
Models, Statistical
Pandemics
SARS-CoV-2
Socioeconomic Factors
Publication Status
Published
Coverage Spatial
United States
Article Number
e1012141
Date Publish Online
2024-05-28