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. 2024 Feb 7:25:101621.
doi: 10.1016/j.ssmph.2024.101621. eCollection 2024 Mar.

Social determinants of spatial inequalities in COVID-19 outcomes across England: A multiscale geographically weighted regression analysis

Esmaeil Khedmati Morasae  1 , Daniel W Derbyshire  2 , Payam Amini  3 , Tahera Ebrahimi  4
Affiliations

Social determinants of spatial inequalities in COVID-19 outcomes across England: A multiscale geographically weighted regression analysis

Esmaeil Khedmati Morasae et al. SSM Popul Health. .

Abstract

A variety of factors are associated with greater COVID-19 morbidity or mortality, due to how these factors influence exposure to (in the case of morbidity) or severity of (in the case of mortality) COVID-19 infections. We use multiscale geographically weighted regression to study spatial variation in the factors associated with COVID-19 morbidity and mortality rates at the local authority level across England (UK). We investigate the period between March 2020 and March 2021, prior to the rollout of the COVID-19 vaccination program. We consider a variety of factors including demographic (e.g. age, gender, and ethnicity), health (e.g. rates of smoking, obesity, and diabetes), social (e.g. Index of Multiple Deprivation), and economic (e.g. the Gini coefficient and economic complexity index) factors that have previously been found to impact COVID-19 morbidity and mortality. The Index of Multiple Deprivation has a significant impact on COVID-19 cases and deaths in all local authorities, although the effect is the strongest in the south of England. Higher proportions of ethnic minorities are associated with higher levels of COVID-19 mortality, with the strongest effect being found in the west of England. There is again a similar pattern in terms of cases, but strongest in the north of the country. Other factors including age and gender are also found to have significant effects on COVID-19 morbidity and mortality, with differential spatial effects across the country. The results provide insights into how national and local policymakers can take account of localized factors to address spatial health inequalities and address future infectious disease pandemics.

Keywords: COVID-19; Deprivation; Multiscale geographically weighted regression; Spatial inequalities; United Kingdom.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Directed Acyclic Graph (DAG) for COVID-19 morbidity (Panel A) and mortality (Panel B)
Fig. 1
Fig. 1
Directed Acyclic Graph (DAG) for COVID-19 morbidity (Panel A) and mortality (Panel B)
Fig. 2
Fig. 2
A map of 326 local authorities in England illustrated by red colored delineations. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3
Fig. 3
Geographical distribution of COVID-19 morbidity and mortality rates across local authorities in England (the legends show the number of cases/deaths in a color-coded format). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
Coefficients of the relationship between COVID-19 morbidity and explanatory variables derived from MGWR analysis
Fig. 4
Fig. 4
Coefficients of the relationship between COVID-19 morbidity and explanatory variables derived from MGWR analysis
Fig. 4
Fig. 4
Coefficients of the relationship between COVID-19 morbidity and explanatory variables derived from MGWR analysis
Fig. 4
Fig. 4
Coefficients of the relationship between COVID-19 morbidity and explanatory variables derived from MGWR analysis
Fig. 5
Fig. 5
Coefficients of relationship between COVID-19 mortality and explanatory variables derived from MGWR analysis
Fig. 5
Fig. 5
Coefficients of relationship between COVID-19 mortality and explanatory variables derived from MGWR analysis
Fig. 5
Fig. 5
Coefficients of relationship between COVID-19 mortality and explanatory variables derived from MGWR analysis
Fig. 5
Fig. 5
Coefficients of relationship between COVID-19 mortality and explanatory variables derived from MGWR analysis
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