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COVID-19 Mortality in English Neighborhoods: The Relative Role of Socioeconomic and Environmental Factors

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Listed:
  • Peter Congdon

    (School of Geography, Queen Mary University of London, London E1 4NS, UK)

Abstract

Factors underlying neighborhood variation in COVID-19 mortality are important to assess in order to prioritize resourcing and policy intervention. As well as characteristics of area populations, such as health status and ethnic mix, it is important to assess the role of more specifically environmental variables (e.g., air quality, green space access). The analysis of this study focuses on neighborhood mortality variations during the first wave of the COVID-19 epidemic in England against a range of postulated area risk factors, both socio-demographic and environmental. We assess mortality gradients across levels of each risk factor and use regression methods to control for multicollinearity and spatially correlated unobserved risks. An analysis of spatial clustering is based on relative mortality risks estimated from the regression. We find mortality gradients in most risk factors showing appreciable differences in COVID mortality risk between English neighborhoods. A regression analysis shows that after allowing for health deprivation, ethnic mix, and ethnic segregation, environment (especially air quality) is an important influence on COVID mortality. Hence, environmental influences on COVID mortality risk in the UK first wave are substantial, after allowing for socio-demographic factors. Spatial clustering of high mortality shows a pronounced metropolitan-rural contrast, reflecting especially ethnic composition and air quality.

Suggested Citation

  • Peter Congdon, 2021. "COVID-19 Mortality in English Neighborhoods: The Relative Role of Socioeconomic and Environmental Factors," J, MDPI, vol. 4(2), pages 1-16, May.
    • Handle: RePEc:gam:jjopen:v:4:y:2021:i:2:p:11-146:d:555005
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    References listed on IDEAS

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    4. Kulu, Hill & Dorey, Peter, 2020. "Infection Rates from Covid-19 in Great Britain by Geographical Units: A Model-based Estimation from Mortality Data," SocArXiv 84f3e, Center for Open Science.
    5. Reich, Brian J. & Fuentes, Montserrat & Dunson, David B., 2011. "Bayesian Spatial Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 6-20.
    6. Harriet A. Washington, 2020. "How environmental racism is fuelling the coronavirus pandemic," Nature, Nature, vol. 581(7808), pages 241-241, May.
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    Cited by:

    1. Mikolai, Júlia & Dorey, Peter & Keenan, Katherine & Kulu, Hill, 2023. "Spatial patterns of COVID-19 and non-COVID-19 mortality across waves of infection in England, Wales, and Scotland," Social Science & Medicine, Elsevier, vol. 338(C).
    2. Miren Hayet-Otero & Fernando García-García & Dae-Jin Lee & Joaquín Martínez-Minaya & Pedro Pablo España Yandiola & Isabel Urrutia Landa & Mónica Nieves Ermecheo & José María Quintana & Rosario Menénde, 2023. "Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-30, April.

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