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Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach.
Adin, A; Congdon, P; Santafé, G; Ugarte, M D.
  • Adin A; Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
  • Congdon P; Institute for Advanced Materials and Mathematics (INAMAT2), Public University of Navarre, Pamplona, Spain.
  • Santafé G; School of Geography, Queen Mary University of London, London, UK.
  • Ugarte MD; Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
Stoch Environ Res Risk Assess ; 36(10): 2995-3010, 2022.
Article in English | MEDLINE | ID: covidwho-1941673
ABSTRACT
The COVID-19 pandemic is having a huge impact worldwide and has highlighted the extent of health inequalities between countries but also in small areas within a country. Identifying areas with high mortality is important both of public health mitigation in COVID-19 outbreaks, and of longer term efforts to tackle social inequalities in health. In this paper we consider different statistical models and an extension of a recent method to analyze COVID-19 related mortality in English small areas during the first wave of the epidemic in the first half of 2020. We seek to identify hotspots, and where they are most geographically concentrated, taking account of observed area factors as well as spatial correlation and clustering in regression residuals, while also allowing for spatial discontinuities. Results show an excess of COVID-19 mortality cases in small areas surrounding London and in other small areas in North-East and and North-West of England. Models alleviating spatial confounding show ethnic isolation, air quality and area morbidity covariates having a significant and broadly similar impact on COVID-19 mortality, whereas nursing home location seems to be slightly less important.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Stoch Environ Res Risk Assess Year: 2022 Document Type: Article Affiliation country: S00477-022-02175-5

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Stoch Environ Res Risk Assess Year: 2022 Document Type: Article Affiliation country: S00477-022-02175-5