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Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach
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 effo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8771626/ https://www.ncbi.nlm.nih.gov/pubmed/35075346 http://dx.doi.org/10.1007/s00477-022-02175-5 |
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author | Adin, A. Congdon, P. Santafé, G. Ugarte, M. D. |
author_facet | Adin, A. Congdon, P. Santafé, G. Ugarte, M. D. |
author_sort | Adin, A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8771626 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-87716262022-01-20 Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach Adin, A. Congdon, P. Santafé, G. Ugarte, M. D. Stoch Environ Res Risk Assess Original Paper 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. Springer Berlin Heidelberg 2022-01-20 2022 /pmc/articles/PMC8771626/ /pubmed/35075346 http://dx.doi.org/10.1007/s00477-022-02175-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Paper Adin, A. Congdon, P. Santafé, G. Ugarte, M. D. Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title | Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title_full | Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title_fullStr | Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title_full_unstemmed | Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title_short | Identifying extreme COVID-19 mortality risks in English small areas: a disease cluster approach |
title_sort | identifying extreme covid-19 mortality risks in english small areas: a disease cluster approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8771626/ https://www.ncbi.nlm.nih.gov/pubmed/35075346 http://dx.doi.org/10.1007/s00477-022-02175-5 |
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