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Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study

BACKGROUND: Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be different across SES in Portugal. This work identifies the coro...

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Autores principales: Alves, Joana, Soares, Patrícia, Rocha, João Victor, Santana, Rui, Nunes, Carla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989252/
https://www.ncbi.nlm.nih.gov/pubmed/33723606
http://dx.doi.org/10.1093/eurpub/ckab036
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author Alves, Joana
Soares, Patrícia
Rocha, João Victor
Santana, Rui
Nunes, Carla
author_facet Alves, Joana
Soares, Patrícia
Rocha, João Victor
Santana, Rui
Nunes, Carla
author_sort Alves, Joana
collection PubMed
description BACKGROUND: Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be different across SES in Portugal. This work identifies the coronavirus disease 2019 (COVID-19) worst-affected municipalities at four different time points in Portugal measured by prevalence of cases, and seeks to determine if these worst-affected areas are associated with SES. METHODS: The worst-affected areas were defined using the spatial scan statistic for the cumulative number of cases per municipality. The likelihood of being in a worst-affected area was then modelled using logistic regressions, as a function of area-based SES and health services supply. The analyses were repeated at four different time points of the COVID-19 pandemic: 1 April, 1 May, 1 June, and 1 July, corresponding to two moments before and during the confinement period and two moments thereafter. RESULTS: Twenty municipalities were identified as worst-affected areas in all four time points, most in the coastal area in the Northern part of the country. The areas of lower unemployment were less likely to be a worst-affected area on the 1 April [adjusted odds ratio (AOR) = 0.36 (0.14–0.91)], 1 May [AOR = 0.03 (0.00–0.41)] and 1 July [AOR = 0.40 (0.16–1.05)]. CONCLUSION: This study shows a relationship between being in a worst-affected area and unemployment. Governments and public health authorities should formulate measures and be prepared to protect the most vulnerable groups.
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spelling pubmed-79892522021-04-01 Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study Alves, Joana Soares, Patrícia Rocha, João Victor Santana, Rui Nunes, Carla Eur J Public Health Covid-19 BACKGROUND: Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be different across SES in Portugal. This work identifies the coronavirus disease 2019 (COVID-19) worst-affected municipalities at four different time points in Portugal measured by prevalence of cases, and seeks to determine if these worst-affected areas are associated with SES. METHODS: The worst-affected areas were defined using the spatial scan statistic for the cumulative number of cases per municipality. The likelihood of being in a worst-affected area was then modelled using logistic regressions, as a function of area-based SES and health services supply. The analyses were repeated at four different time points of the COVID-19 pandemic: 1 April, 1 May, 1 June, and 1 July, corresponding to two moments before and during the confinement period and two moments thereafter. RESULTS: Twenty municipalities were identified as worst-affected areas in all four time points, most in the coastal area in the Northern part of the country. The areas of lower unemployment were less likely to be a worst-affected area on the 1 April [adjusted odds ratio (AOR) = 0.36 (0.14–0.91)], 1 May [AOR = 0.03 (0.00–0.41)] and 1 July [AOR = 0.40 (0.16–1.05)]. CONCLUSION: This study shows a relationship between being in a worst-affected area and unemployment. Governments and public health authorities should formulate measures and be prepared to protect the most vulnerable groups. Oxford University Press 2021-03-16 /pmc/articles/PMC7989252/ /pubmed/33723606 http://dx.doi.org/10.1093/eurpub/ckab036 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the European Public Health Association. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Covid-19
Alves, Joana
Soares, Patrícia
Rocha, João Victor
Santana, Rui
Nunes, Carla
Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title_full Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title_fullStr Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title_full_unstemmed Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title_short Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
title_sort evolution of inequalities in the coronavirus pandemics in portugal: an ecological study
topic Covid-19
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989252/
https://www.ncbi.nlm.nih.gov/pubmed/33723606
http://dx.doi.org/10.1093/eurpub/ckab036
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