tetano
Editor, Senior Moderator
Eur J Public Health
. 2021 Mar 16;ckab036.
doi: 10.1093/eurpub/ckab036. Online ahead of print.
Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
Joana Alves[SUP] 1 2 3 [/SUP], Patr?cia Soares[SUP] 1 2 3 [/SUP], Jo?o Victor Rocha[SUP] 1 2 3 [/SUP], Rui Santana[SUP] 1 2 3 [/SUP], Carla Nunes[SUP] 1 2 3 [/SUP]
Affiliations
Abstract
Background: Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the SARS-CoV-2 infection might be different across SES in Portugal. This work identifies the 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: 1st of April, 1st of May, 1st of June, and 1st of 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 1st of April [AOR = 0.36 (0.14; 0.91)], on the 1st of May [AOR = 0.03 (0.00; 0.41)], and on the 1st of 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.
Keywords: COVID-19; coronavirus; disparities; socioeconomic inequalities; worst-affected areas.
. 2021 Mar 16;ckab036.
doi: 10.1093/eurpub/ckab036. Online ahead of print.
Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study
Joana Alves[SUP] 1 2 3 [/SUP], Patr?cia Soares[SUP] 1 2 3 [/SUP], Jo?o Victor Rocha[SUP] 1 2 3 [/SUP], Rui Santana[SUP] 1 2 3 [/SUP], Carla Nunes[SUP] 1 2 3 [/SUP]
Affiliations
- PMID: 33723606
- DOI: 10.1093/eurpub/ckab036
Abstract
Background: Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the SARS-CoV-2 infection might be different across SES in Portugal. This work identifies the 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: 1st of April, 1st of May, 1st of June, and 1st of 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 1st of April [AOR = 0.36 (0.14; 0.91)], on the 1st of May [AOR = 0.03 (0.00; 0.41)], and on the 1st of 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.
Keywords: COVID-19; coronavirus; disparities; socioeconomic inequalities; worst-affected areas.