Author: Alves, Joana; Soares, PatrÃcia; Rocha, João Victor; Santana, Rui; Nunes, Carla
Title: Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study Cord-id: hrw67jvi Document date: 2021_3_16
ID: hrw67jvi
Snippet: 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 spati
Document: 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: 1(st) of April, 1(st) of May, 1(st) of June, and 1(st) 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 1(st) of April [AOR = 0.36 (0.14; 0.91)], on the 1(st) of May [AOR = 0.03 (0.00; 0.41)], and on the 1(st) 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.
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