Joana Alves1,2,3, Patrícia Soares1,2,3, João Victor Rocha1,2,3, Rui Santana1,2,3, Carla Nunes1,2,3. 1. Escola Nacional de Saúde Pública, Universidade NOVA de Lisboa, Lisboa, Portugal. 2. NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisboa, Portugal. 3. Comprehensive Health Research Center (CHRC), Lisboa, Portugal.
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.
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.
Authors: Ana Gama; João Victor Rocha; Maria J Marques; Sofia Azeredo-Lopes; Ana Rita Pedro; Sónia Dias Journal: Int J Environ Res Public Health Date: 2022-02-04 Impact factor: 3.390
Authors: Florian Beese; Julia Waldhauer; Lina Wollgast; Timo-Kolja Pförtner; Morten Wahrendorf; Sebastian Haller; Jens Hoebel; Benjamin Wachtler Journal: Int J Public Health Date: 2022-08-29 Impact factor: 5.100