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10.1093/eurpub/ckab036 http://scihub22266oqcxt.onion/10.1093/eurpub/ckab036 33723606!7989252!33723606     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Public+Health 2021 ; 31 (5): 1069-1075 Nephropedia Template TP {{tp|p=33723606|t=2021. Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study |pdf=|usr=}}{{33723606}} gab.com Text Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study JO: Eur J Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33723606 #FOAvip 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. Twit Text FOAVip Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study ????Eur J Public Health http://www.kidney.de/pget.php?&tw=1&pmid=33723606 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33723606 #FOAvip English Wikipedia <ref>{{Cite pmid|33723606}}</ref> Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study #MMPMID33723606 Alves J; Soares P; Rocha JV; Santana R; Nunes C Eur J Public Health 2021[Oct]; 31 (5): 1069-1075 PMID33723606show ga 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. |*COVID-19[MESH] |*Pandemics[MESH] |Humans[MESH] |Portugal/epidemiology[MESH] |Prevalence[MESH]Evolution of inequalities in the coronavirus pandemics in Portugal: an(epmc).pdf DeepDyve Pubget Overpricing