tetano
Editor, Senior Moderator
Travel Med Infect Dis
. 2020 Dec 2;101945.
doi: 10.1016/j.tmaid.2020.101945. Online ahead of print.
Spatiotemporal ecological study of COVID-19 mortality in the city of S?o Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions
Patricia Marques Moralejo Bermudi[SUP] 1 [/SUP], Camila Lorenz[SUP] 2 [/SUP], Breno Souza de Aguiar[SUP] 3 [/SUP], Marcelo Antunes Failla[SUP] 3 [/SUP], Ligia Vizeu Barrozo[SUP] 4 [/SUP], Francisco Chiaravalloti Neto[SUP] 1 [/SUP]
Affiliations
Abstract
Background: Currently, Brazil is experiencing one of the fastest increasing coronavirus disease (COVID-19) mortality rates worldwide, with a minimum of 158,000 confirmed deaths presently. The city of S?o Paulo is particularly vulnerable because it is the most populated city in Brazil. Thus, this study aimed to analyse COVID-19 mortality in a spatiotemporal context in S?o Paulo, with respect to socio-economic levels.
Method: We modelled the deaths using spatiotemporal architectures and Poisson probability distributions using a latent Gaussian Bayesian model approach.
Results: Both total deaths and confirmed deaths showed similar spatial patterns. Mortality was higher in men and increased with age. The most critical period regarding mortality occurred between the 20[SUP]th[/SUP] and 23[SUP]rd[/SUP] epidemiological weeks, followed by an apparent stabilisation of the epidemiological trend. The risk of death was greater in areas with the worst social conditions during the study period. However, this pattern was not uniform over time, since we identified a shift of high risk from the areas with the best socio-economic conditions to those with the worst conditions.
Conclusions: Our study corroborated the relationship between COVID-19 mortality and socio-economic conditions, revealing the importance of geographic screening in the integration of better actions to face the pandemic.
Keywords: Integrated Nested Laplace Approximation; Mortality; Pandemics; Spatio-Temporal Analysis; health inequity.
. 2020 Dec 2;101945.
doi: 10.1016/j.tmaid.2020.101945. Online ahead of print.
Spatiotemporal ecological study of COVID-19 mortality in the city of S?o Paulo, Brazil: Shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions
Patricia Marques Moralejo Bermudi[SUP] 1 [/SUP], Camila Lorenz[SUP] 2 [/SUP], Breno Souza de Aguiar[SUP] 3 [/SUP], Marcelo Antunes Failla[SUP] 3 [/SUP], Ligia Vizeu Barrozo[SUP] 4 [/SUP], Francisco Chiaravalloti Neto[SUP] 1 [/SUP]
Affiliations
- PMID: 33278610
- DOI: 10.1016/j.tmaid.2020.101945
Abstract
Background: Currently, Brazil is experiencing one of the fastest increasing coronavirus disease (COVID-19) mortality rates worldwide, with a minimum of 158,000 confirmed deaths presently. The city of S?o Paulo is particularly vulnerable because it is the most populated city in Brazil. Thus, this study aimed to analyse COVID-19 mortality in a spatiotemporal context in S?o Paulo, with respect to socio-economic levels.
Method: We modelled the deaths using spatiotemporal architectures and Poisson probability distributions using a latent Gaussian Bayesian model approach.
Results: Both total deaths and confirmed deaths showed similar spatial patterns. Mortality was higher in men and increased with age. The most critical period regarding mortality occurred between the 20[SUP]th[/SUP] and 23[SUP]rd[/SUP] epidemiological weeks, followed by an apparent stabilisation of the epidemiological trend. The risk of death was greater in areas with the worst social conditions during the study period. However, this pattern was not uniform over time, since we identified a shift of high risk from the areas with the best socio-economic conditions to those with the worst conditions.
Conclusions: Our study corroborated the relationship between COVID-19 mortality and socio-economic conditions, revealing the importance of geographic screening in the integration of better actions to face the pandemic.
Keywords: Integrated Nested Laplace Approximation; Mortality; Pandemics; Spatio-Temporal Analysis; health inequity.