Spatiotemporal dynamic of COVID-19 mortality in the city of Sao Paulo, Brazil: shifting the high risk from the best to the worst socio-economic conditions

TL;DR

This study analyzed the spatiotemporal distribution of COVID-19 mortality in Sao Paulo, revealing a shift in high-risk areas from better to worse socio-economic conditions, emphasizing targeted public health interventions.

Contribution

It introduces a Bayesian spatiotemporal model to identify shifting high-risk areas based on socio-economic factors in Sao Paulo during 2020.

Findings

Mortality increased with age and was higher in men.

Risk was greater in areas with worse social conditions.

High-risk areas shifted from better to worse socio-economic zones.

Abstract

Currently, Brazil has one of the fastest increasing COVID-19 epidemics in the world, that has caused at least 94 thousand confirmed deaths until now. The city of Sao Paulo is particularly vulnerable because it is the most populous in the country. Analyzing the spatiotemporal dynamics of COVID-19 is important to help the urgent need to integrate better actions to face the pandemic. Thus, this study aimed to analyze the COVID-19 mortality, from March to July 2020, considering the spatio-time architectures, the socio-economic context of the population, and using a fine granular level, in the most populous city in Brazil. For this, we conducted an ecological study, using secondary public data from the mortality information system. We describe mortality rates for each epidemiological week and the entire period by sex and age. We modelled the deaths using spatiotemporal and spatial architectures and Poisson probability distributions in a latent Gaussian Bayesian model approach. We obtained the relative risks for temporal and spatiotemporal trends and socio-economic conditions. To reduce possible sub notification, we considered the confirmed and suspected deaths. Our findings showed an apparent stabilization of the temporal trend, at the end of the period, but that may change in the future. Mortality rate increased with increasing age and was higher in men. The risk of death was greater in areas with the worst social conditions throughout the study period. However, this was not a uniform pattern over time, since we identified a shift from the high risk in the areas with best socio-economic conditions to the worst ones. Our study contributed by emphasizing the importance of geographic screening in areas with a higher risk of death, and, currently, worse socio-economic contexts, as a crucial aspect to reducing disease mortality and health inequities, through integrated public health actions.