Spatial pattern of COVID-19 deaths and infections in small areas of Brazil

Autor(es)

Campos de Lima, Everton Emanuel ^[1] ; Gayawan, Ezra ^[2] ; Baptista, Emerson Augusto ^[3] ; Queiroz, Bernardo Lanza ^[4]

Afiliação do(s) autor(es)

^[1] Univ Campinas UNICAMP, Coll Philosophy & Human Sci IFCH, Campinas - Brazil

^[2] Fed Univ Technol Akure, Dept Stat, Akure - Nigeria

^[3] Shanghai Univ, Asian Demog Res Inst ADRI, Shanghai - Peoples R China

^[4] Univ Fed Minas Gerais UFMG, Dept Demog, Belo Horizonte, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 16, n. 2 FEB 11 2021.
• Citações Web of Science: 3

Resumo

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths. (AU)

• Processo FAPESP: 18/18649-7 - Análise de pequenas áreas de níveis e dispersão em mortalidade específica por causas no Brasil e na África Subsaariana através do uso de modelos bayesianos para dados de proporção
• Beneficiário: Everton Emanuel Campos de Lima
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Internacional