Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals

Andrea Brizzi, Charles Whittaker, Luciana M.S. Servo, Iwona Hawryluk, Carlos A. Prete, William M. de Souza, Renato S. Aguiar, Leonardo J.T. Araujo, Leonardo S. Bastos, Alexandra Blenkinsop, Lewis F. Buss, Darlan Candido, Marcia C. Castro, Silvia F. Costa, Julio Croda, Andreza Aruska de Souza Santos, Christopher Dye, Seth Flaxman, Paula L.C. Fonseca, Victor E.V. GeddesBernardo Gutierrez, Philippe Lemey, Anna S. Levin, Thomas Mellan, Diego M. Bonfim, Xenia Miscouridou, Swapnil Mishra, Mélodie Monod, Filipe R.R. Moreira, Bruce Nelson, Rafael H.M. Pereira, Otavio Ranzani, Ricardo P. Schnekenberg, Elizaveta Semenova, Raphael Sonabend, Renan P. Souza, Xiaoyue Xi, Ester C. Sabino, Nuno R. Faria, Samir Bhatt, Oliver Ratmann

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Gamma variant of concern has spread rapidly across Brazil since late 2020, causing substantial infection and death waves. Here we used individual-level patient records after hospitalization with suspected or confirmed coronavirus disease 2019 (COVID-19) between 20 January 2020 and 26 July 2021 to document temporary, sweeping shocks in hospital fatality rates that followed the spread of Gamma across 14 state capitals, during which typically more than half of hospitalized patients aged 70 years and older died. We show that such extensive shocks in COVID-19 in-hospital fatality rates also existed before the detection of Gamma. Using a Bayesian fatality rate model, we found that the geographic and temporal fluctuations in Brazil’s COVID-19 in-hospital fatality rates were primarily associated with geographic inequities and shortages in healthcare capacity. We estimate that approximately half of the COVID-19 deaths in hospitals in the 14 cities could have been avoided without pre-pandemic geographic inequities and without pandemic healthcare pressure. Our results suggest that investments in healthcare resources, healthcare optimization and pandemic preparedness are critical to minimize population-wide mortality and morbidity caused by highly transmissible and deadly pathogens such as SARS-CoV-2, especially in low- and middle-income countries.

Original languageEnglish
Pages (from-to)1476-1485
Number of pages10
JournalNature Medicine
Volume28
Issue number7
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Funding

We thank all contributors to GISAID for making SARS-CoV-2 sequence data and metadata publicly available, as listed in Methods ; all contributors to Rede Genomica Fiocruz for making SARS-CoV-2 variant frequency data publicly available; all members of the CADDE network for their comments throughout the project and earlier versions of the manuscript; O. G. Pybus, A. Rambaut and J. T. McCrone for their insightful comments on SARS-CoV-2 phylogenetic analyses; and the Imperial College Research Computing Service (https://doi.org/10.14469/hpc/2232) for providing the computational resources to perform this study. We thank all contributors to GISAID for making SARS-CoV-2 sequence data and metadata publicly available, as listed in ; all contributors to Rede Genomica Fiocruz for making SARS-CoV-2 variant frequency data publicly available; all members of the CADDE network for their comments throughout the project and earlier versions of the manuscript; O. G. Pybus, A. Rambaut and J. T. McCrone for their insightful comments on SARS-CoV-2 phylogenetic analyses; and the Imperial College Research Computing Service ( https://doi.org/10.14469/hpc/2232 ) for providing the computational resources to perform this study. This study was supported by the Medical Research Council-São Paulo Research Foundation (FAPESP) CADDE partnership award (MR/S0195/1 and FAPESP18/143890) ( https://caddecentre.org ), the Bill & Melinda Gates Foundation (INV-034540 and INV-034652) and the EPSRC through the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning at Imperial and Oxford (EP/S023151/1). The authors acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the UK Medical Reseach Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement, and also part of the EDCTP2 programme supported by the European Union. R.S.A. acknowledges support from the Rede Coronaômica BR MCTI/FINEP affiliated to RedeVírus/MCTI (FINEP 01.20.0029.000462/20, CNPq 404096/20204), from CNPq (312688/2017-2 and 439119/2018-9), from MEC/CAPES (14/2020 -23072.211119/2020-10) and from FINEP (0494/20 01.20.0026.00). S.B. acknowledges support from UK Research and Innovation (MR/V038109/1), the MRC (MR/R015600/1), the Novo Nordisk Young Investigator Award (NNF20OC0059309), the Danish National Research Foundation via a chair position and the NIHR Health Protection Research Unit in Modelling Methodology. L.S.B. acknowledges support from Inova Fiocruz (48401485034116). D.S.C. acknowledges support from the Clarendon Fund, the University of Oxford Department of Zoology and Merton College. N.R.F. acknowledges support from the Wellcome Trust and the Royal Society (Sir Henry Dale Fellowship. 204311/Z/16/Z). S.F. acknowledges support from the EPSRC (EP/V002910/2). C.A.P. acknowledges support from FAPESP (2019/21858-0) and Fundação Faculdade de Medicina and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES). O.T.R. acknowledges support from the Instituto de Salud Carlos III (Sara Borrell fellowship, CD19/00110), from the Spanish Ministry of Science and Innovation and State Research Agency through the ‘Centro de Excelencia Severo Ochoa 2019-2023’ program (CEX2018-000806-S) and from the ‘Generalitat de Catalunya’ through the CERCA program. O.R. acknowledges support from the Bill & Melinda Gates Foundation (OPP1175094) and the Medical Research Council (MR/V038109/1). R.P.S. acknowledges support from the Rede Coronaômica BR MCTI/FINEP affiliated with RedeVírus/MCTI (FINEP 01.20.0029.000462/20 and CNPq 404096/2020-4), from CNPq (310627/2018-4), from MEC/CAPES (14/2020 – 23072.211119/2020-10), from FINEP (0494/20 01.20.0026.00) and from FAPEMIG (APQ-00475-20). W.M.S. acknowledges support from the Global Virus Network Fellowship and the National Institutes of Health (AI12094). C.W. acknowledges support from the Medical Research Council (Doctoral Training Partnership Studentship 1975152). P.L. acknowledges support from the European Research Council (grant 725422).

FundersFunder number
Centro de Excelencia Severo Ochoa 2019-2023CEX2018-000806-S
Commonwealth & Development Office
Fundação Universitaria do ABC, Faculdade de Medicina do ABC
Imperial College Research Computing Service
Inova Fiocruz48401485034116
Medical Research Council-São Paulo Research FoundationFAPESP18/143890, MR/S0195/1
Spanish Ministry of Science and Innovation and State Research Agency
University of Oxford Department of Zoology and Merton College
National Institutes of Health (NIH)AI12094, 1975152
Bill and Melinda Gates FoundationINV-034540, INV-034652
Wellcome Trust
UK Research and Innovation Science and Technology Facilities CouncilMR/V038109/1
Medical Research Council404096/2020-4, 310627/2018-4, MR/R015600/1
Engineering and Physical Sciences Research CouncilEP/S023151/1
National Institute for Health and Care Research
Royal Society of Medicine204311/Z/16/Z, EP/V002910/2, 2019/21858-0
European Commission
H2020 European Research Council725422
Danmarks Grundforskningsfond
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior14/2020 -23072.211119/2020-10
ICREA Foundation-Generalitat de CatalunyaOPP1175094
Ministério da Ciência, Tecnologia e Inovação
Conselho Nacional de Desenvolvimento Científico e Tecnológico312688/2017-2, 439119/2018-9
Medical Engineering Centre King’s College London
Novo Nordisk A/SNNF20OC0059309
Instituto de Salud Carlos IIICD19/00110
Financiadora de Estudos e Projetos0494/20 01.20.0026.00
Fundação de Amparo à Pesquisa do Estado de Minas GeraisAPQ-00475-20
Clarendon Fund
Foreign, Commonwealth and Development Office

    ASJC Scopus subject areas

    • General Biochemistry, Genetics and Molecular Biology

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