Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis

Detalhes bibliográficos
Autor(a) principal: Ferreira, Leonardo Souto [UNESP]
Data de Publicação: 2023
Outros Autores: Darcie Marquitti, Flavia Maria, Paixão da Silva, Rafael Lopes [UNESP], Borges, Marcelo Eduardo, Ferreira da Costa Gomes, Marcelo, Cruz, Oswaldo Gonçalves, Kraenkel, Roberto André [UNESP], Coutinho, Renato Mendes, Prado, Paulo Inácio, Bastos, Leonardo Soares
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.lana.2022.100397
http://hdl.handle.net/11449/248200
Resumo: Background: Vaccines developed between 2020 and 2021 against the SARS-CoV-2 virus were designed to diminish the severity and prevent deaths due to COVID-19. However, estimates of the effectiveness of vaccination campaigns in achieving these goals remain a methodological challenge. In this work, we developed a Bayesian statistical model to estimate the number of deaths and hospitalisations averted by vaccination of older adults (above 60 years old) in Brazil. Methods: We fit a linear model to predict the number of deaths and hospitalisations of older adults as a function of vaccination coverage in this group and casualties in younger adults. We used this model in a counterfactual analysis, simulating alternative scenarios without vaccination or with faster vaccination roll-out. We estimated the direct effects of COVID-19 vaccination by computing the difference between hypothetical and realised scenarios. Findings: We estimated that more than 165,000 individuals above 60 years of age were not hospitalised due to COVID-19 in the first seven months of the vaccination campaign. An additional contingent of 104,000 hospitalisations could have been averted if vaccination had started earlier. We also estimated that more than 58 thousand lives were saved by vaccinations in the period analysed for the same age group and that an additional 47 thousand lives could have been saved had the Brazilian government started the vaccination programme earlier. Interpretation: Our estimates provided a lower bound for vaccination impacts in Brazil, demonstrating the importance of preventing the suffering and loss of older Brazilian adults. Once vaccines were approved, an early vaccination roll-out could have saved many more lives, especially when facing a pandemic. Funding: The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brazil (Finance Code 001 to F.M.D.M. and L.S.F.), Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brazil (grant number: 315854/2020-0 to M.E.B., 141698/2018-7 to R.L.P.d.S., 313055/2020-3 to P.I.P., 311832/2017-2 to R.A.K.), Fundação de Amparo à Pesquisa do Estado de São Paulo – Brazil (contract number: 2016/01343-7 to R.A.K.), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – Brazil (grant number: E-26/201.277/2021 to L.S.B.) and Inova Fiocruz/Fundação Oswaldo Cruz – Brazil (grant number: 48401485034116) to L.S.B., O.G.C. and M.G.d.F.C. The funding agencies had no role in the conceptualization of the study.
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spelling Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysisBayesian modelCOVID-19DeathsHospitalisationPandemicSARS-CoV-2Background: Vaccines developed between 2020 and 2021 against the SARS-CoV-2 virus were designed to diminish the severity and prevent deaths due to COVID-19. However, estimates of the effectiveness of vaccination campaigns in achieving these goals remain a methodological challenge. In this work, we developed a Bayesian statistical model to estimate the number of deaths and hospitalisations averted by vaccination of older adults (above 60 years old) in Brazil. Methods: We fit a linear model to predict the number of deaths and hospitalisations of older adults as a function of vaccination coverage in this group and casualties in younger adults. We used this model in a counterfactual analysis, simulating alternative scenarios without vaccination or with faster vaccination roll-out. We estimated the direct effects of COVID-19 vaccination by computing the difference between hypothetical and realised scenarios. Findings: We estimated that more than 165,000 individuals above 60 years of age were not hospitalised due to COVID-19 in the first seven months of the vaccination campaign. An additional contingent of 104,000 hospitalisations could have been averted if vaccination had started earlier. We also estimated that more than 58 thousand lives were saved by vaccinations in the period analysed for the same age group and that an additional 47 thousand lives could have been saved had the Brazilian government started the vaccination programme earlier. Interpretation: Our estimates provided a lower bound for vaccination impacts in Brazil, demonstrating the importance of preventing the suffering and loss of older Brazilian adults. Once vaccines were approved, an early vaccination roll-out could have saved many more lives, especially when facing a pandemic. Funding: The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brazil (Finance Code 001 to F.M.D.M. and L.S.F.), Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brazil (grant number: 315854/2020-0 to M.E.B., 141698/2018-7 to R.L.P.d.S., 313055/2020-3 to P.I.P., 311832/2017-2 to R.A.K.), Fundação de Amparo à Pesquisa do Estado de São Paulo – Brazil (contract number: 2016/01343-7 to R.A.K.), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – Brazil (grant number: E-26/201.277/2021 to L.S.B.) and Inova Fiocruz/Fundação Oswaldo Cruz – Brazil (grant number: 48401485034116) to L.S.B., O.G.C. and M.G.d.F.C. The funding agencies had no role in the conceptualization of the study.Instituto de Física Teórica Universidade Estadual PaulistaObservatório COVID-19 BRInstituto de Física ‘Gleb Wataghin’ and Instituto de Biologia Universidade Estadual de CampinasFundação Oswaldo Cruz Programa de Computação CientíficaCentro de Matemática Computação e Cognição Universidade Federal do ABCInstituto de Biociências Universidade de São PauloInstituto de Física Teórica Universidade Estadual PaulistaUniversidade Estadual Paulista (UNESP)Observatório COVID-19 BRUniversidade Estadual de Campinas (UNICAMP)Programa de Computação CientíficaUniversidade Federal do ABC (UFABC)Universidade de São Paulo (USP)Ferreira, Leonardo Souto [UNESP]Darcie Marquitti, Flavia MariaPaixão da Silva, Rafael Lopes [UNESP]Borges, Marcelo EduardoFerreira da Costa Gomes, MarceloCruz, Oswaldo GonçalvesKraenkel, Roberto André [UNESP]Coutinho, Renato MendesPrado, Paulo InácioBastos, Leonardo Soares2023-07-29T13:37:16Z2023-07-29T13:37:16Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.lana.2022.100397Lancet Regional Health - Americas, v. 17.2667-193Xhttp://hdl.handle.net/11449/24820010.1016/j.lana.2022.1003972-s2.0-85146326558Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLancet Regional Health - Americasinfo:eu-repo/semantics/openAccess2023-07-29T13:37:17Zoai:repositorio.unesp.br:11449/248200Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:24:48.096101Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
title Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
spellingShingle Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
Ferreira, Leonardo Souto [UNESP]
Bayesian model
COVID-19
Deaths
Hospitalisation
Pandemic
SARS-CoV-2
title_short Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
title_full Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
title_fullStr Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
title_full_unstemmed Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
title_sort Estimating the impact of implementation and timing of the COVID-19 vaccination programme in Brazil: a counterfactual analysis
author Ferreira, Leonardo Souto [UNESP]
author_facet Ferreira, Leonardo Souto [UNESP]
Darcie Marquitti, Flavia Maria
Paixão da Silva, Rafael Lopes [UNESP]
Borges, Marcelo Eduardo
Ferreira da Costa Gomes, Marcelo
Cruz, Oswaldo Gonçalves
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Prado, Paulo Inácio
Bastos, Leonardo Soares
author_role author
author2 Darcie Marquitti, Flavia Maria
Paixão da Silva, Rafael Lopes [UNESP]
Borges, Marcelo Eduardo
Ferreira da Costa Gomes, Marcelo
Cruz, Oswaldo Gonçalves
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Prado, Paulo Inácio
Bastos, Leonardo Soares
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Observatório COVID-19 BR
Universidade Estadual de Campinas (UNICAMP)
Programa de Computação Científica
Universidade Federal do ABC (UFABC)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv Ferreira, Leonardo Souto [UNESP]
Darcie Marquitti, Flavia Maria
Paixão da Silva, Rafael Lopes [UNESP]
Borges, Marcelo Eduardo
Ferreira da Costa Gomes, Marcelo
Cruz, Oswaldo Gonçalves
Kraenkel, Roberto André [UNESP]
Coutinho, Renato Mendes
Prado, Paulo Inácio
Bastos, Leonardo Soares
dc.subject.por.fl_str_mv Bayesian model
COVID-19
Deaths
Hospitalisation
Pandemic
SARS-CoV-2
topic Bayesian model
COVID-19
Deaths
Hospitalisation
Pandemic
SARS-CoV-2
description Background: Vaccines developed between 2020 and 2021 against the SARS-CoV-2 virus were designed to diminish the severity and prevent deaths due to COVID-19. However, estimates of the effectiveness of vaccination campaigns in achieving these goals remain a methodological challenge. In this work, we developed a Bayesian statistical model to estimate the number of deaths and hospitalisations averted by vaccination of older adults (above 60 years old) in Brazil. Methods: We fit a linear model to predict the number of deaths and hospitalisations of older adults as a function of vaccination coverage in this group and casualties in younger adults. We used this model in a counterfactual analysis, simulating alternative scenarios without vaccination or with faster vaccination roll-out. We estimated the direct effects of COVID-19 vaccination by computing the difference between hypothetical and realised scenarios. Findings: We estimated that more than 165,000 individuals above 60 years of age were not hospitalised due to COVID-19 in the first seven months of the vaccination campaign. An additional contingent of 104,000 hospitalisations could have been averted if vaccination had started earlier. We also estimated that more than 58 thousand lives were saved by vaccinations in the period analysed for the same age group and that an additional 47 thousand lives could have been saved had the Brazilian government started the vaccination programme earlier. Interpretation: Our estimates provided a lower bound for vaccination impacts in Brazil, demonstrating the importance of preventing the suffering and loss of older Brazilian adults. Once vaccines were approved, an early vaccination roll-out could have saved many more lives, especially when facing a pandemic. Funding: The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brazil (Finance Code 001 to F.M.D.M. and L.S.F.), Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brazil (grant number: 315854/2020-0 to M.E.B., 141698/2018-7 to R.L.P.d.S., 313055/2020-3 to P.I.P., 311832/2017-2 to R.A.K.), Fundação de Amparo à Pesquisa do Estado de São Paulo – Brazil (contract number: 2016/01343-7 to R.A.K.), Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro – Brazil (grant number: E-26/201.277/2021 to L.S.B.) and Inova Fiocruz/Fundação Oswaldo Cruz – Brazil (grant number: 48401485034116) to L.S.B., O.G.C. and M.G.d.F.C. The funding agencies had no role in the conceptualization of the study.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:37:16Z
2023-07-29T13:37:16Z
2023-01-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.lana.2022.100397
Lancet Regional Health - Americas, v. 17.
2667-193X
http://hdl.handle.net/11449/248200
10.1016/j.lana.2022.100397
2-s2.0-85146326558
url http://dx.doi.org/10.1016/j.lana.2022.100397
http://hdl.handle.net/11449/248200
identifier_str_mv Lancet Regional Health - Americas, v. 17.
2667-193X
10.1016/j.lana.2022.100397
2-s2.0-85146326558
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lancet Regional Health - Americas
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
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