Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic

Detalhes bibliográficos
Autor(a) principal: Carolina Abreu de Carvalho
Data de Publicação: 2021
Outros Autores: Vitória Abreu de Carvalho, Marcos Adriano Garcia Campos, Bruno Luciano Carneiro Alves de Oliveira, Eduardo Moraes Diniz, Alcione Miranda dos Santos, Bruno Feres de Souza, Antônio Augusto Moura da Silva
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Cadernos de Saúde Pública
Texto Completo: https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866
Resumo: This study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic.
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spelling Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemicCOVID-19SARS-CoV-2Mortality RegistriesData AccuracyThis study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic.La propuesta de este estudio es describir la demora en la notificación de muertes por COVID-19, en la ciudad São Luís, Maranhão, Brasil, y demostrar su impacto en el seguimiento puntual, así como en el modelaje de la pandemia de COVID-19. Un objetivo secundario fue confirmar el alcance, donde la previsión inmediata es capaz de mejorar el retraso en la notificación de las muertes. Analizamos los datos de muertes por COVID-19 diariamente en el Boletín Epidemiológico de la Secretaría de Estado de la Salud de Maranhão y calculamos los atrasos notificados desde el 23 de marzo al 29 de agosto, 2020. Con el fin de ilustrar el impacto del retraso en la notificación de muertes, y para probar la efectividad de la predicción inmediata bayesiana en la mejora de los datos de calidad, ajustamos un modelo jerárquico bayesiano semi-mecanicista. Solo un 17.8% de las muertes se notificaron sin atrasos o el día después, mientras que un 40.5% se vieron retrasadas durante más de 30 días. Debido a la demora informada, se produjo una subestimación inicial de muertes. No obstante, desde el 7 de junio al 29 de agosto, se informó de 644 muertes, pero solamente 116 muertes se produjeron durante este periodo. El uso de la técnica de predicción inmediata bayesiana mejoró parcialmente la calidad de la información de mortalidad durante el pico de la epidemia, presentando estimaciones que se ajustan mejor al escenario observado en la ciudad, pero no fue útil casi 2 meses después del pico. El retraso en la notificación de muertes podría interferir directamente en la toma de decisiones asertivas y puntuales, respecto a la pandemia de COVID-19. Por consiguiente, se debe revisar urgentemente el sistema brasileño de vigilancia epidemiológica y la notificación de la fecha de muerte debería ser obligatoria. La técnica de predicción inmediata ha demostrado ser bastante efectiva para mejorar la calidad de los datos de mortalidad solamente en el pico pandémico, pero no después.O estudo teve como objetivos, descrever o atraso na notificação de óbitos por COVID-19 na cidade de São Luís, Maranhão, Brasil, e demonstrar o impacto sobre o monitoramento oportuno e modelagem da pandemia. O estudo teve como objetivo secundário determinar a medida em que a nowcasting é capaz de diminuir a defasagem na notificação de óbitos. Analisamos os dados de mortalidade por COVID-19 registrados diariamente no Boletim Epidemiológico da Secretaria de Estado da Saúde do Maranhão e calculamos o atraso na notificação entre 23 de março e 29 de agosto de 2020. Para ilustrar o impacto do atraso na notificação de óbitos e testar a efetividade de uma nowcasting bayesiana para melhorar a qualidade dos dados, ajustamos um modelo hierárquico bayesiano semi-mecanístico. Apenas 17,8% dos óbitos foram notificados sem atraso ou no dia seguinte, enquanto 40,5% foram atrasados em mais de 30 dias. Devido ao atraso na notificação, houve uma subestimação inicial nos óbitos. Entre 7 de junho e 29 de agosto, 644 óbitos foram notificados, mas apenas 116 mortes ocorreram nesse período. O uso da técnica de nowcasting bayesiana melhorou parcialmente a qualidade dos dados de mortalidade no pico da epidemia, apresentando estimativas mais ajustadas ao cenário observado na cidade, mas não se mostrou útil quase dois meses depois do pico. O atraso na notificação de óbitos pode interferir diretamente nas decisões assertivas e oportunas sobre o combate à pandemia da COVID-19. Portanto, o sistema brasileiro de vigilância epidemiológica deve ser revisto urgentemente, e o registro da data do óbito deve ser obrigatório. A técnica de nowcasting mostrou ser parcialmente eficaz na melhoria dos dados de mortalidade no auge da pandemia, mas não depois.Reports in Public HealthCadernos de Saúde Pública2021-08-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866Reports in Public Health; Vol. 37 No. 7 (2021): JulyCadernos de Saúde Pública; v. 37 n. 7 (2021): Julho1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866/17600https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866/17601Carolina Abreu de CarvalhoVitória Abreu de CarvalhoMarcos Adriano Garcia CamposBruno Luciano Carneiro Alves de OliveiraEduardo Moraes DinizAlcione Miranda dos SantosBruno Feres de SouzaAntônio Augusto Moura da Silvainfo:eu-repo/semantics/openAccess2024-03-06T15:30:13Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/7866Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:09:01.795213Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true
dc.title.none.fl_str_mv Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
title Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
spellingShingle Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
Carolina Abreu de Carvalho
COVID-19
SARS-CoV-2
Mortality Registries
Data Accuracy
title_short Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
title_full Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
title_fullStr Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
title_full_unstemmed Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
title_sort Delay in death reporting affects timely monitoring and modeling of the COVID-19 pandemic
author Carolina Abreu de Carvalho
author_facet Carolina Abreu de Carvalho
Vitória Abreu de Carvalho
Marcos Adriano Garcia Campos
Bruno Luciano Carneiro Alves de Oliveira
Eduardo Moraes Diniz
Alcione Miranda dos Santos
Bruno Feres de Souza
Antônio Augusto Moura da Silva
author_role author
author2 Vitória Abreu de Carvalho
Marcos Adriano Garcia Campos
Bruno Luciano Carneiro Alves de Oliveira
Eduardo Moraes Diniz
Alcione Miranda dos Santos
Bruno Feres de Souza
Antônio Augusto Moura da Silva
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Carolina Abreu de Carvalho
Vitória Abreu de Carvalho
Marcos Adriano Garcia Campos
Bruno Luciano Carneiro Alves de Oliveira
Eduardo Moraes Diniz
Alcione Miranda dos Santos
Bruno Feres de Souza
Antônio Augusto Moura da Silva
dc.subject.por.fl_str_mv COVID-19
SARS-CoV-2
Mortality Registries
Data Accuracy
topic COVID-19
SARS-CoV-2
Mortality Registries
Data Accuracy
description This study describes the COVID-19 death reporting delay in the city of São Luís, Maranhão State, Brazil, and shows its impact on timely monitoring and modeling of the COVID-19 pandemic, while seeking to ascertain how nowcasting can improve death reporting delay. We analyzed COVID-19 death data reported daily in the Epidemiological Bulletin of the State Health Secretariat of Maranhão and calculated the reporting delay from March 23 to August 29, 2020. A semi-mechanistic Bayesian hierarchical model was fitted to illustrate the impact of death reporting delay and test the effectiveness of a Bayesian Nowcasting in improving data quality. Only 17.8% of deaths were reported without delay or the day after, while 40.5% were reported more than 30 days late. Following an initial underestimation due to reporting delay, 644 deaths were reported from June 7 to August 29, although only 116 deaths occurred during this period. Using the Bayesian nowcasting technique partially improved the quality of mortality data during the peak of the pandemic, providing estimates that better matched the observed scenario in the city, becoming unusable nearly two months after the peak. As delay in death reporting can directly interfere with assertive and timely decision-making regarding the COVID-19 pandemic, the Brazilian epidemiological surveillance system must be urgently revised and notifying the date of death must be mandatory. Nowcasting has proven somewhat effective in improving the quality of mortality data, but only at the peak of the pandemic.
publishDate 2021
dc.date.none.fl_str_mv 2021-08-13
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format article
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dc.identifier.uri.fl_str_mv https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866
url https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866/17600
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7866/17601
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
application/pdf
dc.publisher.none.fl_str_mv Reports in Public Health
Cadernos de Saúde Pública
publisher.none.fl_str_mv Reports in Public Health
Cadernos de Saúde Pública
dc.source.none.fl_str_mv Reports in Public Health; Vol. 37 No. 7 (2021): July
Cadernos de Saúde Pública; v. 37 n. 7 (2021): Julho
1678-4464
0102-311X
reponame:Cadernos de Saúde Pública
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Cadernos de Saúde Pública
collection Cadernos de Saúde Pública
repository.name.fl_str_mv Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)
repository.mail.fl_str_mv cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br
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