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Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil

The purpose of this article is to quantify the amount of misclassification of the Coronavirus Disease-2019 (COVID-19) mortality occurring in hospitals and other health facilities in selected cities in Brazil, discuss potential factors contributing to this misclassification, and consider the implicat...

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Autores principales: França, Elisabeth B., Ishitani, Lenice H., de Abreu, Daisy Maria Xavier, Teixeira, Renato Azeredo, Corrêa, Paulo Roberto Lopes, de Jesus, Eliene dos Santos, Marinho, Maria Antonieta Delgado, Bahia, Tauá Vieira, Bierrenbach, Ana Luiza, Setel, Philip, Marinho, Fatima
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021639/
https://www.ncbi.nlm.nih.gov/pubmed/36962159
http://dx.doi.org/10.1371/journal.pgph.0000199
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author França, Elisabeth B.
Ishitani, Lenice H.
de Abreu, Daisy Maria Xavier
Teixeira, Renato Azeredo
Corrêa, Paulo Roberto Lopes
de Jesus, Eliene dos Santos
Marinho, Maria Antonieta Delgado
Bahia, Tauá Vieira
Bierrenbach, Ana Luiza
Setel, Philip
Marinho, Fatima
author_facet França, Elisabeth B.
Ishitani, Lenice H.
de Abreu, Daisy Maria Xavier
Teixeira, Renato Azeredo
Corrêa, Paulo Roberto Lopes
de Jesus, Eliene dos Santos
Marinho, Maria Antonieta Delgado
Bahia, Tauá Vieira
Bierrenbach, Ana Luiza
Setel, Philip
Marinho, Fatima
author_sort França, Elisabeth B.
collection PubMed
description The purpose of this article is to quantify the amount of misclassification of the Coronavirus Disease-2019 (COVID-19) mortality occurring in hospitals and other health facilities in selected cities in Brazil, discuss potential factors contributing to this misclassification, and consider the implications for vital statistics. Hospital deaths assigned to causes classified as garbage code (GC) COVID-related cases (severe acute respiratory syndrome, pneumonia unspecified, sepsis, respiratory failure and ill-defined causes) were selected in three Brazilian state capitals. Data from medical charts and forensic reports were extracted from standard forms and analyzed by study physicians who re-assigned the underlying cause based on standardized criteria. Descriptive statistical analysis was performed and the potential impact in vital statistics in the country was also evaluated. Among 1,365 investigated deaths due to GC-COVID-related causes, COVID-19 was detected in 17.3% in the age group 0–59 years and 25.5% deaths in 60 years and over. These GCs rose substantially in 2020 in the country and were responsible for 211,611 registered deaths. Applying observed proportions by age, location and specific GC-COVID-related cause to national data, there would be an increase of 37,163 cases in the total of COVID-19 deaths, higher in the elderly. In conclusion, important undercount of deaths from COVID-19 among GC-COVID-related causes was detected in three selected capitals of Brazil. After extrapolating the study results for national GC-COVID-related deaths we infer that the burden of COVID-19 disease in Brazil in official vital statistics was probably under estimated by at least 18% in the country in 2020.
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spelling pubmed-100216392023-03-17 Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil França, Elisabeth B. Ishitani, Lenice H. de Abreu, Daisy Maria Xavier Teixeira, Renato Azeredo Corrêa, Paulo Roberto Lopes de Jesus, Eliene dos Santos Marinho, Maria Antonieta Delgado Bahia, Tauá Vieira Bierrenbach, Ana Luiza Setel, Philip Marinho, Fatima PLOS Glob Public Health Research Article The purpose of this article is to quantify the amount of misclassification of the Coronavirus Disease-2019 (COVID-19) mortality occurring in hospitals and other health facilities in selected cities in Brazil, discuss potential factors contributing to this misclassification, and consider the implications for vital statistics. Hospital deaths assigned to causes classified as garbage code (GC) COVID-related cases (severe acute respiratory syndrome, pneumonia unspecified, sepsis, respiratory failure and ill-defined causes) were selected in three Brazilian state capitals. Data from medical charts and forensic reports were extracted from standard forms and analyzed by study physicians who re-assigned the underlying cause based on standardized criteria. Descriptive statistical analysis was performed and the potential impact in vital statistics in the country was also evaluated. Among 1,365 investigated deaths due to GC-COVID-related causes, COVID-19 was detected in 17.3% in the age group 0–59 years and 25.5% deaths in 60 years and over. These GCs rose substantially in 2020 in the country and were responsible for 211,611 registered deaths. Applying observed proportions by age, location and specific GC-COVID-related cause to national data, there would be an increase of 37,163 cases in the total of COVID-19 deaths, higher in the elderly. In conclusion, important undercount of deaths from COVID-19 among GC-COVID-related causes was detected in three selected capitals of Brazil. After extrapolating the study results for national GC-COVID-related deaths we infer that the burden of COVID-19 disease in Brazil in official vital statistics was probably under estimated by at least 18% in the country in 2020. Public Library of Science 2022-05-05 /pmc/articles/PMC10021639/ /pubmed/36962159 http://dx.doi.org/10.1371/journal.pgph.0000199 Text en © 2022 França et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
França, Elisabeth B.
Ishitani, Lenice H.
de Abreu, Daisy Maria Xavier
Teixeira, Renato Azeredo
Corrêa, Paulo Roberto Lopes
de Jesus, Eliene dos Santos
Marinho, Maria Antonieta Delgado
Bahia, Tauá Vieira
Bierrenbach, Ana Luiza
Setel, Philip
Marinho, Fatima
Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title_full Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title_fullStr Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title_full_unstemmed Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title_short Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil
title_sort measuring misclassification of covid-19 as garbage codes: results of investigating 1,365 deaths and implications for vital statistics in brazil
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10021639/
https://www.ncbi.nlm.nih.gov/pubmed/36962159
http://dx.doi.org/10.1371/journal.pgph.0000199
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