Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil.
Elizabeth Barboza FrançaLenice Harumi IshitaniDaisy Maria Xavier de AbreuRenato Teixeira AzeredoPaulo Roberto Lopes CorreaEliene Dos Santos de JesusMaria Antonieta Delgado MarinhoTauá Vieira BahiaAna Luiza de Souza BierrenbachPhilip W SetelMaria de Fatima Marinho de SouzaPublished in: PLOS global public health (2022)
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.
Keyphrases
- coronavirus disease
- sars cov
- respiratory syndrome coronavirus
- healthcare
- public health
- primary care
- mental health
- cardiovascular disease
- emergency department
- intensive care unit
- type diabetes
- quality improvement
- cardiovascular events
- extracorporeal membrane oxygenation
- septic shock
- artificial intelligence
- human health
- health information
- liquid chromatography
- community dwelling
- acute care
- cross sectional