COVID-19 surveillance data quality issues: a national consecutive case series

Data de publicação: 01/12/2021 Data Ahead of Print: 06/12/2021

Autores da FMUP

• Cristina Maria Nogueira Da Costa Santos

  Autor

• Ana Luisa Fernandes Neves Soares

  Autor

• Ricardo João Cruz Correia

  Autor

• Paulo Alexandre Azevedo Pereira Santos

  Autor

• Matilde Filipa Monteiro Soares

  Autor

• José Alberto Da Silva Freitas

  Autor

• Fernanda Inês De Carvalho Pereira Ribeiro

  Autor

• Teresa Sarmento Henriques

  Autor

• Pedro Pereira Rodrigues

  Autor

• 

  Altamiro Manuel Rodrigues Da Costa Pereira

  Autor

• Ana Margarida Barbosa Ribeiro Pereira

  Autor

• João De Almeida Lopes Da Fonseca

  Autor

Unidades de investigação

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• Medicina da Comunidade, Informação e Decisão em Saúde

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

Objectives High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. Settings On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. Participants All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. Primary and secondary outcome measures Data completeness and consistency. Results DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. Conclusions Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Keywords

• COVID-19; information management; health informatics; epidemiology; public health; statistics & research methods