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

ObjectivesHigh-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 epidemiolog...

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Bibliographic Details
Published in:BMJ open 2021-12, Vol.11 (12), p.e047623-e047623
Main Authors: Costa-Santos, Cristina, Neves, Ana Luisa, Correia, Ricardo, Santos, Paulo, Monteiro-Soares, Matilde, Freitas, Alberto, Ribeiro-Vaz, Ines, Henriques, Teresa S, Pereira Rodrigues, Pedro, Costa-Pereira, Altamiro, Pereira, Ana Margarida, Fonseca, Joao A
Format: Article
Language:English
Subjects:
Coronaviruses
COVID-19
Data collection
Datasets
Decision making
Disease prevention
Electronic health records
Epidemics
Epidemiology
Health Informatics
Health surveillance
Hepatitis
Hospitals
Influenza
information management
Medical personnel
Medical research
Metadata
Pandemics
Public health
statistics & research methods
Variables
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Summary:ObjectivesHigh-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.SettingsOn 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.ParticipantsAll COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June.Primary and secondary outcome measuresData completeness and consistency.ResultsDGSAugust 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.ConclusionsImportant 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.
ISSN:2044-6055
2044-6055
DOI:10.1136/bmjopen-2020-047623