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COVID-19 surveillance data quality issues: a national consecutive case series
Cristina Costa-Santos1,2, Ana Luisa Neves3,2,4, Ricardo Correia3,2
1Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal csantos.cristina@gmail.com.
BMJ Open
|December 7, 2021
Summary
Portuguese COVID-19 surveillance data show significant quality issues, including missing information and inconsistencies between datasets. These data frailties hinder evidence-based healthcare and effective pandemic control, necessitating urgent improvements.
Area of Science:
- Epidemiology
- Public Health Data Science
Background:
- High-quality data are essential for evidence-based healthcare and decision-making, particularly when knowledge is limited.
- The COVID-19 pandemic has highlighted global data quality challenges in epidemiological surveillance.
Purpose of the Study:
- To assess data quality issues in a major Portuguese COVID-19 epidemiological surveillance dataset.
- To propose potential solutions for improving the usability of surveillance data.
Main Methods:
- Analysis of two datasets (DGSApril and DGSAugust) from the Portuguese Directorate-General of Health (DGS) obtained on April 27 and August 4, 2020, respectively.
- Evaluation of data completeness and consistency for COVID-19 confirmed cases notified through the National System for Epidemiological Surveillance until the end of June.
Main Results:
- The DGSAugust dataset exhibited significant data quality issues compared to DGSApril, including a substantial number of missing cases and inconsistencies.
- Variables such as 'underlying conditions' showed low completeness and differing values between datasets.
- Discrepancies were observed between the case and death counts in DGSAugust and publicly reported DGS figures.
Conclusions:
- The identified data quality issues in Portuguese COVID-19 surveillance datasets compromise their utility for informed decision-making and research.
- Urgent enhancements are required, including simplified data entry, continuous monitoring, and improved training for healthcare providers.
- Low data quality poses a risk to effective pandemic control efforts.
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