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

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Costa-Santos, C (Author) Other; Luísa Neves, A (Author) FMUP; Ricardo Cruz Correia (Author) FMUP; Paulo Santos (Author) FMUP; Matilde Soares (Author) FMUP; Freitas, A (Author) Other; Ribeiro-Vaz, I (Author) FMUP; Henriques, T (Author) FMUP; Pedro Pereira Rodrigues (Author) FMUP; Costa-Pereira A (Author) FMUP; Pereira, AM (Author) FMUP; Fonseca, J (Author) FMUP

Journal

Title: BMJ Open

ISSN: 2044-6055

Publisher: BMJ PUBLISHING GROUP

Other information

Authenticus ID: P-00T-3QW

DOI: 10.1136/bmjopen-2020-047623

Abstract (EN): <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>High-quality data is crucial for guiding decision making and practicing 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 surveillance dataset, our study aims to assess data quality issues and suggest possible solutions.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>On April 27<jats:sup>th</jats:sup> 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4<jats:sup>th</jats:sup>, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (e.g. 4,075 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 (e.g. 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.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>The low quality of COVID-19 surveillance datasets limits its usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed - e.g. simplification of data entry processes, constant monitoring of data, and increased training and awareness of health care providers - as low data quality may lead to a deficient pandemic control.</jats:p></jats:sec>

Language: English

Type (Professor's evaluation): Scientific