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

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 epidemiol...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649880/
https://www.ncbi.nlm.nih.gov/pubmed/34872992
http://dx.doi.org/10.1136/bmjopen-2020-047623
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author 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
author_facet 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
author_sort Costa-Santos, Cristina
collection PubMed
description 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.
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spelling pubmed-86498802021-12-10 COVID-19 surveillance data quality issues: a national consecutive case series 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 BMJ Open Health Informatics 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. BMJ Publishing Group 2021-12-06 /pmc/articles/PMC8649880/ /pubmed/34872992 http://dx.doi.org/10.1136/bmjopen-2020-047623 Text en © 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. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Health Informatics
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
COVID-19 surveillance data quality issues: a national consecutive case series
title COVID-19 surveillance data quality issues: a national consecutive case series
title_full COVID-19 surveillance data quality issues: a national consecutive case series
title_fullStr COVID-19 surveillance data quality issues: a national consecutive case series
title_full_unstemmed COVID-19 surveillance data quality issues: a national consecutive case series
title_short COVID-19 surveillance data quality issues: a national consecutive case series
title_sort covid-19 surveillance data quality issues: a national consecutive case series
topic Health Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8649880/
https://www.ncbi.nlm.nih.gov/pubmed/34872992
http://dx.doi.org/10.1136/bmjopen-2020-047623
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