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 epidemiolo...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2021-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/11/12/e047623.full |
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| author | Ana Margarida Pereira Ricardo Correia Joao A Fonseca Alberto Freitas Teresa S Henriques Cristina Costa-Santos Altamiro Costa-Pereira Paulo Santos Matilde Monteiro-Soares Inês Ribeiro-Vaz Pedro Pereira Rodrigues |
| author_facet | Ana Margarida Pereira Ricardo Correia Joao A Fonseca Alberto Freitas Teresa S Henriques Cristina Costa-Santos Altamiro Costa-Pereira Paulo Santos Matilde Monteiro-Soares Inês Ribeiro-Vaz Pedro Pereira Rodrigues |
| author_sort | Ana Margarida Pereira |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-86887e90fd9c48d29ff752ecbab4367e |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-86887e90fd9c48d29ff752ecbab4367e2024-12-10T02:35:09ZengBMJ Publishing GroupBMJ Open2044-60552021-12-01111210.1136/bmjopen-2020-047623COVID-19 surveillance data quality issues: a national consecutive case seriesAna Margarida Pereira0Ricardo Correia1Joao A Fonseca2Alberto Freitas3Teresa S Henriques4Cristina Costa-Santos5Altamiro Costa-Pereira6Paulo Santos7Matilde Monteiro-Soares8Inês Ribeiro-Vaz9Pedro Pereira Rodrigues10Allergy Unit, Instituto and Hospital CUF, Porto, PortugalDepartment of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Health Information and Decision Sciences (CIDES) & Center for Health Technology and Services Research (CINTESIS), University of Porto-Faculty of Medicine, Porto, PortugalCentre for Health Technology and Services Research (CINTESIS), Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, PortugalDepartment of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, PortugalMEDCIDS - Departamento de Ciências da Informação e da Decisão em Saúde, Faculty of Medicine, University of Porto, Porto, PortugalCenter for Health Technology and Services Research, Oporto University Faculty of Medicine, CINTESIS, Porto, PortugalCenter for Health Technology and Services Research, Faculty of Medicine, University of Porto, Porto, PortugalObjectives 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.https://bmjopen.bmj.com/content/11/12/e047623.full |
| spellingShingle | Ana Margarida Pereira Ricardo Correia Joao A Fonseca Alberto Freitas Teresa S Henriques Cristina Costa-Santos Altamiro Costa-Pereira Paulo Santos Matilde Monteiro-Soares Inês Ribeiro-Vaz Pedro Pereira Rodrigues COVID-19 surveillance data quality issues: a national consecutive case series BMJ Open |
| 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 |
| url | https://bmjopen.bmj.com/content/11/12/e047623.full |
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