COVID-19 surveillance - a descriptive study on data quality issues

BackgroundHigh-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. MethodsOn April 27th 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4th, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets. ResultsDGSAugust 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. ConclusionsThe 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.

Is scaling-up COVID-19 testing cost-saving?

The World Health Organization currently recommends that governments scale up testing for COVID-19 infection. We performed health economic analyses projecting whether the additional costs from screening would be offset by the avoided costs with hospitalizations. We analysed Portuguese COVID-19 data up until the 22nd March 2020, and estimated the additional number of cases that would be detected if different testing rates and frequencies of positive results would have been observed. We projected that, in most scenarios, the costs with scaling up COVID-19 tests would be lower than savings with hospitalization costs, rendering large scale testing cost-saving.