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. 2023 Jun;62(S 01):e47-e56.
doi: 10.1055/a-2006-1086. Epub 2023 Jan 3.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections

Khalid O Yusuf  1 , Olga Miljukov  2 , Anne Schoneberg  1 , Sabine Hanß  1 , Martin Wiesenfeldt  1 , Melanie Stecher  3   4 , Lazar Mitrov  5 , Sina Marie Hopff  5 , Sarah Steinbrecher  6 , Florian Kurth  6 , Thomas Bahmer  7   8 , Stefan Schreiber  7 , Daniel Pape  7 , Anna-Lena Hofmann  2 , Mirjam Kohls  2 , Stefan Störk  9 , Hans Christian Stubbe  10 , Johannes J Tebbe  11 , Johannes C Hellmuth  12   13 , Johanna Erber  14 , Lilian Krist  15 , Siegbert Rieg  16 , Lisa Pilgram  17   18 , Jörg J Vehreschild  3   4   18 , Jens-Peter Reese  2 , Dagmar Krefting  1   19
Affiliations

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections

Khalid O Yusuf et al. Methods Inf Med. 2023 Jun.

Abstract

Background: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies.

Objectives: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models.

Methods: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source.

Results: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%.

Conclusion: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
Workflow of the consistency assessment using dqGecco R-package and independent evaluation of the assessment results.
Fig. 2
Fig. 2
Comparison of body temperature ( T b ) against fever in the SÜP cohort. T b values not captured at the same time-point as fever were excluded.
See this image and copyright information in PMC

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