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Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data CollectionsFunding The study was carried out using the clinical-scientific infrastructure and data of NUKLEUS, NAPKON, and CODEX of the Network University Medicine (NUM, grant number 01KX2121), with support from the German Center for Cardiovascular Research (DZHK, grant number 81Z0300108) both funded by the Federal Ministry of Education and Research (BMBF) .
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.
* These authors contributed equally.
Availability of Materials and Data
The GECCO83 dataset used for the study can be accessed through the normal use and access procedure of the NAPKON.1 Also, the source code of the implementation is available in the gitlab repository of the project.2
1 NAPKON-Proskive: https://proskive.napkon.de/
2 dqGecco Project: https://gitlab.gwdg.de/medinfpub/dqgecco.git
Received: 18 July 2022
Accepted: 31 October 2022
Accepted Manuscript online:
03 January 2023
Article published online:
30 January 2023
© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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