> Details

Yusuf, Khalid O.; Miljukov, Olga; Schoneberg, Anne; Hanß, Sabine; Wiesenfeldt, Martin; Stecher, Melanie; Mitrov, Lazar; Hopff, Sina Marie; Steinbrecher, Sarah; Kurth, Florian; Bahmer, Thomas; Schreiber, Stefan; Pape, Daniel; Hofmann, Anna-Lena; Kohls, Mirjam; Störk, Stefan; Stubbe, Hans Christian; Tebbe, Johannes J.; Hellmuth, Johannes C.; Erber, Johanna; Krist, Lilian; Rieg, Siegbert; Pilgram, Lisa; Vehreschild, Jörg J.; [...]

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

Reference
management

Bookmarks

Remove from
bookmarks

Share this on Whatsapp

Close

Bookmarks



You can manage bookmarks using lists, please log in to your user account for this.
  • Media type: E-Article
  • Title: Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections
  • Contributor: Yusuf, Khalid O.; Miljukov, Olga; Schoneberg, Anne; Hanß, Sabine; Wiesenfeldt, Martin; Stecher, Melanie; Mitrov, Lazar; Hopff, Sina Marie; Steinbrecher, Sarah; Kurth, Florian; Bahmer, Thomas; Schreiber, Stefan; Pape, Daniel; Hofmann, Anna-Lena; Kohls, Mirjam; Störk, Stefan; Stubbe, Hans Christian; Tebbe, Johannes J.; Hellmuth, Johannes C.; Erber, Johanna; Krist, Lilian; Rieg, Siegbert; Pilgram, Lisa; Vehreschild, Jörg J.; [...]
  • Published: Georg Thieme Verlag KG, 2023
  • Published in: Methods of Information in Medicine, 62 (2023) S 01, Seite e47-e56
  • Language: English
  • DOI: 10.1055/a-2006-1086
  • ISSN: 0026-1270; 2511-705X
  • Origination:
  • Footnote:
  • Description: 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.