The German Corona Consensus Dataset (GECCO): A standardized dataset for COVID-19 research in university medicine and beyond

Abstract

Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.

Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk

“…However, a global standard data model that comprises all COVID-19 related terms is not available as of now. Therefore, we have used three controlled vocabularies to establish the interoperability and mapping towards various datasets and data points, namely, German Corona Consensus Dataset (GECCO) [18] , the COVID-19 Ontology [19] and the Corona Virus Vocabulary (COVOC) (https://github.com/EBISPOT/covoc/raw/master/covoc.owl).…”

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

“…Given the harmonized but individual data collection of the three NAPKON cohorts, each maintains its dedicated eCRF. All NAPKON eCRFs contain the German Corona Consensus Dataset (GECCO-83) [31], ensuring syntactic and semantic interoperability for a core dataset via diverse international terminologies (e.g., International Statistical Classi cation of Diseases and Related Health Problems, 10th revision, German modi cation (ICD-10-GM)[32], Logical Observation Identi ers Names and Codes (LOINC) [33], the Anatomical Therapeutic Chemical Classi cation System (ATC) [34], Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) [35]) and de ned Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR) pro les [36]. The cohorts selected additional data elements by incorporating international data sets (e.g., ISARIC [37]), already established German COVID-19 cohorts (e.g., LEOSS [38], Pa-COVID-19 [39]), and suggestions of scientists (see section "Governance").…”

Dataset of Coronavirus Content From Instagram With an Exploratory Analysis

“…Akindtande et al [19] present a dataset that investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa and the data is collected via an electronic questionnaire method from twenty-one Africa countries. In medicine, Sass et al introduce the ''German Corona Consensus Dataset'' (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data [20].…”