Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond
Creators
- 1. Institute of Health Informatics, University College London, London, UK
- 2. Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- 3. Department of Epidemiology, Jans- sen Research & Development LLC, Raritan, New Jersey, USA
- 4. The Hyve, Utrecht, The Netherlands
- 5. Odysseus Data Services GmbH, Berlin, Germany
- 6. Centre for Statis- tics in Medicine, NDORMS, University of Oxford, Oxford, UK
Description
Objective: The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to con- vert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM.
Materials and Methods: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data.
Results: We identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we trans- formed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medica- tions 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6465399 (89.2%) procedures, 337896333 primary care diagnoses (CTV3, SNOMED-CT), 139966587 (98.74%) prescriptions (dmþd) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demo- graphic, risk factor, and comorbidity factors between source and transformed data.
Discussion and Conclusion: Our study demonstrated that the OMOP CDM can be successfully leveraged to har- monize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncov- ered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.
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2022_JAMIA_Transforming and evaluating the UK Biobank to the OMOP Common Data Model.pdf
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