{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:33:39Z","timestamp":1774157619536,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T00:00:00Z","timestamp":1665619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Health Data & Evidence Network"},{"name":"Innovative Medicines Initiative 2 Joint Undertaking","award":["806968"],"award-info":[{"award-number":["806968"]}]},{"name":"European Union\u2019s Horizon 2020"},{"name":"BHF Data Science Centre led by Health Data Research UK","award":["SP\/19\/3\/34678"],"award-info":[{"award-number":["SP\/19\/3\/34678"]}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_PC_20030"],"award-info":[{"award-number":["MC_PC_20030"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC_PC_20059"],"award-info":[{"award-number":["MC_PC_20059"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Health Data Research UK"},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Applied Research Collaboration South London"},{"DOI":"10.13039\/100010872","name":"King\u2019s College Hospital NHS Foundation Trust","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100010872","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>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 convert the UK Biobank, a study of 500\u200a000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified 502\u200a505 participants (3086 with COVID-19) and transformed 690 fields (1\u200a373\u200a239\u200a555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946\u200a053 (83.91% of all source entries), cancers 37\u200a802 (70.81%), medications 1\u200a218\u200a935 (88.25%), and prescriptions 864\u200a788 (86.96%). In EHR, we transformed 13\u200a028\u200a182 (99.95%) hospital diagnoses, 6\u200a465\u200a399 (89.2%) procedures, 337\u200a896\u200a333 primary care diagnoses (CTV3, SNOMED-CT), 139\u200a966\u200a587 (98.74%) prescriptions (dm+d) and 77\u200a127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion and Conclusion<\/jats:title><jats:p>Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered 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.<\/jats:p><\/jats:sec>","DOI":"10.1093\/jamia\/ocac203","type":"journal-article","created":{"date-parts":[[2022,10,13]],"date-time":"2022-10-13T15:43:14Z","timestamp":1665675794000},"page":"103-111","source":"Crossref","is-referenced-by-count":39,"title":["Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2123-7993","authenticated-orcid":false,"given":"Vaclav","family":"Papez","sequence":"first","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Health Data Research UK , London, UK"}]},{"given":"Maxim","family":"Moinat","sequence":"additional","affiliation":[{"name":"The Hyve , Utrecht, The Netherlands"},{"name":"Erasmus Medical Center Rotterdam , Rotterdam, The 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Netherlands"}]},{"given":"Michael","family":"Kallfelz","sequence":"additional","affiliation":[{"name":"Odysseus Data Services GmbH , Berlin, Germany"}]},{"given":"Folkert W","family":"Asselbergs","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Health Data Research UK , London, UK"},{"name":"Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam , Amsterdam, The Netherlands"}]},{"given":"Daniel","family":"Prieto-Alhambra","sequence":"additional","affiliation":[{"name":"Erasmus Medical Center Rotterdam , Rotterdam, The Netherlands"},{"name":"Centre for Statistics in Medicine, NDORMS, University of Oxford , Oxford, UK"}]},{"given":"Richard J B","family":"Dobson","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, University College London , London, UK"},{"name":"Health Data Research UK , London, UK"},{"name":"Department of Biostatistics and 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