Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond

Abstract

Objective The 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 (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert 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 (EHR) 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 (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, 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 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.

IncidencePrevalence: An R package to calculate population‐level incidence rates and prevalence using the OMOP common data model

“…Version 0.5.1 of IncidencePrevalence is written in R (version 4.2.1), organised using roxygen2, 4 and depends on the following existing packages: checkmate, 5 cli, 6 dbplyr, 7 dplyr, 8 lubridate, 9 glue, 10 magrittr, 11 rlang, 12 purrr, 13 tidyr, 14 tidyselect, 15 stringr 16 and zip 17 . IncidencePrevalence is designed to be used against data mapped to the OMOP CDM, and therefore, data sets must be first converted to the CDM prior to using the package presented in this paper 18–20 . IncidencePrevalence can connect to several database management systems through the DataBase Interface (DBI) R package 21 .…”

“…17 IncidencePrevalence is designed to be used against data mapped to the OMOP CDM, and therefore, data sets must be first converted to the CDM prior to using the package presented in this paper. [18][19][20] Inci-dencePrevalence can connect to several database management systems through the DataBase Interface (DBI) R package. 21 To allow for a pipe friendly syntax, CDM table references are stored in a single object (referred as cdm object) through the CDMConnector R package.…”

Transforming the Information System for Research in Primary Care (SIDIAP) in Catalonia to the OMOP Common Data Model and Its Use for COVID-19 Research

“…In order to standardize both the language and structure of health data, the Observational Health Data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), 10 which has been adapted by numerous health care databases. [11][12][13][14] Our first aim was to convert data from SIDIAP to the OMOP CDM to facilitate distributed network research related to the pandemic. Our second aim was to summarise the occurrence of COVID-19-related outcomes observed and describe the characteristics of those affected and vaccinated against this disease.…”

Transforming Estonian health data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model: lessons learned

“…Previous research has described the successful transformation of data to OMOP CDM, which originate from different sources like biobanks [11], national databases, and registries [12][13][14], hospital databases [15][16][17][18], questionnaires [19], cohort studies [20][21][22]. Some studies focus on specific conditions or some part of a database [12,13,16,17,19,21] while others transfer whole databases with different diagnoses, drug adherence or health care procedures [11,15,20].…”

“…Previous research has described the successful transformation of data to OMOP CDM, which originate from different sources like biobanks [11], national databases, and registries [12][13][14], hospital databases [15][16][17][18], questionnaires [19], cohort studies [20][21][22]. Some studies focus on specific conditions or some part of a database [12,13,16,17,19,21] while others transfer whole databases with different diagnoses, drug adherence or health care procedures [11,15,20]. Despite the existing research, it has been stressed that continued sharing of experiences, methodologies, and challenges of the data transformation process to OMOP is needed as it helps to develop the transformation process and foster collaboration [21,23].…”