Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System
BackgroundObservational health data are collected in different formats and structures, making it challenging to analyze with common tools. The Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) is a standardized data model that can harmonize observational health data.ObjectiveT...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1423621/full |
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| author | Michael Ochola Sylvia Kiwuwa-Muyingo Tathagata Bhattacharjee David Amadi Maureen Ng’etich Damazo Kadengye Henry Owoko Boniface Igumba Jay Greenfield Jim Todd Agnes Kiragga Agnes Kiragga |
| author_facet | Michael Ochola Sylvia Kiwuwa-Muyingo Tathagata Bhattacharjee David Amadi Maureen Ng’etich Damazo Kadengye Henry Owoko Boniface Igumba Jay Greenfield Jim Todd Agnes Kiragga Agnes Kiragga |
| author_sort | Michael Ochola |
| collection | DOAJ |
| description | BackgroundObservational health data are collected in different formats and structures, making it challenging to analyze with common tools. The Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) is a standardized data model that can harmonize observational health data.ObjectiveThis paper demonstrates the use of the OMOP CDM to harmonize COVID-19 sero-surveillance data from the Nairobi Urban Health and Demographic Surveillance System (HDSS).MethodsIn this study, we extracted data from the Nairobi Urban HDSS COVID-19 sero-surveillance database and mapped it to the OMOP CDM. We used open-source Observational Health Data Sciences and Informatics (OHDSI) tools like WhiteRabbit, RabbitInAHat, and USAGI. The steps included data profiling (scanning), mapping the vocabularies using the offline USAGI and online ATHENA, and designing the extract, transform, and load (ETL) process using RabbitInAHat. The ETL process was implemented using Pentaho Data Integration community edition software and structured query language (SQL). The target OMOP CDM can now be used to analyze the prevalence of COVID-19 antibodies in the Nairobi Urban HDSS population.ResultsWe successfully mapped the Nairobi Urban HDSS COVID-19 sero-surveillance data to the OMOP CDM. The standardized dataset included information on demographics, COVID-19 symptoms, vaccination, and COVID-19 antibody test results.ConclusionsThe OMOP CDM is a valuable tool for harmonizing observational health data. Using the OMOP CDM facilitates the sharing and analysis of observational health data, leading to a better understanding of disease conditions and trends and improving evidence-based population health strategies. |
| format | Article |
| id | doaj-art-400363d8387344a0931650d5d16f6bd4 |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-400363d8387344a0931650d5d16f6bd42025-01-30T14:30:09ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-01-01710.3389/fdgth.2025.14236211423621Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance SystemMichael Ochola0Sylvia Kiwuwa-Muyingo1Tathagata Bhattacharjee2David Amadi3Maureen Ng’etich4Damazo Kadengye5Henry Owoko6Boniface Igumba7Jay Greenfield8Jim Todd9Agnes Kiragga10Agnes Kiragga11Data Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaDepartment of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United KingdomDepartment of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United KingdomData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaMachine Learning (AI and ML), Committee on Data of the International Science Council (CODATA), Paris, FranceDepartment of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, University of London, London, United KingdomData Science Program, African Population and Health Research Center (APHRC), Nairobi, KenyaImplementation Network for Sharing Population Information from Research Entities (INSPIRE Network), Nairobi, KenyaBackgroundObservational health data are collected in different formats and structures, making it challenging to analyze with common tools. The Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM) is a standardized data model that can harmonize observational health data.ObjectiveThis paper demonstrates the use of the OMOP CDM to harmonize COVID-19 sero-surveillance data from the Nairobi Urban Health and Demographic Surveillance System (HDSS).MethodsIn this study, we extracted data from the Nairobi Urban HDSS COVID-19 sero-surveillance database and mapped it to the OMOP CDM. We used open-source Observational Health Data Sciences and Informatics (OHDSI) tools like WhiteRabbit, RabbitInAHat, and USAGI. The steps included data profiling (scanning), mapping the vocabularies using the offline USAGI and online ATHENA, and designing the extract, transform, and load (ETL) process using RabbitInAHat. The ETL process was implemented using Pentaho Data Integration community edition software and structured query language (SQL). The target OMOP CDM can now be used to analyze the prevalence of COVID-19 antibodies in the Nairobi Urban HDSS population.ResultsWe successfully mapped the Nairobi Urban HDSS COVID-19 sero-surveillance data to the OMOP CDM. The standardized dataset included information on demographics, COVID-19 symptoms, vaccination, and COVID-19 antibody test results.ConclusionsThe OMOP CDM is a valuable tool for harmonizing observational health data. Using the OMOP CDM facilitates the sharing and analysis of observational health data, leading to a better understanding of disease conditions and trends and improving evidence-based population health strategies.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1423621/fullobservational health dataOMOP CDMpopulation health dataCOVID-19sero-surveillanceNairobi Urban HDSS |
| spellingShingle | Michael Ochola Sylvia Kiwuwa-Muyingo Tathagata Bhattacharjee David Amadi Maureen Ng’etich Damazo Kadengye Henry Owoko Boniface Igumba Jay Greenfield Jim Todd Agnes Kiragga Agnes Kiragga Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System Frontiers in Digital Health observational health data OMOP CDM population health data COVID-19 sero-surveillance Nairobi Urban HDSS |
| title | Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System |
| title_full | Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System |
| title_fullStr | Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System |
| title_full_unstemmed | Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System |
| title_short | Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System |
| title_sort | harmonizing population health data into omop common data model a demonstration using covid 19 sero surveillance data from nairobi urban health and demographic surveillance system |
| topic | observational health data OMOP CDM population health data COVID-19 sero-surveillance Nairobi Urban HDSS |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1423621/full |
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