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ORIGINAL RESEARCH article

Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1423621

Harmonizing Population Health Data into Observational Medical Outcome Partnership Common Data Model: A demonstration using COVID-19 Sero-surveillance Data from Nairobi Urban HDSS 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

Provisionally accepted
Michael Ochola Michael Ochola 1*Sylvia Kiwuwa-Muyingo Sylvia Kiwuwa-Muyingo 1*Tathagata Bhattacharjee Tathagata Bhattacharjee 2David Amadi David Amadi 2Maureen Ng’etich Maureen Ng’etich 1Damazo Kadengye Damazo Kadengye 1Henry Owoko Henry Owoko 1Boniface Igumba Boniface Igumba 1Jay Greenfield Jay Greenfield 3Jim Todd Jim Todd 2Agnes Kiragga Agnes Kiragga 1
  • 1 African Population and Health Research Center (APHRC), Nairobi, Kenya
  • 2 London School of Hygiene and Tropical Medicine, University of London, London, London, United Kingdom
  • 3 Committee on Data of the International Science Council (CODATA), Paris, France

The final, formatted version of the article will be published soon.

    Background: Observational 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. Objective: This 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).Methods: In 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.We 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.The 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.

    Keywords: Formatted: Font: (Default) Calibri, 14 pt, BOLD, Font color: Green, Ligatures: Standard + Contextual Normal, Centered, No bullets or numbering Font: (Default) Calibri, 10 pt Font: (Default) Calibri

    Received: 26 Apr 2024; Accepted: 08 Jan 2025.

    Copyright: © 2025 Ochola, Kiwuwa-Muyingo, Bhattacharjee, Amadi, Ng’etich, Kadengye, Owoko, Igumba, Greenfield, Todd and Kiragga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Michael Ochola, African Population and Health Research Center (APHRC), Nairobi, Kenya
    Sylvia Kiwuwa-Muyingo, African Population and Health Research Center (APHRC), Nairobi, Kenya

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.