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ORIGINAL RESEARCH article

Front. Big Data
Sec. Data Mining and Management
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1435510

Integrating Longitudinal Mental Health Data into a Staging Database: Harnessing DDI-Lifecycle and OMOP Vocabularies within the INSPIRE Network Datahub

Provisionally accepted
Bylhah Mugotitsa Bylhah Mugotitsa 1,2*Tathagata Bhattacharjee Tathagata Bhattacharjee 3Michael Ochola Michael Ochola 1Dorothy Mailosi Dorothy Mailosi 4David Amadi David Amadi 1,3Pauline Andeso Pauline Andeso 1Joseph Kuria Joseph Kuria 1Reinpeter Momanyi Reinpeter Momanyi 1Evans Omondi Evans Omondi 3,5Dan Kajungu Dan Kajungu 6Jim Todd Jim Todd 3Agnes Kiragga Agnes Kiragga 1,6Jay Greenfield Jay Greenfield 4
  • 1 African Population and Health Research Center (APHRC), Nairobi, Kenya
  • 2 Strathmore Business School, Strathmore University, Nairobi, Kenya
  • 3 London School of Hygiene and Tropical Medicine, University of London, London, London, United Kingdom
  • 4 Committee on Data of the International Science Council (CODATA), Paris, France
  • 5 Strathmore University, Nairobi, Kenya
  • 6 Makerere University, Kampala, Central Region, Uganda

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

    Background: Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets.The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves.Results: Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research.The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

    Keywords: Longitudinal Mental Health, OMOP Common Data Model, DDI-Lifecycle, Staging Database, extract, transform and load

    Received: 20 May 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Mugotitsa, Bhattacharjee, Ochola, Mailosi, Amadi, Andeso, Kuria, Momanyi, Omondi, Kajungu, Todd, Kiragga and Greenfield. 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: Bylhah Mugotitsa, 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.