Integrating longitudinal mental health data into a staging database: harnessing DDI-lifecycle and OMOP vocabularies within the INSPIRE Network Datahub.

Autor:	Mugotitsa B; African Population and Health Research Center (APHRC), Nairobi, Kenya.; Strathmore University Business School, Strathmore University, Nairobi, Kenya., Bhattacharjee T; Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom., Ochola M; African Population and Health Research Center (APHRC), Nairobi, Kenya., Mailosi D; Artificial Intelligence and Machine Learning (AI and ML), CODATA-Committee on Data of the International Science Council, Paris, France., Amadi D; Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom., Andeso P; African Population and Health Research Center (APHRC), Nairobi, Kenya., Kuria J; African Population and Health Research Center (APHRC), Nairobi, Kenya., Momanyi R; African Population and Health Research Center (APHRC), Nairobi, Kenya., Omondi E; African Population and Health Research Center (APHRC), Nairobi, Kenya.; Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya., Kajungu D; Iganga Mayuge Health and Demographic Surveillance Site (IMHDSS), Makerere University Centre for Health and Population Research (MUCHAP), Kampala, Uganda., Todd J; Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom., Kiragga A; African Population and Health Research Center (APHRC), Nairobi, Kenya.; Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda., Greenfield J; Artificial Intelligence and Machine Learning (AI and ML), CODATA-Committee on Data of the International Science Council, Paris, France.
Jazyk:	angličtina
Zdroj:	Frontiers in big data [Front Big Data] 2024 Oct 11; Vol. 7, pp. 1435510. Date of Electronic Publication: 2024 Oct 11 (Print Publication: 2024).
DOI:	10.3389/fdata.2024.1435510
Abstrakt:	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. Methods: 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. Conclusion: 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Mugotitsa, Bhattacharjee, Ochola, Mailosi, Amadi, Andeso, Kuria, Momanyi, Omondi, Kajungu, Todd, Kiragga and Greenfield.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu