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

DDI-lifecycle OMOP Common Data Model extract longitudinal mental health staging database transform and load

Journal

Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603

Informations de publication

Date de publication:
2024
Historique:
received: 20 05 2024
accepted: 09 09 2024
medline: 28 10 2024
pubmed: 28 10 2024
entrez: 28 10 2024
Statut: epublish

Résumé

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. 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.

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 39463847
doi: 10.3389/fdata.2024.1435510
pmc: PMC11502395
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1435510

Informations de copyright

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

Déclaration de conflit d'intérêts

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.

Auteurs

Bylhah Mugotitsa (B)

African Population and Health Research Center (APHRC), Nairobi, Kenya.
Strathmore University Business School, Strathmore University, Nairobi, Kenya.

Tathagata Bhattacharjee (T)

Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Michael Ochola (M)

African Population and Health Research Center (APHRC), Nairobi, Kenya.

Dorothy Mailosi (D)

Artificial Intelligence and Machine Learning (AI and ML), CODATA-Committee on Data of the International Science Council, Paris, France.

David Amadi (D)

Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Pauline Andeso (P)

African Population and Health Research Center (APHRC), Nairobi, Kenya.

Joseph Kuria (J)

African Population and Health Research Center (APHRC), Nairobi, Kenya.

Reinpeter Momanyi (R)

African Population and Health Research Center (APHRC), Nairobi, Kenya.

Evans Omondi (E)

African Population and Health Research Center (APHRC), Nairobi, Kenya.
Institute of Mathematical Sciences, Strathmore University, Nairobi, Kenya.

Dan Kajungu (D)

Iganga Mayuge Health and Demographic Surveillance Site (IMHDSS), Makerere University Centre for Health and Population Research (MUCHAP), Kampala, Uganda.

Jim Todd (J)

Department of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Agnes Kiragga (A)

African Population and Health Research Center (APHRC), Nairobi, Kenya.
Infectious Diseases Institute, College of Health Sciences, Makerere University, Kampala, Uganda.

Jay Greenfield (J)

Artificial Intelligence and Machine Learning (AI and ML), CODATA-Committee on Data of the International Science Council, Paris, France.

Classifications MeSH