ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Computational Psychiatry
This article is part of the Research TopicAdvancing Biostatistics and Informatics Applications in Mental Health ResearchView all 7 articles
Migrating Longitudinal African Mental Health Data from Staging to the OMOP Common Data Model within the INSPIRE Network Datahub
Provisionally accepted
Tathagata Bhattacharjee1*
Bylhah Mugotitsa2*
Michael Ochola2
Reinpeter Momanyi2Pauline Andeso2
David Amadi1Dorothy Mailosi3Letisha Najjemba3Jay Greenfield3
Kagiso Mabe4
Emma Slaymaker1
Jim Todd1
Agnes Kiragga2- 1London School of Hygiene and Tropical Medicine, University of London, London, United Kingdom
- 2African Population and Health Research Center, Nairobi, Kenya
- 3Committee on Data of the International Science Council (CODATA), Paris, France
- 4University of Johannesburg, Johannesburg, South Africa
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Background: The standardization and integration of longitudinal mental health data from African cohort studies are critical in advancing research and informing policy. There are several challenges posed by diverse sources, instruments adapted for locals, and the absence of an interoperable framework to allow for meaningful analysis and cross-study comparisons. Methods: We designed and executed a metadata-driven pipeline using the OMOP Common Data Model within the INSPIRE Network Datahub to harmonise multi-country African mental health datasets. Data extracted previously from longitudinal studies, standardised via a snowflake schema staging database, is now mapped to OMOP vocabularies with local extensions, and validated through quality assurance protocols using OHDSI tools. Results: A total of 202,013 person records and over 7 million observations across fourteen cohort studies were successfully migrated. Mapping completeness exceeded 99.9%, with high conformance, completeness, and plausibility across all OMOP domains. Custom vocabularies ensured the coverage of context-specific exposures and outcomes, thereby supporting robust cohort construction, event characterization, and longitudinal analyses. Conclusion: This framework demonstrates scalable harmonisation and integration of African mental health data, bridging the gap between local datasets with global standards. This then enables the performance of federated analysis and reproducible research, increasing the utility and impact of mental health data in informing evidence-based policies and future collaborative studies across Africa.
Keywords: Africa, data harmonization, Data QualityAssurance, health informatics, Longitudinal Studies, Mental Disorders, Mental Health, OHDSI
Received: 21 Nov 2025; Accepted: 09 Feb 2026.
Copyright: © 2026 Bhattacharjee, Mugotitsa, Ochola, Momanyi, Andeso, Amadi, Mailosi, Najjemba, Greenfield, Mabe, Slaymaker, 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:
Tathagata Bhattacharjee
Bylhah Mugotitsa
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