Evidence map›Paper›PMID 41877886›Full record

ArticleFrontiers in psychiatry2026

Migrating longitudinal African mental health data from staging to the OMOP common data model within the INSPIRE network datahub.

Tathagata Bhattacharjee, Bylhah Mugotitsa, Michael Ochola, Reinpeter Momanyi, Pauline Andeso, David Amadi, Dorothy Mailosi, Letisha Najjemba, Jay Greenfield, Kagiso Mabe and 4 more

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Article in Frontiers in psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

14 authors.

Tathagata BhattacharjeeLondon School of Hygiene and Tropical Medicine (LSHTM), London, United Kingdom.
Bylhah MugotitsaAfrican Population and Health Research Center (APHRC), Nairobi, Kenya.
Michael OcholaAfrican Population and Health Research Center (APHRC), Nairobi, Kenya.
Reinpeter MomanyiAfrican Population and Health Research Center (APHRC), Nairobi, Kenya.
Pauline AndesoAfrican Population and Health Research Center (APHRC), Nairobi, Kenya.
David AmadiLondon School of Hygiene and Tropical Medicine (LSHTM), London, United Kingdom.
Dorothy MailosiCODATA-Committee on Data of the International Science Council, Paris, France.
Letisha NajjembaCODATA-Committee on Data of the International Science Council, Paris, France.
Jay GreenfieldCODATA-Committee on Data of the International Science Council, Paris, France.
Kagiso MabeDepartment of Information and Knowledge Management, University of Johannesburg, Johannesburg, South Africa.
Emma SlaymakerLondon School of Hygiene and Tropical Medicine (LSHTM), London, United Kingdom.
Jim ToddLondon School of Hygiene and Tropical Medicine (LSHTM), London, United Kingdom.
Agnes KiraggaAfrican Population and Health Research Center (APHRC), Nairobi, Kenya.
INSPIRE Network

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Africadata harmonizationdata quality assurancehealth informaticslongitudinal studiesmental disordersmental HealthOHDSI

Identifiers

PMID41877886
PMCPMC13006644

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Textmetadata
LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.