Tathagata Bhattacharjee ^1* Sylvia Kiwuwa-Muyingo ^2* Chifundo Kanjala ^1,3 Molulaqhooa L. Maoyi ⁴ David Amadi ¹ Michael Ochola ² Damazo Kadengye ² Arofan Gregory ⁵ Agnes Kiragga ² Amelia Taylor ⁶ Jay Greenfield ⁵ Emma Slaymaker ¹ Jim Todd ¹ INSPIRE Network ⁷

• ¹ London School of Hygiene and Tropical Medicine, University of London, London, London, United Kingdom
• ² African Population and Health Research Center (APHRC), Nairobi, Kenya
• ³ UNICEF (Malawi), Lilongwe, Malawi
• ⁴ South African Population Research Infrastructure Network (SAPRIN), South African Medical Research Council, Durban, South Africa
• ⁵ Committee on Data of the International Science Council (CODATA), Paris, France
• ⁶ Malawi University of Business and Applied Sciences, Blantyre, Malawi
• ⁷ Implementation Network for Sharing Population Information from Research Entities (INSPIRE Network), Nairobi, Kenya

Population health data integration remains a critical challenge in low-and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources.The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis.The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine-and human-readable Implementation Guide (IG), enhancing transparency and usability.The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the wellbeing of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.