Next Generation Global Health Research through a Federated, Truth-First Data Collaboration Framework

Abstract

Background: Traditional global health data-sharing is stifled by centralised repository risks and operational bottlenecks. Progress is frequently stalled by the unwillingness of data producers to share micro-level datasets due to complex legal barriers, sovereignty concerns, and evolving national regulations. For the NIHR RESPIRE network, these challenges are magnified across diverse jurisdictions in the UK and South/Southeast Asia (India, Bangladesh, Indonesia, Malaysia, Bhutan, and Pakistan).   Methods: We propose a transformative framework shifting the paradigm from moving micro-level data to sharing high-level insights via five strategic pillars. The Foundational Pillar establishes a secure Trusted Research Environment (TRE) using the OMOP Common Data Model for global standardisation. The Innovation Pillar deploys federated analytics and dashboards, enabling partners to execute studies locally. To ensure compliance, the Regulatory Pillar implements a "Trust-First" model with standardised ethics processes and cross-jurisdictional governance. Finally, the Evidence Pillar validates the framework through LMIC-based use-cases, while the Growth Pillar ensures sustainability via local data engineering and regional training hubs to maintain partner ownership.   Results: This decentralised ecosystem preserves absolute institutional sovereignty. By eliminating physical data transfer, the framework resolves the primary legal and trust concerns of data producers, enabling seamless, real-time cross-border analytics. Pilot results demonstrate a scalable blueprint for generating high-impact respiratory health insights without compromising security or autonomy.   Conclusion: By integrating federated analytics with robust privacy guardrails, this framework transforms siloed, legally sensitive data into a powerful collaborative asset. It empowers NIHR RESPIRE partners to lead high-fidelity, evidence-based interventions while retaining full data control, providing a sustainable model for global health research in low-resource settings.