Integrating granular personal and clinical data with large-scale administrative records is a frontier in modern public health. In France, linking national epidemiological surveys with the National Health Data System (SNDS)—one of the world's most exhaustive administrative databases—offers a transformative "win-win" methodology to overcome self-reporting biases and loss to follow-up. This paper analyzes the architectural and methodological frameworks of data linkage to SNDS in France, distinguishing between deterministic linkage (via the National Identification Number) and probabilistic approaches. We examine major national integrations, including the prospective Constances cohort, cross-sectional surveys or clinical cohorts, and administrative cohorts like EDP-Santé. Linkage significantly enhances data utility by cross-referencing objective healthcare consumption with socio-economic, environmental, and behavioral health determinants. Beyond data enrichment, this synergy provides a robust methodological platform for the validation of identification algorithms, allowing researchers to calculate sensitivity and specificity against clinical “Gold Standards.” We highlight how these linked datasets facilitate complex longitudinal studies on social health inequalities and care pathways that are unattainable through isolated sources. Survey-SNDS linkage is a “win-win” process that has become the foundational standard for high-impact research in France. By maximizing the utility of national data assets, this methodology provides a replicable model for global real-world evidence (RWE) generation and public health policy evaluation.
Kab et al. (Wed,) studied this question.