Abstract
BackgroundRecord linkage is increasingly used in health research worldwide. Combining the patient information available in healthcare, administrative and clinical databases broadens the research perspectives, particularly for chronic diseases. Recent guidelines highlight the need for transparency on the used record linkage processes and the extracted data to be used by researchers.MethodsTherefore, the aim of this study was to describe the deterministic iterative approach used to link the French Epidemiology and Information Network (REIN), a French national End-Stage Renal Disease registry, with the Système National des Données de Santé (SNDS), a French nationwide medico-administrative healthcare database.ResultsAmong the 22,073 patients included in the REIN registry who started renal replacement therapy between 2014 and 2015 in France, 19,223 (87.1%) were matched with patients in the SNDS database. Comparison of matched and unmatched patients confirmed the absence of any major selection bias. Then, the record linkage was evaluated using the comorbidity status (diabetes).ConclusionsThis fast and efficient method of record linkage with pseudonymized data and without unique and direct identifier might inspire other research teams. It also opens the path for new research on chronic kidney disease.
Highlights
Record linkage is increasingly used in health research worldwide
The aim is to allow researchers using these data to be aware of potential biases, improving the interpretation of results based on linked data and their overall quality [1]
In line with the GUidance for Information about Linking Datasets (GUILD) [1] recommendations, here, we describe the fast and efficient record linkage approach used to link patients in the Renal Epidemiology and Information Network (REIN) registry with patients in the Système National des Données de Santé (SNDS) database
Summary
Record linkage is increasingly used in health research worldwide. Combining the patient information available in healthcare, administrative and clinical databases broadens the research perspectives, for chronic diseases. Recent guidelines highlight the need for transparency on the used record linkage processes and the extracted data to be used by researchers. Linking databases increases the information available on each patient (clinical and administrative data, disease-related mortality, healthcare utilization...) and broadens the research opportunities [4,5,6,7,8,9]. Recent publications underlined the necessity for a greater transparency about the production and the use of linked data in health research [1, 20]. The aim is to allow researchers using these data to be aware of potential biases, improving the interpretation of results based on linked data and their overall quality [1]
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