Assessing the FAIRness of Metabolic Bariatric Surgery Registries: a Comparative Analysis of Data Dictionaries from the UK, Germany, France, Netherlands, Norway, and Sweden

Abstract

Background: This study is part of an initiative to improve the FAIRness (Findability, Accessibility, Interoperability, Reusability) of metabolic bariatric surgery (MBS) registries globally. It explores the extent to which European registry data can be manually integrated without first making them FAIR and assesses these registries' current level of FAIRness. The findings establish a baseline for evaluation and provide recommendations to enhance MBS data management practices.

Methods: Data dictionaries from five national MBS registries in Germany, France, the Netherlands, the UK, and a combined registry for Scandinavia (Norway and Sweden) were evaluated regarding their ability to manually integrate registry datasets with one another. The FAIR Data Maturity Model from the Research Data Alliance (RDA) FAIR Data Maturity Model Working Group was used to assess the FAIRness of both metadata and data of the registries.

Results: The registries showed significant variability in variables and coding structures, with inconsistent numerical formats and without linkage to international standards such as SNOMED CT, LOINC, or NCIt, making data integration labor-intensive and assumption-heavy. Despite the presence of data dictionaries, all registries failed the FAIR assessment because machine-readable data was unavailable, and only human-readable metadata was available in the form of data dictionaries in a spreadsheet.

Conclusion: Our study reveals significant inconsistencies in data structuring and a failure to comply with the FAIR Principles, which limit effective data analysis and comparison. This emphasizes the critical need for standardized data management practices. We recommend four next steps to improve the FAIRness of MBS registries: (1) annotate data elements using standardized terminology systems, (2) deposit registry-level metadata in a repository, (3) request globally unique and persistent identifiers for datasets, and (4) define access restrictions.

Keywords: Data modeling; FAIR; Metabolic bariatric surgery; Registry.

Fig. 1

Example metadata from a fictitious dataset. Links to international standards offering unambiguous codes for variables and values and machine-readable descriptions of them are highlighted in orange