FAIR foundations of a novel indicator vault for non-communicable diseases in the European Union
Background: Comparing health indicators across the European Union (EU) is a challenging endeavour. A feasibility study was conducted to explore opportunities for improvement through the contextualisation of indicators for major non-communicable diseases (NCDs). We aimed to improve the usability and transparency of indicators in the domain of NCDs by describing the contextual information about the data from which they draw and the related data processes. In particular, we sought to illustrate how semantic linkage could be achieved to facilitate interoperability with other metadata models using FAIR data principles. Finally, we aimed to provide recommendations for the implementation of the proposed metadata model at the EU level. Methods: A number of expert group meetings were held between March 2023 and October 2024 to agree on the approach and related technologies to meet the standard requirements for the meaningful comparison of indicators across countries and regions of Europe in the domain of NCDs. Results: The Semantic Ontology-Labelled Indicator Contextualisation Integrative Taxonomy (SOLICIT) was selected as a suitable generic metadata model for contextualising indicators. In this work, we adapted the SOLICIT generic framework to the diabetes sub-domain and extended its applicability more generally across all NCDs. As a proof of concept, we present an example of how to adapt a diabetes indicator and its related contextualisation within SOLICIT. Conclusion: The accurate contextualisation of NCD indicators can substantially improve their use and comparability across national and regional boundaries. This study delivered a set of seven recommendations for implementation in three different areas: (a) contextualisation of common data elements and indicators (use of contextual information
Authors
- Jana Lepiksone (ORCID: https://orcid.org/0000-0002-0690-4524)
- Fabrizio Carinci (ORCID: https://orcid.org/0000-0002-7524-227X)
- Tamara Poljičanin
- Stephen Fava (ORCID: https://orcid.org/0000-0002-2372-7101)
- Iztok Štotl (ORCID: https://orcid.org/0000-0002-2847-6129)
- Astrid Lavens
- Massimo Massi Benedetti
- Scott G. Cunningham
Publication Details
- Journal
- DiRROS repository (University of Maribor)
- Published
- 2025-12-18
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00