In varietate concordia – cluster analysis of EQ-5D-5L value sets in European Union countries

Abstract

Background The transferability of health policies in the European Union (EU) faces challenges due to economic, political, and healthcare system factors, including cross-country differences in health preferences. In order to aid policymaking, previous research has grouped EU countries based on geographical proximity or literature-based criteria, but not on health preference data. The EQ-5D-5L instrument, which measures health-related quality of life and reflects unique national health preferences shaped by cultural and social factors, is used to evaluate policies, technologies and interventions, but has not been used to group countries. Thus, this study aims to identify clusters of EU-27 countries with similar preference patterns using published EQ-5D-5L value sets. Methods Hierarchical agglomerative clustering was employed on 13 EU-27 countries’ EQ-5D-5L value sets, first analyzing regression coefficients for each dimension-level and then creating a simulated coefficient distribution considering uncertainty. Findings Five clusters of EU-27 countries emerged: (1) Belgium, Netherlands, Sweden, Germany); (2) Hungary, Italy, Portugal; (3) Poland, Romania; (4) France, Spain; (5) Denmark, Ireland. All clusters except cluster 5 gave higher importance to “Pain and Discomfort”; all clusters except cluster 1 gave less importance to “Usual Activities”. “Anxiety and Depression” and “Pain and Discomfort” had the largest heterogeneity in valuation across severity level. Interpretation Identified clusters of countries with distinct health preferences provide insights for health policy transferability in the EU. Recognizing countries with similar health preferences can aid EU policymaking and transferability efforts, particularly in early-stage policymaking, while also fostering closer collaboration on health policy decisions and best practice sharing. Further development of national value sets within the EU is necessary for a more accurate representation.