Clustering European populations based on health inequalities

Abstract

MA Beenackers, FJ van Lenthe, CW Looman, JP Mackenbach, for the EURO-GBD-SE consortium Department of Public Health, Erasmus MC, Rotterdam, The Netherlands Contact: m.beenackers@erasmusmc.nl Background Countries are often compared based on average population health, and less often on their pattern of health inequalities. Insights into which countries have similar or contrasting patterns regarding health inequalities may help to identify determinants of health inequalities, and ultimately to find entry points for action to reduce health inequalities. The aim of this paper is therefore to identify different clusters of European populations based on their pattern of health inequalities. Methods Harmonized mortality data by educational level ( 1998-2007) was used to calculate slope indices of inequality (SII) for 24 causes of death for 18 European populations in 14 European countries. An exploratory hierarchical cluster analysis was performed using Wards linkage and squared Euclidian distance, with SII per cause as variables. Results Preliminary analyses identified three main clusters for men: The first cluster (Estonia & Hungary) showed large inequalities for many causes of death. The second cluster contained a large group of countries (Norway, Sweden, Denmark, England, Belgium (2 populations), Austria, Switzerland, Spain (3 populations) & Italy (2 populations)) with low inequalities for many causes of death. The third cluster (Poland, Czech republic & Finland) was a mixed cluster in which inequalities varied by cause of death. For women, four clusters were identified but the differences between the clusters were less pronounced. Preventable causes of death such as alcoholrelated conditions and hypertension seemed to be major drivers in the clustering for both men and women. Conclusions This study shows that clusters of countries that resemble each other in their pattern of health inequalities can indeed be identified. Future studies should analyse the determinants of this clustering in order to identify entry points for action to increase population health and reduce health inequalities. Key message European populations can be clustered based on their pattern of health inequalities. This may provide entry points for action to reduce health inequalities.