Social inequalities in multimorbidity patterns in Europe: A multilevel latent class analysis using the European Social Survey (ESS)

Multimorbidity is associated with lower quality of life, greater disability and higher use of health services and is one of the main challenges facing governments in Europe. There is a need to identify and characterize patterns of chronic conditions and analyse their association with social determinants not only from an individual point of view but also from a collective point of view. This paper aims to respond to this knowledge gap by detecting patterns of chronic conditions and their social determinants in 19 European countries from a multilevel perspective. We used data from the ESS round 7. The final sample consisted of 18,933 individuals over 18 years of age, and patterns of multimorbidity from 14 chronic conditions were detected through Multilevel Latent Class Analysis, which also allows detecting similarities between countries. Gender, Age, Housing Location, Income Level and Educational Level were used as individual covariates to determine possible associations with social inequalities. The goodness-of-fit indices derived in a model with six multimorbidity patterns and five countries clusters. The six patterns were “Back, Digestive and Headaches”, “Allergies and Respiratory”, “Complex Multimorbidity”, “Cancer and Cardiovascular”, “Musculoskeletal” and “Cardiovascular”; the five clusters could be associated with some geographical areas or welfare states. Patterns showed significant differences in the covariates of interest, with differences in education and income being of particular interest. Some significant differences were found among patterns and the country groupings. Our findings show that chronic diseases tend to appear in a combined and interactive way, and socioeconomic differences in the occurrence of patterns are not only of the individual but also of group importance, emphasising how the welfare states in each country can influence in the health of their inhabitants.