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 Multi -morbidity", "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 cova-riates of interest, with differences in education and income being of particular interest. Some significant dif-ferences 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

The relationship between characteristics of nurses and their attitude toward nursing diagnosis: a cluster analysis

Aims and objectives To classify a sample of nurses into groups with similar attitude towards nursing diagnosis (ND), using the cluster analysis method, to explore differences in the characteristics between the identified profile groups. The characteristics tested included sociodemographic and professional aspects, and the degree of contact with ND. Methods A cross-sectional explorative design was used in a convenience sample of 548 registered nurses (RNs) was recruited through snowball sampling between the contacts of collaborators of the research group. It was used the Nursing Diagnosis Scale (PND): a 20-item scale that uses the semantic differential method to assess attitudes towards ND. Descriptive statistics were used to summarise data. A hierarchical cluster analysis was employed to categorise the participants into mutually exclusive clusters with similar attitude profiles based on their responses to the 20 items of the PND. Results A three-cluster solution was considered the most suitable. Clusters 1, 2 and 3 comprise RNs with positive, neutral and negative attitudes towards ND, respectively. Conclusion RNs who work in the management field show a better attitude. The increasing awareness of the Spanish managers regarding the benefits of the use of ND in practice could be related to this finding. Contact with ND appears to be a common factor among nurses with positive attitudes; the greater the interaction with ND by nurses, the better the attitudes