Intersectional inequalities in depressive symptoms according to gender, migration, and education in 30 European countries
摘要
Intersectional approaches offer the opportunity to analyse the interplay of different social factors and reach a deeper understanding of inequalities in depressive symptoms. There is a need for further research on intersectional inequalities in depressive symptoms as the few previous studies focussed on individual countries. Therefore, we aimed to explore intersectional inequalities in depressive symptoms according to gender, migration, and education in 30 European countries.
MethodsAnalyses made use of the recent wave (round 11) of the cross-sectional European Social Survey (ESS, 2023/2024). Samples of the ESS are supposed to be representative of all persons aged 15 years and over resident within private households in each country. Individuals are selected by strict random probability methods at every stage. Overall sample size was N = 50,116. Depressive symptoms were assessed by the CES-D-8. Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was conducted.
ResultsWomen and respondents with a low education reported significantly more depressive symptoms in most European countries. People with a migration background were more affected than those without, although differences were significant in a few countries only. Social inequalities in depressive symptoms were attributable to additive effects of education, gender, and migration, with gender and education contributing most to these inequalities. Analyses across 12 intersectional strata (combining subgroups of education, gender, and migration) showed that the differences between the most (women with a migration background and low education) and least affected strata (men with high education and no migration background) were significant.
ConclusionsThe study revealed additive social inequalities in depressive symptoms according to education, gender, and migration in 30 European countries. Results indicate the potential of MAIHDA to identify vulnerable groups affected by multiple disadvantages that can be addressed in interventions to reduce social inequalities in depression.