Beyond Single-Axis Inequality: An Intersectional MAIHDA Analysis of Major Cardiovascular Disease in Europe

Background Cardiovascular disease (CVD) remains socially patterned across Europe, but single-axis analyses can obscure how social positions combine. We used Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) to examine intersectional inequalities in self-reported major CVD. Methods We analysed cross-sectional European Health Interview Survey wave 3 data from 218 539 adults aged 40 years or older in 30 European countries. Major CVD comprised self-reported myocardial infarction, coronary heart disease or angina, or stroke, including chronic consequences. Twenty-four strata combined gender, household income, country of birth, and area of residence. Multilevel logistic models estimated predicted prevalence and assessed between-stratum heterogeneity and discriminatory accuracy before and after adjustment for age, education, obesity, smoking, and sedentary lifestyle. Results Weighted major CVD prevalence was 7.5%. Fully adjusted predicted prevalence ranged from 4.0% to 12.9% across intersectional strata, with the highest prevalence among low-income men and the lowest among medium/high-income women. In the fully adjusted model, women had lower odds of major CVD than men (odds ratio 0.67, 95% confidence interval 0.62–0.71), whereas low-income participants had higher odds than those with medium/high income (1.40, 1.31–1.49). Residual between-stratum heterogeneity was very small (variance partition coefficient 0.07%; proportional change in variance 98.5%), and the fully adjusted model had an area under the curve of 0.728. Conclusions Major CVD was clearly socially patterned, but inequalities reflected mainly the accumulation of measured disadvantages rather than large effects unique to particular intersections. These findings support population-wide cardiovascular prevention combined with proportionately greater support for socially disadvantaged groups.