Abstract
Background
Cardiovascular risk factors (CVRF) represent a growing public health concern, yet healthcare use remains unequal across social groups. Most studies analyse inequality axes isolated, leaving the intersectional dimension unexplored. We aimed to analyse inequalities in healthcare use among people from 16 to 50 years old with CVRF in Aragón (Spain) through an intersectional approach.
Methods
Cross-sectional observational study in the CARhES cohort (2024), a real-world data (RWD) population-based cohort derived from electronic health records, comprising 98,596 individuals aged 16–50 with CVRF. Associations between sex, country of origin, income and place of residence with primary care (PC) and specialised care (SC) use were examined by age group. Multivariable logistic regression models with interaction terms explored intersecting axes of inequality. Results were expressed as odds ratios (OR) with 95% confidence intervals (95%CI), assessed by Wald test.
Results
Substantial intersectional disparities were identified. PC use ranged from 95.4% in the group of native women patients with high-income and living in rural areas to 77.1% in those immigrant men with high-income living in urban areas. SC also ranged from 60.8% (native, high-income women in urban settings) to 30.7% (immigrant, high-income men in rural areas). Female sex was consistently associated with higher use at both levels. Low income combined with immigrant background was associated with higher PC use than the most privileged group (OR: 1.38; 95%CI: 1.06–1.80), while female sex, immigrant background and low income was associated with a reduced likelihood of SC use (OR: 0.83; 95%CI: 0.72–0.95).
Conclusions
An intersectional approach applied to RWD reveals complex, socially patterned inequalities in healthcare use among young people with CVRF. These patterns are not capture by single-axis analyses. These results provide actionable evidence for designing more equitable healthcare services.
Key messages
• Real-world data intersectional modelling provides actionable evidence to design more equitable healthcare services for socially vulnerable populations.
• Conventional approaches to healthcare inequality measurement systematically underestimate disparity magnitude by failing to capture interaction effects between social axes.