Keywords: Intersectional Framework, Mental Health, Health Inequities

Background:

Intersectionality is a theoretical framework that examines how multiple dimensions of social identity (gender, class, or education) intersect and combine to generate experiences of discrimination, privilege, and marginalisation. In the field of mental health, intersectionality can offer a multidimensional explanation for these health problems.

Research questions:

What is the intersectional effect of sex, age, educational level on the mental health in the elder spanish adults?

Method:

Design: cross-sectional descriptive study from the Spanish National Health Surveys (N=17,903).
Population: participants aged >60 years old
Data analysis: Multilevel logistic regression analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) to examine mental health inequities across 24 intersectional strata defined by age, sex, educational level, and survey year, and to quantify the contribution of their intersections to these inequities. An empty model and an additive model with covariates were estimated to assess additive effects. Estimate measures: Variance Partition Coefficient (VPC) and the percentage change in variance (PCV) were used. Finally, we calculated the difference in predicted probabilities between the total predicted probability for each stratum and the probability based solely on the additive main effects.

Results:

Twenty-four strata were generated. The prevalence of mental health problems was highest among women aged >75 years with a low educational level. The intersectional effect was VPC:5.8%, with a PCV of 97%, largely explained by low educational attainment and female sex. However, no differences were observed in predicted probabilities between the empty model and the additive effects model.

Conclusions:

Although there is moderate variability between social strata, most of the observed inequalities are explained by additive effects. Being a woman and having a low educational level were associated with a higher risk of depression and anxiety, while no relevant intersectional effects beyond these main factors were identified. These findings highlight the usefulness of the I-MAIHDA approach for distinguishing between additive and intersectional inequalities.

Points for discussion:

What dimensions of social inequalities have a higher impact on mental health?

Would an I-MAIHDA approach through social strata better define our patients in Primary Care?