Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Abstract

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.

Mapping intersectional inequalities in biomarkers of healthy ageing and chronic disease in older English adults

“…For the first time, we have intersectionally 'mapped out' the main social disparities in key biomarkers of healthy ageing using nationally representative English data. We found no evidence of multiplicative intersectional effects, consistent with other MAIHDA analyses which have generally found no or negligible effects in a range of health outcomes [13][14][15]37 . We uncovered intersectional disparities both in terms of the intersectional range, as well as intersectional patterning, as a result of the additive (or layered) effects of social attributes.…”

“…Linear regression models were estimated directly in Stata and marginal effects were calculated using the margins command. Given that MAIHDA models are often estimated using the Markov Chain Monte Carlo (MCMC) method [13][14][15]37 we tested whether the lack of multiplicative effects found was due to our use of IGLS estimation. Further, MAIHDA models often use age categories to define the intersectional subgroups 9,13-15,37 , so we also tested whether this strategy would result in multiplicative effects being found, by including age in 10 years bands to define intersectional subgroups.…”

Multilevel versus single-level regression for the analysis of multilevel information: The case of quantitative intersectional analysis

“…Symmetrically, cases of low discriminatory accuracy encourage us to remain skeptical of the use of such labels for determining individual risk, particularly in clinical settings. These cases encourage a more "anticategorical" perspective (Hernández-Yumar et al 2018;Merlo 2018;Wemrell et al 2017a). It is critical to note that the terms (inter)categorical and anticategorical are meant here very differently than how they are frequently used to define distinct approaches within intersectional scholarship.…”

“…Furthermore, intersectional MAIHDA has answered calls from intersectional scholars for innovative new quantitative methods to study intercategorical inequalities (Bauer 2014;Bowleg 2012;McCall 2005;Nash 2008) and calls from social epidemiologists for innovative new ecoepidemiologic approaches (Merlo 2014;Merlo & Wagner 2012). While MAIHDA has been validated thus far using simulations (Bell et al 2019;Evans 2015;Evans et al 2018;Jones et al 2016) and has been applied in a number of empirical scenarios (Axelsson Fisk et al 2018;Evans 2019;Evans & Erickson 2019;Evans et al 2018;Hernández-Yumar et al 2018;Persmark et al 2019), there remain many subtleties to this new approach that are worthy of further exploration and explication.…”

Intersectional inequalities and the U.S. opioid crisis: challenging dominant narratives and revealing heterogeneities

“…As described in detail previously (Axelsson Fisk, et al, 2018;Evans, et al, 2018;Hernández-Yumar, et al, 2018), an intersectional MAIHDA analysis models twolevel hierarchical data with individuals at level 1 nested within intersectional strata at level 2. We modelled the risk of POM using three two-level random-intercept logistic regression models, described below.…”