Interpreting inequalities and causal considerations – reflections on the application of descriptive intersectional MAIHDA

Intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) is a novel method for analysing intersectional inequalities that is increasingly popular in health-related research. The method is characterised by its unusual application of a standard multilevel modelling framework, which allows users to describe inequalities in an outcome across multiple intersecting characteristics simultaneously. It does this by nesting individuals within the relevant combination of intersecting characteristics, or ‘strata’. However, the aims of such analyses are not always clear, and misunderstandings of the fundamental goals of intersectional MAIHDA are evident. Intersectional MAIHDA and its closely related approaches (including multicategorical MAIHDA and Eco-Intersectional Multilevel models) were initially conceived as descriptive methods, yet some recent applications stray into causal imaginings. Care is needed when designing, conducting and interpreting the results of MAIHDA. There are also several notable risks for analyses situated in the grey area between description and causal inference. In this study, we consider three key issues that warrant attention: (1) how to interpret the coefficients of stratum-defining variables, (2) the potential for inadvertent collider adjustment and generalisability bias, illustrated using simulated data, and (3) the inclusion of ‘control’ variables. We provide recommendations for producing more robust descriptive research using MAIHDA.