Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK

Abstract

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualization of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009–2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.