Many studies compare health averages between groups such as neighbourhoods or social categories. Averages are simple but can be misleading, since individuals within the same group often differ widely. We present MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) as a general framework to study how context structures health differences. MAIHDA is not a new statistical model but a way to reorganize standard multilevel analysis to look beyond averages. It integrates three perspectives: (1) Specific Contextual Effects (mean differences between groups), (2) General Contextual Effects (how strongly outcomes cluster within groups, eg the variance partition coefficient), and (3) Discriminatory Accuracy (how well group membership classifies individuals according to the outcome). Interpreting these dimensions together shows to which degree a context shapes outcome and whether interventions should be universal or targeted. Although intersectional studies have recently popularized MAIHDA, the framework predates its intersectional applications. It was first developed within contextual epidemiology to study geographical and institutional settings, and later extended to intersectionality and multicategorical analyses, which added visibility. By shifting attention from averages to heterogeneity and clustering, MAIHDA helps avoid group stigmatization and guides equitable strategies such as proportionate universalism. It offers a practical, theory-agnostic way to understand how contexts structure individual inequalities.
Merlo et al. (2026) studied this question.