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Background  Intersectionality theory combined with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can facilitate our understanding of health disparities. This enables the application of proportionate universalism for resource allocation in public health. Analyzing self-rated health (SRH) in Sweden, we show how an intersectional perspective allows for a detailed mapping of health inequalities while avoiding simplification and stigmatization based on indiscriminate interpretations of differences between group averages.    Methods  We analyzed participants ( n =133,244) in 14 consecutive National Public Health Surveys conducted in Sweden in 2004–2016 and 2018. Applying AIHDA, we investigated the risk of bad SRH across 12 intersectional strata defined by gender, income and migration status, adjusted by age and survey year. We calculated odds ratios (with 95% confidence intervals) to evaluate between-strata differences, using native-born men with high income as the comparison reference. We calculated the area under the receiver operating characteristic curve (AU-ROC) to evaluate the discriminatory accuracy of the intersectional strata for identifying individuals according to their SRH status.    Results  The analysis of intersectional strata showed clear average differences in the risk of bad SRH. For instance, the risk was seven times higher for immigrated women with low income (OR 7.00 [95% CI 6.14–7.97]) than for native men with high income. However, the discriminatory accuracy of the intersectional strata was small (AU-ROC=0.67).    Conclusions  The intersectional AIHDA approach provides more precise information on the existence (or the absence) of health inequalities, and can guide public health interventions according to the principle of proportionate universalism. The low discriminatory accuracy of the intersectional strata found in this study warrants universal interventions rather than interventions exclusively focused on strata with a higher average risk of bad SRH.

An intersectional analysis providing more precise information on inequities in self-rated health