In recent decades, intersectionality has been at the center of feminist and gender theory debates. In the United States, Canada and Europe, it has achieved a hegemonic status, strengthened by its multiple possible applications, precisely because it does not meet the requirements of a theory or conception with defined contours.Intersectionality has been incorporated mainly in qualitative studies, favoring methodologies that are assumed to be best suited to address complexity such as ethnography, deconstruction, genealogy, ethnomethodology and case studies. In the field of population health research in particular, approaches to model intersectionality in quantitative studies are still emerging. Progress has been made in multivariate analysis and logistic regressions models separately for men and women. Other authors work with additive models from multiple linear regressions where different "levels of intersectionality" are included in different steps of the regression. Another possible approach is the inclusion of interaction terms in regression models.The objective of this article is to contribute to these theoretical-methodological discussions through the inclusion of interactions between variables in binary logistic regression models. We propose this intersectional analysis to explain the inequalities in self-perception of the health status of the population aged 18 years and older, living in urban areas of Argentina. First, we address the effect of different sociodemographic and geographical variables on self-perceived health status and then we add an interaction between sex and educational level. We use the data of the National Survey of Risk Factors (ENFR) 2013, provided jointly by the National Institute of Statistics and Censuses (INDEC) and the Ministry of Health of the Argentine Nation. This survey was carried out based on a probabilistic design (by conglomerate and stratified), through four stages (department, area, housing and household member). A sample of 46,555 cases was obtained nationwide.