CART-analysis embedded in social theory: A case study comparing quantitative data analysis strategies for intersectionality-based public health monitoring within and beyond the binaries
Implementation of an intersectionality perspective into quantitative health research might support the process of unravelling complex socio-cultural and economic power relations which underly and shape patterns of health and disease within populations. Intersectionality-informed health monitoring and health reporting integrating a sex/gender-sensitive perspective could serve as a fertile ground to strengthen the essential function of health reporting to support political decision-making. We aimed at the integration of social theory into quantitative data analysis by taking into consideration 4 distinct central sex/gender theoretical concepts in health research. We developed and tested an intersectionality-based, sex/gender-sensitive strategy comparing 5 distinct models based on different combinations of the binary sex/gender variable, socio-cultural and economic variables (defined from an intersectionality perspective) as well as solution-linked sex/gender variables. We used CART-analysis as a quantitative, non-parametric, exploratory method to detect subgroups with high prevalence of frequent mental distress (FMD). Analyses were based on data from a National Health Telephone Interview Survey conducted in Germany. Depending on model and detected subgroup of our comparative approach, prevalence of FMD ranged between approximately 5 %–25%. Within the model including the binary sex/gender variable, socio-cultural and economic variables, sex/gender turned out to be the most important attribute. Comparing the models which included solution-linked sex/gender variables to the model not including these variables illustrated that the CART-algorithm was able to detect subgroups with the same prevalence of FMD, but with approximately 14% as opposed to 4.5% of the study population being affected. For these models, social support served as the primary splitting variable and not the binary sex/gender variable. Including or not including the binary sex/gender variable in the models with the solution-linked variables did not ...