Identifying intersectional groups at risk for missing breast cancer screening: Comparing regression- and decision tree-based approaches

Abstract

Malignant neoplasm of the breast was the fifth leading cause of death among women in Germany in 2020. To improve early detection, nationwide breast cancer screening (BCS) programmes for women 50–69 have been implemented since 2005. However, Germany has not reached the European benchmark of 70% participation, and socio-demographic inequalities persist. At the same time, challenges exist to identify groups of women at high risk for non-participation, since it is likely that this is due to disadvantages on multiple social dimensions. This study, therefore, aimed to identify intersectional groups of women at higher risk of not attending BCS by comparing two analytical strategies: a) evidence-informed regression and b) decision tree-based regression. Participants were drawn from the German 2019 European Health Interview Survey (N = 23,001; 21.6% response rate). Two logistic regressions using cross-classification intersectional groups based on relevant PROGRESS-Plus characteristics adjusted by age were built. The evidence-informed approach selected relevant variables based on the literature and the decision tree approach on the best-performing tree. The first identified low-income women born outside Germany, living in rural areas and not cohabiting with their partner at higher risk of never attending BCS (OR = 9.48, p = 0.002), whereas the second, based on a Classification and Regression Tree (61.91% balanced accuracy), determined widowed women living alone, with children, with a partner and children, or in other arrangements, and residing in specific federal states (i.e. Bavaria, Brandenburg, Bremen, Hamburg, or Saarland) (OR = 3.43, p < 0.001). Compared to the evidence-informed regression, the decision tree-based regression yielded higher discriminatory accuracy (AUC = 0.6726 vs AUC = 0.6618) and added relevant nuances in the identification of at-risk intersectional groups, going beyond known inequality dimensions and, therefore, helping the inclusion of under-studied populations in breast cancer screening.