Intersectional analysis of inequalities in self-reported breast cancer screening attendance using supervised machine learning and PROGRESS-Plus framework - Pedrós Barnils, Núria; Schüz, Benjamin (Frontiers in Public Health, 2024)
Background: Breast cancer is a critical public health concern in Spain, and organized screening programs have been in place since the 1990s to reduce its incidence. However, despite the bi-annual invitation for breast cancer screening (BCS) for women aged 45-69, significant attendance inequalities persist among different population groups. This study employs a quantitative intersectional perspective to identify intersectional positions at risk of not undergoing breast cancer screening in Spain. Methods: Women were drawn from the 2020 European Health interview Survey in Spain, which surveyed the adult population (> 15 years old) living in private households (N = 22,072; 59% response rate). Inequality indicators based on the PROGRESS-Plus framework were used to disentangle existing social intersections. To identify intersectional groups, decision tree models, including Classification and Regression Trees (CART), Chi-square Automatic Interaction Detector (CHAID), Conditional Inference Trees (CIT), and C5.0, and an ensemble algorithm, Extreme Gradient Boosting (XGBoost) were applied.Results: XGBoost (AUC 78.8%) identified regional differences (Autonomous Community) as the most important factor for classifying BCS attendance, followed by education, age, and marital status.The C5.0 model (balanced accuracy 81.1%) highlighted that the relative importance of individual characteristics such as education, marital status, or age for attendance differs based on women's place of residence and their degree of interaction. The highest risk of not attending BCS was observed in illiterate older women in lower social classes born in Spain residing in Asturias,