Background and Objective:
Organised breast cancer screening programmes can be effective in reducing incidence, mortality and illness burden. Currently in Spain, women between 50-69 are invited to attend screenings bi-annually. However, roughly 25% do not utilise this free service. Here, we take an intersectional perspective using machine learning techniques to identify social groups at risk of not utilising breast cancer screening.

Methods:
Women were drawn from the 2020 European Health interview Survey in Spain, which targets the (young) adult population > 15 years old living in private households (N = 22,072; 59% response rate). Using available indicators of socioeconomic status based on the PROGRESS-Plus framework, we applied machine learning (Classification and Regression Trees (CART), Chi-square Automatic Interaction Detector (CHAID), and Conditional Inference Trees (CIT)) to data from the target population (women 50-69) to estimate models that disentangle existing social intersections. We then used accuracy, sensitivity and specificity indicators to identify the best-fitting tree.

Results:
A non-parametric CHAID model suggests (overall accuracy of 75.07%) primary education or below to be the strongest discriminating factor for not attending screening (n=1,060 Pr=0.3448 vs n=2,790 Pr=0.2462). The second strongest factor was country of birth (lower education group, born in Spain: n=983 Pr=0.3313 vs born outside Spain n=77 Pr=0.5195; higher education group, born in Spain: n=2,596 Pr=0.2369 vs born outside Spain: n=194 Pr=0.3711). Finally, for those born in Spain with lower education, being married, widowed or divorced predicted attendance compared to being single or separated (n=869 Pr=0.3026 vs n=124 Pr=0.5081).

Conclusion:
In order to reduce inequities in screening attendance, programs in Spain should particularly support women with lower education and migration background, and potentially focus on social support measures. CHAID is a useful tool to identify groups at higher risk of not utilising an organised public health program and inform tailored prevention programs.