Health disparities are driven by a complex interplay of determinants operating across multiple levels of influence. However, while recognized conceptually, much disparities research fails to capture this inherent complexity in study focus and/or design; little of such work accounts for the interplay across the multiple levels of influence from structural (contextual) to biological or clinical. We developed a novel modeling framework that addresses these challenges and provides new insights. We used data from the Florida Cancer Data System on endometrial cancer patients and geocoded-derived social determinants of health to demonstrate the applicability of a new modeling paradigm we term PRISM regression. PRISM is a new highly interpretable tree-based modeling framework that allows for automatic discovery of potentially non-linear hierarchical interactions between health determinants at multiple levels and differences in survival outcomes between groups of interest, including through a new specific area-level disparity estimate (SPADE) incorporating these multilevel influences. PRISM demonstrates that hierarchical influences on racial disparity in endometrial cancer survival appear to be statistically relevant and that these better predict survival differences than only using individual level determinants. The interpretability of the models allows more careful inspection of the nature of these hierarchical effects on disparity. Additionally, SPADE estimates show distinct geographical patterns across census tracts in Florida. PRISM can provide a powerful new modeling framework with which to better understand racial disparities in cancer survival.