Combining individual and aggregated data to investigate the role of socio-economic disparities on cancer burden in Italy

Quantifying socio-economic disparities and understanding the roots of inequalities are growing topics in cancer research, however socio-economic differences are challenging to investigate mainly due to the lack of accurate data at individual-level, while aggregate indicators are only partially informative. We implemented a multiple imputation algorithm within a statistical matching framework that combines diverse sources of data to estimate individual-level associations between income and risk of breast and lung cancer, adjusting for potential confounding factors in Italy. The framework we developed is computationally flexible to be adapted to similar contexts.