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Combining individual and aggregated data to investigate the role of socioeconomic disparities on cancer burden in Italy
Author(s) -
Mezzetti Maura,
Palli Domenico,
Dominici Francesca
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8392
Subject(s) - socioeconomic status , confounding , matching (statistics) , econometrics , propensity score matching , aggregate data , computer science , breast cancer , imputation (statistics) , statistics , medicine , environmental health , cancer , mathematics , missing data , machine learning , population
Quantifying socioeconomic disparities and understanding the roots of inequalities are growing topics in cancer research. However, socioeconomic 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 is computationally flexible and can be adapted to similar contexts.