Health AI Central
Peer-Reviewed Publication
Biometrics2026;82(4)October 7, 2026Journal Article

Causal decomposition framework for quantifying intervenable cancer health disparities.

Fan Xia1, Sang Kyu Lee2, Seonjin Kim3, Kelly Blum4, Hyokyoung G Hong5, Alison J Canchola1,6, Mi-Ok Kim1,4
1Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94143, United States.
2Department of Applied Statistics, Konkuk University, Seoul, South Korea 05029.
3Department of Statistics, Miami University, Oxford, OH 45056, United States.
4UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94143, United States.
5Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, United States.
6Greater Bay Area Cancer Registry Center, San Francisco, CA 94538, United States.
PubMed DOI

Abstract

This study proposes a nonparametric decomposition framework to quantify and prioritize the impact of multiple intervenable mediators contributing to disparities in health outcomes. Identifying modifiable components of health disparities is essential for informing policy and guiding resource allocation, but several methodological challenges remain. These include formulating hypothetical interventio…

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