Assessing Racial Disparities in Healthcare Expenditures via Mediator Distribution Shifts

Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex and require further investigation. This study develops a framework for decomposing such disparities through shifts in the distributions of mediating variables, rather than treating race itself as a manipulable exposure. We define disparities as differences in covariate-adjusted outcome distributions across racial groups, and decompose the total disparity into two components: one attributable to differences in mediator distributions, and another residual component that would remain even after equalizing these distributions. Using data from the Medical Expenditures Panel Survey, we examine the extent to which expenditure disparities would persist or be reduced if mediators such as socioeconomic status, insurance access, health behaviors, or health status were equalized across racial groups. To ensure valid inference, we derive asymptotically linear estimators based on influence-function techniques and flexible machine learning tools, including super learners and a two-part model designed for the zero-inflated, right-skewed nature of expenditure data.