Racial disparities in healthcare expenditures are well-documented, yet the underlying drivers remain complex. This study develops a framework to decompose 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 a component attributable to differences in mediator distributions, and a residual component that remains after equalizing those distributions. Using data from the Medical Expenditures Panel Survey (MEPS), we examine the extent to which expenditure disparities would persist or be reduced if mediators such as socioeconomic status (SES), 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, including super learners and a two-part model designed for the zero-inflated, right-skewed nature of expenditure data. Applying this framework to MEPS data from 2009 and 2016, substantial disparities were observed across all pairwise racial comparisons, with the largest gaps observed between non-Hispanic Whites and Hispanics in both years. Differences in SES and health status were the largest contributors to these disparities, with insurance access also playing a meaningful role, particularly for Hispanic populations, whereas health behaviors contributed minimally. Residual disparities persisted, especially in comparisons involving non-Hispanic Whites, suggesting the influence of unmeasured or structural factors.

翻译：医疗支出中的种族差异已有充分文献记载，但其潜在驱动因素仍较为复杂。本研究构建了一个框架，通过中介变量分布的偏移分解此类差异，而非将种族本身视为可操纵的暴露因素。我们将差异定义为调整协变量后各群体间结果分布的差异，并将总差异分解为可归因于中介变量分布差异的部分，以及在对这些分布进行均衡化后仍存在的残差部分。利用医疗支出面板调查(MEPS)数据，我们检验了若社会经济地位(SES)、保险可及性、健康行为或健康状况等中介变量在种族群体间实现均衡化时，支出差异将在多大程度上持续存在或减少。为确保推断的有效性，我们基于影响函数技术和灵活机器学习方法推导了渐近线性估计量，包括超级学习器和针对支出数据零膨胀、右偏特性设计的双部分模型。将该框架应用于2009年和2016年MEPS数据，在所有种族配对比较中均观察到显著差异，其中非西班牙裔白人与西班牙裔群体间的差距在两年中均为最大。社会经济地位和健康状况的差异是导致这些差异的最大贡献因素，保险可及性也发挥重要作用（尤其对西班牙裔人口），而健康行为的贡献相对较小。残差差异持续存在，特别是在涉及非西班牙裔白人的比较中，提示存在未测量因素或结构性因素的影响。