Recent causal inference literature has introduced causal effect decompositions to quantify sources of observed inequalities or disparities in outcomes but usually limiting this to pairwise comparisons. In the context of hospital profiling, comparison of hospital performance may reveal inequalities in healthcare delivery between sociodemographic groups, which may be explained by access/selection or actual effect modification. We consider the case of polytomous exposures in hospital profiling where the comparison is often to the system wide average performance, and decompose the observed variance in care delivery as the quantity of interest. For this, we formulate a new eight-way causal variance decomposition where we attribute the observed variation to components describing the main effects of hospital and group membership, modification of the hospital effect by group membership, hospital access/selection, effect of case-mix covariates and residual variance. We discuss the causal interpretation of the components, formulate parametric and nonparametric model based estimators and study the properties of these estimators through simulation. Finally, we illustrate our method by an example of cancer care delivery using data from the SEER database.

翻译：近期因果推断文献引入了因果效应分解方法来量化观察到的结果不平等或差异的来源，但通常仅限于成对比较。在医院绩效评估的背景下，医院绩效的比较可能揭示社会人口学群体之间在医疗保健提供方面的不平等，这可能由就医机会/选择或实际效应修饰所解释。我们考虑医院绩效评估中的多分类暴露情况，其中比较对象通常是系统范围内的平均绩效，并将观察到的医疗服务提供方差分解为关注量。为此，我们提出了一种新的八向因果方差分解方法，将观察到的变异归因于以下组成部分：医院和群体成员身份的主效应、群体成员身份对医院效应的修饰作用、医院就医机会/选择、病例组合协变量的效应以及残差方差。我们讨论了各组成部分的因果解释，构建了基于参数和非参数模型的估计量，并通过模拟研究了这些估计量的性质。最后，我们使用SEER数据库的癌症医疗服务数据通过实例说明了我们的方法。