标题： 因果方差分解法在测量健康不平等中的应用 显示英文标题

标题： Causal Variance Decompositions for Measuring Health Inequalities

摘要： 最近的因果推断文献引入了因果效应分解，以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。 在医院评估的背景下，比较医院绩效可能会揭示不同社会人口群体之间医疗交付的不平等，这可能由可及性/选择或实际效应修饰来解释。 我们考虑医院评估中多分类暴露的情况，其中比较通常是与整体系统平均绩效进行的，并将观察到的护理交付方差作为感兴趣的量。 为此，我们提出了一种新的八向因果方差分解，我们将观察到的变异归因于描述医院和群体成员身份的主要效应、群体成员身份对医院效应的修饰、医院可及性/选择、病例混合协变量的影响和残差方差的组成部分。 我们讨论了各组成部分的因果解释，制定了基于参数和非参数模型的估计器，并通过模拟研究这些估计器的性质。 最后，我们通过使用SEER数据库数据的癌症护理交付示例来说明我们的方法。 显示英文摘要

摘要： 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.