Adjusting for Health Status in Non-Linear Models of Health Care Disparities.
Adjusting for Health Status in Non-Linear Models of Health Care Disparities.
复制标题
DOI：
10.1007/s10742-008-0039-6
复制
发表时间：
2009-03-01
影响因子：
1.5
通讯作者：
Zaslavsky, Alan M
中科院分区：
文献类型：
--
作者：
Cook, Benjamin L;McGuire, Thomas G;Meara, Ellen;Zaslavsky, Alan M
本文比较了使用非线性医疗保健模型估计种族差异的替代方法的概念和经验优势。提出了三种方法（倾向评分、排名和替换以及组合方法），这些方法根据健康状况进行调整，同时允许 SES 变量调节种族和获得护理之间的关系。将这些方法应用于 2003 年和 2004 年医疗支出小组调查 (MEPS) 中调查的黑人和非西班牙裔白人的全国代表性样本，我们评估了每种方法与医学研究所 (IOM) 种族差异定义的一致性，并根据经验比较了这些方法的预测差异估计值、估计值的方差以及估计值对现有数据限制的敏感性。 数据。排名替换法和组合法（但不是倾向评分法）与 IOM 对种族差异的定义一致，因为每种方法都创建了一个健康状况和 SES 变量具有适当边际分布的比较组。排序替换方法和组合方法的预测差异和预测方差相似，但排序替换方法对 SES 信息的限制很敏感。对于所有方法，与更全面的数据集相比，限制健康状况信息显着减少了差异估计。我们得出的结论是，两种 IOM 一致方法足够相似，可以在视差预测中考虑其中任何一种方法。在SES信息有限的数据集中，组合方法是更好的选择。
This article compared conceptual and empirical strengths of alternative methods for estimating racial disparities using non-linear models of health care access. Three methods were presented (propensity score, rank and replace, and a combined method) that adjust for health status while allowing SES variables to mediate the relationship between race and access to care. Applying these methods to a nationally representative sample of blacks and non-Hispanic whites surveyed in the 2003 and 2004 Medical Expenditure Panel Surveys (MEPS), we assessed the concordance of each of these methods with the Institute of Medicine (IOM) definition of racial disparities, and empirically compared the methods' predicted disparity estimates, the variance of the estimates, and the sensitivity of the estimates to limitations of available data. The rank and replace and combined methods (but not the propensity score method) are concordant with the IOM definition of racial disparities in that each creates a comparison group with the appropriate marginal distributions of health status and SES variables. Predicted disparities and prediction variances were similar for the rank and replace and combined methods, but the rank and replace method was sensitive to limitations on SES information. For all methods, limiting health status information significantly reduced estimates of disparities compared to a more comprehensive dataset. We conclude that the two IOM-concordant methods were similar enough that either could be considered in disparity predictions. In datasets with limited SES information, the combined method is the better choice.