Chen et al. respond to "Bias in socioeconomic health disparities--comments".
Chen et al. respond to "Bias in socioeconomic health disparities--comments".
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陈等人。
DOI：
10.1093/aje/kwt040
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发表时间：
2013
影响因子：
5
通讯作者：
Krieger,Nancy
中科院分区：
文献类型：
--
作者：
Chen,JarvisT;Beckfield,Jason;Waterman,PamelaD;Krieger,Nancy
我们感谢塔里赫博士对我们关于健康差距时间比较的论文的评论(1)(2)。奇怪的是，塔利同意我们的关键结论：当比较一段时间的差距时，改变社会经济地位各维度(如教育和收入)之间的关系可能会导致偏差，特别是当研究人员依赖于社会经济地位的单一衡量指标时。塔利的批评是什么？它们是：1)我们将教育减少到收入，并将教育视为线性变量，无视证书的重要性；2)通过使用因果有向非循环图违反因果不可知论；3)断言不平等的斜率指数(SII)不是用于时间比较的。这些错误的担忧都很容易解决。首先，我们同意塔里赫的观点，即教育不能降低为收入，并可能通过其与物质资源的联系之外的途径影响健康。然而，关于收入和健康之间的联系以及教育程度及其与收入的关系的戏剧性变化，存在着广泛的证据(3，4)。因此，即使教育对健康有很强的独立影响(例如，通过识字)，对健康方面的教育差距的估计(如我们的模型所示)将既反映将教育与健康联系起来的独立机制，也反映涉及收入和物质资源的途径可能产生的实质性影响(参见我们的网络附录2，可在http://aje.获得牛津大学的期刊。Org/)，这些担忧是将教育视为连续变量还是绝对变量。我们关于潜在偏见的发现是成立的。第二，我们发现塔利对“因果不可知论”的呼吁是非常有问题的。虽然我们同意他倡导的条件独立性方法(只有无向边)将产生与我们的基于有向无环图的方法相同的模拟数据，但在概念层面上，我们认为因果复杂的情况需要研究人员
We thank Dr. Talih for his commentary (1) on our paper regarding temporal comparisons of health disparities (2). Curiously, Talih agrees with our key conclusion: Changing relationships between dimensions of socioeconomic position (eg, education and income) can result in bias when comparing disparities over time, especially when researchers rely on a single measured indicator of socioeconomic position. What are Talih’s criticisms? They are that we 1)“reduce” education to income and treat education as a linear variable, disregarding the importance of credentials; 2) violate causal agnosticism via our use of causal directed acyclic graphs; and 3) aver that the slope index of inequality (SII) was not intended to be used for temporal comparisons. These erroneous concerns are all easy to address. First, we concur with Talih that education cannot be reduced to income and likely influences health through pathways above and beyond its association with material resources. Nevertheless, extensive evidence exists regarding associations between income and health and dramatic changes in both educational attainment and its relationship with income (3, 4). Thus, even if education has strong independent effects on health (eg, via literacy), estimates of educational disparities in health (as shown by our models) will reflect both the independent mechanisms connecting education to health and the likely substantial effect of pathways involving income and material resources (refer to our Web Appendix 2 available at http://aje. oxfordjournals. org/), and these concerns hold whether education is treated as a continuous or categorical variable. Our findings of potential bias stand.Second, we find Talih’s call for “causal agnosticism” to be highly problematic. Although we concur that the conditional independence approach he advocates (with only undirected edges) would yield the same simulated data as our directed acyclic graph–based approach, on a conceptual level, we argue that causally complex situations require researchers