Huang et al. Respond to "Multigenerational Social Determinants of Health".
Huang et al. Respond to "Multigenerational Social Determinants of Health".
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黄等人。
DOI：
10.1093/aje/kwv147
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发表时间：
2015
影响因子：
5
通讯作者：
Enquobahrie,DanielA
中科院分区：
文献类型：
--
作者：
Huang,JonathanY;Gavin,AmeliaR;Richardson,ThomasS;Rowhani-Rahbar,Ali;Siscovick,DavidS;Enquobahrie,DanielA
我们感谢Cohen女士和Lê-Scherban博士对我们关于2000年代出生的美国婴儿的祖母教育和孙子出生体重之间的关联的论文的深思熟虑的评论（1）。我们赞赏他们努力将我们的工作置于多代研究的更广泛背景下，并同意他们对相关挑战和机遇的评估。我们同意他们在应用新的分析方法时对复杂的社会和生物学理论进行更全面的描述的呼吁，并同意我们的方法，除其他外（3-5），代表了早期的一步。为此，我们强调了我们的工作所隐含的机会，以解决科恩和Lê-Scherban所阐述的挑战，特别是复杂的因果结构和剩余混淆的棘手问题。Cohen和Lê-Scherban将健康选择和社会地位传递确定为流行社会理论的两个关键特征，这些特征使因果关系的识别复杂化。有了适当的数据，这两个问题可以实质性地解决使用边际结构模型（MSM）。例如，健康选择，其中状态的获得可能会受到健康状况不佳的阻碍，可以通过对个人进行低社会经济地位的概率加权来解决，如由一些早期健康状态预测的。在罕见的情况下，早期健康状态本身在感兴趣的途径之外，并且有足够的测量预测因子来随机化早期健康状态（即，满足MSM假设），早期健康状态也可以在结果模型中得到控制。更有可能的是，当我们实施青少年（孕前）体重指数时，该指标被从结果模型中省略，因此被包括在估计的“效应”中。此外，可以通过在更精细的时间尺度上纳入更多的纵向社会经济地位和健康数据来解决反向因果关系。
We thank Ms. Cohen and Dr. Lê-Scherban for their thoughtful commentary (1) on our paper concerning associations between grandmaternal education and grandchild birth weight among US infants born in the 2000s (2). We appreciate their efforts to set our work within the broader context of multigenerational studies and concur with their assessment of the related challenges and opportunities. We agree with their call for a more comprehensive account of complex social and biological theories when applying novel analytical methods and agree that our approach, among others (3–5), represents an early step. To that end, we highlight opportunities implied by our work to address challenges on which Cohen and Lê-Scherban elaborate, specifically the tenacious issues of complex causal structures and residual confounding. Cohen and Lê-Scherban identified health selection and social status transmission as 2 key features from prevailing social theory that complicate the identification of causal relationships. With suitable data, both issues may be substantively addressed using marginal structural models (MSMs). For example, health selection, wherein status attainment may be hampered by poor health, can be addressed by weighting individuals by their probabilities of low socioeconomic status, as predicted by some earlier health state. In the rare case that the earlier health state is itself outside the pathway of interest and there are sufficient measured predictors to randomize earlier health state (ie, to satisfy MSM assumptions), earlier health state can also be controlled for in the outcome model. More likely, as we implemented for adolescent (prepregnancy) body mass index, the measure is omitted from the outcome model and therefore included in the estimated “effect.” Additionally, reverse causality may be addressed by incorporating additional longitudinal socioeconomic status and health data at finer time scales.