Disconcordance in Statistical Models of Bisphenol A and Chronic Disease Outcomes in NHANES 2003-08
Disconcordance in Statistical Models of Bisphenol A and Chronic Disease Outcomes in NHANES 2003-08
复制标题
DOI：
10.1371/journal.pone.0079944
复制
发表时间：
2013-11-06
期刊：
影响因子：
3.7
通讯作者：
Neidell, Matthew
中科院分区：
文献类型：
--
作者：
Casey, Martin F.;Neidell, Matthew
背景：双酚A(BPA)是一种常见于塑料中的高产化学物质，由于其潜在的毒性而引起了研究人员的极大关注。利用三个国家健康和营养检查调查(NHANES)周期的数据，我们探讨了双酚A对冠心病和糖尿病的影响的一致性和稳健性。方法和结果：我们报告了三种不同的统计模型在双酚A分析中的使用：(1)Logistic回归，(2)对数线性回归，(3)剂量-反应Logistic回归。在每个变量中，按人口统计学、尿肌酐、双酚A暴露来源、健康行为和邻苯二甲酸盐暴露六个区块添加混杂因素。结果对我们的统计模型的函数形式的变化很敏感，但没有一个单一的模型在NHANES周期中产生一致的结果。报告的OR也被发现对纳入/排除标准敏感。此外，在NHANES 2003-04中最明显的观察到的影响不能用混淆来解释。结论：NHANES数据的局限性和对BPA作用模式的缺乏使得开发信息丰富的统计模型变得困难。考虑到效应估计对函数形式的敏感性，研究人员应该使用关于双酚A测量的不同假设的多个规范来报告结果，从而允许识别数据中的潜在差异。
Background: Bisphenol A (BPA), a high production chemical commonly found in plastics, has drawn great attention from researchers due to the substance's potential toxicity. Using data from three National Health and Nutrition Examination Survey (NHANES) cycles, we explored the consistency and robustness of BPA's reported effects on coronary heart disease and diabetes.Methods And Findings: We report the use of three different statistical models in the analysis of BPA: (1) logistic regression, (2) log-linear regression, and (3) dose-response logistic regression. In each variation, confounders were added in six blocks to account for demographics, urinary creatinine, source of BPA exposure, healthy behaviours, and phthalate exposure. Results were sensitive to the variations in functional form of our statistical models, but no single model yielded consistent results across NHANES cycles. Reported ORs were also found to be sensitive to inclusion/exclusion criteria. Further, observed effects, which were most pronounced in NHANES 2003-04, could not be explained away by confounding.Conclusions: Limitations in the NHANES data and a poor understanding of the mode of action of BPA have made it difficult to develop informative statistical models. Given the sensitivity of effect estimates to functional form, researchers should report results using multiple specifications with different assumptions about BPA measurement, thus allowing for the identification of potential discrepancies in the data.