American journal of epidemiology
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Childhood obesity, a major public health problem, can lead to cardiovascular disease in adulthood. Studies have implicated exposure to bisphenol A (BPA), a commonly used chemical, in the development of obesity in adults. However, literature is limited on this association in children. ⋯ Compared with children in the lowest quartile of BPA (<1.5 ng/mL), children in the highest quartile (>5.4 ng/mL) had a multivariable odds ratio for obesity of 2.55 (95% confidence interval (CI): 1.65, 3.95) (Ptrend < 0.01). The observed positive association was predominantly present in boys (odds ratio = 3.80, 95% CI: 2.25, 6.43) (Ptrend < 0.001) and in non-Hispanic whites (odds ratio = 5.87, 95% CI: 2.15, 16.05) (Ptrend < 0.01). In a representative sample of children, urinary BPA was associated with obesity, predominantly in non-Hispanic white boys, independent of major risk factors.
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Epidemiologic studies often aim to estimate the odds ratio for the association between a binary exposure and a binary disease outcome. Because confounding bias is of serious concern in observational studies, investigators typically estimate the adjusted odds ratio in a multivariate logistic regression which conditions on a large number of potential confounders. It is well known that modeling error in specification of the confounders can lead to substantial bias in the adjusted odds ratio for exposure. ⋯ Double robustness implies that only one of the 2 modeling strategies needs to be correct in order to make valid inferences about the odds ratio parameter. In this paper, I aim to introduce this recent methodology into the epidemiologic literature by presenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure. A SAS macro (SAS Institute Inc., Cary, North Carolina) is given in an online appendix to facilitate use of the approach in routine epidemiologic practice, and a simulated data example is also provided for the purpose of illustration.