Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Obesity (Silver Spring). 2010 Nov;18(11):2184-90. doi: 10.1038/oby.2010.25. Epub 2010 Feb 18.
Prepregnancy BMI is a widely used marker of maternal nutritional status that relies on maternal self-report of prepregnancy weight and height. Pregravid BMI has been associated with adverse health outcomes for the mother and infant, but the impact of BMI misclassification on measures of effect has not been quantified. The authors applied published probabilistic bias analysis methods to quantify the impact of exposure misclassification bias on well-established associations between self-reported prepregnancy BMI category and five pregnancy outcomes (small for gestational age (SGA) and large for gestational age (LGA) birth, spontaneous preterm birth (sPTB), gestational diabetes mellitus (GDM), and preeclampsia) derived from a hospital-based delivery database in Pittsburgh, PA (2003-2005; n = 18,362). The bias analysis method recreates the data that would have been observed had BMI been correctly classified, assuming given classification parameters. The point estimates derived from the bias analysis account for random error as well as systematic error caused by exposure misclassification bias and additional uncertainty contributed by classification errors. In conventional multivariable logistic regression models, underweight women were at increased risk of SGA and sPTB, and reduced risk of LGA, whereas overweight, obese, and severely obese women had elevated risks of LGA, GDM, and preeclampsia compared with normal-weight women. After applying the probabilistic bias analysis method, adjusted point estimates were attenuated, indicating the conventional estimates were biased away from the null. However, the majority of relations remained readily apparent. This analysis suggests that in this population, associations between self-reported prepregnancy BMI and pregnancy outcomes are slightly overestimated.
孕前 BMI 是一种广泛用于评估母体营养状况的指标，它依赖于孕妇对孕前体重和身高的自我报告。孕前 BMI 与母婴不良健康结局相关，但 BMI 分类错误对效应衡量的影响尚未量化。作者应用已发表的概率偏差分析方法，量化了暴露分类错误偏差对来自宾夕法尼亚州匹兹堡市一家医院分娩数据库（2003-2005 年；n=18362）的五项妊娠结局（小于胎龄儿（SGA）和大于胎龄儿（LGA）分娩、自发性早产（sPTB）、妊娠期糖尿病（GDM）和子痫前期）与自我报告的孕前 BMI 类别之间的既定关联的影响。该偏差分析方法通过假设特定的分类参数，重现了原本可以观察到的 BMI 得到正确分类的数据。偏差分析得出的点估计值既考虑了随机误差，也考虑了因暴露分类错误偏差引起的系统误差，以及因分类错误而增加的不确定性。在传统的多变量逻辑回归模型中，体重不足的女性发生 SGA 和 sPTB 的风险增加，而 LGA 的风险降低，而超重、肥胖和重度肥胖的女性与体重正常的女性相比，发生 LGA、GDM 和子痫前期的风险更高。在应用概率偏差分析方法后，调整后的点估计值减弱，表明传统估计值存在向零值偏倚的情况。然而，大多数关系仍然显而易见。这项分析表明，在该人群中，自我报告的孕前 BMI 与妊娠结局之间的关联被略微高估了。
