From the Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA.
Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA.
Epidemiology. 2021 Mar 1;32(2):248-258. doi: 10.1097/EDE.0000000000001310.
Lifecourse research provides an important framework for chronic disease epidemiology. However, data collection to observe health characteristics over long periods is vulnerable to systematic error and statistical bias. We present a multiple-bias analysis using real-world data to estimate associations between excessive gestational weight gain and mid-life obesity, accounting for confounding, selection, and misclassification biases.
Participants were from the multiethnic Study of Women's Health Across the Nation. Obesity was defined by waist circumference measured in 1996-1997 when women were age 42-53. Gestational weight gain was measured retrospectively by self-recall and was missing for over 40% of participants. We estimated relative risk (RR) and 95% confidence intervals (CI) of obesity at mid-life for presence versus absence of excessive gestational weight gain in any pregnancy. We imputed missing data via multiple imputation and used weighted regression to account for misclassification.
Among the 2,339 women in this analysis, 937 (40%) experienced obesity in mid-life. In complete case analysis, women with excessive gestational weight gain had an estimated 39% greater risk of obesity (RR = 1.4, CI = 1.1, 1.7), covariate-adjusted. Imputing data, then weighting estimates at the guidepost values of sensitivity = 80% and specificity = 75%, increased the RR (95% CI) for obesity to 2.3 (2.0, 2.6). Only models assuming a 20-point difference in specificity between those with and without obesity decreased the RR.
The inference of a positive association between excessive gestational weight gain and mid-life obesity is robust to methods accounting for selection and misclassification bias.
生命历程研究为慢性病流行病学提供了一个重要框架。然而,为了观察长期的健康特征而进行的数据收集容易受到系统误差和统计偏倚的影响。我们使用真实世界的数据进行了多次偏差分析,以估计过多的妊娠体重增加与中年肥胖之间的关联,同时考虑了混杂、选择和分类错误偏差。
参与者来自多民族妇女健康研究(Multiethnic Study of Women's Health Across the Nation)。肥胖的定义是在女性年龄为 42-53 岁时(1996-1997 年)通过腰围测量得出的。妊娠体重增加是通过自我回忆来回顾性测量的,超过 40%的参与者数据缺失。我们估计了在任何一次妊娠中存在或不存在过多妊娠体重增加的情况下,中年肥胖的相对风险(RR)和 95%置信区间(CI)。我们通过多次插补来填补缺失数据,并使用加权回归来校正分类错误。
在这项分析中的 2339 名女性中,有 937 名(40%)在中年时患有肥胖症。在完全病例分析中,有过多妊娠体重增加的女性肥胖的风险估计增加了 39%(RR=1.4,CI=1.1,1.7),经过协变量调整。插补数据后,在灵敏度为 80%和特异性为 75%的指导值下对估计值进行加权,RR(95%CI)增加到 2.3(2.0,2.6)。只有在假设肥胖和非肥胖者之间的特异性差异为 20 分的模型中,RR 才会降低。
在考虑了选择和分类错误偏差的方法下,过多的妊娠体重增加与中年肥胖之间的正相关关系的推断是稳健的。