Abstract

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.

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