Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis.

Hutchins F, Krafty R, El Khoudary SR, Catov J, Colvin A, Barinas-Mitchell E, Brooks MM. Gestational Weight Gain and Long-term Maternal Obesity Risk: A Multiple-Bias Analysis. Epidemiology (Cambridge, Mass.). 2021 Mar 1; 32(2):248-258.

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Abstract:

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.