Prepregnancy body mass index and spina bifida: Potential contributions of bias. Issue 8 (19th February 2021)

• Record Type: Journal Article
• Authors: Johnson, Candice Y.; Honein, Margaret A.; Rasmussen, Sonja A.; Howards, Penelope P.; Strickland, Matthew J.; Flanders, W. Dana
• Is Part Of: Birth defects research. Volume 113:Issue 8(2021)
• Page Start: 633
• Page End: 643
• Publication Date: 2021-02-19
• Subjects: bias -- body mass index -- neural tube defects -- obesity -- spina bifida; Teratology -- Periodicals; Abnormalities, Human -- Periodicals; Congenital Abnormalities; Embryo, Mammalian -- abnormalities; Teratology; Abnormalities, Human; Teratology; Periodicals; Periodicals; 616.043
• DOI: 10.1002/bdr2.1877
• Languages: English
• ISSNs: 2472-1727

Abstract

Abstract: Background: Epidemiologists have consistently observed associations between prepregnancy obesity and spina bifida in offspring. Most studies, however, used self‐reported body mass index (potential for exposure misclassification) and incompletely ascertained cases of spina bifida among terminations of pregnancy (potential for selection bias). We conducted a quantitative bias analysis to explore the potential effects of these biases on study results. Methods: We included 808 mothers of fetuses or infants with spina bifida (case mothers) and 7, 685 mothers of infants without birth defects (control mothers) from a population‐based case–control study, the National Birth Defects Prevention Study (1997–2011). First, we performed a conventional epidemiologic analysis, adjusting for potential confounders using logistic regression. Then, we used 5, 000 iterations of probabilistic bias analysis to adjust for the combination of confounding, exposure misclassification, and selection bias. Results: In the conventional confounding‐adjusted analysis, prepregnancy obesity was associated with spina bifida (odds ratio 1.4, 95% confidence interval: 1.2, 1.7). In the probabilistic bias analysis, we tested nine different models for the combined effects of confounding, exposure misclassification, and selection bias. Results were consistent with a weak to moderate association between prepregnancy obesity and spina bifida, with the median odds ratios across the nine models ranging from 1.1 to 1.4. Conclusions: Given our assumptions about the occurrence of bias in the study, our results suggest that exposure misclassification, selection bias, and confounding do not completely explain the association between prepregnancy obesity and spina bifida.