Literature DB >> 33605566

Prepregnancy body mass index and spina bifida: Potential contributions of bias.

Candice Y Johnson1,2, Margaret A Honein2, Sonja A Rasmussen3, Penelope P Howards1, Matthew J Strickland4, W Dana Flanders1.   

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.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  bias; body mass index; neural tube defects; obesity; spina bifida

Mesh:

Year:  2021        PMID: 33605566      PMCID: PMC8358821          DOI: 10.1002/bdr2.1877

Source DB:  PubMed          Journal:  Birth Defects Res            Impact factor:   2.661


  29 in total

1.  A structural approach to selection bias.

Authors:  Miguel A Hernán; Sonia Hernández-Díaz; James M Robins
Journal:  Epidemiology       Date:  2004-09       Impact factor: 4.822

2.  Does low participation in cohort studies induce bias?

Authors:  Ellen Aagaard Nohr; Morten Frydenberg; Tine Brink Henriksen; Jorn Olsen
Journal:  Epidemiology       Date:  2006-07       Impact factor: 4.822

3.  How far from non-differential does exposure or disease misclassification have to be to bias measures of association away from the null?

Authors:  Anne M Jurek; Sander Greenland; George Maldonado
Journal:  Int J Epidemiol       Date:  2008-01-09       Impact factor: 7.196

4.  Comment on: Association of maternal pre-pregnancy weight with birth defects: Evidence from a case-control study in Western Australia. Aust N Z J Obstet Gynaecol 2009; 49: 11-15.

Authors:  Michael Peek; Ralph Nanan
Journal:  Aust N Z J Obstet Gynaecol       Date:  2009-10       Impact factor: 2.100

Review 5.  Effect of prenatal diagnosis on epidemiologic studies of birth defects.

Authors:  J D Cragan; M J Khoury
Journal:  Epidemiology       Date:  2000-11       Impact factor: 4.822

6.  Maternal obesity and the risk of neural tube defects in offspring: A meta-analysis.

Authors:  Hai-Yan Huang; Hong-Lin Chen; Li-Ping Feng
Journal:  Obes Res Clin Pract       Date:  2016-05-05       Impact factor: 2.288

7.  Declines in Unintended Pregnancy in the United States, 2008-2011.

Authors:  Lawrence B Finer; Mia R Zolna
Journal:  N Engl J Med       Date:  2016-03-03       Impact factor: 91.245

8.  Guidelines for case classification for the National Birth Defects Prevention Study.

Authors:  Sonja A Rasmussen; Richard S Olney; Lewis B Holmes; Angela E Lin; Kim M Keppler-Noreuil; Cynthia A Moore
Journal:  Birth Defects Res A Clin Mol Teratol       Date:  2003-03

9.  Impact of Missing Data for Body Mass Index in an Epidemiologic Study.

Authors:  Hilda Razzaghi; Sarah C Tinker; Amy H Herring; Penelope P Howards; D Kim Waller; Candice Y Johnson
Journal:  Matern Child Health J       Date:  2016-07

10.  Multiple bias analysis using logistic regression: an example from the National Birth Defects Prevention Study.

Authors:  Candice Y Johnson; Penelope P Howards; Matthew J Strickland; D Kim Waller; W Dana Flanders
Journal:  Ann Epidemiol       Date:  2018-06-02       Impact factor: 3.797

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