Exposure Misclassification and Selection Bias in a Case-Control Study of Prepregnancy Body Mass Index and Neural Tube Defects Pubblico

Abstract

Abstract
Exposure Misclassification and Selection Bias in a Case-Control Study of
Prepregnancy Body Mass Index and Neural Tube Defects
We explored potential contributions of exposure misclassification, selection bias, and
confounding to a study of prepregnancy body mass index (BMI) and neural tube defects
(NTDs), severe birth defects of the brain and spinal cord. Over a dozen studies have
found associations between BMI and NTDs, with obese mothers the most likely to have
an affected pregnancy. Investigators have suggested that exposure misclassification or
selection bias could account for the observed associations; however, no previous study
has quantitatively addressed the potential effects of these three biases together. We
investigated hypothesized mechanisms for selection bias, examined effects of making
inaccurate assumptions of nondifferential or differential misclassification when adjusting
for exposure misclassification, and proposed a method to simultaneously adjust for
exposure misclassification, selection bias, and confounding using weighted logistic
regression. Using information from these studies, we simultaneously adjusted for these
three biases in a case-control study of prepregnancy BMI and two common NTDs,
anencephaly and spina bifida, using data from the National Birth Defects Prevention
Study. Given our assumptions, adjustment for multiple biases had little effect on
associations between BMI and anencephaly. However, associations between obesity and
spina bifida were attenuated following multiple bias analysis; it is possible that reported
associations between obesity and spina bifida that do not take into account the potential
effects of exposure misclassification or selection bias are overestimates, partially driven
by bias. Although misclassification, selection bias, and confounding have the potential to
affect results, multiple bias analysis remains uncommonly used. Our proposed method is
one option to incorporate adjustment for multiple biases into epidemiologic studies.

Table of Contents

TABLE OF CONTENTS

Chapter 1: Introduction

Chapter 2: Epidemiologic Evidence for an Association Between Maternal Obesity and Neural Tube Defects: a Systematic Review and Meta-Analysis of the Published Literature

Chapter 3: Pregnancy Termination Following Prenatal Diagnosis of Anencephaly or Spina Bifida: a Systematic Review of the Literature

Chapter 4: Prenatal Diagnosis of Spina Bifida
Chapter 5: Potential Sensitivity of Bias Analysis Results to Incorrect Assumptions of Nondifferential or Differential Exposure Misclassification
Chapter 6: Weighted Logistic Regression for Multiple Bias Analysis
Chapter 7: Potential Impacts of Biases on Associations Between Prepregnancy Body Mass Index and Neural Tube Defects
Chapter 8: Discussion and Conclusions

About this Dissertation

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School	Laney Graduate School
Department	Epidemiology
Degree	PhD
Submission	Dissertation
Language	English
Research Field	Health Sciences, Epidemiology
Parola chiave	misclassification prenatal diagnosis selection bias epidemiologic bias neural tube defects obesity
Committee Chair / Thesis Advisor	Oakley Jr, Godfrey, Emory University Flanders, W Dana, Emory University
Committee Members	Howards, Penelope, Emory University Honein, Margaret, CDC/NCBDDD Strickland, Matthew J, Emory University Sonja, Rasmussen, CDC/NCBDDD

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