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.

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.

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.

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.

For over 20 years, epidemiologists have consistently found associations
between high prepregnancy body mass index (BMI) and increased risk for neural tube
defects, including spina bifida, in offspring (

One hypothesis that has been acknowledged but not quantitatively assessed is that uncontrolled biases in epidemiologic studies could be producing a spurious association between prepregnancy BMI and spina bifida. Many studies of prepregnancy BMI and spina bifida are population-based case–control studies that share important characteristics: a high risk for bias from exposure misclassification and selection bias and no statistical analyses adjusting for these biases.

Exposure misclassification can occur if prepregnancy BMI is calculated from
self-reported height and weight. In the United States, adults tend to overreport
their height and underreport their weight, leading to a lower reported BMI than
their true BMI (

Selection bias can occur if cases are incompletely ascertained among
terminations of pregnancy (

To predict how exposure misclassification and selection bias might affect
the observed association between prepregnancy BMI and spina bifida, we can conduct a
quantitative bias analysis. Quantitative bias analysis refers to a variety of
statistical methods used to model how biases like exposure misclassification and
selection bias affect study results (

To explore how bias could be affecting associations between prepregnancy BMI
and spina bifida, we performed quantitative bias analyses using data from the
National Birth Defects Prevention Study (NBDPS), a population-based
case–control study that has similar potential for exposure misclassification
and selection bias to many previous studies of prepregnancy BMI and spina bifida. To
achieve a large enough sample size for quantitative bias analysis, we updated a
previously published analysis from NBDPS with an additional 9 years of data (

NBDPS is a population-based case–control study of risk factors for
major structural birth defects (

This study followed the U.S. Federal Policy for the Protection of Human Subjects. All participating sites had institutional review board approval and all participants provided informed consent for participation. All analyses were conducted in SAS 9.4 (Cary, NC).

In our analyses, we included mothers who had a fetus or infant with spina
bifida but no other major birth defects (case mothers) and mothers of liveborn
infants without major birth defects (control mothers). We calculated prepregnancy
BMI as self-reported weight in kilograms divided by squared self-reported height in
meters. We used four BMI categories in our analyses: underweight (<18.5
kg/m^{2}), normal weight (18.5–24.9 kg/m^{2}), overweight
(25.0–29.9 kg/m^{2}), and obesity (≥30 kg/m^{2}). We
used categorized instead of continuous BMI as our exposure variable to allow for
nonlinear associations between BMI and spina bifida and to facilitate comparisons
with previous studies, many of which reported BMI categories.

We excluded mothers aged <18 years at conception because the same BMI categories are not typically used for children. We also excluded mothers enrolled at study sites during periods when cases were not ascertained among terminations of pregnancy because our analysis for selection bias required information on these cases: Georgia (1997–1998), Massachusetts (all years), New Jersey (all years), and New York (1997–1999). We excluded mothers with type 1 diabetes, a risk factor for birth defects, but retained mothers with type 2 diabetes because this variable could be on the causal pathway between prepregnancy BMI and spina bifida. For our analysis of exposure misclassification, we could only find suitable data on BMI reporting accuracy for non-Hispanic white, non-Hispanic black, and Hispanic mothers; we restricted our study population to these three racial and ethnic groups.

Our multivariable models were adjusted for two potential confounders,
maternal race/ethnicity and household income, and one study design variable, study
site. We obtained these variables from the maternal questionnaire and excluded
participants with missing data. We considered maternal age, education, and
preconceptional folic acid supplementation as additional covariates; because none
appreciably affected results when included in the models, we removed them from the
models to prevent nonpositivity (zero cells) in the models (

We excluded mothers with missing prepregnancy BMI (4%) from all analyses
except those in which we conducted bias analyses for exposure misclassification; in
those analyses, we could also adjust for missing exposure data. Although including
mothers with missing data in some analyses but not others might mean that our
populations are not comparable between analyses, a previous study of missing
prepregnancy BMI in NBDPS found that results are similar whether adjusting for
missing BMI or excluding participants with missing data (

We used logistic regression to estimate unadjusted (crude) and confounder-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between prepregnancy BMI categories and spina bifida, using the normal weight BMI category as the reference group.

To adjust for exposure misclassification, we chose a previously
described method that reweights participants so that the BMI distribution
reported by mothers better resembles the BMI distribution that most likely
existed in reality (

To use this method, we needed predictive values—the probability
that the BMI category reported by the mother was accurate. We had no such data
for NBDPS. Instead, we found data from the National Health and Nutrition
Examination Survey (NHANES), a cross-sectional survey representative of the U.S.
civilian, noninstitutionalized population (

NHANES participants self-reported their height and weight during an
initial interview. Within 2 weeks, staff measured participants’ heights
and weights; we excluded participants without these measurements. Therefore,
both self-reported and measured BMI categories were available for each included
participant. For women in each self-reported BMI category (underweight, normal
weight, overweight, obesity, and missing data), we calculated the probability
that their measured BMI classification was underweight, normal weight,
overweight, or obesity. We calculated these predictive values within strata of
race/ethnicity and incorporated NHANES’s complex survey sampling design
in the calculation (

Because we did not know if our NHANES estimates were correct for our
study population, we conducted a probabilistic bias analysis. A probabilistic
bias analysis is one that assigns a range of predictive values to account for
uncertainty. For example, in

To conduct the probabilistic analysis, we randomly chose 5,000 values
from our triangular distributions and used them to calculate 5,000 odds ratios
(OR), each time using the predictive value as a weight in the multivariable
logistic regression model to adjust for exposure misclassification in addition
to confounding. Further details on how this method works and how random error is
added into the results have been published previously (

We did not know if case or control mothers more accurately reported their prepregnancy BMI or how any differences might affect results. We made three different assumptions in our analyses: equal accuracy in reporting BMI, case mothers more accurately report BMI, and control mothers more accurately report BMI. Further details along with the predictive values used for each assumption are shown in the Web Appendix.

We adjusted for potential selection bias from two sources: (1) study
participation and (2) underascertainment of cases among terminations of
pregnancy, which might favor inclusion of mothers with obesity and live births
and drive selection bias via the mechanism illustrated in

To adjust for selection bias from study participation, we used available
information on NBDPS participation rates (our best estimate of the probability
of selection): 67% of eligible case mothers and 65% of eligible control mothers
participated in NBDPS (

To adjust for selection bias from underascertainment of cases among
terminations of pregnancy, we first had to address systematic differences in
ascertainment. Some NBDPS sites actively ascertained cases among pregnancies
ending in termination after prenatal diagnosis throughout the study period, but
others used passive ascertaintment at first and began active ascertainment only
partway through (

For each of these two groups, we estimated the selection probabilities
for cases among terminations of pregnancy using a previously described method
(

We tested three selection bias scenarios. First, we assumed that study
participation was the only source of selection bias and these participation
rates did not differ by BMI. Second, we added selection bias from
underascertainment of cases among terminations of pregnancy. Third, we
additionally allowed study selection probabilities to vary by BMI for the
control mothers but not the case mothers; we assumed that mothers of children
with spina bifida were motivated to participate, whereas control mother
participation could be affected by sociodemographic factors for which BMI is a
proxy measure (e.g., income and education). Selection probabilities are
presented in

We conducted probabilistic bias analyses, randomly choosing study
selection probabilities and ascertainment selection probabilities 5,000 times
from triangular distributions. These distributions had the selection
probabilities in

In our final analyses, we adjusted for exposure misclassification,
selection bias, and confounding together. In total, we examined nine models:
combinations of the three exposure misclassification models and the three
selection bias models. For each of the 5,000 iterations of probabilistic bias
analysis, we multiplied the predictive value weight (exposure misclassification)
and IPSW (selection bias) to generate a final multiple-bias-correcting weight
for each observation, a method that has been previously described (

Of the 1,297 spina bifida case mothers and 11,829 control mothers
participating in NBDPS, 808 case mothers and 7,685 control mothers were included.
Major reasons for exclusion were ineligible study sites and years (161 cases, 2,266
controls), missing data on household income (89 cases, 666 controls), ineligible
race/ethnicity groups (66 cases, 610 controls), and spina bifida cases with multiple
major birth defects (111 cases). A participant flow chart is presented in the Web
Appendix. Compared to control mothers, case mothers had higher prevalence of
prepregnancy obesity, Hispanic ethnicity, and low household income and were more
likely to live in California (

In the conventional confounder-adjusted analysis, higher prepregnancy BMI
was associated with increased prevalence of spina bifida in a dose–response
pattern: OR 1.4 (95% CI: 1.2, 1.7) for obesity versus normal weight, OR 1.1 (95% CI:
0.9,1.4) for overweight, and OR 0.6 (95% CI: 0.3, 0.9) for underweight. Overall, the
results from the quantitative bias analyses were similar to those from the
conventional analysis and generally showed the same dose–response association
between prepregnancy BMI and spina bifida (

There was one exception: the dose–response pattern was less apparent when we assumed that participation in the study varied by prepregnancy BMI. For example, for the results where cases had more accurate BMI reporting and selection probabilities varied by prepregnancy BMI (labeled “Exposure 2 and Selection 3” in the Figures), the median OR for obesity was 1.1 (95% SI: 0.9, 1.4), for overweight was 1.0 (95% SI: 0.8, 1.2), and for underweight was 0.6 (95% SI: 0.3, 0.9).

Using quantitative bias analysis, we found that exposure misclassification, selection bias, and confounding did not completely explain the association between prepregnancy BMI and spina bifida under most of our assumptions.

Our conventional crude and confounding-adjusted results showed similar
dose–response associations to those found in other studies, although many of
these studies found stronger associations between obesity and spina bifida than we
did. (

In our bias analyses, each of our three assumptions about exposure misclassification gave similar results. For underweight mothers, all three results were attenuated compared to conventional analyses; however, results were based on small numbers and the 95% SIs were wide and overlapped substantially with the conventional OR and 95% CIs. For mothers who were overweight or had obesity, all three provided results similar to the conventional analyses. We only tested three out of a large number of possible assumptions about exposure misclassification, and had we chosen distributions that had less overlap between them, these more varied distributions could have produced different results.

Ours was not the first bias analysis for selection bias; investigators in
the previous NBDPS study conducted a similar, but nonprobabilistic, analysis (

Simultaneous adjustment for exposure misclassification, selection bias, and
confounding allowed us to see how results would change when these biases were
combined, as opposed to observing their independent effects. We noticed no unusual
synergistic effects when combining biases, something we could not predict without
conducting a formal bias analysis. Another role of bias analysis is the explicit
acknowledgement of uncertainty in the study (

Although we used the best validation data we could find, our data had limitations. For exposure misclassification, we used NHANES data that validated self-reported current weight, not prepregnancy weight, because we could find no other suitable validation data. We also estimated predictive values for the missing BMI category from a small number of NHANES participants with missing data, meaning that our weights could have been imprecisely estimated. For selection bias, we used a Danish study from the same era as NBDPS to estimate selection probabilities conditional on prepregnancy BMI, but we do not know if the results are generalizable to our U.S. population. We chose nine combinations of exposure misclassification and selection bias for our analysis to explore the effects of making different assumptions on occurrence of bias. There likely exist other combinations that would result in different conclusions, but our results were fairly robust across our chosen combinations. These limitations mean that we should not interpret our study results as estimates of the “true” magnitude of the association. Instead, we use these results to illustrate how study results would (or would not) be affected if biases of these magnitudes were occurring in the study. This information can help us to determine if bias is a likely explanation for observed study results.

NBDPS has a large sample size to increase precision and a careful case
classification to minimize outcome misclassification. Our analysis adds adjustment
for exposure misclassification, selection bias, and confounding. Various other
factors could have affected our results, such as misclassification of covariates,
other types of selection bias (e.g., underascertainment of cases among live births
and stillbirths), residual and unmeasured confounding, effect measure modification,
etiologic heterogeneity between spina bifida subtypes, and appropriateness of our
complete-case analysis, triangular distributions, and methods to estimate weights.
Balancing complexity of the analysis and practicality is a necessary part of bias
analysis, and we focused on the sources of bias that we thought might have the
greatest effect on study results (

In summary, the dose–response relationship between prepregnancy BMI and spina bifida largely remained after we accounted for exposure misclassification, selection bias from participation rates, selection bias from case underascertainment, and confounding by select sociodemographic factors, although it was attenuated under certain assumptions about selection bias.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. This project was supported through Centers for Disease Control and Prevention (CDC) cooperative agreements under PA #96043, PA #02081, and FOA #DD09-001 to the Centers for Birth Defects Research and Prevention participating in the National Birth Defects Prevention Study (NBDPS) and by the Laney Graduate School at Emory University. We thank Miriam Siegel for analysis replication and the Helen Riaboff Whiteley Center at the University of Washington for providing an environment conducive to productive research and writing.

Centers for Birth Defects Research and Prevention, Grant/Award Number: FOA #DD09-001; Centers for Disease Control and Prevention, Grant/Award Numbers: PA #02081, PA #96043

CONFLICT OF INTEREST

The authors declare no potential conflict of interest.

SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of this article.

The data used in this study were obtained from the Centers for Birth Defects
Research and Prevention. The procedures for accessing these data can be found at

Directed acyclic graph illustrating one possible mechanism of selection bias. Selection bias (collider stratification bias) could occur if selection into the study is driven by prepregnancy body mass index (via differential prenatal diagnosis and termination of pregnancy) and selection based on outcome (via case–control sampling)

Odds ratios and 95% confidence intervals from conventional analyses and median odds ratios and 95% simulation intervals from bias analyses for the association between prepregnancy obesity (vs. normal weight) and spina bifida. The analyses include one unadjusted model, one adjusted for confounding, three models for exposure misclassification plus confounding, and three adjusting for selection bias plus confounding. The final nine models combine the various assumptions for exposure misclassification, selection bias, and confounding. The assumptions for each model are explained in more detail in the text

Odds ratios and 95% confidence intervals from conventional analyses and median odds ratios and 95% simulation intervals from bias analyses for the association between prepregnancy overweight (vs. normal weight) and spina bifida for the same series of two conventional models and 15 bias analysis models. The assumptions for each model are explained in more detail in the text

Odds ratios and 95% confidence intervals from conventional analyses and median odds ratios and 95% simulation intervals from bias analyses for the association between prepregnancy underweight (vs. normal weight) and spina bifida for the same series of two conventional models and 15 bias analysis models. The assumptions for each model are explained in more detail in the text

Probability of measured body mass index category based on what participants self-reported as their body mass index for three racial and ethnic groups, National Health and Nutrition Examination Survey, 1999–2010

Self-reported body mass index | Measured body mass index | Non-Hispanic white | Non-Hispanic black | Hispanic |
---|---|---|---|---|

Underweight | Underweight | 0.78 | 0.50 | 0.59 |

Normal weight | 0.22 | 0.50 | 0.41 | |

Overweight | 0 | 0 | 0 | |

Obesity | 0 | 0 | 0 | |

| ||||

Normal weight | Underweight | 0.03 | 0.05 | 0.02 |

Normal weight | 0.87 | 0.78 | 0.79 | |

Overweight | 0.10 | 0.16 | 0.18 | |

Obesity | 0 | 0.01 | 0.01 | |

| ||||

Overweight | Underweight | 0 | 0 | 0 |

Normal weight | 0.07 | 0.05 | 0.09 | |

Overweight | 0.75 | 0.72 | 0.71 | |

Obesity | 0.18 | 0.23 | 0.20 | |

| ||||

Obesity | Underweight | 0 | 0 | 0 |

Normal weight | 0 | 0 | 0.01 | |

Overweight | 0.03 | 0.03 | 0.07 | |

Obesity | 0.97 | 0.96 | 0.92 | |

| ||||

Missing | Underweight | 0.02 | 0.01 | 0.02 |

Normal weight | 0.25 | 0.09 | 0.32 | |

Overweight | 0.17 | 0.18 | 0.29 | |

Obesity | 0.56 | 0.72 | 0.37 |

Selection probabilities for case and control mothers given self-reported body mass index under three assumptions about occurrence of selection bias in the study

Assumption 1^{a} | Assumption 2^{b} | Assumption 3^{c} | ||||||
---|---|---|---|---|---|---|---|---|

Self-reported body mass index category | Case mothers | Control mothers | Case mothers (LB, SB) | Case mothers(TOP)^{d} | Control mothers | Cases mothers (LB, SB) | Cases mothers(TOP)^{d} | Control mothers |

Underweight | 0.67 | 0.65 | 0.67 | 0.67*p | 0.65 | 0.67 | 0.67*p | 0.65*0.84 |

| ||||||||

Normal weight | 0.67 | 0.65 | 0.67 | 0.67*p | 0.65 | 0.67 | 0.67*p | 0.65*1.04 |

| ||||||||

Overweight | 0.67 | 0.65 | 0.67 | 0.67*p | 0.65 | 0.67 | 0.67*p | 0.65*0.96 |

| ||||||||

Obesity | 0.67 | 0.65 | 0.67 | 0.67*p | 0.65 | 0.67 | 0.67*p | 0.65*0.89 |

| ||||||||

Missing | 0.67 | 0.65 | 0.67 | 0.67*p | 0.65 | 0.67 | 0.67*p | 0.65*1.00 |

Abbreviations: LB, live birth; SB, stillbirth; TOP, termination of pregnancy.

Study participation rates are the only source of selection bias.

Study participation rates and underascertainment of cases contribute to selection bias.

Study participation rates and underascertainment of cases contribute to selection bias and study participation rates vary by case–control status and prepregnancy body mass index.

Characteristics of included spina bifida case and control mothers, National Birth Defects Prevention Study, 1997–2011

Case | Control | |||
---|---|---|---|---|

| % |
| % | |

Pregnancy outcome | ||||

Live birth | 703 | 87 | 7,685 | 100 |

Fetal death | 16 | 2 | 0 | 0 |

Termination | 89 | 11 | 0 | 0 |

| ||||

Prepregnancy body mass index category | ||||

Underweight | 18 | 2 | 349 | 5 |

Normal weight | 343 | 46 | 3,744 | 51 |

Overweight | 186 | 25 | 1,776 | 24 |

Obesity | 203 | 27 | 1,529 | 21 |

Missing | 58 | 287 | ||

| ||||

Maternal race/ethnicity | ||||

Non-Hispanic white | 463 | 57 | 4,788 | 62 |

Non-Hispanic black | 58 | 7 | 896 | 12 |

Hispanic | 287 | 36 | 2,001 | 26 |

| ||||

Household income | ||||

<$50,000 | 595 | 74 | 5,137 | 67 |

>$50,000 | 213 | 26 | 2,548 | 33 |

| ||||

Study site | ||||

Arkansas | 104 | 13 | 1,183 | 15 |

California | 162 | 20 | 932 | 12 |

Georgia | 101 | 13 | 954 | 12 |

Iowa | 127 | 16 | 1,115 | 15 |

New York | 48 | 6 | 671 | 9 |

North Carolina | 72 | 9 | 790 | 10 |

Texas | 108 | 13 | 1,039 | 14 |

Utah | 86 | 11 | 1,001 | 13 |