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Obstetrics & Gynecology:
doi: 10.1097/01.AOG.0000275264.78506.63
Original Research

The Contribution of Birth Defects to Preterm Birth and Low Birth Weight

Dolan, Siobhan M. MD, MPH1; Gross, Susan J. MD1; Merkatz, Irwin R. MD1; Faber, Vincent MA2; Sullivan, Lisa M. PhD3; Malone, Fergal D. MD1,4,6; Porter, T Flint MD, MPH5; Nyberg, David A. MD6; Comstock, Christine H. MD7; Hankins, Gary D. V. MD8; Eddleman, Keith MD9; Dugoff, Lorraine MD10; Craigo, Sabrina D. MD11; Timor-Tritsch, Ilan MD12; Carr, Stephen R. MD13; Wolfe, Honor M. MD14; Bianchi, Diana W. MD15; D'Alton, Mary E. MD16; for the First and Second Trimester Evaluation of Risk (FASTER) Trial Research Consortium

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Author Information

From 1Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, New York; 2DM-STAT, Inc, Malden, Massachusetts; 3Boston University, Boston, Massachusetts; 4Royal College of Surgeons in Ireland, Dublin, Ireland; 5University of Utah and Intermountain Healthcare, Salt Lake City, Utah; 6Swedish Medical Center, Seattle, Washington; 7William Beaumont Hospital, Royal Oak, Michigan; 8University of Texas Medical Branch, Galveston, Texas; 9Mount Sinai Medical Center, New York, New York; 10University of Colorado Health Sciences, Denver, Colorado; 11Tufts University, Boston, Massachusetts; 12New York University Medical Center, New York, New York; 13Women and Infants' Hospital, Providence, Rhode Island; 14University of North Carolina, Chapel Hill, North Carolina; 15Tufts University, Boston, Massachusetts; and 16Columbia University, New York, New York.

* For members of the FASTER Trial Research Consortium, see the Appendix.

Supported by grant number RO1 HD 38652 from the National Institutes of Health and the National Institute of Child Health and Human Development. Additional funding for analysis provided by The What to Expect Foundation.

Portions of this work were presented at the Society for Maternal-Fetal Medicine Annual Meeting in Reno, Nevada, February 7–12, 2005.

Corresponding author: Siobhan M. Dolan, MD, MPH, Department of Obstetrics and Gynecology and Women's Health, Albert Einstein College of Medicine/Montefiore Medical Center, Belfer 501, 1300 Morris Park Avenue, Bronx, NY 10461; e-mail: sdolan@aecom.yu.edu.

Financial Disclosure Dr. D'Alton served as a consultant for Living Microsystems, Inc, Watertown, MA. The other authors have no potential conflicts of interest to disclose.

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Abstract

OBJECTIVE: To assess the impact of birth defects on preterm birth and low birth weight.

METHODS: Data from a large, prospective multi-center trial, the First and Second Trimester Evaluation of Risk (FASTER) Trial, were examined. All live births at more than 24 weeks of gestation with data on outcome and confounders were divided into two comparison groups: 1) those with a chromosomal or structural abnormality (birth defect) and 2) those with no abnormality detected in chromosomes or anatomy. Propensity scores were used to balance the groups, account for confounding, and reduce the bias of a large number of potential confounding factors in the assessment of the impact of a birth defect on outcome. Multiple logistic regression analysis was applied.

RESULTS: A singleton liveborn infant with a birth defect was 2.7 times more likely to be delivered preterm before 37 weeks of gestation (95% confidence interval [CI] 2.3–3.2), 7.0 times more likely to be delivered preterm before 34 weeks (95% CI 5.5–8.9), and 11.5 times more likely to be delivered very preterm before 32 weeks (95% CI 8.7–15.2). A singleton liveborn with a birth defect was 3.6 times more likely to have low birth weight at less than 2,500 g (95% CI 3.0–4.3) and 11.3 times more likely to be very low birth weight at less than 1,500 g (95% CI 8.5–15.1).

CONCLUSION: Birth defects are associated with preterm birth and low birth weight after controlling for multiple confounding factors, including shared risk factors and pregnancy complications, using propensity scoring adjustment in multivariable regression analysis. The independent effects of risk factors on perinatal outcomes such as preterm birth and low birth weight, usually complicated by numerous confounding factors, may benefit from the application of this methodology, which can be used to minimize bias and account for confounding. Furthermore, this suggests that clinical and public health interventions aimed at preventing birth defects may have added benefits in preventing preterm birth and low birth weight.

LEVEL OF EVIDENCE: II

Much attention has focused on the utility of prenatal screening and diagnosis and its influence on the occurrence and epidemiology of birth defects. A birth defect is defined as an abnormality in structure, function, or body metabolism, which is present at birth1 and affects how the body looks and works or both.2 Many questions remain about the natural history of pregnancies with birth defects, including their outcomes in terms of gestational age and birth weight.

Rasmussen et al3 showed that the risk for birth defects is increased in premature infants. Mili et al4 demonstrated that a very low birth weight infant (less than 1,500 g) is more likely to have a serious birth defect. Although preterm and low birth weight infants are more likely to have birth defects, the effect of birth defects on preterm birth and low birth weight has been difficult to study because of confounding by multiple shared risk factors. Risk factors such as use of assisted reproductive technology,5,6 exposure to cocaine,7,8 low folate levels,9,10 and cigarette smoking11,12 have been shown to contribute to low birth weight and preterm birth, as well as some birth defects. To better understand the relationship between birth defects and preterm birth we proposed the application of propensity scoring to control for confounding by multiple shared risk factors.

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MATERIALS AND METHODS

A large prospective multi-center population-based database, the First and Second Trimester Evaluation of Risk (FASTER) Trial, was studied. The FASTER Trial, sponsored by the National Institute of Child Health and Human Development, compared sonographic and serum screening tests for Down syndrome at various gestational ages as its primary goal. Details of the study and the principal results have been published previously.13 The study enrolled patients from October 1999 to December 2002 at 15 major U.S. medical centers. Written informed consent and institutional review board approval were obtained at each clinical enrollment site. Research coordinators at each clinical site recorded outcome information on study subjects using a computerized tracking system designed specifically for the study to maximize protocol compliance and minimize loss to follow-up. Each woman was contacted after delivery by a data coordinator at the enrolling site and was read a standard list of questions to answer about the outcome of the pregnancy. If she answered yes to the question, “Did the baby have any abnormal physical findings or blood tests after birth?” medical records were obtained. Records were also obtained from all screen-positive women and a large number of controls for quality assurance. At a minimum the records had to include a physical examination of the newborn or infant. There was variation in the length of time the records encompassed, but the focus was on anomalies detected at birth. There was no attempt made to standardize a physical examination at a certain age by specifically trained individuals. Copies of the fetal and pediatric records were submitted for review by a single neonatal nurse and pediatric geneticist for each case in which a possible fetal or neonatal medical problem was suspected. A database was created which included detailed antenatal, birth, and pediatric outcomes on all enrolled patients. The database was then cleaned to consolidate terminology and eliminate redundancy.

For inclusion in the FASTER database, a patient must have had a nuchal translucency scan showing a viable singleton gestation with a fetal crown-rump length on ultrasonography of 36–79 mm (103–136 weeks) at the time of entry into the study. Exclusion criteria included more than one fetus or no live fetus, evidence of a vanishing twin, a septated cystic hygroma or anencephaly noted at nuchal translucency scan, fetal crown-rump length out of range, maternal age younger than 16 years of age, an invasive procedure before first-trimester screen, a chorionic villus sampling procedure, a nuchal translucency scan outside of the study, or multifetal reduction.

All live births after more than 24 weeks with complete data on outcome and confounders were divided into two comparison groups: 1) those with a chromosomal or structural abnormality (birth defect) and 2) those with no abnormality detected in chromosomes or anatomy. Because these comparison groups were different in terms of a large number of potential confounding factors, propensity scores14 were used to match the groups on a range of characteristics or covariates. There are several statistical approaches that can be used to adjust between-group comparisons; a popular application is multivariable regression. Regression techniques assume linearity, and, ideally, that the covariate distributions in the comparison groups are overlapping at least to some extent.15 An alternate approach is statistical matching, and unlike regression, matching does not rely on the same assumptions.

The propensity score method is an efficient technique for balancing the groups on the basis of a large number of covariates, including sociodemographic variables, environmental exposures, obstetric history, and pregnancy complications. Propensity scores were developed by using multiple logistic regression analysis relating presence or absence of birth defects to an array of characteristics that were hypothesized to differ between these two groups. The propensity score is interpreted as the conditional probability of having a birth defect when given a particular profile that is defined by sociodemographic characteristics, environmental exposure, obstetric history, and pregnancy complications. Controlling for the propensity score is a means of accounting for the many differences in groups, both with and without birth defects. The use of propensity scores is a more parsimonious approach than multivariable regression. The technique was used to promote a fair and unbiased comparison of the association between birth defects and preterm birth and low birth weight, controlling for the multiple shared risk factors that can confound this association. Propensity scores are used here as a means to adjust for the many differences between comparison groups in potential confounding variables. Adjustment for propensity scores has been shown to reduce bias by accounting for the conditional probability of falling into one group or the other. To assess comparability between groups defined by the presence or absence of birth defects, multiple linear and logistic regression analysis were used to assess differences between continuous and dichotomous confounding variables, both before and after adjustment for quintiles of the propensity score.

The effect of birth defects on 1) preterm birth (less than 37 completed weeks of gestation at time of birth), 2) preterm birth at less than 34 weeks, 3) very preterm birth (less than 32 completed weeks of gestation at time of birth), 4) low birth weight (less than 2,500 g), and 5) very low birth weight (less than 1,500 g) was assessed by using multiple logistic regression analysis. The SAS software program was used and the analysis adjusted for the propensity scores and the confounders noted above.

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RESULTS

After all eligibility criteria were met, 38,033 women were studied, of whom 5,013 were excluded because of pregnancy termination (n=171), fetal loss at less than 24 weeks (n=349), fetal loss at 24 or more weeks including stillbirths (n=116), neonatal death (n=38), missing data on some aspect of pregnancy outcome (n=2,482), or missing some data on covariates (n=1,857). The number of women included in this analysis was 33,020 (Table 1).

Table 1
Table 1
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The analysis looked at two comparison groups: 1) the group with chromosomal or structural abnormalities (n=1,187) and 2) the group with no abnormality detected (n=31,833). Of the chromosomal abnormalities resulting in live births after 24 weeks of gestation (n=74), the most common were structural chromosomal abnormalities or rearrangements (n=30) and Down syndrome (trisomy 21, n=23). Others included sex chromosome aneuploidies (Klinefelter syndrome [47, XXY], Turner syndrome [45, X], 47, XYY, 47, XXX), tetraploidy, other aneuploidies, Patau syndrome (trisomy 13), and Edwards syndrome (trisomy 18). Structural abnormalities detected (n=1,143) included cardiac, central nervous system, musculoskeletal, and a variety of congenital structural abnormalities such as orofacial cleft, club foot, polydactyly, hypospadias, and spina bifida (Table 2).

Table 2
Table 2
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Comparisons of the sociodemographic characteristics, environmental exposures, obstetric history, and pregnancy complications between those with and those without birth defects, both before and after propensity score adjustment, is shown in Table 3. After adjustment for quintiles of the propensity scores, those with and those without birth defects were better balanced for the majority of confounding factors. Because statistically significant differences (P<.01 to account for multiple testing) remained in parent with a known chromosomal anomaly, diabetic status, other pregnancy complication, placental abruption, and preterm labor, these variables, as well as the quintile of the propensity score, were considered in the final multivariable model. Multiple logistic regression models were then developed to assess the association between birth defects and each outcome, considered separately, adjusted for the quintile of the propensity score and the five confounders noted above.

Table 3
Table 3
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The results, shown in Table 4, demonstrate statistically significant associations between length of gestation, birth weight, and birth defects, with a greater effect size noted at earlier gestation. Singleton liveborns with a birth defect were 2.7 times more likely to deliver preterm at less than 37 weeks (95% confidence interval [CI] 2.3–3.2), 7.0 times more likely to deliver preterm at less than 34 weeks (95% CI 5.5–8.9), and 11.5 times more likely to deliver very preterm at less than 32 weeks (95% CI 8.7–15.2) than unaffected liveborns. A singleton liveborn with a birth defect was 3.6 times more likely to be low birth weight at less than 2,500 g (95% CI 3.0–4.3) and 11.3 times more likely to be very low birth weight at less than 1,500 g (95% CI 8.5–15.1).

Table 4
Table 4
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DISCUSSION

Birth defects and preterm birth are significant contributors to perinatal health, ranking first and second as the leading causes of infant mortality.16 A better understanding of the causes of these adverse perinatal outcomes may lead to improved preventive measures on both clinical and public health fronts. Efforts to improve perinatal and child health will set the stage for improved lifelong health, as well.

This study demonstrates that birth defects are associated with preterm birth and low birth weight when controlling for multiple confounding factors, including shared risk factors and pregnancy complications, using propensity scoring adjustment in multivariable regression analysis. A major strength of our study is that it examines a broad array of risk factors and pregnancy complications in a large prospective cohort to demonstrate that birth defects increase the risk of preterm birth and low birth weight.

There are many plausible but unproven explanations for the association between preterm birth and birth defects. Included among them are the following: 1) certain birth defects, such as those associated with polyhydramnios or abnormal connective tissue, may increase the risk of preterm labor or membrane rupture17–19; 2) prenatal diagnosis of a birth defect may lead to deliberate planned labor induction or cesarean delivery at a gestational age of less than 37 weeks20–23; or 3) preterm birth, low birth weight, and birth defects may share common risk factors.24–26

Although this data set does not allow us to distinguish between planned labor induction or cesarean delivery compared with spontaneous preterm birth, this analysis adds to our understanding of the shared risk factors for birth defects, preterm birth, and low birth weight. Some risk factors, such as body mass index, gestational diabetes, and previous preterm birth were not significantly different between those with and those without birth defects after propensity score adjustment between comparison groups. The factors that did remain significantly different (applying stringent criteria of P<.01) included parent with a known chromosomal abnormality (P<.001), maternal diabetes mellitus (P<.001), other pregnancy complication (P=.004), placental abruption (P<.001), and preterm labor (P<.001). Several of these, including diabetes mellitus and placental abruption, likely point to the importance of vascular disruption and abnormal placentation early in development as common etiologic processes implicated in both birth defects and preterm birth.8,27–29

Of note, folic acid or multivitamin usage was high in the entire FASTER trial study population, approximately 48% in both groups. This is higher than national survey data showing rates of folic acid or multivitamin use ranging from 33% to 40% in reproductive-aged women30 and may explain why we did not see a significant difference in usage between the two groups.

One of the strengths of this study is that it draws on a large population-based cohort, albeit a cohort of women interested in prenatal screening and diagnosis. It analyzes a large number of potential confounders to further refine the association between birth defects, preterm birth, and low birth weight, demonstrating that the association is not strictly due to shared confounders. While propensity scoring has been used in other fields to address confounding by multiple shared risk factors, this is a useful application of this technique in the important field of birth defects and preterm birth.

Effective clinical and public health measures to reduce birth defects and preterm birth are very much needed. This will require continued research to further elucidate the relationship between these adverse perinatal outcomes and a search for shared risk factors. Understanding susceptibility to vascular abnormalities early in pregnancy and decreasing exposure to environmental exposures such as cigarette smoke or teratogens is worthwhile because it may demonstrate the added benefit of reducing birth defects and preterm birth. National efforts to improve preconception care, aimed at improving women's health by optimizing management of medical conditions, limiting environmental exposures, and improving personal behaviors and psychosocial risks,31 will emphasize eradicating risks for both birth defects and preterm birth. With rates of preterm birth rising in this country,32 this study adds emphasis to the importance of preconception care and birth defect prevention as shared mechanisms for improving birth outcomes.

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REFERENCES

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APPENDIX

The authors would like to acknowledge the work of the members of the FASTER Research Consortium: K. Welch, MS, R. Denchy, MS (Columbia University, New York, NY); R. Ball, MD, M. Belfort, MD, B. Oshiro, MD, L. Cannon, BS, K. Nelson, BSN, C. Loucks, RNC, A. Yoshimura (University of Utah, and IHC Perinatal Centers, Salt Lake City, Provo and Ogden, UT); D. Luthy, MD, S. Coe, MS (Swedish Medical Center, Seattle, WA); J. Esler, BS, D. Schmidt, MS (William Beaumont Medical Center, Royal Oak, MI); G. Saade, MD, R. Bukowski, MD, J. Lee, MS, (UTMB, Galveston, TX); R. Berkowitz, MD, Y. Kharbutli, MS (Mount Sinai Medical Center, New York, NY); S. Carter, MS (Montefiore Medical Center, Bronx, NY); J. Hobbins, MD, L. Schultz, RN (University of Colorado Health Science Center, Denver, CO); M. Paidas, MD, J. Borsuk, MS (NYU Medical Center, New York, NY); B. Isquith, MS, B. Berlin, MS (Tufts University, Boston, MA); G. Lambert-Messerlian, PhD, C. Duquette, RDMS (Brown University, Providence, RI); R. Baughman, MS (University of North Carolina, Chapel Hill, NC); T. Tripp, MA, D. Emig, MPH, K. Dukes, PhD (DM-STAT, Inc, Medford, MA). Cited Here...

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