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Cognitive and Behavioral Outcomes of School-Aged Children Who Were Born Preterm
A Meta-analysis
Adnan T. Bhutta, MBBS;
Mario A. Cleves, PhD;
Patrick H. Casey, MD;
Mary M. Cradock, PhD;
K. J. S. Anand, MBBS, DPhil
JAMA. 2002;288:728-737.
ABSTRACT
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Context The cognitive and behavioral outcomes of school-aged children who were born preterm have been reported extensively. Many of these studies have methodological flaws that preclude an accurate estimate of the long-term outcomes of prematurity.
Objective To estimate the effect of preterm birth on cognition and behavior in school-aged children.
Data Sources MEDLINE search (1980 to November 2001) for English-language articles, supplemented by a manual search of personal files maintained by 2 of the authors.
Study Selection We included case-control studies reporting cognitive and/or behavioral data of children who were born preterm and who were evaluated after their fifth birthday if the attrition rate was less than 30%. From the 227 reviewed studies, cognitive data from 15 studies and behavioral data from 16 studies were selected.
Data Extraction Data on population demographics, study characteristics, and cognitive and behavioral outcomes were extracted from each study, entered in a customized database, and reviewed twice to minimize error. Differences between the mean cognitive scores of cases and controls were pooled. Homogeneity across studies was formally tested using a general variance-based method and graphically using Galbraith plots. Linear meta-analysis regression models were fitted to explore the impact of birth weight and gestational age on cognitive outcomes. Study-specific relative risks (RRs) were calculated for the incidence of attention-deficit/hyperactivity disorder (ADHD) and pooled. Quality assessment of the studies was performed based on a 10-point scale. Publication bias was examined using Begg modified funnel plots and formally tested using the Egger weighted-linear regression method.
Data Synthesis Among 1556 cases and 1720 controls, controls had significantly higher cognitive scores compared with children who were born preterm (weighted mean difference, 10.9; 95% confidence interval [CI], 9.2-12.5). The mean cognitive scores of preterm-born cases and term-born controls were directly proportional to their birth weight (R2 = 0.51; P<.001) and gestational age (R2 = 0.49; P<.001). Age at evaluation had no significant correlation with mean difference in cognitive scores (R2 = 0.12; P = .20). Preterm-born children showed increases in externalizing and internalizing behaviors in 81% of studies and had more than twice the RR for developing ADHD (pooled RR, 2.64; 95% CI, 1.85-3.78). No differences were noted in cognition and behaviors based on the quality of the study.
Conclusions Children who were born preterm are at risk for reduced cognitive test scores and their immaturity at birth is directly proportional to the mean cognitive scores at school age. Preterm-born children also show an increased incidence of ADHD and other behaviors.
INTRODUCTION
The infant mortality rate in the United States has decreased from more than 12 per 1000 live births in 1980 to approximately 7 per 1000 live births in 1998. This reduction in mortality has occurred during a period when an increasing percentage of children have been born preterm (<37 weeks) with low birth weights (LBWs) (<2500 g) or very LBWs (<1500 g).1 This decrease can be attributed to improvements in postnatal care provided in the delivery rooms and neonatal intensive care units.
This decrease in mortality is paralleled by an increasing recognition of neurodevelopmental disabilities in these children at school age. A large number of children who were born with a LBW or preterm have adverse outcomes such as cerebral palsy, hydrocephalus, blindness, deafness, or seizures.2-4 Multiple observational studies of children who were born preterm have followed up cohorts from birth to school age ( 5 years) and have reported on their cognitive and behavioral outcomes. Even in children without obvious neurological deficits, subtle abnormalities occur, which include lower cognitive test scores and increased behavioral problems.5-6 However, some studies have found no differences between preterm-born cases and term-born controls.7
The magnitude of the effect of preterm birth on cognitive and behavioral outcomes at school age is unknown. The extreme variability in the published data results from the relatively small numbers of subjects in each study and marked variation in the methods used for their evaluation. Several of these studies have been criticized due to problems with study design, nonrepresentative study samples, inadequate demographic data, high attrition rates, poor selection of control groups, the systematic exclusion of subgroups of patients, and other concerns.8 This has led to difficulties in estimating the true effect of preterm birth on cognition and behavior problems. We performed a meta-analysis to arrive at a better estimate of the effect of preterm birth on cognitive and behavioral outcomes in school-aged children. The 2 available meta-analyses on this patient population were published more than 10 years ago and investigated neurodevelopmental outcomes in preschool children.9-10 We report the first meta-analysis on the cognitive and behavioral outcomes of school-aged children who were born preterm by combining the results from case-control studies published between 1980 and November 2001.
METHODS
Selection of Studies
The guidelines published by Stroup et al11 for the meta-analysis of observational studies were followed in the design, performance, and reporting of this meta-analysis. A MEDLINE search included the period from 1980 to November 2001 and used the subject headings infant-premature; or infant, low birth weight; and cognition; developmental disabilities; child development; personality development; child development disorders; human development; behavior; child behavior disorders; attention deficit; and disruptive behavior disorders. These search terms were combined with the "explode" feature when applicable, the search was limited to English-language publications, and it was supplemented by a manual search of the reference lists of all primary articles and review articles. No attempt was made to contact the authors of any of these studies. We also performed a manual search of files maintained by 2 coauthors (P.H.C. and K.J.S.A.).
We defined a priori criteria for the inclusion of studies in this meta-analysis, selecting only those studies that included an evaluation of concurrent controls. Studies were included if they (1) had a case-control design, (2) reported cognitive data, behavioral data or both, (3) performed evaluations after the fifth birthday of the enrolled subjects, (4) had an attrition rate (loss to follow-up) of less than 30%, and (5) were published in 1980 or later. Studies were excluded if they did not meet all of these inclusion criteria. Studies that primarily examined LBW children were excluded because of the possibility that small-for-gestational-age term infants may be included in these cohorts.
If multiple studies were published from the same cohort of subjects at different ages, only the last published report was included (unless the cognitive and behavioral data were published separately, in which case both reports were included). Studies were also excluded if they reported outcomes on a defined subgroup of the population (eg, only cases with intraventricular hemorrhage) or if the same cognitive test was not used for all subjects. From the 227 studies retrieved and reviewed, only 15 studies with cognitive data and 16 studies with behavioral data met these selection criteria.
Data Extraction
Data were entered into a customized database created for this meta-analysis, with data extracted on the study design, attrition rate, demographic variables, geographic location, socioeconomic status, and detailed information on the cognitive and behavioral evaluations performed. Explanatory variables were chosen based on their significant association with cognitive and behavioral outcomes in the published literature.8-9,12-14 The data extracted from these studies were entered in an open-ended fashion and coding of the variables at the time of data entry was minimized. The data from each study were reviewed twice to minimize the chances of data-entry errors. A wide array of behaviors were assessed and various behavioral methods were used in the selected studies. For the purposes of this meta-analysis, we classified the behavior of subjects into externalizing behaviors (eg, hyperactivity, delinquency) or internalizing behaviors (eg, anxiety, depression, phobias). Data from studies that used standard diagnostic criteria for attention-deficit/hyperactivity disorder (ADHD), either the Diagnostic and Statistical Manual of Mental Disorders, Third Edition (DSM-III), Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition (DSM-III-R), or Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), were extracted for further analysis.
Statistical Methods
This meta-analysis was performed using STATA statistical software (Version 7, STATA Corp, College Station, Tex). For each study, the nonstandardized difference between the mean cognitive test scores of cases (preterm-born children) and controls (term-born children) was weighted by the inverse of the variance for this difference. These weighted mean differences (WMDs) were pooled across studies to compute an overall mean cognitive difference between cases and controls. Cognitive scores from all studies were obtained from comparable tests of cognition (normative data from all reported tests had a mean [SD] of 100 [15]. Therefore, the nonstandardized WMD was chosen as the cognitive outcome measure for combining studies in this meta-analysis.
We used random-effects and fixed-effects least-square regression models for combining the results in this meta-analysis. The fixed-effects and random-effects models produced virtually identical results; therefore, only the results obtained from the random-effects models are presented.
The homogeneity of the WMDs across studies was formally tested using the general variance-based method described by Greenland.15 Because this test is known to have low power, homogeneity was further investigated graphically using Galbraith plots.16-18 These plots allow the contribution of each study to the overall homogeneity test statistic to be examined visually. The Galbraith plot graphs each study's z score (the mean difference divided by the SE of the difference [d/SE{d}]) vs the reciprocal of the SE of the mean difference [1/SE{d}] and fits a least-square regression line constraining the intersect to zero. Studies exhibiting high heterogeneity will have a z score that falls outside 2 SDs above and below the fitted regression line. Linear meta-analysis regression models were fitted to explore the impact of study-specific covariates on heterogeneity.15 The potential for publication bias was visually assessed by examining for possible skewness in Begg modified funnel plots.19 Formal tests for skewness in the funnel plots and thus for publication bias were implemented using the weighted-linear regression approach proposed by Egger et al.20 The relationships between the mean cognitive score and birth weight and gestational age in each study were examined independently using inverse–variance-weighted linear regression.
The relationship between preterm birth and school-aged ADHD in each study was measured by computing (based on the percentages of ADHD children reported in cases and controls) a study-specific relative risk (RR). These RRs were combined to estimate a pooled RR using the same inverse variance-weighted least-squares method described above for the analysis of cognitive data. Statistical analyses similar to those described above were also used to test for homogeneity of the RR for ADHD across studies and to assess for publication bias.
Assessment of Study Quality
We performed assessments of study quality based on a novel 10-point score developed for this meta-analysis. The scoring was based on factors thought to be good quality indicators for observational studies using a case-control design; these criteria are described in Table 1. Studies that scored 8 or higher were grouped as high quality, whereas studies scoring less than 8 were grouped as low quality for the purpose of subgroup analysis.
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Table 1. Quality Criteria for Observational Studies*
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Quality assessment scoring was performed independently by 2 of the authors (A.T.B. and K.J.S.A) and showed high concordance between the 2 raters as measured by the Lin concordance correlation coefficient (Pc = 0.79; 95% CI, 0.62-0.96).21 Furthermore, using the Bland and Altman limits-of-agreement procedure,22 the average disagreement was close to zero (0.14) and had a 95% CI that included zero (-1.64 to 1.93), thus suggesting no evidence for a systematic disagreement bias between the 2 reviewers.
RESULTS
Cognition
From these 15 case-control studies, 17 groups of children (including 1556 cases and 1720 controls) were evaluated after their fifth birthday. The demographic data from these studies are listed in Table 2.6, 23-36 Three studies contained data from the United States, 9 studies looked at regional populations, and the others followed hospital-based cohorts. Sample size of the cases ranged from 15 to 255 and of the controls ranged from 15 to 500. Control populations in all the studies were matched with the cases on 1 or more demographic features. As shown in Table 2, information on demographic variables such as sex, race, and socioeconomic status was not always reported.
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Table 2. Case-Control Studies With Cognitive Data*
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A random-effects meta-analysis showed that the WMD between the mean cognitive scores of the cases and the controls was 10.9 (95% CI, 9.2-12.5) in favor of the controls (z = 13.14; P<.001; Figure 1). However, the  2 test for heterogeneity was significant (216 = 33.65; P = .006). A Galbraith plot to assess study heterogeneity showed that the 2 populations32, 35 with the highest WMD were the cause for this heterogeneity. Taylor et al35 showed the highest WMD between the cases and controls, which is possibly explained by the fact that this study included cognitive assessment of children with severe neurological disability and assigned them the lower limit of IQ scores (39 points) to preserve sample size. Similarly, Stjernqvist and Svenningsen32 also included the IQ scores of children with severe disability (IQ score <70 or 2 SDs below mean). The remaining studies, with the exception of the study by Teplin et al,27 had excluded children who could not be administered the tests of cognition. Heterogeneity was no longer significant after excluding the data reported by Taylor et al35 from this meta-analysis (215 = 19.42; P = .20). The pooled WMD from remaining studies was 10.2 (95% CI, 9.0-11.5) in favor of controls (z = 16.11; P<.001).
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Figure 1. Random-Effects Meta-analysis Comparing Cognitive Test Scores Between Cases and Controls
The test for heterogeneity was significant (216 = 33.65; P = .006). The weighted mean difference significantly favors controls (z = 13.14; P<.001). The size of the data marker corresponds to the weight of that study. Error bars represent 95% confidence intervals.
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The mean cognitive test scores were significantly correlated with the birth weight (R2 = 0.51; P<.001) and gestation at birth (R2 = 0.49; P<.001; Figure 2). There was no significant correlation between the age at evaluation and the WMD between cases and controls (R2 = 0.12 [R2 = -0.35]; P = .20). There were no statistical differences between the cognitive outcomes of US-based populations vs those from other countries (10.6 [95% CI, 6.5-14.8] vs 10.2 [95% CI, 9.1-11.3]; P = .85) or between the studies that compared regional cohorts with hospital-based populations (10.4 [95% CI, 9.3-11.6] vs 9.6 [95% CI, 7.3-11.8]; P = .68).
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Figure 2. Correlations Between Mean Cognitive Scores, Birth Weight, and Gestational Age
Correlations between each variable (birth weight and gestational age) and mean cognitive test scores were significant (birth weight: R2 = 0.51; P<.001; and gestational age: R2 = 0.49; P<.001). The preterm-born children scored lower on tests of cognition for both variables.
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There was a trend toward a greater WMD in the high-quality studies compared with the low-quality studies (11.2 [95% CI, 9.7-12.7] vs 9.4 [95% CI, 8.0-10.8]), but this difference was not statistically significant (P = .17).
We assessed the possibility of publication bias by using a funnel plot to assess for skewness. This method suggested no significant publication bias (P = .82). Further formal testing was done using the method of Egger et al,20 which also showed a lack of publication bias (R2 = 0.30; P = .69).
Behavior
Sixteen studies provided a comparison of the incidence of behavioral problems between 1759 preterm-born cases and 2629 term-born controls. The demographic features and behavioral data from these studies are summarized in Table 3.6, 23, 25, 27-28,30, 32, 35-43
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Table 3. Case-Control Studies With Behavioral Data*
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Children who were born preterm showed increases in externalizing or internalizing behaviors in 13 (81%) of these 16 studies. Ten (67%) of 15 studies that assessed subjects for ADHD found that the cases had a significantly higher prevalence of attention problems compared with controls. Similarly, 9 (69%) of 13 studies found a significantly higher prevalence of externalizing symptoms, while 9 (75%) of 12 studies found a significantly higher prevalence of internalizing symptoms in the cases vs the controls. Further analysis of the behavioral data was not possible due to the variety of tools used to assess and report these behaviors in school-aged children.
Seven populations from 6 studies were assessed by formally defined criteria (DSM-III, DSM-III-R, or DSM-IV) to diagnose ADHD in cases and controls. These studies were selected for a random-effects meta-analysis to calculate the RR of ADHD in children who were born preterm (Figure 3). Cases had a pooled RR of 2.64 (95% CI, 1.85-3.78) compared with controls (z = 5.32; P<.001). The test for heterogeneity was not significant between these studies (26 = 2.60; P = .86). The pooled RR in the cases was similar for the studies labeled high quality vs low quality (2.5 [95% CI, 1.4-4.3] vs 2.8 [95% CI, 1.7-4.4]; P = .86). The tests using methods by Begg (P = .55) and Egger et al (P = .49) showed no significant publication bias. However, the small number of studies (n = 6) makes these tests unreliable.
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Figure 3. Random-Effects Meta-analysis for Studies Assessing Attention-Deficit/Hyperactivity Disorder
Diagnostic criteria used were based on the Diagnostic and Statistical Manual of Mental Disorders, Third Edition; Diagnostic and Statistical Manual of Mental Disorders, Revised Third Edition; or Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. An increased relative risk existed among cases for attention-deficit/hyperactivity disorder (z = 5.32; P<.001). The test for heterogeneity was not significant (26 = 2.60; P = .86). CI indicates confidence interval, illustrated by the error bars. The size of the data marker corresponds to the weight of that study.
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COMMENT
Our meta-analysis shows that preterm birth is associated with lower cognitive scores and increased risks for ADHD and other behaviors at school age compared with term-born controls. Lower cognitive scores for the cases were noted in all the studies selected for this meta-analysis, and a WMD of greater than 10 between the cognitive scores of cases and controls is likely to have significant educational and social consequences.44 Lower birth weight and gestational age were significantly correlated with decreases in cognitive test scores, highlighting the developmental vulnerability of the immature brain. Superimposed on this vulnerability are the factors associated with severity of illness in preterm neonates, their physiological instability and exposure to early adverse experiences, which may have a persistent impact on brain development leading to these cognitive and behavioral outcomes.
The results of this meta-analysis must be viewed in the light of its limitations. Multiple studies conducted over the decades have also demonstrated the significant impact of demographic and environmental factors (such as age, sex, race, and socioeconomic status) on the trajectory of cognitive and behavioral development in both preterm39, 45-48 and full-term12, 49 infants. In a recent study of 118 children at age 10 years who were born preterm, family factors were stronger predictors of school performance than were perinatal complications.50 All the studies included in our meta-analysis featured cases and controls matched for 1 or more demographic variables. Thus, we were unable to determine the specific impact of demographic variables on the measured cognitive and behavioral outcomes.
We limited our search to English-language literature because of practical difficulties in abstracting data from articles published in other languages. Gregoire et al51 showed that in only 1 of 36 consecutive meta-analyses of randomized clinical trials, the inclusion of non–English-language articles produced results different from the published meta-analysis (with a change in pooled odds ratio from 0.70 to 0.67). Such comparisons have not been published for meta-analyses of observational studies similar to ours. Despite the exclusion of non–English-language articles, our meta-analysis contains data from many countries and no significant differences occurred in the cognitive outcomes of children born preterm in the United States vs non-US cohorts. For the sake of completeness, a repeat MEDLINE search found only 4 non–English-language articles (with online abstracts in English) that were designed as case-control studies with cognitive and/or behavioral data for preterm or term-born school-aged children.52-55 Because of the limited information contained in each abstract, we cannot say whether these studies fulfilled all the inclusion and exclusion criteria for this meta-analysis. All 4 of these studies reached conclusions that are consistent with the results of our meta-analysis. Based on these considerations, we propose that including the results from non–English-language studies would not have altered our results or conclusions.
Furthermore, the included studies were published in an era when rapid advances were occurring in the field of fetal medicine and perinatology. Therefore, the care provided to these infants was not uniform and must have evolved over the period studied. The selected studies differed from one another in their baseline characteristics, such as mean birth weight and gestational age. All neonates with a gestational age of less than 37 weeks were defined as cases, and the data obtained from those who were born full-term but were small for their gestational age were excluded. It is possible, however, that some studies may have included those who were born full-term but were small for their gestational age and were not identified in the description of their cohorts.
Data on birth weight and gestational age were not always reported as mean (SD) or range (Table 1). For these control groups, we assumed a mean term gestation of 40 weeks56 and a mean birth weight of 3200 g (50th percentile for age). Cases with severe neurological and cognitive disability were excluded in all but 3 of the studies included in the meta-analysis, although the exact definition of severe disability varied between these studies. The studies that included cases with severe neurological and cognitive disability had the highest WMD between the cases and controls (Figure 1).27, 32, 35
For the purposes of our analysis, we assumed that the cognitive scores from the various tests were comparable because of similar normative data for all the cognitive tests used (mean [SD], 100 [15]). Similarly, we assumed that the different standardized assessments used for the diagnosis of ADHD had comparable sensitivity and specificity (DSM-III, DSM-III-R, and DSM-IV). These assumptions overlook the subtle differences between the cognitive and behavioral tests used, as well as the variability in administering these tests.
The stringent application of our selection criteria resulted in the exclusion of studies with poor methodologic quality (eg, attrition rate >30%) and poor generalizability (eg, reports of subgroup analyses57). Assessment of study quality for meta-analyses of randomized clinical trials has been questioned and may give misleading results in meta-analyses.58 Specific criteria are widely accepted for assessing the quality of randomized controlled trials (eg, randomization, double-blinding, dropouts, or allocation concealment); however, similar criteria have not been developed for observational studies. We devised a quality assessment tool specifically for this meta-analysis, using the objective criteria listed in Table 1. The quality of the studies included in this meta-analysis was assessed independently by 2 of the authors, but showed no differences in cognitive or behavioral outcomes between high-quality and low-quality studies.
This meta-analysis provides evidence from a large number of subjects that children who were born preterm are at significant risk for reduced cognitive performance at school age and that gestational age and birth weight are directly proportional to their mean cognitive test scores. These robust differences should eliminate controversies generated from the variable cognitive outcomes reported by individual follow-up studies. Is a mean difference of 10.9 points between the cognitive scores of school-aged cases and controls clinically significant? McCarton et al44 argued that a 4-point difference between cognitive scores may not produce functionally detectable differences between children, but on a group basis these differences will significantly alter the proportion of children classified as "intellectually deficient and of borderline intelligence." Children who were born preterm or at LBW are 50% more likely to be enrolled in special education classes compared with term-born children, which was conservatively estimated in 1988 to result in an incremental cost of $370.8 million.59 Based on such projections, we propose that the cognitive differences reported in this meta-analysis will have a significant impact on the educational requirements for children who were born preterm and may determine their future socioeconomic potential.
The perinatal course of these children may shed some light on the mechanisms underlying these differences. Preterm neonates are at higher risk for postnatal complications, such as intraventricular hemorrhage, sepsis, metabolic complications (eg, hypoglycemia), and chronic lung disease. They are subjected to multiple painful procedures and maternal separation for prolonged periods. Experimental evidence from animal models shows that all these factors can promote or precipitate neuronal cell death in the immature brain.60 Increased rates of neuronal cell death could lead to volumetric losses in specific brain regions and may at least partially explain the cognitive and behavioral abnormalities noted in these children. Volumetric measurements of brain regions in 8-year-old children born preterm showed disproportionately smaller volumes of the sensorimotor cortex, other cortical areas, the corpus callosum, amygdala, hippocampus, and basal ganglia, which were associated with significantly lower cognitive scores (mean difference = 23.5) and an increased incidence of ADHD and other behavioral disorders.57 Similarly, an increased incidence of neurocognitive and behavioral abnormalities were correlated with magnetic resonance imaging abnormalities in the brains of 14-year-old children who were born preterm.61 Since developing neurons are more vulnerable to cell death during the perinatal period,62-63 we propose that the biological and environmental insults associated with preterm birth may promote some of these anatomical differences.
This meta-analysis further shows that children born preterm have a 2.64-fold risk for developing ADHD and frequently manifest externalizing or internalizing behaviors during school age. Multiple studies report an increased prevalence of psychiatric disorders in preterm-born children,40, 64 which may contribute to increased parenting stress and maternal depression during early childhood.65-66
Could the impairment of preattentional mechanisms (such as the sensory gating of paired stimuli67) lead to ADHD, which ultimately manifests as poor cognition at school age? Preterm-born infants show differences in their threshold for arousal, which supports sustained attention and information encoding.68-69 Children who were born preterm have been shown to be less capable of the selective attention states required for learning.70 Thus, we propose a series of testable hypotheses to examine the pathogenesis of poor cognition in children born preterm.
Proposals to test such hypotheses will require the concerted efforts of clinicians and neuroscientists to develop a complete understanding of the biological, environmental, and psychosocial mechanisms responsible for these cognitive and behavioral differences. Previous research has demonstrated that psychosocial interventions with preterm infants and their parents may improve cognitive and behavioral outcomes,44, 71 whereas others report that socioeconomic factors have minimal effects on neurodevelopmental outcomes.4 With an improved understanding of the underlying biological mechanisms, more focused therapeutic interventions can be developed to decrease or prevent these long-term impairments following survival after preterm birth.
AUTHOR INFORMATION
Author Contributions: Study concept and design: Bhutta, Anand.
Acquisition of data: Bhutta, Anand.
Analysis and interpretation of data: Bhutta, Cleves, Casey, Cradock, Anand.
Drafting of the manuscript: Bhutta, Cleves, Casey, Anand.
Critical revision of the manuscript for important intellectual content: Bhutta, Cleves, Casey, Cradock, Anand.
Statistical expertise: Cleves.
Obtained funding: Anand.
Administrative, technical, or material support: Bhutta, Anand.
Study supervision: Anand.
Funding/Support: Dr Anand received grant HD36484-02 from the National Institute for Child Health and Human Development and grant 01-199 from the Blowitz-Ridgeway Foundation.
Acknowledgment: We gratefully acknowledge the help of Tracy L. Thurman, BS, for constructing the database used for our study.
Corresponding Author and Reprints: K. J. S. Anand, MBBS, DPhil, Arkansas Children's Hospital, 800 Marshall St, S-431, Little Rock, AR 72202 (e-mail: anandsunny{at}exchange.uams.edu).
Author Affiliations: Departments of Pediatrics (Drs Bhutta, Cleves, Casey, Cradock, and Anand), Biostatistics (Dr Cleves), Anesthesiology (Dr Anand), Pharmacology (Dr Anand), and Neurobiology (Dr Anand) and Arkansas Center for Birth Defects Research and Prevention (Dr Cleves), University of Arkansas for Medical Sciences, Little Rock.
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