Centre of Expertise on Environment and Health, commissioned, financed, and steered by the Flemish government (Department of Science, Department of Public Health, and Department of Environment). A total of 1,196 mothers and their newborns were enrolled via 26 maternity wards in Flanders between September 2002 and February 2004. We selected these maternity wards through stratified sampling. We defined the “strata” as geographic areas in Flanders with differ- ent environmental and pollution characteris- tics: two urban areas (Antwerp and Ghent), an area characterized by fruit orchards, a rural area, and four industrial areas. In each of these areas, we selected an average of four maternity wards (one per season), from each of which we expected to recruit 50 participants. Present data are from a longitudinal substudy of a random sample of mothers living in the urban area of Antwerp or in rural Flanders. Inclusion criteria included living for at least 5 years in the area of interest, being able to fill out questionnaires in Dutch, vaginal deliv- ery at term, and an uncomplicated perinatal period (no neonatal or maternal distress, con- genital malformations, or neonatal intensive care unit admission). During the week after delivery, field nurses visited the women in the maternity ward to check for participation eli- gibility, give information on the campaign, and collect completed questionnaires. These questionnaires collected information on the parents, including health status, smoking behavior, age, family composition, socioeco- nomic status, and the height and weight of the parents. We created two variables for smoking: smoked ever (smoked before or during preg- nancy) and smoked during pregnancy. Next, we sent postal questionnaires every 6 months for children between the ages of 3 weeks and 36 months, collecting information about the child’s demography, diet, and health status and whether the child was breast-fed. We defined breast-feeding as being breast-fed for at least 1 day. We then created a separate covariate for children who had been breast-fed for at least 6 months. We used household income as an indicator of socioeconomic status: We defined low income as < €1,240/month. The study protocol was approved by the medical ethics committees at the University of Antwerp. All parents gave written informed consent. Analysis of height and weight. We collected data on height and weight of the children by questionnaires administered at birth and at 12, 18, 24, 30, and 36 months of age. Parents filled in the height and weight measurements for their children obtained during physician visits or with Child and Family Services. We calculated BMI (kilograms per square meter) from questionnaire responses. We further ana- lyzed BMI as BMI standard deviation scores [BMI SDS = (child’s BMI – population mean BMI)/population standard deviation], using Flemish growth curves (Hauspie and Roelants 2004) (Auxology 1.1; Pfizer, Brussels, Belgium). We used BMI SDS because BMI in children is highly dependent on the child’s age and sex and in order to linearize the repeated BMI measurements. Blood collection. We received at least 30 mL of cord blood from each participating birth mother. The cord blood was collected by the participating maternity ward personnel and than aliquoted. We separated plasma by centrifugation in the maternity laboratories within 24 hr of its collection, stored samples under refrigeration, and transported them in cool boxes to the analytical laboratory within 1 week of collection. We stored the plasma samples at the analytical laboratory at –20°C until analysis. Measurements in cord blood. Two laborato- ries measured PCBs (congeners 118, 138, 153, 170, and 180, because of their reported abun- dance in cord serum samples) and chlorinated pesticides by gas chromatography equipped with an electron capture detector using the method of Gomara et al. (2002). All analyses containing PCBs controlled for possible dif- ferences between both laboratories. Both labo- ratories participated in the Arctic Monitoring and Assessment Program (AMAP) proficiency testing scheme (Institut National de Santé Publique, Québec, Canada). Precision (rela- tive standard deviation), estimated using results of the ClinChek and AMAP samples, ranged between 6.7% and 9.3% for all compounds, except for hexachlorobenzene (18.1%). The limit of detection for all chlorinated com- pounds in plasma was 0.02 µg/L. We assessed exposure to dioxin-like compounds via the dioxin-responsive chemical-activated luciferase expression (DR-CALUX) assay, based on the in vitro activation of the aryl hydrocarbon receptor in cultured H4IIE rat hepatoma cells by dioxin-like compounds extracted from 5 mL of cord plasma (BioDetection Systems BV, Amsterdam, the Netherlands). We performed extraction and cleanup procedures as described elsewhere (Koppen et al. 2001). The limit of detection was 0.14 ± 0.07 pg/mL extract or 14 pg CALUX-TEQ (toxic equivalents)/g fat for a plasma sample with a fat content of 200 mg/dL. We express all studied compounds on a lipid weight basis, because most of these pollutants are stored mainly in body fat and because we can reasonably assume that most of our blood samples result from fasting condi- tions. We simultaneously and gravimetrically determined the total plasma lipid concentra- tion using the CALUX assay. If we obtained no value, we calculated the total lipid concen- tration based on routinely measured triglycer- ides and total cholesterol using the following formula: total lipids = 1.33 × (triglycerides + cholesterol) + 50.5 mg/dL (Covaci et al. 2006). Environmental Health Perspectives • volume 117 | number 1 | January 2009 Childhood BMI and environmental pollution Statistical analysis. We present all data as mean ± SD, median, and range, or as percent- ages. We used SAS version 9.1 (SAS Institute Inc., Cary, NC, USA) for all analyses. We calculated Spearman correlation coefficient between the pollutants and possible confound- ers. We balanced the initial design of the study with questionnaires being distributed every 6 months. However, the dates responding to the last measurements of height and weight were highly variable, resulting in an unbalanced design. Furthermore, nonresponses to distrib- uted questionnaires complicated the analy- sis. Therefore, we used linear mixed models (a likelihood-based method), which provides valid results under less restrictive assumptions concerning missing data (missing at random) (Verbeke and Molenberghs 2001). The out- come variables included BMI SDS, weight SDS, and height SDS for children between 1 and 3 years of age. Mixed models determined which covariates significantly influenced the intercept and slope of, for instance, BMI SDS for this age range. We analyzed the outcome variables birth weight and birth length SDS using a multiple regression analysis. We also calculated least-square means. We constructed single-pollutant models because of their high intercorrelation. We assessed the interaction of sex and smoking before and during pregnancy for each pollut- ant. We deleted nonsignificant interactions from the model. Initial analyses also contained the following covariates: age of the child, BMI of both parents, maternal age at time of birth, birth weight SDS, any instance of breast- feeding, maternal smoking before and/or dur- ing pregnancy, and household income. As covariates, models for weight and height SDS contained the weight and length of parents instead of their BMI. In every mixed model, we assessed the interaction of all covariates with time. We deleted nonsignificant inter- actions from the model. We applied a back- ward selection procedure (p = 0.05 as cutoff value), in view of the high number of variables and possible interactions. We applied multiple imputation to inves- tigate the stability of the results due to missing data. For multiple imputations, we replaced each missing value with a set of numbers (Markov chain Monte Carlo method) (Verbeke and Molenberghs 2001). We analyzed the results of each imputation as complete data sets and combined the results of these analyses. We defined statistical significance as p < 0.05. Results Subject characteristics. Table 1 presents sub- ject characteristics (n = 138). The percentages of missing data for BMI SDS at 12, 18, 24, 30, and 36 months were 5, 4, 24, 38, and 40%, respectively, and the estimated mean BMI SDS values ± SE were 1.08 ± 0.87, 0.87 ± 0.89, 0.20 123