Roy et al. were sick or absent from school, rendering us unable to collect a blood sample; the final sam- ple comprised 756 children with all measure- ments (92% of enrolled population). Neurobehavior assessment. Behavioral out- comes were assessed through administration of the following questionnaires, translated into Tamil, to the class teacher of each child: a) the Conners’ ADHD/Diagnostic and Statistical Manual for Mental Disorders, 4th edition (DSM-IV) Scales (CADS-T) (Conners et al. 1998); b) an older version of the Conners’ Teachers Rating Scales (CTRS-39) (Conners 1990) that continues to be widely used in India; and c) the Behavior Rating Inventory of Executive Function (BRIEF) (Gioia et al. 2000). For all questionnaires, higher scores indicate worse behaviors. The CADS-T is a self-administered ques- tionnaire for teachers comprising 27 questions that assess behaviors associated with ADHD. It yields three scores: ADHD index score, DSM-IV inattentive-impulsive subscale, and DSM-IV hyperactive subscale. The ADHD index was empirically derived from the 12 items best differentiating children with ADHD from nonclinical children in several large data sets. The items that comprise the DSM-IV inatten- tive-impulsive and hyperactive scales correspond to the DSM-IV criteria for ADHD diagnosis. Characteristic Age (years) 3 4 5 > 6 Sex Male Female 0 1 2 > 3 No. of other children in family Household average monthly income (rupees) < 4,000 4,000–13,000 > 13,000 Mother’s educational attainment Illiterate/primary school Middle school High school certificate College Father’s educational attainment Illiterate/ primary school Middle school High school certificate College Iron supplementation No Yes Calcium supplementation No Yes *p < 0.05. **p < 0.01. 1608 Table 1. Blood lead levels in relation to individual characteristics among young children in Chennai, India. The CTRS-39 consists of 39 questions used to assess internalizing and externalizing dimen- sions of behavior with five subscales: aggressiv- ity, inattentiveness, anxiety, hyperactivity, and sociability. However, because this is an older version of the CTRS-39, we chose to use only the subscales that did not overlap with those of the CADS-T, namely, anxiety and sociability. The BRIEF questionnaire for teachers was used to assess the children’s executive function. The BRIEF is useful in evaluating children 5–18 years of age with a wide range of devel- opmental and acquired neurologic conditions such as learning disabilities, ADHD, and trau- matic brain injury. This 86-item questionnaire takes 10–15 min to complete. Following the scoring procedure from the test manual (Gioia et al. 2000), the questionnaire responses were used to compute a global executive composite score and eight nonoverlapping clinical scales within the two broader indexes: a) behavioral regulation index (three scales: inhibit, shift, and emotional control), and b) metacognition index (five scales: initiating, working memory, plan/organize, organization of materials, and monitor). These scales were derived theoreti- cally and empirically, and good convergent validity was observed with other measures of inattention, impulsivity, and learning skills (Gioia et al. 2000). Blood lead (µg/dL) No. (%) 127 (16.8) 246 (32.5) 248 (32.8) 135 (17.9) 404 (53.4) 352 (46.6) 168 (22.2) 483 (63.9) 82 (10.8) 23 (3.0) 445 (58.9) 272 (36.0) 39 (5.2) 139 (18.4) 254 (33.6) 256 (33.9) 107 (14.2) 95 (12.6) 213 (28.2) 298 (39.4) 150 (19.8) 671 (90.1) 74 (9.90) 665 (89.4) 79 (10.6) Mean ± SD 10.80 ± 5.36 11.54 ± 5.49 11.57 ± 5.17 11.81 ± 5.32 11.57 ± 5.35 11.37 ± 5.32 10.56 ± 4.68 11.61 ± 5.41* 12.82 ± 5.93** 10.48 ± 4.91 12.14 ± 5.49** 10.88 ± 5.06** 8.06 ± 3.18 11.61 ± 5.28 11.99 ± 5.36 11.39 ± 5.25 10.28 ± 5.42* 11.98 ± 5.36 12.28 ± 5.14 11.34 ± 5.5 10.29 ± 5.05** 11.58 ± 5.36 10.76 ± 5.31 11.52 ± 5.26 10.66 ± 5.11 Data analysis. We examined descriptive statistics for all variables. Blood lead was natural log-transformed to maintain normality of the residuals in the model. Outliers (n = 9) were identified using extreme studentized deviate (Rosner 1983). We performed all analyses with and without the outliers. In general, the results did not change appreciably after removal of the outliers from the final multivariate models. We used analysis of variance for bivariate analysis to explore associations and potential confounders. We performed preliminary anal- yses and model building using multivariate linear regression. Covariates were selected in the regression procedure if they changed the R2 and/or main effect estimate by > 10%. On the basis of biological premise and from previ- ous literature, parental education and hemo- globin were included in the models. Because the study had a nested hierarchical sampling structure, linear models would have narrower standard errors due to correlation between observations at every level of sampling and within the neurobehavioral outcomes of students assessed by common teachers. We examined the intraclass correlation of behav- ioral outcomes at each level (zone, school, class) using random effects mixed models. Because most outcomes were not normally distributed and covariance within the levels did not fit a specified structure, we chose to account for the clustering of observations in the marginal models with generalized estimating equations (GEE) which use quasi-likelihood methods for estimation (Qaqish and Liang 1992). Scores on the different subscales of the CADS-T and CTRS were analyzed separately. For executive function, the BRIEF global exec- utive composite score was analyzed first, and because a significant association was found, further analyses were then carried out on the subscale scores. Because there are no normative data on these questionnaires for Indian children, we standardized raw scores internally by comput- ing Z-scores with respect to sex and age (simi- lar to the process used to create T-scores in psychometric tests). Analyses were carried out on the resulting Z-scores and compared with the analyses using raw scores to determine the consistency of the results. To ascertain whether the associations with blood lead differed across neurobehav- ioral domains (anxiety, sociability, ADHD index, and global executive composite score), we treated the Z-scores as repeated measures of neurobehavior and added an indicator (dummy) variable for each neurobehavioral domain. The association between blood lead and a particular neurobehavioral domain was considered significant if the interaction term for lead and the dummy-coded domain variable had a p < 0.05. Similar analyses were carried out to investigate differences between BRIEF ovlume 117 | number 10 | October 2009 • Environmental Health Perspectives