Cadmium exposure and neurodevelopmental outcomes cumulative cadmium exposure (Lauwerys et al. 1994). Urinary cadmium concentra- tion was determined by inductively coupled plasma mass spectrometry (ICP-MS), and details are accessible online via the NHANES website (CDC-NCHS 2010b). The limit of detection (LOD) was 0.06 μg/L , and cad- mium concentrations below the LOD were imputed as the LOD divided by the square root of 2. Of the 2,199 participants, 222 (10%) had urinary cadmium concentrations below the LOD. Cadmium (Cd) concentra- tions were corrected for interference from tin (Sn) and molybdenum (Mo), because these elements can produce ICP-MS signals (114Sn, 98Mo16O+, 96Mo18O+, 97Mo17OH+) that overlap with 114Cd (CDC-NCHS 2010b; Jarrett et al. 2008). Cadmium concentrations that were below zero after molybdenum cor- rection were left-truncated and listed as zero. Urinary creatinine is often used to correct for the variation in chemical concentration that is attributable to variation in urine dilu- tion when exposure estimates are based on spot urine samples (Barr et al. 2005). Because creatinine enters urine at a fairly constant rate, urine creatinine concentration is inversely proportional to urine flow rate and can be used to correct for differences in urine flow rate (Barr et al. 2005). In this study urine creatinine levels were determined via a Jaffé rate reaction with a Beckman Synchron CX3 Clinical Analyzer (Beckman Instruments, Inc., Brea, CA, USA) (CDC-NCHS 2010b). As recommended by Barr et al. (2005), we included urine creatinine concentration as an independent covariate in the regression mod- els rather than using the ratio of cadmium to creatinine (creatinine standardization), because this approach should be less likely to produce biased effect estimates (Barr et al. 2005; Schisterman et al. 2005). Outcomes. Assessment of neurodevelop- mental outcomes was based on responses of parent, guardian, or other adult proxy responder (CDC-NCHS 2010b). For chil- dren < 12 years of age, LD status was assessed with the following question: “Has a repre- sentative from a school or health professional ever told [you] that [the child] had a learning disability?”; ADHD status was assessed as fol- lows: “Has a doctor or health professional ever told [you] that [the child] had atten- tion deficit disorder?” For those ≥ 12 years of age, the same questions were asked about LD and ADHD but the bracketed words were replaced with: [the child] and [he/she]. Special education utilization was assessed with the same question regardless of the child’s age: “Does [the child] receive Special Education or Early Intervention Services?” Covariates. Data on many potential con- founders were available in NHANES (CDC- NCHS 2010b). We considered the following covariates in our analyses: age (years), sex, race/ethnicity [non-Hispanic white (referent), non-Hispanic black, Mexican American, other Hispanic, other race (including multiracial)], maternal age at birth of child (years), atten- dance at preschool/daycare (yes/no), health insurance coverage (yes/no), receipt of neo- natal intensive care unit or special newborn care (yes/no), low birth weight (< 2,500 g; yes/no), poverty income ratio (the ratio of family income to the federal poverty threshold; ratios ≥ 5 are listed as 5), education level of the household reference person (highest grade achieved: less than 9th grade, at least 9th grade but no high school diploma, high school grad- uate/GED (general educational development) degree or equivalent, some college or associate of arts degree, and college graduate or above), blood lead level (micrograms per deciliter), low hemoglobin level for age and sex (< 10th percentile, ≥ 10th percentile; used as a proxy for iron deficiency), report of a smoker in the home (yes/no), serum cotinine (nanograms per milliliter), prenatal smoke exposure (i.e., the child’s mother smoked while pregnant; yes/no), and, as mentioned above, urine creati- nine (milligrams per deciliter). Detailed infor- mation on the covariates is available online (CDC-NCHS 2010b). Statistical analysis. Analyses were con- ducted using SAS (version 9.1.3; SAS Institute Inc., Cary, NC). We obtained national preva- lence estimates for the outcomes by using the SURVEYMEANS procedure, specifying strata, cluster, and weight variables to account for the complex sampling design characteristics of NHANES (CDC-NCHS 2010a, 2010b). For each of the three outcomes, we estimated the relative odds of each outcome across quartiles of urinary cadmium concentration. To do this we fit multivariable-adjusted logistic regres- sion models with the SURVEYLOGISTIC procedure, again specifying strata, cluster, and weight variables (CDC-NCHS 2010a, 2010b). Urinary cadmium was evaluated in quartiles in order to allow for the detection of nonlinear dose–response relationships, reduce the potential excess influence of data from children with very high cadmium concentra- tions on the effect estimates, and alleviate con- cerns about low cadmium levels in the context of molybdenum correction and left truncation of urinary cadmium levels at zero (when cor- rection resulted in a cadmium concentration below zero). We constructed three main models: a) logistic regression models relating urinary cadmium to each outcome adjusting only for urinary creatinine; b) core models adjusted for urinary creatinine, age, sex, blood lead, smoker in the home, serum cotinine, prenatal smoke exposure (mother smoked while preg- nant), and poverty income ratio; and c) full models also adjusted for additional covariates Environmental Health Perspectives • volume 120 | number 5 | May 2012 of a priori interest that predicted outcomes (p < 0.10) in bivariate logistic regression mod- els. Continuous and discrete covariates were modeled as single linear terms, except for hemoglobin levels. Low hemoglobin levels are a proxy for iron deficiency (Zimmermann 2008). Because normal hemoglobin levels vary by age and sex (Ahsan and Noether 2011), we modeled hemoglobin in categories (< 10th percentile or ≥ 10th percentile for the age and sex of the participant). Prior studies have suggested an interaction between cadmium exposure and sex in rela- tion to LD, as well as an interaction between cadmium exposure and lead exposure in relation to ADHD (Ely et al. 1981; Nation et al. 1989, 1990). We evaluated cadmium– sex and cadmium–lead interactions in the fully adjusted models by including product interaction terms with cadmium modeled as an ordinal trend variable (coded as 0, 1, 2, or 3 based on the quartile of exposure) to obtain p-values for each interaction. For the cadmium–lead interaction, lead was also mod- eled as an ordinal trend variable. To obtain a single (summary) effect estimate for urinary cadmium with each outcome, we evaluated this cadmium quartile trend variable in the fully adjusted models. We also evaluated this cadmium quartile trend variable in the fully adjusted models (with the sex or blood lead term removed) within strata defined by sex or blood lead level (above/below median). In our analyses statistical significance was defined as p < 0.05 or a 95% confidence interval (CI) that excluded the null. Results History of LD was reported for 12.6% (276 of 2,189) of the study population, and participation in special education was reported for 10.5% (231 of 2,196). History of ADHD diagnosis was reported for 9.0% (198 of 2,195) of the study population. The co-occurrence of the outcomes is shown in Figure 1. Accounting for the complex sur- vey design characteristics of NHANES, the estimated lifetime prevalence among U.S. children 6–15 years of age was 12.6% for LD (95% CI: 9.5%, 15.7%), 11.7% for spe- cial education participation (95% CI: 9.5%, 13.9%), and 11.5% for ADHD (95% CI: 8.5%, 14.5%). Urinary cadmium concentra- tions followed an approximately log-normal distribution, with a median of 0.11 gμ/L, an interquartile range (IQR) of 0.06–0.18 gμ/L, and a range of 0.00–14.94 μg/L. Urinary cadmium concentrations by covariate level are listed in Table 1. Urinary cadmium con- centrations were generally higher among non-Hispanic black and Mexican-American children than among white children, and higher among children from more impov- erished households or households where 759