Recruitment and sample

We initially planned to recruit children in grades 3–5 in school districts where prior investigation suggested that the As content of the water supply for some households would be elevated above the EPA Guideline of 10 μg/L [10], and where families used household wells. Recruitment began in 2006–2007, in two New Hampshire (NH) school districts. Examination of well water characteristics for the first 53 recruited NH children revealed very low levels of WAs [on average 2.76 μg/L, with only 5.7% (3 wells) exceeding the US standard of 10 μg/L]. For this reason, in 03/2008, we shifted focus to school districts surrounding Augusta Maine (ME), where colleagues had identified higher exposure [11, 12] . This report considers children attending 11 participating elementary schools in three Regional School Units (RSUs) in ME (Districts A, B, and C).

Recruitment was open to those in grades 3–5 in participating schools. Parents received information distributed by the school, informing them of the study and seeking participation, with notices and response cards sent home in children’s backpacks. Families returning a card indicating interest were given more information by study staff by telephone, and then an appointment was scheduled. We excluded children with conditions with known adverse impact on intellectual functioning (e.g., multiple births, neurodevelopmental disorders) and children who were receiving special education services. These restrictions excluded two children with neurodevelopmental conditions (one a “processing deficit”, and the other “delayed cerebral development impacting motor skills and speech”, both receiving support services at school) that would have made our standard assessment difficult to conduct. Parent report of these conditions was confirmed with school personnel. Exclusion likely biased our findings toward the null. We further limited enrollment to those residing at the present address for three or more years.

From the 1595 children who attended grades 3–5 during the assessment period, 581 families (36%) agreed to participate. One child from each family, selected at random, was eligible for inclusion in analyses. Of the 581, appointments were scheduled with the families of 377 children; appointments were not scheduled with the remainder because of scheduling conflicts, inability to contact the family or change in the family’s interest. Of the children with assessments scheduled, 36 were excluded because they were additional children in the same home; two because they were receiving special education services, and 67 because they had not resided in the home for at least three years. We selected this cutoff to balance minimizing the imprecision of defining exposure using home water As level while maximizing sample size, and because few children were expected to have resided in this home since infancy; indeed, only 61 eligible children had resided in this home since their first year of life. This resulted in a final sample of 272 children in three ME school districts and their families, residing in the present home for an average 7.3 years, which represents more than three-fourths of the children’s lifetimes.

Procedures

Procedures were approved by Columbia University Medical Center and University of New Hampshire Institutional Review Boards. Families received reports summarizing results of well water and developmental testing, information on mitigation procedures (if appropriate) and $25; children received t-shirts.

Home interviews

In home visits, we obtained informed parental consent and child assent, collected water samples and children’s toenail samples, and assessed potential covariates, including mothers’ intelligence, home rearing quality, and socioeconomic status.

Assessment of child intelligence

In the weeks after the home visit, child intelligence was assessed with the WISC-IV in a quiet school setting. Testers were college graduates, experienced in working with children (two social workers and a former teacher), and trained and supervised by the first author (GW).

Water assessments

Water samples were taken at the point of entry into the home (via the connection to the garden hose) and at the consumption point (kitchen sink). For homes using water filters, we took the sample at the post-filtration source. For the point of entry to the home, the home visitor ran the water for 15 minutes, rinsed the collection bottle three times, obtained a 50 ml sample in a polypropylene bottle and sealed the cap. For the point of consumption, the visitor asked how long the family generally let the water run before use, then turned on the faucet and let the water run for that time period, then rinsed and obtained a sample in the same way as for the garden hose. The visitor also inquired about drinking habits, length of residence in the home, well construction and use of filtering procedures.

Laboratory analysis procedures are detailed elsewhere [13, 14]. Water samples were stored at room temperature until shipment to Columbia University for analysis. Following 1% per volume acidification in the laboratory with high-purity hydrochloric acid for at least 48 hours [15], samples were analyzed by high-resolution inductively-coupled plasma mass spectrometry (HR-ICP-MS). The analytical detection limit of the method for As is 0.1 μg/L; the standard deviation of a single measurement is estimated at 4 μg/L for concentrations ≤150 μg/L [14]. Coefficients of Variation (CV’s) of As consistency standards (NIST 1640a and NIST 1643e) for daily runs of HR-ICP-MS data in this study are less than 6% (typically 3-4%) and less than 5% over one year for As concentrations of 10 μg/L and higher.

Toenail collection and assays

Toenail samples from both feet were collected during home visits, cut with standard nail clippers and placed into envelopes, labeled for right and left feet. Nail collection, washing and digestion [16, 17] relied on thorough washing, overnight drying, weighing and digesting in concentrated Ultrex nitric acid. The digested nail samples, diluted to final acid volume of 10%, were analyzed for As using a Perkin-Elmer Elan Dynamic Reaction Cell ICP-MS equipped with a Model AS 93+ autosampler. An ICP-MS- Dynamic Reaction Cell method for metals in nails was developed from a published method [18], with modifications based on suggestions from the Perkin Elmer application laboratory. After calibrating the instrument, and after each batch of 20 samples, we ran quality control nail samples with known As concentrations. During the period when all study samples were analyzed, the intra-precision coefficient of variation for nail As (NAs) in these Quality Control (QC) samples was 2.4%. The inter-precision coefficient of variation for the same QC samples for As was 5.3%. The correlation between NAs concentrations measured from nails on left and right feet was significant [r (247) = 0.86; p < 0.001]. NAs concentrations were subsequently averaged. NAs was available for 248 of 272 participants.

Measures

Child intelligence

The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV: [19]) is an individually administered assessment of intellectual function, for children 6–16 years old. This revised version of the WISC-III [20] has excellent psychometrics, and provides measures of general intellectual ability (FSIQ) and specific cognitive domains (Verbal Comprehension, Perceptual Reasoning, Working Memory, and Processing Speed Indices).

Maternal intelligence was measured on the Wechsler Abbreviated Scale of Intelligence (WASI: [21]), a short and reliable measure of intelligence. It consists of two Performance subtests (Block Design and Matrix Reasoning) and two Verbal (Vocabulary and Similarities) subtests. We examined Vocabulary and Matrix Reasoning subtests.

Additional sociodemographic characteristics were assessed during structured interviews with a parent during home visits. We inquired about the number of children (< 18 years old) residing at home, and maternal and paternal age, ethnicity, and education. Reflecting the well-resourced population studied here, predictive analyses stratified maternal and paternal education into two groups, those with and without post-high school education.

Childrearing characteristics of the home environment were measured by the HOME Inventory [22], a widely used semi-structured assessment that combines interview and direct observation. The HOME consists of eight factor-analytically derived subscales with acceptable psychometrics that assess the childrearing qualities of the home, tapping constructs, on the Middle Childhood version, such as Encouragement of Maturity and Enrichment. Low scores reflect less support for child development. Scores have consistently been linked to child intelligence and achievement [22–24]. The HOME Inventory is intended to be administered at home, with both parent and child available. While all families were seen at home, scheduling interviews when both parent and child were available was a challenge, because of the competing demands of after-school programs, parental employment, and because of distances between homes in this rural setting. When children were not present, interviewers did not assess four observational items on the HOME, of 59 possible items. HOME interviews for which 10% or more items (i.e., 6 or more) were not completed were classified as “missing,” allowing their inclusion in our analyses. For participants with fewer than 6 missing HOME items, we extrapolated HOME scores at the subscale level, and used these values to generate prorated total HOME scores. The median for the distribution of HOME scores for 243 individuals missing fewer than 6 items was 53; those with scores ≥ 53 were designated “High” and those < 53 were designated as “Low” scores.

Exposure mitigation behaviors

During home visits, mothers were interviewed regarding whether or not their household well had been tested for WAs , whether they used a water filtration system, and whether or not they used bottled water as an alternative source.

Our questions about drinking habits were not detailed enough to determine the amount of tap water consumed.

Data analysis

We used linear regression analysis to estimate associations between WAs exposure (from the kitchen tap) and child IQ, with and without adjustment for sociodemographic characteristics (i.e., maternal education and intelligence, number of children in the household, qualities of the HOME environment, school district). Preliminary analyses examined bivariate associations between WAs and covariates, including maternal education, maternal intelligence, HOME scores, well characteristics, arsenic mitigation behavior and school district.

Our analysis plan first considered covariate-unadjusted associations between exposure and child intelligence (Model 1). Next, we determined a set of features to be examined in a covariate-adjusted model (Model 2). From the pool of potential covariates, we considered the contributions of those investigated in our prior work on child intelligence and lead [25, 26] and arsenic [1, 2] exposure, including maternal education and intelligence, number of children in the household, and qualities of the HOME environment. Although maternal education and intelligence were expectably correlated [Spearman r (270) = 0.53, p < 0.0001], both were included in Model 2. Because, as we discuss below, school district was related both to exposure and to other contributors to child intelligence, it was considered in our adjusted models, as well.

In order to describe non-linear patterns, we examined associations between child IQ, with WAs presented both as a continuous (log-transformed) measure, as well as stratified into four categories [WAs < 5 μg/L (n = 141); ≥5 μg/L to <10 (n = 46); ≥10 μg/L to <20 (n = 52); and ≥20 μg/L (n = 33)].