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IMPORTANCE Neighborhood disadvantage is an important social determinant of health in childhood and adolescence. Less is known about the association of neighborhood disadvantage with youth neurocognition and brain structure, and particularly whether associations are similar across metropolitan areas and are attributed to local differences in disadvantage.OBJECTIVE To test whether neighborhood disadvantage is associated with youth neurocognitive performance and with global and regional measures of brain structure after adjusting for family socioeconomic status and perceptions of neighborhood characteristics, and to assess whether these associations (1) are pervasive or limited, (2) vary across metropolitan areas, and (3) are attributed to local variation in disadvantage within metropolitan areas. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study, a cohort study conducted at 21 sites across the US. Participants were children aged 9.00 to 10.99 years at enrollment. They and their parent or caregiver completed a baseline visit between October 1, 2016, and October 31, 2018. EXPOSURES Neighborhood disadvantage factor based on US census tract characteristics. MAIN OUTCOMES AND MEASURES Neurocognition was measured with the NIH ToolboxCognition Battery, and T1-weighted magnetic resonance imaging was used to assess whole-brain and regional measures of structure. Linear mixed-effects models examined the association between neighborhood disadvantage and outcomes after adjusting for sociodemographic factors. RESULTSOf the 11 875 children in the ABCD Study cohort, 8598 children (72.4%) were included in this analysis. The study sample had a mean (SD) age of 118.8 (7.4) months and included 4526 boys (52.6%). Every 1-unit increase in the neighborhood disadvantage factor was associated with lower performance on 6 of 7 subtests, such as Flanker Inhibitory Control and Attention (unstandardized Β = −0.5; 95% CI, −0.7 to −0.2; false discovery rate (FDR)-corrected P = .001) and List Sorting Working Memory (unstandardized Β = −0.7; 95% CI, −1.0 to −0.3; FDR-corrected P < .001), as well as on all composite measures of neurocognition, such as the Total Cognition Composite (unstandardized Β = −0.7; 95% CI, −0.9 to −0.5; FDR-corrected P < .001). Each 1-unit increase in neighborhood disadvantage was associated with lower whole-brain cortical surface area (unstandardized Β = −692.6 mm 2 ; 95% CI, −1154.9 to −230.4 mm 2 ; FDR-corrected P = .007) and subcortical volume (unstandardized Β = −113.9 mm 3 ; 95% CI, −198.5 to −29.4 mm 3 ; FDR-corrected P = .03) as well as with regional surface area differences, primarily in the frontal, parietal, and temporal lobes. Associations largely remained after adjusting for perceptions of neighborhood safety and were both consistent across metropolitan areas and primarily explained by local variation in each area.CONCLUSIONS AND RELEVANCE This study found that, in the US, local variation in neighborhood disadvant...

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Key Points Question Is annual residential exposure to particulate matter 2.5 μm or less in diameter (PM 2.5 ) associated with neuroimaging diffusion markers of white matter microstructure? Findings This cross-sectional study of 7602 children 9 to 10 years of age found evidence of an association between PM 2.5 exposure and hemispheric differences in white matter microstructure. In hemisphere-specific models, adjusted for confounding variables, statistically significant positive associations were observed between PM 2.5 and restricted isotropic diffusion, and statistically significant negative associations were observed between PM 2.5 and mean diffusivity. Meaning Findings from this study suggest that exposure to PM 2.5 may be associated with differences in white matter microarchitecture, supporting a need for further improvements in air quality to protect the developing brain.

A wide range of chemicals was measured in different types of drinking water and urine samples through target and non-target screening (NTS) to estimate human exposure. Tap water samples collected from 42 locations in Barcelona (August–October/2020, May/2021), tap water filtered with domestic activated carbon filters (AC, N = 6) and reverse osmosis (RO, N = 5), commercial bottled water (N = 10), and urine (N = 39) samples were included. 35 per- and polyfluoroalkyl substances (PFAS), bisphenol A, and nonylphenol were analyzed using LC–MS/MS and GC–MS/MS, and NTS using LC–HRMS. 9 PFAS were detected in unfiltered tap water of first sampling (79% samples, median = 30 ng/L), 6 in the second (69%, median = 9.8 ng/L), and 5 in 13% urine samples. NTS tentatively identified pharmaceuticals and other industrial chemicals in drinking water. PFAS were removed by RO and not by AC filters. Findings provide valuable information for exposure science and water quality monitoring of emerging drinking water contaminants.

Background Knowledge about human exposure and health effects associated with non-routinely monitored disinfection by-products (DBPs) in drinking water is sparse. Objective To provide insights to estimate exposure to regulated and non-regulated DBPs in drinking water. Methods We collected tap water from homes (N = 42), bottled water (N = 10), filtered tap water with domestic activated carbon jars (N = 6) and reverse osmosis (N = 5), and urine (N = 39) samples of participants from Barcelona, Spain. We analyzed 11 haloacetic acids (HAAs), 4 trihalomethanes (THMs), 4 haloacetonitriles (HANs), 2 haloketones, chlorate, chlorite, and trichloronitromethane in water and HAAs in urine samples. Personal information on water intake and socio-demographics was ascertained in the study population (N = 39) through questionnaires. Statistical models were developed based on THMs as explanatory variables using multivariate linear regression and machine learning techniques to predict non-regulated DBPs. Results Chlorate, THMs, HAAs, and HANs were quantified in 98–100% tap water samples with median concentration of 214, 42, 18, and 3.2 μg/L, respectively. Multivariate linear regression models had similar or higher goodness of fit (R2) compared to machine learning models. Multivariate linear models for dichloro-, trichloro-, and bromodichloroacetic acid, dichloroacetonitrile, bromochloroacetonitrile, dibromoacetonitrile, trichloropropnanone, and chlorite showed good predictive ability (R2 = 0.8–0.9) as 80–90% of total variance could be explained by THM concentrations. Activated carbon filters reduced DBP concentrations to a variable extent (27–80%), and reverse osmosis reduced DBP concentrations ≥98%. Only chlorate was detected in bottled water samples (N = 3), with median = 13.0 µg/L. Creatinine-adjusted trichloroacetic acid was the most frequently detected HAA in urine samples (69.2%), and moderately correlated with estimated drinking water intake (r = 0.48). Significance Findings provide valuable insights for DBP exposure assessment in epidemiological studies. Validation of predictive models in a larger number of samples and replication in different settings is warranted. Impact statement Our study focused on assessing and describing the occurrence of several classes of DBPs in drinking water and developing exposure models of good predictive ability for non-regulated DBPs.

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