Exposure to Phthalate Mixtures and Inner-City Pediatric Allergic Disease and Airway Inflammation

Phthalate plasticizers are found in consumer products and home furnishing materials. Phthalate urinary metabolites are detected in nearly every sample in population-based studies indicating widespread exposure. Prior epidemiologic studies have associated vinyl flooring, a proxy for phthalate exposure, or house dust concentrations of phthalates with eczema and asthma in children. However, these studies lack adequate exposure measurements, consideration of the early life period, and prospective designs. In light of these gaps in the literature, we designed epidemiologic analyses to address our overarching hypothesis that early life exposure to a mixture of phthalates will have associations with adverse allergic and respiratory health outcomes in children. We tested this hypothesis in five self-contained manuscripts that characterize sources of exposure to phthalates in early life, demonstrate the application of new statistical methods for estimating effects of these highly correlated biomarkers, and test the association between early life exposure to phthalates and eczema and airway inflammation in children. Participants were enrolled from the longitudinal birth cohort of the Columbia Center for Children's Environmental Health (CCCEH) in New York City. Phthalate metabolites were measured in prenatal and child urine samples at the Centers for Disease Control and Prevention. Questionnaires and visual inspections were combined with phthalate measurements from personal and indoor air sampling and urinary metabolite concentrations to examine sources and patterns of phthalate exposure associated with personal care product use and flooring materials in the home. The use of perfume and personal care products was associated with higher exposure to the metabolite of diethyl phthalate (DEP) but not di-n-butyl phthalate (DnBP). Vinyl flooring in the home was associated with higher indoor air and urinary metabolite concentrations for butylbenzyl phthalate (BBzP) but not di(2-ethylhexyl) phthalate (DEHP). Because some phthalates share exposure sources and have multiple metabolites, the urinary biomarker concentrations can be highly correlated. Using a reanalysis of the association between prenatal phthalate metabolites and reduced gestational age, we demonstrate that simple Bayesian models can estimate effects for highly correlated exposure measures without the instability of conventional modeling approaches. We found that prenatal concentrations of the metabolite of butylbenzyl phthalate were associated with the report of early-eczema but not atopy among children in the cohort. In a cross-sectional analysis, children's urinary concentrations of metabolites of diethyl phthalate and butylbenzyl phthalate were both associated with higher fractional exhaled nitric oxide, a marker of airway inflammation. These findings suggest several important sources of exposure to phthalates and demonstrate new methods for highly correlated exposures that have not been widely applied in the environmental health sciences. The association of biomarkers of exposure to butylbenzyl phthalate and eczema extend the findings of previous studies. Our results include the first report of an association between phthalates and airway inflammation in children

DSCOVR-EPIC MAIAC AOD - A Proxy for Understanding Aerosol Diurnal Patterns from Space

The Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. The Earth Polychromatic Imaging Camera (EPIC) onboard DSCOVR views the entire sunlit Earth from sunrise to sunset, every 1-2 hours, at scattering angles between 168.5 and 175.5 with 10 narrowband filters in the range of 317-779 nm. NASA Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm, originally developed for MODIS, has been applied to EPIC data with an Aerosol Optical Depth (AOD) product at 440nm with a 10km spatial resolution. This high temporal resolution product is a unique dataset for investigating diurnal patterns in aerosols from space. Our work analyzed the capability of the satellite-borne data to capture the aerosol diurnal variation by associating it with AERONET AOD at 440nm data over the contiguous US. We validated the DSCOVR MAIAC AOD data over 100 AERONET stations during 2015-2018, and examined the contribution of the surface reflectance and relevant acquisition angles, derived by the MAIAC algorithm, to the predicted error. We used over 180,000 hourly DSCOVR-EPIC MAIAC AOD observations with collocated with AERONET AOD observations averaged over +-30 minutes from the satellite overpass time. The AERONET and DSCOVR AOD temporal patterns show that the diurnal variation is different across US AERONET sites, with higher diurnal variation in the DSCOVR dataset in general

An EAACI “European Survey on Adverse Systemic Reactions in Allergen Immunotherapy (EASSI)”: the methodology:the methodology

At present, there is no European report on clinically relevant systemic reactions due to the regular use of allergen immunotherapy (AIT), administered either subcutaneously or sublingually (SCIT and SLIT, respectively) outside clinical trials. Using an electronic survey and a “harmonised terminology” according to MedDRA, we aimed to prospectively collect systemic adverse reactions due to AIT from real life clinical settings. Under the framework of the EAACI, a team of European specialists in AIT, pharmacovigilance, epidemiology and drugs regulation set up a web-based prospective pilot survey to be conducted in three European countries (France, Germany and Spain). A designated “national coordinator” was responsible for following ethics requirements relative to each country and to select at least 30 doctors per country. Patients were recruited the same day they received their first dose of either SCIT or SLIT. Patient inclusion criteria were: adults and children, with IgE mediated pollen, house dust mite, Alternaria, and/or animal dander respiratory allergies who will initiate AIT. A list of 31 symptoms terms were extracted from the MedDRA (Medical Dictionary for Regulatory Activities) dictionary to harmonize the reporting of all adverse systemic reactions in this survey. The SurveyMonkey® online instrument was used by participant doctors to submit information directly to a blinded central database. Three questionnaires were generated: i) the Doctor Questionnaire, ii) the Patient Questionnaire and iii) the Adverse Reaction Questionnaire. A handbook and a mistake report form were given to each doctor. In this paper, we describe the methodology followed

Estimation of hourly near surface air temperature across Israel using an ensemble model

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies

Estimation of hourly near surface air temperature across Israel using an ensemble model

Mapping of near-surface air temperature (Ta) at high spatio-temporal resolution is essential for unbiased assessment of human health exposure to temperature extremes, not least given the observed trend of urbanization and global climate change. Data constraints have led previous studies to focus merely on daily Ta metrics, rather than hourly ones, making them insufficient for intra-day assessment of health exposure. In this study, we present a three-stage machine learning-based ensemble model to estimate hourly Ta at a high spatial resolution of 1 × 1 km2, incorporating remotely sensed surface skin temperature (Ts) from geostationary satellites, reanalysis synoptic variables, and observations from weather stations, as well as auxiliary geospatial variables, which account for spatio-temporal variability of Ta. The Stage 1 model gap-fills hourly Ts at 4 × 4 km2 from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), which are subsequently fed into the Stage 2 model to estimate hourly Ta at the same spatio-temporal resolution. The Stage 3 model downscales the residuals between estimated and measured Ta to a grid of 1 × 1 km2, taking into account additionally the monthly diurnal pattern of Ts derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In each stage, the ensemble model synergizes estimates from the constituent base learners—random forest (RF) and extreme gradient boosting (XGBoost)—by applying a geographically weighted generalized additive model (GAM), which allows the weights of results from individual models to vary over space and time. Demonstrated for Israel for the period 2004–2017, the proposed ensemble model outperformed each of the two base learners. It also attained excellent five-fold cross-validated performance, with overall root mean square error (RMSE) of 0.8 and 0.9 °C, mean absolute error (MAE) of 0.6 and 0.7 °C, and R2 of 0.95 and 0.98 in Stage 1 and Stage 2, respectively. The Stage 3 model for downscaling Ta residuals to 1 km MODIS grids achieved overall RMSE of 0.3 °C, MAE of 0.5 °C, and R2 of 0.63. The generated hourly 1 × 1 km2 Ta thus serves as a foundation for monitoring and assessing human health exposure to temperature extremes at a larger geographical scale, helping to further minimize exposure misclassification in epidemiological studies

Maternal iron metabolism gene variants modify umbilical cord blood lead levels by gene-environment interaction: a birth cohort study

Background: Given the relationship between iron metabolism and lead toxicokinetics, we hypothesized that polymorphisms in iron metabolism genes might modify maternal-fetal lead transfer. The objective of this study was to determine whether maternal and/or infant transferrin (TF) and hemochromatosis (HFE) gene missense variants modify the association between maternal blood lead (MBL) and umbilical cord blood lead (UCBL). Methods: We studied 476 mother-infant pairs whose archived blood specimens were genotyped for TF P570S, HFE H63D and HFE C282Y. MBL and UCBL were collected within 12 hours of delivery. Linear regression models were used to examine the association between log-transformed MBL and UCBL, examine for confounding and collinearity, and explore gene-environment interactions. Results: The geometric mean MBL was 0.61 μg/dL (range 0.03, 3.2) and UCBL 0.42 (<0.02, 3.9). Gene variants were common with carrier frequencies ranging from 12-31%; all were in Hardy-Weinberg equilibrium. In an adjusted linear regression model, log MBL was associated with log UCBL (β = 0.92, 95% CI: 0.82, 1.03; p < 0.01) such that a 1% increase in MBL was associated with a 0.92% increase in UCBL among infants born to wild-type mothers. In infants born to C282Y variants, however, a 1% increase in MBL is predicted to increase UCBL 0.65% (βMain Effect = −0.002, 95% CI: −0.09, −0.09; p = 0.97; βInteraction = −0.27, 95% CI: −0.52, −0.01; p = 0.04), representing a 35% lower placental lead transfer among women with MBL 5 μg/dL. Conclusions: Maternal HFE C282Y gene variant status is associated with greater reductions in placental transfer of lead as MBL increases. The inclusion of gene-environment interaction in risk assessment models may improve efforts to safeguard vulnerable populations. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-77) contains supplementary material, which is available to authorized users

Perinatal Air Pollutant Exposures and Autism Spectrum Disorder in the Children of Nurses’ Health Study II Participants

Objective: Air pollution contains many toxicants known to affect neurological function and to have effects on the fetus in utero. Recent studies have reported associations between perinatal exposure to air pollutants and autism spectrum disorder (ASD) in children. We tested the hypothesis that perinatal exposure to air pollutants is associated with ASD, focusing on pollutants associated with ASD in prior studies. Methods: We estimated associations between U.S. Environmental Protection Agency–modeled levels of hazardous air pollutants at the time and place of birth and ASD in the children of participants in the Nurses’ Health Study II (325 cases, 22,101 controls). Our analyses focused on pollutants associated with ASD in prior research. We accounted for possible confounding and ascertainment bias by adjusting for family-level socioeconomic status (maternal grandparents’ education) and census tract–level socioeconomic measures (e.g., tract median income and percent college educated), as well as maternal age at birth and year of birth. We also examined possible differences in the relationship between ASD and pollutant exposures by child’s sex. Results: Perinatal exposures to the highest versus lowest quintile of diesel, lead, manganese, mercury, methylene chloride, and an overall measure of metals were significantly associated with ASD, with odds ratios ranging from 1.5 (for overall metals measure) to 2.0 (for diesel and mercury). In addition, linear trends were positive and statistically significant for these exposures (p < .05 for each). For most pollutants, associations were stronger for boys (279 cases) than for girls (46 cases) and significantly different according to sex. Conclusions: Perinatal exposure to air pollutants may increase risk for ASD. Additionally, future studies should consider sex-specific biological pathways connecting perinatal exposure to pollutants with ASD

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region

Gestational Exposure to Endocrine-Disrupting Chemicals and Reciprocal Social, Repetitive, and Stereotypic Behaviors in 4- and 5-Year-Old Children: The HOME Study

Background: Endocrine-disrupting chemicals (EDCs) may be involved in the etiology of autism spectrum disorders, but identifying relevant chemicals within mixtures of EDCs is difficult. Objective: Our goal was to identify gestational EDC exposures associated with autistic behaviors. Methods: We measured the concentrations of 8 phthalate metabolites, bisphenol A, 25 polychlorinated biphenyls (PCBs), 6 organochlorine pesticides, 8 brominated flame retardants, and 4 perfluoroalkyl substances in blood or urine samples from 175 pregnant women in the HOME (Health Outcomes and Measures of the Environment) Study (Cincinnati, OH). When children were 4 and 5 years old, mothers completed the Social Responsiveness Scale (SRS), a measure of autistic behaviors. We examined confounder-adjusted associations between 52 EDCs and SRS scores using a two-stage hierarchical analysis to account for repeated measures and confounding by correlated EDCs. Results: Most of the EDCs were associated with negligible absolute differences in SRS scores (≤ 1.5). Each 2-SD increase in serum concentrations of polybrominated diphenyl ether-28 (PBDE-28) (β = 2.5; 95% CI: –0.6, 5.6) or trans-nonachlor (β = 4.1; 95% CI: 0.8–7.3) was associated with more autistic behaviors. In contrast, fewer autistic behaviors were observed among children born to women with detectable versus nondetectable concentrations of PCB-178 (β = –3.0; 95% CI: –6.3, 0.2), β-hexachlorocyclohexane (β = –3.3; 95% CI: –6.1, –0.5), or PBDE-85 (β = –3.2; 95% CI: –5.9, –0.5). Increasing perfluorooctanoate (PFOA) concentrations were also associated with fewer autistic behaviors (β = –2.0; 95% CI: –4.4, 0.4). Conclusions: Some EDCs were associated with autistic behaviors in this cohort, but our modest sample size precludes us from dismissing chemicals with null associations. PFOA, β-hexachlorocyclohexane, PCB-178, PBDE-28, PBDE-85, and trans-nonachlor deserve additional scrutiny as factors that may be associated with childhood autistic behaviors. Citation: Braun JM, Kalkbrenner AE, Just AC, Yolton K, Calafat AM, Sjödin A, Hauser R, Webster GM, Chen A, Lanphear BP. 2014. Gestational exposure to endocrine-disrupting chemicals and reciprocal social, repetitive, and stereotypic behaviors in 4- and 5-year-old children: the HOME Study. Environ Health Perspect 122:513–520; http://dx.doi.org/10.1289/ehp.130726