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Exposure indices for the National Children's Study: application to inhalation exposures in Queens County, NY.

ABSTRACT: Characterization of environmental exposures to population subgroups within the National Children's Study (NCS), or other large-scale human environmental health studies is essential for developing a high-quality data platform for subsequent investigations. A computational formulation utilizing the tiered exposure ranking framework is presented for calculating inhalation exposure indices (EIs) for population subgroups. This formulation employs a probabilistic approach and combines information from diverse, publicly available exposure-relevant databases and information on biological mechanisms, for ranking study locations or population subgroups with respect to potential for specific end point-related environmental exposures. These EIs capture and summarize, within a set of numerical values/ranges, complex distributions of potential exposures to multiple airborne contaminants. These estimates capture spatial and demographic variability within each study segment, and allow for the relative comparison of study locations based on different statistical metrics of exposures. The EI formulation was applied to characterize and rank segments within Queens County, NY, which is one of the Vanguard centers for the NCS. Inhalation EI estimates relevant to respiratory outcomes, and potentially to pregnancy outcomes (low birth weight and preterm birth rates) were calculated at the study segment level. Results indicate that there is substantial variability across the study segments in Queens County, NY, and within segments, and showed an exposure gradient across the study segments that can help guide and target environmental and personal exposure sampling efforts in this county. The results also serve as an example application of the EI for use in other exposure and outcome studies.

SUBMITTER: Isukapalli SS

PROVIDER: S-EPMC3961756 | biostudies-literature | 2013 Jan-Feb

REPOSITORIES: biostudies-literature

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Exposure indices for the National Children's Study: application to inhalation exposures in Queens County, NY.

Isukapalli Sastry S SS Brinkerhoff Christopher J CJ Xu Shu S Dellarco Michael M Landrigan Philip J PJ Lioy Paul J PJ Georgopoulos Panos G PG

Journal of exposure science & environmental epidemiology 20121017 1

Characterization of environmental exposures to population subgroups within the National Children's Study (NCS), or other large-scale human environmental health studies is essential for developing a high-quality data platform for subsequent investigations. A computational formulation utilizing the tiered exposure ranking framework is presented for calculating inhalation exposure indices (EIs) for population subgroups. This formulation employs a probabilistic approach and combines information from ...[more]

PMID: 23072768

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Project description:BackgroundSocial vulnerability indices were created to measure resiliency to environmental disasters based on socioeconomic and population characteristics of discrete geographic regions. They are composed of multiple validated constructs that can also potentially identify geographically vulnerable populations after injury. Our objective was to determine if these indices correlate with injury fatality rates in the US.MethodsWe evaluated three social vulnerability indices: The Hazards & Vulnerability Research Institute's Social Vulnerability Index (SoVI), the Center for Disease Control's Social Vulnerability Index (SVI), and the Economic Innovation Group's Distressed Community Index (DCI). We analyzed SVI subindices and common individual census variables as indicators of socioeconomic status. Outcomes included age-adjusted county-level overall, firearm, and motor vehicle collision deaths per 100,000 population. Linear regression determined the association of injury fatality rates with the SoVI, SVI, and DCI. Bivariate choropleth mapping identified geographic variation and spatial autocorrelation of overall fatality, SoVI, and DCI.ResultsA total of 3,137 US counties were included. Only 24.6% of counties fell into the same vulnerability quintile for all three indices. Despite this, all indices were associated with increasing fatality rates for overall, firearm, and motor vehicle collision fatality. The DCI performed best by model fit, explanation of variance, and diagnostic performance on overall injury fatality. There is significant geographic variation in SoVI, DCI, and injury fatality rates at the county level across the United States, with moderate spatial autocorrelation of SoVI (Moran's I, 0.35; p < 0.01) and high autocorrelation of injury fatality rates (Moran's I, 0.77; p < 0.01) and DCI (Moran's I, 0.53; p < 0.01).ConclusionWhile the indices contribute unique information, higher social vulnerability is associated with higher injury fatality across all indices. These indices may be useful in the epidemiologic and geographic assessment of injury-related fatality rates. Further study is warranted to determine if these indices outperform traditional measures of socioeconomic status and related constructs used in trauma research.Level of evidenceEpidemiological, level IV.

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Project description:Suffolk County, New York, is a locus for West Nile virus (WNV) infection in the American northeast that includes the majority of Long Island to the east of New York City. The county has a system of light and gravid traps used for mosquito collection and disease monitoring. In order to identify predictors of WNV incidence in mosquitoes and predict future occurrence of WNV, we have developed a spatiotemporal Bayesian model, beginning with over 40 ecological, meteorological, and built-environment covariates. A mixed-effects model including spatially and temporally correlated errors was fit to WNV surveillance data from 2008 to 2014 using the R package "R-INLA," which allows for Bayesian modeling using the stochastic partial differential equation (SPDE) approach. The integrated nested Laplace approximation (INLA) SPDE allows for simultaneous fitting of a temporal parameter and a spatial covariance, while incorporating a variety of likelihood functions and running in R statistical software on a home computer. We found that land cover classified as open water and woody wetlands had a negative association with WNV incidence in mosquitoes, and the count of septic systems was associated with an increase in WNV. Mean temperature at two-week lag was associated with a strong positive impact, while mean precipitation at no lag and one-week lag was associated with positive and negative impacts on WNV, respectively. Incorporation of spatiotemporal factors resulted in a marked increase in model goodness-of-fit. The predictive power of the model was evaluated on 2015 surveillance results, where the best model achieved a sensitivity of 80.9% and a specificity of 77.0%. The spatial covariate was mapped across the county, identifying a gradient of WNV prevalence increasing from east to west. The Bayesian spatiotemporal model improves upon previous approaches, and we recommend the INLA SPDE methodology as an efficient way to develop robust models from surveillance data to develop and enhance monitoring and control programs. Our study confirms previously found associations between weather conditions and WNV and suggests that wetland cover has a mitigating effect on WNV infection in mosquitoes, while high septic system density is associated with an increase in WNV infection.

Project description:BACKGROUND: A standard short-term inhalation study (STIS) was applied for hazard assessment of 13 metal oxide nanomaterials and micron-scale zinc oxide. METHODS: Rats were exposed to test material aerosols (ranging from 0.5 to 50 mg/m3) for five consecutive days with 14- or 21-day post-exposure observation. Bronchoalveolar lavage fluid (BALF) and histopathological sections of the entire respiratory tract were examined. Pulmonary deposition and clearance and test material translocation into extra-pulmonary organs were assessed. RESULTS: Inhaled nanomaterials were found in the lung, in alveolar macrophages, and in the draining lymph nodes. Polyacrylate-coated silica was also found in the spleen, and both zinc oxides elicited olfactory epithelium necrosis. None of the other nanomaterials was recorded in extra-pulmonary organs. Eight nanomaterials did not elicit pulmonary effects, and their no observed adverse effect concentrations (NOAECs) were at least 10 mg/m3. Five materials (coated nano-TiO2, both ZnO, both CeO2) evoked concentration-dependent transient pulmonary inflammation. Most effects were at least partially reversible during the post-exposure period.Based on the NOAECs that were derived from quantitative parameters, with BALF polymorphonuclear (PMN) neutrophil counts and total protein concentration being most sensitive, or from the severity of histopathological findings, the materials were ranked by increasing toxic potency into 3 grades: lower toxic potency: BaSO4; SiO2.acrylate (by local NOAEC); SiO2.PEG; SiO2.phosphate; SiO2.amino; nano-ZrO2; ZrO2.TODA; ZrO2.acrylate; medium toxic potency: SiO2.naked; higher toxic potency: coated nano-TiO2; nano-CeO2; Al-doped nano-CeO2; micron-scale ZnO; coated nano-ZnO (and SiO2.acrylate by systemic no observed effect concentration (NOEC)). CONCLUSION: The STIS revealed the type of effects of 13 nanomaterials, and micron-scale ZnO, information on their toxic potency, and the location and reversibility of effects. Assessment of lung burden and material translocation provided preliminary biokinetic information. Based upon the study results, the STIS protocol was re-assessed and preliminary suggestions regarding the grouping of nanomaterials for safety assessment were spelled out.

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Exposure indices for the National Children's Study: application to inhalation exposures in Queens County, NY.

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Exposure indices for the National Children's Study: application to inhalation exposures in Queens County, NY.

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