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Spatial Exposure Models for Assessing the Relation Between Air Pollution and Childhood Asthma at the Intra-urban ScaleEPA Grant Number: R831845
Title: Spatial Exposure Models for Assessing the Relation Between Air Pollution and Childhood Asthma at the Intra-urban Scale
Investigators: Jerrett, Michael , Gauderman, William , Kuenzli, Nino , Lurmann, Fred , Molitor, John , Thomas, Duncan C. , Winer, Arthur M. , Wu, Jun
Current Investigators: Jerrett, Michael
Institution: University of Southern California , University of California - Los Angeles
Current Institution: University of Southern California
EPA Project Officer: Saint, Chris
Project Period: November 1, 2004 through October 31, 2007
Project Amount: $449,966
RFA: Environmental Statistics Research: Novel Analyses of Human Exposure Related Data (2004) RFA Text | Recipients Lists
Research Category: Environmental Statistics , Health , Health Effects
In developing novel exposure models using existing data, we will test the following hypotheses: (I) different intra-urban spatial exposure models will produce variations in exposure classification within the Children’s Health Study (CHS) subjects, located in 12 communities across southern California; (II) more refined exposure models will have stronger correlations with household and personal exposure, reducing exposure measurement error; and (III) conditional on hypothesis II, more refined exposure models will demonstrate larger health effects for incident asthma. These hypotheses translate into the following research objectives: (1) to derive proximity-based, geostatistical, land use regression, and dispersion models of intra-urban exposure as well as an individual exposure model (IEM) for O3, NO2, NO, and fine particles (PM2.5) for the 12 CHS communities; (2) to assess, with empirical and simulation models, which of the ambient exposure models assigned to subjects in the CHS results in the lowest measurement error when compared to household measurements; (3) to use the modeled ambient and personal exposures to assess air pollution-health associations in CHS data sets for determining whether more refined exposure models produce larger health effects; and (4) to apply resulting exposure and health effects estimates to derive the burden of incident asthma attributable to air pollution in the 12 communities.
We will implement proximity-based, geostatistical, land-use regression, dispersion, and IEM models for the 12 CHS communities with existing data from public agencies and past work on the CHS. The ambient estimates will be cross-validated against and compared to those from field measurements taken at hundreds of houses and schools throughout the study area. Each of the exposures will be assigned to subjects in the CHS and tested for associations with incident asthma through a multilevel Cox model, while controlling for likely confounders. A multi-level Bayesian statistical framework, with Monte Carlo simulation, will be used to assess uncertainties in the estimated health risks. Exposure-response functions from the different models will then be incorporated into a risk assessment model to derive incident asthma cases attributable to air pollution.
This study will provide new estimates of ambient air pollution concentrations for one of the most polluted regions in the U.S. It will also derive potential exposure estimates for a large population of children who live in this region. These estimates will extend a $30 million cohort study on childhood asthma to address important uncertainties in the exposure assignment, and the performance of different spatial exposure models will be evaluated for the first time in the domain of this unparalleled health data set. Additionally, we will use existing air pollution and other relevant data to compare the new estimates of within-community exposure to direct measurements at the homes of study subjects. Through evaluative uncertainty analysis, the marginal benefit of moving from less to more refined exposure models will be assessed, giving guidance to policymakers and researchers on the appropriate methods for future health effects research. Our risk analysis on the burden of incident asthma attributable to air pollution will illustrate the importance of exposure uncertainty to health effects assessment. The within-community risk assessment will emphasize areas of highest risk, potentially leading to more equitable and efficient air pollution policies.