2012 Progress Report: Spatial temporal analysis of health effects associated with sources and speciation of fine PM

EPA Grant Number: R833863
Title: Spatial temporal analysis of health effects associated with sources and speciation of fine PM
Investigators: Fuentes, Montserrat , Bell, Michelle L. , Dominici, Francesca , Frey, H. Christopher , Reich, Brian , Zhang, Yang
Institution: North Carolina State University , The Johns Hopkins University , Yale University
EPA Project Officer: Ilacqua, Vito
Project Period: December 1, 2008 through November 30, 2012 (Extended to November 30, 2013)
Project Period Covered by this Report: December 1, 2011 through November 30,2012
Project Amount: $893,439
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text |  Recipients Lists
Research Category: Health Effects , Particulate Matter , Air

Objective:

The overall objectives of this proposed nationwide spatiotemporal analysis are to investigate the adverse health outcomes associated with population exposure to fine particulate matter (PM2.5) and speciation and to characterize geographic differences, sources, and population heterogeneity in the putatively PM2.5 mediated health effects, combining different sources of data with atmospheric models.

We aim to answer the following research questions:

What is the recommended framework to integrate atmospheric models with monitoring data and other sources of information to obtain a better spatial and temporal characterization of fine PM components and sources? Can we improve the PM component-based epidemiologic studies by using atmospheric and exposure models? How do we integrate the atmospheric models in this epidemiologic framework, while characterizing uncertainties in the epidemiological and numerical models? How do we use source apportionment approaches in national epidemiologic studies, while characterizing different sources of uncertainty in the models and the data?

Progress Summary:

During the one-year no-cost extension provided to the PI (Fuentes), we have accomplished the final statistical objectives and dissemination of our work that were critical to achieve our final goals.  Our major findings and their significances are summarized below.

A crucial step in an epidemiological study of the effects of air pollution is to accurately quantify exposure of the population. The PI as a final step in this STAR project investigated the sensitivity of the health effects estimates associated with short-term exposure to fine particulate matter with respect to three potential metrics for daily exposure: ambient monitor data, estimated values from a deterministic atmospheric chemistry model, and stochastic daily average human exposure simulation output. Each of these metrics has strengths and weaknesses when estimating the association between daily changes in ambient exposure to fine particulate matter and daily emergency hospital admissions. Monitor data are readily available, but is incomplete over space and time. The atmospheric chemistry model output is spatially and temporally complete, but may be less accurate than monitor data. The stochastic human exposure estimates account for human activity patterns and variability in pollutant concentration across microenvironments, but requires extensive input information and computation time. To compare these metrics, we considered a case study of the association between fine particulate matter and emergency hospital admissions for respiratory cases for the Medicare population across three counties in New York. Of particular interest is to quantify the impact and/or benefit to using the stochastic human exposure output to measure health exposure to fine particulate matter. Our results indicate that the stochastic human exposure simulation output indicates approximately the same increase in relative risk associated with emergency admissions as using a chemistry model or monitoring data as exposure metrics. However, the stochastic human exposure simulation output and the atmospheric chemistry model both bring additional information that helps to reduce the uncertainly in our estimated risk.

We proposed a framework to introduce in an epidemiological model that studies the impact of fine particular matter on pregnancy outcomes, exposure metrics obtained by a developed statistical fused approach that combines deterministic chemistry models and data, while characterizing different sources of uncertainty in models and data in our risk assessment. We compared the output from the deterministic chemistry model (CMAQ) and from a spatial-temporal downscaler statistical model that combines information from AQS and CMAQ (DS), and the impact on risk assessment. Using each metric, we analyze ambient ozone and PM effects on low birth weight utilizing a Bayesian temporal probit regression model. Weekly windows of susceptibility are identified and analyzed jointly for all births in a subdomain of Texas, 2001–2004, and results from the different pollution metrics are compared. Increased exposures during weeks 20–23 of the pregnancy are identified as being associated with low birth weight by the DS metric. Use of the CMAQ output alone results in increased variability of the final risk assessment estimates, while calibrating the CMAQ through use of the DS metric provides results more closely resembling those of the AQS.

We introduced a Bayesian spatial–temporal hierarchical multivariate probit regression model that identifies weeks during the first trimester of pregnancy, which are impactful in terms of cardiac congenital anomaly development. The model is able to consider multiple pollutants and a multivariate cardiac anomaly grouping outcome jointly while allowing the critical windows to vary in a continuous manner across time and space. We utilize a dataset of numerical chemical model output that contains information regarding multiple species of PM2.5 . Our introduction of an innovative spatial-temporal semi-parametric prior distribution for the pollution risk effects allows for greater flexibility to identify critical weeks during pregnancy, which are missed when more standard models are applied. The multivariate kernel stick-breaking prior is extended to include space and time simultaneously in both the locations and the masses in order to accommodate complex data settings. Simulation study results suggest that our prior distribution has the flexibility to out perform competitor models in a number of data settings. When applied to the geo-coded Texas birth data, weeks 3, 7 and 8 of the pregnancy are identified as being impactful in terms of cardiac defect development for multiple pollutants across the spatial domain.

Future Activities:

This is the last year of the grant but we will continue to disseminate our research results at national/international conferences/workshops and prepare manuscripts for publications in peer-reviewed journals.


Journal Articles on this Report : 12 Displayed | Download in RIS Format

Other project views: All 90 publications 49 publications in selected types All 49 journal articles
Type Citation Project Document Sources
Journal Article Banerjee S, Fuentes M. Bayesian modeling for large spatial datasets. WIREs Computational Statistics 2012;4(1):59-66. R833863 (2011)
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  • Journal Article Bravo MA, Fuentes M, Zhang Y, Burr MJ, Bell ML. Comparison of exposure estimation methods for air pollutants: ambient monitoring data and regional air quality simulation. Environmental Research 2012;116:1-10. R833863 (2011)
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  • Journal Article Chang HH, Fuentes M, Frey HC. Time series analysis of personal exposure to ambient air pollution and mortality using an exposure simulator. Journal of Exposure Science and Environmental Epidemiology 2012;22(5):483-488. R833863 (2011)
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  • Journal Article Fuentes M, Henry J, Reich B. Nonparametric spatial models for extremes: application to extreme temperature data. Extremes 2013;16(1):75-101. R833863 (2011)
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  • Journal Article Mannshardt E, Sucic K, Jiao W, Dominici F, Frey HC, Reich B, Fuentes M. Comparing exposure metrics for the effects of fine particulate matter on emergency hospital admissions. Journal of Exposure Science and Environmental Epidemiology 2013;23(6):627-636. R833863 (2012)
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  • Journal Article Modlin D, Fuentes M, Reich B. Circular conditional autoregressive modeling of vector fields. Environmetrics 2012;23(1):46-53. R833863 (2011)
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  • Journal Article Reich BJ, Kalendra E, Storlie CB, Bondell HD, Fuentes M. Variable selection for high dimensional Bayesian density estimation: application to human exposure simulation. Journal of the Royal Statistical Society:Series C–Applied Statistics 2012;61(1):47-66. R833863 (2011)
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  • Journal Article Reich BJ, Fuentes M. Nonparametric Bayesian models for a spatial covariance. Statistical Methodology 2012;9(1-2):265-274. R833863 (2011)
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  • Journal Article Warren JL, Fuentes M, Herring AH, Langlois PH. Air pollution metric analysis while determining susceptible periods of pregnancy for low birth weight. ISRN Obstetrics and Gynecology 2013;2013:387452. R833863 (2012)
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  • Journal Article Warren J, Fuentes M, Herring A, Langlois P. Bayesian spatial-temporal model for cardiac congenital anomalies and ambient air pollution risk assessment. Environmetrics 2012;23(8):673-684. R833863 (2012)
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  • Journal Article Warren J, Fuentes M, Herring A, Langlois P. Spatial-temporal modeling of the association between air pollution exposure and preterm birth: identifying critical windows of exposure. Biometrics 2012;68(4):1157-1167. R833863 (2011)
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  • Journal Article Zhou J, Chang HH, Fuentes M. Estimating the health impact of climate change with calibrated climate model output. Journal of Agricultural, Biological, and Environmental Statistics 2012;17(3):377-394. R833863 (2012)
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  • Supplemental Keywords:

    Bayesian inference, epidemiology, public health data, particulate matter, pollution exposure, risk assessment, statistical modelling

    Relevant Websites:

     http://www4.stat.ncsu.edu/~fuentes Exit

    Progress and Final Reports:

    Original Abstract
  • 2009 Progress Report
  • 2010 Progress Report
  • 2011 Progress Report
  • Final Report