2008 Progress Report: Statistical Models for Estimating the Health Impact of Air Quality Regulations

EPA Grant Number: R833622
Title: Statistical Models for Estimating the Health Impact of Air Quality Regulations
Investigators: Dominici, Francesca , Peng, Roger D. , White, Ronald H. , Zeger, Scott L.
Current Investigators: Dominici, Francesca , Samet, Jonathan M. , White, Ronald H.
Institution: The Johns Hopkins University
Current Institution: Harvard T.H. Chan School of Public Health
EPA Project Officer: Nolt-Helms, Cynthia
Project Period: July 1, 2007 through September 30, 2010 (Extended to September 30, 2011)
Project Period Covered by this Report: October 1, 2007 through September 30,2008
Project Amount: $500,000
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Health Effects , Health

Objective:

In this project, we will develop statistical models for estimating consequences of regulation for environmental health outcome indicators, such as the number of premature deaths or morbidity events prevented by regulation. The specific aims of this research are to:

  • A.1:  Assess the chronology of the implementation of the NAAQS for PM10 and the corresponding attainment and non-attainment status for all the U.S. counties for the period 1987-2006.
  • A.2:  Develop exposure indicators: estimates of the association between regulation and longer-term trends in PM.  Specifically, we propose to develop Bayesian hierarchical models for time series data that use all the available air pollution data to:
o   A.2.1:  estimate longer term national, regional and county-specific trends in PM10 for the period 1987-2006, and in fine PM2.5 and coarse PM (PM10-2.5) for the period 1999-2006.
 
o   A.2.2:  estimate the association between county-specific trends in PM10 and nonattainment status accounting for changes in population demographics, industrial activities, and energy demand.
  • A.3:  Develop outcome indicators: estimates of the associations between longer term exposure to PM and human health on a national scale. More specifically, we plan to develop methods for estimating:
o   A.3.1:  cross-sectional associations between longer-term exposure to PM (PM10, PM2.5, PM10-2.5) on mortality and morbidity accounting for individual-level risk factors and area-level confounders.
 
o   A.3.2:  month-to-month variations in mortality rates and average PM [c1] concentrations over the previous year. These methods will provide evidence as to whether counties having steeper decline in PM levels relative to the PM national trend also will have steeper declines in mortality rates relative to the mortality national trend.
  • A.4:  Develop environmental health outcome indicators: estimates of the number of adverse health events prevented by regulation. Specifically, we plan to develop a Bayesian statistical approach for integrating Aims 1-3. We will estimate the percentage decrease in adverse health events prevented by regulation and quantify its uncertainty. We plan to monitor this environmental health outcome indicator over time on a national scale.

Progress Summary:

Aim 1: Completed

Aim 2: One paper is in progress. See the draft abstract below.
 
Estimating associations between attainment status and long-term trends in particulate matter (PM)
 
Aims: The aim of this study is to estimate the percent decrease in PM10 over time for each U.S. county, geographical region and for the all the United States. An additional aim is to estimate the county-specific posterior probability of being in non-attainment with respect to the PM10 NAAQS.
Materials and Methods: We estimated the annual trend of airborne PM smaller than 10 microns (PM10) for the years 1998-2007, using a two-stage Bayesian hierarchical model for 133 communities in the United States.  At the first stage, we fit linear models including smooth functions of time in each location to control for seasonal trend. We combined the location-specific trend estimates across locations within the 7 U.S. regions to estimate regional average trend. We used the same model to estimate the annual trend for airborne particulate matter smaller than 2.5 microns (PM2.5) and ozone (O3).
 
Aim 3: One paper in progress. See the draft abstract below.
 
Spatial Modeling of Air Pollution and Mortality Time Trends in the United States
 
We are interested in the association between long-term exposure to PM and mortality. In cross-sectional comparisons of mean pollution concentrations and mortality between cities, it is difficult to fully control for all potential confounding factors. We instead compare local trends in PM and mortality, with each location acting as its own control, thus minimizing confounding effects. Our data includes PM time series for 7 years in 814 locations in the United States, as well as individual level data on survival from a location-matched subset of the Medicare cohort. While a survival analysis approach reflects that pollution likely will affect longevity rather than overall mortality rates, the size of the data set with more than 3 million deaths makes a direct implementation of a survival model impractical. We use an equivalent Poisson regression model, adjusting for location-specific hazard functions changing smoothly with age. We model potential spatial correlation in the data using penalized splines. To fit this complex model to the high-dimensional data, we develop a suitable backfitting algorithm.
 
Aim 4: Under development.

Future Activities:

Complete methods development and analyses for Aims 2 and 3 and start methods development for Aim 4.


Journal Articles on this Report : 5 Displayed | Download in RIS Format

Other project views: All 23 publications 23 publications in selected types All 23 journal articles
Type Citation Project Document Sources
Journal Article Dominici F, Peng RD, Zeger SL, White RH, Samet JM. Particulate air pollution and mortality in the United States: did the risks change from 1987 to 2000? American Journal of Epidemiology 2007;166(8):880-888. R833622 (2008)
R833622 (2009)
R830548 (Final)
R832417 (Final)
R832417C001 (2007)
R832417C001 (2008)
R832417C001 (2009)
R832417C001 (Final)
  • Abstract from PubMed
  • Full-text: Oxford Journals-Full Text HTML
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  • Abstract: Oxford Journals-Abstract
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  • Other: Oxford Journals-Full Text PDF
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  • Journal Article Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. Fine particulate matter and mortality: a comparison of the six cities and American Cancer Society cohorts with a Medicare cohort. Epidemiology 2008;19(2):209-216. R833622 (2008)
    R833622 (2009)
    R832417 (2008)
    R832417 (Final)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
  • Abstract from PubMed
  • Full-text: Epidemiology-Full Text HTML
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  • Abstract: Epidemiology-Abstract
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  • Other: PennState-Full Text PDF
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  • Journal Article Janes H, Dominici F, Zeger S. Partitioning evidence of association between air pollution and mortality. Epidemiology 2007;18(4):427-428. R833622 (2008)
    R833622 (2009)
    R833622 (Final)
    R832417 (2008)
    R832417 (Final)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
  • Full-text: Epidemiology-Full Text HTML
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  • Abstract: Epidemiology-Article Preview
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  • Other: Epidemiology-Full Text PDF
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  • Journal Article Peng RD, Chang HH, Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. JAMA-Journal of the American Medical Association 2008;299(18):2172-2179. R833622 (2008)
    R833622 (2009)
    R833622 (Final)
    R832417 (2008)
    R832417 (Final)
    R832417C001 (2008)
    R832417C001 (2009)
    R832417C001 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: JAMA-Full Text HTML
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  • Abstract: JAMA-Abstract
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  • Other: JAMA-Full Text PDF
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  • Journal Article Zeger SL, Dominici F, McDermott A, Samet JM. Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000–2005). Environmental Health Perspectives 2008;116(12):1614-1619. R833622 (2008)
    R833622 (2009)
    R833622 (Final)
    R832417 (2008)
    R832417 (2009)
    R832417 (Final)
    R832417C001 (2009)
    R832417C001 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: EHP-Full Text PDF
  • Abstract: EHP-Abstract & Full Text HTML
  • Relevant Websites:

    http://www.biostat.jhsph.edu/~fdominic Exit

    Progress and Final Reports:

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