2009 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. , Samet, Jonathan M. , Rava, Marta
Current Investigators: Dominici, Francesca , White, Ronald H. , Samet, Jonathan M.
Institution: The Johns Hopkins University
Current Institution: Harvard T.H. Chan School of Public Health
EPA Project Officer: Hahn, Intaek
Project Period: July 1, 2007 through September 30, 2010 (Extended to September 30, 2011)
Project Period Covered by this Report: July 1, 2008 through September 30,2009
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 proposal, 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 proposal are to:

A.1: Assess the chronology of the implementation of the NAAQS for PM10 and the corresponding attainment and nonattainment 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:

  • A.2.1: estimate longer term national, regional and county-specific trends in PM10 for the period 1987-2006 as well as in fine PM2.5 and coarse PM (PM10-2.5) for the period 1999-2006.
  • 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:

  • 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.
  • A.3.2: month-to-month variations in mortality rates and average $PM$ 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: Done

Aim 2:

Background: Despite increasingly stringent and cost-demanding national, state and local air quality regulations, adverse health effects associated with ambient exposure to air pollution persist. Accountability research, aimed at evaluating the effects of air quality regulation on health outcome, is increasingly viewed as an essential component of responsible government intervention.

Objectives: In this paper, we focused on assessing the impact of air quality regulations on ambient levels of air pollution.

Methods: We considered two groups of counties: the first group, (A), includes counties that in 1991 were designated as in attainment or unclassifiable with respect to the 1987 NAAQS and maintained their status through 2006; the second group, (Ā), includes counties that in 1991 were designated as nonattainment and were subsequently redesignated as in attainment. We hypothesized that if air pollution control programs adopted to meet the NAAQS are effective in reducing air pollution levels, counties in group Ā will experience a sharper decrease in PM10 levels than counties in group A. To provide evidence to support this hypothesis, Bayesian hierarchical models were developed for estimating: (1) the yearly percentage change in ambient PM10 levels for 100 counties and the entire United Staes during the period 1987-2007; and (2) the change in PM10 ambient levels in counties in group Ā compared to counties in group A.

Results: We found statistically significant evidence of variability across counties in trends of PM10 concentrations. We also found strong evidence that counties transitioning from nonattainment to attainment status during the period 1987-2007 experienced a sharper decline in PM10 when compared to counties that were always in attainment.

Aim 3:

There is substantial observational evidence that chronic exposure to particulate air pollution is associated with premature death in urban populations. Estimates of the magnitude of these effects derive largely from cross-sectional comparisons of adjusted mortality rates among cities with varying pollution levels. Such estimates are potentially confounded by other differences among the populations correlated with air pollution, for example, socioeconomic factors. An alternative approach is to study covariation of particulate matter and mortality across time within a city, as has been done in investigations of acute exposures. In either event, observational studies like these are subject to confounding by unmeasured variables. Therefore, the ability to detect such confounding is a high priority.

In this paper, we describe and apply a method of decomposing the exposure variable into components with variation at distinct temporal, spatial and time by space scales, here focusing on the components involving time. Starting from a proportional hazard model, we derive a Poisson regression model and estimate two regression coefficients: the “global” coefficient, corresponding to the temporal scale and measuring the association between national trends in pollution and mortality; and the “local” coefficient, corresponding to the space by time scale and measuring the association between location-specific trends in pollution and mortality adjusted by the national trends. Absent unmeasured confounders and given valid model assumptions, the scale-specific coefficients should be similar; substantial differences in these coefficients constitute a basis for questioning the model.

We derive a backfitting algorithm to fit our model to very large spatio-temporal data sets. We apply our methods to the Medicare Cohort Air Pollution Study (MCAPS), which includes individual-level information on time of death and age on a population of 18.2 million for the period 2000-2006. Results based on the local coefficient indicate no significant change in life expectancy for any reduction in PM2.5. Results based on the global coefficient indicate that a 10 mg/m3 reduction in the yearly national average of PM2.5 is associated with a significant increase in life expectancy of more than 3 years. While the coefficient based on national trends in PM2.5 and mortality is likely to be confounded by other variables trending on the national level, confounding of the local coefficient by unmeasured factors cannot be ruled out. We use additional survey data available for a subset of the data to investigate sensitivity of results to the inclusion of additional covariates, but both coefficients remain largely unchanged.

Aim 4: Under Development

Databases and Sampling

Samples will not be obtained specifically for this study, which will draw on existing data resources. These databases will be obtained in various forms, including tapes obtained from CMS, data downloaded from the website maintained by the Environmental Protection Agency (http://cms.hhs.gov/) and compact disks containing the 2000 Census data. Similarly, the air pollution data have been downloaded by the Johns Hopkins team, with Dr. McDermott having the lead responsibility. The data will be tracked as they are moved from the initial files to location-based analytic files.

Data Management and Storage

Data will be managed via a relational database management system. Although much of these data are publicly available, the health outcome data are protected in disaggregated form under federal legislation. Consequently, data will be stored on a secure server behind a firewall with password access to the system. In addition, each individual health table will be encrypted and secured under a separate password to ensure confidentiality of the data.

Future Activities:

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


Journal Articles on this Report : 7 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 Greven S, Dominici F, Zeger S. An approach to the estimation of chronic air pollution effects using spatio-temporal information. Journal of the American Statistical Association 2011;106(494):396-406. R833622 (2009)
    R833622 (Final)
    R832416 (Final)
    R832417 (Final)
    R834798 (2013)
    R834798 (2014)
    R834798 (Final)
    R834894 (2012)
    R834894 (2013)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: ResearchGate-Abstract & Full Text PDF
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  • Abstract: Taylor & Francis-Abstract
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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 Rava M, White RH, Dominici F. Does attainment status for the PM10 National Air Ambient Quality Standard change the trend in ambient levels of particulate matter? Air Quality, Atmosphere & Health 2011;4(2):133-143. R833622 (2009)
    R833622 (Final)
    R832417 (Final)
    R832417C001 (Final)
  • Abstract: Springer-Abstract
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  • Other: ResearchGate-Abstract
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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
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  • Final Report