Grantee Research Project Results
Final Report: Estimation of the Risks to Human Health of PM and PM Components
EPA Grant Number: R832417C001Subproject: this is subproject number 001 , established and managed by the Center Director under grant R832417
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Center for the Study of Childhood Asthma in the Urban Environment
Center Director: Hansel, Nadia
Title: Estimation of the Risks to Human Health of PM and PM Components
Investigators: Peng, Roger D. , Bell, Michelle L. , Samet, Jonathan M. , Dominici, Francesca
Institution: The Johns Hopkins University , Yale University
EPA Project Officer: Chung, Serena
Project Period: October 1, 2005 through September 30, 2010
RFA: Particulate Matter Research Centers (2004) RFA Text | Recipients Lists
Research Category: Human Health , Air
Objective:
In this project, we developed and applied statistical methods to national data sources to: (1) carry out multisite time series studies for estimating short-term effects of particulate matter (PM) and PM components on mortality and morbidity (Phase I); (2) carry out cohort studies for estimating long-term effects of PM and PM components in susceptible populations (Phase II); and (3) assess coherence of evidence from bioassays and epidemiological studies on PM toxicity and susceptibility and explore linkages of sources of harmful PM components to human health risks (Phase III).
By providing individual-level health data for the entire U.S. population of elderly, the National Medicare Cohort allowed us to take full advantage of all existing and future air quality databases on PM and its characteristics. This project addressed the following objectives of the Center on a national scale: (1) mapping risks of PM and PM constituents to human health across the United States; (2) using the maps to identify a sampling frame of locations with contrasting higher and lower risks; and (3) carrying out more refined epidemiological studies to estimate further the risks of the more toxic particles to susceptible individuals.
Summary/Accomplishments (Outputs/Outcomes):
Progress is described in relation to the original specific aims for this project.
A. Multi-site time series studies for estimating short-term effects of PM and PM components on mortality and hospitalization (Phase I).
- A1. Characterize spatial and temporal variability of PM2.5, PM2.5 components, and gaseous pollutants across the United States to identify locations for a more in-depth PM characterization for biological studies.
Progress to date:
We have characterized the spatial and temporal variability of PM2.5 components in the United States with the objective of identifying components for assessment in epidemiological studies (Bell, et al., 2007). We constructed a database of 52 PM2.5 component concentrations for 203 U.S. counties for 2000 to 2005 from the EPA’s Speciation Trends Network (STN). First, we described the challenges inherent to analysis of a national PM2.5 chemical composition database. Second, we identified components that substantially contribute to and/or co-vary with PM2.5 total mass. Third, we characterized the seasonal and regional variability of targeted components. We identified substantial seasonal and geographical variation in PM2.5 chemical composition. Only seven of the 52 components contributed 1% or more to total mass for yearly or seasonal averages (NH4+, elemental carbon, organic carbon, NO3-, silicon, NA+, and SO4=). The strongest correlations with PM2.5 total mass were with NH4+ (yearly and each season), organic carbon (winter and autumn), NO3- (winter), and SO4= (yearly, spring, and summer), with particularly high correlations for NH4+ and SO4= in summer. Components that co-varied with PM2.5 total mass, based on daily detrended data, were NH4+, organic carbon, elemental carbon, NO3-, SO4=, and bromine. As a basis for further investigation, we identified a subset of PM2.5 components that should be further investigated to determine whether: (1) their daily variation is associated with daily variation of health indicators; and (2) their seasonal and regional patterns can explain the regional and seasonal heterogeneity in PM10 and PM2.5 health risks. We have developed new statistical models that account for the spatial variability in PM2.5 components when used in community-level time series models of PM2.5 components and health outcomes. The new methodology uses spatial temporal statistical modeling to adjust for the uncertainty in estimating ambient average levels of PM2.5 components in health risk models. In addition to developing new statistical methodology, we also have published a comprehensive characterization of community-level spatial variation in PM2.5 components across the United States. This work identified a number of factors that contribute the spatial variation in PM2.5 components, including distance and season.
- A2. Develop and apply statistical methods for multisite time series studies for estimating short-term effects of PM10, PM2.5, and coarse particles (PM10-PM2.5) on hospitalization and mortality from the National Medicare Cohort over the entire year and by season for the largest 300 counties in the United States with PM data available. Identify locations with the largest and smallest short-term effects of PM on the health indicators (“cold and hot spots”) to identify locations for a more in-depth PM characterization for biological studies.
Progress to date:
Multisite time series studies of PM2.5 and hospital admissions (1999–2005)
As reported in Dominici, et al. (2006), we have conducted a multisite time series study to estimate risks of cardiovascular and respiratory hospital admissions associated with short-term exposure to PM2.5 for Medicare enrollees and explore variation of risks across regions. We assembled a national database comprising daily time-series data for the period 1999–2005 on hospital admission rates for cardiovascular and respiratory diseases and injuries (as a control), ambient PM2.5 levels, and temperature and dew-point for 204 U.S. urban counties. Daily hospital admission rates were constructed from the Medicare National Claims History Files. Our study population included 11.5 million Medicare enrollees living on average 5.9 miles from a PM2.5 monitor. We found that short-term exposure to PM2.5 increases hospital admission risks for cardiovascular and respiratory diseases. By linking Medicare, pollutant, and weather data, we created a national database for continued research that can be updated and analyzed repeatedly to track health risks of air pollution.
Multisite time series studies of PM2.5 and hospital admissions, stratified by season and geographical regions (1999–2005)
As described in Bell, et al., 2008, we investigated whether short-term effects of PM2.5 and risk for cardiovascular and respiratory hospitalizations among the elderly vary by region and season in 202 U.S. counties for 1999–2005. We fit three types of time-series models to provide evidence for: ( 1) consistent PM effects across the year; (2) different PM effects by season; and (3) smoothly varying PM effects throughout the year. We found statistically significant evidence of seasonal and regional variation in PM effect estimates. Respiratory disease effect estimates were highest in winter, with a 1.05% (95% posterior interval [PI] 0.29, 1.82%) increase in hospitalizations per 10 µg/m3 increase in same day PM2.5. Cardiovascular diseases estimates also were highest in winter, with a 1.49% (1.09, 1.89%) increase in hospitalizations per 10 µg/m3 increase in same day PM2.5, with associations also observed in other seasons. The strongest evidence of a relationship between PM2.5 and hospitalizations was in the Northeast for both respiratory and cardiovascular diseases. Heterogeneity of PM2.5 effects on hospitalizations may reflect seasonal and regional differences in emissions and in particles’ chemical constituents. Our results can help guide the development of hypotheses and further epidemiological studies on potential heterogeneity in the toxicity of constituents of the PM mixture.
Multisite time series studies of coarse PM (PM10-2.5) and hospital admissions
As reported in Peng, et al. (2008), we have estimated the risk of hospital admissions for cardiovascular and respiratory diseases associated with exposure to PM10-2.5, controlling for PM2.5. We assembled a database for 108 U.S. counties for the period 1999–2005 with a study population of approximately 12 million Medicare enrollees (age ≥ 65 years) living on average 9 miles from co-located pairs of PM10 and PM2.5 monitors. We found a positive and statistically significant association between same-day concentrations of PM10-2.5 and cardiovascular disease admissions. When adjusted by same-day concentrations of PM2.5, this association was positive but not statistically significant. The associations between PM10-2.5 and respiratory disease admissions, adjusted and unadjusted by PM2.5, were positive but not statistically significant. The effect of PM10-2.5 on cardiovascular disease admissions was statistically significant. The effect of PM10-2.5 on cardiovascular disease admissions was statistically significantly higher in more urban counties compared to less urban counties. We also found continuing evidence of a positive and statistically significant association between same day concentrations of PM2.5 and cardiovascular disease admissions in the most recent Medicare data.
Rationale for sampling strategy for selecting U.S. locations for in-depth PM2.5 characterization and PM collection
Underlying the general approach of the Johns Hopkins PM Center was the proposition that results of epidemiological analyses could be used to identify locations having greater and lesser risk to human health associated with PM exposure. The analyses carried out for the National Morbidity, Mortality and Air Pollution Study (NMMAPS) had shown geographic heterogeneity in the effect of PM on mortality, and PM characteristics are known to vary across the country. In the PM Center application, we proposed to analyze national mortality and Medicare morbidity data to create a sampling frame for selection of locations for Phase II monitoring and PM collection. We recognize that alternative sampling frames could be used (e.g., based on location or source mix); however, for identification of PM characteristics that could contribute to risk, the health risk-based approach provides the most directly relevant sampling frame. We have completed analyses to identify locations for which we have the greatest confidence that they lie at the higher or lower end of the risk distribution.
Analytic Approach
We use time-series data for the period 1999–2005 and for the same 203 U.S. counties included in the study by Dominici, et al. (2006) on PM2.5 and hospital admissions in Medicare enrollees. We focus our analyses on lag 0 PM2.5 concentrations because relative risk estimates are generally larger at this lag. As health outcomes, we use hospital admissions for all cardiovascular disease combined and hospital admissions for all respiratory diseases combined. Specifically, hospital admissions for cardiovascular diseases include five cardiovascular outcomes: heart failure (ICD 9, 428), heart rhythm disturbances (426-427), cerebrovascular events (430-438), ischemic heart disease (410-414, 429), and peripheral vascular disease (440-448). Hospital admissions for respiratory diseases include two respiratory outcomes: chronic obstructive pulmonary disease (COPD) (490-492) and respiratory infections (464-466 and 480-487).
We then:
- Fit Bayesian two-stage normal-normal models to estimate short-term effects of PM2.5 on hospital admissions for cardiovascular and respiratory hospital admissions outcomes;
- Map the Bayesian estimates of the county-specific relative rates and quantify evidence of spatial heterogeneity; and
- Use the maps to identify geographical locations where there are larger and smaller estimates of short-term effects of PM2.5 on hospital admissions. Specifically:
- We group the 203 U.S. counties in five large geographical regions: Northeast, Midwest, Southeast, Northwest, and Southwest. We anticipate that these five large geographical regions have very different mixtures of PM2.5 chemical composition.
- Based on discussions with EPA, we also have replicated these analyses with one additional region, making up the Central United States. We anticipate that these large geographical regions have very different mixtures of PM2.5 chemical composition.
- Within each region, we fit a two-stage Bayesian model to the 203 U.S. counties, and we calculate the posterior t-statistics. A posterior t-statistic is defined as the ratio of the county-specific posterior mean divided by the county-specific posterior standard deviations of the relative risk.
- Within each region and for each outcome, we rank the posterior t-statistics. A location that has a posterior t-statistic above the 75th percentile, consistently across the health outcomes, is identified as a location with a high risk.
- These locations must have at least one STN site (Environmental Science Advisory Committee recommendation on July 9, 2007).
The selected locations are:
- Northwest: King, WA (dropped)
- Northeast: Allegheny County, PA, and Queens County, NY
- Southwest: Sacramento County, CA, and Maricopa County, AZ
- Southeast: Harris County, TX ,and Pinellas County, FL
- Midwest: Jefferson County, KY, and Anoka County, MN
- Characterize the PM2.5 chemical composition of the selected locations.
For each location, we calculated the t statistics for the PM2.5-associated risk for the outcome, along with a categorization of the relative ranking of the estimate. We identified locations that have generally lower estimates across all outcomes, while others have generally higher estimates. Within each of the regions, we identify locations in the strata of lower and higher risk. We plan to use these locations for particle collection and characterization in Project #2 so as to ensure representativeness across the country.
- A3. Develop and apply statistical methods for multisite time series studies that take into account exposure measurement error for: (1) estimating short-term effects of PM2.5 components on hospitalization and mortality from the National Medicare Cohort; and (2) investigating whether spatial and seasonal variability of the PM2.5 components explain spatiotemporal variability of short-term effects of PM10, PM10-2.5, and PM2.5 on hospitalization and mortality estimated in A2.
Progress to date:
Developed new statistical methods for adjustment uncertainty
We have developed new methods to account for “adjustment uncertainty” in time series studies of air pollution and health. Specifically, in the paper by Crainicenau, et al. (2007), we propose a general statistical framework for handling adjustment uncertainty in exposure effect estimation for a large number of confounders, a specific implementation, and associated visualization tools. We also show that when the goal is to estimate an exposure effect accounting for adjustment uncertainty, Bayesian Model Averaging (BMA) can fail to estimate the true exposure effect and over- or underestimate its variance. In the paper by Wang, et al. (2010), we have developed a new method, Bayesian Adjustment for Confounding (BAC). By using BAC, we can estimate an exposure effect while accounting for the confounders, as well as the uncertainty about which of the confounders should be considered.
Bayesian hierarchical distributed lag model for estimating the time course of hospitalization risk associated with particulate matter air pollution
In a paper by Peng, et al. (2007), we developed new methods for estimating the distributed lag function in time series studies of air pollution and health. We have proposed a Bayesian hierarchical distributed lag model that integrates information from national databases with prior knowledge of the time course of hospitalization risk after an air pollution episode. We have applied the model to a database of particulate matter air pollution monitoring and health information on 6.3 million enrollees of the U.S. Medicare system living in 94 counties covering the years 1999–2002. We have obtained estimates of the distributed lag functions relating fine particulate matter pollution to hospitalizations for both ischemic heart disease and acute exacerbation of chronic obstructive pulmonary disease (COPDAE). We found that the effect of an increase in fine particulate matter on ischemic heart disease is immediate, with the bulk of hospitalizations occurring within 2 days of an air pollution increase, whereas for COPDAE, the effect of fine particulate matter appears to be distributed over a week or more.
Estimating trends in the short-term effects of PM10 on mortality
In the paper by Dominic, et al. (2007), we evaluated change in the short-term effect of airborne particles over a period of increasingly stringent regulation that might have changed the chemical composition and toxicity of the airborne particles. We use updated data and methods of the NMMAPS to estimate national average relative rates of the effects of PM10 on all-cause, cardiovascular, and respiratory mortality, and other-cause mortality for the period 1987–2000. We have estimated national average relative rates of the effects of PM2.5 (< 2.5 µm) on all-cause mortality for the period 1999–2000. We found strong evidence that lag 1 exposures to PM10 and PM2.5 continue to be associated with increased mortality. We also found a weak indication that the lag 1 effects of PM10 on mortality declined during the period 1987–2000 and that this decline mostly occurred in the eastern United States. The methodology presented here can be used to track the health effects of air pollution routinely on regional and national scales.
Multisite time series studies of PM2.5 and hospital admissions, stratified by season and geographical regions (1999–2005)
As described in Bell, et al. (2008), we investigated whether short-term effects of PM2.5 on risk for cardiovascular and respiratory hospitalizations among the elderly vary by region and season in 202 U.S. counties for 1999–2005. We fit three types of time-series models to provide evidence for: (1) consistent PM effects across the year; (2) different PM effects by season; and (3) smoothly varying PM effects throughout the year. We found statistically significant evidence of seasonal and regional variation in PM effect estimates. Respiratory disease effect estimates were highest in winter with a 1.05% (95% posterior interval 0.29, 1.82%) increase in hospitalizations per 10 µg/m3 increase in same day PM2.5. Cardiovascular diseases estimates also were highest in winter with a 1.49% (1.09, 1.89%) increase in hospitalizations per 10 µg/m3 increase in same day PM2.5, with associations also observed in other seasons. The strongest evidence of a relationship between PM2.5 and hospitalizations was in the Northeast for both respiratory and cardiovascular diseases. Heterogeneity of PM2.5 effects on hospitalizations may reflect seasonal and regional differences in emissions and in particles’ chemical constituents. Our results can help guide the development of hypotheses and further epidemiological studies on potential heterogeneity in the toxicity of constituents of the PM mixture.
Multisite time series studies of PM2.5 chemical components and hospital admissions (2000–2006)
In Peng, et al. (2009), we estimate the associations between daily levels of PM2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (aged 65 years or older) using Bayesian hierarchical statistical models. We used a national database comprising daily data for 2000–2006 on hospital admission rates for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical components (sulfate, nitrate, silicon, elemental carbon, organic carbon matter, and sodium and ammonium ions), and weather. In multiple-pollutant models, an interquartile range (IQR) increase in elemental carbon was associated with a 0.80 (95% PI, 0.34, 1.27) percent increase in risk of same day cardiovascular admissions, and an IQR increase in organic carbon matter was associated with a 1.01 (95% PI: 0.04, 1.98) percent increase in risk of respiratory admissions on the same day. Ambient levels of elemental carbon and organic carbon matter, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of fine particles.
Bayesian model averaging for clustered data: imputing missing PM data
In Chang, et al. (submitted 2010), we have developed an innovative statistical methodology for imputing missing PM2.5 data when PM10 data from the co-located monitor are available (and vice-versa). We assume that the two daily time series of PM10 and PM2.5 from the pair of co-located monitors form a data cluster. The goal is to best predict the missing PM data at the co-located monitor pair in the presence of multiple competing prediction models. In the presence of multiple competing models, BMA offers a powerful tool to account for model uncertainty and improve prediction. However, the typical application of BMA to clustered data determines model weights (posterior probabilities of the competing models) by comparing how data from all clusters fit each model. In this paper, we develop a BMA approach for clustered data that accounts for differences in the best fitting models among clusters. This is accomplished by allowing the weights of competing regression models to vary between clusters while borrowing information across clusters in estimating model parameters. The work is motivated by the problem of imputing missing observations in clustered data when the performance of the different prediction models varies across clusters. Through simulation and cross-validation studies, we demonstrate that our approach outperforms the standard BMA. Finally, we apply the proposed method to a national dataset of daily ambient particulate matter concentrations between 2003 and 2005. We then estimate the posterior probability of coarse PM nonattainment status for 95 U.S. counties based on EPA’s proposed 24-hour standard.
Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error
In air pollution epidemiology, there is a growing interest in estimating the health effects of coarse PM with aerodynamic diameter between 2.5 and 10 µm. Coarse PM concentrations can exhibit considerable spatial heterogeneity because the particles travel shorter distances and do not remain suspended in the atmosphere for an extended period of time. In Chang, et al. (submitted 2010), we develop a modeling approach for estimating the short-term effects of air pollution in time series analysis when the ambient concentrations vary spatially within the study region. Specifically, our approach quantifies the error in the exposure variable by characterizing, on any given day, the disagreement in ambient concentrations measured across monitoring stations. This is accomplished by viewing monitor-level measurements as error-prone repeated measurements of the unobserved true exposure. Inference is carried out in a Bayesian framework to fully account for uncertainty in the estimation of model parameters. Finally, by using different exposure indicators, we investigate the sensitivity of the association between coarse PM and daily hospital admissions based on a recent national multisite time series analysis. Among Medicare enrollees from 59 U.S. counties during the period 1999–2005, we have found a consistent positive association between coarse PM and same-day admission for cardiovascular diseases.
Spatial misalignment in time series studies of air pollution and health data
Time series studies of environmental exposures often involve comparing daily changes in a toxicant measured at a point in space with daily changes in an aggregate measure of health. Spatial misalignment of the exposure and response variables can bias the estimation of health risk, and the magnitude of this bias depends on the spatial variation of the exposure of interest. In air pollution epidemiology, there is an increasing focus on estimating the health effects of the chemical components of particulate matter. One issue that is raised by this new focus is the spatial misalignment error introduced by the lack of spatial homogeneity in many of the particulate matter components. Current approaches to estimating short-term health risks via time series modeling do not take into account the spatial properties of the chemical components and therefore could result in biased estimation of those risks. In Peng, et al. (2009), we present a spatial-temporal statistical model for quantifying spatial misalignment error and show how adjusted health risk estimates can be obtained using a regression calibration approach and a two-stage Bayesian model. We apply our methods to a database containing information on hospital admissions, air pollution, and weather for 20 large urban counties in the United States.
B. Cohort studies based on the National Medicare Cohort for estimating longer-term effects of PM and PM composition in susceptible populations and for cause-specific health outcomes (Phase II).
- B1. Develop statistical methods for cohort studies for estimating associations between longer-term exposure to PM10, PM2.5, and PM2.5 components and hospitalization and mortality adjusted by individual-level and area-level confounders.
Fine PM and mortality: a comparison of the Harvard Six Cities and American Cancer Society cohorts with a Medicare cohort
In the paper by Eftim, et al. (2008), using Medicare data, we have assessed the association of PM2.5 with mortality for the same locations included in these studies. We have estimated the chronic effects of PM2.5 on mortality for the period 2000–2002 using mortality data for cohorts of Medicare participants and average PM2.5 levels from monitors in the same counties included in the Six Cities Study and the American Cancer Society cohort. Using Medicare data, which lack information on some potential confounding factors, we estimated risks similar to those in the Six Cities Study and the American Cancer Society cohort, which incorporated more extensive information on individual-level confounders. We propose that the Medicare files can be used to construct cohorts for tracking the longer-term risk of air pollution over time.
Methods development for estimation of the long-term effects of PM2.5 on mortality and morbidity outcomes is in progress. The challenge of adequately adjusting for individual- and area-level confounders needs to be addressed.
Mortality in the Medicare population and chronic exposure to fine particulate air pollution in urban centers (2000–2005)
Prospective cohort studies constitute the major source of evidence about the mortality effects of chronic exposure to particulate air pollution. Additional studies are needed to provide evidence on the health effects of chronic exposure to PM2.5, as most previous studies have focused on total suspended particles and PM10. In Zeger et al. (2009), we estimate the relative risk of death in a U.S. population of elderly people associated with long-term exposure to PM2.5 by region and age-groups, for the period 2000–2005. By linking PM2.5 monitoring data to the Medicare billing claims by ZIP code of residence of the enrollees, we have developed a new retrospective cohort study, the Medicare Cohort Air Pollution Cohort Study. The study population comprises 13.2 million participants living in 4,568 ZIP codes having centroids within six miles of a PM2.5 monitor. Relative risks adjusted by socioeconomic status and smoking were estimated by fitting log-linear regression models. In the East and Central regions, a 10 µg/m3 increase in 6-year average of PM2.5 is associated with 6.8% (95% CI: 4.9 to 8.7%) and 13.2% (95% CI: 9.5 to 16.9) increases in mortality, respectively. We did not find evidence of an association in the West and for persons above 85 years of age. We established a cohort of Medicare participants for investigating air pollution and mortality on longer term time frames. Chronic exposure to PM2.5 was associated with mortality in the eastern and central regions, but not in the western United States.
Diagnosing confounding bias in studies of chronic effects of air pollution on health
In the paper by Janes, et al. (2007), we have proposed a new method for diagnosing confounding bias under a model that estimates associations between a spatially and temporally varying exposure and health outcome. Specifically, we have decomposed the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We have shown that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates.
Using this approach, we have estimated the association between monthly averages of PM2.5 during the preceding 12 months and monthly mortality rates in 113 U.S. counties from 2000 to 2002. We have decomposed the association between PM2.5 and mortality into 2 components: (1) the association between “national trends” in PM2.5 and mortality; and (2) the association between “local trends,” defined as county-specific deviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortaility relative to their national trends.
We have found that the exposure effect estimates are different at these two spatiotemporal scales, which raises concerns about confounding bias. We conclude that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.
A spatio-temporal approach for estimating chronic effects of air pollution
In Greven, et al., 2010 (under revision), we propose a new study design and a statistical model, which use spatio-temporal information to estimate the long-term effects of air pollution exposure on life expectancy. Our approach avoids confounding by time-varying covariates and individual or city-level risk factors. By estimating the increase in life expectancy due to decreases in long-term air pollution concentrations, it provides easily interpretable values for public policy purposes. We develop a suitable backfitting algorithm that permits efficient fitting of our model to large spatio-temporal data sets. We evaluate spatio-temporal correlation in the data and obtain appropriate standard errors. We apply our methods to the Medicare Cohort Air Pollution Study, including data on PM2.5 and mortality for 18.2 million Medicare enrollees from 814 locations in the United States during an average of 65 months in 2000–2006.
Progress to date:
Cap and trade legislation: additional benefits from air pollution mitigation
In a commentary in the Journal of the American Medical Association by Barr and Dominici (2010), the authors claimed that substantial human health benefits from cap and trade legislation could potentially come from reductions in ambient levels of harmful pollutants, such as PM and ozone, that share emissions sources with greenhouse gases (GHGs). For example, 94% of CO2 emissions in the United States result from combustion of fossil fuels, with electricity generation and transportation alone composing nearly 70%. These are also the leading source of sulfur dioxide, PM2.5, and precursors to ozone such as mono-nitrogen oxides. While the time scale for potential impacts of cap and trade legislation on climate change and related health benefits is likely decades or centuries, ancillary air pollution mitigation could have immediate health benefits. In two nationwide epidemiological studies, daily levels of ambient ozone and PM2.5 have been linked to increased risk of cardiovascular and respiratory mortality and to increased risk of emergency hospital admissions, especially for heart failure. Estimates of the potential health benefits attributable to reductions in harmful air pollutants resulting from mitigation of GHG emissions, at the city, region and national scales, have been substantial.
Protecting human health from air pollution: shifting from a single pollutant to a multi-pollutant approach
In Dominici, et al. (2010), the authors discusses a multipollutant approach for controlling ambient air pollution that describes multipollutant concepts for different aspects of air quality management and science: (1) scientific estimation of the health risk of multiple pollutants, (2) setting of regulatory standards for multiple pollutants, and (3) simultaneously implementing compliance with regulatory standards for multiple pollutants.
On quantifying the magnitude of confounding
In a paper by Jane et al., we propose a corrected measure of the treatment effect adjusted for confounding that does not depend upon the non linearity effect. More specifically, when estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z, is to compare estimates of the exposure-outcome association with and without adjustment for Z. This approach is widely believed to be problematic due to the nonlinearity of some exposure effect measures. When the expected value of the outcome is modeled as a nonlinear function of the exposure, the adjusted and unadjusted exposure effects can differ even in the absence of confounding (Greenland et al., 1999a); we call this the nonlinearity effect. In this paper, we propose a corrected measure of confounding that does not include the non-linearity effect. The performances of the simple and corrected estimates of confounding are assessed in simulations and illustrated using a study of risk factors for low birth weight infants. We conclude that the simple estimate of confounding is adequate or even preferred in settings where the nonlinearity effect is very small. In settings with a sizable nonlinearity effect, the corrected estimate of confounding has improved performance.
C. Assess coherence of evidence from bioassays and epidemiological studies on PM toxicity and susceptibility and explore linkages of sources of harmful PM components to human health risks (Phase III).
- C1. Building on results from biological and PM characterization studies, use the national databases for estimating short and long-term effects of PM components on those specific clinical endpoints identified in the Biological Assessment project (Project 3).
- C2. Identify emission sources associated with identified PM toxic components.
Develop low-dimensional indicators of the PM source mixture
Investigators for Project 1 have been working with investigators in Project 2 on low-dimensional indicators of the PM mixture in each of eight cities sampled as part of Project 2. We have applied principal components analysis (PCA) to the PM composition data collected in Project 2 to develop “source like” indicators of the overall PM mixture. The PCA produces factor scores that summarize the principal variation in the data and factor loadings that indicate which components contribute the most to the explaining the variation in the data across the eight cities. In Project 3, mice are exposed to the bulk PM collected in Project 2, which has been characterized via the PCA method. The factor scores produced by the PCA will be used as exposure inputs into regression models relating the health responses in the mice with the PM from the eight different cities. Because the number of samples is small, the use of dimension reduction techniques such as PCA is critical so that the number of predictors does not exceed the number of observations.
Journal Articles on this Report : 32 Displayed | Download in RIS Format
Other subproject views: | All 41 publications | 36 publications in selected types | All 34 journal articles |
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Other center views: | All 89 publications | 66 publications in selected types | All 64 journal articles |
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Barr CD, Dominici F. Cap and trade legislation for greenhouse gas emissions: public health benefits from air pollution mitigation. JAMA-Journal of the American Medical Association 2010;303(1):69-70. |
R832417 (Final) R832417C001 (Final) R833622 (Final) |
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Bell ML, Peng RD, Dominici F. The exposure-response curve for ozone and risk of mortality and the adequacy of current ozone regulations. Environmental Health Perspectives 2006;114(4):532-536. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
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Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM. Spatial and temporal variation in PM2.5 chemical composition in the United States for health effects studies. Environmental Health Perspectives 2007;115(7):989-995. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
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Bell ML, Kim JY, Dominici F. Potential confounding of particulate matter on the short-term association between ozone and mortality in multisite time-series studies. Environmental Health Perspectives 2007;115(11):1591-1595. |
R832417 (Final) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
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Bell ML, Dominici F. Effect modification by community characteristics on the short-term effects of ozone exposure and mortality in 98 US communities. American Journal of Epidemiology 2008;167(8):986-997. |
R832417 (Final) R832417C001 (2009) R832417C001 (Final) |
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Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, Zeger SL, Dominici F. Seasonal and regional short-term effects of fine particles on hospital admissions in 202 US counties, 1999-2005. American Journal of Epidemiology 2008;168(11):1301-1310. |
R832417 (2008) R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833622 (Final) |
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Bell ML, Ebisu K, Peng RD, Samet JM, Dominici F. Hospital admissions and chemical composition of fine particle air pollution. American Journal of Respiratory and Critical Care Medicine 2009;179(12):1115-1120. |
R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833863 (2009) R833863 (Final) |
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Bell ML, Ebisu K, Peng RD, Dominici F. Adverse health effects of particulate air pollution: modification by air conditioning. Epidemiology 2009;20(5):682-686. |
R832417 (Final) R832417C001 (Final) R833863 (2009) R833863 (Final) |
Exit Exit |
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Bell ML, Peng RD, Dominici F, Samet JM. Emergency hospital admissions for cardiovascular diseases and ambient levels of carbon monoxide: results for 126 United States urban counties, 1999-2005. Circulation 2009;120(11):949-955. |
R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833863 (2009) R833863 (Final) |
Exit Exit |
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Bell ML, Ebisu K, Peng RD. Community-level spatial heterogeneity of chemical constituent levels of fine particulates and implications for epidemiological research. Journal of Exposure Science & Environmental Epidemiology 2011;21(4):372-384. |
R832417 (Final) R832417C001 (Final) |
Exit Exit |
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Chang HH, Peng RD, Dominici F. Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error. Biostatistics 2011;12(4):637-652. |
R832417 (Final) R832417C001 (Final) R832416 (Final) R833622 (Final) |
Exit Exit Exit |
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Crainiceanu CM, Dominici F, Parmigiani G. Adjustment uncertainty in effect estimation. Biometrika 2008;95(3):635-651. |
R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) |
Exit |
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Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL, Samet JM. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA-Journal of the American Medical Association 2006;295(10):1127-1134. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (2005) |
Exit Exit |
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Dominici F, Peng RD, Ebisu K, Zeger SL, Samet JM, Bell ML. Does the effect of PM10 on mortality depend on PM nickel and vanadium content? A reanalysis of the NMMAPS data. Environmental Health Perspectives 2007;115(12):1701-1703. |
R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
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Dominici F, Peng RD, Zeger SL, White RH, Samet JM. Dominici et al. respond to "Heterogeneity of particulate matter health risks." American Journal of Epidemiology 2007;166(8):892-893. |
R832417 (2008) R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) |
Exit Exit |
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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. |
R832417 (Final) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) R833622 (2008) R833622 (2009) |
Exit Exit Exit |
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Dominici F, Wang C, Crainiceanu C, Parmigiani G. Model selection and health effect estimation in environmental epidemiology. Epidemiology 2008;19(4):558-560. |
R832417 (2008) R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) |
Exit Exit Exit |
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Dominici F, Peng RD, Barr CD, Bell ML. Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach. Epidemiology 2010;21(2):187-194. |
R832417 (Final) R832417C001 (Final) |
Exit |
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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. |
R832417 (2008) R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R833622 (2008) R833622 (2009) |
Exit Exit Exit |
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Janes H, Dominici F, Zeger S. Partitioning evidence of association between air pollution and mortality. Epidemiology 2007;18(4):427-428. |
R832417 (2008) R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R833622 (2008) R833622 (2009) R833622 (Final) |
Exit Exit Exit |
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Janes H, Dominici F, Zeger SL. Trends in air pollution and mortality: an approach to the assessment of unmeasured confounding. Epidemiology 2007;18(4):416-423. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) R833622 (Final) |
Exit Exit Exit |
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Janes H, Dominici F, Zeger S. On quantifying the magnitude of confounding. Biostatistics 2010;11(3):572-582. |
R832417 (Final) R832417C001 (Final) R833622 (Final) |
Exit Exit Exit |
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Peng RD, Dominici F, Zeger SL. Reproducible epidemiological research. American Journal of Epidemiology 2006;163(9):783-789. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
Exit Exit Exit |
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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. |
R832417 (2008) R832417 (Final) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R833622 (2008) R833622 (2009) R833622 (Final) |
Exit Exit Exit |
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Peng RD, Dominici F, Welty LJ. A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution. Journal of the Royal Statistical Society Series C--Applied Statistics 2009;58(1):3-24. |
R832417 (2008) R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833622 (Final) |
Exit Exit |
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Peng RD, Bell ML, Geyh AS, McDermott A, Zeger SL, Samet JM, Dominici F. Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution. Environmental Health Perspectives 2009;117(6):957-963. |
R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833622 (Final) R833863 (2009) R833863 (Final) |
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Peng RD, Bell ML. Spatial misalignment in time series studies of air pollution and health data. Biostatistics 2010;11(4):720-740. |
R832417 (Final) R832417C001 (Final) |
Exit Exit Exit |
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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. |
R832417 (Final) R832417C001 (Final) R833622 (2009) R833622 (Final) |
Exit Exit |
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Symons JM, Wang L, Guallar E, Howell E, Dominici F, Schwab M, Ange BA, Samet J, Ondov J, Harrison D, Geyh A. A case-crossover study of fine particulate matter air pollution and onset of congestive heart failure symptom exacerbation leading to hospitalization. American Journal of Epidemiology 2006;164(5):421-433. |
R832417 (Final) R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) |
Exit Exit Exit |
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Thomas DC, Jerrett M, Kuenzli N, Louis TA, Dominici F, Zeger S, Schwartz J, Burnett RT, Krewski D, Bates D. Bayesian model averaging in time-series studies of air pollution and mortality. Journal of Toxicology and Environmental Health-Part A 2007;70(3-4):311-315. |
R832417 (Final) R832417C001 (2007) R832417C001 (2008) R832417C001 (2009) R832417C001 (Final) R830548 (Final) R831861 (2005) |
Exit |
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Welty LJ, Peng RD, Zeger SL, Dominici F. Bayesian distributed lag models: estimating effects of particulate matter air pollution on daily mortality. Biometrics 2009;65(1):282-291. |
R832417 (2008) R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833622 (Final) |
Exit Exit |
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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. |
R832417 (2008) R832417 (2009) R832417 (Final) R832417C001 (2009) R832417C001 (Final) R833622 (2008) R833622 (2009) R833622 (Final) |
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Supplemental Keywords:
time series, susceptible populations, risk estimates, RFA, Health, PHYSICAL ASPECTS, Scientific Discipline, Air, particulate matter, Health Risk Assessment, Epidemiology, Risk Assessments, Physical Processes, atmospheric particulate matter, acute cardiovascular effects, long term exposure, atmospheric particles, airway disease, exposure, ambient particle health effects, human exposure, ultrafine particulate matter, atmospheric aerosol particles, aersol particles, cardiovascular diseaseRelevant Websites:
http://www.jhsph.edu/particulate_matter ExitProgress and Final Reports:
Original AbstractMain Center Abstract and Reports:
R832417 Center for the Study of Childhood Asthma in the Urban Environment Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R832417C001 Estimation of the Risks to Human Health of PM and PM Components
R832417C002 PM Characterization and Exposure Assessment (Project 2)
R832417C003 Biological Assessment of the Toxicity of PM and PM Components
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- 2009 Progress Report
- 2008 Progress Report
- 2007 Progress Report
- 2006 Progress Report
- Original Abstract
34 journal articles for this subproject
Main Center: R832417
89 publications for this center
64 journal articles for this center