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2007 Progress 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: Johns Hopkins Particulate Matter Research Center
Center Director: Samet, Jonathan M.
Title: Estimation of the Risks to Human Health of PM and PM Components
Investigators: Dominici, Francesca , Bell, Michelle L. , Peng, Roger D. , Samet, Jonathan M. , Zeger, Scott L.
Institution: Johns Hopkins University , Yale University
EPA Project Officer: Stacey Katz/Gail Robarge,
Project Period: October 1, 2005 through September 30, 2010
Project Period Covered by this Report: October 1, 2006 through September 30, 2007
RFA: Particulate Matter Research Centers (2004)
Research Category: Particulate Matter
Description:
Objective:In this project we will develop and apply statistical methods to national data sources to: 1) carry out multi-site time series studies for estimating short-term effects of 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 US population of elderly, the National Medicare Cohort will allow us to take full advantage of all existing and future air quality databases on PM and its characteristics. This project will address 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; 3) carrying out more refined epidemiological studies to estimate further the risks of the more toxic particles to susceptible individuals.
Progress Summary:Progress is described in relation to the original specific aims for this project.
- Multi-site time series studies for estimating short-term effects of PM and PM components on mortality and hospitalization (Phase I)
- Characterize spatial and temporal variability of PM2.5, PM2.5 components, and gaseous pollutants across the US to identify locations for a more in-depth PM characterization for biological studies;
- Develop and apply statistical methods for multi-site 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 USA 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;
- 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
- 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 US counties into four large geographical regions North East (NE), South East (SE), North West (NW), South West (SW). Based on discussions with EPA, we have also replicated these analyses with one additional fifth region, making up the Central US. We anticipate that these four 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 US 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 having a posterior t-statistic above the 75-th percentile, consistently across the health outcomes, is identified as a location with a high risk. A location having a posterior t-statistic below the 25-th percentile, consistently across the outcomes, is identified as a location with a low risk.
- Characterize the PM2.5 chemical composition of the selected locations
- Develop and apply statistical methods for multi-site 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 investigating whether spatial and seasonal variability of the PM2.5 components explain spatio-temporal variability of short-term effects of PM10, (PM10- PM2.5) and PM2.5 on hospitalization and mortality estimated in A.2
- 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)
- Develop statistical methods for cohort studies for estimating associations between longer-term exposure to PM10, PM2.5, PM10, PM2.5, and PM2.5 components and hospitalization and mortality adjusted by individual-level and area-level confounders;
Progress to date:
We have characterized the spatial and temporal variability of PM2.5 components in the U.S. 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-, Si, 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, 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.
Progress to date:
Multi-site time series studies of PM2.5 and hospital admissions
As reported in Dominici et al. (2006) we have conducted a multi-site time series study to estimate risks of cardiovascular and respiratory hospital admissions associated with short-term exposure to PM2.5 for Medicare enrollees and to explore variation of risks across regions. We assembled a national database comprising daily time-series data for the period 1999-2002 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 US urban counties. Daily hospital admission rates were constructed from the Medicare National Claims History Files (NCHF). 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 the health risks of air pollution.
Rationale for Sampling Strategy for Selecting US 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 1999-2002 and for the same 203 US 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 includes 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 includes two respiratory outcomes: chronic obstructive pulmonary disease (COPD) (490-492) and respiratory infections (464-466 and 480-487).
We then
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 assure representativeness across the country.
At the 2006 SAC meeting we were advised to consider seasonal differences in regional particle composition. We are currently evaluating the logistical aspects of visiting some sites more than once to determine if seasonal factors are an important determinant in terms or risk for health effects. The additional visits would involve a subset of sampling equipment and would include the bulk particle sampling system developed as part of Center work. Additionally, we are replicating the site-selection analyses in a database that now includes Medicare data for 2002 – 2005. On the recommendation of the SAC in its July, 2007 meeting, we are limiting the selected sites to those having an STN monitor.
Progress to date:
New methods for adjustment uncertainty
We are developing 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 under-estimate its variance.
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 US 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, while 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 Dominici et al. 2007, we have 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 National Mortality Morbidity Air Pollution Study (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 over the period 1987-2000 and that this decline mostly occurred in the eastern U.S. The methodology presented here can be used to track the health effects of air pollution routinely on regional and national scales.
Progress to date
Diagnosing confounding bias in studies of chronic effects of air pollution on health
In the paper by Janes H 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 approach, we have estimated the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 US 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 mortality relative to their national trends.
We have found that the exposure effect estimates are different at these 2 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.
Fine Particulate Matter and Mortality: A Comparison of the Six Cities and American Cancer Society Cohorts with a Medicare Cohort
In the paper by Eftim et al., 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 SCS and the ACS. Using Medicare data, which lack information on some potential confounding factors, we estimated risks similar to those in the SCS and ACS, 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-level and area-level confounders needs to be addressed.
Journal Articles on this Report: 10 Displayed | Download in RIS Format
| Other subproject views: | All 21 publications | 18 publications in selected types | All 18 journal articles |
| Other center views: | All 21 publications | 18 publications in selected types | All 18 journal articles |
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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. |
R832417C001 (2006) R832417C001 (2007) |
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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. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) |
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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. |
R832417C001 (2007) R832417C001 (2008) |
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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. Journal of the American Medical Association 2006;295(10):1127-1134. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) R830548 (2005) |
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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. |
R832417C001 (2007) R832417C001 (2008) |
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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. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) |
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Peng RD, Dominici F, Louis TA. Model choice in time series studies of air pollution and mortality. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2006;169(2):179-203. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) |
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Peng RD, Dominici F, Zeger SL. Reproducible epidemiological research. American Journal of Epidemiology 2006;163(9):783-789. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) |
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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. |
R832417C001 (2006) R832417C001 (2007) R832417C001 (2008) |
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Thomas DC, Jerrett M, Kuenzli N, Louis TA, Dominici F, Zeger S, Schwarz 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 2007;70(3-4):311-315. |
R832417C001 (2007) R831861 (2005) |
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time series, susceptible populations, risk estimates,
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Air, Scientific Discipline, Health, RFA, PHYSICAL ASPECTS, Risk Assessments, Health Risk Assessment, Physical Processes, Epidemiology, particulate matter, cardiovascular disease, ultrafine particulate matter, aersol particles, human health risk, acute cardiovascular effects, atmospheric particles, ambient particle health effects, airway disease, atmospheric particulate matter, exposure, long term exposure, atmospheric aerosol particles
Progress and Final Reports:
2006 Progress Report
Original Abstract
2008 Progress Report
Main Center Abstract and Reports:
R832417 Johns Hopkins Particulate Matter Research Center
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