2003 Progress Report: Quantifying Exposure Error and its Effect on Epidemiological StudiesEPA Grant Number: R827353C002
Subproject: this is subproject number 002 , established and managed by the Center Director under grant R827353
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: EPA Harvard Center for Ambient Particle Health Effects
Center Director: Koutrakis, Petros
Title: Quantifying Exposure Error and its Effect on Epidemiological Studies
Investigators: Suh, Helen H.
Current Investigators: Suh, Helen H. , Sarnat, Jeremy , Schwartz, Joel , Zanobetti, Antonella
Institution: Harvard University
EPA Project Officer: Chung, Serena
Project Period: June 1, 1999 through May 31, 2005 (Extended to May 31, 2006)
Project Period Covered by this Report: June 1, 2003 through May 31, 2004
Project Amount: Refer to main center abstract for funding details.
RFA: Airborne Particulate Matter (PM) Centers (1999) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air
The objectives of this research project are to: (1) estimate the contribution of particles of outdoor and indoor origin to personal particulate matter (PM2.5) exposures; (2) examine the potential for confounding by gaseous pollutants to affect epidemiological study results; and (3) investigate the ability of particles to penetrate from outdoor to indoor environments. To date, we have published several papers addressing these issues (Chang, et al., 2000; Long, et al., 2000; Sarnat, et al., 2000; Sarnat, et al., 2001; Sarnat, et al., 2002).
This is one of 10 projects funded by the Center. The progress for the other nine projects is reported separately (see reports for R827353C001 and R827353C003 through R827353C011).
We have developed new methods to correct for measurement error in hierarchical models (Schwartz and Coull, 2003). We showed that existing standard two-stage estimators will be biased in the presence of exposure measurement error and that this bias can be away from the null hypothesis of no effect. We proposed two alternative methods for estimating the independent effects of two predictors in a hierarchical model. We applied the new methodology to show that the estimated effect of fine particles on daily deaths, independent of coarse particles, was biased downward by measurement error in an original analysis that did not correct for measurement error. We also used the methods to estimate the effect of gaseous air pollutants on daily deaths. The resulting effect size estimates were very small, and the confidence intervals included zero. Finally, we also have applied this approach to a reanalysis of the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) conducted by Johns Hopkins University. A paper reporting this NMMAPS reanalysis is under review.
Also, using data from multipollutant exposure studies in Boston and Baltimore, simulations were conducted to assess the feasibility of health risks attributed to gases and particles (Schwartz, et al., in preparation, 2004). Results provided evidence that the gaseous pollutants are unlikely confounders of PM health risk estimates for these locations.
Spatial-Temporal Modeling of Exposure
We currently are developing spatial-temporal models of spatially varying exposures, such as traffic pollution, in the Boston area. Given a good model for exposure, this approach yields more accurate measures of spatially heterogeneous exposures than central site monitoring and allows examination of longer averaging times than the limited personal exposures. This approach can decrease the amount of measurement error associated with the central-site measurements, and in turn yield more powerful tests of health effects. A paper describing the methodology and results of this analysis currently is in preparation (Gryparis, et al., in preparation, 2004).
Quantifying Model Uncertainty in Epidemiological Analyses
A criticism of existing PM epidemiologic analyses is the multiple sources of uncertainty involved in obtaining health effect estimates. One key uncertainty is the shape of the concentration-response relation. Another uncertainty is estimating how long one would have to wait after lowering pollution before observing the health improvements. That is, are the associations with 20-year average exposures, which will change slowly, or are they with recent exposures?
We have examined the use of Bayesian model averaging as a way of addressing these two forms of model uncertainty in a reanalysis of the Six Cities Study. This approach avoids relying on effect estimates from a single "final" model, which ignores uncertainty associated with model choice and thus can underestimate the variability associated with these effect estimates. Instead, this approach takes a weighted average of estimates from a range of plausible models. We implemented this approach to average over plausible models for the dose-response relationship of PM as well as the lag structure in the model. Preliminary results suggest that the dose-response curve is approximately linear and the strongest lagged effects occur during the current year (i.e., lag 0) and the immediately preceding year (i.e., lag 1). A paper describing the analysis currently is in preparation (Schwartz, et al., in preparation, 2004).
We will continue to: (1) estimate the contribution of particles of outdoor and indoor origin to personal PM2.5 exposures; (2) examine the potential for confounding by gaseous pollutants to affect epidemiological study results; and (3) investigate the ability of particles to penetrate from outdoor to indoor environments.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other subproject views:||All 3 publications||3 publications in selected types||All 3 journal articles|
|Other center views:||All 200 publications||198 publications in selected types||All 197 journal articles|
||Janssen NAH, Schwartz J, Zanobetti A, Suh HH. Air conditioning and source-specific particles as modifiers of the effect of PM10 on hospital admissions for heart and lung disease. Environmental Health Perspectives 2002;110(1):43-49.||
Supplemental Keywords:exposure, health effects, biology, epidemiology, toxicology, environmental chemistry, monitoring, air pollutants, air pollution, air quality, ambient air, ambient air monitoring, ambient air quality, ambient measurement methods, ambient monitoring, ambient particle health effects, ambient particles, exposure assessment, biological mechanism, biological response, chemical exposure, environmental health hazard, exposure and effects, , health risks, human exposure, human health, human health effects, human health risk, human susceptibility, indoor air quality, indoor exposure, outdoor exposure, inhalation, inhalation toxicology, inhaled particles, measurement methods, particle exposure, particulate exposure, particulates, , PM, PM2.5, exposure modeling, exposure measurement error, uncertainty, Six Cities Study, epidemiological studies., RFA, Health, Scientific Discipline, Air, particulate matter, Toxicology, air toxics, Environmental Chemistry, Epidemiology, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, Environmental Microbiology, genetic susceptability, indoor air, tropospheric ozone, Molecular Biology/Genetics, Biology, ambient air quality, health effects, interindividual variability, molecular epidemiology, monitoring, particulates, risk assessment, sensitive populations, chemical exposure, air pollutants, cardiopulmonary responses, health risks, human health effects, indoor exposure, stratospheric ozone, ambient air monitoring, exposure and effects, ambient air, ambient measurement methods, exposure, pulmonary disease, developmental effects, epidemelogy, respiratory disease, air pollution, ambient monitoring, Human Health Risk Assessment, particle exposure, biological mechanism , cardiopulmonary response, human exposure, inhalation, pulmonary, particulate exposure, ambient particle health effects, mortality studies, inhaled, PM, atmospheric monitoring, human susceptibility, inhalation toxicology, cardiopulmonary, indoor air quality, inhaled particles, human health, measurement methods , quantifying exposure error, air quality, cardiovascular disease, dosimetry, human health risk, metals, respiratory, measurement methods, genetic susceptibility
Progress and Final Reports:Original Abstract
Main Center Abstract and Reports:R827353 EPA Harvard Center for Ambient Particle Health Effects
Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R827353C001 Assessing Human Exposures to Particulate and Gaseous Air Pollutants
R827353C002 Quantifying Exposure Error and its Effect on Epidemiological Studies
R827353C003 St. Louis Bus, Steubenville and Atlanta Studies
R827353C004 Examining Conditions That Predispose Towards Acute Adverse Effects of Particulate Exposures
R827353C005 Assessing Life-Shortening Associated with Exposure to Particulate Matter
R827353C006 Investigating Chronic Effects of Exposure to Particulate Matter
R827353C007 Determining the Effects of Particle Characteristics on Respiratory Health of Children
R827353C008 Differentiating the Roles of Particle Size, Particle Composition, and Gaseous Co-Pollutants on Cardiac Ischemia
R827353C009 Assessing Deposition of Ambient Particles in the Lung
R827353C010 Relating Changes in Blood Viscosity, Other Clotting Parameters, Heart Rate, and Heart Rate Variability to Particulate and Criteria Gas Exposures
R827353C011 Studies of Oxidant Mechanisms
R827353C012 Modeling Relationships Between Mobile Source Particle Emissions and Population Exposures
R827353C013 Toxicological Evaluation of Realistic Emissions of Source Aerosols (TERESA) Study
R827353C014 Identifying the Physical and Chemical Properties of Particulate Matter Responsible for the Observed Adverse Health Effects
R827353C015 Research Coordination Core
R827353C016 Analytical and Facilities Core
R827353C017 Technology Development and Transfer Core