2001 Progress Report: Assessing Life-Shortening Associated with Exposure to Particulate MatterEPA Grant Number: R827353C005
Subproject: this is subproject number 005 , 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: Harvard Particle Center
Center Director: Koutrakis, Petros
Title: Assessing Life-Shortening Associated with Exposure to Particulate Matter
Investigators: Schwartz, Joel
Current Investigators: Schwartz, Joel , Bateson, Thomas F , Zanobetti, Antonella , Coull, Brent , O'Neill, M.
Institution: Harvard T.H. Chan School of Public Health , 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, 2001 through May 31, 2002
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
This project is one of four projects under Theme II: Identifying Populations Susceptible to the Health Effects of Particulate Air Pollution. The objectives of this research project are to develop statistical methods for investigating confounding, dose-response relationships, and to examine whether particles advance mortality by a few days (harvesting) or have a more profound impact on public health.
Harvesting. We have examined whether particles advance mortality by a few days (harvesting) or have a more profound impact on public health. As illustrated in two published papers, a smoothing approach was employed to examine the association of particulate matter (PM) over time with daily deaths in Boston, MA (Schwartz, 2000) and Chicago, IL (Schwartz, 2001). Hospital admissions also were examined in the second paper. The main conclusion of our analyses was that particle effects on mortality and morbidity become stronger as average time increases.
We also have developed a new methodology (smoothed distributed lag models) to investigate the relationship between pollution and daily deaths in Milan, Italy (Zanobetti, Wand, et al., 2000). This paper confirmed that far from reduced effects, "harvesting resistant" estimates are higher by a factor of two. More recently, we extended the distributed lag approach to examine the harvesting effect in 10 cities in Europe. The findings of this study do not provide evidence for the harvesting effect (Zanobetti, Wand, et al., 2000).
Dose-Response. To date, particle health effects studies suggest a no threshold dose-response relationship. If there are thresholds for the effects of particles on deaths or hospital admissions then health effects may be overstated. We have developed a new methodology that allows combining smoothed dose-response curves from multiple locations and demonstrated its effectiveness using simulation studies. Subsequently, we applied this method to analyze daily deaths in 10 United States cities. No deviation from linearity down to the lowest exposure concentrations was observed (Schwartz, 2000).
We extended this methodology to incorporate heterogeneity in response across cities by developing a smoothed estimate that allows heterogeneity to vary by exposure level. This new methodology was then applied to eight cities in Spain (Schwartz, Ballester, et al., 2001). We also used this methodology with two-pollutant models and examined the sensitivity of the dose-response curve shape to the way season and weather was controlled. We found a significant linear association between daily deaths and black smoke. This association was little changed by variations in control for weather, season, or SO2. For SO2, the association was implausible (inverted U-shape) and disappeared after controlling for black smoke. Finally, we have used hierarchical models to identify predictors of heterogeneity in nonlinear dose-response curves. This method was applied to examine the dose-response relationship between PM10 and hospital admissions for heart and lung disease.
Co-pollutant Effects. We have investigated the confounding effect of gaseous co-pollutants for both morbidity and mortality. We have developed a hierarchical model to assess confounding, and applied it to examine the association between PM10 and daily deaths (Schwartz, 2000). The results of this analysis suggested that associations were not confounded by gaseous air pollutants.
Timing of the Effect. We have found that the PM10 effects on myocardial infarction deaths occur on the same day, while for other cardiovascular deaths the lag is about a day. For respiratory deaths, 1 and 2 day-lag were observed. These patterns can be explained physiologically and can help to elucidate biological mechanisms (Braga, Zanobetti, et al., 2001) .
Statistical Methods. We have demonstrated that it is possible to control for season and analyze mortality and morbidity using the Case Crossover approach (Bateson and Schwartz, 1999). During the last year, we showed that there could be a selection bias, which can be estimated and corrected (Bateson and Saldiva, 2001). Using this approach, we have re-investigated the association between PM10 and daily deaths in 10 United States cities.
Over the last 6 months, the validity of generalized additive models has been under examination. Our Center has spent a great deal of time addressing this issue. Towards this end, we have reanalyzed our 10 city mortality study, the Six City time series study, the Six City Source Apportionment Study, our hospital admissions studies, and the long- term distributed lag models from the Air Pollution on Health: a European Approach (APHEA) study. In addition, to reanalyzing these data using different convergence criteria and natural splines, we have developed alternative approaches.
We have shown that mixed models could be used to provide smoothed dose response curves against multiple predictors (Coull and Wellenius, 2002). This approach does not have the shortcomings of the GAM in S-plus, because it provides accurate standard errors and does not use back-fitting. We recently have demonstrated that our approach can be used for Poisson data and have applied it to re-examining the association between PM10 and hospital admissions (a manuscript is in preparation).
In the coming year, we will continue to develop and test statistical methods for investigating confounding, dose-response, and other particle health effects relationships.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
|Other subproject views:||All 22 publications||22 publications in selected types||All 22 journal articles|
|Other center views:||All 200 publications||198 publications in selected types||All 197 journal articles|
||Bateson TF, Schwartz J. Selection bias and confounding in case-crossover analyses of environmental time-series data. Epidemiology 2001;12(6):654-661.||
||Braga ALF, Zanobetti A, Schwartz J. The lag structure between particulate air pollution and respiratory and cardiovascular deaths in 10 US cities. Journal of Occupational and Environmental Medicine 2001;43(11):927-933.||
||Schwartz J. Daily deaths are associated with combustion particles rather than SO2 in Philadelphia. Occupational and Environmental Medicine 2000;57(10):692-697.||
Supplemental Keywords:particulate matter, PM, exposure, statistical methods, life-shortening, mortality, death., RFA, Health, Scientific Discipline, Air, Geographic Area, particulate matter, Toxicology, Environmental Chemistry, Epidemiology, State, Risk Assessments, Microbiology, Susceptibility/Sensitive Population/Genetic Susceptibility, Environmental Microbiology, Environmental Monitoring, genetic susceptability, Atmospheric Sciences, Molecular Biology/Genetics, Biology, Environmental Engineering, ambient air quality, interindividual variability, molecular epidemiology, particulates, risk assessment, sensitive populations, chemical exposure, cardiopulmonary responses, health risks, human health effects, indoor exposure, ambient air monitoring, ambient measurement methods, exposure, pulmonary disease, Utah (UT), developmental effects, epidemelogy, respiratory disease, air pollution, children, Human Health Risk Assessment, Massachusetts (MA), particle exposure, lung cancer, biological mechanism , pre-existing conditions, cardiopulmonary response, human exposure, inhalation, pulmonary, Illinois (IL), particulate exposure, ambient particle health effects, mortality studies, elderly, Connecticut (CT), human susceptibility, inhalation toxicology, indoor air quality, inhaled particles, air quality, cardiovascular disease, dosimetry, human health risk, respiratory, genetic susceptibility
Progress and Final Reports:Original Abstract
Main Center Abstract and Reports:R827353 Harvard Particle Center
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