2005 Progress Report: Chronic Exposure to Particulate Matter and Cardiopulmonary DiseaseEPA Grant Number: R830545
Title: Chronic Exposure to Particulate Matter and Cardiopulmonary Disease
Investigators: Laden, Francine , Camargo, Carlos , Speizer, Frank E. , Suh, Helen H.
Current Investigators: Laden, Francine , Camargo, Carlos , Puett, Robin C. , Schwartz, Joel , Speizer, Frank E. , Suh, Helen H. , Yanosky, Jeff D.
Institution: Brigham and Women’s Hospital
Current Institution: Brigham and Women's Hospital, Inc.
EPA Project Officer: Chung, Serena
Project Period: January 20, 2003 through January 19, 2006 (Extended to January 19, 2008)
Project Period Covered by this Report: January 20, 2005 through January 19, 2006
Project Amount: $933,602
RFA: Epidemiologic Research on Health Effects of Long-Term Exposure to Ambient Particulate Matter and Other Air Pollutants (2002) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Health Effects , Particulate Matter , Air
The objective of this research project is to: (1) develop a model estimating long-term exposure to air pollution in the continental United States using existing databases, including the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS); and (2) evaluate the association of chronic exposure to air pollution with incident coronary and respiratory disease and total mortality in the Nurses’ Health Study , an ongoing prospective cohort study of 121,700 women residing throughout the United States. We hypothesize that the incidence of these diseases and total mortality are positively associated with air pollution and that exposure to air pollution exacerbates existing disease. We also hypothesize that the association with coronary heart disease will be greater among diabetics than nondiabetics and that consumption of antioxidants will modify the association.
We have focused on improving a predictive model of particulate matter less than 10 microns in diameter (PM10) for the northeastern United States and on developing a predictive model of PM2.5 covering the same region for the years 1988 through 2002. These models are used to quantify chronic ambient exposure to air pollution in survival analyses of mortality and chronic disease outcomes in the Nurses’ Health Study. Follow-up questionnaires are mailed to participants every 2 years, thereby providing a complete residential address history. The 1988 through 2002 addresses have been geocoded and will provide a link to the prediction models.
Air Pollution Modeling
Our air pollution prediction models use monthly average PM10 data by monitoring site from EPA’s AQS and various other sources, including the IMPROVE network and Harvard research studies. The following variables were generated for each monitoring location using GIS: block group, tract, and county- level population density; distance to nearest road by Census Feature Class Code road class; elevation from the U .S . Geological Survey (USGS) National Elevation Dataset; land use/land cover from the USGS National Land Cover Dataset; primary criteria pollutant emissions information from the EPA National Emissions Inventory (NEI) by county; and meteorological variables, including temperature, wind speed, and relative humidity, from the National Climatic Data Center (NCDC).
All data were imported into generalized additive statistical models (GAMs) with smooth terms of space, time (separate surfaces for each month), and the GIS variables. The modeling approach combines a monthly pollution surface (the spatial component) and constant (time invariant) effects of predictors derived from the GIS (the non spatial component).
PM2.5 monitoring data are not available on a national level until 1999. Therefore, to predict PM2.5 levels for earlier time periods, we are using observations of horizontal visual range made at Weather Bureau/Army/Navy (WBAN) stations (about 430 nationwide), after correcting for the truncated nature of the observations, as a predictor for PM2.5. Our models use the above mentioned corrected visibility observations, PM10 predictions, and the PM2.5 monitoring data collected from 1999 to 2002 to estimate monthly average PM2.5 levels back in time to 1988.
We have begun our epidemiologic analyses of the associations of PM10 exposure with mortality and chronic health outcomes. To date, we have used Cox proportional hazards models to examine the associations of PM10 with fatal and nonfatal myocardial infarction, strokes, and all cause mortality. We have explored potential confounders and effect modifiers including: smoking, physical activity, body mass index (BMI), physician-diagnosed hypertension and diabetes, hormone use, menopausal status. We are currently refining these models and extending them to explore asthma and chronic obstructive pulmonary disease (COPD) as outcomes. We are exploring the effects of using various lags to estimate pollutant exposure. Additionally, we are examining the pollution exposure and chronic health outcome associations in various geographic subpopulations of the Nurses’ Health Study.
In the coming funding year, we will expand the PM10 model to the entire nation, and we will refine and expand the PM2.5 exposure model. In addition, we will refine our Cox proportional hazards models of the associations of chronic PM10 exposure with mortality and chronic health outcomes, including: cardiovascular disease, lung cancer, asthma, and COPD . We also will use Cox proportional hazards models to examine the associations of chronic PM2.5 exposure with mortality and chronic health outcomes. Through this modeling process, we will examine potential confounders and effect modifiers such as: smoking, physical activity, BMI, physician-diagnosed hypertension, and diabetes.