Relating Cardiovascular Disease Risk to Ambient Air Pollutants Using Geographic Information Systems Technology and Bayesian Neural Networks: The AHSMOG StudyEPA Grant Number: R830547
Title: Relating Cardiovascular Disease Risk to Ambient Air Pollutants Using Geographic Information Systems Technology and Bayesian Neural Networks: The AHSMOG Study
Investigators: Knutsen, Synnove F. , Beeson, Larry , Ghamsary, Mark , Soret, Samuel
Institution: Loma Linda University
EPA Project Officer: Chung, Serena
Project Period: February 1, 2003 through December 31, 2006 (Extended to January 31, 2009)
Project Amount: $964,436
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: Health Effects , Particulate Matter , Air
1. To assess the long-term effects of particulate and gaseous pollutants on risk of cardiovascular disease (CVD), including fatal and non-fatal coronary heart disease (CHD), during 23 years follow-up (1977-1999) using the unique data from the AHSMOG Study.
2. To assess the long-term effects of ambient air pollutants on risk of fatal and non-fatal CHD among sensitive subgroups (e.g., prevalent CVD, hypertensives, diabetics, elderly).
3. To assess the long-term effects of mixed pollutants on the endpoints in objectives 1 and 2.
4. To investigate the effect of lag-times on the ambient air pollution-CVD association.
5. To explore new methods for exposure assessment and analysis.
1. Utilize data from the existing AHSMOG Study, which has been updated through March 2000 through the current EPA STAR Grant (R-82799801-0). This data includes monthly indices of air pollutants to zip code centroids, monthly residence and work location histories, outcome assessment (CHD, fatal and non-fatal) and assessment of relevant confounders (smoking, environmental tobacco smoke, diet, exercise, etc).
2. Develop new indices of ambient air pollutants for the individual subjects in the AHSMOG Study using Geographic Information Systems (GIS) technology and stochastic models that include error estimates of the indices.
3. Develop non-linear statistical models using Bayesian neural networks to develop alternative analytical strategies for modeling the relationship between different ambient air pollutants and risk of CHD where several pollutants and latent (unobserved) and missing values can be incorporated.
4. Compare new methods developed under approaches 2 and 3 to the classic or conventional methods previously used in the AHSMOG Study.
The proposed new methods for assessing ambient air pollution and for analysis using GIS technology and Bayesian neural networks will enhance the ability to estimate health effects associated with ambient air pollution. For public health, more accurate estimates of health risks will aid in setting standards for acceptable levels of ambient air pollutants.