Part 1. Statistical Learning Methods for the Effects of Multiple Air Pollution Constituents.
Journal:
Research report (Health Effects Institute)
Published Date:
Jun 1, 2015
Abstract
INTRODUCTION: The United States Environmental Protection Agency (U.S. EPA*) currently regulates individual air pollutants on a pollutant-by-pollutant basis, adjusted for other pollutants and potential confounders. However, the National Academies of Science concluded that a multipollutant regulatory approach that takes into account the joint effects of multiple constituents is likely to be more protective of human health. Unfortunately, the large majority of existing research had focused on health effects of air pollution for one pollutant or for one pollutant with control for the independent effects of a small number of copollutants. Limitations in existing statistical methods are at least partially responsible for this lack of information on joint effects. The goal of this project was to fill this gap by developing flexible statistical methods to estimate the joint effects of multiple pollutants, while allowing for potential nonlinear or nonadditive associations between a given pollutant and the health outcome of interest.
Authors
Keywords
Aged
Air Pollutants
Air Pollution
Animals
Artificial Intelligence
Bayes Theorem
Computer Simulation
Data Interpretation, Statistical
Dogs
Environmental Exposure
Environmental Monitoring
Hazardous Substances
Humans
Particulate Matter
Prospective Studies
Respiratory Tract Diseases
United States
United States Environmental Protection Agency