Abstract
This chapter illustrates the use of log-linear regression and hierarchical models to estimate the association of daily mortality with acute exposure to particulate air pollution. It focuses on multistage models of daily mortality data in the eighty-eight largest cities in the United States to illustrate the main ideas. These models have been used to quantify the risks of shorter-term exposure to particulate pollution and to address key causal questions.
Original language | English (US) |
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Title of host publication | Monitoring the Health of Populations |
Subtitle of host publication | Statistical Principles and Methods for Public Health Surveillance |
Publisher | Oxford University Press |
ISBN (Electronic) | 9780199864928 |
ISBN (Print) | 9780195146493 |
DOIs | |
State | Published - Sep 1 2009 |
Keywords
- Air pollution
- Bayesian hierarchical models
- Mortality
- Public health monitoring
- Public health surveillance
ASJC Scopus subject areas
- Arts and Humanities(all)