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Monitoring the Health of PopulationsStatistical Principles and Methods for Public Health Surveillance$
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Ron Brookmeyer and Donna F. Stroup

Print publication date: 2003

Print ISBN-13: 9780195146493

Published to Oxford Scholarship Online: September 2009

DOI: 10.1093/acprof:oso/9780195146493.001.0001

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Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality

Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality

Chapter:
(p.267) 10 Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality
Source:
Monitoring the Health of Populations
Author(s):

Scott L. Zeger

Francesca Dominici

Aidan Mcdermott

Jonathan M. Samet

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195146493.003.0010

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.

Keywords:   air pollution, mortality, public health monitoring, public health surveillance, Bayesian hierarchical models

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