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

Scott L. Zeger; Francesca Dominici; Aidan Mcdermott; Jonathan M. Samet

doi:10.1093/acprof:oso/9780195146493.003.0010

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

Scott L. Zeger, Francesca Dominici, Aidan Mcdermott, Jonathan M. Samet

Bloomberg School of Public Health

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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)
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	https://doi.org/10.1093/acprof:oso/9780195146493.003.0010
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)

Access to Document

10.1093/acprof:oso/9780195146493.003.0010

Cite this

Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality. / Zeger, Scott L.; Dominici, Francesca; Mcdermott, Aidan et al.
Monitoring the Health of Populations: Statistical Principles and Methods for Public Health Surveillance. Oxford University Press, 2009.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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AB - 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.

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

Abstract

Keywords

ASJC Scopus subject areas

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