Chapter

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

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

in Monitoring the Health of Populations

Published in print December 2003 | ISBN: 9780195146493
Published online September 2009 | e-ISBN: 9780199864928 | DOI: http://dx.doi.org/10.1093/acprof:oso/9780195146493.003.0010
Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality

More Like This

Show all results sharing this subject:

  • Public Health and Epidemiology

GO

Show Summary Details

Preview

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

Chapter.  7120 words.  Illustrated.

Subjects: Public Health and Epidemiology

Full text: subscription required

How to subscribe Recommend to my Librarian

Buy this work at Oxford University Press »

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.