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

Research output: Chapter in Book/Report/Conference proceedingChapter

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 languageEnglish (US)
Title of host publicationMonitoring the Health of Populations
Subtitle of host publicationStatistical Principles and Methods for Public Health Surveillance
PublisherOxford University Press
ISBN (Electronic)9780199864928
ISBN (Print)9780195146493
DOIs
StatePublished - Sep 1 2009

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Keywords

  • Air pollution
  • Bayesian hierarchical models
  • Mortality
  • Public health monitoring
  • Public health surveillance

ASJC Scopus subject areas

  • Arts and Humanities(all)

Cite this

Zeger, S. L., Dominici, F., Mcdermott, A., & Samet, J. M. (2009). Bayesian Hierarchical Modeling of Public Health Surveillance Data: A Case Study of Air Pollution and Mortality. In Monitoring the Health of Populations: Statistical Principles and Methods for Public Health Surveillance Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195146493.003.0010