A Bayesian hierarchical distributed lag model for estimating the time course of risk of hospitalization associated with particulate matter air pollution

Roger D. Peng, Francesca Dominici, Leah J. Welty

Research output: Contribution to journalArticle

Abstract

Time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased hospitalizations, typically at a single lag of 0, 1 or 2 days after an air pollution episode. Two important scientific objectives are to understand better how the risk of hospitalization that is associated with a given day's air pollution increase is distributed over multiple days in the future and to estimate the cumulative short-term health effect of an air pollution episode over the same multiday period. We propose a Bayesian hierarchical distributed lag model that integrates information from national health and air pollution databases with prior beliefs of the time course of risk of hospitalization after an air pollution episode. This model is applied to air pollution and health data on 6.3 million enrollees of the US Medicare system living in 94 counties covering the years 1999-2002. We obtain estimates of the distributed lag functions relating fine particulate matter pollution to hospitalizations for both ischaemic heart disease and acute exacerbation of chronic obstructive pulmonary disease, and we use our model to explore regional variation in the health risks across the USA.

Original languageEnglish (US)
Pages (from-to)3-24
Number of pages22
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume58
Issue number1
DOIs
StatePublished - Feb 1 2009

Keywords

  • Air pollution
  • Cardiovascular disease
  • Distributed lag model
  • Environmental epidemiology
  • Respiratory disease
  • Time series

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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