Frequency domain log-linear models; air pollution and mortality

Julia E. Kelsall, Scott L. Zeger, Jonathan M. Samet

Research output: Contribution to journalArticlepeer-review

Abstract

Motivated by a study of the association between counts of daily mortality and air pollution, we present a frequency domain estimation approach for log-linear models that accounts for both overdispersion and autocorrelation. The methods also allow for the discounting or down-weighting of information at particular frequencies at which, for example, confounding variables are likely to have greatest influence. This allows flexible sensitivity analyses to be carried out to assess the possible effect of confounders on the estimated effect. We apply the methods to estimate the association between counts of mortality and the concentration of airborne particles in Philadelphia, USA, for the years 1974-1988. We obtain an estimated effect of particulate air pollution on mortality that is significantly greater than zero but less than that obtained by a standard log-linear analysis.

Original languageEnglish (US)
Pages (from-to)331-344
Number of pages14
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume48
Issue number3
DOIs
StatePublished - Jan 1 1999

Keywords

  • Air pollution
  • Autocorrelation
  • Frequency domain
  • Overdispersion
  • Poisson regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Frequency domain log-linear models; air pollution and mortality'. Together they form a unique fingerprint.

Cite this