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Frequency Domain Log‐linear Models; Air Pollution and Mortality

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

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 downweighting 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.

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  • Julia E. Kelsall & Scott L. Zeger & Jonathan M. Samet, 1999. "Frequency Domain Log‐linear Models; Air Pollution and Mortality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 331-344.
    • Handle: RePEc:bla:jorssc:v:48:y:1999:i:3:p:331-344
    DOI: 10.1111/1467-9876.00156
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    Cited by:

    1. Francesca Dominici & Lianne Sheppard & Merlise Clyde, 2003. "Health Effects of Air Pollution: A Statistical Review," International Statistical Review, International Statistical Institute, vol. 71(2), pages 243-276, August.
    2. E. Andres Houseman & Brent Coull & James Shine, 2004. "A Nonstationary Negative Binomial Time Series with Time-Dependent Covariates: Enterococcus Counts in Boston Harbor," Harvard University Biostatistics Working Paper Series 1017, Berkeley Electronic Press.

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