Premium
Frequency Domain Log‐linear Models; Air Pollution and Mortality
Author(s) -
Kelsall Julia E.,
Zeger Scott L.,
Samet Jonathan M.
Publication year - 1999
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00156
Subject(s) - confounding , statistics , overdispersion , air pollution , autocorrelation , linear model , econometrics , mathematics , count data , biology , ecology , poisson distribution
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.