The nature and characteristics of time series data make risk estimation challenging, requiring complex statistical methods su±ciently sensitive to detect e®ects that can be small relative to the combined e®ect of other time-varying covariates. More speci¯cally, the association between air pollution and mortality=morbidity can be confounded by weather and by seasonal °uctuations in health outcomes due to in°uenza epidemics, and to other unmeasured and slowly-varying factors (Schwartz et al., 1996; Katsouyanni et al., 1996; Samet et al., 1997). One widely used approach for a time series analysis of air pollution and health involves a semi-parametric Poisson regression with daily mortality or morbidity counts as the outcome, linear.
