A longitudinal study of emergency room visits and air pollution for Prince George, British Columbia

A new method for regression analysis of longitudinal counts is applied to data from Prince George, British Columbia, previously analysed by Knight et al. The data consist of daily recordings of the number of emergency room visits for each of four categories of respiratory diseases, along with measurements of meteorological variables and air pollution. We use a state-space model assuming conditionally independent Poisson counts for the four categories given a latent morbidity process, the latent process being a gamma Markov process. The main objective of the investigation was to examine the relationship between air pollution and respiratory morbidity, taking into account seasonality and meteorological conditions. We found that total reduced sulphur significantly influences the expected number of emergency room visits for the four disease categories, in agreement with the conclusion by Knight et al. However, our final model is simpler than theirs; in particular we found no evidence of seasonal variation beyond that explained by the meteorological variables.