A Bayesian approach to modeling the interaction between air pollution and temperature

Abstract

Purpose: Investigating the interaction between particulate matter air pollution (PM) and temperature is important for quantifying the effects of PM on mortality. One approach is stratification-estimating the effect of PM within different temperature strata-but this treats the cutpoints that define the strata as fixed, when in fact they are unknown. The purpose of this paper is to propose a new approach that appropriately accounts for uncertainty regarding the cutpoints, and to apply this approach to data from two Australian cities. Methods: We propose a Bayesian model which allows the effects of PM to differ within different temperature strata. The cutpoints that define the strata are parameters that are jointly estimated along with the other model parameters. This is in contrast with the standard stratification approach, where cutpoints are specified a priori and treated as fixed constants. Also, the Bayesian model is formulated in a way that ensures continuity in the effects of PM at the stratum boundaries. Markov chain Monte Carlo methods are used to perform the inferences. Results: Analysis of daily data over several years provides evidence for an interactive effect between PM and temperature in Sydney and no support for such an effect in Melbourne. Conclusions: The proposed Bayesian model provides a means for investigating interactions between PM and temperature which appropriately incorporates uncertainty.