Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study

OBJECTIVE: To demonstrate an application of Bayesian model averaging (BMA) with generalised additive mixed models (GAMM) and provide a novel modelling technique to assess the association between inhalable coarse particles (PM(10)) and respiratory mortality in time-series studies. DESIGN: A time-series study using regional death registry between 2009 and 2010. SETTING: 8 districts in a large metropolitan area in Northern China. PARTICIPANTS: 9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010. MAIN OUTCOME MEASURES: Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM(10) concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NO(x), CO) models. RESULTS: The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM(10), multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM(10) on daily respiratory MR, that is, one IQR increase in PM(10) concentration corresponded to 1.38% vs 1.39%, 1.81% vs 1.83% and 0.87% vs 0.88% increase, respectively, in daily respiratory MR. However, GAMM+BMA gave slightly but noticeable wider CIs for the single-pollutant model (−1.09 to 4.28 vs −1.08 to 3.93) and the PCs-based model (−2.23 to 4.07 vs −2.03 vs 3.88). The CIs of the multiple-pollutant model from two methods are similar, that is, −1.12 to 4.85 versus −1.11 versus 4.83. CONCLUSIONS: The BMA method may represent a useful tool for modelling uncertainty in time-series studies when evaluating the effect of air pollution on fatal health outcomes.