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

Abstract

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.