title: Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study.
abstract: 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 (PM10) and respiratory mortality in time-series studies.
  
  
  
  A time-series study using regional death registry between 2009 and 2010.
  
  
  
  8 districts in a large metropolitan area in Northern China.
  
  
  
  9559 permanent residents of the 8 districts who died of respiratory diseases between 2009 and 2010.
  
  
  
  Per cent increase in daily respiratory mortality rate (MR) per interquartile range (IQR) increase of PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models.
  
  
  
  The Bayesian model averaged GAMM (GAMM+BMA) and the optimal GAMM of PM10, multipollutants and principal components (PCs) of multipollutants showed comparable results for the effect of PM10 on daily respiratory MR, that is, one IQR increase in PM10 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.
  
  
  
  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.
authors.affiliation: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Xin
authors.initials: X
authors.lastname: Fang
authors.affiliation: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.
authors.forename: Runkui
authors.initials: R
authors.lastname: Li
authors.affiliation: Key Laboratory of Public Health Safety of the Ministry of Education and Key Laboratory of Health Technology Assessment of the Ministry of Health, School of Public Health, Fudan University, Shanghai, China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP), Fudan University, Shanghai, China.
authors.forename: Haidong
authors.initials: H
authors.lastname: Kan
authors.affiliation: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Matteo
authors.initials: M
authors.lastname: Bottai
authors.affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Fang
authors.initials: F
authors.lastname: Fang
authors.affiliation: Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.
authors.forename: Yang
authors.identifier: 0000-0002-3552-9153
authors.initials: Y
authors.lastname: Cao
mesh_terms: D000397:Air Pollution; D000399:Air Pressure; D001499:Bayes Theorem; D002681:China; D004781:Environmental Exposure / Q000706:statistics & numerical data*; D006801:Humans; D006813:Humidity; D052638:Particulate Matter; D012042:Registries; D012140:Respiratory Tract Diseases / Q000401:mortality*; D013696:Temperature; D014887:Weather; D014919:Wind
chemical_list: D052638:Particulate Matter
keywords: Bayesian model averaging; Generalized additive mixed model; Model uncertainty; PM<sub>10</sub>; Respiratory mortality; Time-series study