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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-seri...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013441/ https://www.ncbi.nlm.nih.gov/pubmed/27531727 http://dx.doi.org/10.1136/bmjopen-2016-011487 |
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author | Fang, Xin Li, Runkui Kan, Haidong Bottai, Matteo Fang, Fang Cao, Yang |
author_facet | Fang, Xin Li, Runkui Kan, Haidong Bottai, Matteo Fang, Fang Cao, Yang |
author_sort | Fang, Xin |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5013441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50134412016-09-12 Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study Fang, Xin Li, Runkui Kan, Haidong Bottai, Matteo Fang, Fang Cao, Yang BMJ Open Epidemiology 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. BMJ Publishing Group 2016-08-16 /pmc/articles/PMC5013441/ /pubmed/27531727 http://dx.doi.org/10.1136/bmjopen-2016-011487 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
spellingShingle | Epidemiology Fang, Xin Li, Runkui Kan, Haidong Bottai, Matteo Fang, Fang Cao, Yang Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title | Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title_full | Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title_fullStr | Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title_full_unstemmed | Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title_short | Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
title_sort | bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013441/ https://www.ncbi.nlm.nih.gov/pubmed/27531727 http://dx.doi.org/10.1136/bmjopen-2016-011487 |
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