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Bayesian model averaging method for evaluating associations between air pollution and respiratory mortality: a time-series study
Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
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.
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 3 ), Fudan University, Shanghai, China.
Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
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2016 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 6, no 8, e011487Article in journal (Refereed) Published
Abstract [en]

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 (PM10) 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 PM10 concentration and corresponding 95% confidence interval (CI) in single-pollutant and multipollutant (including NOx, CO) models.

Results: 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.

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.

Place, publisher, year, edition, pages
London, England: BMJ Publishing Group Ltd, 2016. Vol. 6, no 8, e011487
National Category
Family Medicine
URN: urn:nbn:se:oru:diva-52843DOI: 10.1136/bmjopen-2016-011487ISI: 000382336700043OAI: oai:DiVA.org:oru-52843DiVA: diva2:1034070

Funding Agencies:

Institute of Environmental Medicine C62412022

Karolinska Institutet, Sweden

Public Welfare Research Program of National Health and Family Planning Commission of China 201402022

Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention LAP3

Available from: 2016-10-11 Created: 2016-10-06 Last updated: 2016-12-13Bibliographically approved

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