An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

Details

Serval ID
serval:BIB_2DED29BA28AF
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
An empirical hierarchical Bayesian unification of occupational exposure assessment methods.
Journal
Statistics in Medicine
Author(s)
Sottas Pierre-Edouard, Lavoué Jérôme, Bruzzi Raffaella, Vernez David, Charrière Nicole, Droz Pierre-Olivier
ISSN
0277-6715
Publication state
Published
Issued date
2009
Peer-reviewed
Oui
Volume
28
Number
1
Pages
75-93
Language
english
Abstract
In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.
Keywords
Air Pollution , Occupational Exposure , Occupational Health , Bayes Theorem , Risk Assessment , Threshold Limit Values , Models, Statistical , Monte Carlo Method , Neural Networks (Computer)
Pubmed
Web of science
Create date
10/02/2009 12:14
Last modification date
20/08/2019 13:12
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