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An empirical hierarchical Bayesian unification of occupational exposure assessment methods
Author(s) -
Sottas PierreEdouard,
Lavoué Jérome,
Bruzzi Raffaella,
Vernez David,
Charrière Nicole,
Droz PierreOlivier
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3411
Subject(s) - bayesian probability , parametric statistics , exposure assessment , computer science , occupational exposure , statistics , monte carlo method , standard deviation , bayesian network , data mining , machine learning , mathematics , artificial intelligence , environmental health , medicine
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. Copyright © 2008 John Wiley & Sons, Ltd.