The Annals of Occupational Hygiene and Occupational Medicine present a
hierarchical Bayesian framework for exposure assessment that makes use of
statistical sampling-based techniques to estimate the posterior probability of
the 95th percentile or arithmetic mean of the exposure distribution being
located in one of several exposure categories. The framework can synthesize
professional judgment and monitoring data to yield an updated posterior
exposure assignment for routine exposure management.

The use of the framework in three settings is illustrated. First, subjective
judgments about exposure magnitude obtained from industrial hygienists for five
tasks were treated as priors in the Bayesian framework. Monitoring data for
each task were used to create a likelihood function in the hierarchical
framework and the posterior was predicted in terms of the 95th percentile being
located in each of the four AIHA exposure categories. The accuracy of the
exposure judgments was then evaluated. Second, the authors illustrate the use
of exposure models to develop priors in this framework and compare with
monitoring data in an iron foundry. Finally, the authors illustrate the use of
this approach for retrospective exposure assessment in a chemical manufacturing
facility, to categorize exposures based on arithmetic mean instead of 95th
percentile.

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AplusA-online.de - Source: The Annals of Occupational Hygiene