Exposure Modeling in Occupational Hygiene Decision Making

Abstract

The primary objective was to develop a framework for using exposure models in conjunction with two-dimensional Monte Carlo methods for making exposure judgments in the context of Bayesian decision analysis. The AIHA exposure assessment strategy will be used for illustrative purposes, but the method has broader applications beyond these specific exposure assessment strategies. A two-dimensional Monte Carlo scheme by which the exposure model output can be represented in the form of a decision chart is presented. The chart shows the probabilities of the 95th percentile of the exposure distribution lying in one of the four exposure categories relative to the occupational exposure limit (OEL): (1) highly controlled (<10% of OEL), (2) well controlled (10–50% of OEL), (3) controlled (50–100% of OEL), and (4) poorly controlled (>100% of OEL). Such a decision chart can be used as a “prior” in the Bayesian statistical framework, which can be updated using monitoring data to arrive at a final decision chart. Hypothetical examples using commonly used exposure models are presented, along with a discussion of how this framework can be used given a hierarchy of exposure models.

Keywords:

• Bayesian decision making
• exposure models
• two-dimensional Monte Carlo

ACKNOWLEDGMENTS

This project was supported by grant 1RO1 OH008513 from the National Institute for Occupational Safety and Health of the Centers of Disease Control and Prevention.

Notes

^A “Infrequently” refers to an event that occurs no more than 5% of the time.

^B “Rarely” refers to an event that occurs no more than 1% of the time.

^C High concentrations are defined as concentrations that exceed the TWA limit or STEL.

^A Relative cost for obtaining the required input parameter information.