Historical Context and Recent Advances in Exposure-Response Estimation for Deriving Occupational Exposure Limits

Virtually no occupational exposure standards specify the level of risk for the prescribed exposure, and most occupational exposure limits are not based on quantitative risk assessment (QRA) at all. Wider use of QRA could improve understanding of occupational risks while increasing focus on identifyi...

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Bibliographic Details
Published in:Journal of occupational and environmental hygiene 2015, Vol.12 (S1), p.S7-S17
Main Authors: Wheeler, M.W., Park, R. M., Bailer, A. J., Whittaker, C.
Format: Article
Language:English
Subjects:
benchmark dose
critical effect
dose-response
Epidemiologic Methods
Exposure
exposure-response
Extrapolation
Federal regulation
Historic
History, 20th Century
Human exposure
Humans
Hygiene
Low level
modeling
Models, Statistical
No-Observed-Adverse-Effect Level
Occupational
Occupational Exposure - history
Occupational Exposure - standards
Occupational hazards
Occupational health
Occupational Health - history
Occupational Health - standards
Occupational safety
Original
Risk
Risk assessment
Risk Assessment - methods
Safety standards
Studies
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Items that cite this one
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Summary:Virtually no occupational exposure standards specify the level of risk for the prescribed exposure, and most occupational exposure limits are not based on quantitative risk assessment (QRA) at all. Wider use of QRA could improve understanding of occupational risks while increasing focus on identifying exposure concentrations conferring acceptably low levels of risk to workers. Exposure-response modeling between a defined hazard and the biological response of interest is necessary to provide a quantitative foundation for risk-based occupational exposure limits; and there has been considerable work devoted to establishing reliable methods quantifying the exposure-response relationship including methods of extrapolation below the observed responses. We review several exposure-response modeling methods available for QRA, and demonstrate their utility with simulated data sets.
ISSN:1545-9624
1545-9632
DOI:10.1080/15459624.2015.1076934