Experiencing a probabilistic approach to clarify and disclose uncertainties when setting occupational exposure limits
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University of Lausanne and Geneva, Lausanne, Switzerland (Institute of Work and Health (IST))
French Agency for Food, Environmental and Occupational Health and Safety (ANSES), Maisons-Alfort, France
National Institute for Research and Security for the Prevention of Occupational Accidents and Diseases (INRS), Vandoeuvre-lès-Nancy, France
David Vernez   

University of Lausanne and Geneva, Institute of Work and Health (IST), Rue de la Corniche 2, 1066 Epalinges, Switzerland
Int J Occup Med Environ Health 2018;31(4):475–489
Objectives: Assessment factors (AFs) are commonly used for deriving reference concentrations for chemicals. These factors take into account variabilities as well as uncertainties in the dataset, such as inter-species and intra-species variabilities or exposure duration extrapolation or extrapolation from the lowest-observed-adverse-effect level (LOAEL) to the noobserved- adverse-effect level (NOAEL). In a deterministic approach, the value of an AF is the result of a debate among experts and, often a conservative value is used as a default choice. A probabilistic framework to better take into account uncertainties and/or variability when setting occupational exposure limits (OELs) is presented and discussed in this paper. Material and methods: Each AF is considered as a random variable with a probabilistic distribution. A short literature was conducted before setting default distributions ranges and shapes for each AF commonly used. A random sampling, using Monte Carlo techniques, is then used for propagating the identified uncertainties and computing the final OEL distribution. Results: Starting from the broad default distributions obtained, experts narrow it to its most likely range, according to the scientific knowledge available for a specific chemical. Introducing distribution rather than single deterministic values allows disclosing and clarifying variability and/or uncertainties inherent to the OEL construction process. Conclusions: This probabilistic approach yields quantitative insight into both the possible range and the relative likelihood of values for model outputs. It thereby provides a better support in decision-making and improves transparency. Int J Occup Med Environ Health 2018;31(4):475–489