EXPERIENCING A PROBABILISTIC APPROACH TO CLARIFY AND DISCLOSE UNCERTAINTIES WHEN SETTING OCCUPATIONAL EXPOSURE LIMITS
Vernez, D; Fraize-Frontier, S; Vincent, R; Binet, S; Rousselle, C
| HERO ID | 10757509 |
|---|---|
| In Press | No |
| Year | 2018 |
| Title | EXPERIENCING A PROBABILISTIC APPROACH TO CLARIFY AND DISCLOSE UNCERTAINTIES WHEN SETTING OCCUPATIONAL EXPOSURE LIMITS |
| Authors | Vernez, D; Fraize-Frontier, S; Vincent, R; Binet, S; Rousselle, C |
| Journal | International Journal of Occupational Medicine and Environmental Health |
| Volume | 31 |
| Issue | 4 |
| Page Numbers | 475-489 |
| Abstract | 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. |
| Doi | 10.13075/ijomeh.1896.01184 |
| Wosid | WOS:000441070500006 |
| Url | https://www.ncbi.nlm.nih.gov/pubmed/29546881 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Keyword | Animals; Models, Statistical; No-Observed-Adverse-Effect Level; Occupational Exposure/standards; Risk Assessment/statistics & numerical data; Toxicology/statistics & numerical data; Uncertainty; assessment factors; chemical toxicity; occupational exposure limits; probabilistic methods; risk management; uncertainty distributions |