Probabilistic exposure assessment
What it's about
Exposure assessment can help to determine the type, nature, frequency and intensity of contacts between the population and the contaminant that is to be assessed. There are various approaches for estimating exposure.
Background
Traditional exposure assessment (also called deterministic estimateEstimateTo glossary or point estimate, "worst case estimates") of risks from chemical substances estimates a value that ensures protection for most of the population. Deviations from the real values are tolerated in order to ensure protection of the consumer using simple methods by, in some cases, considerably overestimating actual exposure.
For some time now the use of probabilistic approaches (also called distribution-based or population-related approaches) has been under discussion for exposure assessment. These methods do not merely describe a single, normally extreme case but rather endeavour to depict overall variabilityVariabilityTo glossary in the data and, by extension, to present all possible forms of exposure. The mathematical tools used in this approach are Monte Carlo simulations, distribution adjustments and other principles taken from the probability theory.
In toxicology risks are normally described by establishing limit values. Below a limit value there should be no risk; above a limit value health effects through contact with the chemicals cannot be ruled out. This approach is frequently challenged. The question has been raised whether this approach does justice to transparent, realistic risk assessment. Probabilistic methods could highlight this supposed lack of clarity, help to characterise uncertainties and take them into account in risk assessment.
Advantages of probabilistic methods
Advantages of probabilistic methods for risk assessment:
- Probabilistic methods offer the possibility of obtaining realistic exposure assessments, encouraging more effective risk management and contributing to more transparent risk communication.
- More information can be obtained by separating variability from uncertaintyUncertaintyTo glossary.
- The possibilities of estimating the success of intervention measures when simulating the entire product chain according to the "farm-to-fork" principle are improved.
- Probabilistic methods provide insight into the overall picture of risks in the population and not just of extreme cases.
- The use of statistical methods instead of subjective expert judgements leads to greater transparency and credibility of the estimates.