Uncertainty in Risk Assessment

© 2004-2005 William Charteris
www.billcharteris.com
www.imperialconsulting.net

In its simplest form, ‘knowledge uncertainty’ can be thought of as comprising uncertainty in the appropriate parameter values for a chosen model, combined with uncertainty in the model itself.

Parameter uncertainty relates to the accuracy and precision with which model parameters can be inferred from input data, judgment, and the literature. It derives from statistical considerations and is usually described either by confidence intervals when using traditional (frequentist) statistical methods, or by probability distributions when using Bayesian statistical methods. Data uncertainties, which are the principal contributors to parameter uncertainty, include (i) measurement errors, (ii) inconsistent or heterogeneous data sets, (iii) data handling and transcription errors, and (iv) non-representative sampling caused by time, space, or financial limitations.

Model uncertainty relates to the degree to which a chosen model accurately represents reality. It may result from the use of surrogate variables (e.g. principal components, biomarkers, etc.), excluded variables (e.g. uncontrollable or noise variables), and approximations including the use of the incorrect mathematical expressions (e.g. low order polynomials) for representing the physical world. It is associated with all models used in all phases of a risk assessment, including (i) animal models used as surrogates for testing human carcinogenicity, (ii) the dose-response models used in extrapolations, and (iii) the computer models used to predict the fate and transport of chemicals in the environment. The use of rodents as surrogates for humans introduces uncertainty into the risk factor because of the considerable interspecies variability in sensitivity. Computer models are simplifications of reality, requiring exclusion of some variables that influence predictions but cannot be included in models because of (i) increased complexity, (ii) a lack of data for these variables, or (iii) difficulties associated with their observation. In general, parameter uncertainty and model uncertainty are generally recognized by risk assessors as major sources of uncertainty.

In a risk analysis, it is not always obvious which uncertainties should be ascribed to ‘natural variability’ and which should be ascribed to ‘knowledge uncertainty’. In this regard, separation of variability and uncertainty in quantitative microbiological risk analysis models has up to now rarely been made, a reflection of the fact that this can be a difficult, if not a daunting task. However, neglecting the difference between them may lead to improper risk estimates and/or incomplete understanding of the results. Also, if the distinction is not clear to the risk analyst, a variability distribution may be used incorrectly, i.e. as if it were an uncertainty distribution. The explicit separation of the two components in the input and output variables is a goal of risk assessors, and such a separation allows risk managers to understand how model outputs might improve if uncertainty is reduced.

This abstract is taken from a paper entitled 'Uncertainty and risk', which was published on December 20, 2004. The paper comprises 3,900 words and 25 references. Individual copies of the paper may be requested by e-mail from the author.