title: Simulations in health risk assessment.
abstract: Health risk assessment procedure provides a clear and systematic form of quantitative (or semi-quantitative) description of environmental health impact. It is well known that this approach is burdened with various types of uncertainties of different origin and nature. Therefore, the results of risk assessment should always contain both the "number" and the "measure of uncertainty". The problem is that even if one does attempt to take account of the uncertainty, one does not know a priori what is the probability of getting a given risk value within the specified range of uncertainty. A promising tool for the assessment of risk which provides a means of describing the sensitivity with respect to different exposure factors and evaluating different intervention scenarios is the technique of Monte Carlo simulation. In this probabilistic approach all variables and parameters used in risk assessment may be regarded as distributions throughout the analysis. A process of repeated simulations is then used, during which the estimated quantity (risk in this case) is calculated many times (usually 10,000 or more) with randomly chosen values of variables and parameters, covering their range of variability and reproducing the assumed distribution density. The final result is given in the form of a probability distribution of risk. The idea of Monte Carlo simulations in health risk assessment concerning the exposure to heavy metals in drinking water is illustrated in the population living in the vicinity of the "Lubna" waste site, taken as an example.
authors.affiliation: Department of Health Risk Assessment, Institute of Occupational Medicine and Environmental Health, Sosnowiec, Poland. m-biesiada@imp.sosnowiec.pl
authors.forename: M
authors.initials: M
authors.lastname: Biesiada
mesh_terms: D006305:Health Status Indicators; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity