PRINCIPLES OF TOXICOLOGY - Biology East Borneo

18.6 PROBABILISTIC VERSUS DETERMINISTIC RISK ASSESSMENTS 463A second problem confronting the risk assessor is management of uncertainty in the risk assessmentprocess. As described elsewhere in this chapter, there are numerous sources of uncertainty in riskcalculations, including uncertainty in the selection of models and assumptions, and in measurementsof risk related parameters. As part of a deterministic calculation of risk, a choice must be made foreach of these so that a risk estimate can be made. For regulatory purposes, conservative choices areusually made; models and assumptions that tend to provide higher estimates of risk are selected fromamong the range of plausible alternatives. The reason for conservative choices by regulatory agenciesin the face of uncertainty is well understood, but the extent of conservatism imparted by the variouschoices is usually unclear. As with the issue of variability, this makes it difficult or impossible for therisk assessor to effectively convey the inherent conservatism associated with the risk estimate.Probabilistic risk assessment is an alternative approach that can address the shortcomings ofdeterministic calculations in terms of variability and uncertainty. In probabilistic risk assessment, inputvariables are entered as probability density functions (PDFs) instead of single values. For example,instead of using a single body weight of 70 kg in the risk calculation, a distribution of body weightswould be entered that reflects the variability in body weight of the exposed population. PDFs mightalso be entered for other variables such as inhalation rate, skin surface area, and frequency of contactwith contaminated media—anything that would be expected to vary from one individual to another.These PDFs are then combined in such a way as to yield a risk distribution, representing the range andfrequency of risks anticipated to exist in the exposed population. Although there are several ways tocombine PDFs, one of the most commonly used techniques is Monte Carlo simulation. With MonteCarlo simulation, a computer program in essence creates a simulated population designed to resemblethe exposed population in every key respect. For each risk calculation, it takes a value from each inputPDF and calculates a numerical risk. This process is repeated, usually thousands of times, and theresulting range of risk values is tallied in the form of a distribution. This distribution represents therisk distribution for the population. From this distribution, the variability in risk among individualscan be visualized and the risk level at various percentiles of the population determined (see Figure18.7).Probabilistic risk assessment can also provide quantitative representation of the uncertainties in therisk calculation. For each input or model, some estimate of the uncertainty is entered. For example,the concentration of chemical X for which a risk estimate is desired is assumed to be 100, but couldbe as low as 50 or as high as 200. In this case, the chemical concentration could be entered as adistribution of values, with 100 as the most likely estimate, but with a range extending from 50 to 200.As with variability, the uncertainty associated with various inputs can be combined to produce a PDFshowing boundaries of uncertainty associated with a risk estimate. An additional benefit of thisapproach is that a sensitivity analysis can be used to rank the various sources of uncertainty in termsof their relative contribution to overall uncertainty. If the uncertainty is unacceptably large, this can beused to identify the best areas for further analysis or research to reduce uncertainty.It is possible for a probabilistic risk assessment to address both variability and uncertaintysimultaneously. This requires the development of PDFs for both uncertainty and variability. Forexample, a PDF might be used to portray variability in body weight in the exposed population, and aseparate PDF would be used to deal with any uncertainty that the body weight distribution selectedaccurately reflects the actual body weight distribution of the population in question. (Note: This is notan unreasonable uncertainty, since risk assessors almost never have the time and resources to actuallyweigh everyone in an exposed population, and therefore must rely on published body weight data forthe general population to create their body weight PDF.) The variability and uncertainty PDFs are thencombined separately to generate a risk distribution with confidence boundaries provided by theuncertainty distributions. This is called a two-dimensional probabilistic risk assessment.The principal advantage of a probabilistic risk assessment is that it provides much greaterinformation on variability and uncertainty associated with risk estimates. The manner in which risk isdistributed within the exposed population is transparent, and the magnitude of uncertainty associatedwith the risk estimate is conveyed in quantitative terms. There are, however, a number of disadvantagesto probabilistic risk assessment, including the facts that (1) it is technically demanding, requiring much