the extent to which the treatment of the data is quantitative. These four classes are

  1. formal expert elicitations;

  2. qualitative assessments that use non-numerical descriptors;

  3. semi-quantitative assessments that combine quantitative data and non-numerical descriptors; and

  4. quantitative assessments in which mathematical modeling uses a large amount of scientific data.

Although semi-quantitative assessments are the most widely used of these classes, Buchanan focused on quantitative methods.

Quantitative Microbial Risk Assessment

A quantitative microbial risk assessment produces a mathematical statement that is based on the cumulative probabilities of certain adverse events happening following an exposure to a hazardous agent. The result of such a risk assessment includes not only an estimate of the risk but also an estimate of the attendant uncertainties. In addition to the quantitative factors, the assessment will also generally consider qualitative factors in its discussion of risk and uncertainty.

Quantitative microbial risk assessments are generally of two basic types: deterministic and probabilistic. Deterministic assessments use point estimates to describe risk at a certain level of exposure (for example, the mean risk or the 95th or 99th percentile of risk). A disadvantage of this approach is that it may overestimate risk if one tries to combine factors. Probabilistic assessments use entire distributions and require advanced modeling techniques, such as those made possible with Monte Carlo simulation software. Using a Monte Carlo simulation, for example, one could determine the impact of changing the refrigerator temperature on the growth rate of a pathogenic microorganism and, therefore, on the final risk posed by an organism in a food.

The advantages of probabilistic models include more accurate results, the ability to modify the model easily to incorporate new data, the ability to produce “what-if” scenarios, and the ability to evaluate the effects of potential actions to mitigate risk. In a “what-if” scenario, one can make substitutions in the model that make it possible to examine how making a specific change would affect an outcome. One disadvantage of probabilistic assessments is that they may be difficult for risk managers to interpret.

Microbial risk assessment could be used in a variety of ways by the U.S. Food and Drug Administration (FDA). Some possibilities are



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