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J Food Prot. 2007 May;70(5):1137-52.

Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats.

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Department of Food Science and Technology, University of Córdoba, Campus de Rabanales, C-1, 14014 Córdoba, Spain.


The risk assessment study of Listeria monocytogenes in ready-to-eat foods conducted by the U.S. Food and Drug Administration is an example of an extensive quantitative microbiological risk assessment that could be used by risk analysts and other scientists to obtain information and by managers and stakeholders to make decisions on food safety management. The present study was conducted to investigate how detailed sensitivity analysis can be used by assessors to extract more information on risk factors and how results can be communicated to managers and stakeholders in an understandable way. The extended sensitivity analysis revealed that the extremes at the right side of the dose distribution (at consumption, 9 to 11.5 log CFU per serving) were responsible for most of the cases of listeriosis simulated. For concentration at retail, values below the detection limit of 0.04 CFU/g and the often used limit for L. monocytogenes of 100 CFU/g (also at retail) were associated with a high number of annual cases of listeriosis (about 29 and 82%, respectively). This association can be explained by growth of L. monocytogenes at both average and extreme values of temperature and time, indicating that a wide distribution can lead to high risk levels. Another finding is the importance of the maximal population density (i.e., the maximum concentration of L. monocytogenes assumed at a certain temperature) for accurately estimating the risk of infection by opportunistic pathogens such as L. monocytogenes. According to the obtained results, mainly concentrations corresponding to the highest maximal population densities caused risk in the simulation. However, sensitivity analysis applied to the uncertainty parameters revealed that prevalence at retail was the most important source of uncertainty in the model.

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