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Prev Vet Med. 1998 Dec 1;37(1-4):129-45.

Quantitative risk assessment of human listeriosis from consumption of soft cheese made from raw milk.

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  • 1Epidemiology and Animal Health Management Laboratory, Alfort Veterinary School, Maisons-Alfort, France.


Microbial hazards have been identified in soft cheese made from raw milk. Quantification of the resulting risk for public health was attempted within the frame of the Codex Alimentarius Commission, 1995 approach to quantitative risk assessment, using Monte Carlo simulation software. Quantitative data could only be found for Listeria monocytogenes. The complete process of cheese making was modeled, from milking to consumption. Using data published on the different sources of milk contamination (environment and mastitis) and bacterial growth, distributions were assumed for parameters of the model. Equations of Farber, J.M., Ross, W.H., Harwing, J. (1996) for general and at-risk populations were used to link the ingested dose of L. monocytogenes to the occurrence of listeriosis. The probability of milk contamination was estimated to be 67% with concentration ranging from 0 to 33 CFU ml-1. The percentage of cheese with a predicted concentration of L. monocytogenes greater than 100 CFU g-1 was low (1.4%). The probability of consuming a contaminated cheese serving was 65.3%. Individual annual cumulative risk of listeriosis, in a population each consuming 50 servings of 31 g, ranged from 1.97 x 10(-9) to 6.4 x 10(-8) in a low-risk sub-population and 1.04 10(-6) to 7.19 10(-5) in a high-risk sub-population. The average number of expected cases of listeriosis per year was 57 for a high-risk sub-population and one for a low-risk healthy sub-population. When the frequency of environmental milk contamination was reduced in the model and L. monocytogenes mastitis was eliminated, the expected incidence of listeriosis decreased substantially; the average number of expected cases was reduced by a factor of 5. Thus the usefulness of simulation to demonstrate the efficiency of various management options could be demonstrated, even if results should be interpreted with care (as many assumptions had to be made on data and their distributions.

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