NUSAP: a method to evaluate the quality of assumptions in quantitative microbial risk assessment

Ides Boone, Yves Van der Stede, Jeroen Dewulf, Winy Messens, Marc Aerts, Georges Daube and Koen Mintiens

Journal of Risk Research, 2010, vol. 13, issue 3, 337-352

Abstract: The Numeral Unit Spread Assessment Pedigree (NUSAP) system was implemented to evaluate assumptions in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. This QMRA model allows the testing of mitigation strategies for the reduction of human salmonellosis and aims to serve as a basis for science-based policy making. The NUSAP method was used to assess the subjective component of assumptions in the QMRA model by a set of four pedigree criteria: 'the influence of situational limitations', 'plausibility', 'choice space' and 'the agreement among peers'. After identifying 13 key assumptions relevant for the QMRA model, a workshop was organized to assess the importance of these assumptions on the output of the QMRA. The quality of the assumptions was visualized using diagnostic and kite diagrams. The diagnostic diagram pinpointed assumptions with a high degree of subjectivity and a high 'expected influence on the model results' score. Examples of those assumptions that should be dealt with care are the assumptions regarding the concentration of Salmonella on the pig carcass at the beginning of the slaughter process and the assumptions related to the Salmonella prevalence in the slaughter process. The kite diagrams allowed a clear overview of the pedigree scores for each assumption as well as a representation of expert (dis)agreement. The evaluation of the assumptions using the NUSAP system enhanced the debate on the uncertainty and its communication in the results of a QMRA model. It highlighted the model's strong and weak points and was helpful for redesigning critical modules. Since the evaluation of assumptions allows a more critical approach of the QMRA process, it is useful for policy makers as it aims to increase the transparency and acceptance of management decisions based on a QMRA model.

Date: 2010
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DOI: 10.1080/13669870903564574

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