title: NUSAP method for evaluating the data quality in a quantitative microbial risk assessment model for Salmonella in the pork production chain.
abstract: The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.
authors.affiliation: Veterinary and Agrochemical Research Centre (VAR), Coordination Centre for Veterinary Diagnostics, Groeselenberg 99, Brussels, Belgium.
authors.forename: Ides
authors.initials: I
authors.lastname: Boone
authors.forename: Yves
authors.initials: Y
authors.lastname: Van der Stede
authors.forename: Kaatje
authors.initials: K
authors.lastname: Bollaerts
authors.forename: David
authors.initials: D
authors.lastname: Vose
authors.forename: Dominiek
authors.initials: D
authors.lastname: Maes
authors.forename: Jeroen
authors.initials: J
authors.lastname: Dewulf
authors.forename: Winy
authors.initials: W
authors.lastname: Messens
authors.forename: Georges
authors.initials: G
authors.lastname: Daube
authors.forename: Marc
authors.initials: M
authors.lastname: Aerts
authors.forename: Koen
authors.initials: K
authors.lastname: Mintiens
mesh_terms: D000818:Animals; D008461:Meat Products / Q000382:microbiology*; D008962:Models, Theoretical; D018570:Risk Assessment; D012475:Salmonella / Q000302:isolation & purification*; D013552:Swine