Ides Boone

NUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain

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.

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

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.

Identification of Salmonella high risk pig farms in belgium suing semi-parametric quantile regression

Since consumption of pork contaminated with Salmonella is an important source of human Salmonellosis in Belgium, policy makers implement the identification of the 10% most Salmonella problematic pig herds. These herds are then encouraged to take control measures to reduce the Salmonella infection burden. To identify high risk herds, serological data were collected, reported as Sample to Positive ratios (SP-ratios). Objectives of the current study are to identify the 10% highest risk herds and to investigate risk factors associated with high Sa/monel/a prevalence. We propose to identify risk herds using semiparametric quantile regression. The risk factor analysis is conducted using Generalized Linear Mixed Models. Finally, practical rules to identify risk farms are deduced.