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See detailAntimicrobial Resistance in the food chain: a review
Verraes, Claire; Van Boxtael, Sigrid; Van Meervenne, Eva et al

in International Journal of Environmental Research and Public Health (2013), 10

Antimicrobial resistant zoonotic pathogens present on food constitute a direct risk to public health. Antimicrobial resistance genes in commensal or pathogenic strains form an indirect risk to public ... [more ▼]

Antimicrobial resistant zoonotic pathogens present on food constitute a direct risk to public health. Antimicrobial resistance genes in commensal or pathogenic strains form an indirect risk to public health, as they increase the gene pool from which pathogenic bacteria can pick up resistance traits. Food can be contaminated with antimicrobial resistant bacteria and/or antimicrobial resistance genes in several ways. A first way is the presence of antibiotic resistant bacteria on food selected by the use of antibiotics during agricultural production. A second route is the possible presence of resistance genes in bacteria that are intentionally added during the processing of food (starter cultures, probiotics, bioconserving microorganisms and bacteriophages). A last way is through cross-contamination with antimicrobial resistant bacteria during food processing. Raw food products can be consumed without having undergone prior processing or preservation and therefore hold a substantial risk for transfer of antimicrobial resistance to humans, as the eventually present resistant bacteria are not killed. As a consequence, transfer of antimicrobial resistance genes between bacteria after ingestion by humans may occur. Under minimal processing or preservation treatment conditions, sublethally damaged or stressed cells can be maintained in the food, inducing antimicrobial resistance build-up and enhancing the risk of resistance transfer. Food processes that kill bacteria in food products, decrease the risk of transmission of antimicrobial resistance. [less ▲]

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See detailNUSAP: a method to evaluate the quality of assumptions in quantitative microbial risk assessment
Boone, Ides; Van der Stede, Yves; Dewulf, Jeroen et al

in Journal of Risk Research (2010), 13

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 ... [more ▼]

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. [less ▲]

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See detailMETZOON : Development of a quantitative microbial risk assessment for human salmonellosis through household consumption of fresh minced pork méat in Belgium.
Bollaerts, Kaatje; Messens, Winy; Delhalle, Laurent ULg et al

in Risk Analysis : An Official Publication of the Society for Risk Analysis (2009), 29(6), 820-840

A quantitative microbial risk assessment according to the Codex Alimentarius Principles is conducted to evaluate the risk on human salmonellosis through household consumption of fresh minced pork meat in ... [more ▼]

A quantitative microbial risk assessment according to the Codex Alimentarius Principles is conducted to evaluate the risk on human salmonellosis through household consumption of fresh minced pork meat in Belgium. The quantitative exposure assessment is carried out by building a modular risk model, called the METZOON-model, which covers the pork production from farm to fork. In the METZOON-model, the food production pathway is split up in six consecutive modules: (1) primary production, (2) transport & lairage, (3) slaughterhouse, (4) post-processing, (5) distribution & storage and (6) preparation & consumption. All the modules are developed to resemble as closely as possible the Belgian situation making use of the available national data. Several statistical refinements and improved modeling techniques are proposed. The model produces highly realistic results. The baseline predicted number of annual salmonellosis cases is 20513 [st. dev. 9061.45]. The risk is estimated higher for the susceptible population [est. 4.713 × 10−5; st. dev. 1.466 × 10−5] compared to the normal population [est. 7.704 × 10−6; st. dev. 5.414 × 10−6] and is mainly due to cross contamination via cook’s hands and only for a small extent to undercooking. [less ▲]

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See detailNUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain
Boone, Ides; Van der Stede, Yves; Bollaerts, Kaatje et al

in Risk Analysis : An Official Publication of the Society for Risk Analysis (2009), 29

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 ... [more ▼]

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. [less ▲]

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