Quantitative Microbiological Risk Assessment Model Research Articles

Risk assessment of proteolytic Clostridium botulinum in canned foie gras

In this study, a risk assessment of proteolytic Clostridium botulinum in canned foie gras was performed, the number of illnesses per year in France due to C. botulinum in foie gras was estimated. Data on initial level in raw materials were collected at manufacturers and analysed using a Negative Binomial distribution. The effect of the usual foie gras heat treatment (equivalent time at 121°C: F0=0.5min) was considered at two levels: first, it led to an inactivation (estimated to 2.3 log); second it led to a spore injury and then to a spore inhibition. This latter effect was assessed by analysing data from a challenge test study carried out with Clostridium sporogenes spores in the foie gras product. The probability of spore recovering after thermal inhibition was estimated to 9.5×10−8 (corresponding to 7.0 log). The data on the consumption pattern were collected on the French market. The Quantitative Microbiological Risk Assessment (QMRA) model and all the assumptions are reported in detail in the study. The initial contamination of raw materials, effect of thermal treatment on microbial inactivation and spore inhibition were handled mathematically using a probabilistic framework, considering only the variability dimension. The model was implemented in Excel and run through Monte Carlo simulation, using @Risk software. In parallel, epidemiological data collected from the French Institute for Public Health Surveillance during the period 2001–2012 were used to estimate an Appropriate Level Of Protection (ALOP) and then a Food Safety Objective (FSO): ALOP equalled to 2.5×10−3 illnesses per million inhabitant per year, FSO equalled to 1.4×10−9 foie gras portions containing C. botulinum spore (expressed in decimal logarithm, FSO=−8.9). The QMRA model output values were smaller, but on the same order of magnitude as these two figures: 8.0×10−4 illnesses per million inhabitants per year, and, 4.5×10−10 (−9.3 log) foie gras portions containing C. botulinum spores able to recover. It was then possible to conclude that the current practices regarding thermal treatment of canned foie gras are sufficient to control the risk of C. botulinum in foie gras in France.

A risk-based microbiological criterion that uses the relative risk as the critical limit

A risk-based microbiological criterion is described, that is based on the relative risk associated to the analytical result of a number of samples taken from a food lot. The acceptable limit is a specific level of risk and not a specific number of microorganisms, as in other microbiological criteria. The approach requires the availability of a quantitative microbiological risk assessment model to get risk estimates for food products from sampled food lots. By relating these food lot risk estimates to the mean risk estimate associated to a representative baseline data set, a relative risk estimate can be obtained. This relative risk estimate then can be compared with a critical value, defined by the criterion. This microbiological criterion based on a relative risk limit is particularly useful when quantitative enumeration data are available and when the prevalence of the microorganism of concern is relatively high. The use of the approach is therefore illustrated with an example of Campylobacter in broiler meat. It shows that this microbiological criterion can be applied in practice. An advantage of the method is that the acceptable limit is directly defined in terms of risk, without the need to define other food safety standards.

Graphical models and Bayesian domains in risk modelling: Application in microbiological risk assessment

Quantitative microbiological risk assessment (QMRA) models are used to reflect knowledge about complex real-world scenarios for the propagation of microbiological hazards along the feed and food chain. The aim is to provide insight into interdependencies among model parameters, typically with an interest to characterise the effect of risk mitigation measures. A particular requirement is to achieve clarity about the reliability of conclusions from the model in the presence of uncertainty. To this end, Monte Carlo (MC) simulation modelling has become a standard in so-called probabilistic risk assessment.In this paper, we elaborate on the application of Bayesian computational statistics in the context of QMRA. It is useful to explore the analogy between MC modelling and Bayesian inference (BI). This pertains in particular to the procedures for deriving prior distributions for model parameters. We illustrate using a simple example that the inability to cope with feedback among model parameters is a major limitation of MC modelling. However, BI models can be easily integrated into MC modelling to overcome this limitation. We refer a BI submodel integrated into a MC model to as a “Bayes domain”. We also demonstrate that an entire QMRA model can be formulated as Bayesian graphical model (BGM) and discuss the advantages of this approach. Finally, we show example graphs of MC, BI and BGM models, highlighting the similarities among the three approaches.

Quantitative microbiological risk assessment (QMRA) of food-borne zoonoses at the European level

At EU level, the European Food Safety Authority (EFSA) is the body responsible for risk assessment in the field of food and feed safety. Following a request from European Union (EU) risk managers, the EFSA Panel on Biological Hazards (BIOHAZ) provided, for the first time, two EU-wide farm-to-fork quantitative microbiological risk assessments (QMRA), with regard to Salmonella in slaughter and breeder pigs and Campylobacter in broilers. The Scientific Opinion on a QMRA of Salmonella in pigs represented a major step forward in terms of modelling from farm to consumption as it took into account the variability between and within EU Member States. This QMRA model was developed to estimate the prevalence of infection/contamination and the microbial load from the farm to the point of consumption (exposure) and then estimating the probability of infection. It was used to investigate the effect of interventions to control Salmonella in pigs at different points of the food chain and resulted in a hierarchy of suggested on farm and slaughterhouse control measures, with estimates of the reduction of human cases. To model the effect of interventions from farm to fork on the incidence of human campylobacteriosis, a QMRA model was developed and used in the framework of the Scientific Opinion on a QMRA of Campylobacter in broiler meat. Reductions to the public health risk of campylobacteriosis could be achieved through a variety of interventions, both in primary production or at the slaughterhouse, with different impacts. Reductions of public health risk using targets at primary production or microbiological criteria were also estimated through modelling using additional models. QMRA of food-borne pathogens at European level has proven a useful/good tool to enable risk managers to evaluate the feasibility and the cost-benefit ratio of introducing control measures and targets to further protect public health of European consumers.

A Risk Assessment of Campylobacteriosis and Salmonellosis Linked to Chicken Meals Prepared in Households in Dakar, Senegal

We used a quantitative microbiological risk assessment model to describe the risk of Campylobacter and Salmonella infection linked to chicken meals prepared in households in Dakar, Senegal. The model uses data collected specifically for this study, such as the prevalence and level of bacteria on the neck skin of chickens bought in Dakar markets, time-temperature profiles recorded from purchase to consumption, an observational survey of meal preparation in private kitchens, and detection and enumeration of pathogens on kitchenware and cooks' hands. Thorough heating kills all bacteria present on chicken during cooking, but cross-contamination of cooked chicken or ready-to-eat food prepared for the meal via kitchenware and cooks' hands leads to a high expected frequency of pathogen ingestion. Additionally, significant growth of Salmonella is predicted during food storage at ambient temperature before and after meal preparation. These high exposures lead to a high estimated risk of campylobacteriosis and/or salmonellosis in Dakar households. The public health consequences could be amplified by the high level of antimicrobial resistance of Salmonella and Campylobacter observed in this setting. A significant decrease in the number of ingested bacteria and in the risk could be achieved through a reduction of the prevalence of chicken contamination at slaughter, and by the use of simple hygienic measures in the kitchen. There is an urgent need to reinforce the hygiene education of food handlers in Senegal.

Quantitative microbiological risk assessment as a tool to obtain useful information for risk managers — Specific application to Listeria monocytogenes and ready-to-eat meat products

The presence of Listeria monocytogenes in a sliced cooked, cured ham-like meat product was quantitatively assessed. Sliced cooked, cured meat products are considered as high risk products. These ready-to-eat, RTE, products (no special preparation, e.g. thermal treatment, before eating is required), support growth of pathogens (high initial pH = 6.2–6.4 and water activity = 0.98–0.99) and has a relatively long period of storage at chilled temperatures with a shelf life equal to 60 days based on manufacturer's instructions. Therefore, in case of post-process contamination, even with low number of cells, the microorganism is able to reach unacceptable levels at the time of consumption. The aim of this study was to conduct a Quantitative Microbiological Risk Assessment (QMRA) on the risk of L. monocytogenes presence in RTE meat products. This may help risk managers to make decisions and apply control measures with ultimate objective the food safety assurance. Examples are given to illustrate the development of practical risk management strategies based on the results obtained from the QMRA model specifically developed for this pathogen/food product combination.

Studying the effects of POs and MCs on the Salmonella ALOP with a quantitative risk assessment model for beef production

The Finnish Salmonella Control Programme and the special guarantees (SG) of import concerning Salmonella in the beef production chain were examined within the risk analysis framework. The appropriate level of protection (ALOP de facto since not referred to as ALOP in regulation), performance objectives (PO), and microbiological criteria (MC) were identified along the beef production chain. A quantitative microbiological risk assessment (QMRA) model using the Bayesian probabilistic method was developed for the beef chain to evaluate the capability of different POs to contribute to the ALOP. The influence of SGs was studied as an intervention protecting Finnish consumers. The QMRA made it possible to translate an ALOP without a stated food safety objective (FSO) to POs when implemented for both ready-to-eat (RTE) and non-RTE products. According to the results, the Finnish ALOP de facto for beef, beef preparations and products (10 human Salmonella cases/100,000) was reached in all of the years 1996–2004. However, if the prevalence at the slaughter, domestic cut beef, and retail levels would increase to the level of POs set (maximum 1%), the ALOP de facto would be exceeded by a factor of roughly two. On the other hand, the zero tolerance applied to MCs would keep the true Salmonella prevalence at production steps with POs clearly below 1%, and the ALOP would then be achievable. The influence of SGs on the total exposure was so small (average 0.1% added to the total prevalence of beef-derived foods at retail) that their relevance may be doubted with the current amount and Salmonella prevalence in beef-derived imports. On the other hand, a change in import profile could increase the protective effect of the SGs. Although practical follow-up has to be carried out as apparent prevalences, the objectives and criteria should be estimated as true prevalences and incidences with quantified uncertainties in order to achieve a sound, transparent scientific-based understanding of the risk. The QMRA model developed here provided a useful tool for studying the connection between the ALOP and other targets in the production chain.

A Risk Assessment Model for Campylobacter in Broiler Meat

A quantitative microbiological risk assessment model describes the transmission of Campylobacter through the broiler meat production chain and at home, from entering the processing plant until consumption of a chicken breast fillet meal. The exposure model is linked to a dose-response model to allow estimation of the incidence of human campylobacteriosis. The ultimate objective of the model is to serve as a tool to assess the effects of interventions to reduce campylobacteriosis in the Netherlands. The model describes some basic mechanistics of processing, including the nonlinear effects of cross-contamination between carcasses and their leaking feces. Model input is based on the output of an accompanying farm model and Dutch count data of Campylobacters on the birds' exterior and in the feces. When processing data are lacking, expert judgment is used for model parameter estimation. The model shows that to accurately assess of the effects of interventions, numbers of Campylobacter have to be explicitly incorporated in the model in addition to the prevalence of contamination. Also, as count data usually vary by several orders of magnitude, variability in numbers within and especially between flocks has to be accounted for. Flocks with high concentrations of Campylobacter in the feces that leak from the carcasses during industrial processing seem to have a dominant impact on the human incidence. The uncertainty in the final risk estimate is large, due to a large uncertainty at several stages of the chain. Among others, more quantitative count data at several stages of the production chain are needed to decrease this uncertainty. However, this uncertainty is smaller when relative risks of interventions are calculated with the model. Hence, the model can be effectively used by risk management in deciding on strategies to reduce human campylobacteriosis.