Quantitative Risk Assessment of Listeria monocytogenes in French Cold-Smoked Salmon: I. Quantitative Exposure Assessment

A quantitative assessment of the exposure to Listeria monocytogenes from cold-smoked salmon (CSS) consumption in France is developed. The general framework is a second-order (or two-dimensional) Monte Carlo simulation, which characterizes the uncertainty and variability of the exposure estimate. The model takes into account the competitive bacterial growth between L. monocytogenes and the background competitive flora from the end of the production line to the consumer phase. An original algorithm is proposed to integrate this growth in conditions of varying temperature. As part of a more general project led by the French Food Safety Agency (Afssa), specific data were acquired and modeled for this quantitative exposure assessment model, particularly time-temperature profiles, prevalence data, and contamination-level data. The sensitivity analysis points out the main influence of the mean temperature in household refrigerators and the prevalence of contaminated CSS on the exposure level. The outputs of this model can be used as inputs for further risk assessment.     

Stochastically Modeling Listeria Monocytogenes Growth in Farm Tank Milk

This article presents a Listeria monocytogenes growth model in milk at the farm bulk tank stage. The main objective was to judge the feasibility and value to risk assessors of introducing a complex model, including a complete thermal model, within a microbial quantitative risk assessment scheme. Predictive microbiology models are used under varying temperature conditions to predict bacterial growth. Input distributions are estimated based on data in the literature, when it is available. If not, reasonable assumptions are made for the considered context. Previously published results based on a Bayesian analysis of growth parameters are used. A Monte Carlo simulation that forecasts bacterial growth is the focus of this study. Three scenarios that take account of the variability and uncertainty of growth parameters are compared. The effect of a sophisticated thermal model taking account of continuous variations in milk temperature was tested by comparison with a simplified model where milk temperature was considered as constant. Limited multiplication of bacteria within the farm bulk tank was modeled. The two principal factors influencing bacterial growth were found to be tank thermostat regulation and bacterial population growth parameters. The dilution phenomenon due to the introduction of new milk was the main factor affecting the final bacterial concentration. The results show that a model that assumes constant environmental conditions at an average temperature should be acceptable for this process. This work may constitute a first step toward exposure assessment for L. monocytogenes in milk. In addition, this partly conceptual work provides guidelines for other risk assessments where continuous variation of a parameter needs to be taken into account.     

A Quantitative Risk Assessment of Waterborne Cryptosporidiosis in France Using Second-Order Monte Carlo Simulation

A pragmatic quantitative risk assessment (QRA) of the risks of waterborne Cryptosporidium parvum infection and cryptosporidiosis in immunocompetent and immunodeficient French populations is proposed. The model takes into account French specificities such as the French technique for oocyst enumeration performance and tap water consumption. The proportion of infective oocysts is based on literature review and expert knowledge. The probability of infection for a given number of ingested viable oocysts is modeled using the exponential dose-response model applied on published data from experimental infections in immunocompetent human volunteers challenged with the IOWA strain. Second-order Monte Carlo simulations are used to characterize the uncertainty and variability of the risk estimates. Daily risk of infection and illness for the immunocompetent and the immunodeficient populations are estimated according to the number of oocysts observed in a single storage reservoir water sample. As an example, the mean daily risk of infection in the immunocompetent population is estimated to be 1.08 x 10(-4) (95% confidence interval: [0.20 x 10(-4); 6.83 x 10(-4)]) when five oocysts are observed in a 100 L storage reservoir water sample. Annual risks of infection and disease are estimated from a set of oocyst enumeration results from distributed water samples, assuming a negative binomial distribution of day-to-day contamination variation. The model and various assumptions used in the model are fully explained and discussed. While caveats of this model are well recognized, this pragmatic QRA could represent a useful tool for the French Food Safety Agency (AFSSA) to define recommendations in case of water resource contamination by C. parvum whose infectivity is comparable to the IOWA strain.     

Gender differences in gambling preferences and problem gambling: a network-level analysis

Most gambling studies have a gender-blind research approach, although a large body of scientific evidence suggests that gambling in females is on the rise and that males and females have different gambling behaviours and experience specific gambling-related harm. This study addressed these gender differences using a network analysis, an innovative approach considering disorders/concepts as dynamic systems of interacting symptoms/items. Data on gambling activities, problem gambling, substance use and mental health were collected in a representative sample of French adult gamblers (n = 8805). The study capitalized on the network analysis directly to compare associations of specific gambling activities with gambling disorder symptoms separately for both genders. The network analysis revealed that problem gambling was strongly associated with gambling machines among females, whereas it was related to sports betting, poker and casino games among males. The networks that included substance use and mental health showed that substance use was related to specific gambling activities. These findings confirm the links between various gender-specific gambling patterns and problem gambling and suggest a need to consider these gender differences to improve prevention efforts. More broadly, the present study further supports the importance of gender differences for gambling research and policy.     

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.     

Uncertainty Distribution Associated with Estimating a Proportion in Microbial Risk Assessment

The uncertainty associated with estimates should be taken into account in quantitative risk assessment. Each input's uncertainty can be characterized through a probabilistic distribution for use under Monte Carlo simulations. In this study, the sampling uncertainty associated with estimating a low proportion on the basis of a small sample size was considered. A common application in microbial risk assessment is the estimation of a prevalence, proportion of contaminated food products, on the basis of few tested units. Three Bayesian approaches (based on beta(0, 0), beta(1/2, 1/2), and beta(l, 1)) and one frequentist approach (based on the frequentist confidence distribution) were compared and evaluated on the basis of simulations. For small samples, we demonstrated some differences between the four tested methods. We concluded that the better method depends on the true proportion of contaminated products, which is by definition unknown in common practice. When no prior information is available, we recommend the beta (1/2, 1/2) prior or the confidence distribution. To illustrate the importance of these differences, the four methods were used in an applied example. We performed two-dimensional Monte Carlo simulations to estimate the proportion of cold smoked salmon packs contaminated by Listeria monocytogenes, one dimension representing within-factory uncertainty, modeled by each of the four studied methods, and the other dimension representing variability between companies.     

A professionalization framework to understand the structuring of work

The notion of professionalization is widely used both by managers and researchers. This use questions the traditional definition of profession. This paper reviews a book published in 1988 – The System of Professions – written by a sociologist, A. Abbott. Though this book had a strong impact in the field of the sociology of professions, it has remained unknown by many researchers in management science. His pragmatic approach to the concepts of profession and professionalization helps achieve better understanding of work structuring and as a consequence can be useful to address issues such as human resources management, organizational control or even the management of information systems.