Vero-Cytotoxigenic Escherichia coli O157 in Pasteurized Milk Containers at the Point of Retail: A Qualitative Approach to Exposure Assessment

Dairies within the United Kingdom are classified into two groups, namely, off-farm and on-farm dairies (the latter often being small scale). We propose a model for the probability of milk sold as pasteurized reaching the point of retail contaminated with Vero-cytotoxigenic Escherichia coli (VTEC) O157 from each of these two pathways. We evaluate qualitatively the exposures inherent in each, and compare and contrast the two situations. The model framework is generic, in that it can in principle be used, with the relevant data modifications, to provide a qualitative assessment of the likely exposure from milk sold as pasteurized to any potentially milk-borne pathogenic organism. Furthermore, the methodological approaches presented are widely applicable in the microbial risk assessment field. The specific example presented will be of particular interest to the UK dairy and public health communities: we conclude that the exposure potential per liter consumed from milk processed in off-farm dairies is negligible, whereas the exposure potential per liter consumed from milk processed on-farm is low, but not sufficiently small to be regarded as negligible. We also identify areas of data sparsity, which need to be addressed for quantitative risk assessment to proceed, and highlight the critical points in the pasteurized milk production chain, which, in the event of a breakdown, have the potential to increase the risk to the consumer.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts’ knowledge about the microbial dynamics of a given food‐borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high‐dimensional second‐order QMRA model. The case study is a farm‐to‐fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time‐temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second‐order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.     

Quantitative Assessment of Human MRSA Risks from Swine

The public health community, news media, and members of the general public have expressed significant concern that methicillin‐resistant Staphylococcus aureus (MRSA) transmitted from pigs to humans may harm human health. Studies of the prevalence and dynamics of swine‐associated (ST398) MRSA have sampled MRSA at discrete points in the presumed causative chain leading from swine to human patients, including sampling bacteria from live pigs, retail meats, farm workers, and hospital patients. Nonzero prevalence is generally interpreted as indicating a potential human health hazard from MRSA infections, but quantitative assessments of resulting risks are not usually provided. This article integrates available data from several sources to construct a conservative (plausible upper bound) probability estimate for the actual human health harm (MRSA infections and fatalities) arising from ST398‐MRSA from pigs. The model provides plausible upper bounds of approximately one excess human infection per year among all U.S. pig farm workers, and one human infection per 31 years among the remaining total population of the United States. These results assume the possibility of transmission events not yet observed, so additional data collection may reduce these estimates further.     

A Comparative Exposure Assessment of Campylobacter in Ontario,Canada

To inform source attribution efforts, a comparative exposure assessment was developed to estimate the relative exposure to Campylobacter, the leading bacterial gastrointestinal disease in Canada, for 13 different transmission routes within Ontario, Canada, during the summer. Exposure was quantified with stochastic models at the population level, which incorporated measures of frequency, quantity ingested, prevalence, and concentration, using data from FoodNet Canada surveillance, the peer‐reviewed and gray literature, other Ontario data, and data that were specifically collected for this study. Models were run with @Risk software using Monte Carlo simulations. The mean number of cells of Campylobacter ingested per Ontarian per day during the summer, ranked from highest to lowest is as follows: household pets, chicken, living on a farm, raw milk, visiting a farm, recreational water, beef, drinking water, pork, vegetables, seafood, petting zoos, and fruits. The study results identify knowledge gaps for some transmission routes, and indicate that some transmission routes for Campylobacter are underestimated in the current literature, such as household pets and raw milk. Many data gaps were identified for future data collection consideration, especially for the concentration of Campylobacter in all transmission routes.     

Sensitivity Analysis for Critical Control Points Determination and Uncertainty Analysis to Link FSO and Process Criteria: Application to Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk‐based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.     

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.     

基于收益分享契约的VMI模型研究

本文讨论了一个季节性商品销售环境下的供应商管理库存(Vendor Managed Inventory,VMI)模型,建立了零售商与供应商之间的Stackelberg博弈模型,并求出了该博弈的均衡解.在此基础上,文章引入了剩余补贴策略对模型进行了优化,并找到了在协调供应链前提下实现供应链成员期望收益帕累托改进的最优集.用一个算例对结论进行了说明.     

A Model for Setting Performance Objectives for Salmonella in the Broiler Supply Chain

A stochastic model for setting performance objectives for Salmonella in the broiler supply chain was developed. The goal of this study was to develop a model by which performance objectives for Salmonella prevalence at various points in the production chain can be determined, based on a preset final performance objective at the end of the processing line. The transmission of Salmonella through the broiler production chain was modeled. The prevalence at flock level was calculated from the measured prevalence at sample level. The transmission model is based on data on the occurrence of Salmonella collected in the Dutch broiler production chain during several years. The developed model can be used by policymakers and industry to determine economically and politically acceptable performance objectives for various points of the production chain and to draw conclusions about which interventions are most appropriate.     

一个考虑回收契约的两级供应链库存决策模型研究

讨论了一个季节性商品销售环境下的两级供应链库存决策模型,在分析传统回收契约模型的基础上,引入了一个特殊的回收契约,讨论不同契约参数下供应链成员的最优决策.研究表明,在该契约下通过合理的契约参数设计,供应链可以实现帕累托改进.用一个算例对结论进行了说明.     

供应链道德风险逾渗的临界状态研究

为了确定能将供应链道德风险控制在不对供应链造成重大危害的临界点,构建以人为力度的供应链道德风险复杂网络模型,并据此给出供应链道德风险诱发的传递特性和拓扑特性的变化规律。按照逾渗理论,根据供应链中道德风险传递的特征,以随机攻击与蓄意攻击两种方式控制供应链道德风险的传递过程,确定出在随机攻击与蓄意攻击两种策略下,需要控制的最小数量的员工,从而确定供应链道德风险逾渗的临界状态。结论认为,随机攻击时供应链道德风险系统呈现鲁棒性,而蓄意攻击时供应链道德风险系统呈现脆弱性,蓄意攻击供应链道德风险这种策略是非常有效,并且这种临界概率取决于供应链系统的结构以及道德风险的特征。     

LIABILITY AS A CATALYST FOR PRODUCT STEWARDSHIP

Product stewardship is the set of practices related to reducing risks from chemical and process hazards in a company's supply chain. This paper develops an economic framework for evaluating supply chain liability as a driver for adopting product stewardship. Companies that outsource production may face residual liability for damages from use of their products, when liabilities are large enough to exceed supply chain partners' assets. The resulting potential liabilities can be mitigated through product stewardship. This paper shows that extended supply chain liabilities provide incentives for investing in reducing environmental hazards throughout the supply chain.     

Heuristic-Systematic Information Processing and Risk Judgment

The heuristic-systematic information processing model (HSM) holds that individuals will use one or both of these modes of information processing when attempting to evaluate information in order to arrive at a judgment. Systematic processing is defined by effortful scrutiny and comparison of information, whereas heuristic processing is defined by the use of cues to arrive more easily at a judgment. Antecedents to the two processing modes include information sufficiency, motivation, and self-efficacy. Structural equation modeling is used to examine competing configuration of this model and to evaluate the model as appropriate for predicting risk judgment. The model also is evaluated across three groups that vary with respect to their level of concern. These analyses are executed within a case study involving an epidemiological investigation of a suspected cancer cluster. The analysis confirms the HSM's theoretically proposed structure and shows it to be a useful vehicle for evaluating risk judgment. In the overall analysis, antecedent variables generally function as specified by theory. Systematic processing is predicted by greater motivation. Heuristic processing is predicted by information sufficiency. Self-efficacy is a significant predictor of both processing modes. And heuristic processing is shown to be associated with judgment of less risk. However, when the analysis is contrasted across three groups (those concerned about cancer, not concerned and uncertain) it is shown that the model is significantly more robust for the uncertain group. This finding may have implications for the use of the HSM in risk research specifically, and in field research generally.     

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.     

Dioxin in the Agricultural Food Chain

Polychlorinated dibenzo-p-dioxin's (PCDD) behavior in the agricultural food chain was modeled in this study. The source of PCDD was a municipal solid waste (MSW) incinerator. Of the farm livestock investigated only the beef cow and the milk cow were shown to contribute significant amounts of PCDD to humans. Milk was the single highest dietary source of PCDD. Comparison of the calculated human dose of PCDD from the agricultural food chain with an estimate human PCDD dose from inhalation of contaminated air revealed that the agricultural food chain could be responsible for a vast majority of the dose to an individual impacted by these facilities. Thus, these data suggest that operating MSW facilities in agricultural areas may result in enhanced PCDD exposure to individuals via the agricultural food chain. The data also suggest that milk and beef may be good sentinels to evaluate environmental impacts of these facilities.     

Risk of Antibiotic-Resistant Staphylococcus aureus Dispersion from Hog Farms: A Critical Review

The World Health Organization has declared antibiotic resistance “one of the biggest threats to global health.” Mounting evidence suggests that antibiotic use in industrial-scale hog farming is contributing to the spread of antibiotic-resistant Staphylococcus aureus. To capture available evidence on these risks, we searched peer-reviewed studies published before June 2017 and conducted a meta-analysis of these studies’ estimates of the prevalence of swine-associated, antibiotic-resistant S. aureus in animals, humans, and the environment. The 166 relevant studies revealed consistent evidence of livestock-associated methicillin-resistant S. aureus (MRSA) in hog herds (55.3%) raised with antibiotics. MRSA prevalence was also substantial in slaughterhouse pigs (30.4%), industrial hog operation workers (24.4%), and veterinarians (16.8%). The prevalence of swine-associated, multidrug-resistant S. aureus (MDRSA)—with resistance to three or more antibiotics—is not as well documented. Nonetheless, sufficient studies were available to estimate MDRSA pooled prevalence in conventional hog operation workers (15.0%), workers’ household members (13.0%), and community members (5.37%). Evidence also suggests that antibiotic-resistant S. aureus can be present in air, soil, water, and household surface samples gathered in or near high-intensity hog operations. An important caveat is that prevalence estimates for humans reflect colonization, not active infection, and the health risks of colonization remain poorly understood. In addition, these pooled results may not represent risks in specific locations, due to wide geographic variation. Nonetheless, these results underscore the need for additional preventive action to stem the spread of antibiotic-resistant pathogens from livestock operations and a streamlined reporting system to track this risk.     

Innovation and Sectoral Employment: A Trade–off between Compensation Mechanisms

The question whether technological progress displaces employment or whether technological advance is beneficial for the level of employment has been at the core of economic debate for over two centuries. The beneficial effect might be achieved by several compensation mechanisms within the economic system. In this paper we categorize these compensation mechanisms into two basic categories that reflect the different nature of the ideas ruling the compensation. We discriminate the mechanisms of employment despite innovation from employment via innovation. In the context of new innovation economics we model an artificial industry implementing both compensation mechanisms. Simulation analysis is used to examine both the short–run and long–run properties of the model. There we focus on the influence of wage restraint policy on the functioning of the compensation mechanism.     

“农户+餐饮企业”有机农产品供应链模式构建研究

有机农产品具有巨大的消费市场，但是目前有机农产品的供应链模式相对单一，不能满足有机市场发展的需要。本文提出"农户+餐饮企业"的新模式，并与农户直销模式及农超合作的模式进行比较。结论表明这种新模式能够使得农户和餐饮企业共同受益，并建议政府采取措施促进市场形成多种模式并存的有机农产品供应链格局。     

A new EPQ model: integrating lower pricing,rework and reject situations

The economic production quantity (EPQ) is a well-known and commonly used inventory control technique. It has been used for well over 50 years to optimize lot sizes in transportation/production. The standard results are easy to apply but are based on a number of assumptions. A common assumption in the EPQ model is that all units produced are of perfect quality, this will underestimate the actual required quantity. Many researchers have studied the effects after relaxing this assumption on the EPQ model. The previous studies had considered that imperfect quality and defective items are either to be reworked instantaneously and kept in stock or rejected at a cost. The objective of this paper is to provide a framework to integrate lower pricing, rework and reject situations into a single EPQ model. A 100% inspection is performed in order to identify the amount of good quality items, imperfect quality items and defective items in each lot. This model assumes that items of imperfect quality, not necessarily defective, could be used in another production situation or sold to a particular purchaser at a lower price. The electronic and clothing industries give good examples for such situations. A mathematical model is developed and a numerical example is presented to illustrate the solution procedures. It is found that the time factor of when to sell the imperfect items is critical, as this decision will affect the inventory cost and the batch quantities.     

Risk Assessment of Human Listeriosis from Semisoft Cheeses Made from Raw Sheep's Milk in Lazio and Tuscany (Italy)

Semisoft cheese made from raw sheep's milk is traditionally and economically important in southern Europe. However, raw milk cheese is also a known vehicle of human listeriosis and contamination of sheep cheese with Listeria monocytogenes has been reported. In the present study, we have developed and applied a quantitative risk assessment model, based on available evidence and challenge testing, to estimate risk of invasive listeriosis due to consumption of an artisanal sheep cheese made with raw milk collected from a single flock in central Italy. In the model, contamination of milk may originate from the farm environment or from mastitic animals, with potential growth of the pathogen in bulk milk and during cheese ripening. Based on the 48‐day challenge test of a local semisoft raw sheep's milk cheese we found limited growth only during the initial phase of ripening (24 hours) and no growth or limited decline during the following ripening period. In our simulation, in the baseline scenario, 2.2% of cheese servings are estimated to have at least 1 colony forming unit (CFU) per gram. Of these, 15.1% would be above the current E.U. limit of 100 CFU/g (5.2% would exceed 1,000 CFU/g). Risk of invasive listeriosis per random serving is estimated in the 10^?12 range (mean) for healthy adults, and in the 10^?10 range (mean) for vulnerable populations. When small flocks (10–36 animals) are combined with the presence of a sheep with undetected subclinical mastitis, risk of listeriosis increases and such flocks may represent a public health risk.