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Dose‐response models in microbial risk assessment consider two steps in the process ultimately leading to illness: from exposure to (asymptomatic) infection, and from infection to (symptomatic) illness. Most data and theoretical approaches are available for the exposure‐infection step; the infection‐illness step has received less attention. Furthermore, current microbial risk assessment models do not account for acquired immunity. These limitations may lead to biased risk estimates. We consider effects of both dose dependency of the conditional probability of illness given infection, and acquired immunity to risk estimates, and demonstrate their effects in a case study on exposure to Campylobacter jejuni. To account for acquired immunity in risk estimates, an inflation factor is proposed. The inflation factor depends on the relative rates of loss of protection over exposure. The conditional probability of illness given infection is based on a previously published model, accounting for the within‐host dynamics of illness. We find that at low (average) doses, the infection‐illness model has the greatest impact on risk estimates, whereas at higher (average) doses and/or increased exposure frequencies, the acquired immunity model has the greatest impact. The proposed models are strongly nonlinear, and reducing exposure is not expected to lead to a proportional decrease in risk and, under certain conditions, may even lead to an increase in risk. The impact of different dose‐response models on risk estimates is particularly pronounced when introducing heterogeneity in the population exposure distribution.

Dairy products are an important source of high-quality animal proteins in developing countries, and increased consumption of these products by pregnant women and young children is advocated to reduce malnutrition and child stunting. However, the nutritional benefits of dairy products can be compromised by the presence of contaminants causing foodborne disease. These food safety risks are increased by frequent consumption of raw or inadequately heated dairy products. The World Health Organization published estimates of the global burden of foodborne disease in 2015, and attribution of this disease burden to specific food groups in 2017. It is estimated that each year, 600 million people fall ill because of foodborne disease, resulting in 435,000 deaths and a disease burden of 33 million Disability Adjusted Life Years (DALYs; equivalent to one healthy life year lost). Of this burden, 38% is attributed to animal-source foods (ASF), with 12% of the burden of ASF attributed to dairy products. The average global burden of dairy products is 20 DALYs per 100,000 population. The major contaminants in dairy are Mycobacterium bovis (9 DALYs/100,000, highest burden in Africa), Campylobacter spp. (4 DALYs/100,000, highest burden in Eastern Mediterranean), nontyphoidal Salmonella enterica (4 DALYs/100,000, highest burden in Africa) and Brucella spp. (1 DALY/100,000, highest burden in Eastern Mediterranean). The burdens of Cryptosporidium spp., Shiga-toxin producing Escherichia coli and Toxoplasma gondii are low (<1 DALY/100,000). Proper heating of dairy products would be effective in reducing these burdens substantially. The burden of chemical contaminants is less well documented. Adulteration is a potential problem, as illustrated by the melamine contamination incident in Chinese infant formula. Aflatoxin M1 (AFM1) is frequently observed in milk in concentrations higher than maximum tolerable limits in the USA and Europe. AFM1, which cannot be destroyed by heating milk, is a metabolite of aflatoxin B1 (AFB1) - a mycotoxin (fungal toxin) frequently found in corn, nuts, and the feed of dairy animals. However, the carcinogenic potential of AFM1 is significantly lower than that of AFB1, which is a known human liver carcinogen. The risk of liver cancer from current exposure levels to AFM1 is likely to be extremely low. There is limited evidence of an association between AFM1 and stunting, which requires further study. Dioxins cause a high disease burden specifically in Southeast Asia (14 DALYs/100,000); and several metals (lead, arsenic, methylmercury) each cause a global burden of 20-70 DALYs per 100,000. The contribution of dairy products to human exposure to these chemicals is unknown. ; United States Agency for International Development ; World Bank ; Government of Japan ; Ministry of Foreign Affairs, the Netherlands ; Centers for Disease Control and Prevention ; United States Food and Drug Administration ; United States Department of Agriculture ; World Health Organization

Campylobacteriosis in humans, caused by Campylobacter jejuni and Campylobacter coli, is the most common recognized bacterial zoonosis in the European Union and the United States. The acute phase is characterized by gastrointestinal symptoms. The long-term sequelae (Guillain-Barré syndrome, reactive arthritis, and postinfectious irritable bowel syndrome) contribute considerably to the disease burden. Attribution studies identified poultry as the reservoir responsible for up to 80% of the human Campylobacter infections. In the European Union, an estimated 30% of the human infections are associated with consumption and preparation of poultry meat. Until now, interventions in the poultry meat production chain have not been effectively introduced except for targeted interventions in Iceland and New Zealand. Intervention measures (eg, biosecurity) have limited effect or are hampered by economic aspects or consumer acceptance. In the future, a multilevel approach should be followed, aiming at reducing the level of contamination of consumer products rather than complete absence of Campylobacter.

The choice of a dose‐response model is decisive for the outcome of quantitative risk assessment. Single‐hit models have played a prominent role in dose‐response assessment for pathogenic microorganisms, since their introduction. Hit theory models are based on a few simple concepts that are attractive for their clarity and plausibility. These models, in particular the Beta Poisson model, are used for extrapolation of experimental dose‐response data to low doses, as are often present in drinking water or food products. Unfortunately, the Beta Poisson model, as it is used throughout the microbial risk literature, is an approximation whose validity is not widely known. The exact functional relation is numerically complex, especially for use in optimization or uncertainty analysis. Here it is shown that although the discrepancy between the Beta Poisson formula and the exact function is not very large for many data sets, the differences are greatest at low doses—the region of interest for many risk applications. Errors may become very large, however, in the results of uncertainty analysis, or when the data contain little low‐dose information. One striking property of the exact single‐hit model is that it has a maximum risk curve, limiting the upper confidence level of the dose‐response relation. This is due to the fact that the risk cannot exceed the probability of exposure, a property that is not retained in the Beta Poisson approximation. This maximum possible response curve is important for uncertainty analysis, and for risk assessment of pathogens with unknown properties.

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables "backward reasoning" when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.

We estimated the true incidence of campylobacteriosis and salmonellosis in the European Union (EU) in 2009. The estimate was based on disease risks of returning Swedish travellers, averaged over the years 2005–2009, and anchored to a Dutch population-based study on incidence and aetiology of gastroenteritis. For the 27 EU member states the incidence of campylobacteriosis was about 9·2 (95% CI 2·8–23) million cases, while the incidence of salmonellosis was 6·2 (95% CI 1·0–19) million cases. Only 1/47 (95% CI 14–117) cases of campylobacteriosis and one 1/58 (95% CI 9–172) cases of salmonellosis were reported in the EU. The incidence rate of campylobacteriosis in EU member states varied between 30 and 13 500/100 000 population and was significantly correlated with the prevalence of Campylobacter spp. in broiler chickens. The incidence rate of salmonellosis in EU member states varied between 16 and 11 800/100 000 population and was significantly correlated with the prevalence of Salmonella Enteritidis in laying hens.

We develop a model for bacterial cross‐contamination during food preparation in the domestic kitchen and apply this to the case of Campylobacter‐contaminated chicken breast. Building blocks of the model are the routines performed during food preparation, with their associated probabilities of bacterial transfer between food items and kitchen utensils. The model is used in a quantitative microbiological risk assessment (QMRA) of Campylobacter in the Netherlands. Using parameter values from the literature and performing elementary sensitivity analyses, we show that cross‐contamination can contribute significantly to the risk of Campylobacter infection and find that cleaning frequency of kitchen utensils and thoroughness of rinsing of raw food items after preparation has more impact on cross‐contamination than previously emphasized. Furthermore, we argue that especially more behavioral data on hygiene during food preparation is needed for a comprehensive Campylobacter risk assessment.