Effectiveness and Efficiency of Controlling Campylobacter on Broiler Chicken Meat

Campylobacter bacteria are an important cause of foodborne infections. We estimated the potential costs and benefits of a large number of possible interventions to decrease human exposure to Campylobacter by consumption of chicken meat, which accounts for 20-40% of all cases of human campylobacteriosis in the Netherlands. For this purpose, a farm-to-fork risk assessment model was combined with economic analysis and epidemiological data. Reduction of contamination at broiler farms could be efficient in theory. However, it is unclear which hygienic measures need to be taken and the costs can be very high. The experimental treatment of colonized broiler flocks with bacteriophages has proven to be effective and could also be cost efficient, if confirmed in practice. Since a major decrease of infections at the broiler farm is not expected in the short term, additional measures in the processing plant were also considered. At this moment, guaranteed Campylobacter-free chicken meat at the retail level is not realistic. The most promising interventions in the processing plant are limiting fecal leakage during processing and separation of contaminated and noncontaminated flocks (scheduling), followed by decontamination of the contaminated flock. New (faster and more sensitive) test methods to detect Campylobacter colonization in broilers flocks are a prerequisite for successful scheduling scenarios. Other methods to decrease the contamination of meat of colonized flocks such as freezing and heat treatment are more expensive and/or less effective than chemical decontamination.     

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.     

A Probabilistic Transmission Model of Salmonella in the Primary Broiler Production Chain

Annual data from the Finnish National Salmonella Control Programme were used to build up a probabilistic transmission model of salmonella in the primary broiler production chain. The data set consisted of information on grandparent, parent, and broiler flock populations. A probabilistic model was developed to describe the unknown true prevalences, vertical and horizontal transmissions, as well as the dynamical model of infections. By combining these with the observed data, the posterior probability distributions of the unknown parameters and variables could be derived. Predictive distributions were derived for the true number of infected broiler flocks under the adopted intervention scheme and these were compared with the predictions under no intervention. With the model, the effect of the intervention used in the programme, i.e., eliminating salmonella positive breeding flocks, could be quantitatively assessed. The 95% probability interval of the posterior predictive distribution for (broiler) flock prevalence under current (1999) situation was [1.3%-17.4%] (no intervention), and [0.9%-5.8%] (with intervention). In the scenario of one infected grandparent flock, these were [2.8%-43.1%] and [1.0%-5.9%], respectively. Computations were performed using WinBUGS and Matlab softwares.     

A QMRA for the Transmission of ESBL‐Producing Escherichia coli and Campylobacter from Poultry Farms to Humans Through Flies

The public health significance of transmission of ESBL‐producing Escherichia coli and Campylobacter from poultry farms to humans through flies was investigated using a worst‐case risk model. Human exposure was modeled by the fraction of contaminated flies, the number of specific bacteria per fly, the number of flies leaving the poultry farm, and the number of positive poultry houses in the Netherlands. Simplified risk calculations for transmission through consumption of chicken fillet were used for comparison, in terms of the number of human exposures, the total human exposure, and, for Campylobacter only, the number of human cases of illness. Comparing estimates of the worst‐case risk of transmission through flies with estimates of the real risk of chicken fillet consumption, the number of human exposures to ESBL‐producing E. coli was higher for chicken fillet as compared with flies, but the total level of exposure was higher for flies. For Campylobacter, risk values were nearly consistently higher for transmission through flies than for chicken fillet consumption. This indicates that the public health risk of transmission of both ESBL‐producing E. coli and Campylobacter to humans through flies might be of importance. It justifies further modeling of transmission through flies for which additional data (fly emigration, human exposure) are required. Similar analyses of other environmental transmission routes from poultry farms are suggested to precede further investigations into flies.     

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.     

A Quantitative Microbiological Risk Assessment for Campylobacter in Petting Zoos

The significance of petting zoos for transmission of Campylobacter to humans and the effect of interventions were estimated. A stochastic QMRA model simulating a child or adult visiting a Dutch petting zoo was built. The model describes the transmission of Campylobacter in animal feces from the various animal species, fences, and the playground to ingestion by visitors through touching these so‐called carriers and subsequently touching their lips. Extensive field and laboratory research was done to fulfill data needs. Fecal contamination on all carriers was measured by swabbing in 10 petting zoos, using Escherichia coli as an indicator. Carrier‐hand and hand‐lip touching frequencies were estimated by, in total, 13 days of observations of visitors by two observers at two petting zoos. The transmission from carrier to hand and from hand to lip by touching was measured using preapplied cow feces to which E. coli WG5 was added as an indicator. Via a Beta‐Poisson dose‐response function, the number of Campylobacter cases for the whole of the Netherlands (16 million population) in a year was estimated at 187 and 52 for children and adults, respectively, so 239 in total. This is significantly lower than previous QMRA results on chicken fillet and drinking water consumption. Scenarios of 90% reduction of the contamination (meant to mimic cleaning) of all fences and just goat fences reduces the number of cases by 82% and 75%, respectively. The model can easily be adapted for other fecally transmitted pathogens.     

Cross-Contamination During Food Preparation: A Mechanistic Model Applied to Chicken-Borne Campylobacter

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.     

Development and Application of a Stochastic Epidemic Model for the Transmission of Salmonella Typhimurium at the Farm Level of the Pork Production Chain

In previous work a deterministic model for the compartment level was built, taking into account the two different syndromes with which  Salmonella  Typhimurium appears at pig farms. Based on this model, a stochastic one was built in this work that simulated different compartmental sizes, taking into account compartments of 200 to 400 pigs. Multiple scenarios of starting conditions of infection (SCI) ranging from 0.25 to 100% were tested for each population size. The effect of each of these two factors on the probability of disease extinctions and the prevalence of each of the classes of the model and the risk groups of pigs were estimated. The results showed that the compartment population had an inverse effect on the probability of disease extinction. On the other hand, low SCI resulted in high levels of early extinctions reaching 45%, while higher SCI led to high levels of late extinctions. Early extinctions resulted in the absence of the pathogen from the compartment, while late extinctions did not assure it. This effect shows that reducing the population of the compartment combined with appropriate cleaning and good farming practices could have a positive effect in the reduction of the risk of introducing  S.  Typhimurium into the slaughtering procedure. On the other hand, the profile of seroprevalence at slaughter age allows for risk characterization of the farm, given the relative stability and the small variation for higher SCI.     

Food Safety in the Domestic Environment: The Effect of Consumer Risk Information on Human Disease Risks

The improvement of food safety in the domestic environment requires a transdisciplinary approach, involving interaction between both the social and natural sciences. This approach is applied in a study on risks associated with Campylobacter on broiler meat. First, some web-based information interventions were designed and tested on participant motivation and intentions to cook more safely. Based on these self-reported measures, the intervention supported by the emotion "disgust" was selected as the most promising information intervention. Its effect on microbial cross-contamination was tested by recruiting a set of participants who prepared a salad with chicken breast fillet carrying a known amount of tracer bacteria. The amount of tracer that could be recovered from the salad revealed the transfer and survival of Campylobacter and was used as a measure of hygiene. This was introduced into an existing risk model on Campylobacter in the Netherlands to assess the effect of the information intervention both at the level of exposure and the level of human disease risk. We showed that the information intervention supported by the emotion "disgust" alone had no measurable effect on the health risk. However, when a behavioral cue was embedded within the instruction for the salad preparation, the risk decreased sharply. It is shown that a transdisciplinary approach, involving research on risk perception, microbiology, and risk assessment, is successful in evaluating the efficacy of an information intervention in terms of human health risks. The approach offers a novel tool for science-based risk management in the area of food safety.     

The BSE Risk of Processing Meat and Bone Meal in Nonruminant Feed: A Quantitative Assessment for the Netherlands

The total ban on use of meat and bone meal (MBM) in livestock feed has been very successful in reducing bovine spongiform encephalopathy (BSE) spread, but also implies a waste of high-quality proteins resulting in economic and ecological loss. Now that the BSE epidemic is fading out, a partial lifting of the MBM ban might be considered. The objective of this study was to assess the BSE risk for the Netherlands if MBM derived from animals fit for human consumption, i.e., category 3 MBM, would be used in nonruminant feed. A stochastic simulation model was constructed that calculates (1) the probability that infectivity of undetected BSE-infected cows ends up with calves and (2) the quantity of infectivity ( Q_inf ) consumed by calves in case of such an incident. Three pathways were considered via which infectivity can reach cattle: (1) cross-contamination in the feed mill, (2) cross-contamination on the primary farm, and (3) pasture contamination. Model calculations indicate that the overall probability that infectivity ends up with calves is 3.2%. In most such incidents the Q_inf is extremely small (median = 6.5 × 10⁻¹² ID₅₀; mean = 1.8 × 10⁻⁴ ID₅₀), corresponding to an average probability of 1.3 × 10⁻⁴ that an incident results in ≥1 new BSE infections. Cross-contamination in the feed mill is the most risky pathway. Combining model results with Dutch BSE prevalence estimates for the coming years, it can be concluded that the BSE risk of using category 3 MBM derived from Dutch cattle in nonruminant feed is very low.     

A Linear Model for Managing the Risk of Antimicrobial Resistance Originating in Food Animals

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.     

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.     

Dose Response for Infectivity of Several Strains of Campylobacter jejuni in Chickens

Although some major risk studies have been done for Campylobacter jejuni, its dose response is not well characterized. Only a single human study is available, providing dose-response information for only a single isolate. As substantial heterogeneity in infectivity has been acknowledged for other pathogens, it remains unknown how well this single study represents the dose-response relation for this pathogen. As future human challenge studies with Campylobacter are unlikely, we have to find other means of studying its infectivity. Several dose-response studies have been done using chickens as host organisms. These studies may be used to obtain quantitative information on the variation in infectivity among different isolates of this pathogen. A hierarchical Bayesian model is well suited to describe heterogeneity, and we demonstrate how the beta-Poisson model of microbial infection may be adapted to allow for within- and between-isolate variation. Isolates tested in chickens can be categorized into two distinct groups: lab-adapted and fresh isolates, and we show how the hierarchical dose-response model can be used to quantitatively describe their differences. Fresh isolates show higher colonization potential and less within-isolate variation than lab isolates. The results indicate that Campylobacter jejuni is highly infectious in chickens. Different isolates show great variation in infectivity, especially between lab and fresh isolates, indicating that human clinical (volunteer) studies on infectivity must be interpreted cautiously.     

Quantitative Risk Assessment of Haemolytic and Uremic Syndrome Linked to O157:H7 and Non‐O157:H7 Shiga‐Toxin Producing Escherichia coli Strains in Raw Milk Soft Cheeses

Shiga‐toxin producing Escherichia coli (STEC) strains may cause human infections ranging from simple diarrhea to Haemolytic Uremic Syndrome (HUS). The five main pathogenic serotypes of STEC (MPS‐STEC) identified thus far in Europe are O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28. Because STEC strains can survive or grow during cheese making, particularly in soft cheeses, a stochastic quantitative microbial risk assessment model was developed to assess the risk of HUS associated with the five MPS‐STEC in raw milk soft cheeses. A baseline scenario represents a theoretical worst‐case scenario where no intervention was considered throughout the farm‐to‐fork continuum. The risk level assessed with this baseline scenario is the risk‐based level. The impact of seven preharvest scenarios (vaccines, probiotic, milk farm sorting) on the risk‐based level was expressed in terms of risk reduction. Impact of the preharvest intervention ranges from 76% to 98% of risk reduction with highest values predicted with scenarios combining a decrease of the number of cow shedding STEC and of the STEC concentration in feces. The impact of postharvest interventions on the risk‐based level was also tested by applying five microbiological criteria (MC) at the end of ripening. The five MCs differ in terms of sample size, the number of samples that may yield a value larger than the microbiological limit, and the analysis methods. The risk reduction predicted varies from 25% to 96% by applying MCs without preharvest interventions and from 1% to 96% with combination of pre‐ and postharvest interventions.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Modeling of interventions for reducing external Enterobacteriaceae contamination of broiler carcasses during processing

This article presents a mathematical model for the Enterobacteriaceae count on the surface of broiler chicken during slaughter and how it may be affected by different processing technologies. The model is based on a model originally developed for Campylobacter and has been adapted for Enterobacteriaceae using a Bayesian updating approach and hitherto unpublished data gathered from German abattoirs. The slaughter process in the model consists of five stages: input, scalding, defeathering, evisceration, washing, and chilling. The impact of various processing technologies along the broiler processing line on the Enterobacteriaceae count on the carcasses’ surface has been determined from literature data. The model is implemented in the software R and equipped with a graphical user interface which allows interactively to choose among different processing technologies for each stage along the processing line. Based on the choice of processing technologies the model estimates the Enterobacteriaceae count on the surface of each broiler chicken at each stage of processing. This result is then compared to a so-called baseline model which simulates a processing line with a fixed set of processing technologies. The model calculations showed how even very effective removal of bacteria on the exterior of the carcass in a previous step will be undone by the cross-contamination with leaked feces, if feces contain high concentrations of bacteria.     

Comparison of the Historic Recycling Risk for BSE in Three European Countries by Calculating the Basic Reproduction Ratio R0

A deterministic model of BSE transmission is used to calculate the R(0) values for specific years of the BSE epidemics in the United Kingdom (UK), the Netherlands (NL), and Switzerland (CH). In all three countries, the R(0) values decreased below 1 after the introduction of a ban on feeding meat and bone meal (MBM) to ruminants around the 1990s. A variety of additional measures against BSE led to further decrease of R(0) to about 0.06 in the years around 1998. The calculated R(0) values were consistent with the observations made on the surveillance results for UK, but were partially conflicting with the surveillance results for NL and CH. There was evidence for a dependency of the BSE epidemic in NL and CH from an infection source not considered in the deterministic transmission model. Imports of MBM and feed components can be an explanation for this discrepancy, and the importance of imports for these observations is discussed.     

Disease Spread Models to Estimate Highly Uncertain Emerging Diseases Losses for Animal Agriculture Insurance Policies: An Application to the U.S. Farm‐Raised Catfish Industry

Emerging diseases (ED) can have devastating effects on agriculture. Consequently, agricultural insurance for ED can develop if basic insurability criteria are met, including the capability to estimate the severity of ED outbreaks with associated uncertainty. The U.S. farm‐raised channel catfish (Ictalurus punctatus) industry was used to evaluate the feasibility of using a disease spread simulation modeling framework to estimate the potential losses from new ED for agricultural insurance purposes. Two stochastic models were used to simulate the spread of ED between and within channel catfish ponds in Mississippi (MS) under high, medium, and low disease impact scenarios. The mean (95% prediction interval (PI)) proportion of ponds infected within disease‐impacted farms was 7.6% (3.8%, 22.8%), 24.5% (3.8%, 72.0%), and 45.6% (4.0%, 92.3%), and the mean (95% PI) proportion of fish mortalities in ponds affected by the disease was 9.8% (1.4%, 26.7%), 49.2% (4.7%, 60.7%), and 88.3% (85.9%, 90.5%) for the low, medium, and high impact scenarios, respectively. The farm‐level mortality losses from an ED were up to 40.3% of the total farm inventory and can be used for insurance premium rate development. Disease spread modeling provides a systematic way to organize the current knowledge on the ED perils and, ultimately, use this information to help develop actuarially sound agricultural insurance policies and premiums. However, the estimates obtained will include a large amount of uncertainty driven by the stochastic nature of disease outbreaks, by the uncertainty in the frequency of future ED occurrences, and by the often sparse data available from past outbreaks.     

Development of a Self‐Regulated Dynamic Model for the Propagation of Salmonella Typhimurium in Pig Farms

A self‐regulated epidemic model was developed to describe the dynamics of Salmonella Typhimurium in pig farms and predict the prevalence of different risk groups at slaughter age. The model was focused at the compartment level of the pig farms and it included two syndromes, a high and a low propagation syndrome. These two syndromes generated two different classes of pigs, the High Infectious and the Low Infectious, respectively, which have different shedding patterns. Given the two different classes and syndromes, the Infectious Equivalent concept was used, which reflected the combination of High and Low Infectious pigs needed for the high propagation syndrome to be triggered. Using the above information a new algorithm was developed that decides, depending on the Infectious Equivalent, which of the two syndromes should be triggered. Results showed that the transmission rate of S. Typhimurium for the low propagation syndrome is around 0.115, pigs in Low Infectious class contribute to the transmission of the infection by 0.61–0.80 of pigs in High Infectious class and that the Infectious Equivalent should be above 10–14% of the population in order for the high propagation syndrome to be triggered. This self‐regulated dynamic model can predict the prevalence of the classes and the risk groups of pigs at slaughter age for different starting conditions of infection.     

Cost-Utility Analysis to Control Campylobacter on Chicken Meat—Dealing with Data Limitations

The current article describes the economic evaluation of interventions to control Campylobacter on chicken meat by means of a cost-utility analysis. Apart from the methodology used, the main focus of this article is on data gaps and assumptions made, and their impact on results and conclusions. The direct intervention costs, the relative risk, the disease burden (expressed in disability-adjusted life years (DALYs)), and the costs of illness for the various interventions are necessary inputs for the cost-utility analysis. The cost-utility ratio (CUR) -- the measure for efficiency -- is expressed in net costs per avoided DALY. Most data gaps were of a biological order, but for some interventions, information on costs was also scarce. As a consequence, assumptions had to be made, which had some impact on the estimated CUR. A higher (lower) incidence of Campylobacter infections associated with chicken meat, higher (lower) effectiveness, and lower (higher) intervention costs, respectively, would result in absolute better (worse) CUR estimates. By taking the perspective of all consumers eating Dutch chicken meat, rather than only the Dutch society, absolute better CUR estimates could be obtained. Indirect costs or a shift toward non-Dutch chicken meat would both result in higher CUR estimates. Despite the assumptions made, three interventions showed for most of the applied sensitivity analyses relatively favorable CUR estimates: limiting fecal leakage during processing, carcass decontamination by dipping in a chemical solution, and the phage therapy. However, all three do have some clauses.