Eric D. Ebel

Estimating the annual fraction of eggs contaminated with Salmonella enteritidis in the United States

Using available data on the occurrence of Salmonella enteritidis (SE) in US layer flocks and eggs, and a probabilistic scenario tree method, an estimate of the fraction of SE-contaminated eggs produced annually is derived with attendant uncertainty. In lieu of a definitive prevalence survey, the approach presented here provides insight to the relative contribution of various pathways leading to contaminated eggs. A Monte Carlo model with four branches is developed. The first branch predicts the proportion of all US flocks that are SE-affected. The second branch apportions SE-affected flocks into three categories (high, moderate, and low level affected flocks) based on population-adjusted epidemiologic data. The third branch predicts the proportion of affected flocks that are molted and producing eggs during a high risk period subsequent to molt. The fourth branch predicts the fraction of contaminated eggs produced by flocks of the type described by the pathway (e.g. high level affected flocks that are not molted) based on egg sampling evidence from naturally infected flocks. The model is simulated to account for uncertainty in the data used to estimate the branch probabilities. Correlation analysis is used to estimate the sensitivity of model output to various model inputs. The output of this model is an uncertainty distribution for the fraction of all eggs that are SE-contaminated during 1 year of production in the US. The expected value of this distribution is approximately one SE-affected egg in every 20,000 eggs annually produced, and the 90% certainty interval is between one SE-contaminated egg in 30,000 eggs, and one SE-contaminated egg in 12,000 eggs. The model estimates that an average of 14% of all eggs (i.e. contaminated and not contaminated) from affected flocks are produced by high level, non-molted affected flocks, but these flocks are estimated to account for more than two-thirds of the total fraction of contaminated eggs produced annually. Sensitivity analysis also suggests that the proportion of affected flocks that are high level flocks - and the egg contamination frequency for these types of flocks - are the most sensitive model inputs. The models pathways provide a framework for evaluating interventions to reduce the number of contaminated eggs produced in the US. Furthermore, sensitivity analysis of the model identifies those inputs whose uncertainty is most influential on the models output. Future farm-level research priorities can be established on the basis of this analysis, but public policy decisions require a fuller exposure assessment and dose-response analysis to account for microbial growth dynamics, meal preparation, and consumption demographics among US egg consumers.

Analysis of Salmonella serotypes from selected carcasses and raw ground products sampled prior to implementation of the Pathogen Reduction; Hazard Analysis and Critical Control Point Final Rule in the US

In July 1996, the US Department of Agriculture (USDA), Food Safety and Inspection Service (FSIS), published the Pathogen Reduction; Hazard Analysis and Critical Control Point (HACCP) Systems final rule to improve food safety of meat and poultry products. The final rule established, among other requirements, pathogen reduction performance standards for Salmonella for food animal carcasses and raw ground products. The final rule is to be fully implemented in three stages in successively smaller federally inspected meat and poultry slaughter and processing establishments. Implementation began in January 1998 and was completed in January 2000. Samples of carcasses of four species of food animals (cattle, swine, chickens, turkeys), and raw ground product from each of these species, were collected by FSIS from establishments prior to implementation of the final rule and cultured for Salmonella. This paper reports Salmonella serotype results of samples collected from June 1997 through August 1998. These results represent a baseline for future comparisons.

Dose-response envelope for Escherichia coli O157:H7.

Escherichia coli O157:H7 is an emerging food and waterborne pathogen in the U.S. and internationally. The objective of this work was to develop a dose-response model for illness by this organism that bounds the uncertainty in the dose-response relationship. No human clinical trial data are available for E. coli O157:H7, but such data are available for two surrogate pathogens: enteropathogenic E. coli (EPEC) and Shigella dysenteriae. E. coli O157:H7 outbreak data provide an initial estimate of the most likely value of the dose-response relationship within the bounds of an envelope defined by beta-Poisson dose-response models fit to the EPEC and S. dysenteriae data. The most likely value of the median effective dose for E. coli O157:H7 is estimated to be approximately 190[emsp4 ]000 colony forming units (cfu). At a dose level of 100[emsp4 ]cfu, the median response predicted by the model is six percent.

Considering the complexity of microbial community dynamics in food safety risk assessment

The potential for competitive inhibition to limit the growth of microbial pathogens in food raises questions about the external validity of typical predictive microbiology studies and suggests the need to consider microbial community dynamics in food safety risk assessment. Ecological theory indicates, however, that community dynamics are highly complex and may be very sensitive to initial conditions and random variation. Seemingly incongruous empirical results for Escherichia coli O157:H7 in ground beef are shown to be consistent with a simple theoretical model of interspecific competition. A potential means of incorporating community-level microbial dynamics into the food safety risk assessment process is explored.

Considering uncertainty in comparing the burden of illness due to foodborne microbial pathogens

The uncertainty attendant to burden-of-illness estimates should be taken into account in comparing the public health impact of different foodborne pathogens. In this paper, decision analysis concepts are applied to the comparisons of pathogen-specific burden-of-illness estimates. In situations wherein the magnitude of uncertainty varies, the rank order of pathogen-specific burden-of-illness estimates is sensitive to the decisional criteria applied. To illustrate the magnitude of attendant uncertainty in pathogen-specific foodborne-illness estimates, probabilistic risk assessment methods are used to characterize the uncertainty regarding the burden of illness due to Escherichia coli O157:H7. The magnitude of uncertainty about the burden of food-related illness due to E. coli O157:H7 is substantial, ranging from less than 50,000 to more than 120,000 cases/year. This example underscores the importance of considering the uncertainty attendant to burden-of-illness estimates in comparing the public health impacts of different pathogens. Although some would argue that the expected value of the number of illnesses provides the best estimate for decision-making, this merely reflects a decision-making rule of convention and not a scientific truism.

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm-to-table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food-safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.

Temporal patterns of Campylobacter contamination on chicken and their relationship to campylobacteriosis cases in the United States

The proportion of Campylobacter contaminated food and water samples collected by different surveillance systems often exhibit seasonal patterns. In addition, the incidence of foodborne campylobacteriosis also tends to exhibit strong seasonal patterns. Of the various product classes, the occurrence of Campylobacter contamination can be high on raw poultry products, and chicken is often thought to be one of the leading food vehicles for campylobacteriosis. Two different federal agencies in the United States collected samples of raw chicken products and tested them for the presence of Campylobacter. During the same time period, a consortium of federal and state agencies operated a nationwide surveillance system to monitor cases of campylobacteriosis in the United States. This study uses a common modeling approach to estimate trends and seasonal patterns in both the proportion of raw chicken product samples that test positive for Campylobacter and cases of campylobacteriosis. The results generally support the hypothesis of a weak seasonal increase in the proportion of Campylobacter positive chicken samples in the summer months, though the number of Campylobacter on test-positive samples is slightly lower during this time period. In contrast, campylobacteriosis cases exhibit a strong seasonal pattern that generally precedes increases in contaminated raw chicken. These results suggest that while contaminated chicken products may be responsible for a substantial number of campylobacteriosis cases, they are most likely not the primary driver of the seasonal pattern in human illness.

Characterizing uncertainty when evaluating risk management metrics: Risk assessment modeling of Listeria monocytogenes contamination in ready-to-eat deli meats

This report illustrates how the uncertainty about food safety metrics may influence the selection of a performance objective (PO). To accomplish this goal, we developed a model concerning Listeria monocytogenes in ready-to-eat (RTE) deli meats. This application used a second order Monte Carlo model that simulates L. monocytogenes concentrations through a series of steps: the food-processing establishment, transport, retail, the consumers home and consumption. The model accounted for growth inhibitor use, retail cross contamination, and applied an FAO/WHO dose response model for evaluating the probability of illness. An appropriate level of protection (ALOP) risk metric was selected as the average risk of illness per serving across all consumed servings-per-annum and the model was used to solve for the corresponding performance objective (PO) risk metric as the maximum allowable L. monocytogenes concentration (cfu/g) at the processing establishment where regulatory monitoring would occur. Given uncertainty about model inputs, an uncertainty distribution of the PO was estimated. Additionally, we considered how RTE deli meats contaminated at levels above the PO would be handled by the industry using three alternative approaches. Points on the PO distribution represent the probability that - if the industry complies with a particular PO - the resulting risk-per-serving is less than or equal to the target ALOP. For example, assuming (1) a target ALOP of -6.41 log10 risk of illness per serving, (2) industry concentrations above the PO that are re-distributed throughout the remaining concentration distribution and (3) no dose response uncertainty, establishment POs of -4.98 and -4.39 log10 cfu/g would be required for 90% and 75% confidence that the target ALOP is met, respectively. The PO concentrations from this example scenario are more stringent than the current typical monitoring level of an absence in 25 g (i.e., -1.40 log10 cfu/g) or a stricter criteria of absence in 125 g (i.e., -2.1 log10 cfu/g). This example, and others, demonstrates that a PO for L. monocytogenes would be far below any current monitoring capabilities. Furthermore, this work highlights the demands placed on risk managers and risk assessors when applying uncertain risk models to the current risk metric framework.

Use of a Markov-chain Monte Carlo model to evaluate the time value of historical testing information in animal populations

Quantitative risk assessments are now required to support many regulatory decisions involving infectious diseases of animals. Current methods, however, do not consider the relative values of historical and recent data. A Markov-chain model can use specific disease characteristics to estimate the present value of disease information collected in the past. Uncertainty about the disease characteristics and variability among animals and herds can be accounted for with Monte Carlo simulation modeling. This results in a transparent method of valuing historical testing information for use in risk assessments. We constructed such a model to value historical testing information in a more-transparent and -reproducible manner. Applications for this method include trade, food safety, and domestic animal-health regulations.

Methods for fitting a parametric probability distribution to most probable number data

Every year hundreds of thousands, if not millions, of samples are collected and analyzed to assess microbial contamination in food and water. The concentration of pathogenic organisms at the end of the production process is low for most commodities, so a highly sensitive screening test is used to determine whether the organism of interest is present in a sample. In some applications, samples that test positive are subjected to quantitation. The most probable number (MPN) technique is a common method to quantify the level of contamination in a sample because it is able to provide estimates at low concentrations. This technique uses a series of dilution count experiments to derive estimates of the concentration of the microorganism of interest. An application for these data is food-safety risk assessment, where the MPN concentration estimates can be fitted to a parametric distribution to summarize the range of potential exposures to the contaminant. Many different methods (e.g., substitution methods, maximum likelihood and regression on order statistics) have been proposed to fit microbial contamination data to a distribution, but the development of these methods rarely considers how the MPN technique influences the choice of distribution function and fitting method. An often overlooked aspect when applying these methods is whether the data represent actual measurements of the average concentration of microorganism per milliliter or the data are real-valued estimates of the average concentration, as is the case with MPN data. In this study, we propose two methods for fitting MPN data to a probability distribution. The first method uses a maximum likelihood estimator that takes average concentration values as the data inputs. The second is a Bayesian latent variable method that uses the counts of the number of positive tubes at each dilution to estimate the parameters of the contamination distribution. The performance of the two fitting methods is compared for two data sets that represent Salmonella and Campylobacter concentrations on chicken carcasses. The results demonstrate a bias in the maximum likelihood estimator that increases with reductions in average concentration. The Bayesian method provided unbiased estimates of the concentration distribution parameters for all data sets. We provide computer code for the Bayesian fitting method.