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.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Food Safety in the Domestic Environment: An Interdisciplinary Investigation of Microbial Hazards During Food Preparation

It has been established that, to a considerable extent, the domestic hygiene practices adopted by consumers can result in a greater or lesser microbial load in prepared meals. In the research presented here, an interdisciplinary study is reported in which interviews, observations of consumers preparing a recipe, and microbial contamination of the finished meals were compared. The results suggest that, while most consumers are knowledgeable about the importance of cross-contamination and heating in preventing the occurrence of foodborne illness, this knowledge is not necessarily translated into behavior. The adoption of habitual cooking practices may also be important. Potentially risky behaviors were, indeed, observed in the domestic food preparation environment. Eighteen of the participants made errors in food preparation that could potentially result in cross-contamination, and seven participants allowed raw meat juices to come in contact with the final meal. Using a tracer microorganism the log reduction as a result of consumer preparation was estimated at an average of log 4.1 cfu/salad. When combining these findings, it was found that cross-contamination errors were a good predictor for log reduction. Procedural food safety knowledge (i.e., knowledge proffered after general open questions) was a better predictor of efficacious bacterial reduction than declarative food safety knowledge (i.e., knowledge proffered after formal questioning). This suggests that motivation to prepare safe food was a better indicator of actual behavior than knowledge about food safety per se.     

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.     

Practice-Specific Risk Perceptions and Self-Reported Food Safety Practices

The relationship between risk perception and risk avoidance is typically analyzed using self-reported measures. However, in domains such as driving or food handling, the validity of responses about usual behavior is threatened because people think about the situations in which they are self-aware, such as when they encounter a hazard. Indeed, researchers have often noted a divergence between what people say about their behavior and how they actually behave. Thus, in order to draw conclusions about risk perceptions and risk avoidance from survey data, it is important to identify particular cognitive elements, such as those measured by questions about risk and safety knowledge, risk perceptions, or information search behavior, which may be effective antecedents of self-reported safety behavior. It is also important to identify and correct for potential sources of bias that may exist in the data. The authors analyze the Food and Drug Administration's 1998 Food Safety Survey to determine whether there are consistent cognitive antecedents for three types of safe food practices: preparation, eating, and cooling of foods. An assessment of measurement biases shows that endogeneity of food choices affects reports of food preparation. In addition, response bias affects reports of cooling practices as evidenced by its relation to knowledge and information search, a pattern of cognitive effects unique to cooling practices. After correcting for these biases, results show that practice-specific risk perceptions are the primary cognitive antecedents of safe food behavior, which has implications for the design of effective education messages about food safety.     

Meta-Analysis of Food Safety Information Based on a Combination of a Relational Database and a Predictive Modeling Tool

The management of microbial risk in food products requires the ability to predict growth kinetics of pathogenic microorganisms in the event of contamination and growth initiation. Useful data for assessing these issues may be found in the literature or from experimental results. However, the large number and variety of data make further development difficult. Statistical techniques, such as meta-analysis, are then useful to realize synthesis of a set of distinct but similar experiences. Moreover, predictive modeling tools can be employed to complete the analysis and help the food safety manager to interpret the data. In this article, a protocol to perform a meta-analysis of the outcome of a relational database, associated with quantitative microbiology models, is presented. The methodology is illustrated with the effect of temperature on pathogenic Escherichia coli and Listeria monocytogenes, growing in culture medium, beef meat, and milk products. Using a database and predictive models, simulations of growth in a given product subjected to various temperature scenarios can be produced. It is then possible to compare food products for a given microorganism, according to its growth ability in these products, and to compare the behavior of bacteria in a given foodstuff. These results can assist decisions for a variety of questions on food safety.     

Modeling Logistic Performance in Quantitative Microbial Risk Assessment

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times—mutually dependent in successive steps in the chain—cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for  Listeria monocytogenes  in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.     

Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model

Sensitivity analysis (SA) methods are a valuable tool for identifying critical control points (CCPs), which is one of the important steps in the hazard analysis and CCP approach that is used to ensure safe food. There are many SA methods used across various disciplines. Furthermore, food safety process risk models pose challenges because they often are highly nonlinear, contain thresholds, and have discrete inputs. Therefore, it is useful to compare and evaluate SA methods based upon applications to an example food safety risk model. Ten SA methods were applied to a draft Vibrio parahaemolyticus (Vp) risk assessment model developed by the Food and Drug Administration. The model was modified so that all inputs were independent. Rankings of key inputs from different methods were compared. Inputs such as water temperature, number of oysters per meal, and the distributional assumption for the unrefrigerated time were the most important inputs, whereas time on water, fraction of pathogenic Vp, and the distributional assumption for the weight of oysters were the least important inputs. Most of the methods gave a similar ranking of key inputs even though the methods differed in terms of being graphical, mathematical, or statistical, accounting for individual effects or joint effect of inputs, and being model dependent or model independent. A key recommendation is that methods be further compared by application on different and more complex food safety models. Model independent methods, such as ANOVA, mutual information index, and scatter plots, are expected to be more robust than others evaluated.     

A Quantitative Microbial Risk Assessment Model for Legionnaires'' Disease: Animal Model Selection and Dose-Response Modeling

Legionnaires' disease (LD), first reported in 1976, is an atypical pneumonia caused by bacteria of the genus Legionella, and most frequently by L. pneumophila (Lp). Subsequent research on exposure to the organism employed various animal models, and with quantitative microbial risk assessment (QMRA) techniques, the animal model data may provide insights on human dose-response for LD. This article focuses on the rationale for selection of the guinea pig model, comparison of the dose-response model results, comparison of projected low-dose responses for guinea pigs, and risk estimates for humans. Based on both in vivo and in vitro comparisons, the guinea pig (Cavia porcellus) dose-response data were selected for modeling human risk. We completed dose-response modeling for the beta-Poisson (approximate and exact), exponential, probit, logistic, and Weibull models for Lp inhalation, mortality, and infection (end point elevated body temperature) in guinea pigs. For mechanistic reasons, including low-dose exposure probability, further work on human risk estimates for LD employed the exponential and beta-Poisson models. With an exposure of 10 colony-forming units (CFU) (retained dose), the QMRA model predicted a mild infection risk of 0.4 (as evaluated by seroprevalence) and a clinical severity LD case (e.g., hospitalization and supportive care) risk of 0.0009. The calculated rates based on estimated human exposures for outbreaks used for the QMRA model validation are within an order of magnitude of the reported LD rates. These validation results suggest the LD QMRA animal model selection, dose-response modeling, and extension to human risk projections were appropriate.     

Improving Food Safety in the Domestic Environment: The Need for a Transdisciplinary Approach

Microbial food safety has been the focus of research across various disciplines within the risk analysis community. Natural scientists involved in food microbiology and related disciplines work on the identification of health hazards, and the detection of pathogenic microorganisms. To perform risk assessment, research activities are increasingly focused on the quantification of microbial contamination of food products at various stages in the food chain, and modeling the impact of this contamination on human health. Social scientists conduct research into how consumers perceive food risks, and how best to develop effective risk communication with consumers in order to improve public health through improved food handling practices. The two approaches converge at the end of the food chain, where the activities regarding food preparation and food consumption are considered. Both natural and social sciences may benefit from input and expertise from the perspective of the alternative discipline, although, to date, the integration of social and natural sciences has been somewhat limited. This article therefore explores the potential of a transdisciplinary approach to food risk analysis in terms of delivering additional improvements to public health. Developing knowledge arising from research in both the natural and social sciences, we present a novel framework involving the integration of the two approaches that might provide the most effective way to improve the consumer health associated with food-borne illness.     

Quantitative Risk Assessment of Norovirus Transmission in Food Establishments: Evaluating the Impact of Intervention Strategies and Food Employee Behavior on the Risk Associated with Norovirus in Foods

We developed a quantitative risk assessment model using a discrete event framework to quantify and study the risk associated with norovirus transmission to consumers through food contaminated by infected food employees in a retail food setting. This study focused on the impact of ill food workers experiencing symptoms of diarrhea and vomiting and potential control measures for the transmission of norovirus to foods. The model examined the behavior of food employees regarding exclusion from work while ill and after symptom resolution and preventive measures limiting food contamination during preparation. The mean numbers of infected customers estimated for 21 scenarios were compared to the estimate for a baseline scenario representing current practices. Results show that prevention strategies examined could not prevent norovirus transmission to food when a symptomatic employee was present in the food establishment. Compliance with exclusion from work of symptomatic food employees is thus critical, with an estimated range of 75–226% of the baseline mean for full to no compliance, respectively. Results also suggest that efficient handwashing, handwashing frequency associated with gloving compliance, and elimination of contact between hands, faucets, and door handles in restrooms reduced the mean number of infected customers to 58%, 62%, and 75% of the baseline, respectively. This study provides quantitative data to evaluate the relative efficacy of policy and practices at retail to reduce norovirus illnesses and provides new insights into the interactions and interplay of prevention strategies and compliance in reducing transmission of foodborne norovirus.     

Estimating Listeria monocytogenes Growth in Ready-to-Eat Chicken Salad Using a Challenge Test for Quantitative Microbial Risk Assessment

Currently, there is a growing preference for convenience food products, such as ready-to-eat (RTE) foods, associated with long refrigerated shelf-lives, not requiring a heat treatment prior to consumption. Because Listeria monocytogenes is able to grow at refrigeration temperatures, inconsistent temperatures during production, distribution, and at consumer's household may allow for the pathogen to thrive, reaching unsafe limits. L. monocytogenes is the causative agent of listeriosis, a rare but severe human illness, with high fatality rates, transmitted almost exclusively by food consumption. With the aim of assessing the quantitative microbial risk of L. monocytogenes in RTE chicken salads, a challenge test was performed. Salads were inoculated with a three-strain mixture of cold-adapted L. monocytogenes and stored at 4, 12, and 16 °C for eight days. Results revealed that the salad was able to support L. monocytogenes’ growth, even at refrigeration temperatures. The Baranyi primary model was fitted to microbiological data to estimate the pathogen's growth kinetic parameters. Temperature effect on the maximum specific growth rate (μ_max) was modeled using a square-root-type model. Storage temperature significantly influenced μ_max of L. monocytogenes (p < 0.05). These predicted growth models for L. monocytogenes were subsequently used to develop a quantitative microbial risk assessment, estimating a median number of 0.00008726 listeriosis cases per year linked to the consumption of these RTE salads. Sensitivity analysis considering different time–temperature scenarios indicated a very low median risk per portion (<−7 log), even if the assessed RTE chicken salad was kept in abuse storage conditions.     

Characterizing Risk for Cumulative Risk Assessments

In assessing environmental health risks, the risk characterization step synthesizes information gathered in evaluating exposures to stressors together with dose–response relationships, characteristics of the exposed population, and external environmental conditions. This article summarizes key steps of a cumulative risk assessment (CRA) followed by a discussion of considerations for characterizing cumulative risks. Cumulative risk characterizations differ considerably from single chemical‐ or single source‐based risk characterization. CRAs typically focus on a specific population instead of a pollutant or pollutant source and should include an evaluation of all relevant sources contributing to the exposures in the population and other factors that influence dose–response relationships. Second, CRAs may include influential environmental and population‐specific conditions, involving multiple chemical and nonchemical stressors. Third, a CRA could examine multiple health effects, reflecting joint toxicity and the potential for toxicological interactions. Fourth, the complexities often necessitate simplifying methods, including judgment‐based and semi‐quantitative indices that collapse disparate data into numerical scores. Fifth, because of the higher dimensionality and potentially large number of interactions, information needed to quantify risk is typically incomplete, necessitating an uncertainty analysis. Three approaches that could be used for characterizing risks in a CRA are presented: the multiroute hazard index, stressor grouping by exposure and toxicity, and indices for screening multiple factors and conditions. Other key roles of the risk characterization in CRAs are also described, mainly the translational aspect of including a characterization summary for lay readers (in addition to the technical analysis), and placing the results in the context of the likely risk‐based decisions.     

Modeling Virus Inactivation on Salad Crops Using Microbial Count Data

Microbial counts of the persistent Bacteroides fragilis bacteriophage B40-8 from a virus decay experiment conducted under glasshouse conditions were used to model the decay of viruses on wastewater-irrigated lettuce and carrot crops. The modeling approach applied gave specific consideration to the discrete nature of microbial count data. The experimental counts were best fit by a negative binomial distribution indicating highly dispersed distribution of viruses on lettuce and carrot crops following irrigation with wastewater. In addition, there was evidence for biphasic inactivation of viruses, signifying the presence of a persistent subpopulation of viruses that decayed slowly, resulting in virus accumulation on the crop surface over subsequent irrigations. Maximum likelihood estimates of initial and persistent subpopulation inactivation rates were 2.48 day(-1) and 0.51 day(-1) for lettuces and 0.84 day(-1) and 0.046 day(-1) for carrots. Maximum likelihood estimates of the persistent virus subpopulation size were 0.12% and 2% for lettuce and carrots, respectively.     

Rapid Benefit-Risk Assessments: No Escape from Expert Judgments in Risk Management

The "human health impacts assessment" described by Cox and Popken (this issue) is intended to be a benefit-risk tool that avoids pitfalls of using expert judgments for policy analysis or during strict application of the precautionary principle in risk management. The proposed benefit-risk calculation uses numerous assumptions and suppositions to calculate a ratio of quality-adjusted life years (QALYs) lost for the number of human illness days prevented by the use of a food-animal antimicrobial drug, to the number of human illness days caused by the use of the antimicrobial drug. Assumptions about data--e.g., expert judgments on the representativeness of parameter estimates--are commonly used in risk assessment and risk management, including Cox and Popken's method. Cox and Popken apply the technique to specific examples of enrofloxacin and macrolides antimicrobial drugs, sometimes used in broiler chickens for human food. Although enthusiastically portrayed as a straightforward calculation of QALYs lost for two decision alternatives, Cox and Popken were silent on the pivotal expert judgment subsumed in their method: quality weights for illnesses caused by antimicrobial-resistant and antimicrobial-sensitive microbes are tacitly assumed to be equal. Yet, the costs in terms of prolonged illness, additional medications, repeat medical visits, and dread of more serious sequelae are expected to differ substantially for antimicrobial-resistant versus antimicrobial-sensitive illnesses. Despite their enthusiasm to the contrary, the "human health impacts assessment" by Cox and Popken is not immune from expert judgments in risk management.     

Work Conditions,Social Incorporations,and Foodborne Diseases Risk: Reflections About the (Non)Compliance of Food Safety Practices

The number of foodborne diseases has increased in all continents, and efforts must be made to control this urgent and expressive public health problem. This article aims to present and discuss situations related to the compliance and noncompliance of food safety practices (FSPs) in light of Bourdieu's social theory. This qualitative study was conducted in commercial restaurants in two cities in São Paulo, Brazil. Participant observation was used in the restaurants, and notes referring to the kitchen workers and their bosses’ work processes were registered in field journals. Thematic type content analysis was used to determine the meaning cores of field journals. It was found that aspects inherent to convenience and haste at work, deficient infrastructure, lack of employees, negative boss examples, exposure to noise, and body pain experienced by workers can contribute to noncompliance of FSPs and consolidate in the habitus and practical sense some dispositions that can increase the risk of foodborne diseases. This study highlights the necessity of creating environments that address food safety, which means being able to perform a service properly.     

Phthalate Exposure Through Food and Consumers' Risk Perception of Chemicals in Food

Phthalates have been detected in various types of retail foods. Consumers' exposure to phthalates is common. Consumers are concerned about chemicals in food. Our aim was to investigate the relationships between consumers' exposure to phthalates through food, consumers' interest in a natural and healthy diet, risk perception of food chemicals, and consumers' diet patterns. We collected data through a mail survey in the adult Swiss-German population ( N  = 1,200). We modeled exposure to di(2-ethylhexyl) phthalate (DEHP), dibutyl phthalate (DBP), benzyl butyl phthalate (BBP), and diethyl phthalate (DEP) based on a food frequency questionnaire and phthalate concentrations reported from food surveys. Using rating scales, we assessed risk perceptions of chemicals in food and interest in a natural and healthy diet. Higher risk perceptions and higher natural and healthy diet interest were associated with higher daily doses of DEHP, BBP, and DEP. No health risk from phthalates in food was identified for the vast majority of the population. Four consumers' diet clusters were discerned, with differences in phthalate exposure, risk perceptions, and interest in a natural and healthy diet. This study shows that even those consumers who express strong interest in natural food and low acceptance of food chemicals, and who try to make respective food choices, are exposed to contaminants such as phthalates.     

Ensuring the Quality of Occupational Safety Risk Assessment

In work environments, the main aim of occupational safety risk assessment (OSRA) is to improve the safety level of an installation or site by either preventing accidents and injuries or minimizing their consequences. To this end, it is of paramount importance to identify all sources of hazards and assess their potential to cause problems in the respective context. If the OSRA process is inadequate and/or not applied effectively, it results in an ineffective safety prevention program and inefficient use of resources. An appropriate OSRA is an essential component of the occupational safety risk management process in industries. In this article, we performed a survey to elicit the relative importance for identified OSRA tasks to enable an in‐depth evaluation of the quality of risk assessments related to occupational safety aspects on industrial sites. The survey involved defining a questionnaire with the most important elements (tasks) for OSRA quality assessment, which was then presented to safety experts in the mining, electrical power production, transportation, and petrochemical industries. With this work, we expect to contribute to the main question of OSRA in industries: “What constitutes a good occupational safety risk assessment?” The results obtained from the questionnaire showed that experts agree with the proposed OSRA process decomposition in steps and tasks (taxonomy) and also with the importance of assigning weights to obtain knowledge about OSRA task relevance. The knowledge gained will enable us, in the near future, to build a framework to evaluate OSRA quality for industrial sites.     

Time-Dose-Response Models for Microbial Risk Assessment

While microbial risk assessment (MRA) has been used for over 25 years, traditional dose-response analysis has only predicted the overall risk of adverse consequences from exposure to a given dose. An important issue for consequence assessment from bioterrorist and other microbiological exposure is the distribution of cases over time due to the initial exposure. In this study, the classical exponential and beta-Poisson dose-response models were modified to include exponential-power dependency of time post inoculation (TPI) or its simplified form, exponential-reciprocal dependency of TPI, to quantify the time of onset of an effect presumably associated with the kinetics of in vivo bacterial growth. Using the maximum likelihood estimation approach, the resulting time-dose-response models were found capable of providing statistically acceptable fits to all tested pooled animal survival dose-response data. These new models can consequently describe the development of animal infectious response over time and represent observed responses fairly accurately. This is the first study showing that a time-dose-response model can be developed for describing infections initiated by various pathogens. It provides an advanced approach for future MRA frameworks.     

Quantitative Risk Assessment Relating to Adventitious Presence of Allergens in Food: A Probabilistic Model Applied to Peanut in Chocolate

Peanut allergy is a public health concern, owing to the high prevalence in France and the severity of the reactions. Despite peanut-containing product avoidance diets, a risk may exist due to the adventitious presence of peanut allergens in a wide range of food products. Peanut is not mentioned in their ingredients list, but precautionary labeling is often present. A method of quantifying the risk of allergic reactions following the consumption of such products is developed, taking the example of peanut in chocolate tablets. The occurrence of adventitious peanut proteins in chocolate and the dose-response relationship are estimated with a Bayesian approach using available published data. The consumption pattern is described by the French individual consumption survey INCA2. Risk simulations are performed using second-order Monte Carlo simulations, which separately propagates variability and uncertainty of the model input variables. Peanut allergens occur in approximately 36% of the chocolates, leading to a mean exposure level of 0.2 mg of peanut proteins per eating occasion. The estimated risk of reaction averages 0.57% per eating occasion for peanut-allergic adults. The 95% values of the risk stand between 0 and 3.61%, which illustrates the risk variability. The uncertainty, represented by the 95% credible intervals, is concentrated around these risk estimates. Children have similar results. The conclusion is that adventitious peanut allergens induce a risk of reaction for a part of the French peanut-allergic population. The method developed can be generalized to assess the risk due to the consumption of every foodstuff potentially contaminated by allergens.