Modeling Growth and Reduction of Microorganisms in Foods as Functions of Temperature and Time

Food safety objectives (FSOs) are established in order to minimize the risk of foodborne illnesses to consumers, but these have not yet been incorporated into regulatory policy. An FSO states the maximum frequency and/or concentration of a microbiological hazard in a food at the time of consumption that provides an acceptable level of protection to the public and leads to a performance criterion for industry. However, in order to be implemented as a regulation, this criterion has to be achievable by the affected industry. In order to determine an FSO, the steps to produce and store that food need to be known, especially where they have an impact on contamination, growth, and destruction. This article uses existing models for growth of Listeria monocytogenes in conjunction with calculations of FSOs to approximate the outcome of more than one introduction of the foodborne organism throughout the food-processing path from the farm to the consumer. Most models for the growth and reduction of foodborne illnesses are logarithmic in nature, which fits the nature of the growth of microorganisms, spanning many orders of magnitude. However, these logarithmic models are normally limited to a single introduction step and a single reduction step. The model presented as part of this research addresses more than one introduction of food contamination, each of which can be separated by a substantial amount of time. The advantage of treating the problem this way is the accommodation of multiple introductions of foodborne pathogens over a range of time durations and conditions.     

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.     

Quantitative Risk Assessment of Listeria monocytogenes in French Cold-Smoked Salmon: II. Risk Characterization

A model for the assessment of exposure to Listeria monocytogenes from cold-smoked salmon consumption in France was presented in the first of this pair of articles (Pouillot et al ., 2007, Risk Analysis, 27:683–700). In the present study, the exposure model output was combined with an internationally accepted hazard characterization model, adapted to the French situation, to assess the risk of invasive listeriosis from cold-smoked salmon consumption in France in a second-order Monte Carlo simulation framework. The annual number of cases of invasive listeriosis due to cold-smoked salmon consumption in France is estimated to be 307, with a very large credible interval ([10; 12,453]), reflecting data uncertainty. This uncertainty is mainly associated with the dose-response model. Despite the significant uncertainty associated with the predictions, this model provides a scientific base for risk managers and food business operators to manage the risk linked to cold-smoked salmon contaminated with L. monocytogenes. Under the modeling assumptions, risk would be efficiently reduced through a decrease in the prevalence of L. monocytogenes or better control of the last steps of the cold chain (shorter and/or colder storage during the consumer step), whereas reduction of the initial contamination levels of the contaminated products and improvement in the first steps of the cold chain do not seem to be promising strategies. An attempt to apply the recent risk-based concept of FSO (food safety objective) on this example underlines the ambiguity in practical implementation of the risk management metrics and the need for further elaboration on these concepts.     

Perceived Risks of Conventional and Organic Produce: Pesticides, Pathogens, and Natural Toxins

Public risk perceptions and demand for safer food are important factors shaping agricultural production practices in the United States. Despite documented food safety concerns, little attempt has been made to elicit consumers' subjective risk judgments for a range of food safety hazards or to identify factors most predictive of perceived food safety risks. In this study, over 700 conventional and organic fresh produce buyers in the Boston area were surveyed for their perceived food safety risks. Survey results showed that consumers perceived relatively high risks associated with the consumption and production of conventionally grown produce compared with other public health hazards. For example, conventional and organic food buyers estimated the median annual fatality rate due to pesticide residues on conventionally grown food to be about 50 per million and 200 per million, respectively, which is similar in magnitude to the annual mortality risk from motor vehicle accidents in the United States. Over 90% of survey respondents also perceived a reduction in pesticide residue risk associated with substituting organically grown produce for conventionally grown produce, and nearly 50% perceived a risk reduction due to natural toxins and microbial pathogens. Multiple regression analyses indicate that only a few factors are consistently predictive of higher risk perceptions, including feelings of distrust toward regulatory agencies and the safety of the food supply. A variety of factors were found to be significant predictors of specific categories of food hazards, suggesting that consumers may view food safety risks as dissimilar from one another. Based on study findings, it is recommended that future agricultural policies and risk communication efforts utilize a comparative risk approach that targets a range of food safety hazards.     

Medical capacity investment for epidemic disease: The effects of policymaker's confidence and public trust

Due to the server bed shortage, which has raised ethical dilemmas in the earliest days of the COVID-19 crisis, medical capacity investment has become a vital decision-making issue in the attempt to contain the epidemic. Furthermore, economic strength has failed to explain the significant performance difference across countries in combatting COVID-19. Unlike common diseases, epidemic diseases add substantial unpredictability, complexity, and uncertainty to decision-making. Knowledge miscalibration on epidemiological uncertainties by policymaker's over- and underconfidence can seriously impact policymaking. Ineffective risk communication may lead to conflicting and incoherent information transmission. As a result, public reactions and attitudes could be influenced by policymakers' confidence due to the level of public trust, which eventually affects the degree to which an epidemic spreads. To uncover the impacts of policymakers' confidence and public trust on the medical capacity investment, we establish epidemic diffusion models to characterize how transmission evolves with (and without) vaccination and frame the capacity investment problem as a newsvendor problem. Our results show that if the public fully trusts the public health experts, the policymaker's behavioral bias is always harmful, but its effect on cost increment is marginal. If a policymaker's behavior induces public reactions due to public trust, both the spread of the epidemic and the overall performance will be significantly affected, but such impacts are not always harmful. Decision bias may be beneficial when policymakers are pessimistic or highly overconfident. Having an opportunity to amend initially biased decisions can debias a particular topic but has a limited cost-saving effect.     

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.     

European Public Perceptions of Food Risk: Cross-National and Methodological Comparisons

This article presents a comparative study of public perceptions of food risk across 25 European member states. A secondary data analysis is conducted on a Eurobarometer survey fielded to nationally representative samples in 2005. The survey included closed questions as well as free associations to map risk perceptions. Taking a quantitative approach, we find that people in a majority of European countries express similar levels of concern about food risks. However, outside this majority a North-South divide is evident, with the Northern countries worrying less than the Southern countries. Multilevel modeling shows that cross-national differences in individual respondents' level of worry are in part attributable to shared country effects and to generalized risk sensitivity about a range of personal risks. On the underlying structure of food risk concerns, factor analysis points to three dimensions described by groupings of risks related to adulteration and contamination, health effects, and production and hygiene. A qualitative analysis of respondents' free associations about problems and risks with food identifies three major themes that are consistent with the quantitative results. However, the free associations also point toward greater cross-national diversity and to striking variations in the range and importance of food risks. Overall, the picture is of a public that frames food risks in a wider context of beliefs about the links between diet and health. We conclude with some implications for research on food risk perceptions in particular and risk perception studies in general.     

Communicating Food Risks in an Era of Growing Public Distrust: Three Case Studies

The communication and regulation of risk has changed significantly over the past 30 years in Europe and to a noticeable but lesser extent in the United States. In Europe, this is partly due to a series of regulatory mishaps, ranging from mad cow disease in the United Kingdom to contamination of the blood supply in France. In the United States, general public confidence in the American government has been gradually declining for more than three decades, driven by a mix of cultural and political conflicts like negative political advertising, a corrosive news media, and cuts in regulatory budgets. While the former approach is based on an objective assessment of the risk, the latter is driven more by the perception of the risk, consumer sentiment, political will, and sectoral advocacy. In this article, the author examines three U.S.‐based food case studies (acrylamide, bisphenol A, and artificial food colorings) where regulations at the local and state levels are increasingly being based on perceived risk advocacy rather than on the most effective response to the risk, be it to food safety or public health, as defined by regulatory interpretation of existing data. In the final section, the author puts forward a series of recommendations for how U.S.‐based regulators can best handle those situations where the perceived risk is markedly different from the fact‐based risk, such as strengthening the communication departments of food regulatory agencies, training officials in risk communication, and working more proactively with neutral third‐party experts.     

Diminishing Willingness to Pay per Quality‐Adjusted Life Year: Valuing Acute Foodborne Illness

We design and conduct a stated‐preference survey to estimate willingness to pay (WTP) to reduce foodborne risk of acute illness and to test whether WTP is proportional to the corresponding gain in expected quality‐adjusted life years (QALYs). If QALYs measure utility for health, then economic theory requires WTP to be nearly proportional to changes in both health quality and duration of illness and WTP could be estimated by multiplying the expected change in QALYs by an appropriate monetary value. WTP is elicited using double‐bounded, dichotomous‐choice questions in which respondents (randomly selected from the U.S. general adult population, n = 2,858) decide whether to purchase a more expensive food to reduce the risk of foodborne illness. Health risks vary by baseline probability of illness, reduction in probability, duration and severity of illness, and conditional probability of mortality. The expected gain in QALYs is calculated using respondent‐assessed decrements in health‐related quality of life if ill combined with the duration of illness and reduction in probability specified in the survey. We find sharply diminishing marginal WTP for severity and duration of illness prevented. Our results suggest that individuals do not have a constant rate of WTP per QALY, which implies that WTP cannot be accurately estimated by multiplying the change in QALYs by an appropriate monetary value.     

Reproducibility of the Dose-Response Curve of Steroid-Induced Cleft Palate in Mice

Pregnant CD-1 mice were exposed to cortisone acetate at doses ranging from 20 to 100 mg/kg/ day on days 10-13 by oral and intramuscular routes. Multiple replicate assays were conducted under identical conditions to assess the reproducibility of the dose–response curve for cleft palate. The data were fitted to the probit, logistic, multistage or Armitage-Doll, and Weibull dose-response model separately for each route of exposure. The curves were then tested for parallel slopes (probit and logistic models) or coincidence of model parameters (multistage and Weibull models). The 19 replicate experiments had a wide range of slope estimates, wider for the oral than for the intramuscular experiments. For all models and both routes of exposure the null hypothesis of equality of slopes was rejected at a significant level of p < 0.001. For the intramuscular group of replicates, rejection of slope equality could in part be explained by not maintaining a standard dosing regime. The rejection of equivalence of dose-response curves from replicate studies showed that it is difficult to reproduce dose-response data of a single study within the limits defined by the dose-response model. This has important consequences for quantitative risk assessment, public health measures, or development of mechanistic theories which are typically based on a single animal bioassay.     

“How Exposed Is Exposed Enough?” Lay Inferences About Chemical Exposure

The concept of exposure is central to chemical risk assessment and plays an important role in communicating to the public about the potential health risks of chemicals. Research on chemical risk perception has found some indication that the model lay people use to judge chemical exposure differs from that of toxicologists, thereby leading to different conclusions about chemical safety. This paper presents the results of a series of studies directed toward developing a model for understanding how lay people interpret the concept of chemical exposure. The results indicate that people's beliefs about chemical exposure (and its risks) are based on two broad categories of inferences. One category of inferences relates to the nature in which contact with a chemical has taken place, including the amount of a chemical involved and its potential health consequences. A second category of inferences about chemical exposure relates to the pragmatics of language interpretation, leading to beliefs about the motives and purposes behind chemical risk communication. Risk communicators are encouraged to consider how alternative models of exposure and language interpretation can lead to conflicting conclusions on the part of the public about chemical safety.     

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh,North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM_2.5) concentrations in ambient air and associated health risks from PM_2.5 exposure in North Carolina's Raleigh‐Durham‐Chapel Hill area. We integrate transportation demand, land‐use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (?0.2%) point estimates of regional annual average PM_2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (?33%) PM_2.5‐attributable mortality. Furthermore, compactness increases local variation in PM_2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM_2.5 in local hotspots and increase population exposure to PM_2.5. Health effects may be magnified if compact neighborhoods and PM_2.5 hotspots are spatially co‐located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile‐dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.     

Bivariate Distributions for Height and Weight of Men and Women in the United States

For the U.S. population, we fit bivariate distributions to estimated numbers of men and women aged 18-74 years in cells representing 1 in. intervals in height and 10 lb intervals in weight. For each sex separately, the marginal histogram of height is well fit by a normal distribution. For men and women, respectively, the marginal histogram of weight is well fit and satisfactorily fit by a lognormal distribution. For men, the bivariate histogram is satisfactorily fit by a normal distribution between the height and the natural logarithm of weight. For women, the bivariate histogram is satisfactorily fit by two superposed normal distributions between the height and the natural logarithm of weight. The resulting distributions are suitable for use in public health risk assessments.     

Modeling the Public Health System Response to a Terrorist Event in the Food Supply

We have developed a simulation model to quantify and characterize the response of the public health system and the impact of public health advisories in the event of an intentional contamination of the food supply. The model has three components: (1) definition of individual exposure over time and the outcomes of exposure, (2) definition of the geographical dispersal of exposures, and (3) response of the public health authorities to symptomatic individuals. The model explicitly considers the variation in the multiple interrelated facets of the response system, including differences among individuals' responses to exposure, variation between health care providers, and the subsequent processing of samples and confirmation of cases. To illustrate use of the model, case studies with  Escherichia coli  O157:H7 and  Salmonella  spp. in three categories of food vehicle were compared. The level of detail required to run the public health component of the model is not trivial. While some data may not be available for hazards of particular interest in potential bioterrorism events, the application of expert judgment permits comparisons between different agents, different system reactions, and other assumptions within the system. The model provides the capacity to study the impact of system changes, to compare scenarios and to quantify the benefits of improvement in terms of averted exposures and risk reduction, and constitutes a significant aid to understanding and managing these threats. Essentially, the model provides an explicit valuation of time saved in the identification and intervention in terrorist events in the food supply.     

Socioeconomic Determinants of Health- and Food Safety-Related Risk Perceptions

Individual and societal perceptions of food-related health risks are multidimensional and complex. Social, political, psychological, and economic factors interact with technological factors and affect perceptions in complex ways. Previous research found that the significant determinants of risk perceptions include socioeconomic and behavioral variables. Most of these past results are based on two-way comparisons and factor analysis. The objective of this study was to analyze the significance of socioeconomic determinants of risk perceptions concerning health and food safety. A multivariate approach was used and the results were compared with earlier bivariate results to determine which socioeconomic predictors were robust across methods. There were two major findings in this study. The first was that the results in the multivariate models were generally consistent with earlier bivariate analysis. That is, variables such as household income, number of children, gender, age, and voting preferences were strong predictors of an individual's risk perceptions. The second result was that the gender of the respondent was the only variable found to be robust across all three classes of health and food safety issues across two time periods.     

Evaluating the Simplified Conjoint Expected Risk Model: Comparing the Use of Objective and Subjective Information

The simplified Conjoint Expected Risk (CER) model by Holtgrave and Weber posits that perceived risk is a linear combination of the subjective judgments of the probabilities of harm, benefit, and status quo, and the expected harm and benefit of an activity. It modifies Luce and Weber's original CER model—that uses objective information to evaluate financial gambles—to accommodate activities such as health/technology activities where values of the model variables are subjective. If the simplified model is a valid modification of the original model, its performance should not be sensitive to the use of subjective information. However, because people may evaluate information differently when objective information is provided to them than when they generate information on their own, the performance of the simplified CER model may not be robust to the source of model-variable information. We compared the use of objective and subjective information, and results indicate that the estimates of the simplified CER model parameters and the proportion of variance in risk judgments accounted for by the model are similar under these two conditions. Thus, the simplified CER model is viable with activities for which harm and benefit information is subjective.     

FOUR APPROACHES TO THE CLASSIFICATION PROBLEM IN DISCRIMINANT ANALYSIS: AN EXPERIMENTAL STUDY*

Four discriminant models were compared in a simulation study: Fisher's linear discriminant function [14], Smith's quadratic discriminant function [34], the logistic discriminant model, and a model based on linear programming [17]. The study was conducted to estimate expected rates of misclassification for these four procedures when observations were sampled from a variety of normal and nonnormal distributions. In contrast to previous research, data were taken from four types of Kurtotic population distributions. The results indicate the four discriminant procedures are robust toward data from many types of distributions. The misclassification rates for both the logistic discriminant model and the formulation based on linear programming consistently decreased as the kurtosis in the data increased. The decreases, however, were of small magnitude. None of these procedures yielded statistically significant lower rates of misclassification under nonnormality. The quadratic discriminant function produced significantly lower error rates when the variances across groups were heterogeneous.     

An Integrated Physical Dispersion and Behavioral Response Model for Risk Assessment of Radiological Dispersion Device (RDD) Events

A radiological dispersion device (RDD) or "dirty" bomb is a conventional explosive wrapped in radiological material. Terrorists may use an RDD to disperse radioactive material across a populated area, causing casualties and/or economic damage. Nearly all risk assessment models for RDDs make unrealistic assumptions about public behavior in their health assessments, including assumptions that the public would stand outside in a single location indefinitely. In this article, we describe an approach for assessing the risks of RDD events incorporating both physical dispersion and behavioral response variables. The general approach is tested using the City of Pittsburgh, Pennsylvania as a case study. Atmospheric models simulate an RDD attack and its likely fallout, while radiation exposure models assess fatal cancer risk. We model different geographical distributions of the population based on time of day. We evaluate aggregate health impacts for different public responses (i.e., sheltering-in-place, evacuating). We find that current RDD models in use can be improved with the integration of behavioral components. Using the results from the model, we show how risk varies across several behavioral and physical variables. We show that the best policy to recommend to the public depends on many different variables, such as the amount of trauma at ground zero, the capability of emergency responders to get trauma victims to local hospitals quickly and efficiently, how quickly evacuations can take place in the city, and the amount of shielding available for shelterers. Using a parametric analysis, we develop behaviorally realistic risk assessments, we identify variables that can affect an optimal risk reduction policy, and we find that decision making can be improved by evaluating the tradeoff between trauma and cancer fatalities for various RDD scenarios before they occur.     

An Empirical Test of the Relative Validity of Expert and Lay Judgments of Risk

This article investigates how accurately experts (underwriters) and lay persons (university students) judge the risks posed by life-threatening events Only one prior study (Slovic, Fischhoff, & Lichtenstein, 1985) has previously investigated the veracity of expert versus lay judgments of the magnitude of risk. In that study, a heterogeneous grouping of 15 experts was found to judge, using marginal estimations, a variety of risks as closer to the true annual frequencies of death than convenience samples of the lay population. In this study, we use a larger, homogenous sample of experts performing an ecologically valid task. We also ask our respondents to assess frequencies and relative frequencies directly, rather than ask for a "risk" estimate--a response mode subject to possible qualitative attributions-as was done in the Slovic et al. study. Although we find that the experts outperformed lay persons on a number of measures, the differences are small, and both groups showed similar global biases in terms of: (1) overestimating the likelihood of dying from a condition (marginal probability) and of dying from a condition given that it happens to you (conditional probability), and (2) underestimating the ratios of marginal and conditional likelihoods between pairs of potentially lethal events. In spite of these scaling problems, both groups showed quite good performance in ordering the lethal events in terms of marginal and conditional likelihoods. We discuss the nature of expertise using a framework developed by Bolger and Wright (1994), and consider whether the commonsense assumption of the superiority of expert risk assessors in making magnitude judgments of risk is, in fact, sensible.     

Comparative Risk Analysis of Dioxins in Fish and Fine Particles from Heavy-Duty Vehicles

Dioxins and airborne fine particles are both environmental health problems that have been the subject of active public debate. Knowledge on fine particles has increased substantially during the last 10 years, and even the current, lowered levels in the Europe and in the United States appear to be a major public health problem. On the other hand, dioxins are ubiquitous persistent contaminants, some being carcinogens at high doses, and therefore of great concern. Our aim was to (a) quantitatively analyze the two pollutant health risks and (b) study the changes in risk in view of the current and forthcoming EU legislations on pollutants. We performed a comparative risk assessment for both pollutants in the Helsinki metropolitan area (Finland) and estimated the health effects with several scenarios. For primary fine particles: a comparison between the present emission situation for heavy-duty vehicles and the new fine particle emission standards set by the EU. For dioxins: an EU directive that regulates commercial fishing of Baltic salmon and herring that exceed the dioxin concentration limit set for fish meat, and a derogation (= exemption) from the directive for these two species. Both of these two decisions are very topical issues and this study estimates the expected changes in health effects due to these regulations. It was found that the estimated fine particle risk clearly outweighed the estimated dioxin risk. A substantial improvement to public health could be achieved by initiating reductions in emission standards; about 30 avoided premature deaths annually in the study area. In addition, the benefits of fish consumption due to omega-3 exposure were notably higher than the potential dioxin cancer risk. Both regulations were instigated as ways of promoting public health.