Consumer Selection of Food-Safety Information Sources

The aim of this study is to examine the preferences of consumers for different information sources when they have a question about food safety. On the basis of a nationally representative survey conducted in the Netherlands, five distinct consumer groups are identified that not only differ on the reported use of information sources but also regarding several personality characteristics and sociodemographic variables. The empirical results show that two-thirds of the consumers are selective in their use of information sources and prefer either institutional or social sources. So, multiple information-acquisition patterns exist among the general public. The study illustrates how these findings can help to develop effective risk communication strategies.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

Risk Assessment of Human Listeriosis from Semisoft Cheeses Made from Raw Sheep's Milk in Lazio and Tuscany (Italy)

Semisoft cheese made from raw sheep's milk is traditionally and economically important in southern Europe. However, raw milk cheese is also a known vehicle of human listeriosis and contamination of sheep cheese with Listeria monocytogenes has been reported. In the present study, we have developed and applied a quantitative risk assessment model, based on available evidence and challenge testing, to estimate risk of invasive listeriosis due to consumption of an artisanal sheep cheese made with raw milk collected from a single flock in central Italy. In the model, contamination of milk may originate from the farm environment or from mastitic animals, with potential growth of the pathogen in bulk milk and during cheese ripening. Based on the 48‐day challenge test of a local semisoft raw sheep's milk cheese we found limited growth only during the initial phase of ripening (24 hours) and no growth or limited decline during the following ripening period. In our simulation, in the baseline scenario, 2.2% of cheese servings are estimated to have at least 1 colony forming unit (CFU) per gram. Of these, 15.1% would be above the current E.U. limit of 100 CFU/g (5.2% would exceed 1,000 CFU/g). Risk of invasive listeriosis per random serving is estimated in the 10^?12 range (mean) for healthy adults, and in the 10^?10 range (mean) for vulnerable populations. When small flocks (10–36 animals) are combined with the presence of a sheep with undetected subclinical mastitis, risk of listeriosis increases and such flocks may represent a public health risk.     

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.     

Integrating Behavioral Science with Human Factors to Address Process Safety

ABSTRACT

Applying the science of human factors to eliminate error across all aspects of process design, management, operation, and maintenance has been a focus in the process safety area for many years. Human error has been attributed as a major cause of many high profile catastrophic accidents around the world. These accidents have resulted in national and international attention, which has led to a focus on improving organizational capabilities, systems, and in many cases, governmental regulations around human factors. This article provides a review of the field of human factors highlighting various topics in the literature, and introduces governmental regulatory bodies currently engaging organizations in a scientific approach to human factors. Finally, the need for integrating behavioral science methodologies with human factors is addressed. This is done with specific focus on how Organizational Behavior Management methodologies can work in concert with human factors to optimize process safety.     

Leadership’s Role in Process Safety: An Understanding of Behavioral Science Among Managers and Executives Is Needed

ABSTRACT

A recent prevalence of high visibility catastrophic events has garnered increased attention to process safety issues. While the use of Behavior-Based Safety interventions demonstrate a reduction in workplace injuries by targeting employee behavior, we believe that process safety requires a greater focus on the behavior of leaders (e.g., creating and executing strategy). One effective method to begin targeting leader behavior for the improvement of process safety is to teach leaders about the principles of behavior, including ways by which the science may be applied within their own organizational models.     

Priority Setting for the Distribution of Localized Hazard Protection

We address the problem of distributing safety-enhancing devices across a region, where each identical device provides for only local protection of the population. The devices protect nonidentical sectors of the population. The sectors of population are exposed to nonidentical intensities of hazard. A method for the screening and prioritizing of needs for the protective devices is described. An approach of risk-benefit-cost analysis under uncertainty is recommended as follows. Measures of hazard intensity and population exposure are identified. Exogenous parameters that influence assessments of risks, benefits, and costs are identified. Uncertainties of the exogenous parameters are propagated by interval analysis. Several tiers of the plausibility of need for protection are identified. The tiers are useful in setting priorities for the distribution of the safety devices. The method is demonstrated in an engineering application to roadway lighting, but has implications for disaster preparedness, anti-terrorism, transportation safety, and other arenas of public safety.     

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.     

Expert Study to Select Indicators of the Occurrence of Emerging Mycotoxin Hazards

This article describes a Delphi‐based expert judgment study aimed at the selection of indicators to identify the occurrence of emerging mycotoxin hazards related to Fusarium spp. in wheat supply chains. A panel of 29 experts from 12 European countries followed a holistic approach to evaluate the most important indicators for different chain stages (growth, transport and storage, and processing) and their relative importance. After three e‐mailing rounds, the experts reached consensus on the most important indicators for each of the three stages: wheat growth, transport and storage, and processing. For wheat growth, these indicators include: relative humidity/rainfall, crop rotation, temperature, tillage practice, water activity of the kernels, and crop variety/cultivar. For the transport and storage stage, they include water activity in the kernels, relative humidity, ventilation, temperature, storage capacity, and logistics. For wheat processing, indicators include quality data, fraction of the cereal used, water activity in the kernels, quality management and traceability systems, and carryover of contamination. The indicators selected in this study can be used in an identification system for the occurrence of emerging mycotoxin hazards in wheat supply chains. Such a system can be used by risk managers within governmental (related) organizations and/or the food and feed industry in order to react proactively to the occurrence of these emerging mycotoxins.     

Methodology for the Calculation of Annualized Incremental Risks in Systems of Dams

In the past few years, the field of dam safety has approached risk informed methodologies throughout the world and several methodologies and programs are appearing to aid in the systematization of the calculations. The most common way of implementing these calculations is through the use of event trees, computing event probabilities, and incremental consequences. This methodology is flexible enough for several situations, but its generalization to the case of systems of several dams is complex and its implementation in a completely general calculation methodology presents some problems. Retaining the event tree framework, a new methodology is proposed to calculate incremental risks. The main advantage of this proposed methodology is the ease with which it can be applied to systems of several dams: with a single risk model that describes the complete system and with a single calculation the incremental risks of the system can be obtained, being able to allocate the risk of each dam and of each failure mode. The article shows how both methodologies are equivalent and also applies them to a case study.