NUSAP Method for Evaluating the Data Quality in a Quantitative Microbial Risk Assessment Model for Salmonella in the Pork Production Chain

The numeral unit spread assessment pedigree (NUSAP) system was implemented to evaluate the quality of input parameters in a quantitative microbial risk assessment (QMRA) model for Salmonella spp. in minced pork meat. The input parameters were grouped according to four successive exposure pathways: (1) primary production (2) transport, holding, and slaughterhouse, (3) postprocessing, distribution, and storage, and (4) preparation and consumption. An inventory of 101 potential input parameters was used for building the QMRA model. The characteristics of each parameter were defined using a standardized procedure to assess (1) the source of information, (2) the sampling methodology and sample size, and (3) the distributional properties of the estimate. Each parameter was scored by a panel of experts using a pedigree matrix containing four criteria (proxy, empirical basis, method, and validation) to assess the quality, and this was graphically represented by means of kite diagrams. The parameters obtained significantly lower scores for the validation criterion as compared with the other criteria. Overall strengths of parameters related to the primary production module were significantly stronger compared to the other modules (the transport, holding, and slaughterhouse module, the processing, distribution, and storage module, and the preparation and consumption module). The pedigree assessment contributed to select 20 parameters, which were subsequently introduced in the QMRA model. The NUSAP methodology and kite diagrams are objective tools to discuss and visualize the quality of the parameters in a structured way. These two tools can be used in the selection procedure of input parameters for a QMRA, and can lead to a more transparent quality assurance in the QMRA.     

Reanalysis of the DEMS Nested Case‐Control Study of Lung Cancer and Diesel Exhaust: Suitability for Quantitative Risk Assessment

The International Agency for Research on Cancer (IARC) in 2012 upgraded its hazard characterization of diesel engine exhaust (DEE) to “carcinogenic to humans.” The Diesel Exhaust in Miners Study (DEMS) cohort and nested case‐control studies of lung cancer mortality in eight U.S. nonmetal mines were influential in IARC's determination. We conducted a reanalysis of the DEMS case‐control data to evaluate its suitability for quantitative risk assessment (QRA). Our reanalysis used conditional logistic regression and adjusted for cigarette smoking in a manner similar to the original DEMS analysis. However, we included additional estimates of DEE exposure and adjustment for radon exposure. In addition to applying three DEE exposure estimates developed by DEMS, we applied six alternative estimates. Without adjusting for radon, our results were similar to those in the original DEMS analysis: all but one of the nine DEE exposure estimates showed evidence of an association between DEE exposure and lung cancer mortality, with trend slopes differing only by about a factor of two. When exposure to radon was adjusted, the evidence for a DEE effect was greatly diminished, but was still present in some analyses that utilized the three original DEMS DEE exposure estimates. A DEE effect was not observed when the six alternative DEE exposure estimates were utilized and radon was adjusted. No consistent evidence of a DEE effect was found among miners who worked only underground. This article highlights some issues that should be addressed in any use of the DEMS data in developing a QRA for DEE.     

Further Beyond the Basic Background Check: Predicting Future Unethical Behavior

In this paper, we analyze the ethical issues of using honesty and integrity tests in employment screening. Our focus will be on the United States context: legal requirements related to applicant privacy differ in other countries, but we posit that our proposed balancing test is broadly applicable. We start by discussing why companies have ethical and legal obligations, based on a stakeholder analysis, to assess the integrity of potential employees. We then move to a consideration of how companies currently use background checks as a pre‐employment screening tool, noting their limitations. We then take up honesty and integrity testing, focusing particularly on the problems of false positives and due process. We offer a balancing test for the use of honesty and integrity testing that takes in three factors: (1) the potential harm posed by a dishonest employee in a particular job, (2) the linkage between the test and the assessment process, and (3) the accuracy and validity of the honesty and integrity test. We conclude with implications for practice and future research.     

Measuring Inequity Aversion in a Heterogeneous Population Using Experimental Decisions and Subjective Probabilities

We combine choice data in the ultimatum game with the expectations of proposers elicited by subjective probability questions to estimate a structural model of decision making under uncertainty. The model, estimated using a large representative sample of subjects from the Dutch population, allows both nonlinear preferences for equity and expectations to vary across socioeconomic groups. Our results indicate that inequity aversion to one's own disadvantage is an increasing and concave function of the payoff difference. We also find considerable heterogeneity in the population. Young and highly educated subjects have lower aversion for inequity than other groups. Moreover, the model that uses subjective data on expectations generates much better in‐ and out‐of‐sample predictions than a model which assumes that players have rational expectations.     

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.     

Flexible SWAps for Strategic Policy‐making: Reflections on the Zambian Experience

This article offers an alternative view on the emergence of new forms of development co‐operation by analysing the emergence of SWAps in Zambia since the early 1990s. SWAps in Zambia emerged not as part of a grand design but in response to the changing environment in which aid agencies were operating. An examination of the relative success of the health and education SWAps and the spectacular failure of the agriculture SWAp suggests that a flexible approach is more effective than the imposition of a planning template. Furthermore, the failure of the agricultural SWAp has been followed by significant new forms of public/private partnership.     

Informing the Public About Uncertain Risks

Many problems arise in connection with the communication of risk information. In this article the content of the information communicated is taken as a starting-point for analyzing the risk communication process. We studied the way in which authorities communicated health risks to local residents in Dutch soil pollution situations and found that communication problems were characterized more by misunderstanding and conflict between parties with different views and interests than by a lack of understanding. Therefore, it is important to look more closely at the way the communicators of information (in our cases, officials) select risk information from risk assessments, and to study the effects that the information selected has on the receivers (in our cases, residents). Both the process of selection by the officials and the process of interpretation by the residents will be shown to be influenced by the different institutional backgrounds. The article presents a new approach to risk communication. Both the risk information presented by the authorities and the public reactions to this risk information are considered to reflect the institutional background of authorities and residents, and can be analyzed in these terms. Such an approach has consequences for the study of risk communication and the manner in which it is practiced.     

Estimation of the Force of Infection from Current Status Data Using Generalized Linear Mixed Models

Based on sero-prevalence data of rubella, mumps in the UK and varicella in Belgium, we show how the force of infection, the age-specific rate at which susceptible individuals contract infection, can be estimated using generalized linear mixed models (McCulloch & Searle, 2001). Modelling the dependency of the force of infection on age by penalized splines, which involve fixed and random effects, allows us to use generalized linear mixed models techniques to estimate both the cumulative probability of being infected before a given age and the force of infection. Moreover, these models permit an automatic selection of the smoothing parameter. The smoothness of the estimated force of infection can be influenced by the number of knots and the degree of the penalized spline used. To determine these, a different number of knots and different degrees are used and the results are compared to establish this sensitivity. Simulations with a different number of knots and polynomial spline bases of different degrees suggest - for estimating the force of infection from serological data - the use of a quadratic penalized spline based on about 10 knots.