Evaluation of the Uncertainty in an Oral Reference Dose for Methylmercury Due to Interindividual Variability in Pharmacokinetics

An analysis of the uncertainty in guidelines for the ingestion of methylmercury (MeHg) due to human pharmacokinetic variability was conducted using a physiologically based pharmacokinetic (PBPK) model that describes MeHg kinetics in the pregnant human and fetus. Two alternative derivations of an ingestion guideline for MeHg were considered: the U.S. Environmental Protection Agency reference dose (RfD) of 0.1 g/kg/day derived from studies of an Iraqi grain poisoning episode, and the Agency for Toxic Substances and Disease Registry chronic oral minimal risk level (MRL) of 0.5 g/kg/day based on studies of a fish-eating population in the Seychelles Islands. Calculation of an ingestion guideline for MeHg from either of these epidemiological studies requires calculation of a dose conversion factor (DCF) relating a hair mercury concentration to a chronic MeHg ingestion rate. To evaluate the uncertainty in this DCF across the population of U.S. women of child-bearing age, Monte Carlo analyses were performed in which distributions for each of the parameters in the PBPK model were randomly sampled 1000 times. The 1st and 5th percentiles of the resulting distribution of DCFs were a factor of 1.8 and 1.5 below the median, respectively. This estimate of variability is consistent with, but somewhat less than, previous analyses performed with empirical, one-compartment pharmacokinetic models. The use of a consistent factor in both guidelines of 1.5 for pharmacokinetic variability in the DCF, and keeping all other aspects of the derivations unchanged, would result in an RfD of 0.2 g/kg/day and an MRL of 0.3 g/kg/day.     

What Should Be the Implications of Uncertainty,Variability, and Inherent “Biases”/“Conservatism” for Risk Management Decision-Making?

This paper is a challenge from a pair of lifelong technical specialists in risk assessment for the risk-management community to better define social decision criteria for risk acceptance vs. risk control in relation to the issues of variability and uncertainty. To stimulate discussion, we offer a variety of straw man proposals about where we think variability and uncertainty are likely to matter for different types of social policy considerations in the context of a few different kinds of decisions. In particular, we draw on recent presentations of uncertainty and variability data that have been offered by EPA in the context of the consideration of revised ambient air quality standards under the Clean Air Act.     

Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.     

Sensitivity Analysis of a Two‐Dimensional Quantitative Microbiological Risk Assessment: Keeping Variability and Uncertainty Separated

The aim of quantitative microbiological risk assessment is to estimate the risk of illness caused by the presence of a pathogen in a food type, and to study the impact of interventions. Because of inherent variability and uncertainty, risk assessments are generally conducted stochastically, and if possible it is advised to characterize variability separately from uncertainty. Sensitivity analysis allows to indicate to which of the input variables the outcome of a quantitative microbiological risk assessment is most sensitive. Although a number of methods exist to apply sensitivity analysis to a risk assessment with probabilistic input variables (such as contamination, storage temperature, storage duration, etc.), it is challenging to perform sensitivity analysis in the case where a risk assessment includes a separate characterization of variability and uncertainty of input variables. A procedure is proposed that focuses on the relation between risk estimates obtained by Monte Carlo simulation and the location of pseudo‐randomly sampled input variables within the uncertainty and variability distributions. Within this procedure, two methods are used—that is, an ANOVA‐like model and Sobol sensitivity indices—to obtain and compare the impact of variability and of uncertainty of all input variables, and of model uncertainty and scenario uncertainty. As a case study, this methodology is applied to a risk assessment to estimate the risk of contracting listeriosis due to consumption of deli meats.     

Regression analysis of hierarchical poisson-like event rate data: superpopulation model effect on predictions

This paper studies prediction of future failure (rates) by hierarchical empirical Bayes (EB) Poisson regression methodologies. Both a gamma distributed superpopulation as well as a more robust (long-tailed) log student-t superpopulation are considered. Simulation results are reported concerning predicted Poisson rates. The results tentatively suggest that a hierarchical model with gamma superpopulation can effectively adapt to data coming from a log-Student-t superpopulation particularly if the additional computation involved with estimation for the log-Student-t hierarchical model is burdensome.     

What Comes After the Prestige Disaster? An Entropic Approach to Modeling the Recurrence of Major Oil Tanker Spills in Galicia

A methodology is presented to investigate the recurrence of extraordinary events. The approach is fully general and complies with a canon of inference establishing a set of basic rationality requirements scientific reasoning should satisfy. In particular, we apply it to model the interarrival time between disastrous oil spills in the Galician coast in the northwest of Spain, one of the greatest risk areas in the world, as confirmed by the Prestige accident of November 2002. We formulate the problem within the logical probability framework, using plausible logic languages with observations to allow the appropriate expression of evidences. Therein, inference is regarded as the joint selection of a pair of reference and inferred probability distributions, which better encode the knowledge about potential times between incidents provided by the available evidences and other higher-order information at hand. To solve it, we employ the REF relative entropy method with fractile constraints. Next, we analyze the variability of the joint entropic solution, as knowledge that a time has elapsed since the last recorded spill is added, by conditioning the evidences. Attention is paid to the variability of two representative parameters: the average reference recurrence time and an inferred characteristic probability fractile for the time to an event. In contrast with classical results, the salient consequence is their nonconstancy with the elapsed time and the appearance of a variability pattern indicating an observational memory, even under the assumption of one-parameter exponential models, traditionally regarded as memoryless. Tanker accidentality is therefore dynamic, changing as time goes on with no further accidents. Generality of the methodology entails that identical conclusions would apply to hazard modeling of any other kind of extraordinary phenomena. This should be considered in risk assessment and management.     

Reduction of control-chart signal variablity for high-quality processes

The design of a control chart is often based on the statistical measure of average run length (ARL). A longer in-control ARL is ensured by the design, but the variance run length distribution may also be large for such a design. In practical terms, the variability in false alarms and true signals may be large. If the sample size for plotting a point is not constant, then the focus is on the average number inspected as against the ARL. This article considers two well-known attribute control chart procedures for monitoring high quality based on the number inspected, and shows how the variability in false alarms and correct signals can be reduced.     

Book Reviews

Books reviewed: V. W. Pentreath, Neurotoxicology in Vitro James T. Hamilton and W. Kip Viscusi, Calculating Risks? The Spatial and Political Dimensions of Hazardous Waste Policy     

An Exploratory Study of Variations in Exposure to Environmental Tobacco Smoke in the United States

There is considerable interest in assessing exposure to environmental tobacco smoke (ETS) and in understanding the factors that affect exposure at various venues. The impact of these complex factors can be researched only if monitoring studies are carefully designed. Prior work by Jenkins et al. gathered personal monitor and diary data from 1,564 nonsmokers in 16 metropolitan areas of the United States and compared workplace exposures to ETS with exposures away from work. In this study, these data were probed further to examine (1) the correspondence between work and away-from-work exposure concentrations of ETS; (2) the variability in exposure concentration levels across cities; and (3) the association of ETS exposure concentrations with select socioeconomic, occupation, and lifestyle variables. The results indicate (1) at the population level, there was a positive association between ETS concentrations at the work and away-from-work environments; (2) exposure concentration levels across the 16 cities under consideration were highly variable; and (3) exposure concentration levels were significantly associated with occupation, education, household income, age, and dietary factors. Workplace smoking restrictions were associated with low ETS concentration levels at work as well as away from work. Generally, the same cities that exhibited either lower or higher away-from-work exposure concentration levels also showed lower or higher work exposure concentration levels. The observations suggest that similar avoidance characteristics as well as socioeconomic and other lifestyle factors that affect exposure to ETS may have been in operation in both away-from-work and work settings.     

BL Lac天体红外谱性质(英文)

对所收集的ＢＬ Ｌａｃ天体的红外资料（Ｆａｎ＆　Ｌｉｎ １９９９）进行讨论，发现具有红移的 ２７个 ＢＬ Ｌａｃ天体的红外谱指数与红移之间有强相关，这种强相关也许意味着谱存在演化效应。