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.     

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     

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.     

Using Instructive Feedback to Increase Response Variability During Intraverbal Training for Children with Autism Spectrum Disorder

We evaluated the effects of instructive feedback on the variability of intraverbal responses for two children with autism spectrum disorder. Specifically, we used an adapted alternating treatments design to compare participants’ novel responses and response combinations during an intraverbal category program across conditions with and without instructive feedback. During instructive feedback, secondary targets were presented during the consequence event of the learning trial and consisted of a therapist’s model of response variability. The results showed that participants engaged in more novel response combinations during instructive feedback conditions. We discussed the clinical implications of these results as well as areas for future research.     

Analysis of variance of paired data without repetition of measurement

Summary The objective of this analysis of variance of paired data is to estimate positive random error variances for each ofN=2 measurement methods. The two methods measure the same item only once without measurement repetition. The well-known unbiased Grubbs’ estimators are not suitable for practical purpose because they can become negative. With the help of Chebyshev’s inequality the probability was determined that Grubbs’ estimators become negative. Based on the Grubbs’ estimators new estimators were derived. The new estimators are indeed always positive, but they are biased. It is shown that the biases are small. In case the Grubbs’ estimators are positive a bias correction of the new estimators may be envisaged.

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Zusammenfassung Das Ziel dieser Varianzanalyse von gepaarten Messungen ist die Sch?tzung zuf?lliger Messfehlervarianzen für jede derN=2 Messmethoden. Die beiden Messmethoden messen das gleiche Merkmal eines Elements nur einmal ohne Messwiederholung. Die bekannten unverzerrten Grubbs-Sch?tzer sind für die praktische Anwendung nicht geeignet, weil sie negativ werden k?nnten. Die Tschebyscheffsche Ungleichung wurde genutzt, um die Wahrscheinlichkeit zu ermitteln, dass Grubbs-Sch?tzer negativ werden. Basierend auf Grubbs-Sch?tzern wurden neue Sch?tzer hergeleitet. Diese neuen Sch?tzer sind zwar immer positiv, aber verzerrt. Es wird gezeigt, dass die Verzerrungen klein sind. Für den Fall, dass die Grubbs-Sch?tzer positiv ausfallen, k?nnte eine Korrektur der Verzerrung in Betracht gezogen werden.

     

Physiologically-Based Pharmacokinetic Modeling of Benzene in Humans: A Bayesian Approach

Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically-based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra- and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human-population variability in benzene dosimetry.     

Understanding Uncertainty

There is more information we don't know than we do know for making most critical decisions involving risks. Our focus must be on understanding and effectively dealing with what we don't know. As a first step in achieving this focus, a classification of the types of uncertainties that must be addressed and the sources of these types of uncertainties is presented. The purpose is to provide a framework for discussion about addressing uncertainty, particularly in risk analyses.
Both uncertainty and variability of information are addressed using four main classes:

• 1) 

  Metrical uncertainty and variability in measurement,
• 2) 

  Structural uncertainty due to complexity, including models and their validation,
• 3) 

  Temporal uncertainty in future and past states
• 4) 

  Translational uncertainty in explaining uncertain results.

The factors that contribute uncertainty and error to these classes are identified, and their interrelationships indicated. Both subjective and objective aspects are addressed.     

汉语惯用语的特征探析

惯用语是汉语中常见的一种熟语类型。从形成来源、表义方式和表达风格角度看，它有着独特鲜明的个性特征：一是形成来源的广泛性，二是语义生成的变异性，三是口语格调的鲜明性，四是语用价值的单纯性。     

经济财富的性质考察与辨析

经济财富除了具有效用性、稀缺性、劳动性、物质性、社会性、历史性等基本特征以外,还具有多样性、变异性、聚集性、流动性等众多衍生性质。在此基础上,笔者认同和支持通过努力劳动与重视科技创新、不断改善社会关系并保护劳动者的利益、积极扩大国内外贸易与技术交流、加强国家安全防卫等基本措施创造与维护国家的经济财富。     

Multivariate Process Variability Monitoring

Understanding multivariate variability is a difficult task because there is no single measure that can be properly used. This article presents a new measure that features good properties. If this measure is simultaneously used with generalized variance, it will give a better understanding of multivariate variability. It can also efficiently be used for large data sets with high dimensions. Furthermore, when it is used for constructing a Shewhart-type chart to monitor multivariate variability, the resulting chart has a much better out-of-control ARL than the generalized variance chart. An example illustrates its advantage.