Potential Climate Change Health Risks from Increases in Heat Waves: Abnormal Birth Outcomes and Adverse Maternal Health Conditions

We investigate the risks presented by heat waves for adverse health conditions for babies and expectant mothers when these mothers have been exposed to heat waves during gestation or during the period just prior to conception. Rather than just birth weight and gestational age, we focus on less common metrics such as abnormal conditions in the newborn (fetal distress, reliance on a ventilator, and meconium aspiration) and adverse health conditions in the mother (pregnancy‐related hypertension, uterine bleeding during pregnancy, eclampsia, and incompetent cervix). We use monthly panel data for over 3,000 U.S. counties, constructed from the confidential version of the U.S. Natality Files for 1989–2008. Our models control for sociodemographic factors and include county, month, and state‐by‐year fixed effects to control for unobserved spatial and timewise heterogeneity in the data. Even within the United States, where there is widespread access to air conditioning, heat waves increase the fraction of babies with abnormal conditions related to maternal stress, as well as the fraction of mothers who experience pregnancy‐related adverse health conditions. The scope for these risks in developing countries is likely to be even greater.     

Reassessing Benzene Cancer Risks Using Internal Doses

Human cancer risks from benzene exposure have previously been estimated by regulatory agencies based primarily on epidemiological data, with supporting evidence provided by animal bioassay data. This paper reexamines the animal-based risk assessments for benzene using physiologically-based pharmacokinetic (PBPK) models of benzene metabolism in animals and humans. It demonstrates that internal doses (interpreted as total benzene metabolites formed) from oral gavage experiments in mice are well predicted by a PBPK model developed by Travis et al. Both the data and the model outputs can also be accurately described by the simple nonlinear regression model total metabolites = 76.4x/(80.75 + x), where x = administered dose in mg/kg/day. Thus, PBPK modeling validates the use of such nonlinear regression models, previously used by Bailer and Hoel. An important finding is that refitting the linearized multistage (LMS) model family to internal doses and observed responses changes the maximum-likelihood estimate (MLE) dose-response curve for mice from linear-quadratic to cubic, leading to low-dose risk estimates smaller than in previous risk assessments. This is consistent with the conclusion for mice from the Bailer and Hoel analysis. An innovation in this paper is estimation of internal doses for humans based on a PBPK model (and the regression model approximating it) rather than on interspecies dose conversions. Estimates of human risks at low doses are reduced by the use of internal dose estimates when the estimates are obtained from a PBPK model, in contrast to Bailer and Hoel's findings based on interspecies dose conversion. Sensitivity analyses and comparisons with epidemiological data and risk models suggest that our finding of a nonlinear MLE dose-response curve at low doses is robust to changes in assumptions and more consistent with epidemiological data than earlier risk models.     

Perinatal loss and parental grief: the challenge of ambiguity and disenfranchised grief

Following perinatal loss, a type of ambiguous loss, bereaved couples struggle with and experience distress due to various forms of ambiguity. Moreover, the juxtaposition of their grief with society's minimization often disenfranchises them from traditional grieving processes. The purpose of this study was to explore sources of ambiguity and disenfranchised grief related to perinatal loss. Audio-taped interviews with 13 bereaved couples at 2, 6, and 13 months following the death of their fetus or infant were analyzed. Several categories of ambiguity and disenfranchised grief emerged, pertaining to: (a) the viability of the pregnancy; (b) the physical process of pregnancy loss; (c) making arrangements for the remains; and (d) sharing the news. This study uncovers the many sources of ambiguity and disenfranchised grief that bereaved couples face in interactions with family, friends, society, and healthcare professionals. These insights may inform healthcare professionals in their attempts to ease distress related to perinatal loss.     

Development of a Single-Meal Fish Consumption Advisory for Methyl Mercury

Methyl mercury (meHg) contamination of fish is the leading cause of fish consumption advisories in the United States. These advisories have focused upon repeated or chronic exposure, whereas risks during pregnancy may also exist from a single-meal exposure if the fish tissue concentration is high enough. In this study, acute exposure to meHg from a single fish meal was analyzed by using the one-compartment meHg biokinetic model to predict maternal hair concentrations. These concentrations were evaluated against the mercury hair concentration corresponding to the U.S. Environmental Protection Agency's reference dose (RfD), which is intended to protect against neurodevelopmental effects. The one-compartment model was validated against blood concentrations from three datasets in which human subjects ingested meHg in fish, either as a single meal or multiple meals. Model simulations of the single-meal scenario at different fish meHg concentrations found that concentrations of 2.0 ppm or higher can be associated with maternal hair concentrations elevated above the RfD level for days to weeks during gestation. A single-meal fish concentration cutoff of > or = 2.0 ppm is an important consideration, especially because this single high exposure event might be in addition to a baseline meHg body burden from other types of fish consumption. This type of single-meal advisory requires that fish sampling programs provide data for individual rather than composited fish, and take into account seasonal differences that may exist in fish concentrations.     

The Use of PBPK Models to Inform Human Health Risk Assessment: Case Study on Perchlorate and Radioiodide Human Lifestage Models

Physiologically‐based pharmacokinetic (PBPK) models are often submitted to or selected by agencies, such as the U.S. Environmental Protection Agency (U.S. EPA) and Agency for Toxic Substances and Disease Registry, for consideration for application in human health risk assessment (HHRA). Recently, U.S. EPA evaluated the human PBPK models for perchlorate and radioiodide for their ability to estimate the relative sensitivity of perchlorate inhibition on thyroidal radioiodide uptake for various population groups and lifestages. The most well‐defined mode of action of the environmental contaminant, perchlorate, is competitive inhibition of thyroidal iodide uptake by the sodium‐iodide symporter (NIS). In this analysis, a six‐step framework for PBPK model evaluation was followed, and with a few modifications, the models were determined to be suitable for use in HHRA to evaluate relative sensitivity among human lifestages. Relative sensitivity to perchlorate was determined by comparing the PBPK model predicted percent inhibition of thyroidal radioactive iodide uptake (RAIU) by perchlorate for different lifestages. A limited sensitivity analysis indicated that model parameters describing urinary excretion of perchlorate and iodide were particularly important in prediction of RAIU inhibition; therefore, a range of biologically plausible values available in the peer‐reviewed literature was evaluated. Using the updated PBPK models, the greatest sensitivity to RAIU inhibition was predicted to be the near‐term fetus (gestation week 40) compared to the average adult and other lifestages; however, when exposure factors were taken into account, newborns were found to be populations that need further evaluation and consideration in a risk assessment for perchlorate.     

Framework for Evaluation of Physiologically-Based Pharmacokinetic Models for Use in Safety or Risk Assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.     

A Distributed Parameter Physiologically-Based Pharmacokinetic Model for Dermal and Inhalation Exposure to Volatile Organic Compounds

Estimates of dermal dose from exposures to toxic chemicals are typically derived using models that assume instantaneous establishment of steady-state dermal mass flux. However, dermal absorption theory indicates that this assumption is invalid for short-term exposures to volatile organic chemicals (VOCs). A generalized distributed parameter physiologically-based pharmacokinetic model (DP-PBPK), which describes unsteady state dermal mass flux via a partial differential equation (Fickian diffusion), has been developed for inhalation and dermal absorption of VOCs. In the present study, the DP-PBPK model has been parameterized for chloroform, and compared with two simpler PBPK models of chloroform. The latter are lumped parameter models, employing ordinary differential equations, that do not account for the dermal absorption time lag associated with the accumulation of permeant chemical in tissue represented by permeability coefficients. All three models were evaluated by comparing simulated post-exposure exhaled breath concentration profiles with measured concentrations following environmental chloroform exposures. The DP-PBPK model predicted a time-lag in the exhaled breath concentration profile, consistent with the experimental data. The DP-PBPK model also predicted significant volatilization of chloroform, for a simulated dermal exposure scenario. The end-exposure dermal dose predicted by the DP-PBPK model is similar to that predicted by the EPA recommended method for short-term exposures, and is significantly greater than the end-exposure dose predicted by the lumped parameter models. However, the net dermal dose predicted by the DP-PBPK model is substantially less than that predicted by the EPA method, due to the post-exposure volatilization predicted by the DP-PBPK model. Moreover, the net dermal dose of chloroform predicted by all three models was nearly the same, even though the lumped parameter models did not predict substantial volatilization.     

Using expected values to simplify decision making under uncertainty

A simulation study examines the impact of a simplification strategy that replaces distributional attribute evaluations with their expected values and uses those expectations in an additive value model. Several alternate simplified forms and approximation approaches are investigated, with results showing that in general the simplified models are able to provide acceptable performance that is fairly robust to a variety of internal and external environmental changes, including changes to the distributional forms of the attribute evaluations, errors in the assessment of the expected values, and problem size. Certain of the simplified models are shown to be highly sensitive to the form of the underlying preference functions, and in particular to extreme non-linearity in these preferences.     

Stochastic Response Surface Methods (SRSMs) for Uncertainty Propagation: Application to Environmental and Biological Systems

Comprehensive uncertainty analyses of complex models of environmental and biological systems are essential but often not feasible due to the computational resources they require. "Traditional" methods, such as standard Monte Carlo and Latin Hypercube Sampling, for propagating uncertainty and developing probability densities of model outputs, may in fact require performing a prohibitive number of model simulations. An alternative is offered, for a wide range of problems, by the computationally efficient "Stochastic Response Surface Methods (SRSMs)" for uncertainty propagation. These methods extend the classical response surface methodology to systems with stochastic inputs and outputs. This is accomplished by approximating both inputs and outputs of the uncertain system through stochastic series of "well behaved" standard random variables; the series expansions of the outputs contain unknown coefficients which are calculated by a method that uses the results of a limited number of model simulations. Two case studies are presented here involving (a) a physiologically-based pharmacokinetic (PBPK) model for perchloroethylene (PERC) for humans, and (b) an atmospheric photochemical model, the Reactive Plume Model (RPM-IV). The results obtained agree closely with those of traditional Monte Carlo and Latin Hypercube Sampling methods, while significantly reducing the required number of model simulations.     

Regression Models and Risk Estimation for Mixed Discrete and Continuous Outcomes in Developmental Toxicology

Multivariate dose-response models have recently been proposed for developmental toxicity data to simultaneously model malformation incidence (a binary outcome), and reductions in fetal weight (a continuous outcome). In this and other applications, the binary outcome often represents a dichotomization of another outcome or a composite of outcomes, which facilitates analysis. For example, in Segment II developmental toxicology studies, multiple malformation types (i.e., external, visceral, skeletal) are evaluated on each fetus; malformation status may also be ordinally measured (e.g., normal, signs of variation, full malformation). A model is proposed is for fetal weight and multiple malformation variables measured on an ordinal scale, where the correlations between the outcomes and between the offspring within a litter are taken into account. Fully specifying the joint distribution of outcomes within a litter is avoided by specifying only the distribution of the multivariate outcome for each fetus and using generalized estimating equation methodology to account for correlations due to litter clustering. The correlations between the outcomes are required to characterize joint risk to the fetus, and are therefore a focus of inference. Dose-response models and their application to quantitative risk assessment are illustrated using data from a recent developmental toxicology experiment of ethylene oxide in mice.     

Managing the Maternal Body: A Comprehensive Review and Transdisciplinary Analysis

This paper comprehensively reviews transdisciplinary and critical perspectives on the employed maternal (pregnant and post‐birth) body in the context of management studies. It highlights the disparities between equal opportunities policies and everyday management practices in relation to pregnancy and new motherhood. In so doing, the review examines the contradictions between equal opportunities policies aimed at protecting pregnant and newly maternal employees and the discouraging treatment that such women receive in practice at work. In analysing the disparity between policy and practice, the review identifies gaps within the field of research on the employed maternal body. It then shows how perceptions about the pregnant and newly maternal body are based more on myth than on evidence. In keeping with policies encouraging family friendly working practices and aimed at enhancing parental health, the paper argues that research on the maternal body is integral to management studies.     

Significance of the Dermal Route of Exposure to Risk Assessment

The skin is a route of exposure that needs to be considered when conducting a risk assessment. It is necessary to identify the potential for dermal penetration by a chemical as well as to determine the overall importance of the dermal route of exposure as compared with inhalation or oral routes of exposure. The physical state of the chemical, vapor or liquid, the concentration, neat or dilute, and the vehicle, lipid or aqueous, is also important. Dermal risk is related to the product of the amounts of penetration and toxicity. Toxicity involves local effects on the skin itself and the potential for systemic effects. Dermal penetration is described in large part by the permeability constant. When permeability constants are not known, partition coefficients can be used to estimate a chemical's potential to permeate the skin. With these concepts in mind, a tiered approach is proposed for dermal risk assessment. A key first step is the determination of a skin-to-air or skin-to-medium partition coefficient to estimate a potential for dermal absorption. Building a physiologically-based pharmacokinetic (PBPK) model is another step in the tiered approach and is useful prior to classical in vivo toxicity tests. A PBPK model can be used to determine a permeability constant for a chemical as well as to show the distribution of the chemical systemically. A detailed understanding of species differences in the structure and function of the skin and how they relate to differences in penetration rates is necessary in order to extrapolate animal data from PBPK models to the human. A study is in progress to examine anatomical differences for four species.     

A study of DEA models without explicit inputs

In performance evaluations, data without explicit inputs (such as index data, pure output data) are widely used. To directly use such data, this paper presents a study on building DEA models without explicit inputs, so-called DEA-WEI models, which are applicable to the evaluation applications where inputs are not directly considered. We provide an axiom foundation of these kinds of models, and further discuss how to incorporate value judgments of decision makers into these DEA-WEI models. Several such models are derived. Finally, applications of the DEA-WEI models are presented.     

Interspecies Extrapolation of Physiological Pharmacokinetic Parameter Distributions

Three methods (multiplicative, additive, and allometric) were developed to extrapolate physiological model parameter distributions across species, specifically from rats to humans. In the multiplicative approach, the rat model parameters are multiplied by the ratio of the mean values between humans and rats. Additive scaling of the distributions is denned by adding the difference between the average human value and the average rat value to each rat value. Finally, allometric scaling relies on established extrapolation relationships using power functions of body weight. A physiologically-based pharmacokinetic model was fitted independently to rat and human benzene disposition data. Human model parameters obtained by extrapolation and by fitting were used to predict the total bone marrow exposure to benzene and the quantity of metabolites produced in bone marrow. We found that extrapolations poorly predict the human data relative to the human model. In addition, the prediction performance depends largely on the quantity of interest. The extrapolated models underpredict bone marrow exposure to benzene relative to the human model. Yet, predictions of the quantity of metabolite produced in bone marrow are closer to the human model predictions. These results indicate that the multiplicative and allometric techniques were able to extrapolate the model parameter distributions, but also that rats do not provide a good kinetic model of benzene disposition in humans.     

STICKY PRICES: A NEW MONETARIST APPROACH

Why do some sellers set nominal prices that apparently do not respond to changes in the aggregate price level? In many models, prices are sticky by assumption; here it is a result. We use search theory, with two consequences: prices are set in dollars, since money is the medium of exchange; and equilibrium implies a nondegenerate price distribution. When the money supply increases, some sellers may keep prices constant, earning less per unit but making it up on volume so profit stays constant. The calibrated model matches price‐change data well. But, in contrast to typical sticky‐price models, money is neutral.     

Artificial Neural Network Models for Pricing Initial Public Offerings

In recent times, managerial applications of neural networks, especially in the area of financial services, has received considerable attention. In this paper, neural network models are developed for a new application: the pricing of Initial Public Offerings (IPOs). Previous empirical studies provide consistent evidence of considerable inefficiency in the pricing of new issues. Neural network models using publicly available financial data as inputs are developed to price IPOs. The pricing performance and the economic benefits of the neural network models are evaluated. Significant economic gains are documented with neural networks. Several tests to establish generalizability and robustness of the results are conducted.     

Symmetry, Identifiability, and Prediction Uncertainties in Multistage Clonal Expansion (MSCE) Models of Carcinogenesis

Many models of exposure-related carcinogenesis, including traditional linearized multistage models and more recent two-stage clonal expansion (TSCE) models, belong to a family of models in which cells progress between successive stages-possibly undergoing proliferation at some stages-at rates that may depend (usually linearly) on biologically effective doses. Biologically effective doses, in turn, may depend nonlinearly on administered doses, due to PBPK nonlinearities. This article provides an exact mathematical analysis of the expected number of cells in the last ("malignant") stage of such a "multistage clonal expansion" (MSCE) model as a function of dose rate and age. The solution displays symmetries such that several distinct sets of parameter values provide identical fits to all epidemiological data, make identical predictions about the effects on risk of changes in exposure levels or timing, and yet make significantly different predictions about the effects on risk of changes in the composition of exposure that affect the pharmacodynamic dose-response relation. Several different predictions for the effects of such an intervention (such as reducing carcinogenic constituents of an exposure) that acts on only one or a few stages of the carcinogenic process may be equally consistent with all preintervention epidemiological data. This is an example of nonunique identifiability of model parameters and predictions from data. The new results on nonunique model identifiability presented here show that the effects of an intervention on changing age-specific cancer risks in an MSCE model can be either large or small, but that which is the case cannot be predicted from preintervention epidemiological data and knowledge of biological effects of the intervention alone. Rather, biological data that identify which rate parameters hold for which specific stages are required to obtain unambiguous predictions. From epidemiological data alone, only a set of equally likely alternative predictions can be made for the effects on risk of such interventions.     

Production planning in practice

The changes and pressures facing the manufacturing and engineering industries today are increasing the importance of effective aggregate manpower and production planning. Several different theoretical optimisation models to tackle this problem have been described in detail in the literature but there have been few applications of them in practice.The reasons for this are many but include: the difficulty in expressing managements' conflicting and mixed objectives in an objective function; the necessity to oversimplify real life systems to enable these methods to be used; the simplistic approach to manpower planning used in these models; the difficulty in gaining managements' acceptance and finally the fact that what management actually wants is a tool to assist them in planning and decision making.What is being used by many managements is a case-study deterministic simulation model. Many companies are adopting this type of model for all types of planning and twelve out of twenty-seven companies visited in a research project described in this paper were using this type of model for aggregate manpower and production planning. It is proving to be an effective management tool and is being readily accepted principally because modern specialised financial modelling languages are enabling these models to be built, understood and used by non-specialist managers.     

SIMPLE MODELS FOR MONITORING NEW PRODUCT PERFORMANCE

Several studies have shown that consumers trying a new brand of frequently purchased consumer goods behave differently from repeat buyers. The “customer mix” for a new brand changes over time. Early in a new brand's life, most of the buyers are triers, while later in its life, a significant portion of the buyers are repeaters. The study presented here tests the hypothesis that an aggregate sales forecast, “averaging” the behaviors of triers and repeaters, would be outperformed by a model that treats them separately. A new product model, giving separate consideration to triers and repeaters, is developed. This model is tested on predictive ability against a widely used single-equation aggregate model. Consumer panel purchase diaries and data on advertising in measured media, covering a period of 42 months following introduction of a major brand of cold tablets, serve as the proving grounds. Data for the early part of this period serve as the data base for both models, while data for the rest of the period serve to test the predictive ability of both models. The disaggregate model is shown to perform significantly better than the aggregate model in terms of predictive accuracy. It also offers several other advantages in use as a decision aid, both for GO-NO decisions and for marketing mix decisions. Finally, several problems in the implementation of the proposed model and implications for research strategy are discussed.     

Stackelberg竞争下考虑消费者转移和平台差异的供应商运营策略

考虑消费者转移和平台差异对需求的影响,构建不同渠道结构和销售契约下的决策模型;基于水平和垂直Stackelberg博弈,获得8种情形下的最优决策。探究佣金率、平台差异和消费者转移率对均衡结果的影响,获得最优的销售契约和渠道配置策略。研究表明:代理模式可以降低产品的销售价格,提升销量;垄断市场中,平台更愿意采取转售模式,而供应商更偏好代理模式;竞争环境下,优势平台一定条件下总是更偏好转售模式,而竞争平台销售模式偏好随着佣金率逐渐变化;供应商的渠道选择与消费者转移量有关,当转移量较高时,供应商偏向双渠道结构,反之亦然。