Causal modelling of multivariate outcomes from developmental toxicity experiments

Exposure to developmental toxicants may cause foetal malformations, increase foetal death or resorption and reduce foetal weight. The correlations among these developmental endpoints have been reported, but their causal relationships have not been investigated. Structural equation models (path models) were applied to study the patterns of causation among the four developmental outcomes, number of viable foetuses, number of malformations, number of deaths/resorptions, and average foetal weight; these outcomes were modelled as response variables with the dose level and number of implants (litter size) modelled as independent variables. Three hypothesized path models were fitted to developmental toxicity data from a study of the herbicide 2,4,5-T exposure in mice. One model, which hypothesized that foetal weight, implicitly a mechanism such as cellular growth, affects both malformations and death/resorption (equivalently, viability) with no causation relationship between malformation and death/resorption, described data consistently well for three strains of mice. The most consistent relationship found was a strong direct effect of foetal weight on malformations, and the correlation between malformations and death/resorption (or reduced viability) was likely spurious.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.     

A framework designation for the assessment of urban ecological risks

Urban ecological risks stemming from urbanization are increasing and limiting the capability of China to effectuate sustainable urban development. Therefore, addressing urban ecological risks is an urgent need. Numerous factors are involved in urban ecological risks, including air, water, and soil. Additionally, risk sources and risk receptors are complex and diverse. In this study, urban ecological risks are defined as adverse effects and possibility of impacts on urban ecosystem services resulting from urbanization. Urbanization is recognized as the risk source, and the urban ecosystem is considered the risk receptor. Based on this understanding, the components of urban ecological risks are defined, and the relationships between the components of urban ecological risks are illuminated by establishing an indicator system. Based on previous studies on urban ecological risks, an explicit framework for identification, assessment, and management of urban ecological risks is proposed. For purposes of identification, there are three types of risk sources: population growth, industrial development, and the expansion of built land. Stressors include the accumulation of contaminants, consumption of resources, and occupation of space. Assessment endpoints are divided into provisioning, regulating, cultural, and supporting services. In response to urban ecological risks having multiple stressors and multiple assessment endpoints, we assessed risks both with a single stressor/single endpoint and comprehensive ecological risks. In our framework, the physical or material assessment of ecosystem services is adopted as the core method for the analysis of urban ecological risk, because it is believed that the analysis of urban ecological risk should be based on the physical or material assessment of ecosystem services instead of the value assessment of ecosystem services. The results of the single value assessment of urban ecosystem services will cause the deviation from the purpose of urban ecosystem services assessment. The purpose of urban ecosystem services assessment is to maintain and/or improve the capability of urban ecosystems of providing physical or material services, and further to reduce or avoid the occurrence risks of unsustainable cities. Additionally, a multi-level characterization method was adopted for the results of urban ecological risk assessment. In this study, we established a platform to manage urban ecological risks based on landscape ecology and environmental internet of things technologies, and to effectuate online urban ecological risk identification, assessment, and management via this platform.     

Bayesian testing for trend in a power model for clustered binary data

Developmental toxicity studies are widely used to investigate the potential risk of environmental hazards. In dose–response experiments, subjects are randomly allocated to groups receiving various dose levels. Tests for trend are then often applied to assess possible dose effects. Recent techniques for risk assessment in this area are based on fitting dose–response models. The complexity of such studies implies a number of non-trivial challenges for model development and the construction of dose-related trend tests, including the hierarchical structure of the data, litter effects inducing extra variation, the functional form of the dose–response curve, the adverse event at dam or at fetus level, the inference paradigm, etc. The purpose of this paper is to propose a Bayesian trend test based on a non-linear power model for the dose effect and using an appropriate model for clustered binary data. Our work is motivated by the analysis of developmental toxicity studies, in which the offspring of exposed and control rodents are examined for defects. Simulations show the performance of the method over a number of samples generated under typical experimental conditions.     

On the use of growth rate parameters for projecting population sizes: Application to aphids

This paper extends the application of the cumulative size based mechanistic model, which has previously been shown to describe diverse aphid population size data well. The mechanistic model is reviewed with a focus on the explanatory role of the birth and death rate formulation. An analysis of two data sets, one on the mustard aphid and the other on the pecan aphid, indicates that multiple linear regression equations based on the estimated birth and death rate parameters alone account for nearly all (R² > 0.95) of the variability in two key population attributes, namely the peak count and the cumulative density. This indicates that population size variables may be projected directly from the growth rate parameters using linear equations. Such linear relationships based on the birth and death rate parameters are shown to hold also for certain generalized mechanistic models for which the analytical solution is not available. The birth and death rate coefficients, therefore, constitute a new succinct set of variables that could be included in the predictive modeling of aphid populations, as well as other insect and animal populations with local collapse which follow similar growth dynamics.     

Valuation of human health: An integrated model of willingness to pay for mortality and morbidity risk reductions

This paper develops and applies an integrated model of mortality and morbidity valuation that is consistent with the principles of welfare economics. To obtain the integrated model, the standard one-period expected utility model of one person facing the prospect of either being alive or dead is extended to incorporate (1) a third health state (sick) with a utility level that is intermediate to utility if healthy and utility if dead, (2) a family perspective in which a parent makes choices about risk exposure both for herself and for a child, and (3) a multi-period framework that allows for possible parent/child differences in illness latency. Monetary benefits of health risk reduction obtained from the integrated model are compared with those that would be computed using the standard model. The integrated model then is applied using data obtained from two field studies of skin cancer and leukemia to demonstrate how it can be used to estimate health benefits of reduced illness and death risks.     

Comparison of Spatial Methods for the Assessment of Planktonic Patches

Planktonic patches are defined as areas where the abundance of plankters is above a threshold value τ. The estimation of patch size and shape can be approached using spatial statistical tools, using truncated random fields or indicator random fields as classifiers. In all cases there is the risk of false positive and false negative errors. In this paper we present the results of a comparative study on the performance of four commonly used methods: conditional simulation and kriging, both in the original measurement units of the data and under an indicator transform. We used a misclassification cost function to compare the four methods. Our results show that conditional simulation in the original measurement units attains the lowest misclassification cost. We also illustrate how the point at which this minimum is attained can be used to chose an optimal cut-off value for binary classification. Received: December 2003 / Revised: June 2005     

Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection

This paper extends the spatial local-likelihood model and the spatial mixture model to the space-time (ST) domain. For comparison, a standard random effect space-time (SREST) model is examined to allow evaluation of each model’s ability in relation to cluster detection. To pursue this evaluation, we use the ST counterparts of spatial cluster detection diagnostics. The proposed criteria are based on posterior estimates (e.g., misclassification rate) and some are based on post-hoc analysis of posterior samples (e.g., exceedance probability). In addition, we examine more conventional model fit criteria including mean square error (MSE). We illustrate the methodology with a real ST dataset, Georgia throat cancer mortality data for the years 1994–2005, and a simulated dataset where different levels and shapes of clusters are embedded. Overall, it is found that conventional SREST models fair well in ST cluster detection and in goodness-of-fit, while for extreme risk detection the local likelihood ST model does best.     

Statistical models of health risk due to microbial contamination of foods

Between 6 million and 33 million cases of food-related illness are estimated to occur in the United States each year, with about 5000 episodes resulting in death. Growing concerns about the safety of food prompted the National Food Safety Initiative of 1997, the goal of which is to reduce the incidence of illness caused by food-borne pathogens. A key component of the food safety initiative is the improvement of farm-to-table risk assessment capabilities, including the development of improved dose-response models for estimating risk. When sufficient data are available, allowable contamination levels of specific micro-organisms in food are established using dose-response models to predict risk at very low doses based on experimental data at much higher doses. This necessitates having reliable models for setting allowable exposures to food-borne pathogens. While only limited data on relatively few micro-organisms that occur in food are available at present for dose-response modeling and risk estimation, still none of the two-parameter models proposed so far, including the popular Beta-Poisson (BP) model, appears to be completely satisfactory for describing and fitting all of the present data (Holcomb et al., 1999). The Weibull–Gamma (WG) model is the only three-parameter model that has been proposed to date. In this paper, new competitive three-parameter models are derived, using a formulation that can be parameterized to represent statistical variation with respect to the dose of micro-organism received by the host and the hosts susceptibility to infection. Parameters of the models are estimated using the maximum likelihood method. Experimental data on several common microbial contaminants in food are used to illustrate the methodology.     

Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation

We apply a semi-nonparametric distribution-free estimator for binary discrete response data to the estimation of a dichotomous choice contingent valuation model. Using this estimator, mean and median compensating and equivalent variation can be consistently estimated without making nontheoretically motivated assumptions on consumer' preferences. The approach is illustrated using a contingent valuation survey of willingness to pay for reduction of risk of premature death due to exposure to hazardous waste. We find that a conventional parametric estimator and the proposed estimator give similar estimates of unconditional WTP, but that conditional on explanatory variables the estimates are quite different.     

The Reliability of Using Population Viability Analysis for Risk Classification of Species

I examine whether or not it is appropriate to use extinction probabilities generated by population viability analyses, based on best estimates for model parameters, as criteria for listing species in Red Data Book categories as recently proposed by the World Conservation Union. Such extinction probabilities are influenced by how accurately model parameters are estimated and by how accurately the models depict actual population dynamics. I evaluate the effect of uncertainty in parameter estimation through simulations. Simulations based on Steller sea lions were used to evaluate bias and precision in estimates of probability of extinction and to consider the performance of two proposed classification schemes. Extinction time estimates were biased (because of violation of the assumption of stable age distribution) and underestimated the variability of probability of extinction for a given time (primarily because of uncertainty in parameter estimation). Bias and precision in extinction probabilities are important when these probabilities are used to compare the risk of extinction between species. Suggestions are given for population viability analysis techniques that incorporate parameter uncertainty. I conclude that testing classification schemes with simulations using quantitative performance objectives should precede adoption of quantitative listing criteria.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Using the bootstrap with two-phase adaptive stratified samples from multiple populations at multiple locations

Recently the two-phase adaptive stratified sampling design proposed by Francis (1984) has been extended by Manly et al. (2002) for situations where several biological populations are sampled simultaneously, and where this is done at several different geographical locations in order to estimate population totals or means. The method uses the results from a first phase sample to decide how best to allocate a second phase sample to locations and strata, in order to maximise a criterion (based on estimated coefficients of variation) that measures the accuracy of estimation for population totals, for all variables at all locations. One potential problem with this method is bias in the estimators of the population totals and means. In this paper bootstrapping is considered as a means of overcoming these biases. It is shown using model populations of Pacific walrus and shellfish, based on real data, that bootstrapping is a useful tool for removing about half of the bias. This is also confirmed from some simulations using artificial data.     

Are ecological groups of species optimal for forest dynamics modelling?

The huge diversity of tree species in tropical rain-forests makes the modelling of its dynamics a difficult task. One-way to deal with it is to define species groups. A classical approach for building species groups consists in grouping species with nearby characteristics, using cluster analysis. A group of species is then characterized by the same list of attributes as a single species, and it is incorporated in the model of forest dynamics in the same way as a single species. In this paper, a new approach for building species group is proposed. It relies on the discrepancy between model predictions when all species are considered separately, and model predictions when species groups are used. An aggregation error that quantifies the bias in model predictions that results from species grouping is thus defined. We then define the optimal species grouping as the one that minimizes the aggregation error. Using data from a tropical rain-forest in French Guiana and a toy model of forest dynamics, this new method for species grouping is confronted to the classical method based on cluster analysis of the species characteristics, and to a combined method based on a cluster analysis that uses the aggregation error as a dissimilarity between species. The optimal species grouping is quite different from the classical species grouping. The ecological interpretation of the optimal groups is difficult, as there is no direct linkage between the species characteristics and the way that they are grouped. The combined approach yields species groups that are closed to the optimal ones, with much less computations. The optimal species groups are thus specific to the model of forest dynamics and lack the generality of those of the classical method, that in turn are not optimal.     

环境风险知觉和评价的整体框架

环境风险通常发生在一定时间之后,以某种可能性发生在某个特定的地方,使得某些人群受到影响。因此,环境风险是典型的多维度事件,它涵盖四个基本的偏好维度：时间、空间、概率与社会性,每个维度都可能导致环境风险知觉和评价出现折扣（discount）现象。但是,传统视角往往关注单一维度,主要是时间,因而割裂了维度的整体性。基于环境风险的多维度特征,提出了一个统一的多维度偏好框架,为环境风险知觉及评价提供整体性视角。这四个偏好维度统一的内在原因是心理距离。维度共性意味着时间维度上的折现机制能够应用到其它维度,为此,对环境风险跨期评价中关于折现率热点问题的研究进展进行了一个关键的梳理。未来有必要在此整体框架下,重点关注三个方面的问题：（1）环境折现率,对环境折现率的要素及其关系进行规范性分析,进而分析环境贴现率与经济贴现率之间的关系,及双率折现对可持续发展的涵义;（2）基于维度共性,将时间维度上的研究结论应用到其它维度;（3）研究多重维度复合情形下的偏好和评价问题,建立综合性描述模型,依托具体案例探讨有效的环境风险沟通策略、环境政策和制度设计。     

Sensitivity analysis on the ecological bias for Seoul tuberculosis data

In ecological studies, researchers often try to convey the analysis results to individual level based on aggregate data. In order to do this correctly, the possibility of ecological bias should be studied and addressed. One of the key ideas used to address the ecological bias issue is to derive the ecological model from the individual model and to check whether the parameter of interest in the individual model is identifiable in the ecological model. However, the procedure depends on unverifiable assumptions, and we recommend checking how sensitive the results are to these unverifiable assumptions. We analyzed the tuberculosis data that was collected in Seoul in 2005 using a spatial ecological regression model for the aggregate count data with spatial correlation, and found that the deprivation index is likely to have a small positive effect on the occurrence risk of tuberculosis in individual level in Seoul. We considered this finding in various aspects by performing in depth sensitivity analyses. In particular, our findings are shown to be robust to the distribution assumptions for the individual exposure and missing binary covariate across various scenarios.     

Accounting for rate instability and spatial patterns in the boundary analysis of cancer mortality maps

Boundary analysis of cancer maps may highlight areas where causative exposures change through geographic space, the presence of local populations with distinct cancer incidences, or the impact of different cancer control methods. Too often, such analysis ignores the spatial pattern of incidence or mortality rates and overlooks the fact that rates computed from sparsely populated geographic entities can be very unreliable. This paper proposes a new methodology that accounts for the uncertainty and spatial correlation of rate data in the detection of significant edges between adjacent entities or polygons. Poisson kriging is first used to estimate the risk value and the associated standard error within each polygon, accounting for the population size and the risk semivariogram computed from raw rates. The boundary statistic is then defined as half the absolute difference between kriged risks. Its reference distribution, under the null hypothesis of no boundary, is derived through the generation of multiple realizations of the spatial distribution of cancer risk values. This paper presents three types of neutral models generated using methods of increasing complexity: the common random shuffle of estimated risk values, a spatial re-ordering of these risks, or p-field simulation that accounts for the population size within each polygon. The approach is illustrated using age-adjusted pancreatic cancer mortality rates for white females in 295 US counties of the Northeast (1970–1994). Simulation studies demonstrate that Poisson kriging yields more accurate estimates of the cancer risk and how its value changes between polygons (i.e., boundary statistic), relatively to the use of raw rates or local empirical Bayes smoother. When used in conjunction with spatial neutral models generated by p-field simulation, the boundary analysis based on Poisson kriging estimates minimizes the proportion of type I errors (i.e., edges wrongly declared significant) while the frequency of these errors is predicted well by the p-value of the statistical test.

The evolution of eusociality in molerats (Bathyergidae): a question of risks,numbers, and costs

Summary This paper addresses risk and energetic considerations fundamentaly causative in the evolution of eusociality in the bathyergid molerats. Three simple mathematical models are presented which predict the probability of successful outbreeding in terms of dispersal risks and the energetic costs of foraging. The predictions of the models are compared with data from the literature, which mostly provide excellent empirical and theoretical support.Inter-habitat dispersal risks are influenced most importantly by group size and resource characteristics, but also by body size, metabolic rate, thermoregulation, soil conditions, and caste structure. Intea-habitat foraging risks are temporaly dependent on rainfall — a factor critical for appropriate dispersal timing. High dispersal and foraging risks necessitate large group sizes and preclude a solitary existence. Outbreeding or inbreeding options are strongly influenced by dispersal risks, with high genetic relatedness in high risk habitats the likely consequence. Offspring should attain inclusive fitness values equal, if not more, than those possible by outbreeding by staying and helping with the colony reproductive effort.Offprint requests to: B.G. Love grove at the German address     

吡草醚原药对大鼠胚胎及胎仔发育的影响

经口染毒大鼠检测妊娠动物接触吡草醚原药后引起致畸的可能性,初步评价吡草醚原药致畸危害程度。依据《GB15670-1995农药登记毒理学试验方法》,选用的大鼠按雄、雌2:1比例同笼交配,次日清晨阴道涂片检查雌鼠,发现精子即确定为受孕第0天,设低、中、高三个剂量组(18.56、92.80、464.00 mg·(kg·d)-1)、一个阴性对照组和一个阳性对照组。在雌鼠受孕第6~15天灌胃给予受试物,连续共10天,在妊娠第19.5天,处死孕鼠检查胚胎发育情况。高剂量组和阳性对照组大鼠孕期体重增重值降低,差异有显著性(p0.05);中、高剂量组和阳性对照组大鼠吸收胎率与阴性对照组比较差异有显著性(p0.05);中、高剂量组和阳性对照组胎鼠重量与阴性对照组比较,差异有显著性(p0.01);阳性对照组胎鼠外观畸形明显,与阴性对照组比较差异有显著性(p0.01);各剂量组胎鼠外观畸形与阴性对照组比较,差异均无显著性(p0.05);中、高剂量组组和阳性对照组胎鼠骨骼及内脏畸形例数明显增多,差异有显著性(p0.01)。实验结果表明吡草醚原药中、高剂量组可明显影响雌鼠体重增长及其胚胎的发育。     

Altruism and environmental risks to health of parents and their children

This paper tests an equilibrium condition from a model that incorporates: (1) altruism of parents toward their young children, and (2) household production of latent health risks. The model demonstrates that an altruistic parent's marginal rate of substitution between an environmental health risk to herself and to her child is equal to the ratio of marginal risk reduction costs. Econometric estimates support this prediction based on data from a stated preference survey involving 488 parents of children aged 3–12 years. This outcome implies that parents reallocate family resources to at least partly offset the effectiveness of public programs that aim to reduce their children's environmental risks.