Benchmark Dose Analysis via Nonparametric Regression Modeling

Estimation of benchmark doses (BMDs) in quantitative risk assessment traditionally is based upon parametric dose‐response modeling. It is a well‐known concern, however, that if the chosen parametric model is uncertain and/or misspecified, inaccurate and possibly unsafe low‐dose inferences can result. We describe a nonparametric approach for estimating BMDs with quantal‐response data based on an isotonic regression method, and also study use of corresponding, nonparametric, bootstrap‐based confidence limits for the BMD. We explore the confidence limits’ small‐sample properties via a simulation study, and illustrate the calculations with an example from cancer risk assessment. It is seen that this nonparametric approach can provide a useful alternative for BMD estimation when faced with the problem of parametric model uncertainty.     

An International Pooled Analysis for Obtaining a Benchmark Dose for Environmental Lead Exposure in Children

Lead is a recognized neurotoxicant, but estimating effects at the lowest measurable levels is difficult. An international pooled analysis of data from seven cohort studies reported an inverse and supra‐linear relationship between blood lead concentrations and IQ scores in children. The lack of a clear threshold presents a challenge to the identification of an acceptable level of exposure. The benchmark dose (BMD) is defined as the dose that leads to a specific known loss. As an alternative to elusive thresholds, the BMD is being used increasingly by regulatory authorities. Using the pooled data, this article presents BMD results and applies different statistical techniques in the analysis of multistudy data. The calculations showed only a limited variation between studies in the steepness of the dose‐response functions. BMD results were quite robust to modeling assumptions with the best fitting models yielding lower confidence limits (BMDLs) of about 0.1–1.0 μ g/dL for the dose leading to a loss of one IQ point. We conclude that current allowable blood lead concentrations need to be lowered and further prevention efforts are needed to protect children from lead toxicity.     

Water Adherence Factors for Human Skin

On incidental dermal exposure to chemicals in water, a key exposure factor is the amount of water adhering to skin. Although soil adherence factors have been developed for risk assessment, measurements of water adherence on human skin have not been described. In the Environmental Protection Agency's (EPA's) dermal risk assessment guidance, dermal dose from environmental exposures is based upon the flux rate across the skin, which assumes that an unlimited amount of chemical is available for absorption. This assumption is applicable to certain exposure scenarios such as swimming and bathing. However, exposures to contaminated water frequently involve scenarios where the available chemical is limited by the amount of water adhering to the skin, for example, during accidental splashes. We conducted studies in human volunteers to investigate water adherence per unit area of skin after brief contact with water. In two sets of experiments, either water was applied with a micropipette to 10‐cm² areas of the lower leg, foot, and hand, or the foot and hand were briefly immersed in water. In males, using a micropipette, water adherence ranged from 1.93 (foot) to 7.13 μL/cm² (lower leg). In females, it ranged from 1.10 (lower leg) to 4.83 μL/cm² (hand). Hand and foot immersion resulted in relatively higher values of 6.89 and 5.17 μL/cm², respectively, in males, and 5.40 and 6.39 μL/cm² in females. Water adherence was affected by amount of body hair and type of exposure. Water adherence factors can be used to calculate the applied dose per unit area for exposures involving intermittent water contact.     

Sensitivity studies of air scattered neutron dose from particle accelerators

Sensitivity analysis of air scattered neutron dose at several distances from a particle accelerator with respect to shield thickness and neutron yield distributions has been carried out. We illustrate the successful use of Response Surface Methodology in studying the behaviour of the sensitivity coefficients for main effects and interaction terms. A comparison of the full six factor design and the orthogonal Central Composite Design has been made. The overhead shield is found to be the most sensitive parameter followed by the high energy part of the neutron energy distribution.     

Robust williams trent test

To investigate the biological activities of a new compound or drug, experimenters usually compare a series of increasing doses to a control. Among other objectives, one may try to investigate any possible dose-response trend and to determine the minimum effective dose among all the experimental doses. Williams (1971, 1972) proposed a procedure to test the dose-response trend and also to identify the minimum effective dose based on the normally distributed data. In this paper, we propose a similar test procedure based on the robust estimate'of the average response to perform similar analysis. The proposed method is more resistant to the outliers and more powerful than the Williams procedure when the data distribution deviates from normality. We illustrate the use of this procedure with data arising from a recent study.     

A generalized scheffé method of multiple comparisons

Standard resulrs on the extrema of quotients of quadratic forms are extended to the non-negative definite case. The maximum and the set over which it is achieved are characterized explicitly both in terms of generalized inverse matrices and generalized eigenvalues. These results become the basis of Scheffe type multiple comparisons in the usual way. To demonstrate their application to statistics with singular covariance matrices, the method is detailed for Mantel-Haenszel, Breslow, and Cox statistics. An example is presented illustrating a situation where the proposed Scheffe type comparisons may be better than the pairwise method.     

A Weighted Composite Dose – Response Model For Human Salmonellosis

This article describes the development of a weighted composite dose – response model for human salmonellosis. Data from previously reported human challenge studies were categorized into two different groups representing low and moderately virulent/pathogenic Salmonella strains based on a disease end point. Because epidemiological data indicate that some Salmonella strains are particularly pathogenic, and in the absence of human feeding study data for such strains, Shigella dysenteriae was used as a proxy for highly virulent strains. Three single-hit dose – response models were applied to the human feeding study data and evaluated for best fit using maximum likelihood estimation: (1) the exponential (E-1pop), (2) the two-subpopulation exponential (E-2pop), and (3) the Beta-Poisson (BP). Based on the goodness-of-fit test, the E-1pop and BP were the best-fit models for low and moderately virulent/pathogenic Salmonella strains, and the E-2pop and BP models were better for highly virulent/pathogenic strains. Epistemic analysis was conducted by determining the degree of confidence associated with the selected models, which was found to be 50%/50% (E-1pop/BP) for low and moderately pathogenic Salmonella strains, and 9.8%/90.2% (E-2pop/BP) for highly virulent strains. The degree of confidence for each component model and variations in the proportion of strains within each virulence/pathogenicity category were incorporated into the overall composite model. This study describes the influence of variation in strain virulence and host susceptibility on the shape of the population dose – response relationship.     

Book Reviews

Books reviewed:
Office International Des Epizooties (World Organization for Animal Health) World Animal Health in 1998, Part 1: Reports on the Animal Health Status and Disease Control Methods and Tables on Incidence of List A Diseases and Part 2: Tables on the Animal Health Status and Disease Control Methods
Peter Sedlmeier. Lawrence Erlbaum Associates, Mahwah Improving Statistical Reasoning: Theoretical Models and Practical Implications
Andrew Ford. Island Press Modeling the Environment. An Introduction to System Dynamics Modeling of Environmental Systems
Jacob I. Bregman. Lewis Publishers, Boca Raton Environmental Impact Statements, Second Edition     

Remedial Policies in Radiologically-Contaminated Forests: Environmental Consequences and Risk Assessment

As a result of the Chernobyl nuclear power plant accident in 1986, large forested areas in Europe were contaminated by radionuclides. Extensive societal pressure has been exerted to decrease the radiation dose to the population and to the environment. Thus, in making abatement and remediation policy decisions not only economic costs, but also human and environmental risk assessment are desired. Forest remediation by organic layer removal, one of the most promising cleanup policies, is considered in this paper. Ecological risk assessment requires evaluation of the radionuclide distribution in forests. The FORESTPATH model^(1,2) is used for predicting the radionuclide fate in forest compartments after deposition as well as for evaluating the application of the remedial policy. Time of intervention and radionuclide deposition profile was predicted as being crucial for the remediation efficiency. Risk assessment conducted for a critical group of forest users in Belarus shows that consumption of forest products (berries and mushrooms) leads to about 0.004% risk of a fatal cancer. Cost-benefit analysis for forest cleanup suggests that complete removal of organic layer is too expensive for application in Belarus.