Logistic regression of inhalation toxicities of perchloroethylene--application in noncancer risk assessment

Towards sustainable and safe apparel cleaning methods: A review

Perchloroethylene (PERC) is a compound commonly used as a solvent in dry cleaning, despite its severe health and environmental impacts. In recent times chemicals such as hydrocarbons, GreenEarth(®), acetal and liquid carbon dioxide have emerged as less damaging substitutes for PERC, and an even more sustainable water-based wet cleaning process has been developed. We employed a systematic review approach to provide a comprehensive overview of the existing research evidence in the area of sustainable and safe apparel cleaning methods and care. Our review describes traditional professional dry cleaning methods, as well as those that utilise solvents other than PERC, and their ecological attributes. In addition, the new professional wet cleaning process is discussed. Finally, we address the health hazards of the various solvents used in dry cleaning and state-of-the-art solvent residue trace analysis techniques.

Risk assessment for benefits analysis: framework for analysis of a thyroid-disrupting chemical

Benefit-cost analysis is of growing importance in developing policies to reduce exposures to environmental contaminants. To quantify health benefits of reduced exposures, economists generally rely on dose-response relationships estimated by risk assessors. Further, to be useful for benefits analysis, the endpoints that are quantified must be expressed as changes in incidence of illnesses or symptoms that are readily understood by and perceptible to the layperson. For most noncancer health effects and for nonlinear carcinogens, risk assessments generally do not provide the dose-response functions necessary for economic benefits analysis. This article presents the framework for a case study that addresses these issues through a combination of toxicology, epidemiology, statistics, and economics. The case study assesses a chemical that disrupts proper functioning of the thyroid gland, and considers the benefits of reducing exposures in terms of both noncancer health effects (hypothyroidism) and thyroid cancers. The effects are presumed to be due to a mode of action involving interference with thyroid-pituitary functioning that would lead to nonlinear dose response. The framework integrates data from animal testing, statistical modeling, human data from the medical and epidemiological literature, and economic methodologies and valuation studies. This interdisciplinary collaboration differs from the more typical approach in which risk assessments and economic analyses are prepared independently of one another. This framework illustrates particular approaches that may be useful for expanded quantification of adverse health effects, and demonstrates the potential of such interdisciplinary approaches. Detailed implementation of the case study framework will be presented in future publications.

Health risk above the reference dose for multiple chemicals

Recent work indicates that the regression of toxicity data viewed as categories of pathological staging is useful for exploring the likely health risk at doses above a Reference Dose (RfD), which is an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. Toxic effects, which may include both quantal and continuous data, are classified into ordered categories of total toxic severity (e.g., none, mild, adverse, severe). These severity categories are regressed on explanatory variables, such as dose or exposure duration, to estimate the probability of observing an adverse or severe effect. In this paper, categorical regression has been expanded to compare the likely risks across multiple chemicals when exposures are above their RfDs. Existing health risk data for diazinon, disulfoton, S-ethyl dipropylthiocarbamate, fenamiphos, and lindane were analyzed. As expected, the estimated risks of adverse effects above the RfD varied among the chemicals. For example, at 10-fold above the RfD these risks were modeled to be 0.002, 0.0001, 0.0007, 0.002, and 0.02, respectively. The results and impacts of this analysis indicate that categorical regression is a useful screening tool to analyze risks above the RfD for specific chemicals and suggest its application in evaluating comparative risks where multiple chemical exposures exist.

Comparison of noncancer risk assessment approaches for use in deriving drinking water criteria

The development and promulgation of drinking water regulations to protect exposed human populations from contaminants that may occur in public drinking water supplies has been a major regulatory concern and effort of the United States Environmental Protection Agency for decades. Risk assessment, as applied in the development of drinking water regulations, involves the quantification of the level below which adverse health effects are not expected to occur. Traditionally, the oral reference dose (RfD) has been the preferred approach for characterizing these noncancer health risks. The benchmark dose approach to derive RfDs has increasingly gained scientific and regulatory acceptance as a risk assessment methodology since its introduction in 1984. Similarly, the use of categorical regression techniques were introduced at about the same time. The objective of this paper is to present an evaluation of the strengths and weaknesses of each risk assessment method as related to the development of drinking water criteria for noncarcinogenic chemicals. The data base requirements, performance record, mathematical or statistical basis, and other parameters are described and compared.

Categorical regression of toxicity data: a case study using aldicarb

Categorical regression is a mathematical tool that can be adapted to estimate potential health risk from chemical exposures. By regressing ordered categories of toxic severity or pathological staging on exposure dose, this method can estimate the likelihood of observing any of the categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence rates for any of the categories in an exposed population or the probability of a new study conducted at a specified dose level being classified as one of the categories. Categorical regression is illustrated using toxicity data on aldicarb. For aldicarb, the data fall into three different groups: human clinical studies, dietary exposures in experimental animals, and accidental human exposure by contaminated crops. The U.S. EPA has assessed this literature and developed a reference dose (RfD) of 0.001 mg/kg-day. The results of applying categorical regression to data from human clinical studies suggests a maximum likelihood risk estimate of adverse effects of 0.008% at a 10-fold higher dose than the RfD when blood cholinesterase inhibition is not considered as an adverse effect. When blood cholinesterase inhibition of 20% or more is considered as an adverse effect, a maximum likelihood risk estimate of adverse effects is 0.1% at a dose 10-fold higher than the RfD.