Health Risk above the Reference Dose for Multiple Chemicals

• Published in: Regulatory toxicology and pharmacology Vol. 30; no. 2; pp. S19 - S26
• Main Authors: Teuschler, Linda K., Dourson, Michael L., Stiteler, William M., McClure, Peter, Tully, Heather
• Format: Journal Article Conference Proceeding
• Language: English
• Published: San Diego, CA Elsevier Inc 01-10-1999; Elsevier
• Subjects: Animals; Biological and medical sciences; Diazinon - toxicity; Disulfoton - toxicity; Dose-Response Relationship, Drug; General aspects. Methods; Humans; Lindane - toxicity; Maximum Allowable Concentration; Medical sciences; Models, Biological; No-Observed-Adverse-Effect Level; Organophosphorus Compounds - toxicity; Pesticides - toxicity; Regression Analysis; Risk Assessment - methods; Thiocarbamates - toxicity; Toxicology; Human; Uncertainty; Health; Toxicity; Chemical compound; Pesticides; No observed effect level; Modeling; Toxic association; Mixture; Limit dose; Investigation method; Residue; Animal
• ISSN: 0273-2300; 1096-0295
• DOI: 10.1006/rtph.1999.1321

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.