Health risk above the reference dose for multiple chemicals

Recent work indicates that the regression of toxicity data viewed as catego ries of pathological staging is useful for exploring the likely health risk at doses above a Reference Dose (RfD), which is an estimate (with uncertai nty spanning perhaps an order of magnitude) of a daily exposure to the huma n population (including sensitive subgroups) that is likely to be without a n appreciable risk of deleterious effects during a lifetime. Toxic effects, which may include both quantal and continuous data, are classified into or dered categories of total toxic severity (e.g., none, mild, adverse, severe ). These severity categories are regressed on explanatory variables, such a s dose or exposure duration, to estimate the probability of observing an ad verse or severe effect. In this paper, categorical regression has been expa nded to compare the likely risks across multiple chemicals when exposures a re above their RfDs. Existing health risk data for diazinon, disulfoton, S- ethyl dipropylthiocarbamate, fenamiphos, and lindane were analyzed. As expe cted, 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 su ggest its application in evaluating comparative risks where multiple chemic al exposures exist.