CATEGORICAL REGRESSION OF TOXICITY DATA - A CASE-STUDY USING ADICARB

Categorical regression is a mathematical tool that can be adapted to e stimate potential health risk from chemical exposures. By regressing o rdered categories of toxic severity or pathological staging on exposur e dose, this method can estimate the likelihood of observing any of th e categories of severity at any dose level. Depending on the nature of the available data, these estimates can take the form of incidence ra tes for any of the categories in an exposed population or the probabil ity of a new study conducted at a specified dose level being classifie d as one of the categories, Categorical regression is illustrated usin g toxicity data on aldicarb, For aldicarb, the data fall into three di fferent groups: human clinical studies, dietary exposures in experimen tal 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 regressio n to data from human clinical studies suggests a maximum likelihood ri sk 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 o f adverse effects is 0.1% at a dose 10-fold higher than the RfD. (C) 1 997 Academic Press.