Probabilistic Characterization of RfD: Empirical Data to Develop Uncertainty Factor Distributions. S. J. S. Baird, Newton, MA
Uncertainty factors are currently thought of as point estimates that adjust and provide an unknown margin of protection for each area of extrapolation required to extrapolate from an animal bioassay NOAEL or benchmark dose to a dose that will be protective of human health, the RfD. However, data are available to characterize the adjustment and uncertainty in the adjustment probabilistically. A probabilistic framework and data based distributions for each of the uncertainty factors was originally presented in Baird et al. (HERA, 2:79, 1995). The current work focuses on characterizing the biologic process underlying each area of extrapolation, the ideal data to address those processes, the data available and its limitations, how to use the data for the extrapolation, and issues that remain when using the available data for data based uncertainty factor distributions. Issues that need to be addressed during the development of default distributions for noncancer risk estimation and by the users of the estimates include: 1) how to interpret the available data in the context of the extrapolation and the attendant uncertainty; 2) how does the available data drive the interpretation; 3) what are the implications of data analysis decisions that get embedded in the distributions (and point estimates); and 4) what are the implications of using the same data sources for multiple decisions.
Work supported in part by Health Canada, under Contract No. 5366.
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