Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure

Biologically based markers (biomarkers) are currently used to provide infor mation on exposure, health effects, and individual susceptibility to chemic al and radiological wastes. However, the development and validation of biom arkers are expensive and time consuming. To determine whether biomarker dev elopment and use offer potential improvements to risk models based on predi ctive relationships or assumed values, we explore the use of uncertainty an alysis applied to exposure models for dietary methyl mercury intake. We com pare exposure estimates based on self-reported fish intake and measured fis h mercury concentrations with biomarker-based exposure estimates (i.e., hai r or blood mercury concentrations) using a published data set covering I mo nth of exposure. Such a comparison of exposure model predictions allowed es timation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important comp onents of uncertainty regarding biomarker-based exposure estimates, while t he diary-based exposure estimate was susceptible to bias. Application of th e proposed methods to a simple case study demonstrates their utility in est imating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk ass essment. Such analyses can guide risk analysts and managers in the appropri ate validation, use, and interpretation of exposure biomarker information. (C) 1998 Academic Press.