Concordance Analysis of Probabilistic Aggregate Exposure Assessment and Biomarkers of ExposureEPA Grant Number: R831844
Title: Concordance Analysis of Probabilistic Aggregate Exposure Assessment and Biomarkers of Exposure
Investigators: Kissel, John C. , Fenske, Richard , Belle, Gerald van , Lu, Chensheng (Alex)
Current Investigators: Kissel, John C.
Institution: University of Washington - Seattle
EPA Project Officer: Klieforth, Barbara I
Project Period: August 1, 2004 through July 31, 2007
Project Amount: $449,193
RFA: Environmental Statistics Research: Novel Analyses of Human Exposure Related Data (2004) RFA Text | Recipients Lists
Research Category: Environmental Statistics , Health , Health Effects
The overall objectives of this study are to examine the accuracy of current pesticide exposure assessment models, and to demonstrate a novel method for the development and evaluation of such models. We propose to conduct second order probabilistic assessments of aggregate pesticide exposures in three existing data sets, characterize the variability of biological exposure measures, and evaluate the concordance of these two exposure assessment approaches. This novel analytical approach will produce new methods for determining the validity of exposure and risk estimates.
Three key areas of scientific inquiry in the study of environmental contaminants are
- assessment of aggregate exposure,
- probabilistic prediction of exposure,
- and biomarkers of exposure.
We propose to integrate these three areas of inquiry through application of second order probabilistic methods to aggregate exposure data and through concordance analysis of predicted and measured levels of urinary pesticide metabolites. This project will generate two-dimensional Monte Carlo simulations based on three key aggregate exposure studies (UW Total Exposure Assessment, NHEXAS-Maryland, and CTEPP) for which corresponding organophosphate (OP) pesticide biomarker data are available. Concordance will be assessed using a measure of total deviation incorporating accuracy, scale and precision.
These analyses will provide new insights into the validity of current pesticide exposure assessment methods, and will permit development of a novel approaches for evaluating pesticide exposure data. This work will improve analytical procedures for second order probabilistic assessments of pesticide exposure data; properly characterize uncertainty as well as variability in pesticide exposure assessment models; characterize variability in pesticide biomarker exposure data; determine the contribution of extraneous sources of pesticide metabolite compounds to estimates of exposure and dose; and, develop a novel analytical approach to evaluate the validity of probabilistic aggregate exposure models with biological monitoring data.