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Using Spot Biomarker Data to Inform Chronic Exposure and Health Risk
Citation:
Sobus, J. AND J. Pleil. Using Spot Biomarker Data to Inform Chronic Exposure and Health Risk. 2015 ISES Annual Meeting, Henderson, NV, October 18 - 23, 2015.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
The U.S. NHANES and other health surveys frequently collect and analyze spot biological specimens to inform chemical exposures. These spot measurements can be compared to biomarker-based reference levels, such as “Biomonitoring Equivalents” (BEs), to evaluate health risks for target populations. Comparisons for non-persistent chemicals can be uncertain, owing to large intra-individual variation in biomarker levels. As such, methods are needed to better interpret spot measures of non-persistent biomarkers against reference levels. Using repeat-measures biomarker data from a previous study, a series of simulation experiments were performed to evaluate relationships between distributions of spot and pooled (averaged) measures. Based on these experiments, a mathematical model was developed for estimating parameters (i.e., geometric mean and standard deviation) of a means distribution using spot measurements and estimates of biomarker intraclass correlation coefficients (ICCs). An Excel-based tool was developed from this model and can be used to rapidly calculate population exceedance above a BE or other biomarker-based reference level. This presentation will introduce the new computational method and provide examples for utilizing the Excel-based tool.
