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Estimating lifetime risk from spot biomarker data and intra‐class correlation coefficients (ICC)
Citation:
Pleil, J. AND J. Sobus. Estimating lifetime risk from spot biomarker data and intra‐class correlation coefficients (ICC). Journal of Exposure Science and Environmental Epidemiology . Nature Publishing Group, London, Uk, 76(12):747-766, (2013).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s 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:
Human biomarker measurements in tissues including blood, breath, and urine can serve as efficient surrogates for environmental monitoring because a single biological sample integrates personal exposure across all environmental media and uptake pathways. However, biomarkers represent a “snapshot” in time, and risk assessment is generally based on long-term averages. In this article, we propose a statistical approach for estimating long-term average exposures from distributions of spot biomarker measurements using intra-class correlations (based on measurement variance components) from the literature. This methodology is developed and demonstrated using a log-normally distributed data set of urinary oh-pyrene taken from our own studies. The calculations are generalized for any biomarker data set of spot measures such as those from the National Health and Nutrition Evaluation Studies (NHANES) requiring only spreadsheet calculations. We develop a three-tiered approach (depending on the availability of meta-data) for converting any collection of spot biomarkers into an estimated distribution of individual means that can then be compared to a biologically relevant risk level. Examples from a Microsoft Excel® based spreadsheet for calculating estimates of the proportion of the population exceeding a given bio-equivalent level are provided as an appendix.
URLs/Downloads:
PLEIL CLEANCOPYJTEHA FINAL FINAL .PDF (PDF, NA pp, 207.568 KB, about PDF)Journal of Toxicology and Environmental Health, Part A
