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Other pesticide exposures not related to the farmwork activities being studied may make interpretation of exposure prediction factors difficult.
The first two parts of this discussion address interpretation of exposure biomarkers within the exposure-to-disease continuum articulated by Perera and Weinstein (2000).
Since there is no individual-level exposure assessment, interpretation of exposure at the individual-level would result in the Berkson measurement error [ 52].
When using hair and finger/toenails to quantify exposure to environmental contaminants, caution is needed in the interpretation of exposure data.
Type III error refers to the lack of relevance of some biomarkers, with uncertain biological meaning, for the interpretation of exposure disease relationships.
Quantitative analysis could then proceed on end points with strong or moderate levels of evidence to provide a full quantitative context for interpretation of exposure data.
For example, the data required for the assessment and interpretation of exposure trends may be different from those necessary for the assessment of health risk.
In the short term, molecular signature studies conducted using laboratory animals and human cells lines will be useful for guiding the interpretation of exposure data from epidemiologic studies.
Large interindividual differences in the formation of the metabolites, and changes of the metabolite profile at different times after exposure further complicate interpretation of exposure and dose using urinary atrazine biomarkers (Buchholz et al. 1999).
This builds on the work of Arbuckle and colleagues in establishing parameters for herbicide exposure (Arbuckle et al. 2002, 2004), and will aid the development and interpretation of exposure models for epidemiologic studies of pesticide-exposed populations (Acquavella et al. 2006; Dosemeci et al. 2002).
Interpretation of exposures for either of these metrics using urinary concentrations of analytes requires assumptions or data about urinary flow rates.
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