Linked Life Data

9927559

Source:http://linkedlifedata.com/resource/pubmed/id/9927559

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1999-4-14
pubmed:abstractText
Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0273-2300
pubmed:author
pubmed:copyrightInfo
Copyright 1998 Academic Press.
pubmed:issnType
Print
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
96-105
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure.
pubmed:affiliation
Department of Environmental Health and Consortium for Risk Evaluation with Stakeholder Participation, University of Washington Institute for Risk Analysis and Risk Communication, Seattle, Washington, 98105, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Review, Research Support, Non-U.S. Gov't