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Accounting for uncertainty in enviro...

Whitney, Melissa Jean.

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Accounting for uncertainty in environmental health risk assessment.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Accounting for uncertainty in environmental health risk assessment./
Author:	Whitney, Melissa Jean.
Description:	81 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: .
Contained By:	Dissertation Abstracts International72-01B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3435438
ISBN:	9781124339177

Accounting for uncertainty in environmental health risk assessment.
Whitney, Melissa Jean.

Accounting for uncertainty in environmental health risk assessment. - 81 p.

Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: .

Thesis (Ph.D.)--Harvard University, 2010.

This dissertation explores several common sources of uncertainty in environmental health risk assessment and proposes new methods to account for uncertainty in a systematic manner. The relationship between prenatal polychlorinated biphenyl (PCB) exposure and early childhood cognitive development serves as an exemplary backdrop for studying uncertainty. Although the effects of large, toxic exposures to PCBs are well-documented, studies examining low-level, chronic PCB exposure have produced seemingly conflicting results. Researchers have proposed various explanations for observed discrepancies, citing several sources of uncertainty in estimating effects of PCBs on cognitive development. The methods described in this dissertation help reconcile past study inconsistencies and provide a framework to formally address uncertainty in both human and animal studies on the health effects of environmental pollutants. Such an integrated approach to uncertainty analysis is necessary to ensure that risk-managers have the means to weigh the strengths and limitations of available research and prioritize regulatory actions to effectively protect the public. The Benchmark Dose (BMD), a common regulatory risk measure, is utilized to demonstrate how quantitative risk assessment summary measures can be modified to explicitly account for uncertainty.

ISBN: 9781124339177Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Accounting for uncertainty in environmental health risk assessment.
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020 $a 9781124339177
035 $a (UMI)AAI3435438
035 $a AAI3435438
040 $a UMI $c UMI
100 1 $a Whitney, Melissa Jean. $3 1675631
245 1 0 $a Accounting for uncertainty in environmental health risk assessment.
300 $a 81 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: .
500 $a Adviser: Louise M. Ryan.
502 $a Thesis (Ph.D.)--Harvard University, 2010.
520 $a This dissertation explores several common sources of uncertainty in environmental health risk assessment and proposes new methods to account for uncertainty in a systematic manner. The relationship between prenatal polychlorinated biphenyl (PCB) exposure and early childhood cognitive development serves as an exemplary backdrop for studying uncertainty. Although the effects of large, toxic exposures to PCBs are well-documented, studies examining low-level, chronic PCB exposure have produced seemingly conflicting results. Researchers have proposed various explanations for observed discrepancies, citing several sources of uncertainty in estimating effects of PCBs on cognitive development. The methods described in this dissertation help reconcile past study inconsistencies and provide a framework to formally address uncertainty in both human and animal studies on the health effects of environmental pollutants. Such an integrated approach to uncertainty analysis is necessary to ensure that risk-managers have the means to weigh the strengths and limitations of available research and prioritize regulatory actions to effectively protect the public. The Benchmark Dose (BMD), a common regulatory risk measure, is utilized to demonstrate how quantitative risk assessment summary measures can be modified to explicitly account for uncertainty.
520 $a Chapter 1 introduces Bayesian Model Averaging (BMA) to combine information from a large set of candidate models and arrive at risk estimates that reflect both statistical variability and model selection uncertainty. This work utilizes BMA to generate a range of plausible risk estimates and argues in favor of examining a full distribution of risk instead of merely presenting traditional summary measures, which are conditional on selecting a single model and observing a significant dose effect.
520 $a Chapter 2 addresses uncertainty due to low-dose extrapolation in BMD estimation. This chapter modifies traditional BMD methodology, which was originally developed for animal toxicology data, to accommodate environmental epidemiology studies designed to detect health effects from ubiquitous, low-level human exposures.
520 $a Chapter 3 compares four methods for implementing BMA in the context of model selection. This chapter explores the relative strengths and limitations of these strategies and urges caution in implementing certain strategies when faced with increasingly large numbers of covariates of varying strengths and predictive values.
520 $a Chapter 4 demonstrates the ability of BMA to account for uncertainty in not only an environmental epidemiology context but for animal toxicology studies as well. This chapter quantifies uncertainty due to model structure choice when analyzing animal toxicology data and addresses covariate selection uncertainty given a particular model structure.
590 $a School code: 0084.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Health Sciences, Toxicology. $3 1017752
650 4 $a Statistics. $3 517247
650 4 $a Health Sciences, Epidemiology. $3 1019544
690 $a 0308
690 $a 0383
690 $a 0463
690 $a 0766
710 2 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 72-01B.
790 1 0 $a Ryan, Louise M., $e advisor
790 $a 0084
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3435438