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Estimating the Health Effects of Env...

Correia, Andrew William.

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Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data./
Author:	Correia, Andrew William.
Description:	121 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
Contained By:	Dissertation Abstracts International74-10B(E).
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3566847
ISBN:	9781303183959

Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data.
Correia, Andrew William.

Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data. - 121 p.

Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.

Thesis (Ph.D.)--Harvard University, 2013.

In the field of environmental epidemiology, there is a great deal of care required in constructing models that accurately estimate the effects of environmental exposures on human health. This is because the nature of the data that is available to researchers to estimate these effects is almost always observational in nature, making it difficult to adequately control for all potential confounders - both measured and unmeasured. Here, we tackle three different problems in which the goal is to accurately estimate the effect of an environmental exposure on various health outcomes.

ISBN: 9781303183959Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data.
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020 $a 9781303183959
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100 1 $a Correia, Andrew William. $3 2104958
245 1 0 $a Estimating the Health Effects of Environmental Exposures: Statistical Methods for the Analysis of Spatio-temporal Data.
300 $a 121 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-10(E), Section: B.
500 $a Adviser: Francesca Dominici.
502 $a Thesis (Ph.D.)--Harvard University, 2013.
520 $a In the field of environmental epidemiology, there is a great deal of care required in constructing models that accurately estimate the effects of environmental exposures on human health. This is because the nature of the data that is available to researchers to estimate these effects is almost always observational in nature, making it difficult to adequately control for all potential confounders - both measured and unmeasured. Here, we tackle three different problems in which the goal is to accurately estimate the effect of an environmental exposure on various health outcomes.
520 $a In Chapter 1, we extend and expand upon a previous study examining the relationship between fine particle air pollution and life expectancy in the United States (US) by analyzing data from the period 2000 to 2007 from 545 counties across the US. Using straightforward regression techniques, we estimate the association between changes in air pollution levels and changes in life expectancy over the period from 2000 to 2007 for the entire US as well as for a number of subpopulations within the US.
520 $a Chapter 2 builds upon the previous chapter by developing a modeling approach for estimating the effects of monthly variations in fine particle air pollution on monthly variations in mortality while controlling for potential sources of confounding. We first show via a simulation study where previous approaches to estimating this relationship break down. We then propose a new model to overcome those deficiencies, and we evaluate this approach using a large Medicare dataset linked with air pollution exposure estimates from across the US.
520 $a In Chapter 3, we evaluate the impact of noise exposure from airports on hospitalizations for cardiovascular disease (CVD) among Medicare enrollees living in zip codes surrounding major airports in the continental US. We begin with a fully Bayesian hierarchical Poisson model for the expected number of CVD hospitalizations in each zip code as a function of exposure to noise as well as several other individual and area-level covariates. We then conduct a thorough sensitivity analysis, examining potential sources of confounding, spatial dependence, and the possibility of a threshold effect.
590 $a School code: 0084.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Environmental Health. $3 578282
690 $a 0308
690 $a 0470
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertation Abstracts International $g 74-10B(E).
790 $a 0084
791 $a Ph.D.
792 $a 2013
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3566847