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Survey designs and spatio-temporal m...

Hund, Lauren Brooke.

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Survey designs and spatio-temporal methods for disease surveillance.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Survey designs and spatio-temporal methods for disease surveillance./
作者:	Hund, Lauren Brooke.
面頁冊數:	165 p.
附註:	Source: Dissertation Abstracts International, Volume: 73-11(E), Section: B.
Contained By:	Dissertation Abstracts International73-11B(E).
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3514448
ISBN:	9781267452535

Survey designs and spatio-temporal methods for disease surveillance.
Hund, Lauren Brooke.

Survey designs and spatio-temporal methods for disease surveillance. - 165 p.

Source: Dissertation Abstracts International, Volume: 73-11(E), Section: B.

Thesis (Ph.D.)--Harvard University, 2012.

By improving the precision and accuracy of public health surveillance tools, we can improve cost-efficacy and obtain meaningful information to act upon. In this dissertation, we propose statistical methods for improving public health surveillance research. In Chapter 1, we introduce a pooled testing option for HIV prevalence estimation surveys to increase testing consent rates and subsequently decrease non-response bias. Pooled testing is less certain than individual testing, but, if more people to submit to testing, then it should reduce the potential for non-response bias. In Chapter 2, we illustrate technical issues in the design of neonatal tetanus elimination surveys. We address identifying the target population; using binary classification via lot quality assurance sampling (LQAS); and adjusting the design for the sensitivity of the survey instrument. In Chapter 3, we extend LQAS survey designs for monitoring malnutrition for longitudinal surveillance programs. By combining historical information with data from previous surveys, we detect spikes in malnutrition rates. Using this framework, we detect rises in malnutrition prevalence in longitudinal programs in Kenya and the Sudan. In Chapter 4, we develop a computationally efficient geostatistical disease mapping model that naturally handles model fitting issues due to temporal boundary misalignment by assuming that an underlying continuous risk surface induces spatial correlation between areas. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between 1990 and 2000. In Chapter 5, we develop a statistical framework for addressing statistical uncertainty associated with denominator interpolation and with temporal misalignment in disease mapping studies. We propose methods for assessing the impact of the uncertainty in these predictions on health effects analyses. Then, we construct a general framework for spatial misalignment in regression.

ISBN: 9781267452535Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Survey designs and spatio-temporal methods for disease surveillance.
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