Date of Award

2004

Level of Access Assigned by Author

Open-Access Thesis

Degree Name

Master of Arts (MA)

Department

Mathematics

Advisor

Ramesh Gupta

Second Committee Member

Pushpa Gupta

Third Committee Member

Henrik Bresinsky

Abstract

The analysis of geographic variation of disease and its representation on a map form an important topic of research in epidemiology and in public health in general. Identification of spatial heterogeneity of relative risk using morbidity and mortality data is required. The usual technique of disease atlas generation consists of data collection (observed number of disease cases). These data are collected during a continuous period of time (5 to 10 years). The second aspect of atlas creation relates to the analysis of these data. A traditional measure of the spatial variation is usually taken as a ratio of the number of observed disease cases to the numbe ro the expected disease cases for the given region. This measure is called the Standardized Mortality (morbidity) ratio (SMR). Our interest is to estimate the spatial variation, I.e. to estimate the mean and the variance of the SMR. In this paper we will focus on the developments that avoid the pitfalls of the crude SMR. We will compare the results of nonparametric and para metric approaches to the SMR estimation. More specifically, we present a mixture model to evaluate the heterogeneity in estimating SMR. Simulation studies are carrried out and the results analyzed.

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