Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item THE SPATIAL ANALYSIS OF OPIOID-RELATED HEALTH OUTCOMES AND EXPOSURES IN THE UNITED STATES OPIOID OVERDOSE CRISIS(2022) Sauer, Jeffery Charles; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The United States continues to endure the Opioid Overdose Crisis. Yet the burden of the crisis is not experienced uniformly across the United States. The discipline of geography offers a framework and spatial analysis methodology that are direct ways to investigate placed-based differences in opioid-related outcomes, exposures, and proxy measures. This dissertation combines the contemporary frameworks of health geography and geographic information science to provide novel studies on both the geographic patterns in opioid-related health measures at different scales across the United States as well as the actual spatial analytic methods that provide evidence on the Opioid Overdose Crisis. Three main research objectives are addressed over the course of the dissertation: 1) Model the space-time risk of heroin-, methadone-, and cocaine-involved emergency department visits in the greater Baltimore metropolitan area from January 2016 to December 2019 at the Zip Code Tabulation Area-level; 2) Estimate the local and neighboring relationship between prescription opioid volume and treatment admissions involving a prescription opioid across the United States from 2006 to 2014 at the county-level; and 3) Investigate and provide a framework as to how geographic information science has been used to provide knowledge over the duration of the crisis from 1999 to 2021. The first study demonstrates how a recently proposed spatio-temporal Bayesian model can produce disease risk surfaces for opioid-related health outcomes in data constrained scenarios. The second study executes spatial lag of X models on a nationwide prescription opioid distribution dataset, allowing for estimates on the relationship between neighboring prescription opioid volume and nonfatal treatment admissions involving a prescription opioid at the county-level. The third and final study of the dissertation developed and implemented a scoping review methodology, ultimately analyzing the study design and geographical elements of 231 peer-reviewed publications using geographic information science on research questions related to the crisis. Examination of the geographical components of these studies reveals a lack of evidence available at sub-state scales and in the Midwest, north Rocky Mountains, and non-continental United States. Several important future research directions - such as geographic meta-analyses and geographical machine learning - are identified. Taken as a whole, the dissertation provides a contemporary geographical framework to understand the ongoing United States Opioid Overdose Crisis.Item Small area estimation: an empirical best linear unbiased prediction approach(2007-09-17) Li, Huilin; Lahiri, Partha; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In a large scale survey, we are usually concerned with estimation of some characteristics of interest for a large area (e.g., a country). But we are frequently interested in estimating similar characteristics for a subpopulation using the same survey data. The direct survey estimator which utilizes data only from the small area of interest has been found to be highly unreliable due to small sample size. Model-based methods have been used in small area estimation in order to combine information available from the survey data and various administrative and census data. We study the empirical best linear unbiased prediction (EBLUP) and its inferences under the general Fay-Herriot small area model. Considering that the currently used variance estimation methods could produce zero estimates, we propose the adjusted density method (ADM) following Morris' comments. This new method always produces positive estimates. Morris only suggested such adjustment to the restricted maximum likelihood. Asymptotic theory of ADM is unknown. We prove the consistency for the ADM estimator. We also propose an alternate consistent ADM estimator by adjusting the maximum likelihood. By comparing these two ADM estimators both in theory and simulation, we find that the ADM estimator using maximum likelihood is better than the one using the restricted likelihood in terms of bias. We provide a concrete proof for the positiveness and consistency of both ADM estimators. We also propose EBLUP estimator of $\theta_i$ where we use two ADM estimators of $A$. The associated second-order unbiased Taylor linearization MSE estimators are also proposed. In addition, a new parametric bootstrap prediction interval method using ADM estimator is proposed. The positiveness of ADM estimators is emphasized in the construction of the prediction interval. We also show that the coverage probability of this new method is accurate up to $O(m^{-3/2})$. Extensive Monte Carlo simulations are conducted. A data analysis for the SAIPE data set is also presented. The positiveness of ADM estimators plays a vital role here since for this data set the method-of-moments, REML, ML and FH methods could be all zero. We observe that ADM methods produce EBLUP's which generally put more weights to the direct survey estimates than the corresponding EBLUP's that use the other methods of variance component estimation.