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Impact of Federally Qualified Health Centers on Rates of Ambulatory Care Sensitive Conditions among Medicaid and Uninsured Populations in Maryland
No full textAmbulatory Care Sensitive Conditions (ACSCs) are conditions for which hospitalization and emergency department (ED) visits can be avoided if a person has better access to ambulatory care services. Rates and costs of hospital admission and ED visits for ACSCs have increased over the decade, especially for people without health insurance and/or on Medicaid. Objectives of this dissertation were to study ACSC rates in Maryland over time, identify areas where ACSC rates had been persistently high, determine factors that were associated with ACSCs, and examine if the expansion of FQHCs had decreased ACSCs in geographical areas over the study period. The study used Maryland hospital discharge data to identify ACSCs based on the Agency for Healthcare Research and Quality (AHRQ) definition of Prevention Quality Indicators (PQIs). A Zip Code Tabulation Area (ZCTA) was used as a unit of analysis. ACSCs and all controlled factors were calculated at the ZCTA level from 2000 to 2010. Negative binomial panel models were used to determine trends, and to estimate the impact of FQHCs and other factors on ACSCs. The study found that ACSC rates among Medicaid and uninsured patients had increased over time for several conditions while such conditions among total populations remained stable or decline. In addition, variations in hospitalization and ED visits for ACSCs existed across Maryland's counties and local areas, but the rates seemed consistent within the areas over time. Proportion of populations living in poverty had the largest and consistently positive relationship with most ACSC hospitalization and ED visit rates. The relationships between ACSCs and other socioeconomic factors are varied by type of condition. Importantly, the expansion of FQHCs had a significant association with lower rates of hospitalization and ED visits for several ACSC conditions. Thus, the expansion of FQHCs is associated with better access to primary care among Medicaid and uninsured populations
Performance Evaluation of Probabilistic Latent Semantic Analysis for Unstructured Social Media Data
No full textBig data analytics is being applied in many fields today to mine unstructured data such as social media blogs or medical records. We focus this thesis on two popular analysis techniques, the methods of Latent Semantic Analysis(LSA) and Probabilistic Latent Semantic Analysis(PLSA), both used for interpreting or extracting concepts and relationships from data. As a use case, we propose to compare their performances in identifying communities from Twitter data sets during natural disasters such as Hurricanes. Latent semantic analysis uses statistical computations, typically singular value decomposition, to find semantic or contextual meaning from the data. It finds relationships between terms and concepts in an unstructured data set. Probabilistic latent semantic analysis or indexing is another method based on Bayesian analysis that typically is used for two-mode data. The objective is to compare these two methods on a large set of social media documents related to Hurricane Sandy in order to form clusters of similar concepts. We then compare the performance of these two methods to determine their relative performance in determining communities and hidden topics, e.g. finding clusters of similar topics like power outages, floods, gas outages, etc. We apply two clustering methods, K-Means and Affinity Propagation to form clusters in the data. Finally, we present the results by applying external methods of evaluation after creating a test data-set to compare the performance of these two methods. Metrics like Precision, Recall, confusion matrix are used to evaluate the performance of our system. The evaluation showed us that in almost all the scenarios, PLSA works better than LSA in finding out hidden relationships and structures. Whereas LSA is slightly faster than PLSA
Development Of Advanced Sandwich Core Topologies Using Fused Deposition Modeling And Electroforming Processes
No full textNew weight efficient materials are needed to enhance the performance of vehicle systems allowing increased speed, maneuverability and fuel economy. This work leveraged a multi-length-scale composite approach combined with hybrid material methodology to create new state-of-the-art additive manufactured sandwich core material. The goal of the research was to generate a new material to expands material space for strength versus density. Fused-Deposition-Modeling (FDM) was used to remove geometric manufacturing constraints, and electrodepositing was used to generate a high specific-strength, bio-inspired hybrid material. Microtension samples (3mm x 1mm with 250?m x 250?m gage) were used to investigate the electrodeposited coatings in the transverse (TD) and growth (GD) directions. Three bath chemistries were tested: copper, traditional nickel sulfamate (TNS) nickel, and nickel deposited with a platinum anode (NDPA). NDPA shows tensile strength exceeding 1600 MPa, significantly beyond the literature reported values of 60MPa. This strengthening was linked to grain size refinement into the sub-30nm range, in addition to grain texture refinement resulting in only 17% of the slip systems for nickel being active. Anisotropy was observed in nickel deposits, which was linked to texture evolution inside of the coating. Microsample testing guided the selection of 15?m layer of copper deposition followed by a 250 ?m NDPA layer. Classical formulas for structural collapse were used to guide an experimental parametric study to establish a weight/volume efficient strut topology. Length, diameter and thickness were all investigated to determine the optimal column topology. The most optimal topology exists when Eulerian buckling, shell micro buckling and yielding failure modes all exist in a single geometric topology. Three macro-scale sandwich topologies (pyramidal, tetrahedral, and strut-reinforced-tetrahedral (SRT) were investigated with respect to strength-per-unit-weight. The topologies were optimized across length scales using texture on the nano-scale microsamples on the micro-scale, and the parametric column study on the meso-scale. The results showed that additive manufacturing as a viable method for removing geometric constraints observed by other manufacturing methods. The SRT was the most optimized topology showing the highest strength-per-unit-weight. The final topology sits in a best-of-both areas of material space exceeding the commercially available honeycombs strength per relative density by 1670%
Surface Reactions During the Atomic Layer Deposition of High-κ Dielectrics on III-V Semiconductor Surfaces
No full textThe quality of the dielectric/semiconductor interface is one of the most critical parameters for the fabrication of high-speed and low-power-consumption III-V semiconductor based metal-oxide-semiconductor field effect transistors (MOSFETs), as it determines the device performance. This dissertation contains investigations of the deposition and interface of binary oxide films on GaAs(100) and InAs(100) surfaces aiming at understanding the removal of the surface native oxides during certain atomic layer deposition (ALD) processes. To accomplish that, two complementary experimental approaches have been used. Initially, films were deposited in a conventional ALD reactor and characterized ex situ using spectroscopic ellipsometry (SE), X-ray photoelectron spectroscopy (XPS), high-resolution transmission electron microscopy (HRTEM), and atomic force microscopy (AFM). The systems examined were Ta2O5 on GaAs(100) surfaces from pentakis(dimethylamino) tantalum (Ta(N(CH3)2)5, PDMAT) and TiO2 on GaAs(100) and InAs(100) surfaces from tetrakis(dimethylamino) titanium (Ti(N(CH3)2)4, TDMAT). For these systems, deposition at the optimal ALD temperature resulted in practically sharp interfaces. Indium oxides were found to diffuse through ~ 6 nm of TiO2 film and accumulate on the topmost film layer. For the ALD of Ta2O5 on GaAs(100) surfaces, native oxide removal was enhanced at deposition temperatures above the ALD window; for ALD of TiO2 on both GaAs(100) and InAs(100) surfaces, native oxide removal was enhanced as the deposition temperatures increased up to 250 �C, while oxidation of the interface was observed for deposition above 300 �C due to the formation of noncontinuous films. To elucidate the surface reactions occurring during the deposition, an in situ attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy apparatus was constructed and used to investigate the surface reactions during the ALD of TiO2 and HfO2 on GaAs(100) surfaces. The existence of a ligand exchange mechanism was verified for both processes. Additionally, the formation of methylmethyleneimine (CH3N=CH2, MMI) was observed, indicating the existence of a beta hydride reaction pathway. Additionally, at 275 �C continuous removal of arsenic oxides was observed during the first 20 process cycles, an observation that challenges the prevailing understanding of the native oxide removal and indicates a much more complex surface chemistry
Bayesian Adaptive Dose-finding Methods in Phase I Drug Combination Trials
No full textIn a single-agent dose-finding Phase I trial, the key underlying assumption is that toxicity probability increases monotonically with the dose level. However, in multi-agent trial, this assumption may not hold because the drug-drug interaction effect can either decrease or increase the joint toxicity as compared to either one used alone, which may lead to an unforeseen toxicity probability surface. In the first part of the dissertation, we develop a novel adaptive dose-finding approach which can be applied to these kinds of multi-drug combination trials under the situation of non-monotonic toxicity probability surface. In the second part of the dissertation, we extend our investigation on the drug combination dose-finding trials with late-onset toxicity outcomes and have proposed a Bayesian adaptive dose-finding design under a nonignorable missing data mechanism, and where surrogate data are available. We evaluate the operating characteristics of the aforementioned methods and also compare them with existing methods through extensive simulation studies under various scenarios. The proposed methods demonstrate satisfactory performance in general
Factors Influencing Registered Nurses' Choice of Graduate Program Concentration and Career Intent
No full textThe shortage of nursing faculty in pre-licensure registered nursing education programs is a problem that could affect the United States health care system. A lack of qualified nursing faculty will limit the number of students accepted into nursing programs and the number of registered nurses produced for the workforce. Research investigating reasons why nurses enter faculty positions is limited. This study examined the effects of external factors on the decision of registered nurses seeking graduate education to choose nursing education as a major or teaching as a career path. Relationships between perceived employment opportunities, faculty salaries, prestige of faculty role, opinions about pre-licensure nursing education, and the choice of graduate concentration and academic career goal were investigated. Analysis models were based on current literature and Krumboltz's Social Learning Theory of Career Decision Making, which states that career decisions are based on multiple influential factors and interactions. A non-experimental research design with a 43 item survey tool developed by the investigator was used for data collection. The tool was administered to 218 graduate students in five university nursing programs in Maryland. The response rate was 88%. Correlation and regression analysis revealed significant relationships (p < .05) between intent to pursue an education major and academic career, and several influential factors. The odds of majoring in nursing education were lower for students who considered employment opportunities important (OR= .60) and for students with the opinion that the bachelor's degree should be the entry-level education for registered nurses (OR = .68). Black nursing graduate students had higher odds than non-white students of choosing a concentration in nursing education (OR= 4.55). Findings also indicated that students who considered faculty salary a consideration in employment (OR =1.50), married students (OR=3.25), and students with at least one child under age 18 (OR = 1.57) had higher odds of expressing intent to teach. These results indicate that faculty salary is not the only variable in nursing graduate education and career decisions. More research is needed to better understand associations between nurses' opinions on entry level education and other factors related to the nursing faculty shortage
An evaluation of the impact of obesity related legislation
No full textIn attempt to address the adult obesity epidemic in the U.S., several state legislatures have enacted laws to curtail rates of adult obesity (Stein & Colditz, 2004). Recent enacted policies include: menu labeling laws, snack taxes, and Complete Streets policy (Robert Wood Johnson, 2009). The aim of this dissertation was to evaluate the effectiveness of existing legislative efforts to limit rates of growth of adult obesity. I examined if anti-obesity legislation effectively reduces rates of adult obesity and if anti-obesity policies differ in their ability to diminish adult obesity. Several panel data sets were constructed using reported data for each state and the District of Columbia from 1995 to 2011 via the Behavioral Risk Factor Surveillance System (BRFSS), U.S. Department of Commerce, Bureau of Economic Analysis and the U.S. Census Bureau. Time series regression analysis was completed to assess the impact of these three policies at a state, group and individual level. At the state level, all three policies are associated with a decrease in adult obesity rates although, the magnitude is small and statistical significance varies by empirical model. At the individual level, all three policies are associated with a decrease in BMI, however, the magnitude is small and the only policy with statistical significance is menu labeling. At the group level, the policies vary in their effect on BMI by race, age, income, education and gender. Only menu labeling and snack tax policy are statistically significant at the group level. Although the magnitude of effect seen with these policies is small, any sign of a reversal in the growing obesity trend, a trend that has been unaltered for the past decade, could be seen as a sign of improvement from a public health perspective. The results of this study highlight how a one size fits all approach will not be effective in combating the obesity epidemic, rather an assortment of legislative policies are necessary
Variable Electromotive-force Generators for Wind Turbines and Hybrid Vehicles, and Wind Tower Technology for Power Generation
No full textIn this dissertation, variable electromotive-force generators, and Wind Tower technology are developed as alternatives to current technologies in power generation and transportation system. Theoretical, experimental, and numerical studies of the generated electromotive force in the modified generator show the reduced torque loss of the generator and an increased rotational speed of the generator rotor by adjusting the overlap between the rotor and the stator. A simple and robust active control system is developed to adjust the overlap between the rotor and stator automatically and provide a stable output power that has less than 3% variations around its average values. This concept can improve the fuel efficiency of hybrid vehicles and increase the efficiency and expand the operational range of wind turbines. A novel multiple generator drivetrain configuration is developed at which a single-generator in a large wind turbine is replaced by multiple generators with the same or different rated powers. An electromagnetic clutch mechanism is employed to run the generators independently or together; this provides higher reliability and reduces scheduled maintenance and power loss especially at low input powers. A novel prototype is designed and fabricated; the system worked smoothly and the results show the possibility of running generators independently and together based on the input speed, using the electromagnetic clutch mechanism. A precise and accurate model for calculating the power and energy densities from measured wind speed data at an arbitrary site is developed to evaluate the potential of generating electricity using the wind power. In this regard, a novel ducted turbine, referred to as the Wind Tower technology, for capturing wind power and generating electricity in either residential or commercial scale applications is studied theoretically, experimentally and numerically. There is a good agreement between the estimated output power values from theoretical and experimental data. Also, the numerical simulation of the fluid flow inside the tower has provided an optimum dimension range of the Wind Tower components. The economic evaluation for designing and manufacturing a Wind Tower shows the potential of having cost reductions and a quick return on investment in areas with higher electricity rates
The Impacts of Service-Learning Participation upon Post-Secondary Students' Academic and Social Development
No full textService-Learning is a form of applied learning that engages students in solving social problems within community-based settings. It is rooted in the social and educational philosophy of John Dewey's Pragmatism. As a pedagogy, service-learning presents students with opportunities for social and intellectual growth by complementing classroom learning with community-based experiential learning. This research aims to improve our understanding of the academic and social impacts of service-learning participation in higher education. This study incorporates a mixed methodological design comprised of a primary quantitative study that meets a gap in the service-learning research, and a complementary qualitative study that allows for additional themes to emerge and illustrate findings of the primary study. The University of Maryland, Baltimore County is an ideal location for this research because of its history of service-learning leadership. Through rigorous, quasi-experimental, longitudinal analysis of a robust data set, the quantitative analysis investigates the relationship between service-learning participation and diverse measures of student academic development. A fixed effects design limits the potential bias stemming from non-random selection into service-learning. Qualitative research complements the quantitative study and provides an in-depth understanding of students' development, with a particular focus upon analyzing academic and pro-social growth through service-learning (e.g., voluntary behavior intended to benefit another such as altruism [Eisenberg, et al., 2006, p. 646]). The qualitative study consists of a non-probability, purposive sample of students (Singleton & Straits, 2005) participating in semi-structured interviews (Singleton & Straits, 2005), followed by thematic analysis (Marks & Yardley, 2004). This research makes methodological contributions and strengthens our understanding of applied and service-learning, particularly as implemented through the innovative institutional practices at the studied university. The quantitative study is strengthened by modeling that addresses threats to internal validity from students' self-selection (i.e., endogenous bias; Meyer, 1995) and allows for significant analytical conclusions. Additionally, the mixed methodological design illustrates student pro-social growth through a number of emergent themes (Johnson & Onwuegbuzie, 2004). The scale and design of this study increases our understanding of the impact of service-learning and the conclusions suggest it deserves further institutional attention as a core pedagogy in higher education
Multivariate spatial anomalous window discovery
No full textThis dissertation investigates and formulates a novel multivariate spatial anomalous window discovery method. This novel method discovers linear shaped spatial anomalous windows corresponding to where unusual phenomenon take place. This method also uses a non- parametric multivariate scan statistic model that does not rely on any prior knowledge of the data distribution. Anomaly detection in spatial data has been a challenging research problem as most of the traditional anomaly detection approaches are not suitable to this work. In contrast, scan statistic approaches have proven to be a promising technique in quantifying spatial anomalous windows, where the anomalous window is a contiguous set of objects in a region forming an unusual cluster with respect to the rest of the data. Anomalous windows are found in several real world applications such as accident hubs along highways, disease outbreaks, crime hot spots to name a few. Existing scan statistic approaches have several limitations. First, they do not identify robust, multivariate, linear form, non parametric windows which are critical in studying phenomenon such as traffic accidents. Second, multivariate data may be sparse or noisy, thus, it is important to identify anomalies taking into account the presence of outliers in the data. Third, real world data does not necessarily follow known distributions, thus, it is important to identify anomalies in a non parametric setting such that it does not rely on specific fitted distributions in the data. Extensive experiments on multiple real-world data sets are conducted. The experimental results demonstrate the efficacy of our novel method in discovering critical anomalies in real-world applications. For example, the linear anomalous window discovery on real-world accident datasets for various highways with known issues were validated with existing domain expert reports to prove our results are not only statistically significant, but also identify the real anomalous traffic accident hubs along multiple intersecting highways