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An Examination of Known Tuberculosis Risk Factors and their Correlation across the United StatesYoung, David 20 November 2010 (has links)
Background: Globally tuberculosis (TB) is one of the leading causes of mortality. There is scientific evidence of sociodemographic, behavioral and health risk factors associated with TB infection and TB disease. In the United States (US), there is a low endemicity of TB and a goal of TB elimination. Objective: The primary objective of the study was to examine the correlation of TB risk factors at the state level in the US to obtain insights specific to the state of TB in the US. The risk factors examined were diabetes rates, smoking rates, alcohol abuse rates, AIDS rates, foreign-born vs. US-born, poverty as expressed by GINI and per capita income and race/ethnicity. Methods: Secondary data from the Centers for Disease Control and Prevention (CDC) and US Census Bureau on line databases were used. Simple linear regression, bivariate correlation and multiple linear regression were carried out. Results: Significant correlations were found at the state level between TB disease rates and being non-Hispanic White (r=-0.856, p<0.001), foreign-born (r=0.649, p<0.001), GINI (r=0.588, p<0.001) and AIDS diagnosis rates (r=0.579, p<0.001). No significant associations were found between TB disease rates and diabetes rates, smoking rates and alcohol abuse rates. Conclusion: The focus of the fight against TB in the US should be on minority communities, those populated by the foreign-born and those with high rates of AIDS particularly where a large degree of income inequality is present.
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Simulation Based Process Flow Improvement for Wood Framing Home Building Production LinesShafai,Leila Unknown Date
No description available.
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An analysis of land use change using GIS and spatial analysis : a case study of the Seoul metropolitan region perimeterKim, Jung-Hoon January 2001 (has links)
This thesis is concerned with analytical and technical capability in using Geographical Information Systems (GIS) and spatial analysis as relevant to the policy challenge of urban fringe management, taking as a case study the Seoul Metropolitan Region Perimeter. The primary purpose of this study is to analyse land use change using the tools of GIS and Spatial Analysis as a spatial decision support system for the task of managing the fragmented development of the urban fringe caused by the policy changes introduced by the Korean government in 1994. The case study analysed in this research shows whether GIS and spatial analysis can be applied to manage and monitor land use change in the urban fringe area at a very detailed level using municipal parcel data which occupies about 80% of administrative affairs, especially at the local government level in Korea. The major determinants of land use change in the study area have been investigated in an attempt to enhance the knowledge of how to provide decision support information for local government in Korea using GIS and spatial analysis. The results of the analysis represent the distribution of land use change from 1994 to 1998, the distribution of parcels with more than 50% of their neighbours in urban use and the transition probability of land use within a GIS, etc. The analysis using GIS and spatial analysis proved to be effective ones when providing the information base for modelling land use change in the urban fringe of the Seoul Metropolitan Region in Korea, to enable informed decisions to be made about land management policies in such areas. The lesson which could be drawn from this study is that a GIS and spatial analysis capacity is quite useful for local government to understand where and why land use change is concentrated, how the pattern of land use has changed, and which areas are susceptible to land use change. This study represented the results of analysis as a map, showed the significant patterns of land use change in the study area, displayed the relationship between neighbouring land use and the pattern of land use change, and suggested the transition probability of land use in the urban fringe in the future. Better understanding of the results may enhance the capability of local government to predict future land use change dynamics and devise more effective land use management strategies. This study brings a new approach to understanding the evolution of development patterns using the methods of combining spatial statistical analysis techniques with GIS application at site level.
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Spatial and temporal analysis of the distribution of bacterial contamination in nearshore areas of Southern Vancouver IslandXu, Kaifeng 19 September 2018 (has links)
This research conducts a spatial and temporal analysis of the distribution of fecal coliform throughout the Capital Regional District (CRD) of southern Vancouver Island. The research is based on 17 years of historical data of stormwater samplings from 1995 to 2011 in the nearshore region. ArcGIS is used to map the fecal coliform data collected within and adjacent to nearshore areas to identify peaks above a regulated threshold. Heavily polluted areas are in Victoria downtown, Esquimalt and the southeastern shore of Oak Bay. Land-use data and drainage patterns are used to determine relationships between fecal coliform levels and land-use by considering relevant, temporally dependent factors. Temperature is positively correlated with FC level and precipitation is negatively correlated. The residential land use is identified as the main source of bacterial contamination. This analysis leads to a regression model that indicates two peaks (July and October) of FC level occur in a 12-month period and positively related to minimum temperature and cloud cover ratio. / Graduate
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Methods for Non-invasive Trustworthy Estimation of Arterial Blood PressureKoohi, Iraj January 2017 (has links)
The trustworthiness of the blood pressure (BP) readings acquired by oscillometric home-based monitoring systems is a challenging issue that requires patients to see the doctor for trusted measurements, especially those who are obese or have cardiovascular diseases such as hypertension or atrial fibrillation. Even with the most accurate monitors one may get different readings if BP is repeatedly measured. Trusted BP readings are those measured with accurate devices at proper measurement conditions. The accurate monitors need an indicator to assure the trustworthiness of the measured BP. In this work, a novel algorithm called the Dynamic Threshold Algorithm (DTA) is proposed that calculates trusted boundaries of the measured systolic and diastolic pressures from the recorded oscillometric waveforms. The DTA determines a threshold from the heart rate of subjects to locate the oscillometric pulse at the mean arterial pressure (PULSEMAP) and uses the peak, trough, and pressure of the located pulse to calculate the trusted boundaries.
In terms of accuracy, a modeling approach is employed to estimate BP from the arterial lumen area oscillations model in the diastolic region (ALA-based). The model requires compliance parameter ‘c’ to estimate BP. To this end, a pre-developed linear regression model between ‘c’ and the corresponding amplitude ratio of the PULSEMAP is employed to evaluate ‘c’. The proposed method uses ‘c’ and estimates BP by minimizing differences between peak and trough amplitudes of the actual and corresponding simulated waveforms.
The proposed DTA and ALA-based methods were tested on two datasets of healthy subjects and one dataset of sick subjects with cardiovascular diseases, and results were validated against corresponding references and compared with two popular maximum amplitude and maximum/minimum slope algorithms. Mean absolute error (MAE) and standard deviation of errors (STDE) are used to evaluate and compare the results. For healthy subjects, the MAE of the estimated systolic (SBP) and diastolic (DBP) blood pressures was improved up to 57% and 57% with an STDE of 55% and 62%, respectively. For sick subjects, the MAE was improved up to 40% and 29% with an STDE of 36% and 20% for SBP and DBP, respectively.
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Health Care Utilization by Rural Patients: What Influences Hospital Choice?Roh, Chul 30 January 2008 (has links)
The bypassing of rural hospitals increased in Colorado's rural communities during the 1990s. To understand this phenomenon, this study explores why rural Medicare patients in Colorado bypassed their local rural hospitals when they could have received health care services at their nearest local hospital. To identify both individual factors and institutional variables associated with hospital choice behavior, the conditional logistic regression model analyzes 4,099 rural Medicare patients who received heart failure and shock procedures. This study determines that both institutional variables (ownership type, number of beds, number of services, accreditation, and distance between the hospital and a patient's residence) and patient variables (age, length of stay, race, and total charge) are significant in patients' hospital choice. This study suggests that rural hospitals could build cooperative relationships with other large rural and urban hospitals.
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Spatial Analysis of Landscape Dynamics to Meteorological Changes in the Gulf of Mexico Coastal RegionLi, Tianyu 11 August 2017 (has links)
The forest ecosystem is a dominant landscape in the Gulf of Mexico (GOM) coastal region. Currently, many studies have been carried out to identify factors that drive forest dynamics. Changes in meteorological conditions have been considered as the main factors affecting the forest dynamics. For this study, the statistical regression analysis was used for modeling forest dynamics. Meteorological impact analysis was driven by observed data from PRISM (parameter-elevation regressions on independent slopes model) climate dataset. The forest dynamics was characterized by an indicator, the normalized difference vegetation index (NDVI). The objectives of this study are to 1) to specify and estimate statistical regression models that account for forest dynamics in the Golf of Mexico coastal region, 2) to assess which model used to capture the relationship between forest dynamics and its explanatory variables with the best explanatory power, and 3) to use the best fitted regression model to explain forest dynamics. By using fixed-effects regression methods: ordinary least squares (OLS) and geographically weighted regression (GWR), the sample-point-based regression analysis showed that meteorological factors could generally explain more than half of variation in forest dynamics. In respect of the unexplained variation of forest dynamics, the necessity of using soil to explain forest dynamics was then discussed. The result suggested that the forest dynamics could be explained by both meteorological parameters and soil texture. One of the basic considerations in this study is to include the spatiotemporal heterogeneity caused by seasonality and forest types. The model explanatory power was found differ among forest types (spatially) and seasons (temporally). By constructing regression models with randomly varying intercepts and varying slopes, the linear mixed-effects model (LMM) was fitted on composite county-based data (e.g., precipitation, temperature and NDVI). The use of LMMs was proved to be appropriate for describing forest dynamics to mixed-effects induced by meteorological changes. Based on this finding, I concluded that meteorological changes could play a significant role in forest dynamics through both fixed-effects and random-effects.
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Factors Affecting Cottage Site DesirabilitySmith, Lawrence January 1974 (has links)
<p> Factors affecting cottage site desirability are investigated two
ways. A step-wise compilation of the regression model is used to
rank the cottage site desirability variables, and determine the proportion
of variation that the variables contribute in explaining
cottage land values. The simple regression and correlation technique
is used to determine the existence of possible relationships.
between distance measures, and measures of both cottage land values
and cottage road values. </p> / Thesis / Master of Arts (MA)
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Spatial Analysis of Chinese Air TransportationMa, Kunlei January 2015 (has links)
No description available.
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Leaders and Followers Among Security AnalystsWang, Li 05 1900 (has links)
<p> We developed and tested procedures to rank the performance of security analysts according to the timeliness of their earning forecasts. We compared leaders and followers among analysts on various performance attributes, such as accuracy, boldness, experience, brokerage size and so on. We also use discriminant analysis and logistic regression model to examine what attributes have an effect on the classification. Further, we examined whether the timeliness of forecasts is related to their impact on stock prices. We found that the lead
analysts identified by the measure of forecast timeliness have a greater impact on stock price
than follower analysts. Our initial sample includes all firms on the Institutional Brokers
Estimate System (I/B/E/S) database and security return data on the daily CRSP file for the
years 1994 through 2003.</p> / Thesis / Master of Science (MSc)
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