Determinants of Obesity in Latinos in Anchorage Alaska

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF PUBLIC HEALTHDeterminants of obesity can be complex and group specific. There is limited data about the Latino population and the health needs of Latinos in the state of Alaska. The goal of this project was to better understand the determinants of obesity in Latinos, including the impact of dietary choices, financial status, mental health, and exercising in the levels of obesity of Latinos. The investigator used Statistical Package for the Social Sciences (SPSS) to study the association of several variables in the Behavioral Risk Factor Surveillance System (BRFSS). After data exploration, univariate analysis and logistical regression were conducted for selected variables related to the causes of obesity in Latinos living in the state of Alaska where the investigator observed higher percentages of obesity in Latinos as compared to other groups. However, results were not statistically significant except for the higher percentages with high blood pressure in obese Latinos when compared to non-obese Latinos and other groups. The results produced by this study are evidence that further research is needed to determine the impact of obesity in Latinos and their differences with other groups.Signature Page / Title Page / Abstract / Table of Contents / List of Figures / List of Tables / List of Appendices / Introduction and Background / Project Goal, Specific Aims, and Objectives / Methods / Results / Discussion / Strengths and Limitations / Public Health Implications / Conclusions and Recommendations / References / Appendice

Determinants of Obesity in Latinos in Anchorage, Alaska: Analysis of BRFSS Alaska Data, 2007-2013

Presented to the Faculty of the University of Alaska Anchorage in Partial Fulfillment of the Requirements for the Degree of MASTER OF PUBLIC HEALTHDeterminants of obesity can be complex and group specific. There is limited data about the Latino population and the health needs of Latinos in the state of Alaska. The goal of this project was to better understand the determinants of obesity in Latinos, including the impact of dietary choices, financial status, mental health, and exercising in the levels of obesity of Latinos. The investigator used Statistical Package for the Social Sciences (SPSS) to study the association of several variables in the Behavioral Risk Factor Surveillance System (BRFSS). After data exploration, univariate analysis and logistical regression were conducted for selected variables related to the causes of obesity in Latinos living in the state of Alaska where the investigator observed higher percentages of obesity in Latinos as compared to other groups. However, results were not statistically significant except for the higher percentages with high blood pressure in obese Latinos when compared to non-obese Latinos and other groups. The results produced by this study are evidence that further research is needed to determine the impact of obesity in Latinos and their differences with other groupsSignature Page / Title Page / Abstract / Table of Contents / List of Figures / List of Tables / Chapter 1: Introduction and Background / Chapter 2: Project Goal, Specific Aims, and Objectives / Chapter 3: Methods / Chapter 4: Results / BMI / Mental Distress / Poverty / Consumption of Fruits and Vegetables / Spatial (Geographic Information Systems) Analysis / Exercise and Limitations in Physical Activities / Health Outcomes / Chapter 5: Discussion / Chapter 6: Strengths and Limitations / Chapter 7: Public Health Implications / Chapter 8: Conclusions and Recommendations / References / Appendi

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Includes abstract. Includes bibliographical references

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Determining optimal number of clusters in a dataset is a challenging task. Though some methods are available, there is no algorithm that produces unique clustering solution. The paper proposes an Automatic Merging for Single Optimal Solution (AMSOS) which aims to generate unique and nearly optimal clusters for the given datasets automatically. The AMSOS is iteratively merges the closest clusters automatically by validating with cluster validity measure to find single and nearly optimal clusters for the given data set. Experiments on both synthetic and real data have proved that the proposed algorithm finds single and nearly optimal clustering structure in terms of number of clusters, compactness and separation.Comment: 13 pages,4 Tables, 3 figure

My Journey From Farquhar College of Arts and Science To becoming a Medical Doctor

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