The study sample was comprised of 18,953 subjects aged 18 years and older who participated in the Third National Health and Nutrition Examination Survey. Preference for diet soda over regular soda was higher in females as compared to males and in Caucasians as compared to other races. Mean alcohol consumption was almost three times higher in males than in females. Undiagnosed hypertension was more common in males than in females, in 65-90 than in 50-64 year olds, and in people with less than high school education compared to those with higher education. Diastolic pressure was correlated with the level of consumption of diet soda, coffee, tea, and alcohol. Diet soda and alcohol consumption had a statistically significant positive relationship with hypertension even after adjusting for demographic variables and body mass index. The study provides useful information of the patterns of beverage uses and the prevalence of hypertension in the United States.

Gender, minority, and regional-related disparities have been documented in diabetes management. Self-efficacy, the belief in one's ability to carry out the actions mandated by a task, has been identified as a key predictor in glycemic control; however, it has not been investigated in rural, female populations. This cross-sectional, correlation investigation examined the relationships among self-efficacy, depression, and diabetes self-care management in women living in Appalachia with type 2 diabetes. Using Bandura's Social Cognitive Theory, 85 women ages ≥ 21 with type 2 diabetes for a minimum of 6 months who were residents in Appalachia completed the 1) Diabetes Self-Efficacy Scale, 2) Beck Depression Inventory-II, 3)Summary of Diabetes Self-Care Activities, and a 4) Diabetes Health-Related Demographics tool. Descriptive statistics detailed the sample characteristics. ANOVA, chi-square, and independent t-tests were computed for between group differences as they related to depression, various physiologic states, presence of self-efficacy sources, and glycosylated hemoglobin. Pearson correlation coefficients were used to describe the relationships between self-efficacy, depression, and self-care management. Multiple linear regression analyses examined prediction models for glucose control while controlling for potential confounders. Eighty-four Caucasian and one African-American enrolled in the study with a mean age of 61. The mean time since diabetes diagnosis was 7 years with a mean glycosylated hemoglobin value of 6.9% (SD=1.3). Higher self-efficacy scores were associated with a lower glycosylated hemoglobin (r-.30, p=.005) and ability to choose foods best to maintain a healthy eating plan(r-.415, p=.001). The sources of self-efficacy associated with enhanced self-care management were mastery experience and vicarious experience. There were no significant relationships between self-efficacy and depression or depression and glycosylated hemoglobin. The diabetes self-care management regression model resulted in self-efficacy and education accounting for 7.5% of the variance in glycosylated hemoglobin. Study findings support the social cognitive theory and the utility of self-efficacy as a predictor of glycemic control. Depression was not found to be a significant obstacle in this Appalachian population. Comprehending the significant relationship between self-efficacy and diabetes self-care management allows providers to modify their interventions when caring for women type 2 diabetes in the region.

Lung cancer remains the leading cause of cancer deaths in the United States despite public health campaigns aimed at reducing its rate of mortality. Kentucky is the state with the highest lung cancer incidence and mortality. This study aims to assess the impact of misclassification of cause of death from Lung Cancer in Kentucky for the period 1979 to 2002. We will examine the potential competing classification of death for two other smoking-related diseases, Chronic Obstructive Pulmonary Disease (COPD) and Emphysema. Age-adjusted mortality rates for these diseases for white males were obtained from the National Center for Health Statistics. There was little evidence that any misclassification between COPD or Emphysema mortality rates was in agreement with the rising lung cancer rates in Kentucky. The long-term increase in lung cancer mortality in Kentucky is likely because of a combination of risk effects between smoking and other risk-factors for this disease.

Stroke and myocardial infarction (MI) are serious conditions whose burdens vary by socio-demographic and geographic factors. Although several studies have investigated and identified disparities in burdens of these conditions at the county and state levels, little is known regarding their geographic epidemiology at the neighborhood level. Both conditions require emergency treatments and therefore timely geographic accessibility to appropriate care is critical. Investigation of disparities in geographic accessibility to stroke and MI care and the role of Emergency Medical Services (EMS) in reducing treatment delays are vital in improving health outcomes. Therefore, the objectives of this work were to: (i) classify neighborhoods based on socio-demographic and geographic characteristics; (ii) investigate spatial patterns of neighborhood level mortality; (iii) identify disparities in geographic accessibility to stroke and MI care; and (iv) identify disparities in EMS transport times for stroke and MI patients in East Tennessee. Fuzzy cluster analysis was used to classify neighborhoods into peer neighborhoods (PNs) based on their socio-demographic and geographic factors. Neighborhood level spatial patterns of stroke and MI mortality risks were investigated using Spatial Empirical Bayesian smoothing techniques and neighborhoods with high mortality risks identified using spatial scan statistics. Travel times to stroke and cardiac care facilities were computed using network analysis to investigate geographic accessibility. Records of over 3,900 suspected stroke and MI patients, from two EMS providers, were used to investigate disparities in EMS transport delays. Four distinct PNs were identified. The highest stroke/MI mortality risks were observed in less affluent, urban PNs, and lowest risks in more affluent, suburban PNs. Several significant (p<0.0001) stroke and MI high mortality risk spatial clusters were identified. Approximately 8% and 15% of the population did not have timely accessibility to appropriate stroke and MI care, respectively. The disparity was greatest for populations in rural areas. Important disparities in EMS transport delays were identified, with the travel time to a hospital contributing the longest delay. The identified disparities in neighborhood characteristics, mortality risks, geographic accessibility, and EMS transport delays are invaluable in guiding resource allocation, service provision, and policy decisions to support evidence-based population health planning and policy.

Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions in settings with severely underreported data. We compare penalized regression approaches to seasonal baseline models and illustrate that incorporation of search data can improve prediction error. This builds on previous research show- ing that search data and recent surveillance data together can be used to create accurate forecasts for diseases such as influenza and dengue fever. This work shows that even in settings where timely surveillance data is not available, using search data in real-time can produce more accurate short-term forecasts than a seasonal baseline prediction. However, forecast accuracy degrades the further into the future the forecasts go. The relative accuracy of these forecasts compared to a seasonal average forecast varies depending on location. Overall, these data and models can improve short-term public health situational awareness and should be incorporated into larger real-time forecasting efforts.

The United States continues to be affected by a severe, long-standing nursing shortage that is not projected to resolve within the next 10 or more years. Unsuccessful passage of the National Council Licensure Examination for Registered Nurses (NCLEX-RN) among graduate nurses remains one of several key contributors to the nursing shortage. The goal of this study was to identify if either cumulative fall semester GPA; the overall prenursing science, mathematics, and English GPA; type of high school background; TOEFL score; clinical pass or fail; and on-time program completion best predicted passage of NCLEX-RN. Archived records from the academic years of 2006-2010 of students/graduates of a small, private BSN program were analyzed. A nonconcurrent, prospective design of secondary data was guided by the theoretical implications of the Seidman retention formula that surmises that early identification of academic problems is a necessary precursor to implementations that promote academic success. Significant, positive correlations were found between GPA of prenursing courses and achievement in clinical courses and on-time nursing program completion. Forward and backward, logistic regression procedures revealed that clinical performance was the strongest predictor of NCLEX-RN success but with an inverse relationship. Implications for positive social change include retention of BSN students to improve graduation rates. This ultimately will foster achievement on the NCLEX-RN, resulting in more graduates will be able to competently serve the health care needs of individuals and communities and alleviation of the nursing shortage.

Inflammation is often a protective reaction against harmful foreign agents. However, in many disease conditions, the mechanisms behind the inflammatory response are poorly understood. Often times, the inflammation causes adverse effects, such as joint pain, abdominal pain, fever, fatigue, and loss of appetite. Thus, many treatments aim to inhibit the inflammatory response in order to control adverse symptoms. Such treatments include TNFα inhibitors. However, a major risk associated with drugs inhibiting tumor necrosis factor alpha (TNFα) is serious infection, including tuberculosis (TB). Anti-TNFα therapy is used to treat patients with Crohn’s disease, for which the risk of tuberculosis may be even more concerning. Recent literature suggests Crohn’s might involve Mycobacterium avium subspecies paratuberculosis (MAP), an intracellular TB-like bacterium. This study seeks to investigate the risk of developing TB in patients with Crohn’s disease treated with TNFα inhibitors. A meta-analysis synthesized existing evidence. Evidence came from published randomized, double-masked, placebo-controlled trials of TNFα inhibitors for treatment of adult Crohn’s disease. Twenty-three trials were identified, including 5,669 patients. The risk of tuberculosis was significantly increased in anti-TNFα treated patients, with a risk difference of 0.028 (95% confidence interval [CI], 0.0011-0.055). The odds ratio was 4.85 (95% CI, 1.02-22.99) when all studies were included and 5.85 (95% CI, 1.13-30.38) when studies reporting zero tuberculosis cases were excluded. The risk of tuberculosis is increased in patients with Crohn’s disease treated with TNFα inhibitors. The medical community should be alerted about this risk and the potential for TNFα inhibitor usage favoring granulomatous infections and worsening the patient condition.

<p>Malnutrition and Anemia in children continue to be major public health challenges in most developing countries, particularly in Africa. Malnutrition and Anemia pervade all aspects of their health, growth, cognitive and social development. They lead to irreversible and lifelong effects that prevent children from realising their full potential. This study was designed to examine the prevalence and determinants of malnutrition and anemia in children under 5 years of age in the Ghanaian population. This research is based on data from the Ghana Demographic and Health Survey (GDHS) 2008, obtained from the Ghana Statistical Service (GSS). The survey is an extensive survey conducted using a stratified, two-stage cluster sampling design. The GDHS data contains a wealth of information on health, demographic, as well as socio-economic factors but is underutilised due to the complexity of the survey data. This study therefore stands out as one of the few that use the GDHS to investigate aspects of child health in Ghana. In this study, we perform subgroup analysis by disaggregating the data by age and gender specific subgroups and then by place of residence and region. This was in order to identify sub level estimates as national estimates have a high tendency of concealing true values and deviations from general trends. Also, subgroup analysis is very significant especially for resource allocation so as to minimize the likelihood of missing the target populations. We investigated associations between the three measurements of malnutrition; stunting, underweight and wasting and anemia (assessed by haemoglobin concentration) and the various risk factors using chi-square test to examine bivariate associations and chi-square trend test to examine linear trends in association. We identified the following variables to be significantly associated with all forms of malnutrition and/or anemia: age of child, mother’s education, financial status and place of residence. Other factors that were identified to be associated with some form of malnutrition and/or anemia include duration of breastfeeding, source of drinking water, mother’s occupation and currently breastfeeding. In view of the high rate of malnutrition, approximately 36% (33.6−37.6) and the alarming prevalence of anemia, 78% (76.7 − 80.2) in children in Ghana, particularly among those less than 2 years old, and the grave consequences on their cognitive and behavioral development even in later years, there is an urgent need for effective and efficient public health interventions.</p> / Master of Science (MSc)

Diabetes is a major risk factor for the development of cataract [3,14,20,22]. In this thesis, we create a model that allows us to understand the incidence of one disease in the context of another; in particular, cataract in the presence of diabetes. The World Health Organization's Vision 2020 blindness-prevention initiative administers surgeries to remove cataracts, the leading cause of blindness worldwide [24]. One of the geographic areas most impacted by cataract-related blindness is Sub-Saharan Africa. In order to plan the number of surgeries to administer, the World Health Organization uses data on cataract prevalence. However, an estimation of the incidence of cataract is more useful than prevalence data for the purpose of resource planning. In 2012, Dray and Williams developed a method for estimating incidence based on prevalence data [5]. Incidence estimates can be further refined by considering associated risk factors such as diabetes. We therefore extend the Dray and Williams model to include diabetes prevalence when calculating cataract incidence estimates. We explore two possible approaches to our model construction, one a detailed extension, and the other, a simplification of that extension. We provide a discussion comparing the two approaches.