Despite national immunization programs existing in each country, the national immunization policy development processes are not well described. One approach used to facilitate the process is that of national immunization technical advisory groups (ITAGs) which make technical recommendations to the national government to guide immunization policies and programs. To better understand the current policy making processes of countries across the globe, a systematic review as well as a global level survey were conducted. This thesis summarizes the characteristics of national immunization policy development processes in all countries as well as the existence of national ITAGs and their characteristics and modes of operation. In conclusion, ITAGs are considered a useful tool by countries for immunization policy development but many countries lack this tool and many of the existing groups need strengthening. Additional evaluation of these groups and further research is needed.

Syncope is defined as sudden transient loss of consciousness with spontaneous, complete recovery. Syncope is usually associated with loss/of postural tone leading to falling. Of the various risk stratification instruments available, the San Francisco Syncope Rule is the only one that has been prospectively derived and validated, included all serious outcomes, focuses on short-term outcomes rather than long-term outcomes and adhered to the accepted guidelines for developing a clinical decision rule. This thesis is on retrospective validation of the San Francisco Syncope rule. The study will also attempt to refine the rule if needed and collect basic epidemiological characteristics of syncope patients presenting to a tertiary care emergency department. The objectives of this study were: (1) To assess the performance of the San Francisco Syncope Rule when applied retrospectively to Canadian syncope patients. (2) To assess the potential, impact on resource utilization (in the form of admission rates) if the San Francisco Syncope Rule was applied in Canada. (3) To determine, if needed, the potential for refining the San Francisco Syncope Rule to improve its performance. (4) To describe the basic epidemiological characteristics of emergency department syncope visits to a tertiary care emergency department. The main outcome of the study was to evaluate the performance characteristics of the San Francisco Syncope Rule in its ability to predict serious outcomes within 30 days. The outcome was a composite serious outcome that included anyone of the serious outcomes: death, myocardial infarction, arrhythmia, pulmonary embolism, stroke, subarachnoid hemorrhage, significant bleeding anywhere, any procedural intervention to treat a related cause of syncope, any condition causing/likely to cause a return emergency visit or hospitalization for a related event within 30 days. 505 patient visits were included in the study. 49 (9.7%) of these visits were associated with serious outcomes with 22 (44.9%) occurring in the emergency department and 27 (55.1%) occurring outside the emergency department either during their inpatient stay or outside the hospital. The sensitivity and specificity of the San Francisco Syncope Rule for all serious outcomes was 90% (95% CI 78, 95) and 40% (95% CI 39, 41), for the serious outcomes occurring after discharge from the emergency department was 96% (95% CI 82, 99) and 40% (95% CI 39, 40). 12.3% of the study patients were admitted to the hospital. The rule would have required 63% of the study patients to be admitted. Exploration for refining the rule yielded a preliminary decision rule by recursive partitioning with 3 variables: (1) Age ≥ 65 years, (2) Lowest emergency department systolic blood pressure < 80 mm of Hg and (3) abnormal electrocardiogram [defined as non-sinus rhythm (supraventricular tachycardia, multifocal atrial tachycardia, atrial flutter, atrial fibrillation, junctional rhythm, idioventricular rhythm), significant atrioventricular block (second and third degree), bifasicular block, first degree atrioventricular block in the presence of left or right bundle branch block and cardiac monitor abnormalities]. This preliminary rule had a sensitivity 100% ((95% CI 93, 100) and 53% (95% CI 52, 53) for predicting serious outcomes within 30 days of the emergency visit for syncope. Using this new rule will require 52% of the patients to be admitted to the hospital. Syncope and pre-syncope were common complaints presenting to the emergency department accounting for I% of all emergency visits. The San Francisco Syncope Rule did not perform as well as previously reported. It is feasible to develop a better clinical decision rule based on the San Francisco Syncope Rule to improve the sensitivity and specificity. It also possible to prospectively clarify and improve the performance the 'abnormal electrocardiogram' variable. (Abstract shortened by UMI.)

Introduction. Current evidence does not provide a clear risk stratification strategy for sickle cell disease patients in the emergency department (ED). The goal of this study was to develop a better understanding of the clinical features among patients with sickle cell disease and to determine the risk factors for short term adverse events. Methods. We conducted a prospective cohort study of sickle cell disease patients presenting to a tertiary care ED over six consecutive months. All patients were assessed by emergency physicians during the ED visit. All the patients had a 2 week structured telephone follow-up or a chart review if they had a repeat ED visit within 2 weeks or admitted. The adverse outcomes were classified as a clinically significant outcome (death, cerebrovascular accidents, acute chest syndrome, sepsis, hyper-hemolytic crisis and exchange blood transfusion) or not. We also classified patients as having acute chest syndrome or not. We analyzed the predictors of adverse outcomes using descriptive statistics and multiple logistic regression. Results. Over six consecutive months, we enrolled 732 patients. Seventy-five patients had a clinically significant outcome and 42 had acute chest syndrome. Using multivariate analysis, we found nine statistically significant predictors of a clinically significant adverse outcome: a prolonged painful episode (OR 10.1; 95%CI 5.3-19.3), age less than 8 years (OR 2.4; 95%CI 1.001 -5.9), oxygen saturation less than 96% (OR 3.9; 95%CI 1.6-10.9), patient appearing toxic (OR 7.8; 95%CI 2.2-27.2), presence of chest crackles (OR 6.5; 95%CI 2.3-18.6), splenomegaly (OR 2.6; 95%CI 1.2-5.5), local limb tenderness (OR 0.2; 95%CI 0.08-0.7), hemoglobin less than 7 g/dL (OR 3.6; 95%CI 1.1-11.6), reticulocyte count more than 15% (OR 4.0; 95%CI 1.4-11.5). Using multivariate analysis, we found seven statistically significant predictors of acute chest syndrome: a prolonged painful episode (OR 22.0; 95%CI 8.7-55.3), oxygen saturation less than 92% (OR 17.7; 95%CI 1.7-184.9), history of cough (OR 4.5; 95%CI 1.7-12.0), history of pneumococcal vaccine (OR 0.33; 95%CI 0.11-0.98), presence of chest crackles (OR 9.5; 95%CI 2.6-34.7), local limb tenderness (OR 0.20; 95%CI 0.05-0.80), and reticulocyte count more than 15% (OR 4.9; 95%CI 1.2-19.7). Conclusion. Our study of a tertiary hospital emergency department found nine identifiable variables which can help to predict the possibility of developing a clinically significant outcome. We also found seven identifiable variables which can help to predict the possibility of developing acute chest syndrome. This might be used in the future to risk stratify the sickle cell disease patients who presents to the emergency department and develop strategies to prevent those adverse outcomes.

Background. Mathematical models are an essential tool in infectious disease epidemiology. The most fundamental, and still widely used mathematical model for looking at infectious diseases, is the simple SIS (susceptible-infected-susceptible) compartmental model. However, this type of model does not integrate the local contact network of individuals making them inappropriate for studying sexually transmitted infections (STIs). Goal. To evaluate the accuracy of the pair-wise network model, proposed by Eames & Keeling (2002), which incorporates the sexual contact network. Results. Compared to observed data, the predictions of the pair-wise model were a much better fit to the prevalence of chlamydia than those of the traditional simple SIS model. Conclusions. The results demonstrate the critical role of the network structure in the spread of chlamydia and other STIs. If decision-makers continue to rely on the simple SIS model, they could underestimate the effectiveness of prevention and control strategies, actually hindering our efforts to eliminate chlamydia. Also, good quality data on the sexual network structure of the target population is necessary in order to make accurate and useful predictions.

Background. Iron is a pro-oxidant and involved in inflammatory response and endogenous nitrosation. It is a candidate modifiable risk factor for cancers of the gastrointestinal (GI) tract. Methods. A gene-disease systematic review was conducted to investigate associations between polymorphisms in iron metabolism genes, including HFE, and GI tract cancers. Data from a case-control study of dietary risk factors and esophageal adenocarcinoma (EAC) were analysed to investigate the association between HFE variants and EAC and reported intakes of red meat, iron (total and heme-bound) and EAC. Results. The results of the systematic review did not support an association between HFE variants and colorectal neoplasia. No studies were identified that investigated genetic variants in iron metabolism genes and EAC. The secondary case-control analyses found no association between HFE variants and EAC. The analyses provided evidence suggestive of an association between intake of heme iron and EAC.

Case-noncase studies, also known as case-control studies, are ubiquitous in epidemiology, where a common goal is to estimate the effect of an exposure on an outcome of interest. In many areas of application, such as policy-informing drug utilization research, this effect is inherently causal. Although logistic regression, the predominant method for analysis of case-noncase data, and other traditional methodologies, may provide associative insights, they are generally inappropriate for causal conclusions. As such, they fail to address the very essence of many epidemiological investigations that employ them. In addition, these methodologies do not allow for outcome-free design (Rubin, 2007) of case-noncase data, which compromises the objectivity of resulting inferences. This thesis is directed at exploring what can be done to preserve objectivity in the causal analysis of case-noncase study data. It is structured as follows. In Chapter 1 we introduce a formal framework for studying causal effects from case-noncase data, which builds upon the well-established Rubin Causal Model for prospective studies. In Chapter 2 we propose a two-party, three-step methodology — PrepDA — for objective causal inference with case-noncase data. We illustrate the application of our methodology in a simple non-trivial setting. Its operating characteristics are investigated via simulation, and compared to those of logistic and probit regression. Chapter 3 focuses on the re-analysis of a subset of data from a published article, Karkouti et al. (2006). We investigate whether PrepDA and logistic regression, when applied to case-noncase data, can generate estimates that are concordant with those from the causal analysis of prospectively collected data. We introduce tools for covariate balance assessment across multiple imputed datasets. We explore the potential for analyst bias with logistic regression, when said method is used to analyze case-noncase data. In Chapter 4 we discuss our technology’s advantages over, and drawbacks as compared to, traditional approaches. / Statistics

Observational studies of newly marketed medications can add important safety and effectiveness information to what is known from pre-approval randomized controlled trials. As new medications are often prescribed to select group of patients, confounding can be particularly large in this setting. Multivariable modeling of the exposure or the outcome is commonly used to reduce confounding, but the modeling can be challenged by rapidly evolving prescribing patterns and the limited number of patients exposed to the new medication. Furthermore, while adjustment for empirically identified potential confounders, and proxies thereof, has been shown to reduce confounding, it is difficult to empirically identify potential confounders in settings with small number of outcomes. In this thesis, we explored two new methods to improve confounding control in the setting of newly marketed medications with few exposures and outcomes and many potential confounders. The first method is the high dimensional disease risk score (DRS) developed using an historical cohort of comparator drug initiators. We developed prediction models for the outcomes of interest in an historical cohort and applied the models to the concurrent study cohort of new and comparator drug initiators. We then used individual patient’s predicted risk score to balance the baseline risk between the two groups. In chapter 1, we compared combinations of shrinkage and dimension reduction approaches to reduce model over-fitting when developing DRSs from large numbers of potential covariates. In chapter 2, we compared the performance of the high dimensional DRSs to the standard high dimensional propensity score (hdPS) approach. In chapter 3, we developed a new method that augments the hdPS variable selection process with data from an historical cohort and compared this approach to standard hdPS. All evaluations were conducted in example comparative studies of newly marketed medications using large US claims database. The hdPS with variable selection augmented by historical data showed good performance in confounding adjustment even in small outcome settings. Future studies should evaluate the use of this method in other settings and should explore improvements in the use of high dimensional DRSs.

Cardiovascular disease (CVD) is the number one cause for mortality and morbidity around the world. Meanwhile, diabetes mellitus (DM) has become an emerging epidemic, causing 1.5 million deaths in 2012, with 80% occurring in low- and middle-income countries. Substantial evidence has linked both lifestyle and metabolic risk factors to increased risk of CVD and death, with suboptimal diet being the single leading modifiable cause of poor health (Lim. SS, et al, Lancet 2012). Of 20 top individual causes of disease burden worldwide, 8 are related to poor nutrition, including suboptimal intakes of various dietary fatty acids. Although previous studies have found divergent health effects of different dietary fatty acids on health, gaps still exist in terms of the scientific knowledge (e.g. how individual circulating vs. dietary trans fatty acid subtypes affect health) and related disease burdens (e.g. national CHD mortality burdens attributable to suboptimal intakes of fatty acids). These gaps have motivated my dissertation researches. In chapter one and two, I investigated the prospective associations of five subtypes of plasma phospholipid trans fatty acid (TFA) levels with the risk of various disease endpoints, including total, CVD and non-CVD mortality, incident coronary heart disease (CHD) and DM. In chapter two, I also examined the prospective associations of total and subclasses dietary TFA with risk of DM. The analyses were conducted using the Cardiovascular Health Study, a community-based multicenter prospective cohort consisted of older Americans. The risks were estimated using the Cox proportional hazard model. The study in chapter three was a collaborative effort of the Nutrition and Chronic Diseases Expert Group as part of the 2010 Global Burden of Diseases, Injuries, and Risk Factors study. Using the comparative risk assessment framework, I comprehensively quantified the CHD mortality attributable to suboptimal intakes of saturated fat, omega-6 polyunsaturated fat, and TFA in 186 countries in 1990 and 2010, by age and sex groups. I also estimated the regional and country level trends of these attributable CHD burdens from 1990 to 2010. The findings of this study are relevant for informing regional and country level public health policy priorities.

The effects of temperature and air pollution on public health are comprehensive and ubiquitous. Therefore, this dissertation deals with the comprehensive topic of climate change and air pollution and their effects on public health. The first chapter examines the effect of temperature on mortality in 148 cities in the U.S. from 1973 through 2006. We focused on the timing of exposure to unseasonal temperature and temporal and spatial acclimation. The second chapter incorporated AOD data from satellite imagery with other predictors such as meteorological variables, land use regression, and spatial smoothing to predict the daily concentration of PM2.5 at a 1 km2 resolution across the southeastern United States, covering the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 through 2011. As the sequel of the result from the second chapter, the last chapter investigated the acute effect of PM2.5 on mortality in the entire population of North Carolina, South Carolina, and Georgia between 2007 and 2011 using the predictions from the second topic as PM2.5 exposure.

This dissertation explores the role of biologic factors and a novel method of measuring cognitive function in investigating mechanisms and risk factors of cognitive aging. With a rapidly increasing aging population, the public health burden of dementia is expected to rise in the future. Therefore, it is important to both better understand the etiology and identify novel risk factors, in order to reduce the incidence of new cases and develop effective treatments. Blood-based biomarkers, such as amyloid-beta and sex hormones, can provide an objective measure of factors likely associated with dementia risk. Additionally, computerized cognitive testing can provide efficient and accurate measurement of cognitive function, yet is seldom used in epidemiologic studies. Our first chapter involves the first systematic review and meta-analysis of prospective studies exploring the association between plasma amyloid-beta and incident Alzheimer’s disease or dementia. While preclinical prediction of Alzheimer’s disease is important for effective intervention, studies have considerably varied in design, assays and sample size, making it difficult to interpret the overall data and thus necessitating a systematic review and meta-analysis. The second chapter explores the association between endogenous hormone levels and cognitive function in a population of older women. While many studies have investigated the role of hormone therapy in cognitive aging, fewer studies have investigated endogenous hormones, which may provide a more objective measure of hormonal status. We therefore present a prospective cohort study investigating the association between several endogenous sex hormones and their prohormones at baseline, and cognitive function over 20 years later. The third chapter evaluates the feasibility and performance of a self-administered computerized cognitive battery. Although most epidemiologic studies of cognitive aging rely on traditional neuropsychological testing to measure cognitive outcomes, such methods can be prone to error, interviewer bias, and demand substantial time and cost. Therefore, we present results demonstrating for the first time the feasibility and performance of an unsupervised self-administered computerized cognitive battery in a population of older men. / Epidemiology