Loss of the renal microvasculature is a common feature of almost all forms of chronic kidney disease (CKD). Here we explored the role of the angiogenic chemokine stromal cell-derived factor-1-alpha (SDF-1) and its cognate receptor CXCR4 in experimental and human CKD. CXCR4 was present on endothelial cells and podocytes, while SDF-1 was detectable on podocytes, arteriolar smooth muscle cells, interstitial fibroblasts and occasional endothelial cells. CXCR4 mRNA was elevated in the kidneys of rats with CKD and chronic antagonism of CXCR4 accelerated renal decline and capillary loss. Acute SDF-1 infusion activated glomerular endothelial nitric oxide synthase (eNOS) in vivo, while functional response to SDF-1 was impaired in glomerular endothelial cells derived from eNOS-/- mice. Finally, CXCR4 mRNA was also found to be increased in biopsies of patients with secondary focal segmental glomerulosclerosis. These observations indicate that local eNOS-dependent SDF-1/CXCR4 signaling exerts a compensatory reno-protective effect in the setting of CKD.

There is a dearth of research that explores the lives and experiences of trans-identified parents. The goal of this study was to explore the experiences of trans people who sought or accessed AHR services in Ontario between 2007 and 2010. Qualitative data that was collected from 7 qualitative interviews with 9 trans people and their partners was analyzed for the present analysis. Results from this study indicate that AHR providers do not possess sufficient knowledge about trans people, trans identities and trans lives to adequately address the needs of trans service users. Specific provider practices that trans people experienced as being unhelpful are illuminated and implications for improving clinical practices are discussed.

With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more)conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a favourable fit to microarray data. In the second method we propose a test statistic for testing whether area under receiver operating characteristic curve (AUC) for each gene is greater than 0.5 allowing different variances for each gene. This proposed method is computationally less intensive and can identify genes that are reasonably stable with satisfactory prediction performance. The third method is based on comparing two AUCs for a pair of genes that is designed for selecting highly correlated genes in the microarray datasets. We propose a nonparametric procedure for selecting genes with expression levels correlated with that of a ``seed" gene in microarray experiments. The test proposed by DeLong et al. (1988) is the conventional nonparametric procedure for comparing correlated AUCs. It uses a consistent variance estimator and relies on asymptotic normality of the AUC estimator. Our proposed method includes DeLong's variance estimation technique in comparing pair of genes and can identify genes with biologically sound implications. In this thesis, we focus on the primary step in the gene selection process, namely, the ranking of genes with respect to a statistical measure of differential expression. We assess the proposed approaches by extensive simulation studies and demonstrate the methods on real datasets. The simulation study indicates that the parametric method performs favorably well at any settings of variance, sample size and treatment effects. Importantly, the method is found less sensitive to contaminated by noise. The proposed nonparametric methods do not involve complicated formulas and do not require advanced programming skills. Again both methods can identify a large fraction of truly differentially expressed (DE) genes, especially if the data consists of large sample sizes or the presence of outliers. We conclude that the proposed methods offer good choices of analytical tools to identify DE genes for further biological and clinical analysis.

Hockey is a popular sport, and at its highest levels, it is a complex contact game characterized by physical strength, speed, and skill. The interaction of these characteristics contributes to the inherent risk of injury athletes must face while playing. Among hockey injuries, concussions are one of the most commonly sustained by athletes across all levels of play and age groups. Significant public attention, combined with poorly understood long-term effects, indicates the importance of tangible preventive strategies. The main goal of this thesis was to understand, through video analysis, how playing characteristics and mechanism of injury contribute to concussions in the National Hockey League (NHL). In the first study, the development and validation of an observational recording tool used to code and analyze NHL concussions observed via video analysis was described. The second study attempted to synthesize the description of players’ characteristics, antecedent events, and contextual variables associated with events leading to concussion at the NHL level. Several specific risk factors for concussion in NHL players were identified, including position, body size, specific locations on the ice, and particular situations based on a player's position. The final study systematically analyzed how concussions occur to identify potential pattern(s) of concussions. A common specific injury mechanism characterized by player-to-player contact and resulting in contact to the head by the shoulder, elbow, or gloves, was also identified. When the principal mechanism was refined further, several important characteristics were discernable: (i) contact was often to the lateral aspect of the head; (ii) the player who suffered a concussion was often not in possession of the puck; and (iii) no penalty was called on the play. Collectively, these studies served to address gaps in the literature; the implications for informing prevention and management strategies are also discussed.

The extent to which economic evaluations of public health programs in Ontario are conducted and used by decision makers is currently very limited. This thesis supports public health decision-making through applied and methodological work. The applied work demonstrates different methods to evaluate the cost-effectiveness of public health interventions using the examples of seasonal and pandemic influenza immunization programs. The methodological component explores whether time horizon choice, one methodological consideration in economic evaluations, introduces bias. The economic evaluation of Ontario’s universal influenza immunization program (UIIP) uses primarily provincial health administrative databases to assess the impact of UIIP on health outcomes (quality-adjusted life years (QALYs), mortality), health care resource use (physician office visits, emergency department visits, and hospitalizations), and costs due to seasonal influenza. Ontario’s UIIP was found to be cost-effective compared to a targeted program. The economic evaluation of Ontario’s H1N1 (2009) mass immunization program uses a mathematical modeling approach to describe the pandemic as observed in Ontario. By removing immunization from the simulation, the impact of the program was evaluated. Outcome measures include health outcomes (attack rate, deaths, QALYs), resource use, and cost (physician office visits, emergency department visits, hospitalizations). The analysis found Ontario’s mass immunization program to be highly cost-effective despite high program cost. The methodological component investigates whether time horizon choice, a major methodological choice, introduces bias to economic evaluations. The existence, magnitude and direction of time horizon bias are demonstrated using a formal model. This work supports current guidelines in recommending a lifetime time horizon and provides a framework to discuss bias in economic evaluations. This thesis demonstrates different approaches to evaluate the cost-effectiveness of public health interventions, informs decision-making, and establishes the groundwork to guide future economic evaluations of public health interventions.

Background: Considerable attention has been paid to evaluating the roles and relationships of professionals participating in team-based or collaborative practice; however, less attention has been paid to exploring the patients’ views and impact of such practice despite claims of it being patient-centred. Objectives: To examine the relationship between patient and provider characteristics and patients’ ratings of measures of quality of care and integration, and to explore the patient views of care delivered in a patient-centred collaborative study involving chiropractors and physicians. Design: Cross-sectional survey. Method: A mixed methods sequential approach with a quantitative priority was used in data analysis. Quantitative data were collected from 2597 patients participating in a collaborative study involving chiropractors and physicians and 530 patients attending chiropractors not involved in collaborative care. All participants presented with musculoskeletal pain. The Primary Care Assessment Survey (PCAS) was modified and scores from six of its scales were used to assess attributes of quality patient-centred care between the two study groups. Qualitative transcript-based data from six purposefully selected focus groups was analyzed using an interpretivist approach. Results: The revised PCAS demonstrated acceptable psychometric properties. Patients in both study groups received quality, patient-centred care. Patients’ reporting being completely satisfied and feeling improved by their care was positively associated with rating chiropractors as high performers on all scales. Survey findings were confirmed in focus groups of study patients. Patients appreciated positive interpersonal interactions, sharing in the treatment decision-making process, having a choice in provider and treatment, and the provision of holistic care. Patients perceived that collaboration between chiropractors and physicians varied, favouring those who were co-located. Patients with chronic or co-morbid conditions desired greater involvement in their care. Patients felt sharing of clinical information was more important than co-location as facilitating coordination and integration of collaborative care. Conclusion: The study suggests that patients suffering from musculoskeletal pain benefit from interprofessional collaborative care that includes improved access to and choice of providers and treatment options, as well as enhanced interprofessional communication and coordination of care.

Osteoporosis is a major public health concern, affecting over 200 million people worldwide. There is valid evidence outlining how osteoporosis can be diagnosed and managed, but gaps exist between evidence and practice. Graham’s “Knowledge to Action” (KTA) process for knowledge translation and the Medical Research Council (MRC) framework for complex interventions were used to address these gaps. The first 4 KTA steps were collapsed into 3 phases of the PhD research plan. In PhD Phase 1, a systematic review was conducted to identify tools that facilitate decision making in osteoporosis disease management (DM). Results showed that few DM tools exist, but promising strategies were those that incorporated reminders and education and targeted physicians and patients. PhD Phase 2 used the findings from the systematic review and consultation with clinical and human factors engineering experts to develop a conceptual design of the tool. Multiple components targeted to both physicians and patients at the point of care, and which could be used as a standalone system or modifiable for integration with electronic health record systems were outlined. PhD Phases 3a and 3b were devoted to the assessment of the barriers to knowledge. In Phase 3a, a qualitative study of focus groups was conducted with physicians to identify attitudes and perceived barriers to implementing decision support tools in practice, and to identify the features that should be included in the design. Findings from 4 focus groups combined with aging research, and input from design and information experts were used to transform the conceptual design into a functional prototype. In Phase 3b, each component of the prototype was tested in 3 usability evaluation studies using an iterative, participant-centered approach to assess how well the prototype met end users’ needs. Findings from the usability study informed the final prototype, which is ready for implementation as part of the post PhD plan to fulfill the requirements of the remaining steps of the KTA and MRC frameworks.

With the development of DNA microarray technology, scientists can now measure the expression levels of thousands of genes (features or genomic biomarkers) simultaneously in one single experiment. Robust and accurate gene selection methods are required to identify differentially expressed genes across different samples for disease diagnosis or prognosis. The problem of identifying significantly differentially expressed genes can be stated as follows: Given gene expression measurements from an experiment of two (or more)conditions, find a subset of all genes having significantly different expression levels across these two (or more) conditions. Analysis of genomic data is challenging due to high dimensionality of data and low sample size. Currently several mathematical and statistical methods exist to identify significantly differentially expressed genes. The methods typically focus on gene by gene analysis within a parametric hypothesis testing framework. In this study, we propose three flexible procedures for analyzing microarray data. In the first method we propose a parametric method which is based on a flexible distribution, Generalized Logistic Distribution of Type II (GLDII), and an approximate likelihood ratio test (ALRT) is developed. Though the method considers gene-by-gene analysis, the ALRT method with distributional assumption GLDII appears to provide a favourable fit to microarray data. In the second method we propose a test statistic for testing whether area under receiver operating characteristic curve (AUC) for each gene is greater than 0.5 allowing different variances for each gene. This proposed method is computationally less intensive and can identify genes that are reasonably stable with satisfactory prediction performance. The third method is based on comparing two AUCs for a pair of genes that is designed for selecting highly correlated genes in the microarray datasets. We propose a nonparametric procedure for selecting genes with expression levels correlated with that of a ``seed" gene in microarray experiments. The test proposed by DeLong et al. (1988) is the conventional nonparametric procedure for comparing correlated AUCs. It uses a consistent variance estimator and relies on asymptotic normality of the AUC estimator. Our proposed method includes DeLong's variance estimation technique in comparing pair of genes and can identify genes with biologically sound implications. In this thesis, we focus on the primary step in the gene selection process, namely, the ranking of genes with respect to a statistical measure of differential expression. We assess the proposed approaches by extensive simulation studies and demonstrate the methods on real datasets. The simulation study indicates that the parametric method performs favorably well at any settings of variance, sample size and treatment effects. Importantly, the method is found less sensitive to contaminated by noise. The proposed nonparametric methods do not involve complicated formulas and do not require advanced programming skills. Again both methods can identify a large fraction of truly differentially expressed (DE) genes, especially if the data consists of large sample sizes or the presence of outliers. We conclude that the proposed methods offer good choices of analytical tools to identify DE genes for further biological and clinical analysis.

Hockey is a popular sport, and at its highest levels, it is a complex contact game characterized by physical strength, speed, and skill. The interaction of these characteristics contributes to the inherent risk of injury athletes must face while playing. Among hockey injuries, concussions are one of the most commonly sustained by athletes across all levels of play and age groups. Significant public attention, combined with poorly understood long-term effects, indicates the importance of tangible preventive strategies. The main goal of this thesis was to understand, through video analysis, how playing characteristics and mechanism of injury contribute to concussions in the National Hockey League (NHL). In the first study, the development and validation of an observational recording tool used to code and analyze NHL concussions observed via video analysis was described. The second study attempted to synthesize the description of players’ characteristics, antecedent events, and contextual variables associated with events leading to concussion at the NHL level. Several specific risk factors for concussion in NHL players were identified, including position, body size, specific locations on the ice, and particular situations based on a player's position. The final study systematically analyzed how concussions occur to identify potential pattern(s) of concussions. A common specific injury mechanism characterized by player-to-player contact and resulting in contact to the head by the shoulder, elbow, or gloves, was also identified. When the principal mechanism was refined further, several important characteristics were discernable: (i) contact was often to the lateral aspect of the head; (ii) the player who suffered a concussion was often not in possession of the puck; and (iii) no penalty was called on the play. Collectively, these studies served to address gaps in the literature; the implications for informing prevention and management strategies are also discussed.

The extent to which economic evaluations of public health programs in Ontario are conducted and used by decision makers is currently very limited. This thesis supports public health decision-making through applied and methodological work. The applied work demonstrates different methods to evaluate the cost-effectiveness of public health interventions using the examples of seasonal and pandemic influenza immunization programs. The methodological component explores whether time horizon choice, one methodological consideration in economic evaluations, introduces bias. The economic evaluation of Ontario’s universal influenza immunization program (UIIP) uses primarily provincial health administrative databases to assess the impact of UIIP on health outcomes (quality-adjusted life years (QALYs), mortality), health care resource use (physician office visits, emergency department visits, and hospitalizations), and costs due to seasonal influenza. Ontario’s UIIP was found to be cost-effective compared to a targeted program. The economic evaluation of Ontario’s H1N1 (2009) mass immunization program uses a mathematical modeling approach to describe the pandemic as observed in Ontario. By removing immunization from the simulation, the impact of the program was evaluated. Outcome measures include health outcomes (attack rate, deaths, QALYs), resource use, and cost (physician office visits, emergency department visits, hospitalizations). The analysis found Ontario’s mass immunization program to be highly cost-effective despite high program cost. The methodological component investigates whether time horizon choice, a major methodological choice, introduces bias to economic evaluations. The existence, magnitude and direction of time horizon bias are demonstrated using a formal model. This work supports current guidelines in recommending a lifetime time horizon and provides a framework to discuss bias in economic evaluations. This thesis demonstrates different approaches to evaluate the cost-effectiveness of public health interventions, informs decision-making, and establishes the groundwork to guide future economic evaluations of public health interventions.