Plusieurs patients atteints d’un cancer de la prostate (CaP) se verront prescrire l'hormonothérapie (HT) en raison du stade avancé ou en cas d’une récidive de la maladie, pour freiner la progression de la maladie et améliorer leur survie. Cependant, l’HT présente des effets pouvant nuire significativement à la santé du patient. / Many prostate cancer patients will be treated with hormonotherapy (HT), depending of the cancer stage. But HT presents some side effects that could be harmful to the patient.

Plusieurs patients atteints d’un cancer de la prostate (CaP) se verront prescrire l'hormonothérapie (HT) en raison du stade avancé ou en cas d’une récidive de la maladie, pour freiner la progression de la maladie et améliorer leur survie. Cependant, l’HT présente des effets pouvant nuire significativement à la santé du patient. / Many prostate cancer patients will be treated with hormonotherapy (HT), depending of the cancer stage. But HT presents some side effects that could be harmful to the patient.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Scoglio / Vector-borne diseases not only cause devastating economic losses, they also significantly impact human health in terms of morbidity and mortality. From an economical and humane point of view, mitigation and control of vector-borne diseases are essential. Studying dynamics of vector-borne disease transmission is a challenging task because vector-borne diseases show complex dynamics impacted by a wide range of ecological factors. Understanding these factors is important for the development of mitigation and control strategies. Mathematical models have been commonly used to translate assumptions concerning biological (medical, demographical, behavioral, immunological) aspects into mathematics, linking biological processes of transmission and dynamics of infection at population level. Mathematical analysis translates results back into biology. Classical deterministic epidemic models do not consider spatial variation, assuming space is homogeneous. Spatial spread of vector-borne diseases observed many times highlights the necessity of incorporating spatial dynamics into mathematical models. Heterogeneous demography, geography, and ecology in various regions may result in different epidemiological characteristics. Network approach is commonly used to study spatial evolution of communicable diseases transmitted among connected populations. In this dissertation, the spread of vector-borne diseases in time and space, is studied to understand factors that contribute to disease evolution. Network-based models have been developed to capture different features of disease transmission in various environments. Network nodes represent geographical locations, and the weights represent the level of contact between regional pairings. Two competent vector populations, Aedes mosquitoes and Culex mosquitoes, and two host populations, cattle and humans were considered. The deterministic model was applied to the 2010 Rift Valley fever outbreak in three provinces of South Africa. Trends and timing of the outbreak in animals and humans were reproduced. The deterministic model with stochastic parameters was applied to hypothetical Rift Valley fever outbreak on a large network in Texas, the United States. The role of starting location and size of initial infection in Rift Valley fever virus spread were studied under various scenarios on a large-scale network. The reproduction number, defined as the number of secondary infections produced by one infected individual in a completely susceptible population, is typically considered an epidemic threshold of determining whether a disease can persist in a population. Extinction thresholds for corresponding Continuous-time Markov chain model is used to predict whether a disease can perish in a stochastic setting. The network level reproduction number for diseases vertically and horizontally transmitted among multiple species on heterogeneous networks was derived to predict whether a disease can invade the whole system in a deterministic setting. The complexity of computing the reproduction number is reduced because the expression of the reproduction number is the spectral radius of a matrix whose size is smaller than the original next generation matrix. The expression of the reproduction number may have a wide range of applications to many vector-borne diseases. Reproduction numbers can vary from below one to above one or from above one to below one by changing movement rates in different scenarios. The observations provide guidelines on executing movement bans in case of an epidemic. To compute the extinction threshold, corresponding Markov chain process is approximated near disease free equilibrium. The extinction threshold for Continuous-time Markov chain model was analytically connected to the reproduction number under some assumptions. Numerical simulation results agree with analytical results without assumptions, proposing a mathematical problem of proving the existence of the relationships in general. The distance of the extinction threshold were shown to be closer to one than the reproduction number. Consistent trends of probability of extinction varying with disease parameters observed through numerical simulations provide novel insights into disease mitigation, control, and elimination.

Doctor of Philosophy / Department of Diagnostic Medicine and Pathobiology / Michael W. Sanderson / The last outbreak for Foot and Mouth Disease (FMD) in the United States (U.S.) was in 1929. Since that time the U.S. has not had any exposure to the disease or vaccination, creating a very susceptible livestock population. The central U.S. has a large susceptible livestock population including cattle, swine, sheep, and goats. The impact of FMD in the U.S. would be devastating. Simulation modeling is the only avenue available to study the potential impacts of an introduction in the U.S. Simulation models are dependent on accurate estimates of the frequency and distance distribution of contacts between livestock operations to provide valid model results for planning and decision making including the relative importance of different control strategies. Due to limited data on livestock movement rates and distance distribution for contacts a survey was conducted of livestock producers in Colorado and Kansas. These data fill a need for region specific contact rates to provide parameters for modeling a foreign animal disease. FMD outbreaks often require quarantine, depopulation and disposal of whole herds in order to prevent the continued spread of the disease. Experts were included in a Delphi survey and round table discussion to critically evaluate the feasibility of depopulating a large feedlot. No clearly acceptable method of rapidly depopulating a large feedlot was identified. Participants agreed that regardless of the method used for depopulation of cattle in a large feedlot, it would be very difficult to complete the task quickly, humanely, and be able to dispose of the carcasses in a timely fashion. Simulation models were developed to assess the impact of livestock herd types and vaccination on FMD outbreaks in the central U.S. using the North American Animal Disease Spread Model (NAADSM), a spatially explicit, stochastic state-transition simulation model. Simulation scenarios with large vaccination zones had decreased outbreak length and number of herds destroyed. Vaccination did not provide additional benefit to control compared to depopulation alone when biosecurity and movement controls were high, however the ability to achieve high levels of biosecurity and movement control may be limited by labor and animal welfare concerns.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Caterina Maria Scoglio / We have analytically discovered the existence of two global epidemic invasion thresholds in a directed meta-population network model of the United States cattle industry. The first threshold describes the outbreak of disease first within the core of the livestock system while the second threshold describes the invasion of the epidemic into a second class of locations where the disease would pose a risk for contamination of meat production. Both thresholds have been verified through extensive numerical simulations. We have further derived the relationship between the pair of thresholds and discovered a unique dependence on the network topology through the fractional compositions and the in-degree distributions of the transit and sink nodes. We then addressed a major challenge for epidemiologists and their efforts to model disease outbreaks in cattle. There is a critical shortfall in the availability of large-scale livestock movement data for the United States. We meet this challenge by developing a method to estimate cattle movement parameters from publicly available data. Across 10 Central States of the US, we formulated a large, convex optimization problem to predict the cattle movement parameters which, having minimal assumptions, provide the best fit to the US Department of Agriculture's Census database and follow constraints defined by scientists and cattle experts. Our estimated parameters can produce distributions of cattle shipments by head which compare well with shipment distributions also provided by the US Department of Agriculture. This dissertation concludes with a brief incorporation of the analytic models and the parameter estimation. We approximated the critical movement rates defined by the global invasion thresholds and compared them with the average estimated cattle movement rates to find a significant opportunity for epidemics to spread through US cattle populations.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd Easton / History has shown that epidemics can occur at random and without warning — devastating the populations which they impact. As a preventative measure, modern medicine has helped to reduce the number of diseases that can instigate such an event, nevertheless natural and man-made disease mutations place us continuously at risk of such an outbreak. As a second line of defense, extensive research has been conducted to better understand spread patterns and the efficacy of various containment and mitigation strategies. However, these simulation models have primarily focused on minimizing the impact to groups of people either from an economic or societal perspective and little study has been focused on determining the utility maximizing strategy for an individual. Therefore, this work explores the decisions of individuals to determine emergent behaviors and characteristics which lead to increased probability of survival during an epidemic. This is done by leveraging linear program optimization techniques and the concept of Agent Based Simulation, to more accurately capture the complexity inherent in most real-world systems via the interactions of individual entities. This research builds on 5 years of study focused on rural epidemic simulation, resulting in the development of a 4,000-line computer code simulation package. This adaptable simulation can accurately model the interactions of individuals to discern the impact of any general disease type, and can be implemented on the population of any contiguous counties within Kansas. Furthermore, a computational study performed on the 17 counties of northwestern Kansas provides game theoretical based insights as to what decisions increase the likelihood of survival. For example, statistically significant findings suggest that an individual is four times more likely to become infected if they rush stores for supplies after a government issued warning instead of remaining at home. This work serves as a meaningful step in understanding emergent phenomena during an epidemic which, subsequently, provides novel insight to an individual’s utility maximizing strategy. Understanding the main findings of this research could save your life.

Background: Self-rated health (SRH) is among the most frequently assessed health perceptions in epidemiological research. While the predominant focus has been the predictive relevance of SRH, there is increasing interest in understanding the nature of SRH. Recently a conceptual interpretation of SRH was proposed. Purpose: To assess the tenability of the recently proposed distinctions of self-rated health, as a spontaneous assessment of overall health, or as an enduring self-concept, or both, and to determine the significance of mental well-being, physical and social health for SRH. Methods: A cohort of individuals undergoing total joint replacement (TJR) for hip or knee osteoarthritis (OA) was followed over 6 months of recovery. Participants (n=449) completed (pre-surgery, 3 and 6 months post-surgery) measures associated with physical health: pain, physical function, sports/recreation, fatigue; mental well-being: anxiety, depression; and social health: social participation, passive/active recreation, community access. Using structural equation modeling, confirmatory factor analyses (CFA) investigated 3 latent health variables, and the responsiveness of SRH to current and changing health status, as well as its predictive significance for future health status was investigated. Results: CFA analyses confirmed relationships between observed health measures and hypothesized latent health dimensions. SRH was responsive to current and changing mental well-being. The effects of physical and social health were mediated through mental well-being. SRH was a strong predictor of future SRH and future health status. Conclusion: SRH displayed features of both an enduring self-concept and spontaneous assessment of health status; evidence is provided that both operate simultaneously. While aspects of physical health have always been perceived as the major determinants of SRH, these findings indicate that mental and social health explain much of the relationship between physical health and SRH. These results are particularly unexpected since this is a sample of individuals undergoing TJR surgery, for which pain and restricted physical function are primary indicators. Since SRH is a significant predictor of health status, TJR outcomes, health care utilization and mortality, this study suggests that health research and care, treatment and management modalities should consider a broad range of health dimensions, not only physical aspects of health, and SRH.

Degenerative disease of the lumbar spine (DDLS) is a common condition for which surgery is beneficial in selected patients. Wide variation in surgical referral and rates of surgery has been observed contributing to unequal access to care. Our objectives were to examine (1) the variation in preferences for referral and surgery among surgeons, family physicians (FPs) and patients, (2) how FP referral practices compare with preferences and guideline recommendations, and (3) how the ‘enthusiasm’ of patients and physicians influence regional variation in surgical rates. We used conjoint analysis in a mailed survey to elicit preferences based on clinical vignettes from surgeons, FPs and patients. A Delphi expert panel provided consensus guideline recommendations for surgical referral to compare with actual FP referral practices. Rates of surgery for DDLS, obtained from Ontario hospital discharge data, were used to quantify regional variation and regression models assessed the relationship with patient and physician enthusiasm. We identified significant differences in preferences for referral and surgery between patients, FPs and surgeons. Surgeons placed high importance on leg-dominant symptoms while patients had high importance for quality of life symptoms (i.e. severity, duration, walking tolerance). Surgical referral practices were poorly predicted by individual FP preferences and guideline recommendations based on clinical factors alone. Variation in Ontario surgical rates was higher than that of hip or knee replacements and was highly associated with the enthusiasm of surgeons (p<0.008), rather than FPs or patients. By appreciating the variation in preferences between patients and physicians, and exploring other non-clinical factors that influence referrals, we may be able to improve the efficiency of referrals and enhance the shared decision making process. With an understanding of the influence that surgeons have in driving variation in surgical rates, further research may allow us to direct strategies to improve access and allow for a more equitable delivery of care for patients suffering from DDLS.

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.

This study builds on previous research showing that breast cancer survival is poorer for First Nations (FN) women compared to other Ontario women. Few studies have examined breast cancer survival in Indigenous populations compared to general populations; all of these report poorer survival among Indigenous people. Fewer still have examined potential factors related to the poorer survival, but these often suggest poorer prognosis even after adjustment for them. Study objectives were: to compare the distribution of demographic, prognostic and treatment factors between FN and non-FN women; to investigate factors associated with later diagnosis in FN women; to compare stage specific survival for FN and non-FN women controlling for important factors potentially associated with breast cancer survival; and to examine potential determinants of survival for FN women by stage at diagnosis. A case-case design was employed to compare FN women (n=287) diagnosed with invasive breast cancer to a frequency-matched random sample of women (n=671) from the general population diagnosed with breast cancer within the Ontario Cancer Registry. Women were matched (2:1) on period of diagnosis (1995-1999 and 2000-2004), age at diagnosis (<50 vs. 50≥), and Regional Cancer Centre (RCC). Stage at diagnosis and data relevant to the determinants of breast cancer survival were collected from medical charts at the RCCs. FN women were diagnosed with breast cancer at later stages compared to non-FN women. Having a non-screened method of detection and increasing BMI were associated with a later breast cancer diagnosis. FN women with comorbidity however were less likely to be diagnosed at a later stage. An unforeseen novel finding was that the survival disadvantage occurred after an early breast cancer diagnosis, whereas the survival experiences for those diagnosed at stages II+ were similar. In a multivariate analysis, elevated risk was observed for FN women in stage I, and significant risk was seen in women with comorbidity. These findings are actionable and can be used to improve the prognosis of FN women with breast cancer. It is likely that the same or similar factors are largely responsible for the survival disadvantage observed among Ontario FN people for most other major cancers.