Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.

Since the publication of the Framingham algorithm for heart disease, tools that predict disease risk have been increasingly integrated into standards of practice. The utility of algorithms at the population level can serve several purposes in health care decision-making and planning. A population-based risk prediction tool for Diabetes Mellitus (DM) can be particularly valuable for public health given the significant burden of diabetes and its projected increase in the coming years. This thesis addresses various aspects related to diabetes risk in addition to incorporating methodologies that advance the practice of epidemiology. The goal of this thesis is to demonstrate and inform the methods of population-based diabetes risk prediction. This is studied in three components: (I) development and validation of a diabetes population risk tool, (II) measurement and (III) obesity risk. Analytic methods used include prediction survival modeling, simulation, and multilevel growth modeling. Several types of data were analyzed including population healthy survey, health administrative, simulation and longitudinal data. The results from this thesis reveal several important findings relevant to diabetes, obesity, population-based risk prediction, and measurement in the population setting. In this thesis a model (Diabetes Population Risk Tool or DPoRT) to predict 10-year risk for diabetes, which can be applied using commonly-collected national survey data was developed and validated. Conclusions drawn from the measurement analysis can inform research on the influence of measurement properties (error and type) on modeling and statistical prediction. Furthermore, the use of new modeling strategies to model change of body mass index (BMI) over time both enhance our understanding of obesity and diabetes risk and demonstrate an important methodology for future epidemiological studies. Epidemiologists are in need of innovative and accessible tools to assess population risk making these types of risk algorithms an important scientific advance. Population-based prediction models can be used to improve health planning, explore the impact of prevention strategies, and enhance our understanding of the distribution of diabetes in the population. This work can be extended to future studies which develop tools for disease planning at the population level in Canada and to enrich the epidemiologic literature on modeling strategies.