Chronic disease is a major health burden in the United States, affecting about half of adults, and leading to poor health, disability, and death. However, the burden of chronic disease is not shared equally among Americans, with some groups (created by determinants such as race/ethnicity and socioeconomic resources) experiencing higher rates of morbidity and mortality. When measures of health and socioeconomic resources are examined together, a stepwise gradient pattern emerges. This social gradient has been established for individual measures, such as household income and social class, and several measures of morbidity and mortality. However, nationally, little research has been conducted using area-level measures, such as county economics, to examine its relationship with chronic disease. Three studies were completed using data from the Behavioral Risk Factor Surveillance System (BRFSS). County economic status was determined using unemployment, per capita market income, and poverty. The first study examined the relationship between county economic status and chronic disease and risk factors, both nationally and by metropolitan classification, using data from BRFSS 2013. Further, the social gradient was explored. The second study also used data from BRFSS 2013 to examine county economic status and prevalence of hypertension, arthritis, and poor health, after controlling for known risk factors. This study also examined results by US region. Finally, the third study assessed changes in disparities between persistently poor and persistently affluent counties for heart disease, hypertension, arthritis, and diabetes using data from BRFSS 2001-2010.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Populations are aging and the prevalence of chronic disease, persisting for many years, is increasing. The most common, non-communicable chronic diseases in developed countries are; cardiovascular disease (CVD), type 2 diabetes, obesity, arthritis and specific cancers. Chronic diseases such as cardiovascular disease, type 2 diabetes and obesity have high prevalence and develop over the course of life due to a number of interrelated factors including genetic predisposition, nutrition and lifestyle. With the development and completion of human genome sequencing, we are able to trace genes responsible for proteins and metabolites that are linked with these diseases. A computerized model focused on organizing knowledge related to genes, nutrition and the three chronic diseases, namely, cardiovascular disease, type 2 diabetes and obesity has been developed for the Ontology-Based Personalized Risk Evaluation for Chronic Disease Project. This model is a Protégé-based ontological representation which has been developed for entering and linking concepts and data for these three chronic diseases. This model facilitates to identify interrelationships between concepts. The ontological representation provides the framework into which information on individual patients, disease symptoms, gene maps, diet and life history can be input, and risks, profiles, and recommendations derived. Personal genome and health data could provide a guide for designing and building a medical health administration system for taking relevant annual medical tests, e.g. gene expression level changes for health surveillance. One method, called transductive neuro-fuzzy inference system with weighted data normalization is used to evaluate personalized risk of chronic disease. This personalized approach has been used for two different chronic diseases, predicting the risk of cardiovascular disease and predicting the risk of type 2 diabetes. For predicting the risk of cardiovascular disease, the National Nutrition Health Survey 97 data from New Zealand population has been used. This data contains clinical, anthropometric and nutritional variables. For predicting risk of type 2 diabetes, data from the Italian population with clinical and genetic variables has been used. It has been discovered that genes responsible for causing type 2 diabetes are different in male and female samples. A framework to integrate the personalized model and the chronic disease ontology is also developed with the aim of providing support for further discovery through the integration of the ontological representation in order to build an expert system in genes of interest and relevant dietary components.

Thesis (Ph. D.)--University of Washington, 1989. / Vita. Includes bibliographical references.

Diss. (sammanfattning) Göteborg : Göteborgs universitet , 2007. / Härtill 4 uppsatser.

Thesis (MA (Research Psychology))-University of Pretoria, 2006. / Includes bibliographical references. Available on the Internet via the World Wide Web.

Thesis (M.H.A.)--University of Michigan, 1961.

Thesis (M.H.A.)--University of Michigan, 1964.

Thesis (M.S.)--University of Michigan, 1975.