Bhat, Ishwari2021-02-012021-02-012019Bhat,Ishwari Ravindra (2019) A multilevel model of self-rated health in Gauteng: a comorbidity study, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/30457>https://hdl.handle.net/10539/30457A research report submitted in fulfilment of the requirements for the degree of Master of Science in GIS and Remote Sensing to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, 2019The report reviewed the self-rated health of Gauteng’s comorbid health in 2015. The outcome variable of this report was defined as the self-rated comorbid health of Gauteng. A multilevel approach examined factors closely associated with comorbid health using both individual and community-level variables. The report addresses the symbiotic relationship between comorbidity prevalence and Gauteng’s socioeconomic conditions that foster poor health. South African healthcare has been characterised by its increasing comorbidity of communicable diseases. As the prevalence of comorbidity varies between spaces, it becomes increasingly important to examine social environments. The aim of this study estimated the prevalence of comorbidity, and determined the factors associated with self-reported comorbidity in the Gauteng province of South Africa. A multilevel model approach was used in this cross-sectional study. Primary data was provided by the Gauteng City-Region Observatory from the Quality of Live survey (QoL) in 2015, it comprised 30 002 participants above the ages of 18 years, who were selected through numerous sampling stages. enumeration areas (EA) were drawn using probability proportional to size as the primary sampling unit. Comorbidity was illustrated as classes of two, three and four-or-more. Prevalence was estimated as a proportion of comorbid health conditions from the health section of the survey. Spatial autocorrelation was used to detect spatial patterns of comorbidity. Regression models were used to determine factors closely associated with comorbidity in Gauteng. The estimated prevalence of self-reported two comorbidities (hypertension and diabetes) was 8.97%, three comorbidities (hypertension, diabetes and influenza/pneumonia) was 3.01% and four-or-more comorbidities (hypertension, heart disease/stroke, diabetes and asthma) was 0.96%. Ordinary Least Squares (OLS) models provided the first step for regression analysis, wherein only two comorbidities illustrated spatial dependence among the residual errors. The spatial error model of two comorbidities was interpreted as the most accurate model owing to the high Hausman test p-value. Three comorbidities and four-or-more comorbidities indicated no spatial dependence among the residuals, reiterating the OLS model as the most appropriate regression model for each class. Conventional multilevel models illustrated that self-rated comorbidity prevalence was more likely to report two comorbidities (n=829,8.97%) for quality of property, child malnutrition, low exercise frequency, high stress and The report reviewed the self-rated health of Gauteng’s comorbid health in 2015. The outcome variable of this report was defined as the self-rated comorbid health of Gauteng. A multilevel approach examined factors closely associated with comorbid health using both individual and community-level variables. The report addresses the symbiotic relationship between comorbidity prevalence and Gauteng’s socioeconomic conditions that foster poor health. South African healthcare has been characterised by its increasing comorbidity of communicable diseases. As the prevalence of comorbidity varies between spaces, it becomes increasingly important to examine social environments. The aim of this study estimated the prevalence of comorbidity, and determined the factors associated with self-reported comorbidity in the Gauteng province of South Africa. A multilevel model approach was used in this cross-sectional study. Primary data was provided by the Gauteng City-Region Observatory from the Quality of Live survey (QoL) in 2015, it comprised 30 002 participants above the ages of 18 years, who were selected through numerous sampling stages. enumeration areas (EA) were drawn using probability proportional to size as the primary sampling unit. Comorbidity was illustrated as classes of two, three and four-or-more. Prevalence was estimated as a proportion of comorbid health conditions from the health section of the survey. Spatial autocorrelation was used to detect spatial patterns of comorbidity. Regression models were used to determine factors closely associated with comorbidity in Gauteng. The estimated prevalence of self-reported two comorbidities (hypertension and diabetes) was 8.97%, three comorbidities (hypertension, diabetes and influenza/pneumonia) was 3.01% and four-or-more comorbidities (hypertension, heart disease/stroke, diabetes and asthma) was 0.96%. Ordinary Least Squares (OLS) models provided the first step for regression analysis, wherein only two comorbidities illustrated spatial dependence among the residual errors. The spatial error model of two comorbidities was interpreted as the most accurate model owing to the high Hausman test p-value. Three comorbidities and four-or-more comorbidities indicated no spatial dependence among the residuals, reiterating the OLS model as the most appropriate regression model for each class. Conventional multilevel models illustrated that self-rated comorbidity prevalence was more likely to report two comorbidities (n=829,8.97%) for quality of property, child malnutrition, low exercise frequency, high stress and low-income bracket in Gauteng. Self-rated comorbidity prevalence was more likely to report three comorbidities (n=76, 3.017%) for migrated from another province, family living nearby, adult malnutrition, medium to high stress, low-income bracket and female in Gauteng. Results of four-or-more comorbidities were defined by the limitation that data set was too small for regression analysis. Comorbidity has a complexity in nature, in that it both influenced by Gauteng socioeconomic environments as well as influenced Gauteng health. Comorbidity became a challenge for Gauteng in addressing complexity, accessibility and cost-effectiveness. It was anticipated that these findings may advise policy interventions in mitigating health disparities in the broader context of South Africa.Online resource (90 leaves)enComorbiditySubstance abuseAnxietyThesis