3. Electronic Theses and Dissertations (ETDs) - All submissions
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Item A multilevel model of self-rated health in Gauteng: a comorbidity study(2019) Bhat, IshwariThe 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.Item The prevalence and predictors of HIV-hypertension comorbidity among youth living with HIV in South Africa, 2010-2016(2018) Makuapane, Lerato PatriciaOver the last two decades, AIDS mortality rates has decreased substantially due to extensive HIV treatment in South Africa, which left HIV as a chronic illness. Therefore, since the disease nature of HIV predisposes patients to other clinical conditions; many HIV patients also suffers from other AIDS-related and non-AIDS diseases resulting in comorbidities. It is most prevalent among population aged 14-35 years. At the same time, NCDs are rapidly increasing among youth with hypertension being amongst the leading cause of HIV comorbidities. High prevalence of both HIV/ AIDS and hypertension in this cohort rise a concern of their simultaneous occurrence to develop HIV-hypertension comorbidity. Abbreviation abstract)Item a clinical ausit of selected predictors of mortality of patients admitted to Charlotte Maxeke Johannesburg academic hospital intensive care unit with human immunodeficiency virus and tuberculosis co-infection(2019) Singh, AvaniBackground: The high level of co-morbid TB/HIV cases with severe organ failure on presentation in South Africa, results in an increased number of ICU admissions often with a poor prognosis at presentation. In this study, the aim was to identify patients admitted with HIV/TB co-infection and calculate the APACHE II scores and SOFA scores for each patient. Predicted percentage mortality was compared with actual mortality. Predictors of mortality were further identified, as well as the benefit of initiating ARV treatment in patients who are ARV naive upon admission to ICU. Methods: A retrospective audit of consecutive cases over a 24 month period was completed. Patient demographics; CD 4 count; ARV treatment status; ICU and 30 day mortality; the APACHE II Score; SOFA scores and correlating predicted percentage mortality were documented. The survival of patients was assessed using Kaplan Meier survival curves, and a univariate analysis was performed to identify risk factors for mortality. Calculated predicted mortality was compared with actual mortality to validate each scoring system and infer which was the better tool. Results: Of 75 patients admitted with pulmonary (43 cases) or extra-pulmonary (32 cases) TB, 23 died in the ICU (mortality 30,7%), and a further 10 died in the first 30 days of hospitalisation (30 day mortality 44%). A survival analysis established ARV treatment and CD 4 counts greater than 50 cells/mm3 were associated with a higher survival rate at any point of the analysis. In the entire study period, only 2 patients were initiated on ARV therapy during their ICU stay, 1 survived to discharge and 1 died in ICU. The APACHE II Predicted Mortality was within the 95% Confidence Intervals for all groups while the SOFA score was outside the upper bound limit of the 95% confidence intervals of actual mortality for those patients taking ARV treatment (52%, 95% CI 43,1% - 59,5% vs actual mortality 30%, 95% CI 17,7% - 46,1%), those with a CD 4 count of more than 50 (53,5% 95% CI 45,4% - 60,6% vs actual mortality 34%, 95% CI 22,1% - 48,4%) and female patients (51,2%, 95% CI 41,6% - 58,1% vs actual mortality 35,1%, 95% CI 21,4% - 50,4%). Conclusion: The study found that both the APACHE II and SOFA scoring systems were both statistically significant in prognosticating mortality in the study population. The APACHE II scoring system however showed a slightly improved prognostication in specific cohorts who had improved survival. It was also confirmed that patients with a CD 4 count of more than 50 cells/mm3, and those on ARV therapy had a statistically significant improved mortality. Further studies reviewing survival benefit of ARV initiation in ICU are warranted. ACKNOWLEDGEMENTS Supervisor: Prof GA Richards Co-Supervisor: Dr SHH Mohamadali Statistician: Mr MH Zondi Assistant - Data Collection: Ms S Madanlall