Predicting district level HIV prevalence in South Africa using medicine ordering data

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dc.contributor.advisorSilal, Sheetal
dc.contributor.advisorEr , Sebnem
dc.contributor.authorLiebenberg, Juandre
dc.date.accessioned2025-09-01T11:29:02Z
dc.date.available2025-09-01T11:29:02Z
dc.date.issued2025
dc.date.updated2025-09-01T11:10:28Z
dc.description.abstractThe Human Immunodeficiency Virus has been at the forefront of South Africa's public health challenges, placing the healthcare system under immense pressure. As a result of HIV planning by policymakers, more than 5.5 million People Living with HIV have access to antiretroviral treatment at present day. Dynamic, mechanistic models such as the Thembisa and Naomi Bayesian models have been used to generate provincial and district-level estimates such as HIV prevalence, People Living with HIV, and the number of residents on antiretroviral treatment. An alternative methodology for estimating drug utilisation and predicting HIV estimates was explored by using medicine ordering data as the primary input for analysis from 2020 to 2022. Two objectives were set out, the first being a drug utilisation analysis aimed at approximating the number of individuals per 1000 inhabitants per day taking antiretroviral drugs to determine if the adequate stock was ordered at district and provincial levels. The second was to predict HIV prevalence by fitting panel data and spatial linear models to predict district prevalence and People Living with HIV; the estimations for People Living with HIV were converted to prevalence to compare the direct estimation of prevalence to the calculated. Results from the drug utilisation analysis suggested that district municipalities hold insufficient stock to meet the demands of those inflicted with the disease. In contrast, larger metropolitan municipalities hold excess medication, implying that people travel across district boundaries to receive treatment. The fitted spatial models generated better prevalence estimates than fixed-effect panel data models for the predicted and calculated prevalence with root mean square error metrics of 0.009 (0.87%) and 0.012(1.24%) compared to that of 0.012(1.21%) and 0.015(1.53%) from the fixed-effect panel data models. The impact of high quantities of antiretroviral drugs ordered by metropolitan municipalities resulted in an underestimation of prevalence in those regions due to the negative relationship between the dependent variable Prevalence and the independent Quantity variable. From the spatial models fitted, the best performing spatial model accurately estimated the prevalence rates for 51 out of 52 districts, which fell within the acceptable range defined by the Naomi Model. The results of the study have shown that the use of ordering data to predict disease prevalence has the potential to serve as an alternative methodology in the absence of established models.
dc.identifier.apacitationLiebenberg, J. (2025). <i>Predicting district level HIV prevalence in South Africa using medicine ordering data</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/41664en_ZA
dc.identifier.chicagocitationLiebenberg, Juandre. <i>"Predicting district level HIV prevalence in South Africa using medicine ordering data."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2025. http://hdl.handle.net/11427/41664en_ZA
dc.identifier.citationLiebenberg, J. 2025. Predicting district level HIV prevalence in South Africa using medicine ordering data. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/41664en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Liebenberg, Juandre AB - The Human Immunodeficiency Virus has been at the forefront of South Africa's public health challenges, placing the healthcare system under immense pressure. As a result of HIV planning by policymakers, more than 5.5 million People Living with HIV have access to antiretroviral treatment at present day. Dynamic, mechanistic models such as the Thembisa and Naomi Bayesian models have been used to generate provincial and district-level estimates such as HIV prevalence, People Living with HIV, and the number of residents on antiretroviral treatment. An alternative methodology for estimating drug utilisation and predicting HIV estimates was explored by using medicine ordering data as the primary input for analysis from 2020 to 2022. Two objectives were set out, the first being a drug utilisation analysis aimed at approximating the number of individuals per 1000 inhabitants per day taking antiretroviral drugs to determine if the adequate stock was ordered at district and provincial levels. The second was to predict HIV prevalence by fitting panel data and spatial linear models to predict district prevalence and People Living with HIV; the estimations for People Living with HIV were converted to prevalence to compare the direct estimation of prevalence to the calculated. Results from the drug utilisation analysis suggested that district municipalities hold insufficient stock to meet the demands of those inflicted with the disease. In contrast, larger metropolitan municipalities hold excess medication, implying that people travel across district boundaries to receive treatment. The fitted spatial models generated better prevalence estimates than fixed-effect panel data models for the predicted and calculated prevalence with root mean square error metrics of 0.009 (0.87%) and 0.012(1.24%) compared to that of 0.012(1.21%) and 0.015(1.53%) from the fixed-effect panel data models. The impact of high quantities of antiretroviral drugs ordered by metropolitan municipalities resulted in an underestimation of prevalence in those regions due to the negative relationship between the dependent variable Prevalence and the independent Quantity variable. From the spatial models fitted, the best performing spatial model accurately estimated the prevalence rates for 51 out of 52 districts, which fell within the acceptable range defined by the Naomi Model. The results of the study have shown that the use of ordering data to predict disease prevalence has the potential to serve as an alternative methodology in the absence of established models. DA - 2025 DB - OpenUCT DP - University of Cape Town KW - HIV LK - https://open.uct.ac.za PB - University of Cape Town PY - 2025 T1 - Predicting district level HIV prevalence in South Africa using medicine ordering data TI - Predicting district level HIV prevalence in South Africa using medicine ordering data UR - http://hdl.handle.net/11427/41664 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/41664
dc.identifier.vancouvercitationLiebenberg J. Predicting district level HIV prevalence in South Africa using medicine ordering data. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2025 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41664en_ZA
dc.language.isoen
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Statistical Sciences
dc.publisher.facultyFaculty of Science
dc.publisher.institutionUniversity of Cape Town
dc.subjectHIV
dc.titlePredicting district level HIV prevalence in South Africa using medicine ordering data
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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