Clustering of longitudinal viral loads in the Western Cape
| dc.contributor.advisor | Lesosky, Maia Rose | |
| dc.contributor.advisor | Myer, Landon | |
| dc.contributor.author | Arua, Eke Nnanna | |
| dc.date.accessioned | 2020-09-09T15:38:42Z | |
| dc.date.available | 2020-09-09T15:38:42Z | |
| dc.date.issued | 2020 | |
| dc.date.updated | 2020-09-09T11:24:31Z | |
| dc.description.abstract | Introduction: Routine viral load (VL) monitoring is important for assessing the effectiveness of ART in South Africa. There is little information however, on how the longitudinal VL patterns change for subgroups of persons living with HIV (PLHIV) who have experienced at least one elevated VL. We investigated the possible longitudinal VL patterns that may exist among this unique population. Methods: This mini-dissertation offers three components; a research protocol (Section A), a literature review (Section B) and a journal ready manuscript (Section C). We examined HIV VL data for the Western Cape from 2008 to 2018, taken from the National Health Laboratory Services (NHLS). Using< 1000 copies/mL as a threshold for viral suppression, we identified 109092 individuals who had at least one instance of an elevated VL. A nonparametric (KML-Shape) and a model-based (LCMM) clustering technique were used to identify latent subgroups of longitudinal VL trajectories among these individuals. Results: Both the KML-Shape and LCMM clustering techniques identified five latent viral load trajectory subgroups. KML-Shape found majority of individuals' trajectories belonged to clusters that had a decreasing longitudinal VL trend (76.6% of individuals), while LCMM found a smaller proportion of individuals' trajectories belonged to clusters that had a decreasing longitudinal trend (52.5% of individuals). Most of the trajectory subgroups identified had long periods of low-level viremia. Conclusion: Although majority of individuals belonged to clusters that had downward trends, further research is needed to better understand factors contributing to membership of clusters that did not have a downward longitudinal trend. Understanding these factors may help in the development of targeted HIV prevention programs for these individuals. | |
| dc.identifier.apacitation | Arua, E. N. (2020). <i>Clustering of longitudinal viral loads in the Western Cape</i>. (). ,Faculty of Health Sciences ,Department of Public Health and Family Medicine. Retrieved from http://hdl.handle.net/11427/32195 | en_ZA |
| dc.identifier.chicagocitation | Arua, Eke Nnanna. <i>"Clustering of longitudinal viral loads in the Western Cape."</i> ., ,Faculty of Health Sciences ,Department of Public Health and Family Medicine, 2020. http://hdl.handle.net/11427/32195 | en_ZA |
| dc.identifier.citation | Arua, E.N. 2020. Clustering of longitudinal viral loads in the Western Cape. . ,Faculty of Health Sciences ,Department of Public Health and Family Medicine. http://hdl.handle.net/11427/32195 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Arua, Eke Nnanna AB - Introduction: Routine viral load (VL) monitoring is important for assessing the effectiveness of ART in South Africa. There is little information however, on how the longitudinal VL patterns change for subgroups of persons living with HIV (PLHIV) who have experienced at least one elevated VL. We investigated the possible longitudinal VL patterns that may exist among this unique population. Methods: This mini-dissertation offers three components; a research protocol (Section A), a literature review (Section B) and a journal ready manuscript (Section C). We examined HIV VL data for the Western Cape from 2008 to 2018, taken from the National Health Laboratory Services (NHLS). Using< 1000 copies/mL as a threshold for viral suppression, we identified 109092 individuals who had at least one instance of an elevated VL. A nonparametric (KML-Shape) and a model-based (LCMM) clustering technique were used to identify latent subgroups of longitudinal VL trajectories among these individuals. Results: Both the KML-Shape and LCMM clustering techniques identified five latent viral load trajectory subgroups. KML-Shape found majority of individuals' trajectories belonged to clusters that had a decreasing longitudinal VL trend (76.6% of individuals), while LCMM found a smaller proportion of individuals' trajectories belonged to clusters that had a decreasing longitudinal trend (52.5% of individuals). Most of the trajectory subgroups identified had long periods of low-level viremia. Conclusion: Although majority of individuals belonged to clusters that had downward trends, further research is needed to better understand factors contributing to membership of clusters that did not have a downward longitudinal trend. Understanding these factors may help in the development of targeted HIV prevention programs for these individuals. DA - 2020_ DB - OpenUCT DP - University of Cape Town KW - Epidemiology LK - https://open.uct.ac.za PY - 2020 T1 - Clustering of longitudinal viral loads in the Western Cape TI - Clustering of longitudinal viral loads in the Western Cape UR - http://hdl.handle.net/11427/32195 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/32195 | |
| dc.identifier.vancouvercitation | Arua EN. Clustering of longitudinal viral loads in the Western Cape. []. ,Faculty of Health Sciences ,Department of Public Health and Family Medicine, 2020 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/32195 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Public Health and Family Medicine | |
| dc.publisher.faculty | Faculty of Health Sciences | |
| dc.subject | Epidemiology | |
| dc.title | Clustering of longitudinal viral loads in the Western Cape | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MPH |