Doctoral Degrees (Epidemiology and Biostatistics)

Permanent URI for this collection

https://scholar.sun.ac.za/handle/10019.1/100763

Browse

Browsing Doctoral Degrees (Epidemiology and Biostatistics) by Subject "Highly active antiretroviral therapy -- Africa, Sub-Saharan"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    The effects of longitudinal HIV viral load exposure on immune outcomes, mortality, and opportunistic infections in people on ART in sub-Saharan Africa
    (Stellenbosch : Stellenbosch University, 2017-12) Sempa, Joseph Bukulu; Nieuwoudt, Martin; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Global Health. Epidemiology and Biostatistics.
    ENGLISH SUMMARY : Introduction: Longitudinal viral load monitoring is used as a cross-sectional marker for treatment failure in HIV infected people receiving antiretroviral therapy. Cumulative viral load, as quantified by area under the viral load curve during combination antiretroviral therapy, has been correlated with treatment outcomes in studies outside, but not within, sub-Saharan Africa. We investigate the effects of exposure to longitudinal viral load on, the incidence of opportunistic infections, mortality and immune recovery in local, previously combination antiretroviral therapy naïve, cohorts. Further, we systematically review statistically derived immune response models and use this to define priors for Bayesian models for application on a previously undescribed treatment cohort. Methods: We analyze data from the Infectious Diseases Institute (IDI) cohort, Kampala-Uganda, and the Antiretroviral Clinic at Tshwane District Hospital in Gauteng-South Africa. For the systematic review, we use ‘Preferred Reporting Items for Systematic Review and Meta- Analyses’ guidelines. We also compare cumulative viral load as numerically estimated using two methods: area under the viral load curve, which is then log-transformed, named, ‘untransformed cumulative viral load’; and area under the log-transformed viral load curve, above the kit-based detection limit of 400 copies/mL, named, ‘transformed cumulative viral load’. We use Cox Proportional Hazards and Bayesian Generalized Mixed Effects to define treatment outcome models. Results: In the IDI cohort most recent viral load, not cumulative viral load, is associated with a 1.34-fold (95% confidence interval: 1.12, 1.61) increase in the risk of opportunistic infections. Transformed, not untransformed, cumulative viral load is associated with mortality and immune response. Each log10 copy-yr/mL increase corresponds to a 1.63-fold (95% confidence interval: 1.02, 2.60) increase in risk of mortality. Systematic review of immune response statistical models also reveals many differences in the number and type of variables adjusted-for, variable transformations and scales and scant details regarding the modelling methods employed. In the Tshwane cohort, using Bayesian methods, for the slope of longitudinal CD4 counts, each log10 copy-yr/mL increase cumulative viral load corresponds to a mean annual CD4 count decrease of -19.5 cells/μL (95% credible interval: -28.34, -10.72). Further, in the asymptote model, each log10 copy-yr/mL increase reduced the odds of having a CD4 count ≥500 cells/μL to 0.42 (95% credible interval: 0.242, 0.724). Modelling inherently variable absolute CD4 count using a Student’s t-distribution produced better fits than assuming a Gaussian normal distribution. Discussion: Transformed cumulative viral load is associated with both mortality and long-term immune response, while most recent viral load is associated with incidence of opportunistic infections. This thesis emphasizes the need for the review of existing literature prior to any statistical analyses, so that more comparable and robust statistical models than have been available to date will be constructed. In particular, comparing immunological outcomes (CD4 counts), statistical models for sub-Saharan African cohorts would benefit from the application of more uniform modelling techniques. Adjusting for transformed cumulative viral load and the use of appropriate distributional assumptions, improves the modelling of immune response to antiretroviral therapy. Future statistical immune response models would benefit from the use of Bayesian methods owing to their flexibility in the selection of prior distributions and hierarchical model designs.