Department of Computing, Mathematical and Statistical Sciences

Permanent URI for this community

http://hdl.handle.net/11070/3407

Browse

Browsing Department of Computing, Mathematical and Statistical Sciences by Subject "Antiretroviral therapy"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A copula approach to sample selection modeling of treatment adherence and viral suppression among HIV patients on antiretroviral therapy (ART) in Namibia
    (University of Namibia, 2019) Nakaluudhe, Jason
    Namibia has a generalized human immunodeficiency virus (HIV) epidemic, with HIV mainly transmitted through heterosexual transmission. Although the number of people receiving ART has increased, the achievement of the 90-90-90 strategy on testing, treatment and suppression has not been evaluated. Moreover, examining factors associated with treatment adherence and viral suppression will assist in designing appropriate accelerated interventions. However, modelling treatment adherence and viral suppression may result in biased estimates if sample selection is ignored. The study fit a joint distributional model of ART treatment adherence and viral suppression, to adjust for sample selection bias among HIV patients on ART in Namibia, to examine the presence of tail dependence in sample selection bias, and investigate the factors associated with viral suppression, viral load and ART adherence. The study used two datasets; HIV data of patients, aged above 16 years, on antiretroviral therapy in Erongo region and the selected health facilities in Windhoek, Namibia. A Heckman-type selection analysis using copula were used on the two models: (i) ART adherence with viral suppression margins and (ii) ART adherence with viral load. The families of copulas i.e. Normal, Frank, FGM, AMH, Student-t and the 0, 90, 180 and 270 degrees rotated versions of Joe, Gumbel and Clayton, to capture dependence in the outcomes, were modelled and selected based on the lowest AIC and BIC. The results showed a strong negative correlation between adherence to ART treatment and viral load suppression. The results also showed the dependence structure between ART adherence and viral load margins. The results further showed that Frank and the 180 degrees rotated versions of Gumbel, and Clayton copulas were the best models. Antiretroviral therapy adherence with viral suppression and ART adherence with viral loads exhibit positive dependence structures, even though some demographic and clinical characteristics were not associated with ART adherence, viral suppression and viral load. Similar studies in the future need to consider socio-economic factors in addition to those considered in this study.
  • Thumbnail Image
    Item
    Dynamic models for time-varying outcomes: An application to the 2015-2017 patient cohort on antiretroviral therapy at Luderitz hospital, Namibia
    (University of Namibia, 2020) Gabriel, Lineekela
    Patients' adherence to a prescribed medication regimen is one of the most significant barriers to successful antiretroviral therapy (ART). In addition, adherence to ART is one of the key determinants of Human Immunodeficiency Virus (HIV) disease progression, while non-adherence severely compromises treatment effectiveness and leads to unsuppressed virus. The extent of the impact of poor adherence on resulting health measures is often unknown, and typical analyses ignore the time-varying nature of adherence. The main objective of this study was to model time-varying outcomes of patients, while accounting for data missingness and measurement error using dynamic models, with application to a cohort of 154 adult patients initiated on ART between January 2015 and December 2017 at the Luderitz hospital. The outcome variable of this study was viral load which was measured at scheduled follow-up visits of patients. Baseline CD4 count, baseline weight, age at start of ART and gender were the non-dynamic covariates which were measured at the ART initiation, while adherence to ART and weight at follow up were the dynamic covariates measured at follow up visits. This study used mixed effects model and Generalized Estimating Equations (GEE) to model longitudinally measured viral load as a function of the dynamic as well as non-dynamic covariates. To account for missingness in the outcome variable as well as potential measurement error in covariates, a Simulation Extrapolation Inverse Probability Weighted Generalized Estimating Equations (SWGEE) model that incorporates missing and measurement error was used to model the data. The study found that adherence was good in female patients as compared to male patients. Furthermore, the study found that patients with a good adherence rate achieved viral suppression within 12 months of treatment unlike non-adherent patients. In conclusion, viral load of patient’s on ART differ across the patients’ baseline demographic and clinical characteristics.