Browsing by Author "Maqutu, Dikokole."

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Determinants of optimal adherence over time to antiretroviral therapy amongst HIV positive adults in South Africa : a longitudinal study.
(Springer Verlag., 2011) Maqutu, Dikokole.; Zewotir, Temesgen Tenaw.; North, Delia Elizabeth.; Grobler, Anna Christina.
Highly active antiretroviral therapy (HAART) requires strict adherence to achieve optimal clinical and survival benefits. A study was done to explore the factors affecting HAART adherence among HIV positive adults by reviewing routinely collected patient information in the Centre for the AIDS Programme of Research in South Africa’s (CAPRISA) AIDS Treatment Programme. Records of 688 patients enrolled between 2004 and 2006 were analysed. Patients were considered adherent if they had taken at least 95% of their prescribed drugs. Generalized estimating equations were used to analyse the data. The results showed that HAART adherence increased over time, however, the rate of increase differed by some of the socio-demographic and behavioural characteristics of the patients. For instance, HAART adherence increased in both urban and rural treatment sites over time, but the rate of increase was higher in the rural site. This helped identify sub-populations, such as the urban population, that required ongoing adherence counseling.
Factors affecting first-month adherence to antiretroviral therapy among HIV-positive adults in South Africa.
(Taylor & Francis co-published with NISC., 2010) Maqutu, Dikokole.; Zewotir, Temesgen Tenaw.; North, Delia Elizabeth.; Naidoo, Kogieleum.; Grobler, Anna Christina.
This study explores the influence of baseline factors on first-month adherence to highly active antiretroviral therapy (HAART) among adults. The study design involved a review of routinely collected patient information in the CAPRISA AIDS Treatment (CAT) programme, at a rural and an urban clinic in KwaZulu-Natal Province, South Africa. The records of 688 patients enrolled in the CAT programme between June 2004 and September 2006 were analysed. Adherence was calculated from pharmacy records (pill counts) and patients were considered adherent if they had taken at least 95% of their prescribed drugs. Logistic regression was used to analyse the data and account for confounding factors. During the first month of therapy, 79% of the patients were adherent to HAART. HAART adherence was negatively associated with a higher baseline CD4 count. Women had better adherence if they attended voluntarily testing and counselling or if they had taken an HIV test because they were unwell, while men had higher adherence if they were tested due to perceived risk of HIV infection. HAART adherence was positively associated with higher age among patients who possessed cell phones and among patients who provided a source of income in the urban setting, but not in the rural setting. Though long-term data from this cohort is required to fully evaluate the impact of non-adherence in the first month of treatment, this study identifies specific groups of patients at higher risk for whom adherence counselling should be targeted and tailored. For example, first-month HAART adherence can be improved by targeting patients initiated on treatment with a high CD4 count.
Statistical methods for longitudinal binary data structure with applications to antiretroviral medication adherence.
(2010) Maqutu, Dikokole.; Zewotir, Temesgen Tenaw.
Longitudinal data tend to be correlated and hence posing a challenge in the analysis since the correlation has to be accounted for to obtain valid inference. We study various statistical methods for such correlated longitudinal binary responses. These models can be grouped into five model families, namely, marginal, subject-specific, transition, joint and semi-parametric models. Each one of the models has its own strengths and weaknesses. Application of these models is carried out by analyzing data on patient’s adherence status to highly active antiretroviral therapy (HAART). One other complicating issue with the HAART adherence data is missingness. Although some of the models are flexible in handling missing data, they make certain assumptions about missing data mechanisms, the most restrictive being missing completely at random (MCAR). The test for MCAR revealed that dropout did not depend on the previous outcome. A logistic regression model was used to identify predictors for the patients’ first month’s adherence status. A marginal model was then fitted using generalized estimating equations (GEE) to identify predictors of long-term HAART adherence. This provided marginal population-based estimates, which are important for public health perspective. We further explored the subject’s specific effects that are unique to a particular individual by fitting a generalized linear mixed model (GLMM). The GLMM was also used to assess the association structure of the data. To assess whether the current optimal adherence status of a patient depended on the previous adherence measurements (history) in addition to the explanatory variables, a transition model was fitted. Moreover, a joint modeling approach was used to investigate the joint effect of the predictor variables on both HAART adherence status of patients and duration between successive visits. Assessing the association between the two outcomes was also of interest. Furthermore, longitudinal trajectories of observed data may be very complex especially when dealing with practical applications and as such, parametric statistical models may not be flexible enough to capture the main features of the longitudinal profiles, and so a semiparametric approach was adopted. Specifically, generalized additive mixed models were used to model the effect of time as well as interactions associated with time non-parametrically.