ABSTRACT
Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL. Data from the HIV Incidence District Surveillance System (HIPSS), a longitudinal study undertaken between June 2014 to June 2016 among men and women aged 15−49 years in rural and peri-urban KwaZulu-Natal, South Africa, were analysed. A Bayesian geoadditive regression model which includes a spatial effect for a small enumeration area was applied using an integrated nested Laplace approximation (INLA) function while accounting for unobserved factors, non-linear effects of selected continuous variables, and spatial autocorrelation. The prevalence of unsuppressed HIV viral load was 46.1% [95% CI: 44.3−47.8]. Predictors of unsuppressed HIV viral load were incomplete high school education, being away from home for more than a month, alcohol consumption, no prior knowledge of HIV status, not ever tested for HIV, not on antiretroviral therapy (ART), on tuberculosis (TB) medication, having two or more sexual partners in the last 12 months, and having a CD4 cell count of <350 cells/µL. A positive non-linear effect of age, household size, and the number of lifetime HIV tests was identified. The higher-risk pattern of unsuppressed HIV viral load occurred in the northwest and northeast of the study area. Identifying predictors of unsuppressed viral load in a localized geographic area and information from spatial risk maps are important for targeted prevention and treatment programs to reduce the transmission of HIV.
ABSTRACT
BACKGROUND: Although, Tuberculosis (TB) is curable if the treatment is adhered to and completed it is still a major cause of death globally including South Africa. The success rate for TB treatment was 77.2% in 2014, of which more than 37 000 lives were lost because of it in South Africa. Several studies have been carried out on this subject, but the difference between the present study and the previous work done is the methodology proposed to establish the determinants of anti-TB medication compliance. Understanding the determinant of anti-TB medication compliance will help the policymakers on the appropriate decision to reduce the menace of the disease. METHODS: In this study, we proposed logistic regression to a sample of individuals taken from the National Income Dynamics Survey data that self-reported to have been TB diagnosed. A comparison of the classical logistic regression and parametric bootstrap estimation methods was done for this data to determine the model that best describes the data. RESULTS: The results obtained from the two methods were similar and identified gender, language, alcohol, English literacy, belief in religion and household SES as the determinants of TB patients on medication. The standard errors for the bootstrap logistic model were bigger than the standard errors of the classical model. CONCLUSION: We conclude that the classical model is better and for this scenario, there was no need to resample. The outcome of this study supports the existing findings that controlling the social and economic determinants of health will help eradicate TB.
