Statistical models to analyse a baseline survey on rural KwaZulu-Natal adults’ HIV prevalence and associated risk factors.

Abstract

South Africa is at the global epicentre of the HIV-AIDS pandemic. Though there has been an increase in prevention and control measures that has led to a significant reduction in HIV-AIDS mortality rates globally, South Africa has experienced a high share of the HIV burden. HIV-AIDS imposes a substantial economic burden on both individuals and governments. It has had a considerable effect on poverty by affecting potentially economically active citizens who would otherwise have entered the workforce and contributed to the local and national economy. This has hindered economic growth and development in South Africa. The 2016 UNAIDS Gap Report estimates that in 2015 there were seven million people living with HIV in South Africa and that this resulted in 180,000 AIDS related deaths in the same year. The same year saw an unprecedented 380,000 new reported infections. The prevalence of HIV-AIDS in South Africa remains high at 19.2% among the general population. This study was an investigation into the determinants of HIV in adults in the age group 15-49 years. The study used the HIV Incidence Provincial Surveillance System (HIPSS) to collect data between June 2014 and June 2015. The final data set comprised 9,804 observations and consisted of explanatory variables pertaining to individuals’ socio-economic, socio-demographic and behavioural circumstances. The response variable was binary indicating whether a participant tested positive or negative for HIV. Incorporating survey weights into the data owing to the complex sample design, necessitated the use multilevel regression procedures. To this end, survey logistic regression and the generalised linear mixed models were employed. The results emanating from these models revealed that factors encompassing socioi economic, demographic and selected behavioural characteristics were significantly associated with HIV prevalence in the study location. In some instances, it is possible that households in close proximity exhibit some similarities with the inevitable result of spatial autocorrelation requiring the use of geographically weighted regression techniques able to account for spatial autocorrelation. The application of a spatial multilevel model showed that the influence between households in close proximity is greater than between those further away, a phenomenon that would be ignored in conventional multilevel models.