Statistical models to analyse a baseline survey on rural KwaZulu-Natal adults’ HIV prevalence and associated risk factors.
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Date
2020
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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.
Description
Masters Degree. University of KwaZulu-Natal, Durban.