Spatial modelling of the relative risk of HIV/AIDS and associated risk factors in South Africa using integrated nested Laplace approximations

The coalescence of Human Immunodeficiency Virus (HIV), Tuberculosis (TB), and substance abuse in South Africa presents a multifaceted public health challenge. Individually catastrophic, these diseases create a synergistic relationship, intensifying morbidity and mortality rates. This study, therefore, aims to estimate HIV prevalence using TB risk factors, socioeconomic issues, and substance abuse in South Africa by leveraging Bayesian spatial modelling techniques. The study used Integrated Nested Laplace Approximation (INLA) to model the geographic distribution of HIV risk. Furthermore, a Local Indicator of Spatial Autocorrelation (LISA) technique was employed to identify the clustering pattern of the risk surface derived using INLA. Spatial predictors included condom use, the proportion of whites and blacks, tuberculosis cases, and needle sharing. We found a significant spatial disparity in HIV risk, with some districts exceeding an RR of 11. However, nearly 35% of districts had a reduced risk of RR