In populations where most subjects know their HIV status, population-based prevalence HIV estimates can be heavily biased due to high rates of non-response to HIV testing. Inverse probability weighting could potentially be used to correct for non-response to HIV testing in order to derive sub-national level HIV statistics, especially where the data at these levels are sparse. Its usefulness can be enhanced by incorporating antenatal clinics’ HIV data, often the only source of HIV prevalence.
Disease mapping models are used in spatial epidemiologic studies to investigate the causes and distributions of diseases. Most of the studies looking at mapping of health problems in the Sub-Saharan African region have concentrated on using univariate spatial models. However, there is a need to use and apply joint mapping models to measure co-morbidities of common illnesses in the region. This article aims to show the utility of joint mapping models in estimating co-morbidities in two important health problems in South Africa: HIV and Syphilis, and vascular diseases.