Investigating spatial disparities in high-risk women and HIV infections using generalized additive models: Results from a cohort of South African women. (August 2019)

• Record Type: Journal Article
• Authors: Wand, Handan; Reddy, Tarylee; Ramjee, Gita
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 30(2019)
• Publication Date: 2019-08
• Subjects: HIV -- Spatial clustering -- Generalized additive models -- Community-level characteristics -- South Africa; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2019.100283
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: South Africa has still has the highest prevalence and incidence of HIV in the world. The complex and multifactorial nature of the epidemic linked to a range of individual, community and geographical level data sources. High HIV infection rates were spatially clustered and overlapped with the areas where high-risk women lived. These areas may represent the priority populations. Aligned with the global recommendations, results will guide the policy makers to allocate scarce funding resources. Abstract: Objective: We identified the geographical clustering of HIV as well as those at highest risk of infection using a decade long data (2002–2012) from KwaZulu-Natal, South Africa. Methods: A total of 5, 776 women who enrolled in several HIV prevention trials were included in the study. Geo-coded individual-level data were linked to the community-level characteristics using the South African Census. High-risk women were identified using a risk scoring algorithm. Generalized additive models were used to identify the significant geographical clustering of high-risk women and HIV. Results: Overall, 60% of the women were classified as high risk of HIV. HIV infection rates were estimated as high as 10 to 15 per 100 person year. Areas with high rates of HIV infections were spatially clustered and overlapped particularly in the Northern part of Durban. Conclusion: Targeting multifactorial and complex nature of the epidemic is urgently needed to identify the "high transmission" areas.