Handan Wand1, Tarylee Reddy2, Gita Ramjee3. 1. Kirby Institute, University of New South Wales, Kensington 2052, New South Wales, Australia. Electronic address: hwand@kirby.unsw.edu.au. 2. Biostatistics Unit, South African Medical Research Council, Durban, Kwazulu-Natal, South Africa. Electronic address: tarylee.reddy@mrc.ac.za. 3. HIV Prevention Research Unit, South African Medical Research Council, Westville, 3630 KwaZulu-Natal, South Africa; Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK; Department of Global Health, School of Medicine, University of Washington, United States. Electronic address: gita.ramjee@mrc.ac.za.
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.
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.
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