https://doi.org/10.4081/gh.2022.1084
Assessing spatial patterns of HIV prevalence and interventions in semi-urban settings in South Africa. Implications for spatially targeted interventions
Submitted: 28 February 2022
Accepted: 19 June 2022
Published: 29 August 2022
Accepted: 19 June 2022
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Supplementary 1: 62
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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Equitable allocation of resources targeting the human immunodeficiency virus (HIV) at the local level requires focusing interventions in areas of the greatest need. Understanding the geographical variation in the HIV epidemic and uptake of selected HIV prevention and treatment programmes are necessary to identify such areas. Individual-level HIV data were obtained from a 2012 national HIV survey in South Africa. Spatial regression models on each outcome measure (HIV infection, sub-optimal condom use or non-anti-retroviral treatment (ART) adjusted for spatial random effects at the ward level were fitted using WINBUGS software. In addition, ward-level data was utilized to estimate condom use coverage and ART initiation rates which were obtained from routinely collected data in 2012. Ordinary Kriging was used to produce smoothed maps of HIV infection, condom use coverage and ART initiation rates. HIV infection was associated with individuals undertaking tertiary education [posterior odds ratio (POR): 19.53; 95% credible intervals (CrI): 3.22- 84.93]. Sub-optimal condom use increased with age (POR: 1.09; 95%CrI: 1.06-1.11) and was associated with being married (POR: 4.14; 95%CrI: 1.23-4.28). Non-ART use was associated with being married (POR: 6.79; 95%CrI: 1.43-22.43). There were clusters with high HIV infection, sub-optimal condom use, and non- ART use in Ekurhuleni, an urban and semi-urban district in Gauteng province, South Africa. Findings show the need for expanding condom programmes and/or strengthening other HIV prevention programmes such as pre-exposure prophylaxis and encouraging sustained engagement in HIV care and treatment in the identified areas with the greatest need in Ekurhuleni Metropolitan Municipality.
Aral SO, Torrone E, Bernstein K, 2015. Geographical targeting to improve progression through the sexually transmitted infection/HIV treatment continua in different populations. Curr Opin HIV AIDS 10:477-82.
DOI: https://doi.org/10.1097/COH.0000000000000195
Ayiga N, 2012. Rates and predictors of consistent condom-use by people living with HIV/AIDS on antiretroviral treatment in Uganda. J Health Popul Nutr 30:270-80.
DOI: https://doi.org/10.3329/jhpn.v30i3.12290
Bekker L-G, Hosek S, 2015. HIV and adolescents: focus on young key populations. J Int AIDS Soci 18:20076.
DOI: https://doi.org/10.7448/IAS.18.2.20076
Birdthistle I, Tanton C, Tomita A, De Graaf K, Schaffnit SB, Tanser F, Slaymaker E, 2019. Recent levels and trends in HIV incidence rates among adolescent girls and young women in ten high-prevalence African countries: a systematic review and meta-analysis. Lancet Glob Health 7:e1521-40.
DOI: https://doi.org/10.1016/S2214-109X(19)30410-3
Boyda DC, Holzman SB, Berman A, Grabowski MK, Chang LW, 2019. Geographic information systems, spatial analysis, and HIV in Africa: a scoping review. PLoS One 14:e0216388.
DOI: https://doi.org/10.1371/journal.pone.0216388
Chib S, Greenberg E, 1995. Understanding the metropolis-hastings algorithm. Am Stat 49:327-35.
DOI: https://doi.org/10.1080/00031305.1995.10476177
Chimoyi LA, Musenge E, 2014. Spatial analysis of factors associated with HIV infection among young people in Uganda, 2011. BMC Public Health 14:1-11.
DOI: https://doi.org/10.1186/1471-2458-14-555
Coburn BJ, Okano JT, Blower S, 2017. Using geospatial mapping to design HIV elimination strategies for sub-Saharan Africa. Sci Transl Med 9.
DOI: https://doi.org/10.1126/scitranslmed.aag0019
Cressie N, 2015. Statistics for spatial data. John Wiley & Sons, New York, NY, USA.
Cuadros DF, Awad SF, Abu-Raddad LJ, 2013. Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa. Int J Health Geogr 12:28.
DOI: https://doi.org/10.1186/1476-072X-12-28
Cuadros DF, Li J, Branscum AJ, Akullian A, Jia P, Mziray EN, Tanser F, 2017. Mapping the spatial variability of HIV infection in Sub-Saharan Africa: effective information for localized HIV prevention and control. Sci Rep 7:9093.
DOI: https://doi.org/10.1038/s41598-017-09464-y
Cuadros DF, Sartorius B, Hall C, Akullian A, Barnighausen T, Tanser F, 2018. Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data. Int J Health Geogr 17:27.
DOI: https://doi.org/10.1186/s12942-018-0146-8
Dellar RC, Dlamini S, Karim QA, 2015. Adolescent girls and young women: key populations for HIV epidemic control. J Int AIDS Soc 18:19408.
DOI: https://doi.org/10.7448/IAS.18.2.19408
Environmental Systems Research Institute, 2019. ArcGIS Release. 10.7.1 ed. ESRI, Redlands, CA, USA.
Gaal B, Vassanyi I, Kozmann G, 2005. A novel artificial intelligence method for weekly dietary menu planning. Methods Inf Med 44:655-64.
DOI: https://doi.org/10.1055/s-0038-1634022
Grabowski MK, Serwadda DM, Gray RH, Nakigozi G, Kigozi G, Kagaayi J, Ssekubugu R, Nalugoda F, Lessler J, Lutalo T, Galiwango RM, Makumbi F, Kong X, Kabatesi D, Alamo ST, Wiersma S, Sewankambo NK, Tobian AaR, Laeyendecker O, Quinn TC, Reynolds SJ, Wawer MJ, Chang LW, Rakai P, 2017. HIV prevention efforts and incidence of HIV in Uganda. N Engl J Med 377:2154-66.
DOI: https://doi.org/10.1056/NEJMoa1702150
Granich RM, Gilks CF, Dye C, De Cock KM, Williams BG, 2009. Universal voluntary HIV testing with immediate antiretroviral therapy as a strategy for elimination of HIV transmission: a mathematical model. Lancet 373:48-57.
DOI: https://doi.org/10.1016/S0140-6736(08)61697-9
Haffejee F, Koorbanally D, Corona R, 2018. Condom use among South African University students in the Province of KwaZulu-Natal. Sexual Cult 22:1279-89.
DOI: https://doi.org/10.1007/s12119-018-9523-5
Hallman K, 2005. Gendered socioeconomic conditions and HIV risk behaviours among young people in South Africa. Afr J AIDS Res 4:37-50.
DOI: https://doi.org/10.2989/16085900509490340
Hrycej T, 1990. Gibbs sampling in Bayesian networks. Artif Intell 46:351-63.
DOI: https://doi.org/10.1016/0004-3702(90)90020-Z
Human Sciences Research Council, 2018. The fifth South African national HIV prevalence, incidence, behaviour and communication survey, 2017 (sabssm v1). HSRCs Pretoria, South Africa. Available from: http://www.hsrc.ac.za/uploads/pageContent/9225/SABSSMV_Impact_Assessment_Summary_ZA_ADS_cleared1%20(002).pdf Accessed: 08/11/2018.
Jama Shai N, Jewkes R, Levin J, Dunkle K, Nduna M, 2010. Factors associated with consistent condom use among rural young women in South Africa. AIDS Care 22:1379-85.
DOI: https://doi.org/10.1080/09540121003758465
Lall P, Lim SH, Khairuddin N, Kamarulzaman A, 2015. Review: an urgent need for research on factors impacting adherence to and retention in care among HIV-positive youth and adolescents from key populations. J Int AIDS Soc 18:19393.
DOI: https://doi.org/10.7448/IAS.18.2.19393
Magege A, Grimwood A, Fatti G. High priority district strategy to actualise the NSP by 2011 and beyond. 4th SA AIDS Conference, March 31st - April 3rd 2009, Durban.
Manda S, Haushona N, Bergquist R, 2020. A scoping review of spatial analysis approaches using health survey data in Sub-Saharan Africa. Int J Environ Res Public Health 17.
DOI: https://doi.org/10.3390/ijerph17093070
Muchiri E, Odimegwu C, De Wet N, 2017. HIV risk perception and consistency in condom use among adolescents and young adults in urban Cape Town, South Africa: a cumulative risk analysis. Southern Afr J Infect Dis 32:105-110.
DOI: https://doi.org/10.4102/sajid.v32i3.48
National Department of Health, 2017. South Africa's National Strategic Plan for HIV, TB and STIs 2017-2022. SANAC, Pretoria, South Africa. Available from: http://sanac.org.za/wp-content/uploads/2017/05/NSP_FullDocument_FINAL.pdf Accessed: 15/01/2018.
Nutor JJ, Duah HO, Agbadi P, Duodu PA, Gondwe KW, 2020. Spatial analysis of factors associated with HIV infection in Malawi: indicators for effective prevention. BMC Public Health 20:1167.
DOI: https://doi.org/10.1186/s12889-020-09278-0
Oliver MA, Webster R, 1990. Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4:313-23.
DOI: https://doi.org/10.1080/02693799008941549
Ramjee G, Sartorius B, Morris N, Tanser F, 2019. A decade of sustained geographic spread of HIV infections among women in Durban, South Africa. BMC Infect Dis 19:500.
DOI: https://doi.org/10.1186/s12879-019-4080-6
Schaefer R, Gregson S, Takaruza A, Rhead R, Masoka T, Schur N, Anderson SJ & Nyamukapa C 2017. Spatial patterns of HIV prevalence and service use in East Zimbabwe: implications for future targeting of interventions. J Int AIDS Soc 20:21409.
DOI: https://doi.org/10.7448/IAS.20.1.21409
Simbayi L, Zuma K, Moyo S, Marinda E, Mabaso M, Ramlagan S, North A, Mohlabane N, Dietrich C, Naidoo I, 2019. South African national HIV prevalence, incidence , behaviour and communication survey, 2017. Human Sciences Research Council, Pretoria, South Africa. Available from: http://www.hsrc.ac.za/en/research-outputs/view/9881 Accessed: 02/11/2020.
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A, 2002. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol 64:583-639.
DOI: https://doi.org/10.1111/1467-9868.00353
Tanser F, De Oliveira T, Maheu-Giroux M, Barnighausen T, 2014. Concentrated HIV subepidemics in generalized epidemic settings. Curr Opin HIV AIDS 9:115-25.
DOI: https://doi.org/10.1097/COH.0000000000000034
Versteeg M, Murray M, 2008. Condom use as part of the wider HIV prevention strategy: experiences from communities in the North West Province, South Africa. Sahara J 5:83-93.
DOI: https://doi.org/10.1080/17290376.2008.9724905
Wilson DP, 2012. HIV treatment as prevention: natural experiments highlight limits of antiretroviral treatment as HIV prevention. PLoS Med 9:e1001231.
DOI: https://doi.org/10.1371/journal.pmed.1001231
Zanoni BC, Sibaya T, Cairns C, Haberer JE, 2019. Barriers to retention in care are overcome by adolescent-friendly services for adolescents living with HIV in South Africa: a qualitative analysis. AIDS Behav 23:957-65.
DOI: https://doi.org/10.1007/s10461-018-2352-6
Zulu LC, Kalipeni E, Johannes E, 2014. Analyzing spatial clustering and the spatiotemporal nature and trends of HIV/AIDS prevalence using GIS: the case of Malawi, 1994-2010. BMC Infect Dis 14:285.
DOI: https://doi.org/10.1186/1471-2334-14-285
How to Cite
Chimoyi, L., Matsena-Zingoni, Z. ., Charalambous, S., Marinda, E., Manda, S., & Musenge, E. (2022). Assessing spatial patterns of HIV prevalence and interventions in semi-urban settings in South Africa. Implications for spatially targeted interventions. Geospatial Health, 17(2). https://doi.org/10.4081/gh.2022.1084
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