TY - JOUR
T1 - Estimating the contribution of key populations towards HIV transmission in South Africa
AU - Stone, Jack
AU - Mukandavire, Christinah
AU - Boily, Marie Claude
AU - Fraser, Hannah
AU - Mishra, Sharmistha
AU - Schwartz, Sheree
AU - Rao, Amrita
AU - Looker, Katharine J.
AU - Quaife, Matthew
AU - Terris-Prestholt, Fern
AU - Marr, Alexander
AU - Lane, Tim
AU - Coetzee, Jenny
AU - Gray, Glenda
AU - Otwombe, Kennedy
AU - Milovanovic, Minja
AU - Hausler, Harry
AU - Young, Katherine
AU - Mcingana, Mfezi
AU - Ncedani, Manezi
AU - Puren, Adrian
AU - Hunt, Gillian
AU - Kose, Zamakayise
AU - Phaswana-Mafuya, Nancy
AU - Baral, Stefan
AU - Vickerman, Peter
N1 - Funding Information:
This publication resulted in part from research funded by a supplement to the Johns Hopkins University Center for AIDS Research, an NIH funded programme (P30AI094189) and R01NR016650 with support specifically from the Office of AIDS Research (OAR) and the National Institutes of Nursing Research. The programme also received support from Linkages across the Continuum of HIV Services for Key Populations Affected by HIV project (LINKAGES, Cooperative Agreement AID‐OAA‐A‐14‐00045) and the parent study HIV Prevention 2.0 (HP2): Achieving an AIDS‐Free Generation in Senegal (AID‐OAA‐A‐13‐00089). The supplement, LINKAGES and HP2 received support from the United States Agency for International Development (USAID) and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). PV also acknowledges support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. SM is supported by a Canadian Institutes of Health and Ontario HIV Treatment Network Research New Investigator Award. JC acknowledges support received through the University of California San Diego, an NIH funded CFAR award (P30 AI036214), funding from the South African Medical Research Council and the Wellcome Trust. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
Funding Information:
None declared. This publication resulted in part from research funded by a supplement to the Johns Hopkins University Center for AIDS Research, an NIH funded programme (P30AI094189) and R01NR016650 with support specifically from the Office of AIDS Research (OAR) and the National Institutes of Nursing Research. The programme also received support from Linkages across the Continuum of HIV Services for Key Populations Affected by HIV project (LINKAGES, Cooperative Agreement AID-OAA-A-14-00045) and the parent study HIV Prevention 2.0 (HP2): Achieving an AIDS-Free Generation in Senegal (AID-OAA-A-13-00089). The supplement, LINKAGES and HP2 received support from the United States Agency for International Development (USAID) and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). PV also acknowledges support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. SM is supported by a Canadian Institutes of Health and Ontario HIV Treatment Network Research New Investigator Award. JC acknowledges support received through the University of California San Diego, an NIH funded CFAR award (P30 AI036214), funding from the South African Medical Research Council and the Wellcome Trust. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. This publication resulted in part from research funded by a supplement to the Johns Hopkins University Center for AIDS Research, an NIH funded programme (P30AI094189) and R01NR016650 with support specifically from the Office of AIDS Research (OAR) and the National Institutes of Nursing Research. The programme also received support from Linkages across the Continuum of HIV Services for Key Populations Affected by HIV project (LINKAGES, Cooperative Agreement AID-OAA-A-14-00045) and the parent study HIV Prevention 2.0 (HP2): Achieving an AIDS-Free Generation in Senegal (AID-OAA-A-13-00089). The supplement, LINKAGES and HP2 received support from the United States Agency for International Development (USAID) and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR). PV also acknowledges support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol. SM is supported by a Canadian Institutes of Health and Ontario HIV Treatment Network Research New Investigator Award. JC acknowledges support received through the University of California San Diego, an NIH funded CFAR award (P30 AI036214), funding from the South African Medical Research Council and the Wellcome Trust. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.
Publisher Copyright:
© 2021 The Authors. Journal of the International AIDS Society published by John Wiley & Sons Ltd on behalf of the International AIDS Society.
PY - 2021/1
Y1 - 2021/1
N2 - Introduction: In generalized epidemic settings, there is insufficient understanding of how the unmet HIV prevention and treatment needs of key populations (KPs), such as female sex workers (FSWs) and men who have sex with men (MSM), contribute to HIV transmission. In such settings, it is typically assumed that HIV transmission is driven by the general population. We estimated the contribution of commercial sex, sex between men, and other heterosexual partnerships to HIV transmission in South Africa (SA). Methods: We developed the “Key-Pop Model”; a dynamic transmission model of HIV among FSWs, their clients, MSM, and the broader population in SA. The model was parameterized and calibrated using demographic, behavioural and epidemiological data from national household surveys and KP surveys. We estimated the contribution of commercial sex, sex between men and sex among heterosexual partnerships of different sub-groups to HIV transmission over 2010 to 2019. We also estimated the efficiency (HIV infections averted per person-year of intervention) and prevented fraction (% IA) over 10-years from scaling-up ART (to 81% coverage) in different sub-populations from 2020. Results: Sex between FSWs and their paying clients, and between clients with their non-paying partners contributed 6.9% (95% credibility interval 4.5% to 9.3%) and 41.9% (35.1% to 53.2%) of new HIV infections in SA over 2010 to 2019 respectively. Sex between low-risk groups contributed 59.7% (47.6% to 68.5%), sex between men contributed 5.3% (2.3% to 14.1%) and sex between MSM and their female partners contributed 3.7% (1.6% to 9.8%). Going forward, the largest population-level impact on HIV transmission can be achieved from scaling up ART to clients of FSWs (% IA = 18.2% (14.0% to 24.4%) or low-risk individuals (% IA = 20.6% (14.7 to 27.5) over 2020 to 2030), with ART scale-up among KPs being most efficient. Conclusions: Clients of FSWs play a fundamental role in HIV transmission in SA. Addressing the HIV prevention and treatment needs of KPs in generalized HIV epidemics is central to a comprehensive HIV response.
AB - Introduction: In generalized epidemic settings, there is insufficient understanding of how the unmet HIV prevention and treatment needs of key populations (KPs), such as female sex workers (FSWs) and men who have sex with men (MSM), contribute to HIV transmission. In such settings, it is typically assumed that HIV transmission is driven by the general population. We estimated the contribution of commercial sex, sex between men, and other heterosexual partnerships to HIV transmission in South Africa (SA). Methods: We developed the “Key-Pop Model”; a dynamic transmission model of HIV among FSWs, their clients, MSM, and the broader population in SA. The model was parameterized and calibrated using demographic, behavioural and epidemiological data from national household surveys and KP surveys. We estimated the contribution of commercial sex, sex between men and sex among heterosexual partnerships of different sub-groups to HIV transmission over 2010 to 2019. We also estimated the efficiency (HIV infections averted per person-year of intervention) and prevented fraction (% IA) over 10-years from scaling-up ART (to 81% coverage) in different sub-populations from 2020. Results: Sex between FSWs and their paying clients, and between clients with their non-paying partners contributed 6.9% (95% credibility interval 4.5% to 9.3%) and 41.9% (35.1% to 53.2%) of new HIV infections in SA over 2010 to 2019 respectively. Sex between low-risk groups contributed 59.7% (47.6% to 68.5%), sex between men contributed 5.3% (2.3% to 14.1%) and sex between MSM and their female partners contributed 3.7% (1.6% to 9.8%). Going forward, the largest population-level impact on HIV transmission can be achieved from scaling up ART to clients of FSWs (% IA = 18.2% (14.0% to 24.4%) or low-risk individuals (% IA = 20.6% (14.7 to 27.5) over 2020 to 2030), with ART scale-up among KPs being most efficient. Conclusions: Clients of FSWs play a fundamental role in HIV transmission in SA. Addressing the HIV prevention and treatment needs of KPs in generalized HIV epidemics is central to a comprehensive HIV response.
KW - clients
KW - female sex workers
KW - key populations
KW - mathematical modelling
KW - men who have sex with men
KW - population attributable fraction
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U2 - 10.1002/jia2.25650
DO - 10.1002/jia2.25650
M3 - Article
C2 - 33533115
AN - SCOPUS:85100441113
SN - 1758-2652
VL - 24
JO - Journal of the International AIDS Society
JF - Journal of the International AIDS Society
IS - 1
M1 - e25650
ER -