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Evaluating the Cost-Effectiveness of Pre-Exposure Prophylaxis (PrEP) and Its Impact on HIV-1 Transmission in South Africa

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  • Carel Pretorius
  • John Stover
  • Lori Bollinger
  • Nicolas Bacaër
  • Brian Williams

Abstract

Background: Mathematical modelers have given little attention to the question of how pre-exposure prophylaxis (PrEP) may impact on a generalized national HIV epidemic and its cost-effectiveness, in the context of control strategies such as condom use promotion and expanding ART programs. Methodology/Principal Findings: We use an age- and gender-structured model of the generalized HIV epidemic in South Africa to investigate the potential impact of PrEP in averting new infections. The model utilizes age-structured mortality, fertility, partnership and condom use data to model the spread of HIV and the shift of peak prevalence to older age groups. The model shows that universal PrEP coverage would have to be impractically high to have a significant effect on incidence reduction while ART coverage expands. PrEP targeted to 15–35-year-old women would avert 10%–25% (resp. 13%–28%) of infections in this group and 5%–12% (resp. 7%–16%) of all infections in the period 2014–2025 if baseline incidence is 0.5% per year at 2025 (resp. 0.8% per year at 2025). The cost would be $12,500–$20,000 per infection averted, depending on the level of ART coverage and baseline incidence. An optimistic scenario of 30%–60% PrEP coverage, efficacy of at least 90%, no behavior change among PrEP users and ART coverage less than three times its 2010 levels is required to achieve this result. Targeting PrEP to 25–35-year-old women (at highest risk of infection) improves impact and cost-effectiveness marginally. Relatively low levels of condom substitution (e.g., 30%) do not nullify the efficacy of PrEP, but reduces cost-effectiveness by 35%–40%. Conclusions/Significance: PrEP can avert as many as 30% of new infections in targeted age groups of women at highest risk of infection. The cost-effectiveness of PrEP relative to ART decreases rapidly as ART coverage increases beyond three times its coverage in 2010, after which the ART program would provide coverage to more than 65% of HIV+ individuals. To have a high relative cost-effective impact on reducing infections in generalized epidemics, PrEP must utilize a window of opportunity until ART has been scaled up beyond this level.

Suggested Citation

  • Carel Pretorius & John Stover & Lori Bollinger & Nicolas Bacaër & Brian Williams, 2010. "Evaluating the Cost-Effectiveness of Pre-Exposure Prophylaxis (PrEP) and Its Impact on HIV-1 Transmission in South Africa," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0013646
    DOI: 10.1371/journal.pone.0013646
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    References listed on IDEAS

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    1. Leigh F. Johnson & Rob Dorrington, 2006. "Modelling the demographic impact of HIV/AIDS in South Africa and the likely impact of interventions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 14(22), pages 541-574.
    2. Leigh Johnson & Rob Dorrington & Debbie Bradshaw & Victoria Pillay-Van Wyk & Thomas Rehle, 2009. "Sexual behaviour patterns in South Africa and their association with the spread of HIV: insights from a mathematical model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 21(11), pages 289-340.
    3. Ume L Abbas & Roy M Anderson & John W Mellors, 2007. "Potential Impact of Antiretroviral Chemoprophylaxis on HIV-1 Transmission in Resource-Limited Settings," PLOS ONE, Public Library of Science, vol. 2(9), pages 1-11, September.
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    2. Brooke E Nichols & Charles A B Boucher & Janneke H van Dijk & Phil E Thuma & Jan L Nouwen & Rob Baltussen & Janneke van de Wijgert & Peter M A Sloot & David A M C van de Vijver, 2013. "Cost-Effectiveness of Pre-Exposure Prophylaxis (PrEP) in Preventing HIV-1 Infections in Rural Zambia: A Modeling Study," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
    3. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    4. Ghayoori, Arash & Nagi, Rakesh, 2022. "A Markov model examining intervention effects on the HIV prevalence/incidence amongst the overall population," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    5. Tarun Bhatnagar & Tapati Dutta & John Stover & Sheela Godbole & Damodar Sahu & Kangusamy Boopathi & Shilpa Bembalkar & Kh Jitenkumar Singh & Rajat Goyal & Arvind Pandey & Sanjay M Mehendale, 2016. "Fitting HIV Prevalence 1981 Onwards for Three Indian States Using the Goals Model and the Estimation and Projection Package," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.
    6. Dobromir Dimitrov & Marie-Claude Boily & Elizabeth R Brown & Timothy B Hallett, 2013. "Analytic Review of Modeling Studies of ARV Based PrEP Interventions Reveals Strong Influence of Drug-Resistance Assumptions on the Population-Level Effectiveness," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-9, November.
    7. Sabina S Alistar & Douglas K Owens & Margaret L Brandeau, 2014. "Effectiveness and Cost Effectiveness of Oral Pre-Exposure Prophylaxis in a Portfolio of Prevention Programs for Injection Drug Users in Mixed HIV Epidemics," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.

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