Optimism regarding prospects for eliminating HIV by expanding antiretroviral treatment has been emboldened in part by projections from several mathematical modeling studies. Drawing from a detailed empirical assessment of rates of progression through the entire HIV care cascade, we quantify for the first time the extent to which models may overestimate health benefits from policy changes when they fail to incorporate a realistic understanding of the cascade.
Rural KwaZulu-Natal, South Africa.
We estimated rates of progression through stages of the HIV treatment cascade using data from a longitudinal population-based HIV surveillance system in rural KwaZulu-Natal. Incorporating empirical estimates in a mathematical model of HIV progression, infection transmission, and care, we estimated life expectancy and secondary infections averted under a range of treatment scale-up scenarios reflecting expanding treatment eligibility thresholds. We compared the results with those implied by the conventional assumptions that have been commonly adopted by existing models.
Survival gains from expanding the treatment eligibility threshold from CD4 350–500 cells/μL and from 500 cells/μL to treating everyone irrespective of their CD4 count may be overestimated by 3.60 and 3.79 times in models that fail to capture realities of the care cascade. HIV infections averted from raising the threshold from CD4 200 to 350, 350 to 500, and 500 cells/μL to treating everyone may be overestimated by 1.10, 2.65, and 1.18 times, respectively.
Models using conventional assumptions about cascade progression may substantially overestimate health benefits. As implementation of treatment scale-up proceeds, it is important to assess the effects of required scale-up efforts in a way that incorporates empirical realities of how people move through the HIV cascade.
*Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA;
†Africa Health Research Institute, KwaZulu-Natal, South Africa;
‡Institute of Public Health, University of Heidelberg, Heidelberg, Germany;
§Division of Infection and Immunity, University College London, London, United Kingdom; and
║Department of Medicine, Stanford University School of Medicine, Stanford, CA.
Correspondence to: Angela Y. Chang, ScD, Department of Global Health and Population, Harvard T. H. Chan School of Public Health, 665 Huntington Ave, Boston, MA 02115 (e-mail: email@example.com).
A.Y.C. and J.A.S. were supported by funding from the Bill & Melinda Gates Foundation, through the HIV Modeling Consortium. T.B. is funded by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professorship endowed by the German Federal Ministry of Education and Research. He is also supported by the Wellcome Trust, the European Commission, the Clinton Health Access Initiative and NICHD of NIH [R01-HD084233], NIAID of NIH [R01-AI124389 and R01-AI112339], and FIC of NIH [D43-TW009775]. The remaining authors have no funding to disclose.
Presented in part at HTAi 2017 Annual Meeting; July 18, 2017; Rome, Italy.
The authors have no conflicts of interest to disclose.
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Received March 19, 2018
Accepted August 03, 2018