Modelling the Epidemiological Impact and Cost-Effectiveness of PrEP for HIV Transmission in MSM in China
Risk of HIV infection is high in Chinese MSM, with an annual HIV incidence ranging from 3.41 to 13.7/100 person-years. Tenofovir-based PrEP is effective in preventing HIV transmission in MSM. This study evaluates the epidemiological impact and cost-effectiveness of implementing PrEP in Chinese MSM over the next two decades. A compartmental model for HIV was used to forecast the impact of PrEP on number of infections, deaths, and disability-adjusted life years (DALY) averted. We also provide an estimate of the incremental cost-effectiveness ratio (ICER) and the cost per DALY averted of the intervention. Without PrEP, there will be 1.1–3.0 million new infections and 0.7–2.3 million HIV-related deaths in the next two decades. Moderate PrEP coverage (50%) would prevent 0.17–0.32 million new HIV infections. At Truvada’s current price in China, daily oral PrEP costs $46,813–52,008 per DALY averted and is not cost-effective; on-demand Truvada reduces ICER to $25,057–27,838 per DALY averted, marginally cost-effective; daily generic tenofovir-based regimens further reduce ICER to $3675–8963, wholly cost-effective. The cost of daily oral Truvada PrEP regimen would need to be reduced by half to achieve cost-effectiveness and realize the public health good of preventing hundreds of thousands of HIV infections among MSM in China.
KeywordsPrEP Mathematical modeling MSM Cost-effectiveness China
The authors wish to thank Dr. Junjie Xu (The First Affiliated Hospital, China Medical University in Shenyang), Dr. Xia Li (Yunnan AIDS Care Center in Kunming), Dr. Yinzhong Shen (Shanghai Public Health Clinical Center) and Dr. Junwei Su (First Affiliated Hospital, Zhejiang University, Hangzhou) for providing pricing information for routine screening tests and costs of ARV drugs. Finally, we thank the anonymous reviewer whose insightful comments were critical to shaping the final manuscript.
LZ and KM designed the overall framework for the analysis; KM, YW, LZ, and PP conducted literature reviews and consulted expert opinion to generate assumptions for inputs into the model; LZ and PP created the mathematic model and did the analysis; XH, HW, and MM gave overall feedback to the inputs into model and provided critical insights for framing of the discussion. XM and NNS contributed in checking the key findings, generating supplementary figures and revising the manuscript. KM, YW, and LZ wrote the manuscript. All authors reviewed drafts and contributed to the overall final manuscript.
HW and XH contributions are supported by the Chinese Government 13th Five-Year Plan (2017ZX10201101-001-002), and the National Natural Science Foundation of China (No. 81701984). KM is supported by Grant # UL1TR001866 from the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program.
Compliance with Ethical Standards
Conflict of interest
MM receives research support in the form of grants from GSK, ViiV, Merck, and Gilead. He is a consultant to Merck and GSK and participates in the speaker bureau for Gilead Sciences. KM receives research support from a grant from GSK. No other authors report any conflict of interest.
Research Involving Human and Animal Participants
This article does not contain any studies with human participants or animals performed by any of the authors.
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