Cost-effectiveness of oral pre-exposure prophylaxis and expanded antiretroviral therapy for preventing HIV infections in the presence of drug resistance among men who have sex with men in China: A mathematical modelling study


Abstract

Background: Oral pre-exposure prophylaxis (PrEP) and antiretroviral therapy (ART) can effectively prevent HIV infections among men who have sex with men (MSM), but the emergence and transmission of HIV drug-resistance (HIVDR) may compromise their benefits. The costs and benefits of expanding PrEP and ART coverage in the presence of HIVDR in China remain unknown.

Methods: We developed a comprehensive dynamic transmission model incorporating the transmitted (TDR) and acquired (ADR) HIV drug resistance. The model was calibrated by the HIV surveillance data from 2009 to 2019 among MSM in Jiangsu Province, China, and validated by the dynamic prevalence of ADR and TDR. We aimed to investigate the impact of eight intervention scenarios (no PrEP, 20%, 50% or 80% of PrEP, without (77% coverage) or with (90% coverage) expanded ART) on the HIV epidemic trend and cost-effectiveness of PrEP over the next 30 years.

Findings: 20% or 50% PrEP + 90% ART would be cost-effective, with an incremental cost-effectiveness ratio (ICER) of 25,417 (95% confidence interval [CI]: 12,390-38,445) or 47,243 (23,756-70,729), and would yield 154,949 (89,662-220,237) or 179,456 (102,570-256,342) incremental quality-adjusted life-years (QALYs) over the next 30 years. No PrEP + 90% ART would yield 125,211 (73,448-176,974) incremental QALYs and be cost-saving. However, 20-80% PrEP + 77% ART and 80% PrEP + 90% ART with ICER of $77,862-$98,338 and $63,332, respectively, and were not cost-effective. A reduction of 64% in the annual cost of oral PrEP would make it highly cost-effective for 50% PrEP + 90% ART.

Interpretation: 20% or 50% PrEP + 90% ART is cost-effective for HIV control in the presence of HIVDR. Expanded ART alone may be the optimal policy under the current limited budgets.

Funding: National Natural Science Foundation of China, the National S&T Major Project Foundation of China.

Keywords: ADR, acquired drug resistance; ART, antiretroviral therapy; Chinese MSM; Cost-effectiveness; GDP, gross domestic product; HIV drug-resistance; HIVDR, HIV drug resistance; ICER, incremental cost-effectiveness ratio; MSM, men who have sex with men; Mathematical model; NLS, nonlinear least-squares; PrEP, pre-exposure prophylaxis; Pre-exposure prophylaxis; QALYs, quality-adjusted life years; TDF/FTC, tenofovir disoproxil fumarate/emtricitabine; TDR, transmitted drug resistance.

Conflict of interest statement

All authors declare that they have no competing interests.

Figures

Fig 1
Figure 1
Flow diagram of the PrEP intervention model. The population was divided into 26 compartments (susceptible individuals without PrEP (S), susceptible individuals with PrEP (SP), undiagnosed infections with drug-sensitive (ISj) or drug-resistant strains (IRj), diagnosed but untreated infections with drug-sensitive (DSj) or drug-resistant strains (DRj), and treated infections with drug-sensitive (TSj) or drug-resistant strains (TRj), j?=?1, 2, 3, 4 denote the stages of CD4 >=500 cells/μL, 350–499 cells/μL, 200–349 cells/μL and <200 cells/μL. Subscripts S and R denote infected with drug sensitive (blue compartments) and drug-resistant strains (red compartments). Denote λn as the force of HIV infections, n?=?1, 2, 3, 4, where λ1 (λ2) was the per capita rate for the susceptible without PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, and λ3 (λ4) is the per capita rate for the susceptible with PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, respectively. Denote π as the recruitment rate and m as the exiting rate due to behavior changes (i.e., not engaging in high-risk sexual behavior). ? is the PrEP using rate, and ?off is the rate discontinuing PrEP. Denote the time-dependent diagnose and treatment rates as φj and δj,j?=?1, 2, 3, 4. τj is the rate of acquired drug resistance after first-line therapy. Denote the disease progression rates from stage of CD4 >=500 cells/μL to CD4 350–499 cells/μL, from CD4 350–499 cells/μL to CD4 200–349 cells/μL, and from CD4 200–349 cells/μL to CD4 <200 cells/μL as θq1Uθq2Uθq3U (θq1Tθq2Tθq3T) among untreated (treated) individuals, respectively, where the superscript U, T denote the untreated and treated individuals. The reversion rates of the above stages after effective treatment are w1,w2,w3, respectively, we assumed reversion rates are not differ in drug-sensitive and drug-resistance infections. The natural death rate among general population (d) and the HIV-related death rates (μqjU,μqjT) were not shown in this figure.
Fig 2
Figure 2
Model fit (blue lines) to annually reported data (black dots) of diagnosed but untreated individuals in CD4 >=500 cells/μL, CD4 350–499 cells/μL, CD4 200–349 cells/μL and CD4 <200 cells/μL (a–d), treated individuals in CD4 >=500 cells/μL, CD4 350–499 cells/μL, CD4 200–349 cells/μL and CD4 <200 cells/μL (e–h), annual HIV-related deaths among treated MSM (i), total number of HIV infections on treatment (j) and the prevalence of the transmitted drug-resistance (k) and acquired drug-resistance (l). Dashed vertical black lines show rollout of PrEP starting in 2022. Right side of the dash line is the model predictions for eight different PrEP coverage levels, with or without expanded ART (Fig. S1). PrEP, pre-exposure prophylaxis; ART, antiretroviral therapy; MSM, men who have sex with men.
Fig 3
Figure 3
Projection of HIV epidemic trend among Chinese MSM from 2022 to 2052. (a) new infections; (b) new drug-resistant infections; (c) proportion of drug-resistant infections in new infections; (d) HIV/AIDS prevalence; (e) HIV-related death among diagnosed and treated. PrEP pre-exposure prophylaxis; ART antiretroviral therapy.
Fig 4
Figure 4
Incremental costs and QALYs of no PrEP, 20%, 50% or 80% PrEP with or without expanded ART (90% ART), compared with the base case (no PrEP?+?77% ART). The black solid lines show the incremental cost-effectiveness ratio (ICER) relative to the next best strategy when expanded ART is not implemented. The dashed black lines show the ICER relative to the next best strategy when ART is expanded. Incremental costs and QALYs are calculated over a 30-year time horizon (2022–2052) and are discounted to the present at 3% annually. The dashed blue and red lines represent the GDP per capita in Jiangsu Province, China ($18,100 in 2020) and three times the GDP per capita, respectively. Interventions above the red dashed line are defined as not cost-effective, between red dashed line and blue dash line as cost-effective, below the blue dash line as highly cost-effective. Negative value denotes cost-saving. PrEP, pre-exposure prophylaxis; ICER, incremental cost-effectiveness ratio; QALY, quality-adjusted life-year; GDP, gross domestic product.
Fig 5
Figure 5
One-way sensitivity analysis of the cost-effectiveness of 50% PrEP?+?90% ART, compared with the base case (no PrEP?+?77% ART). The horizontal bars represent the range of the incremental cost-effectiveness ratios (ICERs) as each parameter varies across its plausible range. The bars with negative ICERs in some cases are marked as ‘cost-saving’. The solid vertical black line indicates the base case ICER ($45,222 per QALY gained). The dashed vertical blue and red lines represent the GDP per capita in Jiangsu Province, China ($18,100 in 2020) and three times the GDP per capita, respectively. ICER
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