key: cord-0962016-y9smti54 authors: Jin, Xinye; Shi, Lingen; Wang, Congyue; Qiu, Tao; Yin, Yi; Shen, Mingwang; Fu, Gengfeng; Peng, Zhihang title: 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 date: 2022-05-03 journal: Lancet Reg Health West Pac DOI: 10.1016/j.lanwpc.2022.100462 sha: cff07d88f93e020df3282de14a9ca43c5867ff98 doc_id: 962016 cord_uid: y9smti54 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. Antiretroviral therapy (ART) has effectively reduced human immunodeficiency virus (HIV)-related mortality and the risk of HIV transmission, but HIV drug resistance (HIVDR) has increased with the scaling-up of ART. 1−4 In 2019, the World Health Organization (WHO) reported that the HIVDR to non-nucleoside reverse transcriptase inhibitor (NNRTI) among ARTnaÿve exceeded 10% in 7/18 countries. 5 The emergence and transmission of HIVDR may limit treatment options and pose a potential threat to the long-term effectiveness of ART. 6, 7 HIV prevention strategy based on first-line ART drugs, pre-exposure prophylaxis (PrEP), has shown an effectiveness of 44−86% to prevent new HIV infections among men who has sex with men (MSM). 8−13 Although HIVDR is rare among PrEP users who have acquired HIV due to frequent monitoring, 9 PrEP-selected drug resistance may compromise the effectiveness of first-line ART once PrEP users infected. Therefore, the WHO recommends that PrEP scaling-up should be accompanied by surveillance of HIVDR, especially in these low-and middle-income countries like China. 14 Understanding how the HIVDR affects the effectiveness of PrEP is essential to control the transmission of HIV. Since 2012, oral PrEP based on tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) has been approved by the US Food and Drug Administration (FDA), 15 and subsequently recommended by the WHO in 2015, 16 and then more than 50 countries had released national guidelines following the WHO's PrEP recommendations by the end of 2019. 17 Oral PrEP is not popular in China, which may be because that China has not released the PrEP guideline yet. 17 To promote the PrEP use among Chinese MSM, a pilot programme of post-exposure prophylaxis (PEP) and PrEP among MSM was conducted in seven provinces and the CROPrEP project (China Real-world Study of Oral PrEP) in four cities in 2018. These studies provided the experience for the implementation of PrEP, primary data of the cascade of PrEP implementation, effectiveness, safety, and possible effects of PrEP use on sexual behaviors in China. 18 Based on the evidences from these studies, the first Chinese expert consensus on PrEP was published in 2020. 19 However, the cost-effectiveness of PrEP in the presence of HIVDR remains unclear in China. Evidence before this study Daily oral pre-exposure prophylaxis (PrEP), based on first-line antiretroviral drugs, has been shown an effectiveness of 44%-86% to prevent new HIV infections among men who has sex with men (MSM). However, the overlapping resistance profiles between antiretroviral drugs used for both PrEP and first-line antiretroviral drugs may compromise the effectiveness of firstline ART once PrEP users infected. And HIV drug resistance (HIVDR) may increase the costs of second-line drugs, and decrease the quality of life. We searched PubMed for studies up to Sep 15, 2021 , with the terms "HIV" and "mathematical model" or "mathematical modeling" or "compartment model", and "PrEP" or "preexposure prophylaxis" or "pre-exposure prophylaxis", and "cost-effectiveness" or "cost-effective", and "China" or "Chinese" and "men who has sex with men" or "MSM" with no language or date restrictions, there were no relevant articles related to HIV modelling incorporating drug resistance of PrEP cost-effectiveness (and related terms) in Chinese MSM. Our wider search, removing "China" or "Chinese" yielded two relevant articles related to modelling studies on PrEP cost-effectiveness incorporating the HIVDR among US MSM. They found that PrEP use was cost-effective in the presence of HIVDR among US MSM. However, differences in HIV control strategies, HIV incidence, drug types and costs, resistance testing, cost-effectiveness threshold between China and US, it is necessary to investigate the cost-effectiveness of PrEP in the condition of HIVDR in China. We developed a comprehensive dynamic transmission model to study the costs and benefits of expanding PrEP and ART coverage in the presence of HIVDR among MSM in Jiangsu Province, China. The model was calibrated and validated by multiple surveillance datasets from Jiangsu CDC and published literatures. Our findings showed that expanding PrEP and ART would be cost-effective among MSM even considering HIVDR, but expanded ART alone may be the optimal policy under the current budgets in Jiangsu province. Our results have important implications for the provision of PrEP among MSM in the presence of Research in Context HIVDR. Expanding PrEP to 20% (or 50%) MSM combined with expanded ART in China would prevent a great number of total and drug resistant infections but also require significant investment of money. The cost of PrEP have the largest impact on the results and 64% reduction in it would achieve highly cost-effectiveness under 50% PrEP coverage combined with expanded ART. Mathematical model is a useful tool to access the effectiveness and cost-effectiveness of expanding PrEP coverage and ART with HIVDR. 20−23 Supervie et al. 20 developed a mathematical model to predict how PrEP might affect population-level HIV resistance. Abbas et al. 21 evaluated the effects of implementing ART alone, PrEP alone, or PrEP plus ART on HIV incidence and drug resistance incidence in South Africa. Drabo et al. 22 estimated the costs and benefits of PrEP combined with test-and-treat among MSM in Los Angeles and Shen et al. 23 evaluated the cost-effectiveness of earlier ART plus PrEP among MSM in San Francisco. However, there are many differences in HIV control strategies, such as HIV incidence, drug types and costs, resistance testing, cost-effectiveness threshold between China and US, so it is necessary to investigate the costeffectiveness of PrEP in the presence of HIVDR in China. This will fill the gaps of previous studies on the cost-effectiveness of PrEP in China without considering HIVDR 24−26 or only considering acquired drug resistance (ADR). 27 Jiangsu Province is a well-developed province with 80¢7 million people (2019) in China, which GDP per capita closely follows Beijing and Shanghai. There were over 31,000 people living with HIV (PLWH) in Jiangsu by the end of 2020. 28 Among newly diagnosed people living with HIV, the proportion infected through same sex sexual activity increased from 28¢0% in 2008 to 56¢0% in 2020. 28 Since 2019, Jiangsu province has set up HIV PrEP and PEP service network with 28 outpatient service points of PEP. 29 However, PrEP have not been implemented in Jiangsu to date. In this paper, we proposed a dynamic compartmental model including transmitted drug resistance (TDR) and ADR, and calibrated this model with the multi-source data: the numbers of annual newly diagnosed people living with HIV and newly treated individuals at four levels of CD4 cell counts (CD4 >=500 cells/mL, CD4 350−499 cells/mL, CD4 200−349 cells/mL, and CD4 <200 cells/mL), total treated individuals, and deaths among the treated individuals from 2009 to 2019 in Jiangsu Province, China. The model was validated by the dynamic prevalence of TDR and ADR. We evaluated the HIV epidemic under several PrEP coverages with or without expanded ART, and then calculated the cost-effectiveness of various PrEP scenarios. Our results will provide theoretical support for Jiangsu to expand PrEP and ART in the presence of TDR and ADR. We extracted the following data (Appendix Table S1 ) during 2009−2019 from the HIV/AIDS information system of Jiangsu Provincial Center for Disease Control and Prevention (Jiangsu CDC): the numbers of annual newly diagnosed people living with HIV and newly treated individuals at four CD4 cell counts (CD4 >=500 cells/mL, CD4 350−499 cells/mL, CD4 200−349 cells/ mL, and CD4 <200 cells/mL), annual total treated individuals, and annual deaths among the treated individuals. The virological failure rates during 2014−2018 were also obtained from the Jiangsu CDC, and the probability of the ADR among the virally unsuppressed individuals was 54¢17%. 30 For example, the virological failure rate was 7¢8% in 2018, and the prevalence of ADR was calculated as 54¢17%*7¢8%=4¢2%. The dynamic prevalence rates of TDR were obtained from the published literatures (Appendix Table S2 ). 31−34 The demographic data about population size of total males and those aged 0−14 years old were obtained from Jiangsu Population and Employment Statistics Yearbook. 35 We estimated the population size of MSM based on the proportion of MSM in adult males (Appendix Table S3 ) from the published literatures. 36, 37 We developed a mathematical model to capture the transmission trends of HIV drug-sensitive and drugresistant strains among Chinese MSM, based on the natural history of HIV infection, HIV diagnosis and treatment. The total MSM population N was divided into 26 compartments ( Figure 1 ): susceptible individuals without PrEP (S), susceptible individuals with PrEP (S P ), undiagnosed infections (I qj ), diagnosed but untreated infections (D qj ) and treated infections (T qj ), where q 2 {S, R} denoted drug-sensitive and drug-resistant strains, j = 1, 2, 3, 4 denote the four stages of CD4 >=500 cells/mL, CD4 350−499 cells/mL, CD4 200 −349 cells/mL and CD4 <200 cells/mL. λ n is denoted as the force of HIV infections, n = 1, 2, 3, 4, where λ 1 (λ 2 ) is the per capita rate for the susceptible without PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, and λ 3 (λ 4 ) is the per capita rate for the susceptible with PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, respectively (see Appendix for details). p is denoted as the recruitment rate and m as the exiting rate due to behavior changes (i.e., not engaging in highrisk sexual behavior). f is the PrEP using rate, and f off is the rate discontinuing PrEP. A proportion of people using PrEP would be diagnosed earlier and move straight from susceptible to diagnosed due to regular testing. In our model, we did not consider this for simplicity, but we performed the sensitivity analysis ( Figure 5 ) by increasing the diagnose rate from 68% to 90%.The time-dependent diagnose and treatment rates are denoted as ' j and d j , j = 1, 2, 3, 4 (see Appendix for details). 38 t j is the rate of acquired drug resistance after first-line therapy. The disease progression rates from stage of CD4 >=500 cells/mL to CD4 350−499 cells/ mL, from CD4 350−499 cells/mL to CD4 200−349 cells/mL, and from CD4 200−349 cells/mL to CD4 where the superscript U, T denote the untreated and treated individuals. The reversion rates of the above stages after effective treatment are w 1 ,w 2 ,w 3 , respectively, and we assumed reversion rates were not different in drug-sensitive and drug-resistance infections. 27 The natural death rate among general population (d) and the HIV-related death rates (m U qj ,m T qj ) were not shown in the Figure 1 . We assumed that first-line and second-line ART reduced infectivity by 96% 39 and 80%, 40 respectively. PrEP effectiveness was assumed to be 66% against drug-sensitive strain according to a meta-analysis, 41 and the relative PrEP effectiveness against resistant strains was 50% (the ratio of PrEP effectiveness against drugresistant versus drug-sensitive strains). 23 ART can increase the life expectancy of infected individuals with drug-sensitive (drug-resistant) by three (two) times, compared with those without treatment. We assumed all individuals with ART would receive lifelong treatment and without droping out of care, and the drugresistant individuals switched to the second-line therapy timely. Here we assumed drug resistance specifically referred to the resistance to the first-line drugs. We did not differentiate the categorizations of HIVDR to any drugs (NNRTI, nucleoside reverse transcriptase inhibitor [NRTI] or protease inhibitor [PI]) in this study for two reasons. First, data to estimate drug-specific resistance was not readily available. Second, drug-specific parameters were unnecessary to achieve our goal of estimating intervention cost-effectiveness of PrEP. In the sensitive analysis we varied the relative effectiveness of PrEP on drug resistant strains from 0 to 1. We did not consider the PrEP-mediated resistance either, because meta-analysis 9 showed that infection with drug- 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 (S P ), undiagnosed infections with drug-sensitive (I Sj ) or drug-resistant strains (I Rj ), diagnosed but untreated infections with drug-sensitive (D Sj ) or drug-resistant strains (D Rj ), and treated infections with drug-sensitive (T Sj ) or drug-resistant strains (T Rj ), j = 1, 2, 3, 4 denote the stages of CD4 >=500 cells/mL, 350−499 cells/mL, 200−349 cells/mL and <200 cells/mL. Subscripts S and R denote infected with drug sensitive (blue compartments) and drug-resistant strains (red compartments). Denote λ n as the force of HIV infections, n = 1, 2, 3, 4, where λ 1 (λ 2 ) was the per capita rate for the susceptible without PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, and λ 3 (λ 4 ) is the per capita rate for the susceptible with PrEP to acquire the infection with the HIV drug-sensitive (drug-resistant) strains, respectively. Denote p as the recruitment rate and m as the exiting rate due to behavior changes (i.e., not engaging in high-risk sexual behavior). f is the PrEP using rate, and f off is the rate discontinuing PrEP. Denote the time-dependent diagnose and treatment rates as ' j and d j ,j = 1, 2, 3, 4. t j is the rate of acquired drug resistance after first-line therapy. Denote the disease progression rates from stage of CD4 >=500 cells/mL to CD4 350 −499 cells/mL, from CD4 350−499 cells/mL to CD4 200−349 cells/mL, and from CD4 200−349 cells/mL to CD4 <200 cells/mL as u U q1 u U q2 u U q3 (u T q1 u T q2 u T q3 ) 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 w 1 ,w 2 ,w 3 , 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 HIVrelated death rates (m U qj ,m T qj ) were not shown in this figure. resistant HIV while on PrEP was very rare, and drug resistance mutations were more likely to occur in those individuals with acute HIV infection. We estimated some of the model parameters (including time-dependent diagnose rate and treatment rate, perpartnership transmission rate, acquired resistance rate and HIV-related death rate) using the nonlinear leastsquares method (NLS). These parameters were estimated by calibrating the model to the following data: Figure S2 ). These estimated parameter values and the initial population size of each compartment were listed in Appendix Tables S4, S5. In each simulation, we calculated the sum of square errors between the model output and data, and selected the top 10% with the least square errors to generate 95% confidence intervals (CI). Other model parameters were obtained from the published literatures or the database from Jiangsu CDC (see Appendix Table S6 ). All analyses and simulations were performed in MATLAB R2019b. Based on the above estimated parameters, we projected that ART coverage would reach 77% in 2027 under the status quo. We assumed all uninfected MSM in Jiangsu Province had the potential to use PrEP. The PrEP coverage rate among any time t is S P (t)/(S(t) + S P (t)). We simulated eight different scenarios with the combination of no PrEP or expanding PrEP coverage to 20% (low), 50% (medium), 80% (high), and ART coverage is 77% or expanding it to 90% after 5 years (in 2027) beginning from 2022 as follows (Appendix Figure S1 ): Scenario 1: PrEP coverage 0, ART coverage 77% in 2027(status quo); Scenarios 2−4: PrEP coverage 20% (50%, 80%), ART coverage 77% in 2027; Scenarios 5−8: PrEP coverage 0 (20%, 50%, 80%) +ART coverage 90% in 2027. We estimated the costs of PrEP, first-and second-line drugs, HIV testing, genotype resistance testing, associated opportunistic infections, diagnosis and counselling from Jiangsu CDC and published literatures (Appendix Tables S7−S17). The annual costs of PrEP and first-line drugs were $1638¢9 ($147¢9−$3444¢8) 42 (Fig. S1) . PrEP, pre-exposure prophylaxis; ART, antiretroviral therapy; MSM, men who have sex with men. and Prevention 2017 (US CDC 2017). 19 For drug resistance infections, the annual cost of on second-line ART was $1254¢3 ($1012¢3−$1486¢4) and the cost of HIV genotype resistance test was $145¢0($72¢5−$217¢4). 25,27,43−45 We also estimated the quality of life of each health stage based on published literatures (Appendix Table S18 ). 23,25,27,46−50 We assumed that PrEP did not affect the quality of life, but it decreased by 5% for drug-resistant individuals relative to drug-sensitive individuals at the same stage. 23 We calculated quality-adjusted life years (QALYs) and costs of various strategies over the next 30 years. Costs and QALYs were discounted by 3% per year, 23 and all costs were expressed in 2020 U.S. dollars (U.S. $). 51 The incremental cost-effectiveness ratio (ICER) for each strategy was calculated and compared with those of the status quo and the next best strategy. Using WHO standards, 52 strategies with an ICER less than the gross domestic product per capita (GDP per capita $18,100 for Jiangsu in 2020 53 ) were considered as highly cost-effective, those with an ICER less than three times the GDP per capita as cost-effective ($54,300), other with an ICER more than three times the GDP per capita as not cost-effective, and those ICER<0 as cost-saving. (Table 1 and Appendix Table S19 ). We conducted sensitivity analyses to evaluate the potential impact of various model parameters on cost-effectiveness, including PrEP effectiveness on preventing infections with wild-type strains ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We found that no PrEP + 90%ART would be cost-saving over the next 30 years compare to the base case, and 20% or 50% PrEP + 90% ART would be cost-effective according to WHO standards. 52 Our findings are qualitatively robust to parameter uncertainty. In one-way sensitivity analysis, we found that the annual costs and coverage of PrEP, and coverage of ART had the largest impact on the costeffectiveness ( Figure 5 ). If the annual cost of PrEP was 27% lower than the base case ($1200), then 20% PrEP +90% ART coverage would be highly cost-effective. If the annual cost of PrEP continued to decrease and became 64% lower than the base case ($588), then 50% PrEP + 90% ART would be highly cost-effective ( Figure S9 ). The sensitivity analyses results suggest that PrEP should be initiated after ART coverage has increased to a high level. Results of one-way sensitivity analyses of other interventions are shown in Appendix Figures S5−S10. In 49 and three times the GDP per capita, respectively. ICER