Preexposure Prophylaxis to Eliminate HIV in Taiwan by 2030: A Modeling Study

Preexposure Prophylaxis to Eliminate HIV in Taiwan by 2030: A Modeling Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Preexposure Prophylaxis to Eliminate HIV in Taiwan by 2030: A Modeling Study Chi-Tai Fang, Huei-Jiuan Wu, Ya-Ping Cheng, Yi-Hsuan Chen, Chia-Chen Chang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3311713/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Abstract Preexposure prophylaxis (PrEP) demonstrated 86% efficacy in randomized trials. However, globally, PrEP remains underutilized. The role of PrEP in achieving HIV elimination has been underappreciated and understudied. In Taiwan, the HIV epidemic predominantly affects young, sexually active men who have sex with men (MSM). Our stochastic modeling indicates that the HIV test-and-treat strategy has minimal impact on HIV transmissions that occur during the acute HIV infection. In contrast, a PrEP program providing access to 50% of young, high-risk MSM will halve transmissions during the acute stage and suppress the basic reproduction number (R0) of HIV to below 1, thereby facilitating its elimination. Risk compensation (i.e., none of the PrEP users using condom), imperfect adherence (at 75%), or drug resistance (at a 1% rate) do not undermine such a program's effectiveness. Deterministic modeling further indicates that implementing a 50% coverage PrEP program will reduce the trajectory of the HIV epidemic in Taiwan to below the World Health Organization’s HIV elimination threshold (1/1,000 person-years) by 2030, and such a program is highly cost-saving from a societal perspective, yielding a benefit-cost ratio of 7.16. Our findings strongly support the broad administration of PrEP to high-risk, HIV-negative MSM to achieve HIV elimination by 2030. Health sciences/Health care/Public health/Epidemiology Health sciences/Diseases/Infectious diseases/HIV infections Figures Figure 3 Figure 4 Figure 5 Introduction Two randomized trials, the IPERGAY study 1 and the PROUD study 2 , have demonstrated that preexposure prophylaxis (PrEP) with oral emtricitabine/tenofovir (Truvada™) is highly effective when taken either daily or on demand, preventing up to 86% of HIV infections among high-risk, HIV-negative men who have sex with men (MSM). An open-label extension of the IPERGAY study further indicates that, with good adherence, the efficacy of PrEP can be as high as 97% 3 . Despite these promising results, global utilization of PrEP remains limited 4 , 5 . Financial constraints are a major barrier 4 , 5 , 6 . Additional barriers include concerns about risk compensation (i.e., increased risk-taking behaviors due to perceived reductions in HIV risk from PrEP use) 7 , 8 , non-adherence 9 , 10 , and the drug resistance 11 , 12 . These challenges could hamper the control of the HIV epidemic. To date, the role of PrEP in achieving the elimination of HIV remains underappreciated and understudied 13 , 14 . In Taiwan, the HIV epidemic predominantly affects young, sexually active MSM, with more than 2,000 new HIV diagnoses annually from 2012 to 2017 15 . Since 2015, the Taiwan Centers for Disease Control (CDC) have implemented the World Health Organization (WHO) guidelines on initiating antiretroviral therapy (ART) immediately after an HIV diagnosis 16 , aiming to achieve the UNAIDS 90-90-90 goal to decrease both HIV transmission and mortality through early ART 17 . The proportion of HIV patients on ART in Taiwan improved from 67% in 2014 to 88% in 2018 18, 19 . The number of new HIV diagnoses decreased to 1,991 in 2018 and 1,755 in 2019, respectively 15 . In 2020, Taiwan achieved the UNAIDS 90-90-90 targets, with 90% of people living with HIV knowing their status, 93% of diagnosed patients receiving ART, and 95% of treated patients reaching an undetectable viral load 20 . However, up to one-third (30–40%) of new HIV patients in 2018 and thereafter were not diagnosed until the late stage 19 . Moreover, during the period from 2020 through 2022, the COVID-19 pandemic led to a 30%-40% decline in HIV testing rates in Taiwan. Therefore, additional strategies that are more resilient to such disruptions than intensive HIV testing and treatment are required for eliminating HIV among this highly vulnerable population. Cost has been a major barrier to access to PrEP in Taiwan 21 , 22 , particularly for young MSM who are at the highest risk for HIV. The 2018–2019 PrEP demonstration project in Taiwan, with approximately 1,000 MSM participants, was supported by a donation from Gilead Science Inc. To date, public funding for the PrEP program for MSM in Taiwan remains limited, covering only Truvada™ 20 tablets every three months. Rapid, targeted, high-coverage roll-out of PrEP was associated with a rapid decline in new HIV diagnoses in New South Wales, Australia 23 . Modeling studies suggested that HIV elimination might be possible in the Netherlands and the Paris region with PrEP coverage rates of 82% and 55%, respectively 24 , 25 . However, the necessity, impact on achieving the WHO's goal to end the AIDS epidemic by 2030, and cost-effectiveness of implementing a high-coverage PrEP program for HIV elimination have not yet been evaluated. The present modeling study, based on comprehensive real-world Taiwan national HIV surveillance and cascade data, aimed to address the following four key questions regarding a high-coverage PrEP program targeting sexually active, high-risk, HIV-negative MSM in Taiwan: The impact on the basic reproduction number (R0) of HIV, compared with the HIV test-and-treat strategy alone The effects of risk compensation, drug resistance, and non-adherence on the impact of the PrEP program. The impact on the trajectory of HIV epidemic in the next 20 years, compared with the HIV test-and-treat strategy alone The cost-effectiveness, from an elimination perspective. Results Necessity of a high coverage PrEP program for HIV elimination We estimated the R0 as the average number of HIV transmissions that would occur during the life course of an HIV-positive young MSM with high-risk sexual behavior (50 partners/year), from 20 years to 45 years, during 1,000 simulations (see Methods: Stochastic modeling). Table 1 shows that, in the absence of PrEP, even an intense HIV test-and-treat campaign with annual HIV testing followed by immediate ART would be insufficient to suppress R0 to below 1. In contrast, a PrEP program that targets young high-risk MSM with a coverage rate of 50% will suppress the R0 of HIV to below 1, even if HIV test-and-treat remains at the status quo. Table 1 further reveal that HIV test-and-treat strategy have only minimal impact on the number of HIV transmissions that occur during the acute HIV infection stage (highlighted with brackets). Conversely, a 50% coverage PrEP program halves the HIV transmissions occurring during the acute infection stage—a reduction uniquely achievable through PrEP. Moreover, the program's impact on curtailing acute-stage transmissions increases proportionally with the coverage rate. Thus, PrEP is indispensable for the elimination of HIV among MSM. Table 1 Impact of Test-and-Treat strategy, without or with PrEP, on the basic reproduction number, Ro, of HIV among high-risk MSM population (sexual partners = 50/year) Basic Reproductive Number, R0 (Transmissions occur in acute stage) PrEP coverage rate 0% 25% 50% 75% 100% Testing rate Without testing, care and ART 2.98 (0.97) 2.36 (0.76) 1.70 (0.55) 1.05 (0.35) 0.41 (0.14) Once every 2.5 years HIV testing with HIV cascade (Status Quo) 1.46 (0.95) 1.11 (0.72) 0.79 (0.53) 0.54 (0.37) 0.20 (0.13) Annual HIV testing with HIV cascade 1.24 (0.97) 0.92 (0.73) 0.67 (0.52) 0.44 (0.34) 0.16 (0.13) Annual HIV testing followed by immediate ART 1.11 (0.88) 0.90 (0.74) 0.62 (0.50) 0.41 (0.33) 0.16 (0.13) Table 2 Effect of (A) risk compensation and (B) 1% drug resistance on the basic reproduction number, R0, of HIV among high-risk MSM population (number of sexual partners: 50/year) (A) Risk compensation (none of PrEP users using condom) Basic Reproductive Number, R0 (Transmissions occur in acute stage) PrEP coverage rate 0% 25% 50% 75% 100% Testing Rate Without testing, care and ART 2.88 (0.94) 2.24 (0.71) 1.66 (0.56) 1.00 (0.31) 0.40 (0.11) Once every 2.5 years HIV testing with HIV cascade (Status Quo) 1.51 (1.01) 1.18 (0.73) 0.79 (0.53) 0.50 (0.32) 0.21 (0.13) Annual HIV testing with HIV cascade 1.23 (0.94) 0.94 (0.75) 0.70 (0.52) 0.45 (0.34) 0.18 (0.14) Annual HIV testing followed by immediate ART 1.16 (0.95) 0.93 (0.75) 0.65 (0.52) 0.40 (0.32) 0.16 (0.12) Table 2 (Continued) (B) 1% drug resistance (efficacy of PrEP drops to zero for an encounter with HIV-positive patient infected with emtricitabine/ tenofovir-resistant HIV strain) Basic Reproductive Number, R0 (Transmissions occur in acute stage) PrEP coverage rate 0% 25% 50% 75% 100% Testing rate Without testing, care and ART 2.96 (0.96) 2.34 (0.78) 1.70 (0.56) 1.06 (0.35) 0.45 (0.15) Once every 2.5 years HIV testing with HIV cascade (Status Quo) 1.45 (0.94) 1.16 (0.76) 0.84 (0.55) 0.51 (0.33) 0.22 (0.15) Annual HIV testing with HIV cascade 1.20 (0.94) 0.96 (0.75) 0.68 (0.53) 0.44 (0.34) 0.18 (0.14) Annual HIV testing followed by immediate ART 1.14 (0.92) 0.89 (0.72) 0.64 (0.53) 0.41 (0.34) 0.17 (0.14) Effect of risk compensation on the impact of a PrEP program We modeled the effect of risk compensation, defined as a decrease in the condom use rate from the status quo (30%) to 0% among all high-risk MSM who take PrEP in the program. Table 2 A shows that, in contrast to popular concern, risk compensation actually does not have an apparent effect on the impact of a PrEP program, as seen when comparing Table 1 with Table 2 A. Effect of drug resistance on the impact of a PrEP program We modeled the effect of 1% drug resistance (where the efficacy of PrEP drops to zero during an encounter with an HIV-positive patient infected with an emtricitabine/tenofovir-resistant HIV strain). Table 2 B shows that a drug resistance level of 1% does not have an apparent effect on the impact of a PrEP program, as seen when comparing Table 1 with Table 2 B. Effect of non-adherence on the impact of a PrEP program We then modeled the effect of non-adherence to taking PrEP (assuming that 100% adherence corresponds to an efficacy of 97%) on the R0 of HIV among the high-risk MSM population (with 50 sexual partners per year), under the status quo HIV test-and-treat. Table 3 shows that at least 75% adherence is required for a 50% coverage rate in a PrEP program to suppress the R0 of HIV to less than 1. Figure 1 illustrates the isolines for R0, demonstrating the trade-off between adherence and coverage rate among the high-risk MSM population. Table 3 Effect of non-adherence (assuming an efficacy of PrEP is 97% when adherence is 100%) on the basic reproduction number, Ro, of HIV among high-risk MSM population (number of sexual partners: 50/year), under status quo HIV test-and-treat (once every 2.5 years HIV testing with HIV cascade) Basic Reproductive Number, R0 (Transmissions occur in acute stage) PrEP coverage rate 0% 25% 50% 75% 100% Compliance 100% 1.49 (0.99) 1.11 (0.74) 0.73 (0.51) 0.41 (0.26) 0.04 (0.02) 75% 1.47 (0.97) 1.19 (0.74) 0.98 (0.64) 0.67 (0.44) 0.40 (0.26) 50% 1.43 (0.95) 1.27 (0.82) 1.11 (0.75) 0.96 (0.63) 0.78 (0.50) 25% 1.52 (0.99) 1.34 (0.87) 1.39 (0.90) 1.17 (0.78) 1.12 (0.74) 0% 1.44 (0.90) 1.47 (0.97) 1.55 (1.02) 1.42 (0.88) 1.44 (0.96) Under the status quo, HIV will become endemic in Taiwan after 2030 To model the impact of a PrEP program on the trajectory of the HIV epidemic over the next 20 years, we constructed a deterministic model that takes into account the natural history of HIV disease progression, the HIV care cascade (from testing and linking to care, through to ART) (Fig. 2 ), as well as risk and age structure (See Methods: deterministic modeling). We calibrated the model to fit HIV surveillance data among MSM from 1990 to 2019 in Taiwan (Fig. 3 ). We then simulated the model under the status quo (current HIV test-and-treat rate, with limited PrEP provision at the level of the 2018–2019 demonstration project) from 2020 to 2050. Figure 4 A shows that by maintaining the status quo, the HIV epidemic in MSM will initially drop rapidly from 2020 to 2026, and then enter an endemic stage. The lowest number of new HIV diagnoses will be 395 in the year 2035; thereafter, the number of HIV diagnoses will rise again. High coverage PrEP will eliminate HIV by 2030 In accordance with stochastic modeling results, Fig. 4 A shows that, in the absence of the disruption by the COVID-19 pandemic, initiating a PrEP program, scaled up over a one-year period (2021), and achieving 50% coverage in 2022 and thereafter among young high-risk MSM (15 to 44 years old), will effectively suppress HIV incidence to levels below the WHO HIV elimination threshold (HIV incidence: 1/1,000 person-years) by 2030. This approach could avert as many as 5,615 (57.7%) new HIV infections by 2040. If the PrEP coverage rate in 2022 and thereafter among young high-risk MSM increases to 75%, the time point for achieving HIV elimination will be accelerated to the year 2026. Intensive HIV Test-and-Treat alone will not be sufficient to eliminate HIV In keeping with the stochastic modelling results, Fig. 4 B shows that an intensive HIV test-and-treat strategy alone, scaled up over a three-year period (2021–2024) with annual HIV testing for high-risk MSM aged 15 to 44 years, and immediate ART for 90% of diagnosed HIV patients, will not suffice to reduce HIV incidence to levels below the WHO HIV elimination threshold. However, it will avert 3,863 (39.7%) new HIV infections by 2040. Synergism between PrEP and HIV Test-and-Treat Similar to the stochastic modelling results, Fig. 4 C illustrates the synergism between the PrEP program and HIV Test-and-Treat. Neither a 25% coverage rate in the PrEP program alone (among high-risk MSM aged 15 to 44 years) nor an HIV Test-and-Treat strategy alone (annual HIV testing targeted at high-risk MSM aged 15–44 with immediate ART for 90% of new HIV cases among MSM) will suffice to suppress the trajectory of the HIV epidemic to levels below the WHO HIV elimination threshold. However, the combination of both the above-stated interventions will suppress the HIV epidemic to levels below the elimination threshold by 2030. HIV elimination by PrEP: sensitivity analysis by deterministic modeling Table 4 shows the impact of key parameters on the feasibility of HIV elimination by PrEP, and if applicable, the timing to reach the WHO HIV elimination threshold, as determined by deterministic modeling. The coverage rate of PrEP among young high-risk MSM is the single key factor. If the coverage rate drops to 25%, then HIV elimination will not be feasible. In contrast, the often-raised concerns—including a drop in the condom use rate among PrEP users (from the current 30–0%), imperfect (75%) adherence to PrEP, an increase in the rate (to 10%) of PrEP drug resistance, an increase (to 25%) in the proportion of MSM engaging in high-risk behaviors, or no increase in the HIV testing rate among PrEP users—may delay the timing to reach the WHO HIV elimination threshold but will not change the outcome of HIV elimination by PrEP. These results also highlight that to successfully eliminate HIV by 2030, it is important to continuously educate people to ensure regular HIV testing among PrEP users, good adherence to minimize the emergence of drug resistance, and the minimization of high-risk behaviors. Table 4 HIV elimination by PrEP: sensitivity analyses, by deterministic modelling Scenario HIV infections averted* N (%) HIV Elimination† Year (C.E.) Base scenario a 5,615 (57.7) 2030 Coverage of PrEP decreased to 25% 3,464 (35.6) Not feasible Coverage of PrEP increased to 75% 6,953 (71.5) 2026 Scaling up PrEP program in 2019 6,295 (64.7) 2029 Delayed scaling up PrEP program in 2023 5,276 (54.3) 2029 Condom usage reduced from 30–0% among PrEP users 5,446 (56.0) 2031 Adherence to PrEP reduced to 75% 5,233 (53.8) 2032 Drug resistance to PrEP increased to 10% 5,330 (54.8) 2032 HIV testing rate not increased by PrEP program scaling up 5,268 (54.2) 2031 Proportion of high-risk group increasing from 17–25% between 2021 and 2024 5,049 (51.9) 2036 *Number of HIV infections and number of HIV-related death averted from Year of 2021 to Year of 2040, compared to the trajectory under the current status quo. †HIV epidemic elimination defined by WHO as incidence lower than 1/1,000 per year. a The base scenario is a 50% coverage rate of PrEP targeting high-risk MSM aged 15 to 44, with 86% efficacy, scaled up over a one-year period (2021), and achieving 50% coverage in 2022 and thereafter among young high-risk MSM (15 to 44 years old). This PrEP program is associated with a 50% increase in the HIV testing rate (to be eligible for PrEP) among high-risk MSM aged 15 to 44. The proportion of the high-risk group remains stable at 17% of all MSM aged 15 to 44. Cost and cost-effectiveness of a 50% coverage PrEP program A high-coverage PrEP program, which includes HIV testing, renal function testing, tests for sexually transmitted infections at follow-up, and PrEP case management services, will cost 84,216 NTD (or 2,807 USD, at an exchange rate of 30 NTD for 1 USD) per person in the first year. Then it will cost 77,576 NTD (2,585.87 USD) per person in each following year (Supplementary Table 4). For comparison, each new HIV diagnosis is associated with a lifetime medical cost of 5.3 million NTD (177,165.5 USD) for patients initially without AIDS, and 4.9 million NTD (163,243.7 USD), respectively (Supplementary Table 5). A PrEP program with a 50% coverage rate for MSM aged 15–44 years at high risk for HIV over a 20-year period (2021–2040) will cost 6.8 billion NTD (227.2 million USD). However, it will avert 4,458 new HIV diagnoses (3,329 non-late diagnoses and 1,129 late diagnoses) and gain 33,864.5 quality-adjusted life years (QALYs), including the 18,109.3 QALYs gained from averting 3,329 non-late HIV diagnoses and the 15,755.1 QALYs gained from averting 1,129 late HIV diagnoses and death (Table 5 ). This will save 23.2 billion NTD (774.1 million USD) in HIV-associated medical costs and 25.6 billion NTD (851.9 million USD) in HIV-associated losses in human capital. From a societal perspective, a PrEP program with a 50% coverage rate for young high-risk MSM is highly cost-saving, with a benefit-cost ratio of 7.16 (for every 1 NTD spent on the PrEP program, 7.16 NTD of total societal cost, including medical costs and losses in human capital, will be saved). Table 5 HIV diagnoses averted and quality-adjusted life year (QALY) gained by a high- coverage PrEP program. HIV diagnosed QALY gained b Cases Averted cases a N (%) (a) Non-AIDS patients Status quo (under 2020 situation) 8,365 - 25% coverage of PrEP 6,321 2,044 (24.4) 9,716.3 50% coverage of PrEP 5,036 3,329 (39.8) 18,109.3 75% coverage of PrEP 4,178 4,187 (50.1) 22,777.6 (b) AIDS patients Status quo (under 2020 situation) 4,493 - 25% coverage of PrEP 3,824 669 (14.9) 8,101.3 50% coverage of PrEP 3,363 1,129 (25.1) 15,755.3 75% coverage of PrEP 3,035 1,458 (32.4) 20,333.4 Note: PrEP program is targeted at high risk MSM aged 15–44 years. Time horizon: 20 years. PrEP was scaled-up in 2021, achieving targeted coverage in 2022 and thereafter a Averted HIV cases indicates those prevented HIV diagnosed regards to interventions; b QALY gained: averted quality-adjusted life-year loss; Cost saving by a high-coverage coverage PrEP program: sensitivity analysis Figure 5 shows the impact of nine key parameters on the benefit-cost ratio estimate of a PrEP program. These parameters include the cost of PrEP (generic vs. brand name drugs), timing of PrEP rollout (2019 vs. 2023), mode of PrEP use (on-demand vs. daily use), time horizon (10 years vs. 30 years), cost of ART (cheapest first-line ART vs. the most expensive second-line ART), coverage rate of PrEP (25% vs. 75%), adherence to PrEP (75% vs. 100%), resistance to PrEP drugs (0% vs. 10%), and condom use (0% vs. 30%). The use of a generic drug (with one-third of the unit drug cost) and shifting from an on-demand mode of PrEP use (the base scenario) to daily use (associated with a 2-fold increase in drug use/cost) have the largest impact on the benefit-cost ratio estimate. Nevertheless, a high-coverage PrEP program remains cost-saving under the daily use scenario, with a benefit-cost ratio of 3.80. Other parameters have an even smaller impact on the benefit-cost ratio of a PrEP program. Discussion To the best of our knowledge, this is the first study to establish the necessity of high-coverage PrEP for the elimination of HIV, while also demonstrating its high cost-effectiveness from an elimination perspective. Our modeling results show that an HIV test-and-treat strategy alone would be insufficient for the elimination of HIV. In contrast, a PrEP program achieving 50% coverage among young, high-risk MSM will decrease the basic reproductive number (R0) of HIV to below 1, thereby facilitating the path toward HIV elimination. Importantly, our analyses indicate that factors such as risk compensation (leading to a 0% condom use rate among PrEP users), imperfect adherence at a rate of 75%, and sporadic drug resistance at a rate of 1% do not significantly undermine the effectiveness of such a PrEP program. Deterministic modeling suggests that the implementation of this program among high-risk MSM aged 15 to 44 years, will drive the trajectory of the HIV epidemic in Taiwan below the WHO's HIV elimination threshold (1 per 1,000 person-years) by the year 2030. The cumulative cost of implementing such a PrEP program over a 20-year period would amount to 227.2 million USD; however, it would yield savings of 774.1 million USD in HIV-related medical expenses and an additional 851.9 million USD by averting losses in human capital. Consequently, the benefit-cost ratio stands at 7.16. Importantly, the cost-saving potential of this PrEP intervention remains robust when subjected to variations in key parameters. The most salient finding of our study underscores the necessity of PrEP, highlighting that a high-coverage PrEP program is instrumental for the elimination of HIV in Taiwan by 2030. Our preliminary deterministic modeling results were first presented at the 21st International AIDS Conference in 2016, where we demonstrated that achieving a 50% coverage rate could eradicate the HIV epidemic among MSM in Taiwan 26 , 27 . Subsequent studies, such as that by Rozhnova et al. (2018), indicated that an 82% PrEP coverage rate, in the context of existing ART coverage, could theoretically eliminate HIV among MSM in the Netherlands 24 . More recently, Jijón et al. (2021) showed that a minimum PrEP coverage of 55%, which has yet to be achieved, could eliminate the HIV epidemic among high-risk MSM in the Paris region 25 . In the current study, we employed stochastic modeling to demonstrate that, without a high-coverage PrEP program, the elimination of HIV in Taiwan remains unattainable. Given that successful HIV elimination hinges on attaining a high PrEP coverage among young, high-risk MSM—a demographic group particularly vulnerable due to financial constraints in accessing Truvada™ with a monthly cost ranging from 5,000 (on demand use) to 10,000 NTD (daily use), or 167 to 333 USD, in Taiwan—policy interventions such as comprehensive public funding or health insurance reimbursements are imperative to broaden access to ensure the successful elimination of HIV in Taiwan by 2030. We found that an intensive HIV Test-and-Treat, comprising annual HIV testing followed by immediate initiation of ART upon diagnosis, is insufficient on its own for eradicating the HIV epidemic among young, high-risk MSM, as evidenced by both our stochastic and deterministic modeling outcomes (refer to Table 1 and Fig. 4 , respectively). These results underscore the essential role of PrEP in achieving HIV elimination within this population. The limitation of the HIV Test-and-Treat strategy is elucidated in Table 1 , which indicates its minimal impact on curtailing HIV transmissions during the acute infection stage—a short window lasting between one and three months that is unlikely to be captured by an annual testing regimen. Importantly, this stage is characterized by heightened infectiousness compared to the chronic stage of the infection. Transmissions occurring in the acute stage account for approximately one-third of all HIV transmissions and can only be effectively mitigated through the implementation of a high-coverage PrEP program. Granich et al. (the WHO modelling group), initially posited in 2009 that a universal HIV Test-and-Treat strategy would effectively eliminate the generalized, heterosexual HIV epidemic in South Africa within a decade of implementation 28 . However, subsequent research by Powers et al. in 2011 challenged these findings, particularly critiquing the assumed relative infectiousness during the acute stage—30.3-fold as opposed to the 3.2-fold cited by Granich et al. 29 , 30 . In the present study, we employed a relative infectiousness rate during the acute phase of 26-fold, in alignment with research by Hollingworth et al. 31 . Our data corroborate the limitations of an HIV Test-and-Treat approach for the eradication of HIV among MSM, echoing the conclusions drawn by Powers et al. (2011) and Akullian et al. (2020) regarding the strategy's inadequacy for eliminating HIV among heterosexual populations in Africa 29 , 32 . Additionally, the objective of achieving annual HIV testing for young, high-risk MSM in Taiwan presents a formidable challenge due to factors such as stigma, discrimination, and legal complexities surrounding an HIV diagnosis. In comparison to an intensive HIV Test-and-Treat strategy, high-coverage PrEP demonstrates greater resilience to disruptions caused by pandemics. This advantage arises from the potential for over-the-counter distribution of PrEP 33 , 34 , whereas the former approach necessitates an operational medical care system. However, our modeling data highlight that suboptimal adherence could negate the benefits of high PrEP coverage, as demonstrated by the isoline of the basic reproduction number R0 under different adherence and coverage rates (see Fig. 1 ). Consequently, while pharmacist-led distribution or over-the-counter availability may expedite reaching the target coverage rate among young, high-risk MSM 33 and may be particularly useful during pandemic disruptions 34 , educational interventions aimed at the client population are crucial for ensuring optimal adherence. One of the major concerns for PrEP is risk compensation. While randomized controlled trials did not support that PrEP is associated with a decrease in condom use 1 , 2 , 35 , most observational studies suggested that PrEP users are more likely to have new sexually transmitted infections than non-users 8 , 36 although this could be a result of the higher baseline risk behaviors in PrEP users. Even if risk compensation is 100% (all users stop using condoms), our modelling results still showed that such a decrease in condom use among those who use PrEP (which acts like a molecular condom) would, in fact, have a negligible effect on HIV epidemic control (Table 2 A for stochastic modelling, and Table 4 for deterministic modelling). Jijón et al. (2021) also reported similar results, indicating that risk compensation with none of the PrEP users using condom only minimally increase the PrEP coverage rate required for elimination 25 . Likewise, our study further revealed that the other two often-raised concerns for PrEP, imperfect (75%) adherence and occasional (1%) drug resistance, do not have a meaningful impact on the effect of a high-coverage PrEP program in eliminating the HIV epidemic among MSM. Cost and cost-effectiveness are critical considerations in policymaking. Previous studies on the cost-effectiveness of PrEP generally show that, under a persistent HIV epidemic, PrEP is cost-effective but not cost-saving unless over a very long (80 years) time horizon or with massive price reduction 12 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 . In contrast, we found that PrEP using brand name Truvada™ is highly cost-saving over a 20-year time horizon when implemented to eliminate the HIV epidemic by averting a substantial amount of HIV-associated lifetime medical costs and HIV-associated losses in human capital. Although the cost required to provide high-coverage PrEP could be a constraint for implementation, our results actually show that, using the brand name Truvada™, funding for 50% of young high-risk MSM in Taiwan requires a total of 227.2 million USD over a 20-year period from 2021 to 2040, or an annual budget of approximately 11.4 million USD. This amount is only a quarter of the current annual budget (approximately 50 million USD for 6 million doses) for publicly funded annual seasonal influenza vaccination in Taiwan. Similar to previous studies 38 , 39 , a massive reduction in the cost of PrEP drugs after the expiration of patents will make the case for high-coverage PrEP even more compelling. On the contrary, if comprehensive publicly funded or health insurance-reimbursed PrEP is not provided for young high-risk MSM, there will be danger ahead. First, the unmet need will force potential users to purchase cheap illegal generic drugs, often of questionable quality, overseas (although some studies support the equivalence in bioavailability between generic and brand name drugs 47 ). Second, without a public program that requires regular HIV testing at entry and at three-month intervals thereafter, illegal generic drug users are at high risk of unknowingly taking PrEP in the presence of HIV infection. Third, in the absence of case management services or professional counseling by physicians or pharmacists, there will be no way to ensure good adherence among users. The end result could be a disastrous emergence of resistance to PrEP drugs. Furthermore, because emtricitabine/tenofovir are also important components of ART, an emergence of drug resistance to emtricitabine/tenofovir could compromise not only the efficacy of PrEP but also the efficacy of ART. The strength of the present study is the precise model parameterization based on high-quality Taiwan national data, including HIV surveillance, HIV cascade, and mortality data (provided by Taiwan CDC), vital statistics (from Ministry of Interior), and HIV-associated medical cost (based on Taiwan National Health Insurance database). Additional advantages include the use of risk/age-structured model, the use of both stochastic and deterministic modelling to yield robust conclusion, and the use of the best available estimates for key parameters, including the relative infectiousness in acute stage as well as the rate of HIV disease progression. Our study is subject to several notable limitations. First, our modeling analysis does not account for the disruptive impact of the COVID-19 pandemic that emerged in 2019. Due to the absence of reliable HIV surveillance data spanning the years 2020–2022, our Taiwan MSM HIV Model was calibrated using data collected from 1990 through 2019. Consequently, projections in our counterfactual scenario relied on the epidemiological landscape of 2019, and the high-coverage PrEP program was assumed to be initiated in 2021, although a sensitivity analysis on scenario to initiate the high-coverage PrEP program in 2023 does not significantly alter the outcome. Second, while our study emphasized cost as a primary barrier to the PrEP adoption in Taiwan, we did not scrutinize other non-financial determinants that could significantly influence PrEP acceptability among Taiwan's MSM population. Factors such as distrust of health authorities and stigmatization associated with PrEP usage may pose substantial obstacles to program implementation. Third, our argument for PrEP funding was framed strictly within an economic context, drawing conclusions from cost-effectiveness analyses. However, the societal dimensions cannot be ignored; public support is pivotal, and the viability of PrEP programs could be compromised in a socio-political climate marked by citizens harboring negative perceptions of MSM In conclusion, a high-coverage PrEP program, aiming to provide a 50% coverage rate for young, high-risk MSM is necessary, effective, and highly cost-saving to eliminate HIV in Taiwan by 2030. Our findings strongly support the broad administration of PrEP to high-risk HIV-negative MSM to achieve HIV elimination. Methods Study design and data source The threshold of HIV elimination, is set as an R0 of HIV less than 1, or an annual HIV incidence of less than 1/1000 person-years. 28 We reviewed the published literature for parameterization, including the natural course of disease progression and transmission of HIV, as well as the effects of ART 48 , 49 and PrEP 1 , 2 on HIV transmission and HIV-associated mortality. We obtained Taiwan national HIV surveillance, HIV cascade, and HIV survival data from the Taiwan CDC, vital statistic data from the Taiwan Ministry of Interior 50 , and data on the costs of ART, PrEP, antimicrobial therapy for HIV-associated opportunistic infections, outpatient care, inpatient care, physician fee, case management, and other medical expenditure in Taiwan, from the Taiwan National Health Insurance database. The study procedure was approved by the Research Ethic Committee of National Taiwan University Hospital (NTUH) (REC# 201703099RINA). Stochastic modeling Our stochastic modelling framework has been described elsewhere 51 . In brief, we simulated the life course of a young, HIV-positive MSM with high-risk sexual behaviour, from the ages of 20 to 45. On each day, the index patient may engage in sexual activity with other MSM, receive HIV testing, or die. We further consider 35 different subsequent scenarios, including whether HIV transmission occurs, whether a condom is used, whether linkage to care and the start of ART occurs, and whether a partner uses PrEP 51 . We set the relative infectiousness in the acute stage of HIV infection compared to the chronic stage, a key parameter in HIV models, at 26, based on the work of Hollingworth et al. 31 (Supplementary Fig. 1). We estimated R0 as the average number of HIV transmissions that would occur over 1,000 simulations. The mean number of sexual partners for a young, high-risk MSM was estimated to be 50, based on calibration results from our deterministic model using Taiwan HIV surveillance data. See Supplementary Table 1 for model parameterization. We used R for computing the results (see Supplementary Note 1 for R code). Deterministic modeling Our deterministic modelling framework has been described elsewhere 26 , 27 , 52 . In brief, the Taiwan MSM HIV Model considers the natural history of HIV disease progression 53 , 54 , 55 (from the acute stage to chronic stages with CD4 count > 500, 350–500, 200–350, < 200, then the AIDS stage), the HIV cascade 56 (from HIV testing, to being linked to care, then on ART), risk structure (high-risk vs. low-risk, with assortative mixing 57 , 58 , 59 ), age structure (15–44 years vs. 45–64 years vs. >65 years, also with assortative mixing 60 ), and the demographic transition of Taiwanese population (birth rate statistics from 1990 to 2019 50 ) (Fig. 2 ). The parameters for HIV disease progression are based on the work of Longini et al. 54 on 1,796 patients in US Army and the work of Veugelers et al. 55 on the effect of age on rate of HIV disease progression. See Supplementary Table 2 for details in the parameterization of all variables in the models. HIV-associated mortality by period, age, and stage is based on our comprehensive literature review and modeling work 61 (Supplementary Table 3). The numbers of MSM who started to use PrEP in Taiwan during the 2018–2019 PrEP demonstration project were based on data from administrators 21 , 62 and the Taiwan AIDS Society 63 . We calibrated the model to fit Taiwan’s national HIV surveillance data (1990–2019), HIV cascade data, and the mortality statistics of HIV patients. We estimated the HIV transmission coefficients across different periods among young high-risk MSM and their effective population size using the least square method (with an estimate of approximately 17% among all MSM aged 15–44 years). We used STELLAR software version 10.0.6 (ISEE Systems Inc. New Hampshire, USA) to compute the results (see Supplementary Note 2 for differential equations). Economic analyses Our framework for economic analyses has been previously described 26 , 27 , 52 . In brief, we compare HIV epidemic scenarios with or without a high-coverage PrEP program, projected into future using the deterministic model. We adopt a societal perspective over a 20-year time horizon. All future costs, including those-related to PrEP and HIV-associated medical cost, are adjusted by a 3% annual discount rate to present-day value. Supplementary Fig. 2 illustrates the cost structure for a publicly funded PrEP program 1 , 2 , 16 , 64 , while Supplementary Fig. 3 outlines the cost structure for HIV-associated medical cost, stratified by whether AIDS is present at diagnosis. Both of the above were based on Taiwan National Health Insurance data from 2018 data (Supplementary Table 4 and Supplementary Table 5). We estimated lifetime HIV-associated medical cost by integrating projected lifetime survival curves with the mean HIV-associated medical cost per patient in 2018. The projected lifetime survival curves (mean survival time: 41.3 year after HIV diagnosis without AIDS, and 29.7 years after HIV diagnosis with AIDS, Supplemental Fig. 2) 65 , 66 , used for estimating HIV-associated lifetime medical cost under Taiwan National Health Insurance, and the losses in quality-adjusted life expectancy (QALE) for each Taiwanese MSM with a new HIV diagnosis (5.44 QALY for each new HIV diagnosis without AIDS, and 13.95 QALY for each new HIV diagnosis with AIDS, Supplemental Fig. 3), are based on the work of Lo et al 66 . Quality of life among HIV-infected patients is empirically measured from HIV-positive patients using EuroQol-5D questionnaire (EQ-5D) 66 , 67 , and integrated with projected lifetime survival for estimating QALE and QALE loss relative to general population 68 . We calculate the societal human capital saved by averting new HIV diagnoses by multiplying the total number of QALY gained, under the intervention scenario, with Taiwan’s Year 2018 per capita gross domestic product (GDP) at 754,711 NTD (25,157 USD at 30 NTD for 1 USD) 69 , in line with the WHO’s recommended threshold for cost-effectiveness of interventions 70 . For cost-saving interventions, we further calculate the benefit-cost ratio by dividing the sum of the saved HIV-associated medical cost and averted human capital loss, by the cost of the intervention. Declarations Author Contributions H.J.W. and C.T.F. conceived and designed the study. H.J.W., Y.P.C., and C.T.F. conducted epidemic modelling analyses. C.C.C. and C.T.F. constructed the Taiwan MSM HIV model. Y.H.C. and C.T.F. reviewed the literature for parameterization and estimated HIV-associated mortality parameters in the model. H.J.W. and C.T.F. conducted cost-effectiveness analysis. T.L. and C.T.F. conducted lifetime survival extrapolation of MSM living with HIV. T.L. analysed National Health Insurance data on cost of ART and medical care in Taiwan. H.J.W. and C.T.F. wrote the manuscript. All authors critically reviewed the draft and approved the final version of the manuscript. H.J.W., Y.H.C., and Y.P.C. contributed equally to this work. Competing interests: All authors declare that they have no conflicts of interest. Additional information Supplementary information accompanies this paper at http://www.nature.com/srep . References Molina JM, et al. On-demand preexposure prophylaxis in men at high risk for HIV-1 infection. N Engl J Med 373, 2237–2246 (2015). McCormack S, et al. Pre-exposure prophylaxis to prevent the acquisition of HIV-1 infection (PROUD): effectiveness results from the pilot phase of a pragmatic open-label randomised trial. Lancet 387, 53–60 (2016). Molina JM, et al. 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Impact of pre-exposure prophylaxis on basic reproductive number of HIV among men who have sex with men in Taiwan: a stochastic modeling study [Master Thesis] (Advisor: Chi-Tai Fang, funded by MOST-106-2314-B-002-115-MY3 to Chi-Tai Fang.). National Taiwan University (2018). Wu HJ. HIV pre-exposure prophylaxis for men who have sex with men in Taiwan: a mathematical modeling study [Master Thesis] (Advisor: Chi-Tai Fang.). National Taiwan University (2016). Centers for Disease Control and Prevention. 1993 revised classification system for HIV infection and expanded surveillance case definition for AIDS among adolescents and adults. MMWR Recomm Rep 41, 1–19 (1992). Longini IM, Jr., Clark WS, Gardner LI, Brundage JF. The dynamics of CD4 + T-lymphocyte decline in HIV-infected individuals: a Markov modeling approach. Journal of acquired immune deficiency syndromes 4, 1141–1147 (1991). Veugelers PJ, et al. Determinants of HIV disease progression among homosexual men registered in the Tricontinental Seroconverter Study. American journal of epidemiology 140, 747–758 (1994). Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis 52, 793–800 (2011). Koblin BA, et al. Risk factors for HIV infection among men who have sex with men. AIDS 20, 731–739 (2006). Liu WC. Comparisons of three methods to estimate incidence rates of HIV infection among persons seeking voluntary, anonymous counseling and testing services (VCT)​ [Master Thesis] (Advisor: Chi-Tai Fang.). National Taiwan University (2011). Ackers ML, et al. High and persistent HIV seroincidence in men who have sex with men across 47 U.S. cities. PLoS One 7, e34972 (2012). Shen HC. Attitude and preparedness for aging among middle age homosexual men in Taiwan [in Chinese][Master Thesis], Institute of Sociology.). National Chengchi University (2004). Chen YH, Wu HJ, Fang CT. Mortality of HIV infection, by age, period, disease stage, care cascade and antiretroviral therapy status [Poster]. In: 10th IAS Conference on HIV Science (IAS 2019). 2019/7/21–24, Mexico City, Mexico (Corresponding author; Chi-Tai Fang.) (2019). Wu HJ. Experience of 2018–2019 PrEP demonstration project in Taiwan (personal communication.) (2019). Hung CC. Experience of 2018–2019 PrEP demonstration project in Taiwan (personal communication.) (2019). Taiwan AIDS society. Guideline for the use of pre-exposure oral prophylaxis (PrEP) in Taiwan. http://www.aids-care.org.tw/DB/News/file/254-1.pdf . (2016). Fang CT, et al. Life expectancy of patients with newly-diagnosed HIV infection in the era of highly active antiretroviral therapy. QJM 100, 97–105 (2007). Lo T. Impact of early HIV diagnosis on quality-adjusted life expectancy in HIV-infected men who having sex with men (MSM) [Master Thesis] (Advisor: Chi-Tai Fang.). National Taiwan University (2015). EuroQoL Group. What is EQ-5D. Available from: http://www.euroqol.org/ . (accessed on 2014 Aug 15).) (2014). Hwang JS, Hu TH, Lee LJ, Wang JD. Estimating lifetime medical costs from censored claims data. Health economics 26, e332-e344 (2017). Director-General of Budget and Accounting and Statistics. (2018). Per Capita Gross Domestic Product. http://statdb.dgbas.gov.tw/pxweb/Dialog/Saveshow.asp. ) (2018). Marseille E, Larson B, Kazi DS, Kahn JG, Rosen S. Thresholds for the cost-effectiveness of interventions: alternative approaches. Bulletin of the World Health Organization 93, 118–124 (2015). Additional Declarations There is NO Competing Interest. Supplementary Files nreditorialpolicychecklist.pdf nrreportingsummary.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Apr, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3311713","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":237197675,"identity":"b729d52b-2ea3-484c-b002-3c67924bec8d","order_by":0,"name":"Chi-Tai 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chia-Chen","middleName":"","lastName":"Chang","suffix":""},{"id":237197680,"identity":"01860e79-7100-4856-a4c5-04ac7ef46429","order_by":5,"name":"Tung Lo","email":"","orcid":"","institution":"Taiwan National Health Insurance Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tung","middleName":"","lastName":"Lo","suffix":""}],"badges":[],"createdAt":"2023-08-31 00:50:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3311713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3311713/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-025-00833-7","type":"published","date":"2025-04-17T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44212510,"identity":"ac84baaa-a76b-4f3e-a8bd-ef4da2c5dedd","added_by":"auto","created_at":"2023-10-06 21:03:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36080,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration of the deterministic model with HIV surveillance data, 1990-2019.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/7091ea1c0d492f4fb65af99f.png"},{"id":44212513,"identity":"45a94471-269a-4cc7-8500-e8627ab287ce","added_by":"auto","created_at":"2023-10-06 21:03:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80542,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of high-coverage PrEP program, HIV test-and-treat and combination strategies on the trajectory of the HIV epidemic among MSM in Taiwan, 2020–2050\u003c/p\u003e\n\u003cp\u003e(A) Effect of a high-coverage PrEP program targeting at young (15 to 44 years old) MSM at high risk for HIV.\u003c/p\u003e\n\u003cp\u003e(B) Effect of HIV Test-and-Treat strategy targeting at young MSM at high risk for HIV: Annual HIV testing followed by immediate ART in 90% of all new HIV cases\u003c/p\u003e\n\u003cp\u003e(C) Synergism between PrEP program and HIV Test-and-Treat\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/ec5ecece1a6d1798aef9770d.png"},{"id":44212511,"identity":"71148382-8b6f-4947-9b84-c8594ca32f77","added_by":"auto","created_at":"2023-10-06 21:03:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41368,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis on benefit-cost ratio of a high-coverage PrEP program targeting young MSM at high risk for HIV. The base scenario is a 50% coverage rate of PrEP targeting high-risk MSM aged 15 to 44, with 86% efficacy, scaled up over a one-year period (2021), and achieving 50% coverage in 2022 and thereafter among young high-risk MSM (15 to 44 years old), without risk compensation or drug resistance. In the base scenario, PrEP costs 2,301 USD per-person-year (Truvada\u003csup\u003eTM\u003c/sup\u003e, on-demand use); and ART costs 6,768 USD per-person-year (real world average cost in Taiwan, 2018). The time horizon is 20 years.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/28cfef59ab6a9f9431498893.png"},{"id":80878537,"identity":"f4305f0d-43bf-4d97-abd9-1ea61a41d672","added_by":"auto","created_at":"2025-04-18 07:10:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1312688,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/3ec80c44-a9a3-492a-aeb4-cb3edc0336e6.pdf"},{"id":44212515,"identity":"b18710d1-045b-43b1-80b1-262d9f4b204a","added_by":"auto","created_at":"2023-10-06 21:03:23","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1681857,"visible":true,"origin":"","legend":"","description":"","filename":"nreditorialpolicychecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/9176e6afc890fc0210f6ecce.pdf"},{"id":44212516,"identity":"e226ffc0-47a7-4acf-ba07-e6ae85349443","added_by":"auto","created_at":"2023-10-06 21:03:24","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1664747,"visible":true,"origin":"","legend":"","description":"","filename":"nrreportingsummary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3311713/v1/4e7346414d2a1e1215ac3f8c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Preexposure Prophylaxis to Eliminate HIV in Taiwan by 2030: A Modeling Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTwo randomized trials, the IPERGAY study \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and the PROUD study \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, have demonstrated that preexposure prophylaxis (PrEP) with oral emtricitabine/tenofovir (Truvada\u0026trade;) is highly effective when taken either daily or on demand, preventing up to 86% of HIV infections among high-risk, HIV-negative men who have sex with men (MSM). An open-label extension of the IPERGAY study further indicates that, with good adherence, the efficacy of PrEP can be as high as 97% \u003csup\u003e3\u003c/sup\u003e. Despite these promising results, global utilization of PrEP remains limited \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Financial constraints are a major barrier \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additional barriers include concerns about risk compensation (i.e., increased risk-taking behaviors due to perceived reductions in HIV risk from PrEP use) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, non-adherence \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and the drug resistance \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These challenges could hamper the control of the HIV epidemic. To date, the role of PrEP in achieving the elimination of HIV remains underappreciated and understudied \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn Taiwan, the HIV epidemic predominantly affects young, sexually active MSM, with more than 2,000 new HIV diagnoses annually from 2012 to 2017 \u003csup\u003e15\u003c/sup\u003e. Since 2015, the Taiwan Centers for Disease Control (CDC) have implemented the World Health Organization (WHO) guidelines on initiating antiretroviral therapy (ART) immediately after an HIV diagnosis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, aiming to achieve the UNAIDS 90-90-90 goal to decrease both HIV transmission and mortality through early ART \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The proportion of HIV patients on ART in Taiwan improved from 67% in 2014 to 88% in 2018 \u003csup\u003e18, 19\u003c/sup\u003e. The number of new HIV diagnoses decreased to 1,991 in 2018 and 1,755 in 2019, respectively \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In 2020, Taiwan achieved the UNAIDS 90-90-90 targets, with 90% of people living with HIV knowing their status, 93% of diagnosed patients receiving ART, and 95% of treated patients reaching an undetectable viral load \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, up to one-third (30\u0026ndash;40%) of new HIV patients in 2018 and thereafter were not diagnosed until the late stage \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Moreover, during the period from 2020 through 2022, the COVID-19 pandemic led to a 30%-40% decline in HIV testing rates in Taiwan. Therefore, additional strategies that are more resilient to such disruptions than intensive HIV testing and treatment are required for eliminating HIV among this highly vulnerable population.\u003c/p\u003e \u003cp\u003eCost has been a major barrier to access to PrEP in Taiwan \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, particularly for young MSM who are at the highest risk for HIV. The 2018\u0026ndash;2019 PrEP demonstration project in Taiwan, with approximately 1,000 MSM participants, was supported by a donation from Gilead Science Inc. To date, public funding for the PrEP program for MSM in Taiwan remains limited, covering only Truvada\u0026trade; 20 tablets every three months.\u003c/p\u003e \u003cp\u003eRapid, targeted, high-coverage roll-out of PrEP was associated with a rapid decline in new HIV diagnoses in New South Wales, Australia \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Modeling studies suggested that HIV elimination might be possible in the Netherlands and the Paris region with PrEP coverage rates of 82% and 55%, respectively \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, the necessity, impact on achieving the WHO's goal to end the AIDS epidemic by 2030, and cost-effectiveness of implementing a high-coverage PrEP program for HIV elimination have not yet been evaluated. The present modeling study, based on comprehensive real-world Taiwan national HIV surveillance and cascade data, aimed to address the following four key questions regarding a high-coverage PrEP program targeting sexually active, high-risk, HIV-negative MSM in Taiwan:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe impact on the basic reproduction number (R0) of HIV, compared with the HIV test-and-treat strategy alone\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe effects of risk compensation, drug resistance, and non-adherence on the impact of the PrEP program.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe impact on the trajectory of HIV epidemic in the next 20 years, compared with the HIV test-and-treat strategy alone\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe cost-effectiveness, from an elimination perspective.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eNecessity of a high coverage PrEP program for HIV elimination\u003c/h2\u003e\n \u003cp\u003eWe estimated the R0 as the average number of HIV transmissions that would occur during the life course of an HIV-positive young MSM with high-risk sexual behavior (50 partners/year), from 20 years to 45 years, during 1,000 simulations (see Methods: Stochastic modeling). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows that, in the absence of PrEP, even an intense HIV test-and-treat campaign with annual HIV testing followed by immediate ART would be insufficient to suppress R0 to below 1. In contrast, a PrEP program that targets young high-risk MSM with a coverage rate of 50% will suppress the R0 of HIV to below 1, even if HIV test-and-treat remains at the status quo. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e further reveal that HIV test-and-treat strategy have only minimal impact on the number of HIV transmissions that occur during the acute HIV infection stage (highlighted with brackets). Conversely, a 50% coverage PrEP program halves the HIV transmissions occurring during the acute infection stage\u0026mdash;a reduction uniquely achievable through PrEP. Moreover, the program\u0026apos;s impact on curtailing acute-stage transmissions increases proportionally with the coverage rate. Thus, PrEP is indispensable for the elimination of HIV among MSM.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of Test-and-Treat strategy, without or with PrEP, on the basic reproduction number, Ro, of HIV among high-risk MSM population (sexual partners\u0026thinsp;=\u0026thinsp;50/year)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eBasic Reproductive Number, R0\u003c/p\u003e\n \u003cp\u003e(Transmissions occur in acute stage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePrEP coverage rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTesting rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout testing, care and ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.98 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36 (0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70 (0.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOnce every 2.5 years HIV testing with HIV cascade\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Status Quo)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54 (0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual HIV testing with HIV cascade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual HIV testing followed by immediate ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of (A) risk compensation and (B) 1% drug resistance on the basic reproduction number, R0, of HIV among high-risk MSM population (number of sexual partners: 50/year) (A) Risk compensation (none of PrEP users using condom)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eBasic Reproductive Number, R0\u003c/p\u003e\n \u003cp\u003e(Transmissions occur in acute stage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePrEP coverage rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTesting Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout testing, care and ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.24 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66 (0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOnce every 2.5 years HIV testing with HIV cascade\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Status Quo)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51 (1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual HIV testing with HIV cascade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45 (0.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnual HIV testing followed by immediate ART\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 (0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e(Continued) (B) 1% drug resistance (efficacy of PrEP drops to zero for an encounter with HIV-positive patient infected with emtricitabine/ tenofovir-resistant HIV strain)\u003c/div\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cdiv class=\"SimplePara\"\u003eBasic Reproductive Number, R0\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e(Transmissions occur in acute stage)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cdiv class=\"SimplePara\"\u003ePrEP coverage rate\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0%\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e25%\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e50%\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e75%\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e100%\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eTesting rate\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eWithout testing, care and ART\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.96 (0.96)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e2.34 (0.78)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.70 (0.56)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.06 (0.35)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.45 (0.15)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eOnce every 2.5 years HIV testing with HIV cascade\u003c/span\u003e\u003c/div\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003e(Status Quo)\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.45 (0.94)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.16 (0.76)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.84 (0.55)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.51 (0.33)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.22 (0.15)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAnnual HIV testing with HIV cascade\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.20 (0.94)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.96 (0.75)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.68 (0.53)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.44 (0.34)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.18 (0.14)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e\u003cspan type=\"Bold\" class=\"Bold\" name=\"Emphasis\"\u003eAnnual HIV testing followed by immediate ART\u003c/span\u003e\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e1.14 (0.92)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.89 (0.72)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.64 (0.53)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.41 (0.34)\u003c/div\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cdiv class=\"SimplePara\"\u003e0.17 (0.14)\u003c/div\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eEffect of risk compensation on the impact of a PrEP program\u003c/h2\u003e\n \u003cp\u003eWe modeled the effect of risk compensation, defined as a decrease in the condom use rate from the status quo (30%) to 0% among all high-risk MSM who take PrEP in the program. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA shows that, in contrast to popular concern, risk compensation actually does not have an apparent effect on the impact of a PrEP program, as seen when comparing Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e with Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eEffect of drug resistance on the impact of a PrEP program\u003c/h2\u003e\n \u003cp\u003eWe modeled the effect of 1% drug resistance (where the efficacy of PrEP drops to zero during an encounter with an HIV-positive patient infected with an emtricitabine/tenofovir-resistant HIV strain). Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB shows that a drug resistance level of 1% does not have an apparent effect on the impact of a PrEP program, as seen when comparing Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e with Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eEffect of non-adherence on the impact of a PrEP program\u003c/h2\u003e\n \u003cp\u003eWe then modeled the effect of non-adherence to taking PrEP (assuming that 100% adherence corresponds to an efficacy of 97%) on the R0 of HIV among the high-risk MSM population (with 50 sexual partners per year), under the status quo HIV test-and-treat. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that at least 75% adherence is required for a 50% coverage rate in a PrEP program to suppress the R0 of HIV to less than 1. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the isolines for R0, demonstrating the trade-off between adherence and coverage rate among the high-risk MSM population.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of non-adherence (assuming an efficacy of PrEP is 97% when adherence is 100%) on the basic reproduction number, Ro, of HIV among high-risk MSM population (number of sexual partners: 50/year), under status quo HIV test-and-treat (once every 2.5 years HIV testing with HIV cascade)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eBasic Reproductive Number, R0\u003c/p\u003e\n \u003cp\u003e(Transmissions occur in acute stage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003ePrEP coverage rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompliance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04 (0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78 (0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52 (0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.12 (0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44 (0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 (0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55 (1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.44 (0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eUnder the status quo, HIV will become endemic in Taiwan after 2030\u003c/h2\u003e\n \u003cp\u003eTo model the impact of a PrEP program on the trajectory of the HIV epidemic over the next 20 years, we constructed a deterministic model that takes into account the natural history of HIV disease progression, the HIV care cascade (from testing and linking to care, through to ART) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), as well as risk and age structure (See Methods: deterministic modeling). We calibrated the model to fit HIV surveillance data among MSM from 1990 to 2019 in Taiwan (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). We then simulated the model under the status quo (current HIV test-and-treat rate, with limited PrEP provision at the level of the 2018\u0026ndash;2019 demonstration project) from 2020 to 2050. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA shows that by maintaining the status quo, the HIV epidemic in MSM will initially drop rapidly from 2020 to 2026, and then enter an endemic stage. The lowest number of new HIV diagnoses will be 395 in the year 2035; thereafter, the number of HIV diagnoses will rise again.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eHigh coverage PrEP will eliminate HIV by 2030\u003c/h3\u003e\n\u003cp\u003eIn accordance with stochastic modeling results, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA shows that, in the absence of the disruption by the COVID-19 pandemic, initiating a PrEP program, scaled up over a one-year period (2021), and achieving 50% coverage in 2022 and thereafter among young high-risk MSM (15 to 44 years old), will effectively suppress HIV incidence to levels below the WHO HIV elimination threshold (HIV incidence: 1/1,000 person-years) by 2030. This approach could avert as many as 5,615 (57.7%) new HIV infections by 2040. If the PrEP coverage rate in 2022 and thereafter among young high-risk MSM increases to 75%, the time point for achieving HIV elimination will be accelerated to the year 2026.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eIntensive HIV Test-and-Treat alone will not be sufficient to eliminate HIV\u003c/h2\u003e\n \u003cp\u003eIn keeping with the stochastic modelling results, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB shows that an intensive HIV test-and-treat strategy alone, scaled up over a three-year period (2021\u0026ndash;2024) with annual HIV testing for high-risk MSM aged 15 to 44 years, and immediate ART for 90% of diagnosed HIV patients, will not suffice to reduce HIV incidence to levels below the WHO HIV elimination threshold. However, it will avert 3,863 (39.7%) new HIV infections by 2040.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSynergism between PrEP and HIV Test-and-Treat\u003c/h3\u003e\n\u003cp\u003eSimilar to the stochastic modelling results, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC illustrates the synergism between the PrEP program and HIV Test-and-Treat. Neither a 25% coverage rate in the PrEP program alone (among high-risk MSM aged 15 to 44 years) nor an HIV Test-and-Treat strategy alone (annual HIV testing targeted at high-risk MSM aged 15\u0026ndash;44 with immediate ART for 90% of new HIV cases among MSM) will suffice to suppress the trajectory of the HIV epidemic to levels below the WHO HIV elimination threshold. However, the combination of both the above-stated interventions will suppress the HIV epidemic to levels below the elimination threshold by 2030.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eHIV elimination by PrEP: sensitivity analysis by deterministic modeling\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the impact of key parameters on the feasibility of HIV elimination by PrEP, and if applicable, the timing to reach the WHO HIV elimination threshold, as determined by deterministic modeling. The coverage rate of PrEP among young high-risk MSM is the single key factor. If the coverage rate drops to 25%, then HIV elimination will not be feasible. In contrast, the often-raised concerns\u0026mdash;including a drop in the condom use rate among PrEP users (from the current 30\u0026ndash;0%), imperfect (75%) adherence to PrEP, an increase in the rate (to 10%) of PrEP drug resistance, an increase (to 25%) in the proportion of MSM engaging in high-risk behaviors, or no increase in the HIV testing rate among PrEP users\u0026mdash;may delay the timing to reach the WHO HIV elimination threshold but will not change the outcome of HIV elimination by PrEP. These results also highlight that to successfully eliminate HIV by 2030, it is important to continuously educate people to ensure regular HIV testing among PrEP users, good adherence to minimize the emergence of drug resistance, and the minimization of high-risk behaviors.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHIV elimination by PrEP: sensitivity analyses, by deterministic modelling\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHIV infections averted*\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHIV Elimination\u0026dagger; Year (C.E.)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBase scenario\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,615 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage of PrEP decreased to 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,464 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot feasible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoverage of PrEP increased to 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,953 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScaling up PrEP program in 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,295 (64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelayed scaling up PrEP program in 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,276 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCondom usage reduced from 30\u0026ndash;0% among PrEP users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,446 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdherence to PrEP reduced to 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,233 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug resistance to PrEP increased to 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,330 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHIV testing rate not increased by PrEP program scaling up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,268 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProportion of high-risk group increasing from 17\u0026ndash;25% between 2021 and 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,049 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e*Number of HIV infections and number of HIV-related death averted from Year of 2021 to Year of 2040, compared to the trajectory under the current status quo.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u0026dagger;HIV epidemic elimination defined by WHO as incidence lower than 1/1,000 per year.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003e The base scenario is a 50% coverage rate of PrEP targeting high-risk MSM aged 15 to 44, with 86% efficacy, scaled up over a one-year period (2021), and achieving 50% coverage in 2022 and thereafter among young high-risk MSM (15 to 44 years old). This PrEP program is associated with a 50% increase in the HIV testing rate (to be eligible for PrEP) among high-risk MSM aged 15 to 44. The proportion of the high-risk group remains stable at 17% of all MSM aged 15 to 44.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCost and cost-effectiveness of a 50% coverage PrEP program\u003c/h2\u003e\n \u003cp\u003eA high-coverage PrEP program, which includes HIV testing, renal function testing, tests for sexually transmitted infections at follow-up, and PrEP case management services, will cost 84,216 NTD (or 2,807 USD, at an exchange rate of 30 NTD for 1 USD) per person in the first year. Then it will cost 77,576 NTD (2,585.87 USD) per person in each following year (Supplementary Table\u0026nbsp;4). For comparison, each new HIV diagnosis is associated with a lifetime medical cost of 5.3\u0026nbsp;million NTD (177,165.5 USD) for patients initially without AIDS, and 4.9\u0026nbsp;million NTD (163,243.7 USD), respectively (Supplementary Table\u0026nbsp;5). A PrEP program with a 50% coverage rate for MSM aged 15\u0026ndash;44 years at high risk for HIV over a 20-year period (2021\u0026ndash;2040) will cost 6.8\u0026nbsp;billion NTD (227.2\u0026nbsp;million USD). However, it will avert 4,458 new HIV diagnoses (3,329 non-late diagnoses and 1,129 late diagnoses) and gain 33,864.5 quality-adjusted life years (QALYs), including the 18,109.3 QALYs gained from averting 3,329 non-late HIV diagnoses and the 15,755.1 QALYs gained from averting 1,129 late HIV diagnoses and death (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This will save 23.2\u0026nbsp;billion NTD (774.1\u0026nbsp;million USD) in HIV-associated medical costs and 25.6\u0026nbsp;billion NTD (851.9\u0026nbsp;million USD) in HIV-associated losses in human capital. From a societal perspective, a PrEP program with a 50% coverage rate for young high-risk MSM is highly cost-saving, with a benefit-cost ratio of 7.16 (for every 1 NTD spent on the PrEP program, 7.16 NTD of total societal cost, including medical costs and losses in human capital, will be saved).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHIV diagnoses averted and quality-adjusted life year (QALY) gained by a high- coverage PrEP program.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHIV diagnosed\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eQALY gained\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverted cases\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e(a) Non-AIDS patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatus quo (under 2020 situation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,044 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,716.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,329 (39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,109.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,187 (50.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22,777.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e(b) AIDS patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStatus quo (under 2020 situation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,101.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,129 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15,755.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75% coverage of PrEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,458 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20,333.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: PrEP program is targeted at high risk MSM aged 15\u0026ndash;44 years. Time horizon: 20 years. PrEP was scaled-up in 2021, achieving targeted coverage in 2022 and thereafter\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eAverted HIV cases indicates those prevented HIV diagnosed regards to interventions;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eQALY gained: averted quality-adjusted life-year loss;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCost saving by a high-coverage coverage PrEP program: sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the impact of nine key parameters on the benefit-cost ratio estimate of a PrEP program. These parameters include the cost of PrEP (generic vs. brand name drugs), timing of PrEP rollout (2019 vs. 2023), mode of PrEP use (on-demand vs. daily use), time horizon (10 years vs. 30 years), cost of ART (cheapest first-line ART vs. the most expensive second-line ART), coverage rate of PrEP (25% vs. 75%), adherence to PrEP (75% vs. 100%), resistance to PrEP drugs (0% vs. 10%), and condom use (0% vs. 30%). The use of a generic drug (with one-third of the unit drug cost) and shifting from an on-demand mode of PrEP use (the base scenario) to daily use (associated with a 2-fold increase in drug use/cost) have the largest impact on the benefit-cost ratio estimate. Nevertheless, a high-coverage PrEP program remains cost-saving under the daily use scenario, with a benefit-cost ratio of 3.80. Other parameters have an even smaller impact on the benefit-cost ratio of a PrEP program.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first study to establish the necessity of high-coverage PrEP for the elimination of HIV, while also demonstrating its high cost-effectiveness from an elimination perspective. Our modeling results show that an HIV test-and-treat strategy alone would be insufficient for the elimination of HIV. In contrast, a PrEP program achieving 50% coverage among young, high-risk MSM will decrease the basic reproductive number (R0) of HIV to below 1, thereby facilitating the path toward HIV elimination. Importantly, our analyses indicate that factors such as risk compensation (leading to a 0% condom use rate among PrEP users), imperfect adherence at a rate of 75%, and sporadic drug resistance at a rate of 1% do not significantly undermine the effectiveness of such a PrEP program. Deterministic modeling suggests that the implementation of this program among high-risk MSM aged 15 to 44 years, will drive the trajectory of the HIV epidemic in Taiwan below the WHO's HIV elimination threshold (1 per 1,000 person-years) by the year 2030. The cumulative cost of implementing such a PrEP program over a 20-year period would amount to 227.2\u0026nbsp;million USD; however, it would yield savings of 774.1\u0026nbsp;million USD in HIV-related medical expenses and an additional 851.9\u0026nbsp;million USD by averting losses in human capital. Consequently, the benefit-cost ratio stands at 7.16. Importantly, the cost-saving potential of this PrEP intervention remains robust when subjected to variations in key parameters.\u003c/p\u003e \u003cp\u003eThe most salient finding of our study underscores the necessity of PrEP, highlighting that a high-coverage PrEP program is instrumental for the elimination of HIV in Taiwan by 2030. Our preliminary deterministic modeling results were first presented at the 21st International AIDS Conference in 2016, where we demonstrated that achieving a 50% coverage rate could eradicate the HIV epidemic among MSM in Taiwan \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Subsequent studies, such as that by Rozhnova et al. (2018), indicated that an 82% PrEP coverage rate, in the context of existing ART coverage, could theoretically eliminate HIV among MSM in the Netherlands \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. More recently, Jij\u0026oacute;n et al. (2021) showed that a minimum PrEP coverage of 55%, which has yet to be achieved, could eliminate the HIV epidemic among high-risk MSM in the Paris region \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In the current study, we employed stochastic modeling to demonstrate that, without a high-coverage PrEP program, the elimination of HIV in Taiwan remains unattainable. Given that successful HIV elimination hinges on attaining a high PrEP coverage among young, high-risk MSM\u0026mdash;a demographic group particularly vulnerable due to financial constraints in accessing Truvada\u0026trade; with a monthly cost ranging from 5,000 (on demand use) to 10,000 NTD (daily use), or 167 to 333 USD, in Taiwan\u0026mdash;policy interventions such as comprehensive public funding or health insurance reimbursements are imperative to broaden access to ensure the successful elimination of HIV in Taiwan by 2030.\u003c/p\u003e \u003cp\u003eWe found that an intensive HIV Test-and-Treat, comprising annual HIV testing followed by immediate initiation of ART upon diagnosis, is insufficient on its own for eradicating the HIV epidemic among young, high-risk MSM, as evidenced by both our stochastic and deterministic modeling outcomes (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively). These results underscore the essential role of PrEP in achieving HIV elimination within this population. The limitation of the HIV Test-and-Treat strategy is elucidated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which indicates its minimal impact on curtailing HIV transmissions during the acute infection stage\u0026mdash;a short window lasting between one and three months that is unlikely to be captured by an annual testing regimen. Importantly, this stage is characterized by heightened infectiousness compared to the chronic stage of the infection. Transmissions occurring in the acute stage account for approximately one-third of all HIV transmissions and can only be effectively mitigated through the implementation of a high-coverage PrEP program. Granich et al. (the WHO modelling group), initially posited in 2009 that a universal HIV Test-and-Treat strategy would effectively eliminate the generalized, heterosexual HIV epidemic in South Africa within a decade of implementation \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. However, subsequent research by Powers et al. in 2011 challenged these findings, particularly critiquing the assumed relative infectiousness during the acute stage\u0026mdash;30.3-fold as opposed to the 3.2-fold cited by Granich et al. \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In the present study, we employed a relative infectiousness rate during the acute phase of 26-fold, in alignment with research by Hollingworth et al. \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our data corroborate the limitations of an HIV Test-and-Treat approach for the eradication of HIV among MSM, echoing the conclusions drawn by Powers et al. (2011) and Akullian et al. (2020) regarding the strategy's inadequacy for eliminating HIV among heterosexual populations in Africa \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, the objective of achieving annual HIV testing for young, high-risk MSM in Taiwan presents a formidable challenge due to factors such as stigma, discrimination, and legal complexities surrounding an HIV diagnosis.\u003c/p\u003e \u003cp\u003eIn comparison to an intensive HIV Test-and-Treat strategy, high-coverage PrEP demonstrates greater resilience to disruptions caused by pandemics. This advantage arises from the potential for over-the-counter distribution of PrEP \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, whereas the former approach necessitates an operational medical care system. However, our modeling data highlight that suboptimal adherence could negate the benefits of high PrEP coverage, as demonstrated by the isoline of the basic reproduction number R0 under different adherence and coverage rates (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, while pharmacist-led distribution or over-the-counter availability may expedite reaching the target coverage rate among young, high-risk MSM \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and may be particularly useful during pandemic disruptions \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, educational interventions aimed at the client population are crucial for ensuring optimal adherence.\u003c/p\u003e \u003cp\u003eOne of the major concerns for PrEP is risk compensation. While randomized controlled trials did not support that PrEP is associated with a decrease in condom use \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, most observational studies suggested that PrEP users are more likely to have new sexually transmitted infections than non-users \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e although this could be a result of the higher baseline risk behaviors in PrEP users. Even if risk compensation is 100% (all users stop using condoms), our modelling results still showed that such a decrease in condom use among those who use PrEP (which acts like a molecular condom) would, in fact, have a negligible effect on HIV epidemic control (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e2\u003c/span\u003eA for stochastic modelling, and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for deterministic modelling). Jij\u0026oacute;n et al. (2021) also reported similar results, indicating that risk compensation with none of the PrEP users using condom only minimally increase the PrEP coverage rate required for elimination \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Likewise, our study further revealed that the other two often-raised concerns for PrEP, imperfect (75%) adherence and occasional (1%) drug resistance, do not have a meaningful impact on the effect of a high-coverage PrEP program in eliminating the HIV epidemic among MSM.\u003c/p\u003e \u003cp\u003eCost and cost-effectiveness are critical considerations in policymaking. Previous studies on the cost-effectiveness of PrEP generally show that, under a persistent HIV epidemic, PrEP is cost-effective but not cost-saving unless over a very long (80 years) time horizon or with massive price reduction \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In contrast, we found that PrEP using brand name Truvada\u0026trade; is highly cost-saving over a 20-year time horizon when implemented to eliminate the HIV epidemic by averting a substantial amount of HIV-associated lifetime medical costs and HIV-associated losses in human capital. Although the cost required to provide high-coverage PrEP could be a constraint for implementation, our results actually show that, using the brand name Truvada\u0026trade;, funding for 50% of young high-risk MSM in Taiwan requires a total of 227.2\u0026nbsp;million USD over a 20-year period from 2021 to 2040, or an annual budget of approximately 11.4\u0026nbsp;million USD. This amount is only a quarter of the current annual budget (approximately 50\u0026nbsp;million USD for 6\u0026nbsp;million doses) for publicly funded annual seasonal influenza vaccination in Taiwan. Similar to previous studies \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, a massive reduction in the cost of PrEP drugs after the expiration of patents will make the case for high-coverage PrEP even more compelling.\u003c/p\u003e \u003cp\u003eOn the contrary, if comprehensive publicly funded or health insurance-reimbursed PrEP is not provided for young high-risk MSM, there will be danger ahead. First, the unmet need will force potential users to purchase cheap illegal generic drugs, often of questionable quality, overseas (although some studies support the equivalence in bioavailability between generic and brand name drugs \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e). Second, without a public program that requires regular HIV testing at entry and at three-month intervals thereafter, illegal generic drug users are at high risk of unknowingly taking PrEP in the presence of HIV infection. Third, in the absence of case management services or professional counseling by physicians or pharmacists, there will be no way to ensure good adherence among users. The end result could be a disastrous emergence of resistance to PrEP drugs. Furthermore, because emtricitabine/tenofovir are also important components of ART, an emergence of drug resistance to emtricitabine/tenofovir could compromise not only the efficacy of PrEP but also the efficacy of ART.\u003c/p\u003e \u003cp\u003eThe strength of the present study is the precise model parameterization based on high-quality Taiwan national data, including HIV surveillance, HIV cascade, and mortality data (provided by Taiwan CDC), vital statistics (from Ministry of Interior), and HIV-associated medical cost (based on Taiwan National Health Insurance database). Additional advantages include the use of risk/age-structured model, the use of both stochastic and deterministic modelling to yield robust conclusion, and the use of the best available estimates for key parameters, including the relative infectiousness in acute stage as well as the rate of HIV disease progression.\u003c/p\u003e \u003cp\u003eOur study is subject to several notable limitations. First, our modeling analysis does not account for the disruptive impact of the COVID-19 pandemic that emerged in 2019. Due to the absence of reliable HIV surveillance data spanning the years 2020\u0026ndash;2022, our Taiwan MSM HIV Model was calibrated using data collected from 1990 through 2019. Consequently, projections in our counterfactual scenario relied on the epidemiological landscape of 2019, and the high-coverage PrEP program was assumed to be initiated in 2021, although a sensitivity analysis on scenario to initiate the high-coverage PrEP program in 2023 does not significantly alter the outcome. Second, while our study emphasized cost as a primary barrier to the PrEP adoption in Taiwan, we did not scrutinize other non-financial determinants that could significantly influence PrEP acceptability among Taiwan's MSM population. Factors such as distrust of health authorities and stigmatization associated with PrEP usage may pose substantial obstacles to program implementation. Third, our argument for PrEP funding was framed strictly within an economic context, drawing conclusions from cost-effectiveness analyses. However, the societal dimensions cannot be ignored; public support is pivotal, and the viability of PrEP programs could be compromised in a socio-political climate marked by citizens harboring negative perceptions of MSM\u003c/p\u003e \u003cp\u003eIn conclusion, a high-coverage PrEP program, aiming to provide a 50% coverage rate for young, high-risk MSM is necessary, effective, and highly cost-saving to eliminate HIV in Taiwan by 2030. Our findings strongly support the broad administration of PrEP to high-risk HIV-negative MSM to achieve HIV elimination.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eStudy design and data source\u003c/h2\u003e \u003cp\u003eThe threshold of HIV elimination, is set as an R0 of HIV less than 1, or an annual HIV incidence of less than 1/1000 person-years.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e We reviewed the published literature for parameterization, including the natural course of disease progression and transmission of HIV, as well as the effects of ART \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and PrEP \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e on HIV transmission and HIV-associated mortality. We obtained Taiwan national HIV surveillance, HIV cascade, and HIV survival data from the Taiwan CDC, vital statistic data from the Taiwan Ministry of Interior \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and data on the costs of ART, PrEP, antimicrobial therapy for HIV-associated opportunistic infections, outpatient care, inpatient care, physician fee, case management, and other medical expenditure in Taiwan, from the Taiwan National Health Insurance database. The study procedure was approved by the Research Ethic Committee of National Taiwan University Hospital (NTUH) (REC# 201703099RINA).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStochastic modeling\u003c/h2\u003e \u003cp\u003eOur stochastic modelling framework has been described elsewhere \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. In brief, we simulated the life course of a young, HIV-positive MSM with high-risk sexual behaviour, from the ages of 20 to 45. On each day, the index patient may engage in sexual activity with other MSM, receive HIV testing, or die. We further consider 35 different subsequent scenarios, including whether HIV transmission occurs, whether a condom is used, whether linkage to care and the start of ART occurs, and whether a partner uses PrEP \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. We set the relative infectiousness in the acute stage of HIV infection compared to the chronic stage, a key parameter in HIV models, at 26, based on the work of Hollingworth et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1). We estimated R0 as the average number of HIV transmissions that would occur over 1,000 simulations. The mean number of sexual partners for a young, high-risk MSM was estimated to be 50, based on calibration results from our deterministic model using Taiwan HIV surveillance data. See Supplementary Table\u0026nbsp;1 for model parameterization. We used R for computing the results (see Supplementary Note 1 for R code).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDeterministic modeling\u003c/h2\u003e \u003cp\u003eOur deterministic modelling framework has been described elsewhere \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In brief, the Taiwan MSM HIV Model considers the natural history of HIV disease progression \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e (from the acute stage to chronic stages with CD4 count\u0026thinsp;\u0026gt;\u0026thinsp;500, 350\u0026ndash;500, 200\u0026ndash;350, \u0026lt;\u0026thinsp;200, then the AIDS stage), the HIV cascade \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e (from HIV testing, to being linked to care, then on ART), risk structure (high-risk vs. low-risk, with assortative mixing \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e), age structure (15\u0026ndash;44 years vs. 45\u0026ndash;64 years vs. \u0026gt;65 years, also with assortative mixing \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e), and the demographic transition of Taiwanese population (birth rate statistics from 1990 to 2019 \u003csup\u003e50\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The parameters for HIV disease progression are based on the work of Longini et al. \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e on 1,796 patients in US Army and the work of Veugelers et al. \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e on the effect of age on rate of HIV disease progression. See Supplementary Table\u0026nbsp;2 for details in the parameterization of all variables in the models. HIV-associated mortality by period, age, and stage is based on our comprehensive literature review and modeling work \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e (Supplementary Table\u0026nbsp;3). The numbers of MSM who started to use PrEP in Taiwan during the 2018\u0026ndash;2019 PrEP demonstration project were based on data from administrators \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and the Taiwan AIDS Society \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. We calibrated the model to fit Taiwan\u0026rsquo;s national HIV surveillance data (1990\u0026ndash;2019), HIV cascade data, and the mortality statistics of HIV patients. We estimated the HIV transmission coefficients across different periods among young high-risk MSM and their effective population size using the least square method (with an estimate of approximately 17% among all MSM aged 15\u0026ndash;44 years). We used STELLAR software version 10.0.6 (ISEE Systems Inc. New Hampshire, USA) to compute the results (see Supplementary Note 2 for differential equations).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEconomic analyses\u003c/h2\u003e \u003cp\u003eOur framework for economic analyses has been previously described \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In brief, we compare HIV epidemic scenarios with or without a high-coverage PrEP program, projected into future using the deterministic model. We adopt a societal perspective over a 20-year time horizon. All future costs, including those-related to PrEP and HIV-associated medical cost, are adjusted by a 3% annual discount rate to present-day value. Supplementary Fig.\u0026nbsp;2 illustrates the cost structure for a publicly funded PrEP program \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, while Supplementary Fig.\u0026nbsp;3 outlines the cost structure for HIV-associated medical cost, stratified by whether AIDS is present at diagnosis. Both of the above were based on Taiwan National Health Insurance data from 2018 data (Supplementary Table\u0026nbsp;4 and Supplementary Table\u0026nbsp;5). We estimated lifetime HIV-associated medical cost by integrating projected lifetime survival curves with the mean HIV-associated medical cost per patient in 2018. The projected lifetime survival curves (mean survival time: 41.3 year after HIV diagnosis without AIDS, and 29.7 years after HIV diagnosis with AIDS, Supplemental Fig.\u0026nbsp;2) \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e, used for estimating HIV-associated lifetime medical cost under Taiwan National Health Insurance, and the losses in quality-adjusted life expectancy (QALE) for each Taiwanese MSM with a new HIV diagnosis (5.44 QALY for each new HIV diagnosis without AIDS, and 13.95 QALY for each new HIV diagnosis with AIDS, Supplemental Fig.\u0026nbsp;3), are based on the work of Lo et al \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Quality of life among HIV-infected patients is empirically measured from HIV-positive patients using EuroQol-5D questionnaire (EQ-5D) \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e, and integrated with projected lifetime survival for estimating QALE and QALE loss relative to general population \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. We calculate the societal human capital saved by averting new HIV diagnoses by multiplying the total number of QALY gained, under the intervention scenario, with Taiwan\u0026rsquo;s Year 2018 per capita gross domestic product (GDP) at 754,711 NTD (25,157 USD at 30 NTD for 1 USD) \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, in line with the WHO\u0026rsquo;s recommended threshold for cost-effectiveness of interventions \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. For cost-saving interventions, we further calculate the benefit-cost ratio by dividing the sum of the saved HIV-associated medical cost and averted human capital loss, by the cost of the intervention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.J.W. and C.T.F. conceived and designed the study. H.J.W., Y.P.C., and C.T.F. conducted epidemic modelling analyses. C.C.C. and C.T.F. constructed the Taiwan MSM HIV model. Y.H.C. and C.T.F. reviewed the literature for parameterization and estimated HIV-associated mortality parameters in the model. H.J.W. and C.T.F. conducted cost-effectiveness analysis. T.L. and C.T.F. conducted lifetime survival extrapolation of MSM living with HIV. T.L. analysed National Health Insurance data on cost of ART and medical care in Taiwan. H.J.W. and C.T.F. wrote the manuscript. All authors critically reviewed the draft and approved the final version of the manuscript. H.J.W., Y.H.C., and Y.P.C. contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e All authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e accompanies this paper at \u003ca href=\"http://www.nature.com/srep\"\u003ehttp://www.nature.com/srep\u003c/a\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMolina JM, \u003cem\u003eet al.\u003c/em\u003e On-demand preexposure prophylaxis in men at high risk for HIV-1 infection. 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Bulletin of the World Health Organization 93, 118\u0026ndash;124 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3311713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3311713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePreexposure prophylaxis (PrEP) demonstrated 86% efficacy in randomized trials. However, globally, PrEP remains underutilized. The role of PrEP in achieving HIV elimination has been underappreciated and understudied. In Taiwan, the HIV epidemic predominantly affects young, sexually active men who have sex with men (MSM). Our stochastic modeling indicates that the HIV test-and-treat strategy has minimal impact on HIV transmissions that occur during the acute HIV infection. In contrast, a PrEP program providing access to 50% of young, high-risk MSM will halve transmissions during the acute stage and suppress the basic reproduction number (R0) of HIV to below 1, thereby facilitating its elimination. Risk compensation (i.e., none of the PrEP users using condom), imperfect adherence (at 75%), or drug resistance (at a 1% rate) do not undermine such a program's effectiveness. Deterministic modeling further indicates that implementing a 50% coverage PrEP program will reduce the trajectory of the HIV epidemic in Taiwan to below the World Health Organization\u0026rsquo;s HIV elimination threshold (1/1,000 person-years) by 2030, and such a program is highly cost-saving from a societal perspective, yielding a benefit-cost ratio of 7.16. Our findings strongly support the broad administration of PrEP to high-risk, HIV-negative MSM to achieve HIV elimination by 2030.\u003c/p\u003e","manuscriptTitle":"Preexposure Prophylaxis to Eliminate HIV in Taiwan by 2030: A Modeling Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-06 21:03:18","doi":"10.21203/rs.3.rs-3311713/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"092c4038-2a2c-40bb-ac70-cde6207e6cde","owner":[],"postedDate":"October 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":25108620,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":25108621,"name":"Health sciences/Diseases/Infectious diseases/HIV infections"}],"tags":[],"updatedAt":"2025-04-18T07:10:41+00:00","versionOfRecord":{"articleIdentity":"rs-3311713","link":"https://doi.org/10.1038/s43856-025-00833-7","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2025-04-17 04:00:00","publishedOnDateReadable":"April 17th, 2025"},"versionCreatedAt":"2023-10-06 21:03:18","video":"","vorDoi":"10.1038/s43856-025-00833-7","vorDoiUrl":"https://doi.org/10.1038/s43856-025-00833-7","workflowStages":[]},"version":"v1","identity":"rs-3311713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3311713","identity":"rs-3311713","version":["v1"]},"buildId":"-HB7Z8yhvgn0wM9Nzuekk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}