Clinical Impact and Cost-Effectiveness of Rapid vs. Non-Rapid Initiation Antiretroviral Therapy Regimens in HIV-Positive Men Who Have Sex With Men in China: A Study Combining Modelling and 96-Week Multicenter Cohort Data

Clinical Impact and Cost-Effectiveness of Rapid vs. Non-Rapid Initiation Antiretroviral Therapy Regimens in HIV-Positive Men Who Have Sex With Men in China: A Study Combining Modelling and 96-Week Multicenter Cohort Data | 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 Clinical Impact and Cost-Effectiveness of Rapid vs. Non-Rapid Initiation Antiretroviral Therapy Regimens in HIV-Positive Men Who Have Sex With Men in China: A Study Combining Modelling and 96-Week Multicenter Cohort Data Kejia Zhou, Xi Wang, Dachuang Zhou, Lili Dai, Wenxi Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8529813/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background International treatment guidelines recommend rapid initiation of antiretroviral therapy (ART) for individuals newly diagnosed with HIV-1 infection; however, robust long-term real-world evidence remains limited in China. We aimed to evaluate the clinical outcomes and cost-effectiveness of efavirenz (400 mg) plus lamivudine and tenofovir disoproxil fumarate (EFV + 3TC + TDF) versus the coformulated bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in rapid (≤ 14 days from diagnosis) versus non-rapid (> 14 days) initiation among HIV-diagnosed men who have sex with men (MSM). Methods A real-world cohort of 301 ART-naïve HIV-positive MSM was stratified into four groups: rapid BIC (G1, n = 74), rapid EFV (G2, n = 77), non-rapid BIC (G3, n = 84), non-rapid EFV (G4, n = 66). Primary endpoint was CD4 + T-cell count change from baseline at weeks 48 and 96; secondary endpoints included viral suppression (< 50 copies/mL) and treatment persistence. Confounding was mitigated via the cloning, censoring, and weighting (CCW) method to emulate a randomized controlled trial (RCT), with cost-effectiveness analyzed using a hybrid economic evaluation model referencing China’s 2024 per capita GDP. Results At week 96, rapid initiation was associated with superior CD4 + T-cell recovery, particularly in the EFV-based regimen (mean difference: 144 cells/µL, p < 0.001 for rapid vs. non-rapid EFV). BIC/FTC/TAF demonstrated significantly better immunological recovery (mean difference: 151 cells/µL, p < 0.001 for non-rapid BIC vs. EFV) and higher treatment persistence (75.68%-84.52% vs. 53.25% for rapid EFV). Economic evaluation indicated a favorable profile for BIC/FTC/TAF. Conclusion Rapid ART initiation achieved excellent virological suppression, superior long-term immunological recovery, and cost-effectiveness. Compared with EFV + 3TC + TDF, BIC/FTC/TAF exhibited advantages in efficacy, economic value, and treatment persistence. Health sciences/Diseases Biological sciences/Immunology Health sciences/Medical research Biological sciences/Microbiology HIV Men who have sex with men Rapid ART initiation Real-World cohort Cost effectiveness Figures Figure 1 Figure 2 Figure 3 Introduction The global human immunodeficiency virus (HIV) epidemic remains a critical public health issue decades after its emergence. In China, an estimated 1.05 million people were living with HIV by 2020, with the epidemic imposing a substantial and rising health-economic burden, evidenced by the surge in national funding for HIV/AIDS control from 160 million CNY in 2004 to 3.7 billion CNY in 2020[ 1 , 2 ]. The management of HIV has undergone a paradigm shift over the past decade, moving from deferred treatment to the universal and immediate initiation of ART for all people with HIV (PWH). This strategy, termed rapid ART initiation, was defined by the World Health Organization (WHO) as starting treatment within seven days of HIV diagnosis for all individuals, with same-day initiation encouraged for prepared patients[ 3 ]. Compelling evidence has demonstrated that rapid ART accelerated viral suppression, enhanced immunological recovery, improved treatment retention, and reduced HIV-related mortality and transmission[ 4 – 6 ]. Consequently, it has become a cornerstone of global efforts to achieve the UNAIDS 95-95-95 targets and is strongly endorsed by major international guidelines from the WHO, the U.S. Department of Health and Human Services (DHHS)[ 7 ], and the European AIDS Clinical Society (EACS)[ 8 ]. Consistent with this global consensus, Chinese national guidelines have progressively incorporated rapid ART into clinical practice. The 2021 Chinese Guidelines for Diagnosis and Treatment of HIV/AIDS and the 2023 National Free ART Manual formally recommend ART initiation within 30 days of diagnosis, while actively encouraging a shorter interval (≤ 7 days) for eligible patients[ 9 , 10 ]. Recommended first-line regimens for rapid initiation consist of two nucleoside reverse transcriptase inhibitors (NRTIs) plus a third agent, such as an integrase strand transfer inhibitor (INSTI) or a non-nucleoside reverse transcriptase inhibitor (NNRTI). Among these, the single-tablet regimen bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and the multi-tablet regimen efavirenz/lamivudine/tenofovir disoproxil fumarate (EFV + 3TC + TDF) are widely used in China, however, their comparative long-term effectiveness and economic value within rapid initiation frameworks remained insufficiently established[ 11 , 12 ]. This evidence gap was particularly salient for key populations, such as MSM, bearing a disproportionate burden of the HIV epidemic in China. While prior studies like the nationwide cohort analysis by Zhao et al[ 13 ] and the randomized trial by Wang et al[ 12 ] have demonstrated the short-term efficacy and safety of rapid ART in Chinese MSM, critical questions persisted. Specifically, there remained a scarcity of robust, long-term real-world evidence comparing the clinical and economic outcomes of rapid versus non-rapid initiation strategies, especially when contrasting modern INSTI-based regimens (e.g., BIC/FTC/TAF) with traditional NNRTI-based regimens (e.g., EFV + 3TC + TDF)[ 14 , 15 ]. To fill these gaps, we conducted a 96-week real-world cohort study to evaluate the comparative clinical effectiveness and cost-effectiveness of rapid versus non-rapid ART initiation, focusing on the BIC or EFV-based regimens among HIV-positive MSM in China. Our findings aimed to inform clinical practice and health policy by providing much-needed evidence on optimizing ART initiation strategies in real-world settings. Methods Study Design We conducted a retrospective cohort study using a prospectively maintained clinical database of PWH at Beijing Youan Hospital, Capital Medical University. Since March 2021, this database has systematically collected standardized clinical information from all visiting patients. Patients were retrospectively identified from this database between March 2021 and March 2023, with follow-up lasting until March 2025 (96 weeks). Eligible participants were adult patients (age ≥ 18 years) diagnosed with HIV-1 infection between March 2021 and March 2023 who initiated ART with either an EFV-based regimen (efavirenz 400 mg + lamivudine + tenofovir disoproxil fumarate) or a BIC-based regimen (bictegravir/emtricitabine/tenofovir alafenamide); ART initiation was classified as "rapid" (≤ 14 days from diagnosis) or "non-rapid" (> 14 days from diagnosis). Based on the initiation timing and treatment regimen, patients were stratified into four mutually exclusive groups: G1: rapid initiation BIC group; G2: rapid initiation EFV group; G3: non-rapid initiation BIC group; G4: non-rapid initiation EFV group. This study aimed to evaluate the comparative clinical effectiveness of BIC/FTC/TAF versus EFV + 3TC + TDF under rapid and non-rapid ART initiation in HIV-1-infected adult MSM in China. Additionally, a comprehensive cost-effectiveness evaluation was performed to compare the long-term economic outcomes across groups. Uncertainty analyses were conducted to assess result robustness and identify key influential parameters, thereby generating evidence to support clinical policy decision-making regarding rapid ART initiation in China. Participant Recruitment and Eligibility Eligible participants were required to meet the following inclusion criteria: (1) Initiating ART for the first time (ART-naïve); (2) Availability of complete baseline demographic information and clinical laboratory data; (3) Availability of at least baseline and follow-up data at either week 48 or week 96. Exclusion criteria included: (1) Pregnancy or lactation; (2) Co-existing other severe immunodeficiency diseases or ongoing immunosuppressive therapy; (3) Significant missing baseline data. Ultimately, 301 patients met the criteria and were included in the final analysis. The study was conducted in collaboration with Beijing Youan Hospital, with ethical approval obtained (Approval No: LL-2020-127-K) .The requirement for individual patient consent was waived by the ethics committee due to the retrospective nature of the study and the use of de-identified clinical data. The data were collected between March 2021 and March 2025. Variables and Data Sources Patient follow-up visits were conducted at baseline, and at weeks 4, 8, 12, 24, 36, 48, and 96. At each visit, measurements included body weight, sleep quality assessment, and evaluation of adverse reactions, along with tests for total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, alanine aminotransferase, aspartate aminotransferase, serum creatinine, and eGFR, Plasma HIV-1 RNA viral load ,CD4 + T-cell count, CD8 count and the CD4/CD8 ratio. The clinical efficacy of EFV + 3TC + TDF or BIC/FTC/TAF was assessed based on viral suppression and changes in CD4 + T-cell count from baseline to week 48 and 96, under both rapid and non-rapid ART initiation strategies. The primary study endpoint was the proportion of participants achieving viral suppression (plasma HIV-1 RNA < 50 copies/mL) at weeks 48 or 96. Secondary endpoints included treatment safety, virological efficacy within predefined subgroups, analyzed according to changes from baseline in CD4 + T-cell count and viral suppression rate at weeks 48 and 96. Data Processing Data cleaning addressed missing values, outliers, and inconsistencies. Incomplete or erroneous records were either corrected or excluded to ensure data quality. Records failing to meet follow-up requirements (e.g., incomplete baseline characteristics or missing CD4 + T-cell count change data) were excluded. Baseline patient characteristics were summarized using descriptive statistics, with variables defined based on research objectives, including baseline characteristics (e.g., CD4 + T-cell count, viral load), treatment regimen, and treatment outcomes (e.g., viral suppression rate). To address potential baseline confounding and ensure group comparability, we employed the cloning, censoring, and weighting (CCW) approach, which was a causal inference method that emulates a target randomized trial using observational data. This framework mitigates selection bias and time-varying confounding often encountered in conventional treatment comparisons[ 16 ]. Each participant was artificially cloned into four copies at baseline, with each copy assigned to one of the treatment strategies of interest (G1-G4). This cloning procedure aligns with the intention-to-treat principle by allowing all individuals to be eligible for each strategy, thereby breaking the link between baseline characteristics and treatment assignment. Thereafter, clones were dynamically censored once their observed treatment pathway deviated from their assigned strategy. For example, a patient assigned to rapid initiation with BIC but who switched to EFV by week 12 would have the corresponding clone censored at that time point. To account for selection bias introduced by censoring and to balance baseline covariates across strategy groups, we applied longitudinal inverse probability of censoring weights (IPCW). This weighting creates a pseudopopulation in which the assignment of treatment strategies is independent of baseline covariates, approximating the conditions of a randomized trial. Two primary analysis datasets were defined. The primary efficacy estimates were derived from the intention-to-treat (ITT) dataset, which included all cloned participants according to their randomly assigned treatment strategies, irrespective of subsequent treatment deviations or discontinuations. This approach preserved the baseline randomization mimicry achieved by cloning and provided an unbiased estimate of the strategy's effectiveness in a real-world clinical setting. To evaluate the robustness of our findings and to estimate efficacy under ideal conditions, a secondary per-protocol (PP) analysis was conducted. The PP dataset included only the clones who adhered to their assigned treatment strategy throughout the entire follow-up period. Results from this scenario analysis were presented as supplementary to the primary ITT analysis. A comprehensive analysis of clinical characteristics was conducted within the MSM population, detailing baseline characteristics, disease progression, treatment responses, and adverse events. The effects of the two treatment regimens (BIC and EFV) across different initiation timings (rapid vs. non-rapid) were systematically evaluated in both the ITT and PP datasets. Key endpoints (CD4 + T-cell count changes, viral suppression rate, discontinuation rate, adverse event incidence) were incorporated into an infectious disease dynamic model for cost-effectiveness evaluation. Statistical Analysis Continuous variables were presented as median and interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical data were summarized as frequency and percentage, and comparisons were performed using the X 2 test or Fisher’s exact test, as appropriate. A two-sided P value < 0.05 was considered statistically significant. Additionally, 95% confidence intervals (CIs) were calculated for the difference in viral suppression rates and changes in CD4 + T-cell counts. All statistical analyses were performed using R (The R Project for Statistical Computing, version 4.3.3). Economic Evaluation Model Model Design This study integrated a decision tree-Markov model with an infectious disease dynamic model to simulate, respectively, the clinical progression and the population-level transmission of HIV among MSM in China and to evaluate the cost-effectiveness of treatment groups under different initiation conditions, model frameworks and datasets. The Markov model adopted a lifetime horizon with monthly cycles, whereas the infectious disease dynamic model projected outcomes over a 30-year time horizon using annual cycles[ 17 ]. Health states were defined based on treatment line (first-line, second-line regimen or treatment failure), viral suppression status (suppressed or unsuppressed), and immunological stage according to the WHO classification for HIV/AIDS (CD4 + T-cell count: >500, 350–500, 200–350, 100–200, and ≤ 100 cells/µL)[ 18 , 19 ]. Detailed algorithms for state transitions, model explanations, and parameters were provided in the Appendix . The overall model structure was summarized in Fig. 1 . Parameter Explanations in Markov Model: No_P 1 − 5 : Natural disease progression rates in ineffective states; ART_P i1−5 : ART effectiveness rates (i means different treatment); d_n: Natural mortality rate; d_1 ~ 5: Additional mortality rate due to HIV/AIDS in different CD4 + T-cell count health states; ade_1 ~ 5: Incidence rates of AIDS-defining events in different CD4 + T-cell count health states; ae_i: Incidence rates of adverse events in different treatment; First-line_vd: Discontinuation rate of first-line treatment due to drug resistance; First-line_nd: Discontinuation rate of first-line treatment due to non-resistance reasons; Second-line_vd: Discontinuation rate of second-line treatment due to drug resistance; Second-line_nd: Discontinuation rate of second-line treatment due to non-resistance reasons. Parameter Explanations in Infectious Disease Dynamic Model: α: Number of individuals entering the model; γ: Natural mortality rate; δ: Additional mortality rate due to HIV/AIDS; ε 1−4 : Natural disease progression rates in ineffective states; ζ 1−4 : First-line treatment effectiveness rates; η 1−4 : Second-line treatment effectiveness rates; λ: Diagnosis and treatment rate; µ: Viral suppression rate (effectiveness of first-line treatment); ξ: Viral suppression rate (effectiveness of second-line treatment); θ: Discontinuation rate of first-line treatment; κ: Discontinuation rate of second-line treatment; ω: Infection rate Cost-effectiveness Analysis and Uncertainty Analysis A comparative evaluation of the two ART regimens was performed by calculating the incremental cost-effectiveness ratio (ICER) under rapid and non-rapid initiation strategies and across different datasets. Cost-effectiveness thresholds were defined based on China's 2024 per capita GDP. ( $ 13,445) To assess parameter uncertainty, both one-way sensitivity analysis (OWSA) and probabilistic sensitivity analysis (PSA) were conducted. In the OWSA, cost and utility parameters were varied by ± 20% from their baseline values, while clinical parameters were evaluated using their 95% CI. For the PSA, appropriate probability distributions were assigned to probabilities and costs. A cost-effectiveness acceptability curve (CEAC) was subsequently generated through Monte Carlo simulations. Results Clinical Outcomes Analysis Patient Enrollment Figure 2 outlined the patient enrollment and follow-up flowchart. Between March 2021 and March 2022, a total of 340 newly diagnosed MSM HIV patients who met the inclusion criteria were enrolled. They were divided based on the interval from diagnosis to ART initiation. Among the 149 patients with rapid ART initiation (≤ 14 days), 73 were treated with BIC/FTC/TAF and 76 with EFV + 3TC + TDF. In this group, 13 patients were excluded: 4 due to withdrawal (loss to follow-up or transfer out) and 9 due to treatment failure. Subsequently, among the 73 BIC/FTC/TAF-treated patients who completed follow-up, 59 remained on the original regimen and 14 discontinued for proactive reasons (e.g., improving compliance). Among the 63 EFV + 3TC + TDF-treated patients who completed follow-up, 38 remained on the original regimen, 16 discontinued due to passive reasons, and 9 discontinued for proactive reasons. Among the 191 patients without rapid ART initiation (> 14 days), 84 received BIC/FTC/TAF and 66 received EFV + 3TC + TDF. In the BIC/FTC/TAF subgroup, 4 patients were excluded: 2 due to withdrawal and 2 due to treatment failure. In the EFV + 3TC + TDF subgroup, 3 patients were excluded: 1 due to withdrawal and 2 due to treatment failure. Thereafter, among the 80 BIC/FTC/TAF-treated patients who completed follow-up, 71 remained on the original regimen, 6 discontinued due to passive reasons, and 3 discontinued for proactive reasons. Among the 63 EFV + 3TC + TDF-treated patients who completed follow-up, 54 remained on the original regimen, 8 discontinued due to passive reasons, and 1 discontinued for proactive reasons. Patient Baseline Characteristics The patient baseline characteristics were summarized in Table 1 . The median age across all groups was approximately 31 years, with no significant between-group differences in either the unmatched (p = 0.2503) or matched (ITT dataset; p = 0.659) analyses. Regarding biochemical profiles, high-density lipoprotein cholesterol (HDL-C) levels differed significantly among unmatched groups (p = 0.006), with the highest median values in G3 (1.02 mmol/L; IQR: 0.86–1.14) and G4 (1.02 mmol/L; IQR: 0.82–1.17), and the lowest in G1 and G2 (both 0.91 mmol/L; IQR, 0.80–1.09 and 0.80–1.01, respectively). Similarly, triglycerides showed significant variation (p = 0.0413), with the highest median in G4 (1.38 mmol/L; IQR: 1.06–1.93) and the lowest in G2 (1.12 mmol/L; IQR: 0.86–1.64). Fasting blood glucose levels also exhibited substantial differences (p < 0.001), increasing progressively from G1 (4.99 mmol/L; IQR: 4.69–5.47) to G3 (5.45 mmol/L; IQR: 5.18–5.96). No other biochemical markers—including AST, ALT, total cholesterol, LDL-C, and creatinine—showed statistically significant differences in the unmatched analysis. After matching, all biochemical indicators, including HDL-C, triglycerides, and blood glucose, were balanced and showed no significant variation among groups (all p > 0.05). For HIV-specific indicators at baseline, CD4 + T-cell counts differed significantly in the unmatched analysis (p = 0.0391), with the highest median in G2 (374.60 cells/µL; IQR: 262.98–507.00) and the lowest in G3 (289.50 cells/µL; IQR: 167.75–442.25). In contrast, CD4/CD8 ratio and plasma HIV-1 RNA levels were comparable across all unmatched groups (p = 0.362 and p = 0.7127, respectively). Following matching, all HIV-related baseline characteristics, including CD4 + T-cell count, CD4/CD8 ratio, and HIV-1 RNA were well-balanced and showed no significant differences (all p > 0.05), indicating that the matching procedure effectively minimized baseline confounding between groups. Table 1 Patient Baseline Characteristics (A) Unmatched Characteristic, median (IQR) G1: Rapid Initiation BIC group (n = 74) G2: Rapid Initiation EFV group (n = 77) G3: Non-Rapid Initiation BIC group (n = 84) G4: Non-Rapid Initiation EFV group (n = 66) P Value Initiation time, day 3.00 (2.00–7.00) 4.00 (2.00–8.00) 26.00 (20.00-51.25) 24.00 (18.00-34.50) < 0.001*** Age, year 30.19 (26.16–36.65) 30.48 (25.42–37.42) 30.59 (27.31–35.99) 35.48 (25.58–46.80) 0.2503 Biochemical indicators AST, U/L 24.00 (21.00-28.75) 23.00 (19.00–28.00) 25.00 (21.00–35.00) 23.75 (21.25–27.75) 0.1753 ALT,n 22.50 (17.00-32.75) 20.00 (15.00–29.00) 25.00 (18.00-35.25) 20.00 (15.25-29.00) 0.0848 Cholesterol, mmol/L 4.25 (3.76–4.80) 4.20 (3.77–4.77) 4.45 (3.87–4.91) 4.20 (3.83–4.78) 0.5794 HDL-C, mmol/L 0.91 (0.80–1.01) 0.91 (0.80–1.09) 1.02 (0.86–1.14) 1.02 (0.82–1.17) 0.006 LDL-C, mmol/L 2.68 (2.24–3.16) 2.59 (2.09–3.09) 2.83 (2.39–3.17) 2.59 (2.32–3.04) 0.3555 Triglycerides, mmol/L 1.32 (1.02–1.83) 1.12 (0.86–1.64) 1.38 (0.98–1.84) 1.38 (1.06–1.93) 0.0413 Blood Glucose, mmol/L 4.99 (4.69–5.47) 5.11 (4.69–5.38) 5.45 (5.18–5.96) 5.25 (4.95–5.70) < 0.001 Creatinine, µmol/L 70.50 (63.00–75.00) 69.00 (64.00–76.00) 67.00 (62.75–75.25) 68.00 (61.00-75.75) 0.4694 HIV-related indicators CD4 + T-cell count at baseline, cells/µL 350.25 (242.46-459.64) 374.60 (262.98–507.00) 289.50 (167.75-442.25) 333.50 (239.25-435.75) 0.0391 CD4/CD8 ratio at baseline 0.32 (0.20–0.43) 0.37 (0.23–0.61) 0.32 (0.22–0.48) 0.34 (0.24–0.48) 0.362 Plasma HIV-1 RNA at baseline, copies/mL 19,555.00 (5,396.50–85,940.00) 23,239.00 (6,739.00–67,267.00) 25,370.50 (5,361.75-90,344.50) 13,972.00 (5,710.00–59,303.50) 0.7127 (B) Matched (ITT dataset) Characteristic, median (IQR) G1: Rapid Initiation BIC group (n = 72.27) G2: Rapid Initiation EFV group (n = 73.81) G3: Non-Rapid Initiation BIC group (n = 80.19) G4: Non-Rapid Initiation EFV group (n = 65.59) P Value Initiation time, day 3.00 (2.00–7.00) 4.00 (2.00–8.00) 24.00 (18.00–33.00) 26.00 (20.00–51.00) < 0.001*** Age, year 30.08 (26.13–37.95) 30.47 (25.42–37.42) 35.34 (25.42–46.92) 30.48 (26.44–36.35) 0.659 Biochemical indicators AST, U/L 24.00 (20.00–28.00) 23.00 (19.00–28.00) 24.00 (21.00–28.00) 24.00 (21.00–34.00) 0.311 ALT,n 22.00 (17.00–33.00) 20.00 (15.00–29.00) 20.00 (16.00–29.00) 25.00 (18.00–34.00) 0.687 Cholesterol, mmol/L 4.25 (3.75–4.80) 4.19 (3.76–4.77) 4.20 (3.83–4.78) 4.45 (3.89–4.95) 0.892 HDL-C, mmol/L 0.93 (0.80–1.01) 0.90 (0.80–1.09) 1.02 (0.81–1.17) 1.02 (0.86–1.15) 0.492 LDL-C, mmol/L 2.68 (2.23–3.18) 2.59 (2.07–3.09) 2.60 (2.31–3.04) 2.84 (2.42–3.19) 0.441 Triglycerides, mmol/L 1.30 (1.02–1.83) 1.12 (0.86–1.64) 1.38 (1.05–1.95) 1.39 (0.97–1.84) 0.533 Blood Glucose, mmol/L 4.99 (4.69–5.48) 5.07 (4.69–5.38) 5.26 (4.99–5.71) 5.45 (5.19–5.96) 0.856 Creatinine, µmol/L 70.00 (63.00–75.00) 69.00 (63.00–76.00) 68.00 (61.00–76.00) 67.00 (63.00–75.00) 0.792 HIV-related indicators CD4 + T-cell count at baseline, cells/µL 350.51 (246.83–463.00) 334.00 (236.50-465.39) 340.00 (249.00-473.00) 328.00 (203.00-509.00) 1 CD4/CD8 ratio at baseline 0.36 (0.21–0.47) 0.31 (0.22–0.57) 0.34 (0.24–0.47) 0.32 (0.24–0.48) 1 Plasma HIV-1 RNA at baseline, copies/mL 19,489.00 (4,643.00–90,594.00) 24,625.00 (7,060.00–70,056.00) 14,320.00 (5,694.00–62,024.00) 24,595.00 (4,067.00–77,321.00) 0.418 Efficacy Analysis Treatment persistence and immunological recovery were evaluated across the four study groups. As summarized in Table 2 , a significantly higher proportion of participants in the non-rapid initiation groups remained on their initial regimen at the end of follow-up (G3: 84.52%; G4: 81.82%), compared to the rapid initiation groups (G1: 75.68%; G2: 53.25%; χ² = 28.685, df = 6, p < 0.001). Notably, the G2 group exhibited the highest rate of discontinuation due to treatment failure (11.69%) and the highest proportion of switches for reactive reasons such as drug resistance (65.22%). In contrast, the G1 group demonstrated a markedly higher rate of proactive regimen switches (93.76%), suggesting better tolerability or adherence management. Immunological outcomes, as depicted in Fig. 3 and corresponding longitudinal data, revealed consistent CD4 + T-cell count recovery across all groups over 96 weeks. At baseline, no significant differences in median CD4 + T-cell counts were observed between rapid and non-rapid initiation groups within each regimen (all p > 0.05). By week 48, the rapid initiation groups showed numerically greater increases in CD4 + T-cell counts compared to non-rapid initiators, particularly in the EFV group (difference: 85.5 cells/µL, p = 0.122). By week 96, this difference became statistically significant in the EFV group (144 cells/µL, p < 0.001), while the BIC groups maintained stable and comparable counts between rapid and non-rapid arms (3 cells/µL difference, p = 0.613). Importantly, between-regimen comparisons at week 96 revealed a significant advantage of BIC over EFV in the non-rapid initiation group (151 cells/µL, p < 0.001). Table 2 Treatment Regimen Persistence and Discontinuation Variable, n(%) G1: Rapid Initiation BIC group (n = 74) G2: Rapid Initiation EFV group (n = 77) G3: Non-Rapid Initiation BIC group (n = 84) G4: Non-Rapid Initiation EFV group (n = 66) Statistical Test Results Remained on Initial Regimen (first-line treatment) 56 (75.68%) 41 (53.25%) 71 (84.52%) 54 (81.82%) χ²(6) = 28.685 p < 0.001 Second-line Regimen 16 (21.62%) 23 (29.87%) 9 (10.72%) 9 (13.63%) Switched for Reactive Reasons (e.g., drug resistance) 1 (6.24%) 15 (65.22%) 6 (66.67%) 8 (88.86%) Switched for Proactive Reasons (e.g., improving adherence) 15 (93.76%) 8 (34.78%) 3 (33.33%) 1 (11.14%) Discontinuation due to Treatment Failure 0 (0.00%) 9 (11.69%) 2 (2.38%) 2 (3.03%) Discontinued Study Participation 2 (2.70%) 4 (5.19%) 2 (2.38%) 1 (1.52%) Economic Outcomes Analysis Base Case Analyses The base-case analysis results were summarized in Table 3 . Panels A and B present the findings from the Markov model over a lifetime horizon. When comparing the BIC group to the EFV group (Panel A), rapid initiation was associated with substantially higher incremental costs ( $ 43,970.58) for a minimal gain in effectiveness (0.01 QALYs), resulting in an ICER of $ 5,637,253.31 per QALY gained (419.28 times the per-capita GDP). In contrast, non-rapid initiation of BIC versus EFV yielded an ICER of $ 4,330.04 per QALY gained (0.32 times GDP), indicating cost-effectiveness. When comparing rapid versus non-rapid initiation strategies (Panel B), the BIC group led to an ICER of $ 89,491.68 per QALY gained (6.66 times GDP), while the EFV group resulted in a highly favorable ICER of $ 395.88 per QALY gained (0.03 times GDP). Panels C and D displayed the results from the infectious disease dynamic model over a 30-year horizon. The comparison of BIC versus EFV group (Panel C) showed that under both rapid and non-rapid initiation, the ICERs were markedly lower than in the Markov model, at $ 2,202.20 and $ 8,824.12 per QALY gained, respectively (both below 1 times GDP). The comparison of initiation strategies (Panel D) revealed that for the BIC group, rapid initiation was dominant (less costly and more effective) with an ICER of $ 419.41 per QALY. For the EFV group, rapid initiation was associated with an ICER of $ 134,509.38 per QALY gained (10.00 times GDP). Table 3 Cost-Effectiveness Results: Cost, Effectiveness and ICER (A) Markov model-BIC group vs EFV group ART regimens IC, $ IE, QALY ICER ICER/GDP Rapid Initiation 43,970.58 0.01 5,637,253.31 419.28 Non-Rapid Initiation 11,814.96 2.73 4,330.04 0.32 (B) Markov model-Rapid Initiation vs Non-Rapid Initiation ART regimens IC, $ IE, QALY ICER ICER/GDP BIC group 33,380.40 0.37 89,491.68 6.66 EFV group 1,224.78 3.09 395.88 0.03 (C) Infectious disease dynamic model-BIC group vs EFV group ART regimens IC, $ IE, QALY ICER ICER/GDP Rapid Initiation 80.81 0.04 2,202.20 0.16 Non-Rapid Initiation 265.05 0.03 8,824.12 0.66 (D) Infectious disease dynamic model-Rapid Initiation vs Non-Rapid Initiation ART regimens IC, $ IE, QALY ICER ICER/GDP BIC group 3.38 0.01 419.41 0.03 EFV group 187.62 1.39E-03 134,509.38 10.00 Abbreviations: IC: incremental cost; IE: incremental effectiveness. Sensitivity Analyses In OWSA, conducted on the intention-to-treat dataset within the Markov model framework for the non-rapid BIC versus EFV group comparison, identified the drug cost of the BIC group as the primary driver of uncertainty. Discontinuation rate and viral suppression rate also emerged as influential parameters in other dataset or model scenarios, with the complete results visualized in Appendix Figures 2-5 , panels A and B. Throughout the analysis, all outcome variations consistently remained within the pre-specified acceptable range defined by the willingness-to-pay (WTP) threshold. PSA reflected substantial robustness of the model outcomes. The distribution of 10,000 ICERs calculated via PSA corroborated the stability of the base-case results, indicating consistent model performance. For example, the cost-effectiveness acceptability curve (CEAC) showed that at this WTP threshold (China’s 2024 per capita GDP), the probability ofthe non-rapid BIC group being cost-effective compared to the non-rapid EFV group was 100% in the ITT dataset based on the Markov model ( Appendix Figure 2-5 , panels C-F). Discussion This 96-week real-world cohort study provided comprehensive evidence on the comparative clinical and cost-effectiveness outcomes of rapid (≤14 days) versus non-rapid ART initiation with two widely prescribed first-line regimens (BIC/FTC/TAF and EFV+3TC+TDF), among HIV-positive MSM in China. Our findings demonstrated that rapid ART initiation was associated with superior long-term immunological recovery. Furthermore, BIC/FTC/TAF consistently showed advantages over EFV+3TC+TDF in immunologic efficacy, treatment persistence, and cost-effectiveness, which is attributed to its higher genetic barrier to resistance, better tolerability, and single-tablet formulation that improves adherence. A key methodological strength of economic evaluation was the adoption of a hybrid modeling approach that integrated a decision tree-Markov model with an infectious disease dynamic transmission model. This dual-model framework captured both individual-level clinical progression and population-level HIV transmission dynamics, addressing a critical limitation of traditional Markov models, which often overlook the secondary transmission benefits of viral suppression. The decision tree-Markov model, structured according to WHO immunological staging and treatment lines, was well-suited to estimate lifetime costs and QALYs from a healthcare system perspective, reflecting the natural history of HIV and treatment pathways at the patient level[18, 20]. However, traditional Markov models often fail to account for the secondary transmission benefits of viral suppression, potentially underestimating the full value of effective ART[21]. To address this, we integrated a HIV-applicable Susceptible–Infectious–Removed (SIR) dynamic model, which projected the HIV epidemic among Chinese MSM over a 30-year horizon and incorporated behavioral parameters specific to this population, such as condom use rates and sexual contact patterns ( Appendix Table 7 )[17, 22]. This dual-model approach allowed us to not only evaluate the direct clinical and economic impacts of rapid versus non-rapid initiation but also to quantify the indirect benefits of reduced HIV transmission, leading to a critical consideration for public health planning and resource allocation. The divergence in results between the two models, particularly the more favorable cost-effectiveness of rapid initiation in the dynamic model, underscores the importance of capturing herd immunity effects in infectious disease economic evaluations[23, 24]. The superior CD4+ T-cell count recovery observed in the rapid initiation groups, especially in G2, aligned with prior studies highlighting the benefits of early ART in preserving immune function and reducing time to viral suppression[4, 5, 12]. Notably, the 144 cells/μL difference in CD4+ T-cell count at week 96 between rapid and non-rapid EFV groups underscored the potential of rapid initiation to mitigate early immune decline, even with a less contemporary regimen. However, the fact that BIC/FTC/TAF achieved comparable CD4+ T-cell count gains regardless of initiation timing suggests that regimens with higher genetic barriers to resistance and better tolerability may be less dependent on initiation speed for optimal immunological outcomes[11, 25]. Regimen choice emerged as a critical determinant of long-term success. The significantly higher treatment persistence in the BIC groups (75.68%–84.52%) compared to the EFV group (53.25%) reflects the well-documented tolerability and safety profile of INSTI-based regimens[11, 26]. The high rate of proactive switches in the rapid BIC group (93.76%) further suggested that patients and clinicians might be more confident in managing side effects or adherence issues with BIC/FTC/TAF, whereas the EFV group experienced more reactive switches due to resistance and adverse events. This was consistent with earlier trials reporting higher discontinuation rates with EFV due to neuropsychiatric and metabolic side effects[27, 28]. Our economic evaluation further supported the preferential use of BIC/FTC/TAF, particularly in non-rapid initiation settings where it was highly cost-effective compared to EFV+3TC+TDF. The Markov model indicated that rapid BIC initiation was associated with a favorable ICER relative to China’s per-capita GDP, whereas the EFV-based rapid strategy, while clinically beneficial, was less economically attractive in the long term[29, 30]. The infectious disease dynamic model further highlighted the population-level benefits of rapid BIC initiation, which was both cost-saving and more effective over a 30-year horizon—a finding that underscores the public health value of combining modern regimens with rapid initiation protocols[17, 31]. The robustness of our findings was confirmed through extensive sensitivity analyses, including one-way and probabilistic sensitivity analyses, which showed consistent results across a range of assumptions. The use of cloning, censoring, and weighting methods to emulate a target trial also strengthens the causal interpretation of our observational data, reducing biases inherent in conventional cohort studies[32]. Several limitations should be acknowledged. First, despite rigorous statistical adjustment, unmeasured confounding might persist in this real-world study[33]. Second, the single-center design might limit generalizability, although the study population was representative of a key demographic in China’s HIV epidemic[34]. Third, we did not capture patient-reported outcomes or qualitative data on barriers to rapid initiation, which could inform implementation strategies[35]. Finally, while the 96-week follow-up was longer than many real-world studies, even longer-term outcomes beyond two years warranted further investigation[36]. In conclusion, this study supported the adoption of rapid ART initiation among MSM in China, particularly with BIC/FTC/TAF, which offered a favorable balance of efficacy, tolerability, and cost-effectiveness. These findings aligned with global trends toward INSTI-based regimens and same-day or rapid-start models, and they provided locally relevant evidence to inform updates to Chinese national HIV treatment guidelines[3, 9, 10]. Conclusion Rapid initiation of ART led to excellent rates of virological suppression, superior long-term immunological recovery, and was shown to be cost-effective among HIV-positive MSM in China. Compared with the EFV+3TC+TDF, BIC/FTC/TAF demonstrated superior efficacy, economic benefit, and treatment durability. These findings support the prioritization of rapid ART initiation in clinical practice and national HIV treatment guidelines for this key population. Abbreviations HIV: Human Immunodeficiency Virus AIDS: Acquired Immunodeficiency Syndrome ART: Antiretroviral Therapy MSM: Men who have sex with men CCW: Cloning, Censoring, and Weighting QALY: Quality-adjusted Life Year ICER: Incremental Cost Effectiveness Ratio PWH: People with HIV NRTI: Nucleoside Reverse Transcriptase Inhibitor NNRTI: Non-nucleoside Reverse Transcriptase Inhibitor INSTI: Integrase Strand Transfer Inhibitor IPCW: Inverse Probability of Censoring Weights ITT: Intention-to-Treat PP: Per-Protocol CI: Confidence Interval IQR: Interquartile range OWSA: One-way Sensitivity Analysis PSA: Probabilistic Sensitivity Analysis WHO: World Health Organization DHHS: U.S. Department of Health and Human Services UNAIDS: The Joint United Nations Programme on HIV/AIDS WTP: Willingness-to-Pay GDP: Gross Domestic Product CPI: Consumer Price Index AE: Adverse Event ADE: AIDS-Defining Event Declarations Ethics approval and consent to participate Ethical standards of the relevant national and institutional committees on human experimentation and the Helsinki Declaration of 1975, as revised in 2008 were followed in all procedures. Ethical approval was obtained from the Ethics Committee of Capital Medical University-affiliated Beijing Youan Hospital (Batch number: LL-2020-127-K). Participants also signed informed consent forms before data collection began. Competing interests The authors declare that they have no competing interests. Authors’ contributions KZ analyzed and interpreted the data, and was a major contributor in drafting and revising the manuscript. XW coordinated across multiple institutions and contributed to the study's conception and critical manuscript revisions. DZ contributed to data acquisition, visualization, and manuscript drafting. KZ, XW and DZ contributed to data acquisition, visualization, and manuscript revision. WT, and LD provided financial support, contributed to the study's conception, and critically revised the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. Funding This work was supported by the National Natural Science Foundation of China, International Cooperative Research Project (2023YFVA1002). Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. References Chinese Society of Infectious Diseases, AIDS and, Hepatitis, C. & Group Chinese Center for Disease Control and Prevention, China HIV/AIDS Diagnosis and Treatment Guidelines Xiehe Medical Journal, 2022. 13(02): pp. 203–226.(in Chinese). (2021) Edition. Suguimoto, S. P. et al. Changing patterns of HIV epidemic in 30 years in East Asia. Curr. HIV/AIDS Rep. 11 (2), 134–145 (2014). World Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. (2015). https://apps.who.int/iris/bitstream/handle/10665/186275/9789241509565_eng.pdf.?sequence=1 Boyd, M. A. et al. Rapid initiation of antiretroviral therapy at HIV diagnosis: definition, process, knowledge gaps. HIV Med. 20 (Suppl 1), 3–11 (2019). Girometti, N. et al. Rapid ART start in early HIV infection: Time to viral load suppression and retention in care in a London cohort. HIV Med. 21 (9), 613–615 (2020). Gomillia, C. E. S. et al. Rapid Antiretroviral Therapy (ART) Initiation at a Community-Based Clinic in Jackson, MS. AIDS Res. Ther. 17 (1), 60 (2020). Department of Health and Human Services. Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents with HIV. (2024). Available from: https://aidsinfo.nih.gov/guidelines European, A. I. D. S. & Clinical Society.. EACS Guidelines version 12.1. https://eacs.sanfordguide.com.2024 Acquired Immunodeficiency Syndrome Professional Group Of Society Of Infectious, Diseases, C. M. A. & Chinese, C. F. D. C. Chinese Guideline for Diagnosis and Treatment of Human Immunodeficiency Virus Infection/Acquired Immunodeficiency Syndrome (2024 Edition). Med. J. Peking Union Med. Coll. Hosp. 15 (6), 1261–1288 (2024). National Center for AIDS/STD Control and Prevention. Chinese Center for Disease Control and Prevention. National Free AIDS Antiretroviral Treatment Manual. (2023). Orkin, C. et al. Fixed-dose combination bictegravir, emtricitabine, and tenofovir alafenamide versus dolutegravir-containing regimens for initial treatment of HIV-1 infection: week 144 results from two randomised, double-blind, multicentre, phase 3, non-inferiority trials. Lancet HIV . 7 (6), e389–e400 (2020). Wang, R. et al. Rapid Initiation of Antiretroviral Therapy With Coformulated Bictegravir, Emtricitabine, and Tenofovir Alafenamide Versus Efavirenz, Lamivudine, and Tenofovir Disoproxil Fumarate in HIV–Positive Men Who Have Sex With Men in China: Week 48 Results of the Multicenter, Randomized Clinical Trial. Clin. Infect. Dis. 79 (1), 169–176 (2024). Zhao, Y. et al. Immediate Antiretroviral Therapy Decreases Mortality Among Patients With High CD4 Counts in China: A Nationwide, Retrospective Cohort Study. Clin. Infect. Dis. 66 (5), 727–734 (2018). Cohen, M. S. et al. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl. J. Med. 365 (6), 493–505 (2011). Lundgren, J. D. et al. Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. N Engl. J. Med. 373 (9), 795–807 (2015). Hernán, M. A. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ 360 , k182 (2018). Wu, J. Constructing a Dynamic Model to Evaluate the Effectiveness of HIV/AIDS Prevention and Control in China (Chinese Center for Disease Control and Prevention, 2014). (in Chinese). Girouard, M. P. et al. The Cost-effectiveness and Budget Impact of 2-Drug Dolutegravir-Lamivudine Regimens for the Treatment of HIV Infection in the United States. Clin. Infect. Dis. 62 (6), 784–791 (2016). Peng, S. et al. Cost-effectiveness of DTG + ABC/3TC versus EFV/TDF/FTC for first-line treatment of HIV-1 in the United States. J. Med. Econ. 18 (10), 763–776 (2015). Moreno Guillen, S. et al. Cost-utility analysis of the fixed-dose combination of dolutegravir/abacavir/lamivudine as initial treatment of HIV + patients in Spain. Farm. Hosp. 41 (5), 601–610 (2017). Cohen, M. S. et al. Antiretroviral treatment of HIV-1 prevents transmission of HIV-1: where do we go from here? Lancet 382 (9903), 1515–1524 (2013). Zhao, R. et al. Optimising HIV pre-exposure prophylaxis and testing strategies in men who have sex with men in Australia, Thailand, and China: a modelling study and cost-effectiveness analysis. Lancet Glob Health . 12 (2), e243–e256 (2024). Rodger, A. J. et al. Sexual Activity Without Condoms and Risk of HIV Transmission in Serodifferent Couples When the HIV-Positive Partner Is Using Suppressive Antiretroviral Therapy. JAMA 316 (2), 171–181 (2016). Rodger, A. J. et al. Risk of HIV transmission through condomless sex in serodifferent gay couples with the HIV-positive partner taking suppressive antiretroviral therapy (PARTNER): final results of a multicentre, prospective, observational study. Lancet 393 (10189), 2428–2438 (2019). Chen, I., Sun, H. & Hung, C. Meta-Analysis of Efficacy and Safety of Coformulated Bictegravir, Emtricitabine, and Tenofovir Alafenamide Among People Living with HIV. Infect. Dis. Ther. 10 (3), 1331–1346 (2021). Chivite, I. et al. Effectiveness, safety and discontinuation rates of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in people with HIV using real-world data: a systematic review and meta-analysis. J. Antimicrob. Chemother. 79 (8), 1775–1783 (2024). Orrell, C. et al. Efavirenz and rifampicin in the South African context: is there a need to dose-increase efavirenz with concurrent rifampicin therapy? Antivir Ther. 16 (4), 527–534 (2011). Molina, J. et al. Which HIV-infected adults with high CD4 T-cell counts benefit most from immediate initiation of antiretroviral therapy? A post-hoc subgroup analysis of the START trial. Lancet HIV . 5 (4), e172–e180 (2018). Phillips, A. N. et al. Evaluation of antiretroviral regimen switching options in adults with HIV with sustained viral load non-suppression on dolutegravir, lamivudine, and tenofovir in eastern, central, southern, and western Africa: a modelling study. Lancet HIV . 12 (8), e578–e586 (2025). Restelli, U. et al. Cost-effectiveness analysis of dolutegravir plus backbone compared with raltegravir plus backbone, darunavir + ritonavir plus backbone and efavirenz/tenofovir/emtricitabine in treatment naïve and experienced HIV-positive patients. Ther. Clin. Risk Manag . 13 , 787–797 (2017). Krebs, E. et al. Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis. PLoS One . 14 (5), e0217559 (2019). Byrnes, J. E. K. & Dee, L. E. Causal Inference With Observational Data and Unobserved Confounding Variables. Ecol. Lett. 28 (1), e70023 (2025). Bezabhe, W. M. et al. Barriers and facilitators of adherence to antiretroviral drug therapy and retention in care among adult HIV-positive patients: a qualitative study from Ethiopia. PLoS One . 9 (5), e97353 (2014). Gieselmann, L. et al. Profiling a large HIV-1 elite neutralizer cohort reveals remarkable CD4bs bNAb for HIV-1 prevention and therapy (bioRxiv, 2025). Ford, N. et al. The WHO public health approach to HIV treatment and care: looking back and looking ahead. Lancet Infect. Dis. 18 (3), e76–e86 (2018). Lederman, M. M. et al. Immunologic failure despite suppressive antiretroviral therapy is related to activation and turnover of memory CD4 cells. J. Infect. Dis. 204 (8), 1217–1226 (2011). Additional Declarations No competing interests reported. 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3","display":"","copyAsset":false,"role":"figure","size":161406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunological outcomes from Baseline to 48 \u0026nbsp;and 96 wks of Follow-up\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8529813/v1/dbab2785eb5facc196ad2758.png"},{"id":104251556,"identity":"079f9b5e-62f3-45e8-9aa1-5dcb1af384d6","added_by":"auto","created_at":"2026-03-09 16:13:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2198765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8529813/v1/7bbaeeaf-c6df-433f-b3b4-7f4a75634443.pdf"},{"id":99889880,"identity":"76bb831b-a411-42b0-b69d-ecb6072a0a1f","added_by":"auto","created_at":"2026-01-09 13:34:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2316284,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8529813/v1/3bae745ca3b170f3b5ab2a6e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Impact and Cost-Effectiveness of Rapid vs. Non-Rapid Initiation Antiretroviral Therapy Regimens in HIV-Positive Men Who Have Sex With Men in China: A Study Combining Modelling and 96-Week Multicenter Cohort Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global human immunodeficiency virus (HIV) epidemic remains a critical public health issue decades after its emergence. In China, an estimated 1.05\u0026nbsp;million people were living with HIV by 2020, with the epidemic imposing a substantial and rising health-economic burden, evidenced by the surge in national funding for HIV/AIDS control from 160\u0026nbsp;million CNY in 2004 to 3.7\u0026nbsp;billion CNY in 2020[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe management of HIV has undergone a paradigm shift over the past decade, moving from deferred treatment to the universal and immediate initiation of ART for all people with HIV (PWH). This strategy, termed rapid ART initiation, was defined by the World Health Organization (WHO) as starting treatment within seven days of HIV diagnosis for all individuals, with same-day initiation encouraged for prepared patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Compelling evidence has demonstrated that rapid ART accelerated viral suppression, enhanced immunological recovery, improved treatment retention, and reduced HIV-related mortality and transmission[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, it has become a cornerstone of global efforts to achieve the UNAIDS 95-95-95 targets and is strongly endorsed by major international guidelines from the WHO, the U.S. Department of Health and Human Services (DHHS)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and the European AIDS Clinical Society (EACS)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Consistent with this global consensus, Chinese national guidelines have progressively incorporated rapid ART into clinical practice. The 2021 Chinese Guidelines for Diagnosis and Treatment of HIV/AIDS and the 2023 National Free ART Manual formally recommend ART initiation within 30 days of diagnosis, while actively encouraging a shorter interval (\u0026le;\u0026thinsp;7 days) for eligible patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Recommended first-line regimens for rapid initiation consist of two nucleoside reverse transcriptase inhibitors (NRTIs) plus a third agent, such as an integrase strand transfer inhibitor (INSTI) or a non-nucleoside reverse transcriptase inhibitor (NNRTI). Among these, the single-tablet regimen bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) and the multi-tablet regimen efavirenz/lamivudine/tenofovir disoproxil fumarate (EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF) are widely used in China, however, their comparative long-term effectiveness and economic value within rapid initiation frameworks remained insufficiently established[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis evidence gap was particularly salient for key populations, such as MSM, bearing a disproportionate burden of the HIV epidemic in China. While prior studies like the nationwide cohort analysis by Zhao et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and the randomized trial by Wang et al[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] have demonstrated the short-term efficacy and safety of rapid ART in Chinese MSM, critical questions persisted. Specifically, there remained a scarcity of robust, long-term real-world evidence comparing the clinical and economic outcomes of rapid versus non-rapid initiation strategies, especially when contrasting modern INSTI-based regimens (e.g., BIC/FTC/TAF) with traditional NNRTI-based regimens (e.g., EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo fill these gaps, we conducted a 96-week real-world cohort study to evaluate the comparative clinical effectiveness and cost-effectiveness of rapid versus non-rapid ART initiation, focusing on the BIC or EFV-based regimens among HIV-positive MSM in China. Our findings aimed to inform clinical practice and health policy by providing much-needed evidence on optimizing ART initiation strategies in real-world settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using a prospectively maintained clinical database of PWH at Beijing Youan Hospital, Capital Medical University. Since March 2021, this database has systematically collected standardized clinical information from all visiting patients.\u003c/p\u003e \u003cp\u003ePatients were retrospectively identified from this database between March 2021 and March 2023, with follow-up lasting until March 2025 (96 weeks). Eligible participants were adult patients (age\u0026thinsp;\u0026ge;\u0026thinsp;18 years) diagnosed with HIV-1 infection between March 2021 and March 2023 who initiated ART with either an EFV-based regimen (efavirenz 400 mg\u0026thinsp;+\u0026thinsp;lamivudine\u0026thinsp;+\u0026thinsp;tenofovir disoproxil fumarate) or a BIC-based regimen (bictegravir/emtricitabine/tenofovir alafenamide); ART initiation was classified as \"rapid\" (\u0026le;\u0026thinsp;14 days from diagnosis) or \"non-rapid\" (\u0026gt;\u0026thinsp;14 days from diagnosis). Based on the initiation timing and treatment regimen, patients were stratified into four mutually exclusive groups: G1: rapid initiation BIC group; G2: rapid initiation EFV group; G3: non-rapid initiation BIC group; G4: non-rapid initiation EFV group.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate the comparative clinical effectiveness of BIC/FTC/TAF versus EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF under rapid and non-rapid ART initiation in HIV-1-infected adult MSM in China. Additionally, a comprehensive cost-effectiveness evaluation was performed to compare the long-term economic outcomes across groups. Uncertainty analyses were conducted to assess result robustness and identify key influential parameters, thereby generating evidence to support clinical policy decision-making regarding rapid ART initiation in China.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant Recruitment and Eligibility\u003c/h3\u003e\n\u003cp\u003eEligible participants were required to meet the following inclusion criteria: (1) Initiating ART for the first time (ART-na\u0026iuml;ve); (2) Availability of complete baseline demographic information and clinical laboratory data; (3) Availability of at least baseline and follow-up data at either week 48 or week 96. Exclusion criteria included: (1) Pregnancy or lactation; (2) Co-existing other severe immunodeficiency diseases or ongoing immunosuppressive therapy; (3) Significant missing baseline data.\u003c/p\u003e \u003cp\u003eUltimately, 301 patients met the criteria and were included in the final analysis. The study was conducted in collaboration with Beijing Youan Hospital, with ethical approval obtained (Approval No: LL-2020-127-K) .The requirement for individual patient consent was waived by the ethics committee due to the retrospective nature of the study and the use of de-identified clinical data. The data were collected between March 2021 and March 2025.\u003c/p\u003e\n\u003ch3\u003eVariables and Data Sources\u003c/h3\u003e\n\u003cp\u003ePatient follow-up visits were conducted at baseline, and at weeks 4, 8, 12, 24, 36, 48, and 96. At each visit, measurements included body weight, sleep quality assessment, and evaluation of adverse reactions, along with tests for total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, alanine aminotransferase, aspartate aminotransferase, serum creatinine, and eGFR, Plasma HIV-1 RNA viral load ,CD4\u0026thinsp;+\u0026thinsp;T-cell count, CD8 count and the CD4/CD8 ratio. The clinical efficacy of EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF or BIC/FTC/TAF was assessed based on viral suppression and changes in CD4\u0026thinsp;+\u0026thinsp;T-cell count from baseline to week 48 and 96, under both rapid and non-rapid ART initiation strategies.\u003c/p\u003e \u003cp\u003eThe primary study endpoint was the proportion of participants achieving viral suppression (plasma HIV-1 RNA\u0026thinsp;\u0026lt;\u0026thinsp;50 copies/mL) at weeks 48 or 96. Secondary endpoints included treatment safety, virological efficacy within predefined subgroups, analyzed according to changes from baseline in CD4\u0026thinsp;+\u0026thinsp;T-cell count and viral suppression rate at weeks 48 and 96.\u003c/p\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eData cleaning addressed missing values, outliers, and inconsistencies. Incomplete or erroneous records were either corrected or excluded to ensure data quality. Records failing to meet follow-up requirements (e.g., incomplete baseline characteristics or missing CD4\u0026thinsp;+\u0026thinsp;T-cell count change data) were excluded. Baseline patient characteristics were summarized using descriptive statistics, with variables defined based on research objectives, including baseline characteristics (e.g., CD4\u0026thinsp;+\u0026thinsp;T-cell count, viral load), treatment regimen, and treatment outcomes (e.g., viral suppression rate).\u003c/p\u003e \u003cp\u003eTo address potential baseline confounding and ensure group comparability, we employed the cloning, censoring, and weighting (CCW) approach, which was a causal inference method that emulates a target randomized trial using observational data. This framework mitigates selection bias and time-varying confounding often encountered in conventional treatment comparisons[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEach participant was artificially cloned into four copies at baseline, with each copy assigned to one of the treatment strategies of interest (G1-G4). This cloning procedure aligns with the intention-to-treat principle by allowing all individuals to be eligible for each strategy, thereby breaking the link between baseline characteristics and treatment assignment. Thereafter, clones were dynamically censored once their observed treatment pathway deviated from their assigned strategy. For example, a patient assigned to rapid initiation with BIC but who switched to EFV by week 12 would have the corresponding clone censored at that time point. To account for selection bias introduced by censoring and to balance baseline covariates across strategy groups, we applied longitudinal inverse probability of censoring weights (IPCW). This weighting creates a pseudopopulation in which the assignment of treatment strategies is independent of baseline covariates, approximating the conditions of a randomized trial.\u003c/p\u003e \u003cp\u003eTwo primary analysis datasets were defined. The primary efficacy estimates were derived from the intention-to-treat (ITT) dataset, which included all cloned participants according to their randomly assigned treatment strategies, irrespective of subsequent treatment deviations or discontinuations. This approach preserved the baseline randomization mimicry achieved by cloning and provided an unbiased estimate of the strategy's effectiveness in a real-world clinical setting. To evaluate the robustness of our findings and to estimate efficacy under ideal conditions, a secondary per-protocol (PP) analysis was conducted. The PP dataset included only the clones who adhered to their assigned treatment strategy throughout the entire follow-up period. Results from this scenario analysis were presented as supplementary to the primary ITT analysis.\u003c/p\u003e \u003cp\u003eA comprehensive analysis of clinical characteristics was conducted within the MSM population, detailing baseline characteristics, disease progression, treatment responses, and adverse events. The effects of the two treatment regimens (BIC and EFV) across different initiation timings (rapid vs. non-rapid) were systematically evaluated in both the ITT and PP datasets. Key endpoints (CD4\u0026thinsp;+\u0026thinsp;T-cell count changes, viral suppression rate, discontinuation rate, adverse event incidence) were incorporated into an infectious disease dynamic model for cost-effectiveness evaluation.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as median and interquartile range (IQR) and compared using the Mann-Whitney U test. Categorical data were summarized as frequency and percentage, and comparisons were performed using the X\u003csup\u003e2\u003c/sup\u003e test or Fisher\u0026rsquo;s exact test, as appropriate. A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Additionally, 95% confidence intervals (CIs) were calculated for the difference in viral suppression rates and changes in CD4\u0026thinsp;+\u0026thinsp;T-cell counts. All statistical analyses were performed using R (The R Project for Statistical Computing, version 4.3.3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEconomic Evaluation Model\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eModel Design\u003c/h2\u003e \u003cp\u003eThis study integrated a decision tree-Markov model with an infectious disease dynamic model to simulate, respectively, the clinical progression and the population-level transmission of HIV among MSM in China and to evaluate the cost-effectiveness of treatment groups under different initiation conditions, model frameworks and datasets.\u003c/p\u003e \u003cp\u003eThe Markov model adopted a lifetime horizon with monthly cycles, whereas the infectious disease dynamic model projected outcomes over a 30-year time horizon using annual cycles[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Health states were defined based on treatment line (first-line, second-line regimen or treatment failure), viral suppression status (suppressed or unsuppressed), and immunological stage according to the WHO classification for HIV/AIDS (CD4\u0026thinsp;+\u0026thinsp;T-cell count: \u0026gt;500, 350\u0026ndash;500, 200\u0026ndash;350, 100\u0026ndash;200, and \u0026le;\u0026thinsp;100 cells/\u0026micro;L)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDetailed algorithms for state transitions, model explanations, and parameters were provided in the \u003cb\u003eAppendix\u003c/b\u003e. The overall model structure was summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eParameter Explanations in Markov Model: No_P\u003csub\u003e1\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/sub\u003e: Natural disease progression rates in ineffective states; ART_P\u003csub\u003ei1\u0026minus;5\u003c/sub\u003e: ART effectiveness rates (i means different treatment); d_n: Natural mortality rate; d_1\u0026thinsp;~\u0026thinsp;5: Additional mortality rate due to HIV/AIDS in different CD4\u0026thinsp;+\u0026thinsp;T-cell count health states; ade_1\u0026thinsp;~\u0026thinsp;5: Incidence rates of AIDS-defining events in different CD4\u0026thinsp;+\u0026thinsp;T-cell count health states; ae_i: Incidence rates of adverse events in different treatment; First-line_vd: Discontinuation rate of first-line treatment due to drug resistance; First-line_nd: Discontinuation rate of first-line treatment due to non-resistance reasons; Second-line_vd: Discontinuation rate of second-line treatment due to drug resistance; Second-line_nd: Discontinuation rate of second-line treatment due to non-resistance reasons.\u003c/p\u003e \u003cp\u003eParameter Explanations in Infectious Disease Dynamic Model: α: Number of individuals entering the model; γ: Natural mortality rate; δ: Additional mortality rate due to HIV/AIDS; ε\u003csub\u003e1\u0026minus;4\u003c/sub\u003e: Natural disease progression rates in ineffective states; ζ\u003csub\u003e1\u0026minus;4\u003c/sub\u003e: First-line treatment effectiveness rates; η\u003csub\u003e1\u0026minus;4\u003c/sub\u003e: Second-line treatment effectiveness rates; λ: Diagnosis and treatment rate; \u0026micro;: Viral suppression rate (effectiveness of first-line treatment); ξ: Viral suppression rate (effectiveness of second-line treatment); θ: Discontinuation rate of first-line treatment; κ: Discontinuation rate of second-line treatment; ω: Infection rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCost-effectiveness Analysis and Uncertainty Analysis\u003c/h3\u003e\n\u003cp\u003eA comparative evaluation of the two ART regimens was performed by calculating the incremental cost-effectiveness ratio (ICER) under rapid and non-rapid initiation strategies and across different datasets. Cost-effectiveness thresholds were defined based on China's 2024 per capita GDP. (\u003cspan\u003e$\u003c/span\u003e13,445)\u003c/p\u003e \u003cp\u003eTo assess parameter uncertainty, both one-way sensitivity analysis (OWSA) and probabilistic sensitivity analysis (PSA) were conducted. In the OWSA, cost and utility parameters were varied by \u0026plusmn;\u0026thinsp;20% from their baseline values, while clinical parameters were evaluated using their 95% CI. For the PSA, appropriate probability distributions were assigned to probabilities and costs. A cost-effectiveness acceptability curve (CEAC) was subsequently generated through Monte Carlo simulations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Outcomes Analysis\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003ePatient Enrollment\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e outlined the patient enrollment and follow-up flowchart. Between March 2021 and March 2022, a total of 340 newly diagnosed MSM HIV patients who met the inclusion criteria were enrolled. They were divided based on the interval from diagnosis to ART initiation.\u003c/p\u003e \u003cp\u003eAmong the 149 patients with rapid ART initiation (\u0026le;\u0026thinsp;14 days), 73 were treated with BIC/FTC/TAF and 76 with EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF. In this group, 13 patients were excluded: 4 due to withdrawal (loss to follow-up or transfer out) and 9 due to treatment failure. Subsequently, among the 73 BIC/FTC/TAF-treated patients who completed follow-up, 59 remained on the original regimen and 14 discontinued for proactive reasons (e.g., improving compliance). Among the 63 EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF-treated patients who completed follow-up, 38 remained on the original regimen, 16 discontinued due to passive reasons, and 9 discontinued for proactive reasons.\u003c/p\u003e \u003cp\u003eAmong the 191 patients without rapid ART initiation (\u0026gt;\u0026thinsp;14 days), 84 received BIC/FTC/TAF and 66 received EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF. In the BIC/FTC/TAF subgroup, 4 patients were excluded: 2 due to withdrawal and 2 due to treatment failure. In the EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF subgroup, 3 patients were excluded: 1 due to withdrawal and 2 due to treatment failure. Thereafter, among the 80 BIC/FTC/TAF-treated patients who completed follow-up, 71 remained on the original regimen, 6 discontinued due to passive reasons, and 3 discontinued for proactive reasons. Among the 63 EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF-treated patients who completed follow-up, 54 remained on the original regimen, 8 discontinued due to passive reasons, and 1 discontinued for proactive reasons.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePatient Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThe patient baseline characteristics were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age across all groups was approximately 31 years, with no significant between-group differences in either the unmatched (p\u0026thinsp;=\u0026thinsp;0.2503) or matched (ITT dataset; p\u0026thinsp;=\u0026thinsp;0.659) analyses. Regarding biochemical profiles, high-density lipoprotein cholesterol (HDL-C) levels differed significantly among unmatched groups (p\u0026thinsp;=\u0026thinsp;0.006), with the highest median values in G3 (1.02 mmol/L; IQR: 0.86\u0026ndash;1.14) and G4 (1.02 mmol/L; IQR: 0.82\u0026ndash;1.17), and the lowest in G1 and G2 (both 0.91 mmol/L; IQR, 0.80\u0026ndash;1.09 and 0.80\u0026ndash;1.01, respectively). Similarly, triglycerides showed significant variation (p\u0026thinsp;=\u0026thinsp;0.0413), with the highest median in G4 (1.38 mmol/L; IQR: 1.06\u0026ndash;1.93) and the lowest in G2 (1.12 mmol/L; IQR: 0.86\u0026ndash;1.64). Fasting blood glucose levels also exhibited substantial differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increasing progressively from G1 (4.99 mmol/L; IQR: 4.69\u0026ndash;5.47) to G3 (5.45 mmol/L; IQR: 5.18\u0026ndash;5.96). No other biochemical markers\u0026mdash;including AST, ALT, total cholesterol, LDL-C, and creatinine\u0026mdash;showed statistically significant differences in the unmatched analysis. After matching, all biochemical indicators, including HDL-C, triglycerides, and blood glucose, were balanced and showed no significant variation among groups (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For HIV-specific indicators at baseline, CD4\u0026thinsp;+\u0026thinsp;T-cell counts differed significantly in the unmatched analysis (p\u0026thinsp;=\u0026thinsp;0.0391), with the highest median in G2 (374.60 cells/\u0026micro;L; IQR: 262.98\u0026ndash;507.00) and the lowest in G3 (289.50 cells/\u0026micro;L; IQR: 167.75\u0026ndash;442.25). In contrast, CD4/CD8 ratio and plasma HIV-1 RNA levels were comparable across all unmatched groups (p\u0026thinsp;=\u0026thinsp;0.362 and p\u0026thinsp;=\u0026thinsp;0.7127, respectively). Following matching, all HIV-related baseline characteristics, including CD4\u0026thinsp;+\u0026thinsp;T-cell count, CD4/CD8 ratio, and HIV-1 RNA were well-balanced and showed no significant differences (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the matching procedure effectively minimized baseline confounding between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient Baseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e(A) Unmatched\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic, median (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG1: Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG2: Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG3: Non-Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG4: Non-Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitiation time, day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (2.00\u0026ndash;8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.00 (20.00-51.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.00 (18.00-34.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.19 (26.16\u0026ndash;36.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.48 (25.42\u0026ndash;37.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.59 (27.31\u0026ndash;35.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.48 (25.58\u0026ndash;46.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2503\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00 (21.00-28.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.00 (19.00\u0026ndash;28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00 (21.00\u0026ndash;35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.75 (21.25\u0026ndash;27.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALT,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.50 (17.00-32.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00 (15.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.00 (18.00-35.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.00 (15.25-29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 (3.76\u0026ndash;4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20 (3.77\u0026ndash;4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.45 (3.87\u0026ndash;4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.20 (3.83\u0026ndash;4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.80\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.80\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.86\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.82\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 (2.24\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (2.09\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83 (2.39\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.59 (2.32\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3555\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.02\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.86\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (0.98\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.38 (1.06\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood Glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99 (4.69\u0026ndash;5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.11 (4.69\u0026ndash;5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.45 (5.18\u0026ndash;5.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.25 (4.95\u0026ndash;5.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.50 (63.00\u0026ndash;75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.00 (64.00\u0026ndash;76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.00 (62.75\u0026ndash;75.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.00 (61.00-75.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV-related indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T-cell count at baseline, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350.25 (242.46-459.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e374.60 (262.98\u0026ndash;507.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e289.50 (167.75-442.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e333.50 (239.25-435.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD4/CD8 ratio at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 (0.20\u0026ndash;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37 (0.23\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32 (0.22\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34 (0.24\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasma HIV-1 RNA at baseline, copies/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,555.00 (5,396.50\u0026ndash;85,940.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23,239.00 (6,739.00\u0026ndash;67,267.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,370.50 (5,361.75-90,344.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,972.00 (5,710.00\u0026ndash;59,303.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(B) Matched (ITT dataset)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristic, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eG1: Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;72.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eG2: Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;73.81)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eG3: Non-Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;80.19)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eG4: Non-Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;65.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInitiation time, day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00 (2.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00 (2.00\u0026ndash;8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.00 (18.00\u0026ndash;33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.00 (20.00\u0026ndash;51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.08 (26.13\u0026ndash;37.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.47 (25.42\u0026ndash;37.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.34 (25.42\u0026ndash;46.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.48 (26.44\u0026ndash;36.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBiochemical indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.00 (20.00\u0026ndash;28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.00 (19.00\u0026ndash;28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.00 (21.00\u0026ndash;28.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.00 (21.00\u0026ndash;34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALT,n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.00 (17.00\u0026ndash;33.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00 (15.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.00 (16.00\u0026ndash;29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.00 (18.00\u0026ndash;34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 (3.75\u0026ndash;4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.19 (3.76\u0026ndash;4.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.20 (3.83\u0026ndash;4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45 (3.89\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.80\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.80\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.81\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.86\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.68 (2.23\u0026ndash;3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (2.07\u0026ndash;3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.60 (2.31\u0026ndash;3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.84 (2.42\u0026ndash;3.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30 (1.02\u0026ndash;1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.86\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (1.05\u0026ndash;1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39 (0.97\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood Glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.99 (4.69\u0026ndash;5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.07 (4.69\u0026ndash;5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.26 (4.99\u0026ndash;5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.45 (5.19\u0026ndash;5.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.00 (63.00\u0026ndash;75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.00 (63.00\u0026ndash;76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.00 (61.00\u0026ndash;76.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.00 (63.00\u0026ndash;75.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV-related indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T-cell count at baseline, cells/\u0026micro;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350.51 (246.83\u0026ndash;463.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e334.00 (236.50-465.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340.00 (249.00-473.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328.00 (203.00-509.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD4/CD8 ratio at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36 (0.21\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31 (0.22\u0026ndash;0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34 (0.24\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32 (0.24\u0026ndash;0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlasma HIV-1 RNA at baseline, copies/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,489.00 (4,643.00\u0026ndash;90,594.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,625.00 (7,060.00\u0026ndash;70,056.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,320.00 (5,694.00\u0026ndash;62,024.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24,595.00 (4,067.00\u0026ndash;77,321.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEfficacy Analysis\u003c/h2\u003e \u003cp\u003eTreatment persistence and immunological recovery were evaluated across the four study groups. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, a significantly higher proportion of participants in the non-rapid initiation groups remained on their initial regimen at the end of follow-up (G3: 84.52%; G4: 81.82%), compared to the rapid initiation groups (G1: 75.68%; G2: 53.25%; χ\u0026sup2; = 28.685, df\u0026thinsp;=\u0026thinsp;6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, the G2 group exhibited the highest rate of discontinuation due to treatment failure (11.69%) and the highest proportion of switches for reactive reasons such as drug resistance (65.22%). In contrast, the G1 group demonstrated a markedly higher rate of proactive regimen switches (93.76%), suggesting better tolerability or adherence management.\u003c/p\u003e \u003cp\u003eImmunological outcomes, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and corresponding longitudinal data, revealed consistent CD4\u0026thinsp;+\u0026thinsp;T-cell count recovery across all groups over 96 weeks. At baseline, no significant differences in median CD4\u0026thinsp;+\u0026thinsp;T-cell counts were observed between rapid and non-rapid initiation groups within each regimen (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). By week 48, the rapid initiation groups showed numerically greater increases in CD4\u0026thinsp;+\u0026thinsp;T-cell counts compared to non-rapid initiators, particularly in the EFV group (difference: 85.5 cells/\u0026micro;L, p\u0026thinsp;=\u0026thinsp;0.122). By week 96, this difference became statistically significant in the EFV group (144 cells/\u0026micro;L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the BIC groups maintained stable and comparable counts between rapid and non-rapid arms (3 cells/\u0026micro;L difference, p\u0026thinsp;=\u0026thinsp;0.613). Importantly, between-regimen comparisons at week 96 revealed a significant advantage of BIC over EFV in the non-rapid initiation group (151 cells/\u0026micro;L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTreatment Regimen Persistence and Discontinuation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG1: Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG2: Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3: Non-Rapid Initiation BIC group (n\u0026thinsp;=\u0026thinsp;84)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG4: Non-Rapid Initiation EFV group (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStatistical Test Results\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRemained on Initial Regimen (first-line treatment)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (75.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (53.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71 (84.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54 (81.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eχ\u0026sup2;(6)\u0026thinsp;=\u0026thinsp;28.685\u003c/p\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSecond-line Regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (21.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (29.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (10.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (13.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwitched for Reactive Reasons (e.g., drug resistance)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (6.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (65.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (88.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSwitched for Proactive Reasons (e.g., improving adherence)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (93.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (34.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (33.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (11.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiscontinuation due to Treatment Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (11.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (3.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiscontinued Study Participation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (2.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (2.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEconomic Outcomes Analysis\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eBase Case Analyses\u003c/h2\u003e \u003cp\u003eThe base-case analysis results were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Panels A and B present the findings from the Markov model over a lifetime horizon. When comparing the BIC group to the EFV group (Panel A), rapid initiation was associated with substantially higher incremental costs (\u003cspan\u003e$\u003c/span\u003e43,970.58) for a minimal gain in effectiveness (0.01 QALYs), resulting in an ICER of \u003cspan\u003e$\u003c/span\u003e5,637,253.31 per QALY gained (419.28 times the per-capita GDP). In contrast, non-rapid initiation of BIC versus EFV yielded an ICER of \u003cspan\u003e$\u003c/span\u003e4,330.04 per QALY gained (0.32 times GDP), indicating cost-effectiveness. When comparing rapid versus non-rapid initiation strategies (Panel B), the BIC group led to an ICER of \u003cspan\u003e$\u003c/span\u003e89,491.68 per QALY gained (6.66 times GDP), while the EFV group resulted in a highly favorable ICER of \u003cspan\u003e$\u003c/span\u003e395.88 per QALY gained (0.03 times GDP).\u003c/p\u003e \u003cp\u003ePanels C and D displayed the results from the infectious disease dynamic model over a 30-year horizon. The comparison of BIC versus EFV group (Panel C) showed that under both rapid and non-rapid initiation, the ICERs were markedly lower than in the Markov model, at \u003cspan\u003e$\u003c/span\u003e2,202.20 and \u003cspan\u003e$\u003c/span\u003e8,824.12 per QALY gained, respectively (both below 1 times GDP). The comparison of initiation strategies (Panel D) revealed that for the BIC group, rapid initiation was dominant (less costly and more effective) with an ICER of \u003cspan\u003e$\u003c/span\u003e419.41 per QALY. For the EFV group, rapid initiation was associated with an ICER of \u003cspan\u003e$\u003c/span\u003e134,509.38 per QALY gained (10.00 times GDP).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost-Effectiveness Results: Cost, Effectiveness and ICER\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e(A) Markov model-BIC group vs EFV group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eART regimens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIC, \u003cspan\u003e$\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIE, QALY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICER\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICER/GDP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43,970.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,637,253.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e419.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Rapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,814.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,330.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(B) Markov model-Rapid Initiation vs Non-Rapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eART regimens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIC, $\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIE, QALY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eICER\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eICER/GDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33,380.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89,491.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,224.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(C) Infectious disease dynamic model-BIC group vs EFV group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eART regimens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIC, $\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIE, QALY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eICER\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eICER/GDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,202.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Rapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,824.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(D) Infectious disease dynamic model-Rapid Initiation vs Non-Rapid Initiation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eART regimens\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIC, $\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIE, QALY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eICER\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eICER/GDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIC group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e419.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134,509.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003eAbbreviations: IC: incremental cost; IE: incremental effectiveness.\u003c/p\u003e\n\u003ch4\u003eSensitivity Analyses\u003c/h4\u003e\n\u003cp\u003eIn OWSA, conducted on the intention-to-treat dataset within the Markov model framework for the non-rapid BIC versus EFV group comparison, identified the drug cost of the BIC group as the primary driver of uncertainty. Discontinuation rate and viral suppression rate also emerged as influential parameters in other dataset or model scenarios, with the complete results visualized in \u003cstrong\u003eAppendix Figures 2-5\u003c/strong\u003e, panels A and B. Throughout the analysis, all outcome variations consistently remained within the pre-specified acceptable range defined by the willingness-to-pay (WTP) threshold.\u003c/p\u003e\n\u003cp\u003ePSA reflected substantial robustness of the model outcomes. The distribution of 10,000 ICERs calculated via PSA corroborated the stability of the base-case results, indicating consistent model performance. For example, the cost-effectiveness acceptability curve (CEAC) showed that at this WTP threshold (China’s 2024 per capita GDP), the probability ofthe non-rapid BIC group being cost-effective compared to the non-rapid EFV group was 100% in the ITT dataset based on the Markov model (\u003cstrong\u003eAppendix Figure 2-5\u003c/strong\u003e, panels C-F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis 96-week real-world cohort study provided comprehensive evidence on the comparative clinical and cost-effectiveness outcomes of rapid (≤14 days) versus non-rapid ART initiation with two widely prescribed first-line regimens (BIC/FTC/TAF and EFV+3TC+TDF), among HIV-positive MSM in China. Our findings demonstrated that rapid ART initiation was associated with superior long-term immunological recovery. Furthermore, BIC/FTC/TAF consistently showed advantages over EFV+3TC+TDF in immunologic efficacy, treatment persistence, and cost-effectiveness, which is attributed to its higher genetic barrier to resistance, better tolerability, and single-tablet formulation that improves adherence.\u003c/p\u003e\n\u003cp\u003eA key methodological strength of economic evaluation was the adoption of a hybrid modeling approach that integrated a decision tree-Markov model with an infectious disease dynamic transmission model. This dual-model framework captured both individual-level clinical progression and population-level HIV transmission dynamics, addressing a critical limitation of traditional Markov models, which often overlook the secondary transmission benefits of viral suppression. The decision tree-Markov model, structured according to WHO immunological staging and treatment lines, was well-suited to estimate lifetime costs and QALYs from a healthcare system perspective, reflecting the natural history of HIV and treatment pathways at the patient level[18, 20]. However, traditional Markov models often fail to account for the secondary transmission benefits of viral suppression, potentially underestimating the full value of effective ART[21]. To address this, we integrated a HIV-applicable Susceptible–Infectious–Removed (SIR) dynamic model, which projected the HIV epidemic among Chinese MSM over a 30-year horizon and incorporated behavioral parameters specific to this population, such as condom use rates and sexual contact patterns (\u003cstrong\u003eAppendix Table 7\u003c/strong\u003e)[17, 22]. This dual-model approach allowed us to not only evaluate the direct clinical and economic impacts of rapid versus non-rapid initiation but also to quantify the indirect benefits of reduced HIV transmission, leading to a critical consideration for public health planning and resource allocation. The divergence in results between the two models, particularly the more favorable cost-effectiveness of rapid initiation in the dynamic model, underscores the importance of capturing herd immunity effects in infectious disease economic evaluations[23, 24].\u003c/p\u003e\n\u003cp\u003eThe superior CD4+ T-cell count recovery observed in the rapid initiation groups, especially in G2, aligned with prior studies highlighting the benefits of early ART in preserving immune function and reducing time to viral suppression[4, 5, 12]. Notably, the 144 cells/μL difference in CD4+ T-cell count at week 96 between rapid and non-rapid EFV groups underscored the potential of rapid initiation to mitigate early immune decline, even with a less contemporary regimen. However, the fact that BIC/FTC/TAF achieved comparable CD4+ T-cell count gains regardless of initiation timing suggests that regimens with higher genetic barriers to resistance and better tolerability may be less dependent on initiation speed for optimal immunological outcomes[11, 25].\u003c/p\u003e\n\u003cp\u003eRegimen choice emerged as a critical determinant of long-term success. The significantly higher treatment persistence in the BIC groups (75.68%–84.52%) compared to the EFV group (53.25%) reflects the well-documented tolerability and safety profile of INSTI-based regimens[11, 26]. The high rate of proactive switches in the rapid BIC group (93.76%) further suggested that patients and clinicians might be more confident in managing side effects or adherence issues with BIC/FTC/TAF, whereas the EFV group experienced more reactive switches due to resistance and adverse events. This was consistent with earlier trials reporting higher discontinuation rates with EFV due to neuropsychiatric and metabolic side effects[27, 28].\u003c/p\u003e\n\u003cp\u003eOur economic evaluation further supported the preferential use of BIC/FTC/TAF, particularly in non-rapid initiation settings where it was highly cost-effective compared to EFV+3TC+TDF. The Markov model indicated that rapid BIC initiation was associated with a favorable ICER relative to China’s per-capita GDP, whereas the EFV-based rapid strategy, while clinically beneficial, was less economically attractive in the long term[29, 30]. The infectious disease dynamic model further highlighted the population-level benefits of rapid BIC initiation, which was both cost-saving and more effective over a 30-year horizon—a finding that underscores the public health value of combining modern regimens with rapid initiation protocols[17, 31].\u003c/p\u003e\n\u003cp\u003eThe robustness of our findings was confirmed through extensive sensitivity analyses, including one-way and probabilistic sensitivity analyses, which showed consistent results across a range of assumptions. The use of cloning, censoring, and weighting methods to emulate a target trial also strengthens the causal interpretation of our observational data, reducing biases inherent in conventional cohort studies[32].\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. First, despite rigorous statistical adjustment, unmeasured confounding might persist in this real-world study[33]. Second, the single-center design might limit generalizability, although the study population was representative of a key demographic in China’s HIV epidemic[34]. Third, we did not capture patient-reported outcomes or qualitative data on barriers to rapid initiation, which could inform implementation strategies[35]. Finally, while the 96-week follow-up was longer than many real-world studies, even longer-term outcomes beyond two years warranted further investigation[36].\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study supported the adoption of rapid ART initiation among MSM in China, particularly with BIC/FTC/TAF, which offered a favorable balance of efficacy, tolerability, and cost-effectiveness. These findings aligned with global trends toward INSTI-based regimens and same-day or rapid-start models, and they provided locally relevant evidence to inform updates to Chinese national HIV treatment guidelines[3, 9, 10].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eRapid initiation of ART led to excellent rates of virological suppression, superior long-term immunological recovery, and was shown to be cost-effective among HIV-positive MSM in China. Compared with the EFV+3TC+TDF, BIC/FTC/TAF demonstrated superior efficacy, economic benefit, and treatment durability. These findings support the prioritization of rapid ART initiation in clinical practice and national HIV treatment guidelines for this key population.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHIV: Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eAIDS: Acquired Immunodeficiency Syndrome\u003c/p\u003e\n\u003cp\u003eART: Antiretroviral Therapy\u003c/p\u003e\n\u003cp\u003eMSM: Men who have sex with men\u003c/p\u003e\n\u003cp\u003eCCW: Cloning, Censoring, and Weighting\u003c/p\u003e\n\u003cp\u003eQALY: Quality-adjusted Life Year\u003c/p\u003e\n\u003cp\u003eICER: Incremental Cost Effectiveness Ratio\u003c/p\u003e\n\u003cp\u003ePWH: People with HIV\u003c/p\u003e\n\u003cp\u003eNRTI: Nucleoside Reverse Transcriptase Inhibitor\u003c/p\u003e\n\u003cp\u003eNNRTI: Non-nucleoside Reverse Transcriptase Inhibitor\u003c/p\u003e\n\u003cp\u003eINSTI: Integrase Strand Transfer Inhibitor\u003c/p\u003e\n\u003cp\u003eIPCW: Inverse Probability of Censoring Weights\u003c/p\u003e\n\u003cp\u003eITT: Intention-to-Treat\u003c/p\u003e\n\u003cp\u003ePP: Per-Protocol\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u003c/p\u003e\n\u003cp\u003eOWSA: One-way Sensitivity Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSA: Probabilistic Sensitivity Analysis\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eDHHS: U.S. Department of Health and Human Services\u003c/p\u003e\n\u003cp\u003eUNAIDS: The Joint United Nations Programme on HIV/AIDS\u003c/p\u003e\n\u003cp\u003eWTP: Willingness-to-Pay\u003c/p\u003e\n\u003cp\u003eGDP: Gross Domestic Product\u003c/p\u003e\n\u003cp\u003eCPI: Consumer Price Index\u003c/p\u003e\n\u003cp\u003eAE: Adverse Event\u003c/p\u003e\n\u003cp\u003eADE: AIDS-Defining Event\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eEthical standards of the relevant national and institutional committees on human experimentation and the Helsinki Declaration of 1975, as revised in 2008 were followed in all procedures. Ethical approval was obtained from the Ethics Committee of Capital Medical University-affiliated Beijing Youan Hospital (Batch number: LL-2020-127-K). Participants also signed informed consent forms before data collection began.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3\u003eAuthors’ contributions\u003c/h3\u003e\n\u003cp\u003eKZ analyzed and interpreted the data, and was a major contributor in drafting and revising the manuscript. XW coordinated across multiple institutions and contributed to the study's conception and critical manuscript revisions. DZ contributed to data acquisition, visualization, and manuscript drafting. KZ, XW and DZ contributed to data acquisition, visualization, and manuscript revision. WT, and LD provided financial support, contributed to the study's conception, and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China, International Cooperative Research Project (2023YFVA1002).\u003c/p\u003e\n\u003ch3\u003eData Availability Statement\u003c/h3\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChinese Society of Infectious Diseases, AIDS and, Hepatitis, C. \u0026amp; Group Chinese Center for Disease Control and Prevention, China HIV/AIDS Diagnosis and Treatment Guidelines Xiehe Medical Journal, 2022. 13(02): pp. 203\u0026ndash;226.(in Chinese). (2021) Edition.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuguimoto, S. P. et al. Changing patterns of HIV epidemic in 30 years in East Asia. \u003cem\u003eCurr. HIV/AIDS Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (2), 134\u0026ndash;145 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Guideline on when to start antiretroviral therapy and on pre-exposure prophylaxis for HIV. (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/bitstream/handle/10665/186275/9789241509565_eng.pdf.?sequence=1\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/bitstream/handle/10665/186275/9789241509565_eng.pdf.?sequence=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyd, M. A. et al. Rapid initiation of antiretroviral therapy at HIV diagnosis: definition, process, knowledge gaps. \u003cem\u003eHIV Med.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (Suppl 1), 3\u0026ndash;11 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirometti, N. et al. Rapid ART start in early HIV infection: Time to viral load suppression and retention in care in a London cohort. \u003cem\u003eHIV Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (9), 613\u0026ndash;615 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomillia, C. E. S. et al. Rapid Antiretroviral Therapy (ART) Initiation at a Community-Based Clinic in Jackson, MS. \u003cem\u003eAIDS Res. Ther.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (1), 60 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepartment of Health and Human Services. Guidelines for the Use of Antiretroviral Agents in Adults and Adolescents with HIV. (2024). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aidsinfo.nih.gov/guidelines\u003c/span\u003e\u003cspan address=\"https://aidsinfo.nih.gov/guidelines\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean, A. I. D. S. \u0026amp; Clinical Society.. EACS Guidelines version 12.1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eacs.sanfordguide.com.2024\u003c/span\u003e\u003cspan address=\"https://eacs.sanfordguide.com.2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcquired Immunodeficiency Syndrome Professional Group Of Society Of Infectious, Diseases, C. M. A. \u0026amp; Chinese, C. F. D. C. Chinese Guideline for Diagnosis and Treatment of Human Immunodeficiency Virus Infection/Acquired Immunodeficiency Syndrome (2024 Edition). \u003cem\u003eMed. J. Peking Union Med. Coll. Hosp.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (6), 1261\u0026ndash;1288 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Center for AIDS/STD Control and Prevention. Chinese Center for Disease Control and Prevention. National Free AIDS Antiretroviral Treatment Manual. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrkin, C. et al. Fixed-dose combination bictegravir, emtricitabine, and tenofovir alafenamide versus dolutegravir-containing regimens for initial treatment of HIV-1 infection: week 144 results from two randomised, double-blind, multicentre, phase 3, non-inferiority trials. \u003cem\u003eLancet HIV\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e (6), e389\u0026ndash;e400 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, R. et al. Rapid Initiation of Antiretroviral Therapy With Coformulated Bictegravir, Emtricitabine, and Tenofovir Alafenamide Versus Efavirenz, Lamivudine, and Tenofovir Disoproxil Fumarate in HIV\u0026ndash;Positive Men Who Have Sex With Men in China: Week 48 Results of the Multicenter, Randomized Clinical Trial. \u003cem\u003eClin. Infect. Dis.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e (1), 169\u0026ndash;176 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y. et al. Immediate Antiretroviral Therapy Decreases Mortality Among Patients With High CD4 Counts in China: A Nationwide, Retrospective Cohort Study. \u003cem\u003eClin. Infect. Dis.\u003c/em\u003e \u003cb\u003e66\u003c/b\u003e (5), 727\u0026ndash;734 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, M. S. et al. Prevention of HIV-1 infection with early antiretroviral therapy. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e365\u003c/b\u003e (6), 493\u0026ndash;505 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundgren, J. D. et al. Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e373\u003c/b\u003e (9), 795\u0026ndash;807 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;n, M. A. How to estimate the effect of treatment duration on survival outcomes using observational data. \u003cem\u003eBMJ\u003c/em\u003e \u003cb\u003e360\u003c/b\u003e, k182 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. \u003cem\u003eConstructing a Dynamic Model to Evaluate the Effectiveness of HIV/AIDS Prevention and Control in China\u003c/em\u003e (Chinese Center for Disease Control and Prevention, 2014). (in Chinese).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGirouard, M. P. et al. The Cost-effectiveness and Budget Impact of 2-Drug Dolutegravir-Lamivudine Regimens for the Treatment of HIV Infection in the United States. \u003cem\u003eClin. Infect. Dis.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (6), 784\u0026ndash;791 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, S. et al. Cost-effectiveness of DTG\u0026thinsp;+\u0026thinsp;ABC/3TC versus EFV/TDF/FTC for first-line treatment of HIV-1 in the United States. \u003cem\u003eJ. Med. Econ.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (10), 763\u0026ndash;776 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno Guillen, S. et al. Cost-utility analysis of the fixed-dose combination of dolutegravir/abacavir/lamivudine as initial treatment of HIV\u0026thinsp;+\u0026thinsp;patients in Spain. \u003cem\u003eFarm. Hosp.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (5), 601\u0026ndash;610 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, M. S. et al. Antiretroviral treatment of HIV-1 prevents transmission of HIV-1: where do we go from here? \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e382\u003c/b\u003e (9903), 1515\u0026ndash;1524 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, R. et al. Optimising HIV pre-exposure prophylaxis and testing strategies in men who have sex with men in Australia, Thailand, and China: a modelling study and cost-effectiveness analysis. \u003cem\u003eLancet Glob Health\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (2), e243\u0026ndash;e256 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodger, A. J. et al. Sexual Activity Without Condoms and Risk of HIV Transmission in Serodifferent Couples When the HIV-Positive Partner Is Using Suppressive Antiretroviral Therapy. \u003cem\u003eJAMA\u003c/em\u003e \u003cb\u003e316\u003c/b\u003e (2), 171\u0026ndash;181 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodger, A. J. et al. Risk of HIV transmission through condomless sex in serodifferent gay couples with the HIV-positive partner taking suppressive antiretroviral therapy (PARTNER): final results of a multicentre, prospective, observational study. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e393\u003c/b\u003e (10189), 2428\u0026ndash;2438 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, I., Sun, H. \u0026amp; Hung, C. Meta-Analysis of Efficacy and Safety of Coformulated Bictegravir, Emtricitabine, and Tenofovir Alafenamide Among People Living with HIV. \u003cem\u003eInfect. Dis. Ther.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (3), 1331\u0026ndash;1346 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChivite, I. et al. Effectiveness, safety and discontinuation rates of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in people with HIV using real-world data: a systematic review and meta-analysis. \u003cem\u003eJ. Antimicrob. Chemother.\u003c/em\u003e \u003cb\u003e79\u003c/b\u003e (8), 1775\u0026ndash;1783 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrrell, C. et al. Efavirenz and rifampicin in the South African context: is there a need to dose-increase efavirenz with concurrent rifampicin therapy? \u003cem\u003eAntivir Ther.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (4), 527\u0026ndash;534 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolina, J. et al. Which HIV-infected adults with high CD4 T-cell counts benefit most from immediate initiation of antiretroviral therapy? A post-hoc subgroup analysis of the START trial. \u003cem\u003eLancet HIV\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (4), e172\u0026ndash;e180 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhillips, A. N. et al. Evaluation of antiretroviral regimen switching options in adults with HIV with sustained viral load non-suppression on dolutegravir, lamivudine, and tenofovir in eastern, central, southern, and western Africa: a modelling study. \u003cem\u003eLancet HIV\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e (8), e578\u0026ndash;e586 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRestelli, U. et al. Cost-effectiveness analysis of dolutegravir plus backbone compared with raltegravir plus backbone, darunavir\u0026thinsp;+\u0026thinsp;ritonavir plus backbone and efavirenz/tenofovir/emtricitabine in treatment na\u0026iuml;ve and experienced HIV-positive patients. \u003cem\u003eTher. Clin. Risk Manag\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 787\u0026ndash;797 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrebs, E. et al. Developing a dynamic HIV transmission model for 6 U.S. cities: An evidence synthesis. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (5), e0217559 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrnes, J. E. K. \u0026amp; Dee, L. E. Causal Inference With Observational Data and Unobserved Confounding Variables. \u003cem\u003eEcol. Lett.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), e70023 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBezabhe, W. M. et al. Barriers and facilitators of adherence to antiretroviral drug therapy and retention in care among adult HIV-positive patients: a qualitative study from Ethiopia. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (5), e97353 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGieselmann, L. et al. \u003cem\u003eProfiling a large HIV-1 elite neutralizer cohort reveals remarkable CD4bs bNAb for HIV-1 prevention and therapy\u003c/em\u003e (bioRxiv, 2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord, N. et al. The WHO public health approach to HIV treatment and care: looking back and looking ahead. \u003cem\u003eLancet Infect. Dis.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (3), e76\u0026ndash;e86 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLederman, M. M. et al. Immunologic failure despite suppressive antiretroviral therapy is related to activation and turnover of memory CD4 cells. \u003cem\u003eJ. Infect. Dis.\u003c/em\u003e \u003cb\u003e204\u003c/b\u003e (8), 1217\u0026ndash;1226 (2011).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, Men who have sex with men, Rapid ART initiation, Real-World cohort, Cost effectiveness","lastPublishedDoi":"10.21203/rs.3.rs-8529813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8529813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e International treatment guidelines recommend rapid initiation of antiretroviral therapy (ART) for individuals newly diagnosed with HIV-1 infection; however, robust long-term real-world evidence remains limited in China. We aimed to evaluate the clinical outcomes and cost-effectiveness of efavirenz (400 mg) plus lamivudine and tenofovir disoproxil fumarate (EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF) versus the coformulated bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in rapid (\u0026le;\u0026thinsp;14 days from diagnosis) versus non-rapid (\u0026gt;\u0026thinsp;14 days) initiation among HIV-diagnosed men who have sex with men (MSM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA real-world cohort of 301 ART-na\u0026iuml;ve HIV-positive MSM was stratified into four groups: rapid BIC (G1, n\u0026thinsp;=\u0026thinsp;74), rapid EFV (G2, n\u0026thinsp;=\u0026thinsp;77), non-rapid BIC (G3, n\u0026thinsp;=\u0026thinsp;84), non-rapid EFV (G4, n\u0026thinsp;=\u0026thinsp;66). Primary endpoint was CD4\u0026thinsp;+\u0026thinsp;T-cell count change from baseline at weeks 48 and 96; secondary endpoints included viral suppression (\u0026lt;\u0026thinsp;50 copies/mL) and treatment persistence. Confounding was mitigated via the cloning, censoring, and weighting (CCW) method to emulate a randomized controlled trial (RCT), with cost-effectiveness analyzed using a hybrid economic evaluation model referencing China\u0026rsquo;s 2024 per capita GDP.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt week 96, rapid initiation was associated with superior CD4\u0026thinsp;+\u0026thinsp;T-cell recovery, particularly in the EFV-based regimen (mean difference: 144 cells/\u0026micro;L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for rapid vs. non-rapid EFV). BIC/FTC/TAF demonstrated significantly better immunological recovery (mean difference: 151 cells/\u0026micro;L, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for non-rapid BIC vs. EFV) and higher treatment persistence (75.68%-84.52% vs. 53.25% for rapid EFV). Economic evaluation indicated a favorable profile for BIC/FTC/TAF.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRapid ART initiation achieved excellent virological suppression, superior long-term immunological recovery, and cost-effectiveness. Compared with EFV\u0026thinsp;+\u0026thinsp;3TC\u0026thinsp;+\u0026thinsp;TDF, BIC/FTC/TAF exhibited advantages in efficacy, economic value, and treatment persistence.\u003c/p\u003e","manuscriptTitle":"Clinical Impact and Cost-Effectiveness of Rapid vs. Non-Rapid Initiation Antiretroviral Therapy Regimens in HIV-Positive Men Who Have Sex With Men in China: A Study Combining Modelling and 96-Week Multicenter Cohort Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 13:34:03","doi":"10.21203/rs.3.rs-8529813/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T04:30:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T17:09:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T23:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202349627163119200431657625823844317079","date":"2026-01-28T15:18:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T20:20:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214789416628904423603153458734799643631","date":"2026-01-13T06:44:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164890265461502346463646015501113834000","date":"2026-01-09T22:43:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T13:43:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-07T12:42:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T03:02:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T03:01:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-06T09:51:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29e54433-0589-4d35-9c13-eb73a2484793","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60830396,"name":"Health sciences/Diseases"},{"id":60830397,"name":"Biological sciences/Immunology"},{"id":60830398,"name":"Health sciences/Medical research"},{"id":60830399,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-09T16:10:04+00:00","versionOfRecord":{"articleIdentity":"rs-8529813","link":"https://doi.org/10.1038/s41598-026-43075-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-08 15:59:59","publishedOnDateReadable":"March 8th, 2026"},"versionCreatedAt":"2026-01-09 13:34:03","video":"","vorDoi":"10.1038/s41598-026-43075-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43075-w","workflowStages":[]},"version":"v1","identity":"rs-8529813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8529813","identity":"rs-8529813","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}