ABSTRACT
-
Background/Aims
The World Health Organization set the goal of eliminating hepatitis C virus (HCV) by 2030, with 80% and 65% reductions in HCV incidence and mortality rates, respectively. We aimed to evaluate the health benefits, cost-effectiveness and return on investment (ROI) of HCV elimination.
-
Methods
Using an HCV transmission compartmental model, we evaluated the benefits and costs of different strategies combining screening and treatment for Chinese populations. We identified strategies to achieve HCV elimination and calculated the incremental cost-effectiveness ratios (ICERs) per disability-adjusted life year (DALY) averted for 2022–2030 to identify the optimal elimination strategy. Furthermore, we estimated the ROI by 2050 by comparing the required investment with the economic productivity gains from reduced HCV incidence and deaths.
-
Results
The strategy that results in the most significant health benefits involves conducting annual primary screening at a rate of 14%, re-screening people who inject drugs annually and the general population every five years, and treating 95% of those diagnosed (P14-R4-T95), preventing approximately 5.75 and 0.44 million HCV infections and deaths, respectively, during 2022–2030. At a willingness-to-pay threshold of $12,615, the P14-R4-T95 strategy is the most cost-effective, with an ICER of $5,449/DALY. By 2050, this strategy would have a net benefit of $120,997 million (ROI=0.868).
-
Conclusions
Achieving HCV elimination in China by 2030 will require significant investment in large-scale universal screening and treatment, but it will yield substantial health and economic benefits and is cost-effective.
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Keywords: Hepatitis C; Disease eradication; Cost-effectiveness analysis; Outcome assessment, health care; China
Study Highlights
• The costs, health benefits, and ROIs associated with various HCV elimination strategies were compared with those associated with the status quo in China.
• Compared with the status quo, HCV elimination strategies are cost-effective. The strategy with an annual primary screening rate of 14%, re-screening of the PWID every year and the general population every five years, and the treatment of 95% of diagnosed patients was the most cost-effective
• Achieving HCV elimination could yield a net economic benefit of $117–$121 billion by 2050, with an investment return ratio of 0.818–0.868.
Graphical Abstract
INTRODUCTION
Hepatitis C is a liver infection caused by the hepatitis C virus (HCV), and it can lead to liver fibrosis, cirrhosis and even hepatocellular carcinoma (HCC). Newly infected individuals are usually asymptomatic. At present, there is no vaccine available for HCV. Since the progression of hepatitis C is closely linked to the timing of diagnosis and treatment, screening and treatment are crucial in the prevention and control of HCV [
1].
China has the highest burden of hepatitis C disease, accounting for 16.7% of the global infected population, and approximately 45,000 patients die annually from hepatitis C-related cirrhosis and HCC [
2,
3]. In China, efforts to achieve the World Health Organization’s (WHO) targets for 2030, which include a 90% diagnosis rate, an 80% treatment rate, an 80% reduction in HCV infection incidence, and a 65% decrease in HCV-related mortality compared with 2015 [
4], will have a significant and positive impact on the prevention and control of HCV worldwide.
The diagnosis rate of hepatitis C in China is low, at only 22.51%, and the treatment rate is 3.49% [
5]. With the advent of highly effective direct-acting antivirals (DAAs) in China, the latest Chinese Guidelines for the Prevention and Treatment of Hepatitis C (2022 version) recommend HCV testing for all adults to facilitate early screening and treatment, thereby minimizing the public health impact of hepatitis C [
6]. However, the considerable cost of a universal screening programme, combined with limited financial resources, necessitates that the potential health and economic benefits of the HCV elimination project be considered for China while determining future courses of action. Recently, several countries have assessed the time frame for achieving elimination and evaluated the cost-effectiveness and impact on the disease burden of hepatitis C [
7-
10]. However, no studies have been conducted to provide a comprehensive economic evaluation of HCV elimination strategies in China. Therefore, the aim of this study was to evaluate the health impact and cost-effectiveness of different HCV elimination strategies to determine the optimal screening strategy and assess the return on investment (ROI) of elimination strategies from a long-term perspective to inform policy development for HCV elimination in China.
MATERIALS AND METHODS
Study design
In this economic evaluation, we assessed the cost-effectiveness of various HCV elimination strategies in China on the basis of our previous HCV dynamic transmission model [
11,
12]. We first modelled the health effects of 81 strategies for the years 2022–2030 to identify the strategies that could be used to achieve the 2030 WHO incidence and mortality reduction targets, referred to as target-achieving strategies. We then compared the costs and benefits of all target-achieving strategies and identified strategies at the cost-effectiveness frontier (frontier strategies) and the most cost-effective strategy (optimal strategy). In addition, we extended the simulation through 2050 to assess the long-term benefits of HCV elimination strategies. Uncertainty analyses were performed to assess the impact of uncertainty in the model parameters on the results. Model construction and data analysis were performed in R software (R version 4.0.5;
http://www.r-project.org). This study was reported following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [
13].
For the compartmental model, population growth, the HCV care cascade and liver disease stages were incorporated to simulate HCV transmission and the natural history of hepatitis C among the general population and people who inject drugs (PWID) in China and to obtain the incidence and deaths associated with HCV infection. The model includes the following population groups: the general population aged 0–19 years, the general population aged 20–34 years, the general population aged 35+ years, PWID aged 20–34 years, PWID aged 35+ years, ex-PWID aged 20–34 years, and ex-PWID aged 35+ years. Simulated individuals transition between states that represent susceptible, infected, and recovered states owing to spontaneous viral clearance or a sustained virological response (SVR). Individuals were further classified on the basis of their care cascade status, including unscreened, screened but undiagnosed, diagnosed, receiving treatment, treatment success and treatment failure. Only individuals who tested positive for both anti-HCV antibody (Ab) and RNA were diagnosed with HCV infection. Individuals with chronic infection gradually progress to liver fibrosis in stages (F0–F3), followed by cirrhosis (F4), decompensated cirrhosis (DC), HCC, and even death. The model structure was shown in
Supplementary Figure 1.
The model was calibrated using demographic data for 2021, epidemiological characteristics of PWID, national survey anti-HCV prevalence data from 2006, anti-HCV prevalence among PWID in 2010, and treatment numbers from 2009 to 2014. We used the least squares method and differential evolutionary algorithm to identify the best-fit parameter sets. The calibration process was repeated until 100 sets of best-fit parameters were obtained. This enabled the reporting of uncertainty intervals (UIs). We compared the model outputs with data not used for calibration or parameterization to validate the model. The validation results indicated that the 2015 prevalence rates for anti-HCV (95% UI 0.89–1.04%) and overall HCV (0.60–0.72%) aligned with the published estimates, which were 0.93–1.49% and 0.5–0.8%, respectively [
14]. The results of calibration and validation were shown in
Supplementary Figures 2-
6.
Information on population size, age and sex distribution was obtained from the State Statistical Bureau of China for the general population [
15,
16]. Estimates of anti‐HCV prevalence among the Chinese general population were obtained from a 2006 national survey [
17]. The proportion of PWID among the whole population and the anti-HCV prevalence among PWID in China were taken from recent systematic reviews [
18,
19]. More information on the model parameters is listed in
Supplementary Table 1, and all calibration data are presented in
Supplementary Table 2.
We incorporated both the direct costs associated with screening, treatment, and disease management, as well as the indirect costs related to lost productivity for each strategy. We followed Scott’s methodology for evaluating the economics of global HCV elimination of [
7]. The cost of the screening included the price of the Ab test and HCV-RNA test, which were $3.88 and $46.51, respectively, as obtained from the Health Care Security Administration [
20]. Treatment costs included the cost of pretreatment liver tests, medication costs, and regular monitoring costs [
21,
22]. We assumed that disease management costs were included only for diagnosed HCV-infected patients and varied with the stage of liver disease. Disease management costs were derived from an economic analysis based on real-world data that investigated annual disease management costs associated with stages F0–F4, DC, and HCC for hepatitis C patients in nine economic regions [
23].
Indirect costs refer to the loss of value to society and households due to reduced work time (absenteeism), reduced work capacity (presenteeism) and premature deaths of the workforce associated with hepatitis C, which are calculated by multiplying the potential productive years lost due to hepatitis C by the gross domestic product (GDP) per capita in China. Days of productivity lost due to absenteeism were derived from a cross-sectional study comparing this metric between individuals with non-cirrhosis (17 days) and cirrhosis (44 days) status using a standardized questionnaire, and from a multicenter cross-sectional study in China evaluating this metric in patients with HCC (73 days) [
24,
25]. Data on presenteeism were obtained from a published study that estimated the impact of hepatitis C on labor force participation and productivity [
26]. The model was designed to assume that if people with HCV infection at stage F4-HCC achieved SVR, they would have a 44% reduction in the number of days of productivity lost by 44.0% if they achieved SVR [
27]. To calculate the years of potential productive life lost due to premature death, we dynamically tracked the population who died due to hepatitis C, starting at the age of death for males up to 60 years and females up to 55 years, which is the mandatory retirement age in China, or until they died from other causes. All costs were expressed in 2021 US dollars via the government-reported exchange rate ($1=¥6.45, Sep 2021) [
28]. The cost and productivity parameters used in the model are listed in
Table 1
Disability-adjusted life years (DALYs) were used to evaluate the health outcomes of implementing different intervention strategies and were calculated by summing years of life lost and years lived with disability, with DALYs averted being the net health benefit. Disability weights for progressive stages of chronic hepatitis C were taken from the Global Burden of Disease Study [
29,
30]. All costs and DALYs were discounted at 5% per year on the basis of the China Guidelines for Pharmacoeconomic Evaluations, and discounts of 0% and 8% were tested in sensitivity analyses [
31].
We defined a status quo scenario in which current HCV screening and treatment policies remained constant from 2022 onward. We modelled strategies that encompassed primary screening, re-screening, and treatment in alignment with the WHO’s 2030 elimination targets: 90% of patients with HCV infection will be diagnosed and 80% of those who are diagnosed will be treated [
4]. For primary screening, individuals who were never tested for HCV Abs were targeted via five strategies with annual screening rates of 6%, 8%, 10%, 12%, and 14%. The model was designed to assume that all Ab+ individuals underwent HCV RNA testing. Re-screening involved retesting individuals who were previously screened but undiagnosed or those who were diagnosed and cured. There were four rescreening strategies: no re-screening (R1), annual rescreening of PWID but not the general population (R2), annual re-screening of PWID and the general population every 10 years (R3), and annual re-screening for PWID and every five years for the general population (R4). In these screening strategies, high-risk populations were prioritized to prevent more new infections. With this model, we emphasize PWID because injection drug use is now the primary route of HCV transmission in China. We designed scenarios with varying treatment coverage rates (80%, 85%, 90%, 95%) for each screening strategy to assess the influence of screening and treatment on HCV elimination in China. Individuals remained in treatment until they either achieved SVR or experienced three failures. Our model was used to simulate a total of 81 HCV elimination strategies. The sensitivity of the anti-HCV test is 98.1%, and the specificity is 99.8% [
32].
We calculated the hepatitis C prevalence, incidence, and deaths for each strategy, and identified elimination strategies that would yield results meeting the WHO 2030 elimination targets. From a health system perspective, by incorporating the costs of screening, treatment and disease management, we estimated the costs and health benefits of each target-achieving strategy from 20022 to 2030. Additionally, we estimated the incremental cost-effectiveness ratios (ICERs, costs per DALY averted) between the status quo and the different HCV elimination strategies. In accordance with the China Guidelines for Pharmacoeconomic Evaluations, the threshold for willingness-to-pay (WTP) was set at China’s GDP per capita, which was $12,615 in 2021 [
31]. Moreover, from a social perspective (accounting for both direct costs and costs of lost productivity), we calculated the net economic benefits of each strategy from 2022 to 2050 by subtracting the baseline costs from the total cumulative costs of each strategy. We further estimated the ROI by dividing these net benefits by the corresponding investment costs. The ROI is a performance measure used to evaluate the efficiency or profitability of an investment and to compare the efficiency of multiple investments. When the ROI of a healthcare intervention has a positive value, it generally indicates that the intervention is potentially profitable [
33].
We performed one-way sensitivity analyses to evaluate the impact of individual variables on the model results, within the plausible ranges or ±20% of the baseline values of the parameters. Furthermore, to assess the joint uncertainty of all the parameters in the model simultaneously, we also performed a probabilistic sensitivity analysis (PSA) with 1000 Monte Carlo simulations, sampling the parameters from their specific distributions. The results of the PSA were used to calculate the probability of the strategy being cost-effective.
Considering that the price of DAAs may decrease if generic DAAs are introduced, we conducted a scenario analysis of the price of DAAs to explore changes in the strategies used. In addition, we conducted a scenario analysis to assess the impact of implementing strategies from 2024 onward on health outcomes and economic benefits. The use of HCV self-testing as an additional screening method provides privacy and convenience. Recent report suggests that universal HCV self-testing is cost-effective [
34]. Therefore, we also conducted a scenario analysis using HCV self-testing in place of Ab testing to evaluate its impact on cost [
35].
RESULTS
Under the status quo HCV screening and treatment scenario, projections indicated an increase in HCV infections to 8,265,404 and HCV-related deaths to 763,569 between 2022 and 2030. This translated to a total of 13,088,386 people with hepatitis C by 2030, a significant increase from the 2015 burden. In 2015, the hepatitis C incidence and mortality rates were 57.93 per 100,000 population and 4.47 per 100,000 population, respectively. For the strategies evaluated, the incidence was projected to be 60.63-3.32 per 100,000 population by 2030, and the liver-related mortality was 3.63–1.28 per 100,000 population. The incidence and liver-related mortality rates projected by 2030 for each strategy are shown in
Table 2. A review of 80 scale-up strategies with different screening and treatment intensities revealed changes in HCV incidence ranging from +5.3% to –94.23%, and HCV-related mortality changes between –18.75% and –71.44% compared with those in 2015 (
Table 3). Among these strategies, 40 achieved an 80% reduction in incidence, but only 12 achieved both an 80% reduction in incidence and a 65% reduction in mortality.
Strategies that could be used to achieve the WHO 2030 HCV elimination targets were listed in
Table 4. The results suggested that to achieve HCV elimination by 2030, the primary screening rate should be increased to at least 10% per year. This should be accompanied by annual re-screening of PWID and five-year re-screening of the general population. In addition, 80% of those diagnosed should receive treatment. Compared with the status quo scenario, the most aggressive strategy, P14-R4-T95, would result in 2,512,779 new HCV infections and 320,116 related deaths by 2030, averting 5,752,625 infections and 443,453 deaths (
Table 4).
Between 2022 and 2030, the status quo scenario was projected to cost $5,751 million ($828 million from screening, $1,296 million from treatment, and $3,627 million from disease management) and result in 12.927 million DALYs. Among the 12 strategies proposed for HCV elimination by 2030, the estimated total direct costs ranged from $43,145 million to $46,051 million, with the number of DALYs averted ranging from 6,628 million to 7,395 million. Among these strategies, strategy P10-R4-T80 had the lowest projected cost, whereas strategy P14-R4-T95 was the most expensive. From a health system perspective, compared with those of the status quo, the ICERs for these strategies range from $5,449 to $5,642 per DALY averted by 2030. In particular, strategy P14-R4-T95 had the lowest ICER. However, all these strategies were considered cost-effective, with a WTP threshold of $12,615 per DALY averted.
Furthermore, a direct comparison was made across the 12 strategies. After excluding the strategies that were dominated or extended dominated, the ICERs were calculated for the remaining strategies and the cost-effectiveness frontier is shown in
Figure 1. The frontier comprised four HCV elimination strategies: P10-R4-T80, P10-R4-T90, P10-R4-T95, and P14-R4-T95. Among all the strategies evaluated, the P14-R4-T95 strategy was the most cost-effective, with an ICER of $4,614 per DALY averted compared with the P10-R4-T95 strategy.
Each of the 4 frontier strategies was projected to have a positive net benefit value and ROI by 2050 (
Table 5). The implementation of the most cost-effective strategy, P14-R4-T95, between 2022 and 2050 necessitates an estimated investment of $139,383 million. This includes direct costs of $77,845 million and indirect costs of $61,538 million. The expected net benefit resulting from this strategy equates to $120,997 million, corresponding to an optimal ROI of 0.868.
The results of the one-way sensitivity analyses revealed that variations in individual variables had no substantial effect on the results (
Fig. 2). Compared with the status quo, the ICER for strategy P14-R4-T95 ranged from $3,872 to $6,658 per DALY averted. The model was most sensitive to the spontaneous clearance rate, transition probability of F4 to HCC, and Ab test cost. The probability of a strategy being cost-effective is related to the WTP threshold (
Fig. 3). When the WTP threshold was less than $1,892/DALY, P10-R4-T80 had a 100% probability of being cost-effective. However, the probability of P14-R4-T95 being cost-effective was greatest when the threshold exceeded $3,735/DALY, and is 100% when the threshold exceeds $7,570/DALY. The ICERs of different target-achieving strategies compared with the status quo decreased as the price of DAAs decreased. Notably, the P14-R4-T95 strategy consistently remains the most cost-effective option, even as the DAA price decreases (
Supplementary Table 3). If interventions are implemented from 2024 onward, all four frontier strategies are expected to achieve the target of an 80% reduction in incidence by 2030. However, a 65% reduction in mortality would not be achieved until approximately 2032 (
Supplementary Table 4). Starting the intervention in 2024 was expected to result in an increase in cumulative incidence and deaths by 2050 compared with those starting in 2022. For strategy P14-R4-T95, the total costs for 2024–2050 were projected to be $145,373 million, with additional expenditures of $5,991 million and a net benefit reduction of $4,324 million compared with starting implementation in 2022 (
Supplementary Table 5). With self-testing, the total cost of screening would be significantly lower. For strategy P14-R4-T95, the total cost was projected to be $4,931 million lower than that of Ab screening by 2030 (
Supplementary Table 6), and the total cost savings for this strategy were projected to reach $10,395 million by 2050 (
Supplementary Table 7).
DISCUSSION
We used a dynamic HCV transmission model based on Chinese populations to simulate HCV epidemiology under different strategies and evaluate their cost-effectiveness and social ROI. Our study shows that all strategies that could be used to achieve HCV elimination by 2030 are cost-effective compared with the status quo. The strategy that offers the greatest value for money requires an annual primary screening rate of 14%, annual re-screening of PWID, and re-screening of the general population every five years, in addition to treating 95% of those diagnosed. This strategy results in an additional cost of $40.3 million compared with the status quo in 2022–2030, but averts $7.395 million in DALYs, resulting in an ICER of $5,449 per DALY averted. Additionally, this strategy results in a social ROI of $120,997 million due to reduced HCV-related productivity losses. Our analysis also demonstrates that delaying the initiation of HCV elimination efforts leads to a higher cumulative disease burden and costs, along with a reduced ROI. Therefore, early intervention is important to maximizing health benefits and economic returns.
Given the economic and healthcare resource disparities across Chinese regions and provinces, the high expenditure associated with the P14-R4-T95 strategy may be a significant challenge for less affluent areas. Therefore, strategies that are less expensive but still likely to result in HCV eliminating, such as the P10-R4-T80 approach, might be preferable in those areas. By 2022, eight DAAs had been covered by basic health insurance following national price negotiations. However, the cost of hepatitis C treatment remains high for patients. The healthcare sector could negotiate a more competitive price with manufacturers on the benefits of expanding coverage of screening and treatment, making HCV elimination more cost-effective. In addition, the introduction of generic DAAs represents a significant opportunity to improve the cost-effectiveness of HCV elimination strategies, while also reducing the financial burden of implementing these strategies [
36]. The use of simple self-testing methods can significantly reduce the cost of screening, and further expansion of the use of such methods is expected to yield even more significant benefits [
34].
The model results indicate that re-screening plays an important role in improving health outcomes. This is because without re-screening, individuals, particularly high-risk groups such as PWID, remain at risk of transmitting HCV even after primary screening, which contributes to a continued increase in infection rates. Moreover, these individuals may lose the opportunity for timely diagnosis and treatment, exacerbating transmission dynamics and leading to a significant increase in incidence. Therefore, re-screening, especially in high-risk groups, is essential. Given the higher rates of hepatitis C transmission and prevalence among PWID, more frequent screening interventions are needed to effectively identify HCV-infected individuals.
In this present study, we assessed the costs required and the economic benefits generated by HCV elimination by considering both direct costs and lost productivity costs, identifying the most cost-effective pathway to elimination. Similar to this study, previous studies have shown that scaling up screening and treatment for HCV is highly cost-effective and even cost-saving in many settings, such as Pakistan, Myanmar, and Egypt [
7,
8,
37]. Therefore, the findings of the present study provide convincing information for policy makers.
In our study, we identified cost-effective strategies for HCV elimination, but the feasibility of modelled interventions needs to be further discussed. Obviously, increasing annual screening rates to at least 10% and treating at least 80% of newly diagnosed infections is challenging and ambitious. However, in recent years, countries such as Egypt and Georgia have implemented strategies to expand diagnosis and treatment coverage. Egypt, in particular, may be the first country in the world to eradicate hepatitis C completely [
38,
39]. In addition, on the basis of the successful experience of universal screening and tracking for the prevention and control of the COVID-19 pandemic in China [
40], the target of scaling up HCV screening and treatment is plausible and achievable. Although scaling up screening and treatment would require significant resources and costs, it appears to remain a feasible option given China’s strategic direction. China is focused on achieving universal screening, diagnosis, and treatment of HCV within an integrated healthcare delivery system [
41]. Moreover, the Chinese government has proposed a program for hepatitis C elimination from 2021 to 2030, which sets a target of testing over 95% of newly reported antibody-positive individuals for HCV RNA and achieving an antiviral treatment rate of over 80% for diagnosed patients by 2030 [
42]. Our findings indicate that while ensuring a high testing rate for newly reported antibody-positive individuals is important, it alone is insufficient. The study highlights the need to increase both initial screening and rescreening efforts to identify potential cases, thereby maximizing the impact on reducing the hepatitis C disease burden and supporting the achievement of elimination goals. These actions demonstrate China’s commitment to eliminating hepatitis C.
Currently, there is a lack of public awareness of HCV infection, and China needs to strengthen HCV awareness and education and encourage the public to actively screen and prioritize high-risk populations [
5,
41]. Given the large population size and high investment costs, to increase the accessibility of screening and availability of medicines, governments need to establish more transparent and competitive tendering processes to negotiate special prices for HCV testing and treatment to be used in elimination programs. Numerous people who test positive for HCV Abs are not appropriately treated, reducing the benefit of screening [
43]. It is therefore necessary to establish a comprehensive HCV cascade of care monitoring and management mechanisms to minimize loss to follow-up between the testing and treatment stages and to improve treatment rates among newly diagnosed individuals.
Our model-based study demonstrates the effectiveness and economics of eliminating hepatitis C in China, but there are several limitations that must be acknowledged. First, in our model, we did not consider loss to follow-up during the test-to-treat period, which may have resulted in overestimation of the benefits of public health strategies. Second, we calculated the economics of averted production losses using per capita GDP, which may overestimate the ROI of the intervention because the employment or income of people living with HCV is probably lower. Third, the disability weights for HCV-related diseases used in the model were obtained from the Global Burden of Disease Study, which does not cover all HCV infection disease stages and may not fully represent the conditions of patients with hepatitis C in China. Fourth, the data sources used for modelling were from different time periods, which may introduce discrepancies between the historical data and more recent trends in HCV prevalence and treatment patterns. Finally, the indirect cost may have been overestimated because absenteeism was included in the indirect cost estimation and the productivity loss cost due to premature death was estimated by applying the human capital approach.
The target of eliminating hepatitis C by 2030 could be achieved in China through large-scale universal screening and treatment. The most cost-effective strategy is to screen 14% of the initial population annually, re-screen PWID annually and re-screen the general population every 5 years, while treating 95% of those diagnosed. To achieve the target, significant investments would be required, but such investments would be worthwhile from both health and economic perspectives, as this achievement would prevent 5.75 million new HCV infections and 0.44 million HCV-related deaths in China between 2022 and 2030, and even save approximately US$121 billion, yielding an ROI of 0.868 by 2050.
FOOTNOTES
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Authors’ contribution
MW, JM, XWang, XWan developed the model and performed the analyses, and interpreted the results. MW, OX, SL, and AdL collected data and drafted the manuscript. AL and XWan contributed to the conception, design of the primary model. MW, CT, and XWan had access to and verified the data. MW, AL, and XWan were responsible for the decision to submit the manuscript. All authors have read and approved the final article.
-
Acknowledgements
This work was supported by a grant from the National Natural Science Foundation of China (grant number 71874209); the research project of the Health Commission of Hunan province (grant number 202113050283); the Fundamental Research Funds for the Central Universities of Central South University (grant number 2023ZZTS0924); Hunan Provincial Natural Science Foundation of China (grant number 2023JJ60503). The authors would like to thank the High Performance Computing Center of Central South University for partial support of this work. AGL acknowledges support from the UK National Institute for Health and Care Research (NIHR) Health Protection Research Unit (HPRU) in Behavioural Science and Evaluation at the University of Bristol (NIHR200877). AGL also acknowledges support from the Wellcome Trust (WT 220866/Z/20/Z) and UK NIHR (NIHR133208). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
-
Conflicts of Interest
The authors have no conflicts to disclose.
SUPPLEMENTAL MATERIAL
Supplementary material is available at Clinical and Molecular Hepatology website (
http://www.e-cmh.org).
Supplementary Figure 1.
Model structure. F0-F3, liver fibrosis stages; F4, compensated cirrhosis; DC, decompensated cirrhosis; HCC, hepatocellular carcinoma; SVR, sustained virological response; PWID, people who inject drugs.
Supplementary Figure 2.
Model calibration results. (A) Population change targets for 1950 to 2021. (B) 2021 population age proportion. (C) 2006 age-specific anti-HCV prevalence. (D) Number of HCV treatments. Results are the median of 100 model runs, with shaded areas and error bars indicating 95% uncertainty intervals.
Supplementary Figure 3.
Model projections for anti-HCV prevalence with the status quo scenario. The asterisk and error bar show the point estimate and 95% uncertainty interval of available anti-HCV prevalence data, respectively. Our model’s projections indicate an estimated overall prevalence of anti-HCV at 0.98% (0.89–1.04%) in 2015, which is within the range of 2015 anti-HCV prevalence estimated by experts (1.21% [0.93–1.49%]).
Supplementary Figure 4.
Model projections for overall chronic HCV prevalence with the status quo scenario. The asterisk and error bar show the point estimate and 95% uncertainty interval of available HCV prevalence data (0.7% [0.5–0.8%]), respectively. Our model projections revealed an estimated overall HCV prevalence of 0.63% (0.60–0.72%) in 2015, which is close to available HCV prevalence data that were not used for parameterization or calibration.
Supplementary Figure 5.
Validation of model outputs against CCDC reported new infection diagnoses. The shaded area represents a ±20% deviation from the reported value. CCDC, Chinese Center for Disease Control and Prevention.
Figure 1.Cost-effectiveness frontiers of 12 strategies that could achieve hepatitis C elimination targets. The solid line is the cost-effectiveness frontier, strategies above the frontier are dominated by strategies on the frontier. DALY, disability-adjusted life year; GDP, gross domestic product.
Figure 2.One-way sensitivity analyses for the P14-R4-T95 strategy vs. status quo strategy. ICER, incremental cost-effectiveness ratio; DALY, disability-adjusted life year; TP, transition probability; DAAs, direct antiviral agents; F0–F3, liver fibrosis stages; F4, compensated cirrhosis; DC, decompensated cirrhosis; HCC, hepatocellular carcinoma.
Figure 3.Cost-effectiveness acceptability curves for the four strategies on the cost-effectiveness frontier. DALY, disability-adjusted life year.
Table 1.Cost and productivity parameters used in the model
Table 1.
|
Parameter |
Baseline value |
Range |
Source |
|
Direct costs, US$ |
|
|
|
|
Anti-HCV test |
3.88 |
±20% |
[20] |
|
HCV self-test |
1.55 |
- |
[35] |
|
HCV-RNA test |
46.51 |
±20% |
[20] |
|
DAA treatment* (per month) |
510 |
±20% |
[21] |
|
Liver fibrosis test |
21 |
±20% |
[22] |
|
Routine surveillance |
302 |
±20% |
[22] |
|
Annual healthcare |
|
|
|
|
Stage F0-F3 |
881 |
596–1,167 |
[23] |
|
Stage F4 |
2,509 |
889–4,129 |
[23] |
|
Stage DC |
5,588 |
3,395–7,780 |
[23] |
|
Stage HCC |
11,794 |
8,482–15,108 |
[23] |
|
Cost per year of productive life lost |
12,615 |
Fixed |
[31] |
|
Productivity parameters |
|
|
|
|
Days of absence due to hepatitis C |
|
|
|
|
Stage F0-F3 |
17 |
7–43 |
[24] |
|
Stage F4-DC |
44 |
17–79 |
[24] |
|
Stage HCC |
73 |
58–88 |
[25] |
|
Relative reduction in absenteeism if SVR |
|
|
|
|
Stage F4-HCC |
0.44 |
0.352–0.528 |
[27] |
|
Disability weights |
|
|
|
|
Stage F0-F2 |
0.006 |
0.002–0.012 |
[29] |
|
Stage F3-F4 |
0.051 |
0,032–0,074 |
[29] |
|
Stage DC |
0.178 |
0.123–0.250 |
[29] |
|
Stage HCC |
0.569 |
0.389–0.727 |
[29] |
|
Percentage of hepatitis C-related deaths in different age groups in 2019 |
|
|
|
|
15–29 |
0.005 |
Fixed |
[30] |
|
30–49 |
0.144 |
Fixed |
[30] |
|
50–59 |
0.200 |
Fixed |
[30] |
|
60–69 |
0.260 |
Fixed |
[30] |
|
70+ |
0.392 |
Fixed |
[30] |
Table 2.HCV incidence and HCV-related deaths for each strategy by 2030 (per 100,000 population)
Table 2.
|
Re-screening |
Treatment |
Incidence
|
Mortality
|
Primary screening
|
Primary screening
|
|
6% |
8% |
10% |
12% |
14% |
6% |
8% |
10% |
12% |
14% |
|
R1 |
80% |
60.63 |
59.17 |
57.83 |
57.83 |
58.02 |
3.63 |
3.33 |
2.96 |
2.79 |
2.74 |
|
85% |
60.59 |
59.11 |
57.78 |
57.81 |
58.01 |
3.61 |
3.31 |
2.93 |
2.78 |
2.73 |
|
90% |
60.56 |
59.05 |
57.74 |
57.78 |
57.99 |
3.59 |
3.29 |
2.91 |
2.76 |
2.72 |
|
95% |
60.52 |
58.99 |
57.69 |
57.77 |
57.98 |
3.57 |
3.26 |
2.88 |
2.75 |
2.71 |
|
R2 |
80% |
17.51 |
16.21 |
14.07 |
13.65 |
13.55 |
3.48 |
3.18 |
2.83 |
2.62 |
2.56 |
|
85% |
17.25 |
15.95 |
13.77 |
13.42 |
13.35 |
3.46 |
3.16 |
2.80 |
2.60 |
2.54 |
|
90% |
17.04 |
15.72 |
13.53 |
13.24 |
13.17 |
3.44 |
3.14 |
2.78 |
2.58 |
2.53 |
|
95% |
16.84 |
15.51 |
13.32 |
13.08 |
13.02 |
3.42 |
3.12 |
2.75 |
2.56 |
2.52 |
|
R3 |
80% |
11.12 |
9.85 |
7.71 |
7.20 |
7.07 |
2.64 |
2.35 |
2.00 |
1.84 |
1.78 |
|
85% |
10.82 |
9.55 |
7.40 |
6.91 |
6.80 |
2.61 |
2.32 |
1.97 |
1.82 |
1.75 |
|
90% |
10.56 |
9.29 |
7.14 |
6.68 |
6.57 |
2.59 |
2.29 |
1.93 |
1.79 |
1.73 |
|
95% |
10.32 |
9.04 |
6.91 |
6.46 |
6.37 |
2.56 |
2.26 |
1.90 |
1.77 |
1.71 |
|
R4 |
80% |
8.13 |
6.36 |
4.73 |
4.15 |
4.01 |
2.20 |
1.91 |
1.56 |
1.41 |
1.35 |
|
85% |
7.86 |
6.06 |
4.43 |
3.88 |
3.74 |
2.17 |
1.88 |
1.52 |
1.38 |
1.32 |
|
90% |
7.61 |
5.81 |
4.17 |
3.65 |
3.52 |
2.14 |
1.85 |
1.50 |
1.35 |
1.30 |
|
95% |
7.39 |
5.57 |
3.94 |
3.45 |
3.32 |
2.11 |
1.81 |
1.47 |
1.33 |
1.28 |
Table 3.Changes in HCV incidence and HCV-related deaths for each strategy by 2030 compared to 2015 (%)
Table 3.
|
Re-screening |
Treatment |
Incidence
|
Mortality
|
Primary screening
|
Primary screening
|
|
6% |
8% |
10% |
12% |
14% |
6% |
8% |
10% |
12% |
14% |
|
R1 |
80% |
5.39 |
2.85 |
0.52 |
0.52 |
0.86 |
–18.75 |
–25.53 |
–33.74 |
–37.49 |
–38.65 |
|
85% |
5.33 |
2.75 |
0.44 |
0.48 |
0.83 |
–19.18 |
–26.02 |
–34.37 |
–37.85 |
–38.93 |
|
90% |
5.27 |
2.64 |
0.36 |
0.44 |
0.81 |
–19.60 |
–26.49 |
–34.97 |
–38.18 |
–39.18 |
|
95% |
5.20 |
2.53 |
0.28 |
0.41 |
0.79 |
–20.05 |
–26.99 |
–35.60 |
–38.49 |
–39.43 |
|
R2 |
80% |
–69.56 |
–71.82 |
–75.54 |
–76.27 |
–76.44 |
–22.21 |
–28.79 |
–36.73 |
–41.41 |
–42.82 |
|
85% |
–70.01 |
–72.28 |
–76.06 |
–76.68 |
–76.80 |
–22.64 |
–29.26 |
–37.33 |
–41.90 |
–43.14 |
|
90% |
–70.39 |
–72.68 |
–76.47 |
–76.99 |
–77.10 |
–23.05 |
–29.72 |
–37.90 |
–42.30 |
–43.39 |
|
95% |
–70.73 |
–73.04 |
–76.84 |
–77.26 |
–77.37 |
–23.48 |
–30.21 |
–38.49 |
–42.62 |
–43.64 |
|
R3 |
80% |
–80.67 |
–82.88 |
–86.60 |
–87.49 |
–87.71 |
–40.90 |
–47.41 |
–55.21 |
–58.76 |
–60.19 |
|
85% |
–81.20 |
–83.41 |
–87.13 |
–87.99 |
–88.18 |
–41.52 |
–48.10 |
–55.99 |
–59.32 |
–60.81 |
|
90% |
–81.64 |
–83.86 |
–87.59 |
–88.40 |
–88.58 |
–42.13 |
–48.76 |
–56.74 |
–59.85 |
–61.31 |
|
95% |
–82.06 |
–84.28 |
–87.99 |
–88.76 |
–88.94 |
–42.77 |
–49.46 |
–57.51 |
–60.43 |
–61.81 |
|
R4 |
80% |
–85.87 |
–88.95 |
–91.77 |
–92.79 |
–93.04 |
–50.76 |
–57.28 |
–65.08 |
–68.47 |
–69.88 |
|
85% |
–86.34 |
–89.46 |
–92.30 |
–93.26 |
–93.50 |
–51.42 |
–58.00 |
–65.89 |
–69.13 |
–70.44 |
|
90% |
–86.77 |
–89.90 |
–92.74 |
–93.65 |
–93.89 |
–52.06 |
–58.69 |
–66.53 |
–69.73 |
–70.94 |
|
95% |
–87.15 |
–90.31 |
–93.16 |
–94.00 |
–94.23 |
–52.73 |
–59.42 |
–67.21 |
–70.33 |
–71.44 |
Table 4.Effectiveness and cost-effectiveness of various strategies to achieve hepatitis C elimination, 2022–2030
Table 4.
|
Strategy |
Cumulative incidence |
Cumulative deaths |
Prevalence in 2030 |
Direct Costs ($ millions)
|
DALY (thousand) |
Averted DALY (thousand) |
ICER ($/DALY)
|
|
Screen |
Treatment |
Disease management |
Incremental costs |
VS status quo |
VS previous*
|
|
Status quo |
8,265,404 |
763,569 |
13,088,386 |
828 |
1,296 |
3,627 |
- |
12,927 |
- |
- |
- |
|
P10-R4-T80†
|
2,874,617 |
366,596 |
1,507,586 |
8,457 |
19,341 |
15,347 |
37,393 |
6,299 |
6,628 |
5,642 |
- |
|
P10-R4-T85 |
2,804,904 |
359,866 |
1,442,575 |
8,455 |
19,464 |
15,540 |
37,707 |
6,187 |
6,740 |
5,594 |
ED |
|
P10-R4-T90 |
2,735,258 |
353,203 |
1,377,907 |
8,455 |
19,581 |
15,731 |
38,016 |
6,075 |
6,852 |
5,548 |
2,778 |
|
P10-R4-T95 |
2,661,737 |
345,962 |
1,316,114 |
8,455 |
19,710 |
15,940 |
38,353 |
5,954 |
6,973 |
5,500 |
2,780 |
|
P12-R4-T80 |
2,798,074 |
351,884 |
1,268,054 |
8,881 |
19,691 |
15,805 |
38,625 |
6,058 |
6,869 |
5,623 |
D |
|
P12-R4-T85 |
2,725,338 |
345,000 |
1,215,494 |
8,881 |
19,804 |
16,002 |
38,935 |
5,943 |
6,984 |
5,575 |
ED |
|
P12-R4-T90 |
2,655,188 |
338,206 |
1,169,711 |
8,881 |
19,908 |
16,198 |
39,235 |
5,830 |
7,097 |
5,528 |
ED |
|
P12-R4-T95 |
2,580,017 |
330,847 |
1,124,822 |
8,881 |
20,016 |
16,408 |
39,553 |
5,707 |
7,220 |
5,478 |
ED |
|
P14-R4-T80 |
2,731,490 |
341,234 |
1,204,596 |
9,191 |
19,808 |
16,121 |
39,368 |
5,884 |
7,043 |
5,590 |
D |
|
P14-R4-T85 |
2,658,203 |
334,311 |
1,159,301 |
9,188 |
19,903 |
16,317 |
39,656 |
5,768 |
7,158 |
5,540 |
D |
|
P14-R4-T90 |
2,587,343 |
327,491 |
1,118,603 |
9,188 |
20,009 |
16,509 |
39,955 |
5,655 |
7,272 |
5,494 |
ED |
|
P14-R4-T95 |
2,512,779 |
320,116 |
1,079,108 |
9,187 |
20,147 |
16,717 |
40,300 |
5,532 |
7,395 |
5,449 |
4,614 |
Table 5.Effectiveness and return on investment of frontier strategies, 2022–2050
Table 5.
|
Strategy |
Cumulative incidence |
Cumulative deaths |
Prevalence in 2050 |
Direct Costs ($ millions)
|
Indirect Costs ($ millions)
|
Net benefit ($ millions) |
ROI |
|
Screening |
Treatment |
Healthcare |
Excluding absenteeism and premature deaths |
Absenteeism and premature deaths |
|
Status quo |
29,985,034 |
3,784,076 |
19,458,491 |
1,393 |
2,760 |
9,276 |
97,603 |
149,347 |
- |
- |
|
P10-R4-T80*
|
3,120,830 |
561,115 |
35,469 |
18,260 |
20,728 |
36,930 |
29,527 |
37,800 |
117,135 |
0.818 |
|
P10-R4-T90 |
2,956,380 |
542,210 |
33,524 |
18,253 |
20,841 |
37,149 |
28,763 |
36,645 |
118,728 |
0.838 |
|
P10-R4-T95 |
2,873,721 |
532,209 |
32,589 |
18,250 |
20,916 |
37,270 |
28,352 |
36,024 |
119,568 |
0.849 |
|
P14-R4-T95 |
2,706,443 |
501,120 |
31,499 |
18,925 |
21,183 |
37,737 |
27,349 |
34,189 |
120,997 |
0.868 |
Abbreviations
HCV antibody seroprevalence
disability-adjusted life year
differential evolutionary algorithm
incremental cost-effectiveness ratio
probabilistic sensitivity analysis
sustained virologic response
World Health Organization
years lived with disability
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, Xiaomin Wan1,2
