Generalized Cost-Effectiveness Analysis to Assess Treatment Value in Hepatitis C

ABSTRACT

Objectives: To estimate the comprehensive value of direct-acting antivirals (DAAs) for the treatment of hepatitis C virus (HCV) compared with peginterferon alfa and ribavirin (PEG/riba) employing a generalized cost-effectiveness analysis (GCEA).

Study Design: To assess the societal-level cost-effectiveness of DAA treatment for HCV, we extended a previously published discrete-time Markov simulation model of HCV transmission and progression to include market dynamics and broader elements of value.

Methods: We followed a stepwise process to add novel value elements to a traditional CEA model for HCV treatments. For each additional element of value, we estimated incremental cost-effectiveness ratios (ICERs) of DAAs compared with PEG/riba.

Results: The health technology assessment (HTA)–style model yielded an ICER value of $64,512 per quality-adjusted life-year (QALY). Adding transmission dynamics resulted in an ICER value of $52,971 per QALY, whereas accounting for transmission dynamics and dynamic price and efficacy further decreased ICER values by 90% to $6406 per QALY. Incorporating genericization, productivity loss, caregiver spillover, and differential valuations of LYs vs quality of life, disease severity, and insurance value further decreased the ICER value to $4487 per QALY, a 93% reduction from the baseline HTA-style CEA to the fully realized GCEA.

Conclusions: Our GCEA study results confirm that DAAs are a cost-effective treatment for HCV compared with PEG/riba even when using conventional cost-effectiveness approaches. Incorporating broader elements of value resulted in more than a 10-fold improvement in cost-effectiveness, emphasizing the substantive impact of a generalized approach and the importance of incorporating GCEAs into decision-making.

Am J Manag Care. 2023;29(12):696-703. https://doi.org/10.37765/ajmc.2023.89468

Takeaway Points

A generalized cost-effectiveness analysis has the ability to more comprehensively estimate the value of innovative treatments than a traditional cost-effectiveness approach.

• Disease transmission is a critical dynamic to capture in a model for treatments of viral diseases that can affect viral load and the ability to infect additional individuals.
• Market dynamics on pricing and efficacy are key factors, with market competition and loss of exclusivity having the potential to substantially decrease prices from launch.
• Additional aspects of value, such as productivity, caregiver burden, quality of life compared with life-year extensions, disease severity, and insurance value, are important elements in estimating the full value of innovations in health care.

Long employed by health technology assessment (HTA) agencies and regulators, cost-effectiveness analyses (CEAs) have been increasingly used by health care decision makers in the United States to assess the comparative economic value of new and existing technologies. CEAs monetize health benefits and typically produce incremental cost-effectiveness ratios (ICERs) to estimate the costs required to gain an additional quality-adjusted life-year (QALY) under a new technology. There remains some debate regarding the validity of QALYs as a decision-making tool in health.^1,2 Additionally, questions remain around the ability of standard CEA approaches to adequately capture relevant drivers of value for a given treatment.^3,4

In 2018, The Professional Society for Health Economics and Outcomes Research (ISPOR) cautioned that traditional CEAs fail to account for key drivers of treatment value beyond posttreatment QALY gains.⁴ The report introduced a value flower, categorizing potential elements of treatment value into core, common, and potential/novel considerations for expanded CEA. Core elements include health gains (eg, increases in QALYs) and net costs of therapy (eg, difference between wholesale acquisition costs and rebates). More broadly used novel value elements include productivity gains (eg, increased work performance and prolonged working ages) and impact on informal caregivers (eg, reduced time away from work, lower levels of physical and emotional distress). Lastly, less commonly incorporated value elements include reduced uncertainty, improved future treatment options, and reductions in health inequity. At the heart of the value flower is the criticism that traditional CEAs employ a narrow definition of value, thereby inhibiting development and/or coverage of therapies that—by HTA standards—would not be considered “value for money.”⁵

This study aims to show that accounting for a broader definition of value through a generalized CEA (GCEA) more accurately reflects the true value that novel therapies can offer to society. To illustrate this argument, we conducted a class-level GCEA of direct-acting antiviral drugs (DAAs) for the treatment of chronic hepatitis C virus (HCV) genotype 1. We selected DAAs for the treatment of HCV because they are a uniquely well-suited technology to be evaluated in a GCEA due to (1) the vast public health impact of this viral disease, supporting the need for a societal perspective and (2) the duration of DAAs on the market, providing evidence of market dynamics for an innovative drug class.

To derive a comprehensive estimate of societal value for DAAs, we adapted the HCV Transmission and Progression (TaP) model, developed by Van Nuys and colleagues,⁶ into a GCEA, including additional novel aspects of value. The expanded CEA by Van Nuys and colleagues⁶ and several extensions published thereafter^7,8 contained elements not traditionally included in CEAs, such as life cycle pricing and productivity. Fully representing the value of HCV treatments requires considering novel aspects of value4 such as productivity, reduction in uncertainty, caregiver burden, and new scientific spillover.⁹ Since the launch of sofosbuvir in 2013, new DAAs have come to market, driving down prices and generating evidence on different elements of value.

METHODS

Model Overview

The HCV TaP model is a discrete-time Markov model simulating HCV treatment and progression as depicted in Figure A1 of Van Nuys et al.⁶ The simulation was conducted over a 70-year time horizon, and population outcomes (such as number of people in each disease state) are collected at the end of each cycle.

The simulation modeled the following 3 main subpopulations, categorized by HCV exposure risk:

• people who inject drugs (PWID), who have the highest risk of contracting HCV—the global seroprevalence of HCV is 52.9% among PWID¹⁰;
• HIV-infected men who have sex with men (MSM-HIV), who are at risk for HCV infection through sexual transmission, exacerbated by HIV coinfection¹¹; and
• other adults (OA), a catchall population composed of adults born before 1992 (when systematic testing of the blood supply was implemented in the United States). This group includes those infected by modes of transmission other than high-risk behaviors, including needle-stick pricks and blood transfusions. This group has minimal infectivity but represents a large number of prevalent cases in the population.¹² Given the minimal infectivity in this group, the model assumes there are no new infections in this cohort. Approximately 70% of infected individuals in this group are baby boomers (those born 1946-1964) who were primarily infected via blood transfusions.¹² In addition, persons infected with HCV in an occupational setting (eg, health care workers) are represented in this group, albeit at a small percentage (3%).¹³ Finally, HCV-infected persons with unknown exposure are included in this group, which is estimated at 20% to 30% of HCV cases.^13,14

In each cycle, cohorts transitioned through each disease state at assumed probabilities. Transmission occurred only within given subpopulations. States modeled include (1) not-infected states (ie, susceptible) or cured, (2) infected states, and (3) treatment states. The model defines infectious disease states as acute or chronic, with chronic consisting of 7 stages of liver damage: fibrosis stages F0 through F4 using the METAVIR—meta-analysis of histological data in viral hepatitis—scoring system, decompensated cirrhosis, and hepatocellular carcinoma. F0 is classified as having no fibrosis; F1, periportal fibrotic expansion; F2, periportal septa; F3, portocentral septa; and F4, compensated cirrhosis.¹⁵

HCV treatment in all scenarios depends on diagnosis and treatment rates. Diagnosis rates are 25%, 50%, and 75% for the infected PWID, OA, and MSM-HIV cohorts, respectively,¹⁶ and the treatment rate was 26% of the diagnosed population.¹⁷

Model Scenarios

This study evaluated the cost-effectiveness of DAAs vs status quo therapy of peginterferon alfa plus ribavirin (PEG/riba) in a baseline scenario consistent with a more conventional CEA approach. Additional value elements were added incrementally in the following order:

1. Disease transmission
2. Market pricing and competition effect on aggregate efficacy
3. Genericization after patent expiry
4. Expanded productivity costs
5. Caregiver burden
6. Differential values of LYs vs quality of life (QOL), disease severity, and insurance value (ie, the value a healthy individual places on a new medical technology’s or intervention’s ability to reduce financial and physical risk from a potential disease) as accounted for in the Generalized Risk-Adjusted Cost-Effectiveness (GRACE) framework.¹⁸

A detailed discussion of novel value parameter estimates (Table 1^6,16-34 [part A and part B]) as well as steps taken to implement the GRACE framework are presented in the eAppendix (available at ajmc.com).

Model Outcomes and Parameters

Parameters and outcomes of the GCEA are listed in Table 1.^6,16-34 Parameters in the original HCV TaP model include HCV treatment costs, medical expenditures (nontreatment medical costs), QOL weights, and mortality rates collected from the published literature. Treatment costs and mortality rates were reestimated, and all HCV TaP parameters were examined for relevance and accuracy and updated as needed.

We updated baseline mortality rates and the starting population total for the MSM-HIV subgroup from the HCV TaP model. Baseline mortality rates were previously calculated from the US Life Tables, 2010, and were updated using the US Life Tables, 2015.³⁵ The starting population total for the MSM-HIV subgroup was updated to match the CDC HIV Surveillance Report³⁶ cited by the TaP model. Although the previous model referenced a separate source for this parameter, this parameter was updated to match the CDC report to ensure consistency with the other starting population parameters.

All cost parameters were inflated to 2021 US$, and future costs and QALYs were discounted at 3% annually. Treatment costs, medical expenditures, and QOL weights varied by disease state, with late-stage cohorts (F3 and higher) experiencing higher treatment costs due to differing treatment durations.⁶ The model assumes that PWID have the same medical expenditures as the OA cohort and that nontreatment medical expenditures do not vary over time.

Sensitivity Analyses

Six sensitivity analyses (SAs) were conducted to test how model results respond to key assumptions around market dynamics, inflation, and time horizon.

SA 1 through SA 3: time horizon. The baseline model had a time horizon of 70 years. We also produced results for 5, 20, and 40 years.

SA 4: inflation adjustment. The baseline model applied a discount rate of 3% to future costs and benefits. We also produced results for a model that assumes health care costs have an inflation rate of 2.8% relative to an all-cost inflation rate of 2.5%.

SA 5 and SA 6: generic pricing discounts. Upon patent cliff, our baseline model assumes generic pricing based on Vondeling et al.¹⁹ We also tested impacts of setting generic pricing discounts at 30% and 99.6%37 of original branded prices.

Additionally, Van Nuys et al examined HCV TaP model sensitivity to several changes in key model inputs, including the initial size of the infected population, value of a QALY, retreatment rates for patients failing initial baseline therapy, diagnosis rates among patients in the health care exposure group, treatment rates following diagnosis, drug costs, and QOL in early disease states, finding that results remained consistent with the base case, as shown in Tables A8 and A9 of their study.⁶

RESULTS

Core Results

Across a reasonable range of model specifications and assumptions, DAAs are cost-effective HCV treatments compared with PEG/riba (Figure 1). The baseline HTA-style model yielded an ICER value of $64,512 per QALY, deemed cost-effective at conventional levels of $100,000 to $150,000 per QALY typically employed by HTA agencies. Adding transmission dynamics yielded an ICER value of $52,971 per QALY, in line with the strictest cost-effectiveness thresholds of $50,000 per QALY sometimes applied by HTA agencies. After accounting for transmission dynamics and dynamic price/efficacy, the ICER value decreased by 90% to $6406 per QALY, the largest decrease seen across all model graduations. When sequentially accounting for compounding additions of genericization, productivity loss, and caregiver spillover, the model yielded ICER values of $6194 per QALY, $5629 per QALY, and $5609 per QALY, respectively. In the final graduation of the model, we incorporated differential valuations of LYs vs QOL, disease severity, and insurance value through the GRACE framework, which further decreased the ICER value to $4487 per QALY. Overall, from the baseline HTA-style model to the fully actioned model, the ICER value decreased by 93%, from $64,512 per QALY to $4487 per QALY (Figure 2 and Table 2). Thus, after accounting for a wide range of novel value elements, DAAs are highly cost-effective treatments for HCV, even at the strictest cost-per-QALY thresholds applied by HTA agencies today. Additionally, the GRACE-adjusted willingness-to-pay threshold, as described by Lakdawalla and Phelps,¹⁸ suggests that any treatment for HCV should be considered cost-effective compared with PEG/riba at ICERs as high as $174,781 per QALY.

SA Results

Results were robust to SAs around medical cost inflation, model time horizon, and generic pricing discounts (Table 2). For the 20- and 40-year time horizon scenarios (SA 2 and SA 3), the ICERs decreased, indicating improved cost-effectiveness compared with baseline model results at 70 years ($913/QALY and $3259/QALY vs $4487/QALY, respectively). However, for the 5-year time horizon scenario (SA 1), the ICER value increased compared with core results at 70 years ($45,201/QALY vs $4487/QALY). Accounting for medical cost inflation (SA 4) did not significantly alter ICER values across model scenarios. For the genericization sensitivity analyses (dynamic market share for generic drugs and generic price discount of 30% [SA 5] or 99.6% [SA 6]), ICER values remained below $6500 ($4587/QALY and $4425/QALY, respectively).

Results confirm DAAs are cost-effective compared with PEG/riba across a wide range of scenarios, even prior to accounting for broader elements of value, and cost-effectiveness increases substantially when accounting for a wider definition of the potential value that DAAs can offer at the US societal level.

DISCUSSION

Based on the results of this analysis, DAAs are cost-effective therapies for the treatment of HCV across a range of scenarios and inputs. Compared with baseline CEA model outcomes, mirroring the traditional CEA approaches used by most HTA organizations, incorporating dynamic transmission and market pricing leads to the largest improvements in cost-effectiveness (decrease in ICER values of ~90%). This substantial decrease in ICER values emphasizes the significant amount of value missed if disease transmission is not incorporated when evaluating treatments of viral disease that not only treat infected individuals but also limit their ability to transmit the disease to others. Additionally, the importance of considering the impact of price competition between both multiple branded and generic pharmaceutical agents is evident in our model results. Therefore, incentivizing continued innovation in all treatment spaces to establish differentiating factors between therapies is critical. Between the baseline analysis and subsequent model graduations, productivity and caregiver burden had little impact on ICER value compared with transmission dynamics and dynamic price/efficacy. In consequence, future models examining the broader societal perspective of viral diseases ought to prioritize inclusion of market dynamics and other aspects of value to more accurately capture the comprehensive value a treatment offers.

The conclusions of this study align with similar analyses in the HCV therapeutic area, yet differences remain in key areas: Overall, trends in cost-effectiveness among this analysis, Van Nuys et al,⁶ and the Institute for Clinical and Economic Review (Tice et al)³⁸ are similar, indicating DAAs are a highly cost-effective treatment for HCV compared with PEG/riba. Moreover, DAAs were deemed cost-effective in baseline analyses, prior to incorporating additional elements of value.^6,38 Close examinations of the CEAs conducted by Van Nuys et al⁶ and Tice et al³⁸ elicited several differentiating factors between the 2 models. Van Nuys et al examined cost-effectiveness from a societal perspective while modeling specific health policy scenarios to determine their impact on health care expenditures with concern to varying high-risk patient populations with several genotypes of HCV.⁶ In comparison, Tice et al examined cost-effectiveness from the payer perspective while modeling the outcomes of 6 treatment regimens for HCV genotype 1 alone.³⁸ Tice et al examined differing health policy scenarios; however, scenarios were not the focus of the analysis, and patient populations were limited to treatment experienced and treatment naive in the base case.³⁸ The investigators considered a limited perspective on indirect cost, focusing on direct medical costs of treatment.³⁸ The analysis did not include broader elements of value.³⁸ Notably, Tice et al did not account for disease transmission. Similar to our analysis, Van Nuys et al incorporated transmission as an additional value element and found that as time progressed and transmission of disease declined, fewer patients required treatment relative to baseline.⁶ Furthermore, Van Nuys et al demonstrated lower ICER values, suggesting that omission of broader elements of value would underestimate the cost-effectiveness of viral disease treatments.^6,38

At the time of writing and to the best knowledge of the authors, this study is the first GCEA in the HCV therapeutic area, including a wide range of novel value elements that could be reasonably estimated from the published literature. The importance of novel elements of value has been demonstrated and explained in various published works. Inclusion of novel value elements, like those outlined by Lakdawalla et al,⁴ results in a more comprehensive approach to cost-effectiveness modeling and a more accurate reflection of the value a therapy can provide to society.4 Our study illustrates the importance of GCEA through a stepwise approach of layering on novel value elements to a baseline traditional CEA model. Compared with the base analysis, integrating dynamic transmission and pricing led to a 90% decrease in ICER value ($64,512 vs $6406). Augmenting the GCEA to include expanded productivity loss and caregiver burden led to a further, albeit much smaller, decrease in ICER value ($6406 vs $5609). This is notable because most current attempts at incorporating broader aspects of value typically consider only productivity loss and caregiver burden, which, in our model, are demonstrated to be smaller aspects of value. Lastly, when accounting for differential valuations of LYs vs QOL, disease severity, and insurance value through the GRACE framework, ICER values further decreased ($5609 vs $4487), reflecting uncertain aspects of disease particularly relevant for severe illnesses and infectious diseases.

In consequence, modeling guideline organizations, such as ISPOR, should consider developing best practices and recommendations for broader applications of a GCEA or elements of a GCEA to ensure appropriate valuation of medicines.

Limitations

Like any model, this analysis comes with limitations related to assumptions made, model capability to reflect real-world dynamics, and currently available knowledge about model parameters and underlying disease dynamics. Considerable uncertainty exists around the prevalence of HCV overall as well as the impact of the COVID-19 pandemic on intravenous drug use rates and overdose deaths in the PWID group of our model. Our estimates for both measures do not include potential COVID-19–related dynamics and are therefore likely conservative, potentially underestimating the societal value of DAAs. Similarly, when accounting for dynamic pricing and genericization, limited data were available to inform discounts once products become generic and discounts other products in the therapeutic area may experience and to gauge implications for shifting market shares of branded and unbranded products. To compensate, we employed an SA to explore broader ranges of discount rates and tested a 99% discount rate to examine a scenario on the potential impact that a radical shift in pricing strategies, such as those of the Mark Cuban Cost Plus Drug Company, might have for generic pricing.^37,39 The GRACE model is a newer framework for understanding risk reduction and insurance value offered by treatments. Because some required underlying inputs, such as societal risk preferences, have not been well established in the literature, we employed reasonable approximations.

In analyzing our results, we noted an interesting long-term trend of moderately increasing relative medical expenditures using DAAs. Because of the high efficacy associated with use of DAAs for HCV treatment, we observed a large increase in patients eligible for liver transplants post HCV cure, leading to increased expenditures associated with liver transplantation. From a broader societal perspective, this increase in expenditures could be offset by fewer HCV infections in the long run and potential reductions in the number of required future liver transplants. However, our analysis did not capture this aspect of value. Even though the base case time horizon is 70 years, the model does not allow for new market entrants or other innovations in the HCV space. Stagnation of innovation in the HCV space is unlikely, but hypothetical treatments or preventives would be difficult to incorporate (and might benefit from the existence of current DAAs in ways beyond current modeling capabilities) without invalidating the model.

CONCLUSIONS

Our GCEA study results confirm DAAs are cost-effective therapies compared with PEG/riba. Results were consistent across scenarios and model specifications, even prior to incorporating broader elements of value. Although DAAs were cost-effective by common thresholds in a traditional baseline HTA-style model, accounting for transmission dynamics, dynamic price/efficacy, genericization, productivity loss, caregiver spillover, and differential valuations of LYs vs QOL, disease severity, and insurance value further decreased ICER values by 93%. This illustrates the importance of incorporating novel elements of value to represent the full value that treatments contribute to society.

Author Affiliations: PRECISIONheor (JWC, MG, OD, IB, ZH, JB), Bethesda, MD.

Source of Funding: Funding for this study was provided by No Patient Left Behind.

Author Disclosures: Ms Chou is employed by PRECISIONheor, a research consultancy that provides health economics research services to life sciences companies, and holds equity with PRECISIONheor’s parent company, Precision Medicine Group. Dr Graf, Dr Diaz, Ms Brewer, Mr Heim, and Dr Baumgardner are employed by PRECISIONheor. All authors report receiving payment from No Patient Left Behind for their involvement in preparing this article.

Authorship Information: Concept and design (JWC, MG, OD, IB, ZH, JB); acquisition of data (MG, IB, ZH); analysis and interpretation of data (JWC, MG, OD, IB, ZH, JB); drafting of the manuscript (JWC, MG, OD, IB, ZH, JB); critical revision of the manuscript for important intellectual content (JWC, MG, OD, IB, ZH, JB); statistical analysis (MG, OD); obtaining funding (JWC, MG); administrative, technical, or logistic support (JWC, MG, IB, ZH); and supervision (JWC, MG).

Address Correspondence to: Jacquelyn W. Chou, MPP, MPL, PRECISIONheor, 2 Bethesda Metro Center, Ste 850, Bethesda, MD 20814. Email: jacki.chou@precisionvh.com.

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