The American Journal of Managed Care
Special Issue: HCV
Volume 22
Issue SP 6

Costs and Spillover Effects of Private Insurers' Coverage of Hepatitis C Treatment

Expanding private-payer coverage of hepatitis C treatment may yield significant long-term cost savings for private payers, reduced costs to Medicare, and increased social value.


Objectives: Hepatitis C virus (HCV) treatment incentives for private payers may be misaligned because payers must bear immediate costs and may not realize long-term benefits. However, these benefits may accrue to future payers, including Medicare. We examined how and to what extent private payers’ current HCV treatment coverage decisions impact Medicare’s and private payers’ future costs.

Study Design: Discrete-time Markov model.

Methods: We modelled HCV disease progression and transmission to simulate the economic and social effects of different private-payer HCV treatment scenarios on Medicare. The model examined differences between a baseline scenario (current practice guidelines) and 2 alternative scenarios that expand treatment coverage. Spillover effects were measured as reduced HCV treatment costs and medical expenditures in Medicare. We calculated the spillover effects and net social value of each scenario (total value of quality-adjusted life-years accrued over time minus cumulative treatment and medical costs).

Results: With expanded HCV treatment coverage, private payers experience reduced medical expenditures in the 3-to-5-year time horizon; however, they still face higher treatment costs. Over a 20-year horizon, private payers experience overall savings of $10 billion to $14 billion after treatment costs. The expansion of coverage by private payers generates positive spillover benefits to Medicare of $0.3 billion to $0.7 billion over a 5-year horizon, and $4 billion to $11 billion over a 20-year horizon.

Conclusions: When private payers increase HCV treatment coverage, they may achieve significant savings while inducing spillover benefits to Medicare. Future savings, however, may not motivate immediate treatment investments among private payers who experience high beneficiary turnover.

Am J Manag Care. 2016;22(5 Spec Issue No. 6):SP236-SP244

Take-Away Points

Although the treatment costs for hepatitis C virus (HCV) are borne immediately, many benefits of treatment (eg, improved quality of life, longer survival) accrue in the future. Private payers with high beneficiary turnover may be reluctant to cover HCV treatment today, but foregoing treatment today results in larger future healthcare costs for both Medicare and private payers.

  • Expanding private coverage of HCV treatments would generate $10 billion to $14 billion in private-payer savings and $4 billion to $11 billion in spillover benefits to Medicare after 20 years.
  • Costs of private-coverage expansion exceed benefits for private payers in the short term; however, this investment would break even after 16 to 17 years.
  • Total social value of comprehensive HCV treatment is positive for society, but private insurers do not have the incentive to provide comprehensive HCV treatment.

Hepatitis C virus (HCV) infection is a slowly progressing disease that often remains asymptomatic for at least 10 years post exposure. Left untreated, HCV infection can lead to serious hepatic complications, liver failure, and death.1-5 Through recent pharmaceutical breakthroughs, short courses of treatment with direct-acting antivirals (DAAs) provide a high likelihood of cure.6,7 DAAs are the current standard of care in the United States, but they generate higher upfront costs relative to previous treatments.

The higher cost and efficacy of DAAs present a quandary for health system decision makers: treatment costs are borne in the short term, yet a significant portion of the benefits, including reduced disease transmission and HCV-related costs, accrue over the longer term. Few doubt that patients with HCV should be treated with these highly effective novel therapies; the salient policy question is when treatment should occur. A growing body of research indicates that early HCV treatment likely generates the greatest value for patients and society7; however, it is much less clear that early treatment generates benefits for the private payers providing this treatment to patients with HCV. Private payers retain enrollees less than 10 years on average,8 which discourages investments in therapies with long-term benefits. These incentives may conflict with the value other payers receive when they enroll patients who were treated earlier in their disease state, before complications arose.7

The misalignment between short-term costs and long-term benefits is of particular concern for Medicare, which insures individuals 65 or older.9 An estimated 75% of individuals currently infected with HCV in the United States are baby boomers (born between 1945 and 1965)—most of whom will be enrolled in Medicare within the next 10 to 15 years.10 Further, the majority of HCV-positive baby boomers was likely infected decades ago, and therefore, is at serious risk of disease complications when entering Medicare.11

Because private insurers currently cover a large population of HCV-positive baby boomers, their HCV coverage decisions today will impact their own costs, as well as the long-term costs borne by the publicly funded Medicare system.12 In this article, we simulate the transmission and progression of HCV under various private-payer HCV treatment coverage policies, quantify how these polices affect healthcare costs and benefits borne by private insurers and Medicare, and compute the social impact of these policies.


Overview of the Markov Model of HCV Transmission and Disease Progression

We developed a discrete-time Markov model (Microsoft Excel 2010/VBA, Microsoft Corporation, Redmond, Washington) that incorporates patient insurance status, categorized as Medicare, Medicaid, private, or uninsured. We quantified the downstream indirect effects on Medicare from earlier private-insurer investment in HCV treatment, which we refer to as “spillover effects”13,14 (see Figure 1; a detailed description of the model is available in the eAppendix [available at]).

The model categorizes patients into 3 subpopulations by risk of HCV exposure: 1) people who inject drugs (PWID); 2) HIV-infected men who have sex with men (MSM-HIV); and 3) all other adults born before 1992, when systematic testing of the blood supply for HCV began (“Other Adults”), of which baby boomers account for approximately 39%.15 The model assumes that individuals belong to the same risk group throughout the simulation. The risk groups of infected patients are further stratified by HCV genotypes 1, 2, and 3, which account for 70%, 16%, and 12% of the HCV-infected populations in the United States, respectively.16

The model assumes Other Adults is a closed cohort in which infected individuals do not transmit HCV to uninfected individuals.17-19 This assumption is based on the introduction of mandatory HCV screening of blood products in 1992 and the observed low disease-transmission risk among adults who are not PWID or MSM-HIV.17,20 HCV can be transmitted by an infected individual in the PWID or MSM-HIV groups to an uninfected individual in the same group; uninfected individuals and those previously cured of HCV are at risk of becoming infected. Furthermore, patients can only be infected with 1 genotype at a time, but can become re-infected with any genotype after being cured.

Once infected with HCV, patients progress through disease states based on transition probabilities obtained from the literature (see eAppendix). The initial distribution of fibrosis stages is assumed independent of age and insurance status.21 We use the METAVIR scoring system to categorize liver fibrosis stages from F0 (no fibrosis) to F4 (most severe). Successfully treated patients return to the pool of susceptible individuals and have the same reinfection probability as individuals who were never infected.

Patients are eligible for treatment if they meet the treatment coverage criteria specified by their insurance type. For the PWID and Other Adults groups, we computed the population’s initial insurance distribution and HCV prevalence by age, using the 5 most recent waves of the National Health and Nutrition Examination Survey (NHANES) (2003-2004 through 2011-2012), applying appropriate weights to generate nationally representative estimates.15,22 Due to the small sample of NHANES respondents in the MSM-HIV exposure group, we used prevalence estimates from the published literature.16,23,24 For all groups, insurance type was assumed constant throughout the simulation, except when patients entered Medicare at age 65. Only diagnosed patients may receive treatment, and based on published estimates of diagnosis rates, we assumed 50% of patients infected with HCV were diagnosed during any given model cycle.25

HCV treatment costs, medical expenditures (nontreatment medical costs), quality-adjusted life-year (QALY) weights, and mortality rates were derived from estimates in the published literature.26,27 All cost parameters were inflated to 2015 US dollars, and future costs and QALYs were discounted at an annual 3% rate.28 Treatment costs vary by genotype and fibrosis stage, while medical expenditures and QALY weights vary by disease state. Treatment efficacy, treatment costs, and medical expenditures do not vary by insurance status or over time, with the exception of treatment costs, which are discounted after 2 years to account for future market competition29 (see eAppendix for full details).

Treatment Scenarios

We simulated 3 treatment coverage scenarios over a 20-year period: a baseline scenario and 2 alternative scenarios with varying degrees of expanded treatment coverage. All scenarios assume that patients with Medicare, Medicaid, or private insurance coverage who meet the fibrosis stage criteria are treated with DAAs, while uninsured patients have no access to treatment.30-32

In the baseline scenario, we assumed all diagnosed publicly and privately insured patients infected with HCV in fibrosis stages F3 or F4 would receive treatment, following the American Association for the Study of Liver Diseases and Infectious Diseases Society of America guidelines for “highest priority for treatment,” which is consistent with general trends regarding payer coverage of HCV treatment.30,33-36 The alternative scenarios assumed that private insurers expand treatment coverage to either fibrosis stages F2-F4 or F0-F4.

Baseline Distribution of Insurance Status

Table 1 presents the baseline distribution of insurance status by risk group. Although more than 40% of the PWID and MSM-HIV groups are uninsured or covered by Medicaid, the majority (53%) of the Other Adults group has private insurance coverage. Even though treatment scenarios assume uninsured patients do not receive treatment, these patients may benefit from expanded private insurance treatment coverage through lower HCV transmission rates since fewer patients will transmit the disease after treatment.

Alternative Simulations

In addition to the treatment scenarios described above, we conducted 3 additional simulations. First, we assessed the spillover effects to Medicare solely driven by individuals transitioning into Medicare at age 65, by measuring Medicare spillovers for the Other Adults risk group only, which has no disease transmission in the model. Second, we calculated the impact of Medicare treatment coverage expansion on Medicare costs and computed spillover effects to private insurers, while holding constant private treatment coverage at the baseline. Finally, we explored the impact on Medicare if Medicaid expands treatment coverage simultaneously with private insurers.

Model Outputs

Across all scenarios, the spillover effects on Medicare are computed as the change in HCV cost burden resulting from private payers expanding HCV treatment coverage minus the change in annual total healthcare expenditures (ie, medical and HCV treatment costs), relative to the baseline scenario. This captures the net benefit from the payer perspective.

To capture the impact from patients’ perspectives, we computed the change in net social value of HCV treatment relative to the baseline scenario. We define net social value as the total value of QALYs accrued over time (ie, value of health gains from treatment) minus cumulative treatment and medical costs. We assumed a $150,000 value per QALY, given the range of values cited in the literature.37,38 We focused on outcomes for 5- and 20-year horizons, but results for 10 years are presented in the eAppendix.


Effects of Private Insurance Treatment Expansion

Table 2 shows the impact on private payers and on Medicare 5 and 20 years after private payers expand treatment from the baseline (F3-F4) to either F2-F4 or F0-F4. When treatment is expanded to F2-F4, private payers, as a class, realize net savings from reduced medical expenditures within 20 years. Relative to the baseline scenario, Medicare’s cumulative costs are reduced by $0.25 billion ($74 per HCV-positive Medicare beneficiary) after 5 years and $4.4 billion ($427 per HCV-positive Medicare beneficiary) after 20 years. These cost reductions represent the spillover effect on Medicare from private payers’ treatment expansion even though Medicare does not expand coverage. In other words, if private payers restrict treatment coverage to F3-F4, Medicare would face an additional $4.4 billion cost burden over 20 years, relative to F2-F4 treatment coverage.

Medicare receives a larger net benefit if private treatment coverage is expanded to F0-F4. After 20 years, Medicare’s medical expenditures declined by $9 billion and treatment costs declined by $2 billion, for a net benefit of $11 billion ($1134 per HCV-positive Medicare beneficiary). In both alternative scenarios, private payers with time horizons of less than 20 years have the incentive to restrict treatment coverage, even though this policy harms both Medicare and private insurers in the long term. Figure 2 makes clear, however, that private payers do not enjoy the same net benefit as Medicare in the short term, because they bear the upfront treatment costs. Private payers face increased total costs of $25 billion to $78 billion after 5 years of expanding treatment. It takes roughly 16 to 17 years for private payers to break even on their upfront investment in treatment; after 20 years, they enjoy net savings of $10 billion to $14 billion. When treatment is expanded to F0-F4, net benefits to Medicare are 2.5 times higher compared with expanding to F2-F4. In addition, despite 5-year costs that are almost 3 times larger compared to expansion to F2-F4, expanding to F0-F4 results in an additional $4 billion in savings to private payers over a 20-year period.

Expanding HCV treatment coverage also greatly benefits society, through both reduced mortality and improved health-related quality of life for treated patients.39 Relative to treating at stages F3-F4, expanding treatment to F2-F4 generates 0.9 million additional discounted life-years over a 20-year period, and this figure increases to 1.17 million discounted QALYs after applying health utilities to the model population. Similarly, expansion to F0-F4 generates 1.7 million discounted life-years and 2.35 million QALYs over 20 years, relative to treating F3-F4.

QALY gains represent considerable value for society (see Figure 3). The cumulative net social value for each treatment expansion scenario is positive within 8 years. However, expanding treatment to F0-F4 provides the largest societal benefits after 20 years, at $382 billion, compared with only $192 billion generated by only expanding to F2-F4.

Alternative Simulation Results

In the simulation of the private-payer treatment expansion for the Other Adults group only, we assessed the Medicare spillovers driven solely by individuals transitioning into Medicare at age 65. We found that the Other Adults group accounts for 88% of the 20-year spillover effect when treatment is expanded to F2-F4 and 84% when treatment is expanded to F0-F4. This suggests that the majority of the net benefits to Medicare result from improved health of currently infected patients rather than reduced transmission.

Next, we assessed the impact on private payers’ costs, attributable to Medicare expanding treatment coverage from F3-F4 to F2-F4, holding private-insurer treatment coverage constant at the baseline. In this simulation, because individuals do not transition from Medicare to private insurance, private insurers benefit from reduced treatment costs and medical expenditures exclusively due to a reduced transmission effect. Therefore, the spillovers generated by expanded Medicare treatment coverage are smaller than those generated by expanded private insurance treatment. After 20 years, private insurers experience cost savings of $0.97 billion compared with the $4.4 billion Medicare saves in the analogous private expansion scenario. Additionally, Medicare requires 25 years to reach positive net benefits of expanding treatment coverage to F2-F4.

Finally, we explored the impact of expanding Medicaid treatment coverage simultaneously with private insurance coverage. If both Medicaid and private payers expand treatment from F3-F4 to F2-F4, Medicare saves $5.1 billion over 20 years—an additional $0.6 billion in savings, relative to a private-payer expansion alone. Simultaneous expansion by Medicaid and private insurers from F3-F4 to F0-F4 saves Medicare $12.8 billion in total costs—an additional $1.2 billion in savings. Given the significantly larger size of the privately insured population relative to the Medicaid population, it is not surprising that private insurance treatment expansion generates the majority of spillovers to Medicare.

Expanding Medicaid treatment coverage also has a small effect on costs to private payers through reduced transmission rates. Over 20 years, private payers experience an additional $0.3 billion in total cost savings when Medicaid expands treatment to F2-F4 and an additional $0.82 billion in total cost savings when Medicaid expands treatment to F0-F4.

The eAppendix presents additional sensitivity analyses conducted on model parameters and assumptions; specifically, sensitivities to changes in treatment costs and in the value of a QALY. Our findings suggest that increasing treatment costs by 50% does not substantively change spillovers to Medicare.6 For example, when treatment costs increase by 50%, expanding treatment to F2-F4 results in a $5.1-billion savings to Medicare compared with a $4.4-billion savings in the main analysis. We also found that reducing the value of a QALY to $50,000 yields a positive net social value by year 10 compared with year 8 in the main analysis.


Investments in HCV treatment pay off over the long term; unfortunately, however, private payers face patient turnover, which makes it difficult for them to enjoy the long-term return on their investment. Although most enrollees switch insurers within 10 years, it takes roughly 15 years for private-payer treatment investments to pay off, from their own individual perspective. Therefore, enrollee turnover creates a short-term focus among private payers that might discourage HCV treatment and other similar long-term investments. The costs of this incentive problem are borne by all future payers—including Medicare and private payers.

The vast majority of HCV-positive baby boomers will transition from private insurance coverage to Medicare within the next decade. Our analysis shows that when private payers expand treatment coverage to fibrosis stages F2-F4, they accrue cost savings over the next 20 years and save Medicare $4.4 billion over the same period. However, if private insurers expand coverage to all fibrosis stages (F0-F4), the spillover benefits to Medicare increase to $11.1 billion over 20 years.

Future costs are also borne by private payers. This may seem counterintuitive since these costs result from private payers’ own decisions; however, this is a classic economic problem of “free-riding.” Private payers recognize that other firms today cover the enrollees they will cover in 15 years; thus, they understand that their current treatment decisions have a smaller impact on the health of their enrollee population 15 years from now. Consequently, a single insurer can only hope that other payers will treat its future beneficiaries. Unfortunately, all private payers face the same harsh calculus, which discourages short-term treatment investments and imposes long-term costs on everyone.

This study adds to the growing evidence that earlier HCV treatment generates considerable value to patients and society, contributing to the ongoing debate about when to initiate HCV treatment, given its costs. Although treatment guidelines do not recommend limiting treatment by fibrosis stage, insurers must balance the increasing evidence for early treatment against their actuarial costs.30 Additionally, this study demonstrates the magnitude of the wedge between payer incentives today, and when patients infected with HCV age into Medicare in the future. Decision makers charged with allocating limited healthcare resources are faced with difficult tradeoffs between short-term costs and long-term benefits. Although this issue is salient in the treatment of HCV, it applies to numerous other clinical settings as well.

Our results present a challenge to policy makers regarding who should be responsible for HCV treatment coverage decisions. Although the socially optimal strategy is for private insurers to expand treatment coverage, this conflicts with their short-term financial incentives. A blunt solution would subsidize HCV treatments directly, since their long-term social benefits exceed the benefits internalized by private payers.

A more nuanced solution would allow private payers to capture the long-term benefits they create from expanded treatment, even if patients switch coverage. An insurer who treats a patient lowers the actuarial cost of covering that patient in the future, which could be rebated to the original insurer as a “handoff payment” if the patient were to switch coverage.40 Alternatively, an explicit credit could be granted to each payer that treats a patient.41 An example is the idea of “Healthcoins,” or a similar market-based tradable asset pegged at the value society derives from treatment (benefits of treatment minus costs).41 These and other policy options remain an open area of further research and discussion.


As in any modeling-based analysis, our approach has some limitations. Markov models are designed to capture cohort-level effects and therefore cannot forecast individual disease processes and outcomes.42,43 We assumed individuals belong to a single risk group to avoid double counting, but some overlap is likely to exist in reality. We also assumed no retreatment for individuals who initially failed treatment, but this limitation likely results in a small impact on cost estimates, since existing research indicates that the number of nonresponders is low.6,44-46

Although NHANES provides reliable population-level estimates, subpopulation estimates are less reliable due to small sample sizes. Additionally, due to NHANES’ self-reporting design, it is possible that stigmatized behaviors, such as sexual activities and intravenous drug use, are underreported, which would affect subpopulation estimates. However, NHANES-based estimates are similar to other estimates reported in the literature.47 Finally, NHANES also does not capture homeless and incarcerated populations, both of which have high HCV prevalence and limited treatment access.15,48

Lastly, our model was designed to examine spillover effects of private coverage policies to Medicare; thus, transitions between insurance types are not modeled, except for individuals aging into Medicare at 65. Spillovers to Medicaid and uninsured populations are limited in our model to the impact of reduced disease transmission. Incorporating insurance transitions could generate additional positive spillover effects from healthier individuals switching among Medicaid, private plans, and uninsured. Additionally, our model assumes uniform coverage among private insurers and does not capture variation in insurance coverage levels. We did not, therefore, study the effects of private insurers who offer different levels of coverage. Differences in coverage may be relevant in a competitive private market or one in which employers and beneficiaries vary in their demand for benefit generosity. Differences in benefit design and competition among insurers present valuable questions for further research.


Expanding HCV treatment coverage significantly benefits patients and society through a reduced disease burden; however, the optimal approach to paying for these treatments is less clear. As our results demonstrate, expanding private insurance coverage of HCV treatments reduces treatment costs and medical expenditures for Medicare over all time horizons. It also generates net savings for private insurers in the longer term and benefits to society in terms of QALYs. The misalignment between short-term treatment costs and long-term benefits that private payers face, however, may not promote socially optimal treatment strategies. Public policies may be required to realize the benefits of expanding HCV treatment coverage. Author Affiliations: Precision Health Economics (GAM, KM, CH, MTL), Los Angeles, CA; Arete Analytics (DD), Andover, MA; AbbVie, Inc (TJ, SEM, YSG), North Chicago, IL; Department of Biostatistics, University of California (RB), Los Angeles, CA; Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California (DNL), Los Angeles, CA.

Source of Funding: Support for this research was provided by AbbVie, Inc.

Author Disclosures: Drs Juday, Marx, and Sanchez Gonzalez are employees and stockholders of Abbvie, Inc, which develops and markets treatments for hepatitis C virus. Drs Moreno and Mulligan, and Ms Huber and Mr Linthicum are employees of Precision Health Economics (PHE), a healthcare consultancy to life science firms. Dr Lakdawalla is the chief strategy officer and owns equity in PHE, and Drs Dreyfus and Brookmeyer are consultants for PHE.

Authorship Information: Concept and design (GAM, KM, DD, TJ, SEM, YSG, RB, DNL); acquisition of data (GAM, KM, CRH); analysis and interpretation of data (GAM, KM, CRH, MTL, DD, TJ, SEM, YSG, RB, DNL); drafting of the manuscript (GAM, KM, CRH, MTL, TJ, SEM, YSG); critical revision of the manuscript for important intellectual content (GAM, KM, CRH, MTL, DD, TJ, SEM, YSG, RB, DNL); statistical analysis (KM,DD, TJ); obtaining funding (TJ, YSG); administrative, technical, or logistic support (GAM, KM, MTL); and supervision (GAM, TJ, YSG, DNL).


Address correspondence to: Gigi A. Moreno, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. E-mail:

1. Razavi H, ElKhoury AC, Elbasha E, et al. Chronic hepatitis C virus (HCV) disease burden and cost in the United States. Hepatology. 2013;57(6):2164-2170. doi: 10.1002/hep.26218.

2. Butt AA, Yan P, Lo Re V 3rd, et al; ERCHIVES (Electronically Retrieved Cohort of HCV Infected Veterans) Study Team. Liver fibrosis progression in hepatitis C virus infection after seroconversion. JAMA Intern Med. 2015;175(2):178-185. doi: 10.1001/jamainternmed.2014.6502.

3. Institute of Medicine (US) Committee on the Prevention and Control of Viral Hepatitis Infection; Colvin HM, Mitchell AE (eds). Hepatitis and Liver Cancer: A National Strategy for Prevention and Control of Hepatitis B and C. Washington, DC: National Academies Press; 2010.

4. Eckman MH, Talal AH, Gordon SC, Schiff E, Sherman KE. Cost-effectiveness of screening for chronic hepatitis C infection in the United States. Clin Infect Dis. 2013;56(10):1382-1393. doi: 10.1093/cid/cit069.

5. Moorman AC, Gordon SC, Rupp LB, et al; Chronic Hepatitis Cohort Study Investigators. Baseline characteristics and mortality among people in care for chronic viral hepatitis: the chronic hepatitis cohort study. Clin Infect Dis. 2013;56(1):40-50. doi: 10.1093/cid/cis815.

6. Leidner AJ, Chesson HW, Xu F, Ward JW, Spradling PR, Holmberg SD. Cost-effectiveness of hepatitis C treatment for patients in early stages of liver disease. Hepatology. 2015;61(6):1860-1869. doi: 10.1002/hep.27736.

7. Van Nuys K, Brookmeyer R, Chou JW, Dreyfus D, Dieterich D, Goldman DP. Broad hepatitis C treatment scenarios return substantial health gains, but capacity is a concern. Health Aff (Millwood). 2015;34(10):1666-1674. doi: 10.1377/hlthaff.2014.1193.

8. Cunningham PJ. Few Americans switch employer health plans for better quality, lower costs [NIHCR research brief No. 12]. National Institute for Health Care Reform website. Published January 2013. Accessed April 8, 2015.

9. Original Medicare (Part A and B) eligibility and enrollment. CMS webite. Updated November 3, 2015. Accessed December 10, 2015.

10. Smith BD, Morgan RL, Beckett GA, et al; Centers for Disease Control and Prevention. Recommendations for the identification of chronic hepatitis C virus infection among persons born during 1945-1965. MMWR Recomm Rep. 2012;61(RR-4):1-32.

11. Davis GL, Alter MJ, El—Serag H, Poynard T, Jennings LW. Aging of hepatitis C virus (HCV)-infected persons in the United States: a multiple cohort model of HCV prevalence and disease progression. Gastroenterology. 2010;138(2):513-521, 521.e1-6. doi: 10.1053/j.gastro.2009.09.067.

12. Stepanova M, Kanwal F, El-Serag HB, Younossi ZM. Insurance status and treatment candidacy of hepatitis C patients: analysis of population-based data from the United States. Hepatology. 2011;53(3):737-745. doi: 10.1002/hep.24131.

13. Chernew M, Baicker K, Martin C. Spillovers in health care markets: implications for current law projections. CMS website. Published April 16, 2010. Accessed November 25, 2015.

14. Romley JA, Axeen S, Lakdawalla DN, Chernew ME, Bhattacharya J, Goldman DP. The relationship between commercial health care prices and Medicare spending and utilization. Health Serv Res. 2015;50(3):883-896. doi: 10.1111/1475-6773.12262.

15. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey; Accessed May 25, 2015.

16. Manos MM, Shvachko VA, Murphy RC, Arduino JM, Shire NJ. Distribution of hepatitis C virus genotypes in a diverse US integrated health care population. J Med Virol. 2012;84(11):1744-1750. doi: 10.1002/jmv.23399.

17. Coffin PO, Reynolds A. Ending hepatitis C in the United States: the role of screening. Hepat Med. 2014;6:79-84. doi: 10.2147/HMER.S40940.

18. Henderson DK. Managing occupational risks for hepatitis C transmission in the health care setting. Clin Microbiol Rev. 2003;16(3):546-568.

19. Williams IT, Bell BP, Kuhnert W, Alter MJ. Incidence and transmission patterns of acute hepatitis C in the United States, 1982-2006. Arch Internal Med. 2011;171(3):242-248. doi: 10.1001/archinternmed.2010.511.

20. Hepatitis C FAQs for health professionals. CDC website. Updated March 11, 2016. Accessed November 5, 2015.

21. Hagan LM, Sulkowski MS, Schinazi RF. Cost analysis of sofosbuvir/ribavirin versus sofosbuvir/simeprevir for genotype 1 hepatitis C virus in interferon-ineligible/intolerant individuals. Hepatology. 2014;60(1):37-45. doi: 10.1002/hep.27151.

22. US Census Bureau. Annual estimates of the resident population by single year and sex for the United States: April 1, 2010 to July 1, 2014. 2014; Accessed April 6, 2015.

23. Denniston MM, Jiles RB, Drobeniuc J, et al. Chronic hepatitis C virus infection in the United States, National Health and Nutrition Examination Survey 2003 to 2010. Ann Intern Med. 2014;160(5):293-300. doi: 10.7326/M13-1133.

24. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 dependent areas—2011 [HIV Surveillance Supplemental Report]. CDC website. Published October 2013. Accessed January 16, 2015.

25. Yehia BR, Schranz AJ, Umscheid CA, Lo Re V 3rd. The treatment cascade for chronic hepatitis C virus infection in the United States: a systematic review and meta-analysis. PLoS One. 2014;9(7):e101554. doi: 10.1371/journal.pone.0101554.

26. Mathers BM, Degenhardt L, Bucello C, Lemon J, Wiessing L, Hickman M. Mortality among people who inject drugs: a systematic review and meta-analysis. Bull World Health Organ. 2013;91(2):102-123. doi: 10.2471/BLT.12.108282.

27. Whiteside YO, Selik R, An Q, et al. Comparison of rates of death having any death-certificate mention of heart, kidney, or liver disease among persons diagnosed with HIV infection with those in the general US population, 2009-2011. Open AIDS J. 2015;9:14-22. doi: 10.2174/1874613601509010014.

28. Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-Effectiveness in Health and Medicine: Report of the Panel on Cost-Effectiveness in Health and Medicine. New York: Oxford University Press; 1996.

29. Friedman SL, Bansal MB. Reversal of hepatic fibrosis—fact or fantasy? Hepatology. 2006;43(2, suppl 1):S82-S88.

30. AASLD IDSA HCV Guidance Panel. Hepatitis C guidance: AASLD-IDSA recommendations for testing, managing, and treating adults infected with hepatitis C virus. Hepatology. 2015;62(3):932-954. doi: 10.1002/hep.27950.

31. Ditah I, Al Bawardy B, Gonzalez HC, et al. Lack of health insurance limits the benefits of hepatitis C virus screening: insights from the National Health and Nutrition Examination Hepatitis C Follow-Up Study. Am J Gastroenterol. 2015;110(8):1126-1133. doi: 10.1038/ajg.2015.31.

32. McGowan CE, Fried MW. Barriers to hepatitis C treatment. Liver Int. 2012;32(suppl 1):151-156. doi: 10.1111/j.1478-3231.2011.02706.x.

33. Pharmacy clinical policy bulletins Aetna non-Medicare prescription drug plan: subject: hepatitis C. 2015. Aetna website. Accessed May 21, 2015.

34. Canary LA, Klevens RM, Holmberg SD. Limited access to new hepatitis C virus treatment under state Medicaid programs. Ann Intern Med. 2015;163(3):226-228. doi: 10.7326/M15-0320.

35. United American Medicare Part D. Prior Authorization Guidelines- Essential (PDP). 2015. Accessed September 30, 2015.

36. Clinical pharmacy programs [program number 2014 P 2023-4]. United Healthcare website. Published September 1, 2014. Accessed March 5, 2014.

37. Hirth RA, Chernew ME, Miller E, Fendrick AM, Weissert WG. Willingness to pay for a quality-adjusted life year: in search of a standard. Med Decis Making. 2000;20(3):332-342.

38. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371(9):796-797. doi: 10.1056/NEJMp1405158.

39. Younossi ZM, Stepanova M, Afdhal N, et al. Improvement of health-related quality of life and work productivity in chronic hepatitis C patients with early and advanced fibrosis treated with ledipasvir and sofosbuvir. J Hepatol. 2015;63(2):337-345. doi: 10.1016/j.jhep.2015.03.014.

40. Bhattacharya J, Chandra A, Chernew M, et al. Best of both worlds uniting universal coverage and personal choice in health care. American Enterprise Institute website. Published August 2013. Accessed November 25, 2015.

41. Basu A. Financing cures in the United States. Expert Rev Pharmacoecon Outcomes Res. 2015;15(1):1-4. doi: 10.1586/14737167.2015.990887.

42. Briggs MA, Sculpher M. An introduction to Markov modelling for economic evaluation. Pharmacoeconomics. 1998;13(4):397-409.

43. Amiri M, Kelishadi R. Comparison of models for predicting outcomes in patients with coronary artery disease focusing on microsimulation. Int J Prev Med. 2012;3(8):522-530.

44. Gutierrez JA, Lawitz EJ, Poordad F. Interferon-free, direct-acting antiviral therapy for chronic hepatitis C. J Viral Hepat. 2015;22(11):861-870. doi: 10.1111/jvh.12422.

45. Younossi ZM, Singer ME, Mir HM, Henry L, Hunt S. Impact of interferon free regimens on clinical and cost outcomes for chronic hepatitis C genotype 1 patients. J Hepatol. 2014;60(3):530-537. doi: 10.1016/j.jhep.2013.11.009.

46. Tice JA, Ollendorf DA, Pearson SD; Institute for Clinical and Economic Review. The comparative clinical effectiveness and value of simperevir and sofosbuvir in the treatment of chronic hepatitis C infection. California Technology Assessment Forum website. Published April 15, 2014. Accessed October 8, 2014.

47. Denniston MM, Klevens RM, McQuillan GM, Jiles RB. Awareness of infection, knowledge of hepatitis C, and medical follow-up among individuals testing positive for hepatitis C: National Health and Nutrition Examination Survey 2001-2008. Hepatology. 2012;55(6):1652-1661. doi: 10.1002/hep.25556.

48. Edlin BR, Eckhardt BJ, Shu MA, Holmberg SD, Swan T. Toward a more accurate estimate of the prevalence of hepatitis C in the United States. Hepatology. 2015;62(5):1353-1363. doi: 10.1002/hep.27978. 

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