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Value of Expanding HCV Screening and Treatment Policies in the United States

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

Expanding screening for hepatitis C virus infection may generate substantial benefits for patients and society, but only when paired with expanded treatment policies.


Objectives: To investigate the value of expanding screening and treatment for hepatitis C virus (HCV) infection in the United States.

Study Design: Discrete-time Markov model.

Methods: We modeled HCV progression and transmission to analyze the costs and benefits of investment in screening and treatment over a 20-year time horizon. Population-level parameters were estimated using National Health and Nutrition Examination Survey data and published literature. We considered 3 screening scenarios that vary in terms of clinical guidelines and physician awareness of guidelines. For each screening scenario, we modeled 3 approaches to treatment, varying the fibrosis stage of treatment initiation. Net social value was the key model outcome, calculated as the value of benefits from improved quality-adjusted survival and reduced transmission minus screening, treatment, and medical costs.

Results: Expanded screening policies generated the largest value to society. However, this value is constrained by the availability of treatment to diagnosed patients. Screening all individuals in the population generates $0.68 billion in social value if diagnosed patients are treated in fibrosis stages F3-F4 compared with $824 billion if all diagnosed patients in stages F0-F4 are treated. Moreover, increased screening generates cumulative net social value by year 8 to 9 under expanded treatment policies compared with 20 years if only patients in stages F3-F4 are treated.

Conclusions: Although increasing screening for HCV may generate some value to society, only when paired with expanded access to treatment at earlier disease stages will it produce considerable value. Such a “test and treat” strategy is likely to entail higher short-term costs but also yield the greatest social benefits.

Am J Manag Care. 2016;22(5 Spec Issue No. 6):SP227-SP235

Take-Away Points

We developed a discrete-time Markov model to simulate the effects of expanding screening for hepatitis C virus (HCV) infection and initiating treatment at different fibrosis stages. We compare screening and treatment policies in terms of net social value over a 20-year horizon.

  • Increased screening generates positive social value in 20 years, but this benefit is reduced without concurrent expansion of treatment.
  • Investments in HCV screening and treatment are expected to “break even” from a social perspective after 20 to 22 years when treatment is limited to fibrosis stages F3-F4 and after only 8 to 9 years when treatment is expanded to include stages F0-F2.

Chronic infection with hepatitis C virus (HCV) is estimated to affect at least 3.5 million individuals in the United States,1 and the incidence is increasing.2 Chronic HCV infection can lead to hepatic damage, including cirrhosis and hepatocellular carcinoma, and is the most common cause of liver transplantation in the United States.3,4

Because symptoms of HCV infection are usually absent or nonspecific until late stages of the disease, an estimated 50% to 75% of infected individuals are unaware of their HCV status and get tested only after significant symptoms develop.5-7 Prior research suggests that earlier identification and treatment of patients infected with HCV generates benefits for patients and society, but the potential social value of increased screening, whether alone or in combination with early treatment, is not well understood.3,8-12 Novel HCV regimens, including direct-acting antivirals (DAAs), have increased cure rates dramatically, which may affect the value of expanded screening.13,14 For example, rates of sustained virologic response (SVR) observed in clinical trials of DAA treatments generally exceed 98% for patients infected with genotype 1 HCV without cirrhosis or prior treatment failure.15-19

Despite rapid innovation in HCV treatment, however, unmet need remains significant. Only 13% to 36% of patients diagnosed with chronic HCV infection have received treatment,3 and even fewer patients completed the treatment regimen and achieved SVR.20 Failures to screen, diagnose, and treat all contribute to this current state of affairs.

Broad consensus exists on the need for inclusive screening. In 2012, the CDC updated its guidelines and recommended expanding screening to include all individuals born between 1945 and 1965 (baby boomers)—a cohort comprising an estimated 75% of existing HCV infections.21 Similarly, the American Association for the Study of Liver Diseases (AASLD) and the Infectious Diseases Society of America (IDSA) updated their guidelines in 2015 to recommend one-time screening for asymptomatic baby boomers.3 Unfortunately, more than 40% of physicians are unaware of current guidelines,21,22 and many individuals infected with HCV may have limited contact with the healthcare system. For these and other reasons, HCV screening rates remain below recommended levels.

It remains unclear whether and to what extent expanded screening benefits society. All-oral DAA regimens present considerable up-front costs23; yet recent research suggests the value of their long-term health benefits is likely to be even higher.12 Screening can identify potentially treatable patients, with implications for both healthcare costs and health benefits. In this article, we explore whether and to what extent expanded screening policies provide net value to society and assess the net social value of varying levels of access to treatment after diagnosis.


Overview of the Markov Model of HCV Transmission and Progression

In this article, we present results of a discrete-time Markov simulation model (Microsoft Excel 2010/VBA, Microsoft Corporation, Redmond, Washington) that simulates the detection, treatment, and progression of populations susceptible to HCV infection, as well as associated costs and health benefits, under different screening and treatment policies. The model builds on previous work that simulates the effects of treatment policies (without screening) on population-level costs, health benefits, and disease dynamics.12

The model tracks infected and uninfected individuals in 3 groups, stratified by risk of HCV exposure: a) people who inject drugs (PWID), b) HIV-positive men who have sex with men (MSM-HIV), and c) all other adults born before 1992, when systematic testing of the blood supply for HCV began (Other Adults). Of the last group, approximately 39% were baby boomers.5 The model further stratifies the infected population in each risk group by HCV genotypes 1, 2, and 3, which account for 70%, 16%, and 12% of the US population infected with HCV, respectively.24

Once infected, individuals progress through disease states according to transition probabilities drawn from the literature (see eAppendix, available at www.ajmc.com). Undiagnosed patients face some probability of screening, which varies across the 3 scenarios described below. Diagnosed patients face a probability of treatment that varies according to 3 treatment policy scenarios. If successfully treated, cured patients return to the pool of susceptible, uninfected individuals and experience the same probability of reinfection as those without a previous infection.

The 3 HCV risk groups were modeled independently, such that individuals do not switch among risk groups and cannot infect individuals in a different risk group. Although patients are infected with only 1 genotype at a time, once cured they can be re-infected with any genotype. HCV transmission in the MSM-HIV and PWID risk groups is based on the number of infected individuals in each risk group and genotype, and is described in detail in the eAppendix.25 Outside of the PWID and MSM-HIV groups, the risk of HCV transmission is low.8,25-27 Therefore, we made the simplifying assumption of no further transmission of HCV in the Other Adults group.

Key model inputs included starting population size, transmission probabilities, and progression rates in each risk group; genotype and disease state at diagnosis; HCV treatment costs; nontreatment medical expenditures; screening costs; quality-adjusted life-year (QALY) utility weights; and mortality rates. Model parameters were obtained from the published literature or computed from National Health and Nutrition Examination Survey (NHANES) data for the years 2003 through 2012.5 All cost estimates were adjusted to 2015 US dollars, and all future costs and QALYs were discounted at 3% per year.

Base drug costs reflect wholesale acquisition costs as of December 2014. However, since treatment duration varies by genotype, this results in different treatment costs by genotype. All treatments considered are currently patent-protected and face price competition from other branded products. To account for branded competition, the model reduced treatment costs by 46% in years 2 to 20 of the simulation.28,29 Screening costs included the cost of an HCV antibody test (enzyme-linked immunoassay) and a level-1 outpatient visit.4 Medical expenditures for diagnosed patients were computed by disease state and diagnosis status.9,10,30

QALY weights were assigned based on disease state and diagnosis status. We assumed that individuals diagnosed with HCV incur associated psychological costs; therefore, patients who are HCV-infected, but undiagnosed, have QALY weights 2% higher than their diagnosed and untreated counterparts.23 For details on model parameters, dynamics, and assumptions, see the eAppendix.

Scenarios Analyzed

HCV screening. The model explores 3 scenarios for the frequency and inclusiveness of screening in clinical practice (see Table 1). We used AASLD/IDSA screening guidelines to define screening practice,3 adjusting for 2 important realities. First, screening can occur only if a patient interacts with a healthcare provider. NHANES data were used to determine the annual rate at which patients received healthcare services. Second, patients might decline offered screening. In all scenarios, we assumed that 91% of those offered screening would accept it.7

Real-world screening rates also depend on physician awareness of, and adherence to, screening guidelines. The baseline scenario (Current Screening) assumes that 58% of clinicians are aware of HCV screening guidelines based on data reported in the literature.22 To assess the effects of expanded screening on costs and patient outcomes, we considered 2 alternative scenarios: Physician Education explores the effect of increasing physician awareness of screening guidelines to 100%, with no change in the guidelines themselves, and Screen All assumes that, in addition to increasing physician awareness of guidelines to 100%, guidelines are expanded to provide one-time HCV screening to all individuals born before 1992. Because data for guideline adherence were not available, we made the simplifying assumption in all scenarios that all physicians aware of screening guidelines also adhere to them. In practice, physician adherence to clinical guidelines is likely to be imperfect31; however, assuming full adherence yields the maximum possible value that could be generated by screening. We examined the sensitivity of screening rates to physician adherence in the eAppendix.

Treatment practices. The effect of HCV screening on patient health depends on whether a diagnosed patient is subsequently treated. To better understand the relationship between screening and treatment access, we varied the fibrosis stages at which treatment would be available to diagnosed individuals in each screening scenario. Using the METAVIR scoring system to categorize liver fibrosis stages from F0 (no fibrosis) to F4 (most severe), we considered 3 levels of treatment access: a) treatment at fibrosis stages F3-F4, which reflects current practice32-34 and serves as the baseline; b) treatment at F2-F4; and c) treatment at F0-F4. We assumed that all screened and diagnosed individuals receive all-oral DAAs if they are insured (see eAppendix).

Key Model Outputs

The key model output was net social value, defined as the difference between: a) the economic value of clinical benefits from improved quality-adjusted survival and reduced transmission, which is calculated as total QALYs multiplied by $150,00035,36; and b) total healthcare costs, measured as the sum of treatment costs, screening costs, and other medical expenditures. We reported results as changes relative to the baseline scenario (ie, Current Screening with treatment at F3-F4). Therefore, net social value is reported as the difference between a given alternative scenario and the baseline. We also examined the value of expanding screening while treatment remained constant. In this case, Current Screening serves as the baseline for comparing expanded screening scenarios.

In addition to net social value, we reported incremental cost-effectiveness ratios relative to the baseline. Also, since HCV treatment incurs short-term costs but generates long-term benefits, we calculated the break-even point (ie, the years required to switch from negative to positive cumulative net social value) for each screening and treatment combination. Cumulative social value and cost-effectiveness results are presented for a 20-year time horizon. For results at the 10-year time horizon, see the eAppendix.

Sensitivity Analyses

Each parameter in our model is characterized by some degree of uncertainty. For example, estimates for disease and transmission dynamics vary in the literature. Additionally, our model includes a number of important assumptions that affect our results.

To test the sensitivity of our model to disease progression and transmission parameters, we conducted sensitivity tests of key model parameters within each scenario. For each key parameter, we varied the parameter across a range and report how the scenario’s value changes in percentage terms when using the upper and lower bounds of the range. We also examined several key assumptions, including physician adherence to screening guidelines, future reductions in treatment costs, and others. For details, see the eAppendix.


Annual Net Value

Figure 1 reports the annual net value of screening scenarios stratified by treatment scenario. More inclusive screening policies involve net costs in the short term, but generate positive net value after 5 to 7 years. More comprehensive treatment policies cause inclusive screening policies to rise in value more quickly, but also make them more costly in the short run. Relative to Current Screening, annual net values in Screen All are approximately double those in Physician Education.

Cumulative Net Value

Costs and QALY gains used to calculate cumulative net social value over the 20-year time horizon are presented in Table 2. Total cost is driven primarily by medical expenditures and treatment costs. In both expanded screening scenarios, medical expenditures increase under treatment at F3-F4. By contrast, savings from reduced medical expenditures exceed the costs of treatment in all scenarios under treatment at F0-F4.

Over a 20-year time horizon, Screen All generates the greatest cumulative net social value at all levels of treatment access compared with the baseline (see Table 3). In general, however, screening expansion has a relatively small effect on cumulative net social value, unless treatment is similarly expanded. Relative to Current Screening, Screen All generates a net gain of $0.68 billion under the most restrictive access to treatment (F3-F4) and Physician Education generates a net loss in social value of $1.76 billion. The relative gain from increased screening rises with more comprehensive access to treatment. Under treatment at F2-F4, Physician Education generates net social value of $421 billion and Screen All generates net social value of $464 billion over 20 years, relative to baseline. These gains increase to $752 billion (Physician Education) and $824 billion (Screen All) under more comprehensive treatment (F0-F4).

Under any given treatment strategy, Screen All is the highest-value screening strategy (see Figure 2). With treatment at F2-F4, Screen All generates nearly twice the value generated by Physician Education over 20 years. The value of screening approximately doubles when treatment is expanded to F0-F4, under which Physician Education and Screen All generate $83.7 billion and $155.1 billion, respectively, in cumulative discounted social value. Broader treatment increases the costs and benefits by roughly the same proportion. Therefore, even though net social value doubles with wider treatment, incremental cost-effectiveness ratios (ICERs) for screening do not vary ($42,000/QALY for treatment at F2-F4 and $19,000/QALY for F0-F4).

Incremental Cost-Effectiveness

All screening strategies are highly cost-effective after 20 years when combined with treatment at F2 or earlier (Table 3). In these expanded treatment scenarios, Screen All exhibits the highest ICER under treatment at F2-F4, at $6747/QALY gained, and 4 of the 6 scenarios are cost-saving. Expanded screening is less cost-effective when treatment is restricted to F3-F4, however, with ICERs reaching $163,933/QALY for Physician Education.

Break-Even Analysis

Varying screening policy has little impact on the number of years required to break even (see Table 3). The accompanying treatment scenario has a much larger effect. For example, with treatment restricted to F3-F4, Screen All breaks even in 20 years—just slightly earlier than Physician Education (22 years). With expansion of treatment to either F2-F4 or F0-F4, all screening scenarios break even in 8 or 9 years.

Sensitivity Analyses

Sensitivity tests within screening/treatment combinations highlight 4 key drivers of uncertainty in our results: starting size of the total Other Adult population, QALY utility weights, discount rate, and economic value of QALYs. Combining the maximum and minimum values from these parameters’ ranges generates 16 permutations that allow us to approximate the upper and lower bounds on our results, given uncertainty in model parameters.

For scenarios with expanded treatment, net social value always remains positive after 20 years and all scenarios remain cost-effective or cost saving. For screening expansion under treatment at F3-F4, however, cumulative social value ranges from —$27 billion, at the minimum, to $69 billion at the maximum (both under the Screen All scenario); cost-effectiveness under treatment at F3-F4 ranges from $114,819 to $206,992/QALY gained. This result highlights the interdependence between screening and treatment policies. For detailed results and additional sensitivity analyses, see the eAppendix.


Both expanded screening and expanded treatment are valuable. However, they are each more valuable when used together. Screening is more effective when diagnosed patients are treated earlier, and treatment expansions generate greater benefits when more patients are diagnosed. Conversely, increasing screening without expanding treatment leads to minimal gains or net losses to society. Newly diagnosed patients derive less benefit and some may even be harmed by the knowledge of their HCV infection if they remain untreated.

For example, the strategy of expanding both screening and treatment breaks even after 8 to 9 years, but expanding screening alone takes 20 to 22 years to break even. The strong complementarity between screening and treatment policies remains over a wide range of cost estimates. Even under the most optimistic screening scenario sensitivities, expanding screening alone takes a minimum of 16 years to break even. One might see greater returns from screening alone, if diagnosed and untreated patients reduce risky transmission behaviors. We do not investigate this possibility, which should be considered in future research.

Our findings suggest that screening expansions are robustly cost-effective and socially valuable, but only when paired with expanded treatment. This is consistent with previous research suggesting that screening for HCV is cost-effective when paired with treatment, even when treating with more expensive DAAs; Rein et al (2012), for example, reported an ICER of $35,700/QALY saved by birth-cohort screening policies focused on the baby boomer population.7 A more recent study of novel DAAs suggest ICERs ranging from $24,921 to $72,169/QALY saved.37 At the 10-year time horizon, our results suggest similar levels of cost-effectiveness (see eAppendix).

The pursuit of both expanded screening and treatment for HCV is consistent with current trends in HIV management, where public health agencies and experts have increasingly supported a “test and treat” strategy, as the value of aggressive screening and early treatment for patients with HIV has become clear.38,39 Existing research, including this study, suggests that such a policy may be beneficial in HCV management as well.11-13

Policy makers and payers in a fiscally constrained environment face a conundrum highlighted by our results. Expanding screening and treatment pays off in as few as 8 years, but the up-front costs are high in the scenarios examined. Because of patient turnover, private payers and state Medicaid systems may not retain patients long enough to directly benefit from their investments in HCV treatment. Furthermore, whereas the costs of screening and treatment are borne by insurers and other payers, only a small portion of the benefits accrue directly to them (in the form of reduced future medical costs).40 The vast majority of the benefits from treatment accrue to patients and society in longer lives and higher quality of life,12,40 potentially resulting in a “race to the bottom” in which public and private payers make decisions based on short-term costs alone.


Although Markov modeling as a tool for understanding chronic disease management policies is well established in the literature,41 the approach has limitations. First, as with any simulation, Markov models are not designed to generate predictions or forecasts. Similarly, as with all population-level studies, results from a Markov simulation cannot inform individual-level understanding of disease processes and outcomes.41,42 The results of our model should be approached as a guide for decision making rather than being predictive of real-world outcomes.

Second, each parameter carries a degree of uncertainty. We present sensitivity analyses and alternative model scenarios in the eAppendix in order to characterize this uncertainty. The model also assumes that parameter estimates are stable for the duration of the simulation and that this is a reasonable representation of HCV disease progression in the modeled risk groups.

Third, the model does not capture some important dynamics of the HCV epidemic. For example, the model does not account for the recent outbreak of HCV due to the increase in intravenous drug use among rural youth.43,44 In addition, the model does not capture the “treatment cascade” that occurs as patients are lost to follow-up between screening, treatment, and, ultimately, the achievement of SVR.20 We also lack concrete data on the extent to which physicians adhere to treatment guidelines.31,45 Our results are therefore an upper bound on the value of increased screening.

Fourth, while NHANES provides reliable population-level estimates, it is subject to several limitations. Small sample sizes make subpopulation estimates less reliable. NHANES also excludes the incarcerated and homeless populations, each of which is thought to have high rates of HCV.1,46,47 In addition, because NHANES relies on self-reported behavioral data, such as sexual behavior and injection drug use, there is a risk of underreporting. Nonetheless, use of NHANES is preferable to parameters from the literature because its sample is representative of the housed, civilian population of the United States.

Finally, more than half of new HCV infections occur in the PWID population, and evidence suggests that combining increased outreach efforts with prevention, testing, and antiviral treatment may have considerable effects on incidence and prevalence in this group.48 Effective prevention includes outreach, education, testing, needle and syringe access, and access to opioid substitution therapy.48,49 Our model does not incorporate the effect of outreach or prevention efforts, however, and assumes that effects on transmission are due to treatment effects alone. Future research should explicitly model the additional effects of programs that offer targeted outreach, screening, prevention, treatment, and wraparound services for high-risk populations.


Increasing screening for HCV infection may generate considerable value for society, but only when paired with access to treatment at earlier stages of the disease. This result highlights the importance of implementing policies to ensure patients who receive an HCV-positive diagnosis remain in the healthcare system until they receive treatment and achieve SVR. Resource constraints in the healthcare system require difficult allocation decisions, and HCV has been at the center of many recent debates. Our findings suggest that expansions in screening coupled with treatment of all infected patients could break even within 8 years and accrue an additional $823.53 billion in discounted net social benefits over a 20-year horizon. Thus, expanded screening and treatment may pay substantial dividends, but only when effective mechanisms are in place to ensure that patients are retained in care and able to access treatment.


The authors would like to thank Caroline Huber, MPH, and Chelsea Kamson, BA, for valuable research support. Caroline Huber is an employee of Precision Health Economics (PHE) and Chelsey Kamson was employed by PHE at the time of the research.Author Affiliations: Precision Health Economics (MTL, KM, GAM), Los Angeles, CA; AbbVie, Inc (YSG, TJ, SEM), North Chicago, IL; Arete Analytics (DD), Andover, MA; Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California (DNL), Los Angeles, CA; Weill Cornell Medical College, Cornell University (BRE), New York, NY; National Development and Research Institutes (BRE), New York, NY; Department of Biostatistics, University of California (RB), 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. Mr Linthicum and Drs Moreno and Mulligan 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. Dr Edlin reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

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


Address correspondence to: Mark T. Linthicum, MPP, Precision Health Economics, 11100 Santa Monica Blvd, Suite 500, Los Angeles, CA 90025. E-mail: mark.linthicum@precisionhealtheconomics.com.

1. 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.

2. CDC. Hepatitis C virus infection among adolescents and young adults—Massachusetts, 2002—2009. MMWR Morb Mortal Wkly Rep. 2011;60(17):537-541.

3. 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.

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. National Health and Nutrition Examination Survey Data [waves 2003-2004 through 2011-2012]. CDC website. http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm. Acessed March 1, 2015.

6. Hagan H, Campbell J, Thiede H, et al. Self-reported hepatitis C virus antibody status and risk behavior in young injectors. Public Health Rep. 2006;121(6):710-719.

7. Rein DB, Smith BD, Wittenborn JS, et al. The cost-effectiveness of birth-cohort screening for hepatitis C antibody in U.S. primary care settings. Ann Intern Med. 2012;156(4):263-270. doi: 10.7326/0003-4819-156-4-201202210-00378.

8. Coffin PO, Scott JD, Golden MR, Sullivan SD. Cost-effectiveness and population outcomes of general population screening for hepatitis C. Clin Infect Dis. 2012;54(9):1259-1271. doi: 10.1093/cid/cis011.

9. Gordon SC, Hamzeh FM, Pockros PJ, et al. Hepatitis C virus therapy is associated with lower health care costs not only in noncirrhotic patients but also in patients with end-stage liver disease. Aliment Pharmacol Ther. 2013;38(7):784-793. doi: 10.1111/apt.12454.

10. Gordon SC, Pockros PJ, Terrault NA, et al. Impact of disease severity on healthcare costs in patients with chronic hepatitis C (CHC) virus infection. Hepatology. 2012;56(5):1651-1660. doi: 10.1002/hep.25842.

11. Moorman AC, Xing J, Ko S, et al; CHeCS Investigators. Late diagnosis of hepatitis C virus infection in the Chronic Hepatitis Cohort Study (CHeCS): missed opportunities for intervention. Hepatology. 2015;61(5):1479-1484. doi: 10.1002/hep.27365.

12. 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.

13. Doyle JS, Aspinall E, Liew D, Thompson AJ, Hellard ME. Current and emerging antiviral treatments for hepatitis C infection. Br J Clin Pharmacol. 2013;75(4):931-943. doi: 10.1111/j.1365-2125.2012.04419.x.

14. 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 Hepatology. 2014;60(3):530-537. doi: 10.1016/j.jhep.2013.11.009.

15. Afdhal N, Reddy KR, Nelson DR, et al; ION-2 Investigators. Ledipasvir and sofosbuvir for previously treated HCV genotype 1 infection. N Engl J Med. 2014;370(16):1483-1493. doi: 10.1056/NEJMoa1316366.

16. Afdhal N, Zeuzem S, Kwo P, et al; ION-1 Investigators. Ledipasvir and sofosbuvir for untreated HCV genotype 1 infection. N Engl J Med. 2014;370(20):1889-1898. doi: 10.1056/NEJMoa1402454.

17. Kowdley KV, Gordon SC, Reddy KR, et al; ION-3 Investigators. Ledipasvir and sofosbuvir for 8 or 12 weeks for chronic HCV without cirrhosis. N Engl J Med. 2014;370(20):1879-1888. doi: 10.1056/NEJMoa1402355.

18. Andreone P, Colombo MG, Enejosa JV, et al. ABT-450, ritonavir, ombitasvir, and dasabuvir achieves 97% and 100% sustained virologic response with or without ribavirin in treatment-experienced patients with HCV genotype 1b infection. Gastroenterology. 2014;147(2):359-365.e351. doi: 10.1053/j.gastro.2014.04.045.

19. Ferenci P, Bernstein D, Lalezari J, et al; PEARL-III Study; PEARL-IV Study. ABT-450/r—ombitasvir and dasabuvir with or without ribavirin for HCV. N Engl J Med. 2014;370(21):1983-1992. doi: 10.1056/NEJMoa1402338.

20. 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.

21. Lutchman G, Kim WR. A glass half full: implications of screening for hepatitis C virus in the era of highly effective antiviral therapy. Hepatology. 2015;61(5):1455-1458. doi: 10.1002/hep.27718.

22. Kallman JB, Arsalla A, Park V, et al. Screening for hepatitis B, C and non-alcoholic fatty liver disease: a survey of community-based physicians. Aliment Pharmacol Ther. 2009;29(9):1019-1024. doi: 10.1111/j.1365-2036.2009.03961.x.

23. 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.

24. 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.

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

26. Hepatitis C FAQs for health professionals. CDC website. http://www.cdc.gov/hepatitis/hcv/hcvfaq.htm#b4. Updated March 11, 2016. Accessed April 15, 2015.

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

28. Grabowski HG, Vernon JM. Brand loyalty, entry, and price competition in pharmaceuticals after the 1984 Drug Act. J Law Econ. 1992;35(2):331-350.

29. Tirrell M. Pricing wars heat up over hepatitis C drugs. CNBC website. http://www.cnbc.com/id/102396903. Published February 4, 2015. Accessed July 3, 2015.

30. Davis KL, Mitra D, Medjedovic J, Beam C, Rustgi V. Direct economic burden of chronic hepatitis C virus in a United States managed care population. J Clin Gastroenterol. 2011;45(2):e17-e24. doi: 10.1097/MCG.0b013e3181e12c09.

31. Cabana MD, Rand CS, Powe NR, et al. Why don’t physicians follow clinical practice guidelines?: a framework for improvement. JAMA. 1999;282(15):1458-1465.

32. Aetna. Pharmacy Clinical Policy Bulletins Aetna Non-Medicare Prescription Drug Plan: Subject: Hepatitis C. 2015. http://www.aetna.com/products/rxnonmedicare/data/2014/GI/hepatitis_c.html. Accessed September 1, 2015.

33. 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.

34. Prior authorization criteria: United American—essential (PDP). United American Medicare Part D website. https://www.uamedicarepartd.com/PDF/Formulary/UA_Essential_PriorAuthorization_2016.pdf. Updated March 01, 2016. Accessed April 14, 2016.

35. 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.

36. 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.

37. Rein DB, Wittenborn JS, Smith BD, Liffmann DK, Ward JW. The cost-effectiveness, health benefits, and financial costs of new antiviral treatments for hepatitis C virus. Clin Infect Dis. 2015;61(2):157-68. doi: 10.1093/cid/civ220.

38. Gardner EM, McLees MP, Steiner JF, Del Rio C, Burman WJ. The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clin Infect Dis. 2011;52(6):793-800. doi: 10.1093/cid/ciq243.

39. Skarbinski J, Rosenberg E, Paz-Bailey G, et al. Human immunodeficiency virus transmission at each step of the care continuum in the United States. JAMA Intern Med. 2015;175(4):588-596. doi: 10.1001/jamainternmed.2014.8180.

40. Moreno G, Mulligan K, Huber C, et al. Costs and spillover effects of private insurers’ coverage of hepatitis C treatment. Am J Manag Care. 2016;x(x):In Press.

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

42. 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.

43. Suryaprasad AG, White JZ, Xu F, et al. Emerging epidemic of hepatitis C virus infections among young nonurban persons who inject drugs in the United States, 2006—2012. Clin Infect Dis. 2014;59(10):1411-1419. doi: 10.1093/cid/ciu643.

44. Jones CM, Logan J, Gladden RM, Michele K. Bohm MK. Vital signs: demographic and substance use trends among heroin users—United States, 2002—2013. MMWR Morb Mortal Wkly Rep. 2015;64(26):719-725.

45. Southern WN, Drainoni M-L, Smith BD, et al. Physician nonadherence with a hepatitis C screening program. Qual Manag Health Care. 2014;23(1):1-9. doi: 10.1097/QMH.0000000000000007.

46. He T, Li K, Roberts MS, et al. Prevention of hepatitis C by screening and treatment in U.S. prisons. Ann Intern Med. 2016;164(2):84-92. doi: 10.7326/M15-0617.

47. Rich JD, Allen SA, Williams BA. Responding to hepatitis C through the criminal justice system. N Engl J Med. 2014;370(20):1871-1874. doi: 10.1056/NEJMp1311941.

48. Martin NK, Hickman M, Hutchinson SJ, Goldberg DJ, Vickerman P. Combination interventions to prevent HCV transmission among people who inject drugs: modeling the impact of antiviral treatment, needle and syringe programs, and opiate substitution therapy. Clin Infect Dis. 2013;57(suppl 2):S39-S45. doi: 10.1093/cid/cit296.

49. Edlin BR, Winkelstein ER. Can hepatitis C be eradicated in the United States? Antiviral Res. 2014;110:79-93. doi: 10.1016/j.antiviral.2014.07.015. 

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