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The American Journal of Managed Care Special Issue: HCV
Real-World Outcomes of Ledipasvir/Sofosbuvir in Treatment-Naïve Patients With Hepatitis C
Zobair M. Younossi, MD, MPH, FACG, AGAF, FAASLD; Haesuk Park, PhD; Stuart C. Gordon, MD; John R. Ferguson; Aijaz Ahmed, MD; Douglas Dieterich, MD; and Sammy Saab, MD, MPH
Sofosbuvir Initial Therapy Abandonment and Manufacturer Coupons in a Commercially Insured Population
Taruja D. Karmarkar, MHS; Catherine I. Starner, PharmD; Yang Qiu, MS; Kirsten Tiberg, RPh; and Patrick P. Gleason, PharmD
Improving HCV Cure Rates in HIV-Coinfected Patients - A Real-World Perspective
Seetha Lakshmi, MD; Maria Alcaide, MD; Ana M. Palacio, MD, MPH; Mohammed Shaikhomer, MD; Abigail L. Alexander, MS; Genevieve Gill-Wiehl, BA; Aman Pandey, BS; Kunal Patel, BS; Dushyantha Jayaweera, MD; and Maria Del Pilar Hernandez, MD
Does Patient Cost Sharing for HCV Drugs Make Sense?
Darius N. Lakdawalla, PhD; Mark T. Linthicum, MPP; and Jacqueline Vanderpuye-Orgle, PhD
A Way Out of the Dismal Arithmetic of Hepatitis C Treatment
Jay Bhattacharya, MD, PhD, Center for Primary Care and Outcomes Research, Stanford University School of Medicine; Guest Editor-in-Chief for the HCV special issue of The American Journal of Managed
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Value of Expanding HCV Screening and Treatment Policies in the United States
Mark T. Linthicum, MPP; Yuri Sanchez Gonzalez, PhD; Karen Mulligan, PhD; Gigi A. Moreno, PhD; David Dreyfus, DBA; Timothy Juday, PhD; Steven E. Marx, PharmD; Darius N. Lakdawalla, PhD; Brian R. Edlin, MD; and Ron Brookmeyer, PhD
Costs and Spillover Effects of Private Insurers' Coverage of Hepatitis C Treatment
Gigi A. Moreno, PhD; Karen Mulligan, PhD; Caroline Huber, MPH; Mark T. Linthicum, MPP; David Dreyfus, DBA; Timothy Juday, PhD; Steven E. Marx, PharmD; Yuri Sanchez Gonzalez, PhD; Ron Brookmeyer, PhD; and Darius N. Lakdawalla, PhD
Coverage for Hepatitis C Drugs in Medicare Part D
Jeah Kyoungrae Jung, PhD; Roger Feldman, PhD; Chelim Cheong, PhD; Ping Du, MD, PhD; and Douglas Leslie, PhD

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

Mark T. Linthicum, MPP; Yuri Sanchez Gonzalez, PhD; Karen Mulligan, PhD; Gigi A. Moreno, PhD; David Dreyfus, DBA; Timothy Juday, PhD; Steven E. Marx, PharmD; Darius N. Lakdawalla, PhD; Brian R. Edlin, MD; and Ron Brookmeyer, PhD
Expanding screening for hepatitis C virus infection may generate substantial benefits for patients and society, but only when paired with expanded treatment policies.
Limitations
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.
 
CONCLUSIONS
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.


Acknowledgments
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.
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