The American Journal of Managed Care
August 2018
Volume 24
Issue 8

Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers

An optimized hepatitis C virus screening and linkage-to-care process reduces the number of patients lost to follow-up and improves linkage to care for Medicare, Medicaid, and commercially insured patients.


Objectives: We examined how a population susceptible to hepatitis C virus (HCV) moves through the HCV screening and linkage-to-care (SLTC) continuum across insurance providers (Medicare, Medicaid, commercial) and identified opportunities for increasing the number of patients who complete the SLTC process and receive treatment.

Study Design: Discrete-time Markov model.

Methods: A cohort of 10,000 HCV-susceptible patients was simulated through the HCV SLTC process using a Markov model with parameters from published literature. Three scenarios were explored: baseline, in which each step required a separate visit and all infected saw a specialist; reflex, which reflexed antibody and RNA testing; and consolidated, which reflexed antibody, RNA, fibrosis staging, and genotype testing into 1 step, with an optional specialist visit. For each scenario, we estimated the number of patients lost at each stage, yield, and cost.

Results: Streamlining the SLTC process by reducing the number of required visits results in more patients completing the process and receiving treatment. Among antibody-positive patients, 76% of those with Medicaid and 71% of those with Medicare and commercial insurance are lost to follow-up in baseline. In reflex and consolidated, these proportions fall to 26% and 27% and 4% and 5%, respectively. The cost to identify and link 1 additional infected patient to care ranges from $1586 to $2546 in baseline and $212 to $548 in consolidated. Total cost, inclusive of treatment, ranges from $1.0 million to $3.1 million in baseline and increases to $3.8 million to $15.1 million in reflex and $5.3 million to $21.0 million in consolidated.

Conclusions: Reducing steps in the HCV SLTC process increases the number of patients who learn their HCV status, receive appropriate care, and initiate treatment.

Am J Manag Care. 2018;24(8):e257-e264Takeaway Points

This study evaluated the impact of streamlining the hepatitis C virus (HCV) screening and linkage-to-care (SLTC) process on costs, yield, and patients lost to follow-up by integrating reflex testing for early steps in the process. The findings are relevant for clinicians and managed care decision makers involved in HCV SLTC programs.

  • Reducing the number of required visits during the SLTC process decreases the number of patients lost to follow-up by 62% to 95%.
  • Streamlining the HCV SLTC process results in more patients who are aware of their HCV status, receive appropriate care, and are ultimately treated.

Up to 3.5 million people in the United States are infected with chronic hepatitis C virus (HCV), and half are unaware of their infection status.1,2 Current guidelines recommend 1-time HCV screening for individuals born between 1945 and 1965 and individuals with increased risk of infection, but initial screening represents only the first stage in the screening and linkage-to-care (SLTC) process.3 To detect chronic infection, patients with a positive HCV antibody test must have confirmatory RNA testing, and for those with chronic HCV, additional diagnostics, including genotype testing and fibrosis staging, are recommended before treatment.

Reflex testing, in which RNA is tested immediately following a reactive antibody test using the same blood draw, represents a simplified SLTC process and allows patients to definitively know their HCV status following 1 visit.4 Without reflex testing, an estimated 33% to 47% of patients who receive a positive antibody test do not receive confirmatory RNA testing, highlighting the importance of a streamlined process for patient awareness.5-7

Although fewer visits in the SLTC process may result in fewer patients lost to follow-up, other barriers may result in patients dropping out of the process prior to initiating treatment. For example, HCV guidelines still recommend subspecialty consultation for patients with advanced fibrosis or cirrhosis.3 The need for specialty care may disproportionately impact patients with less access to care, such as those who use community health centers.8,9 Even if patients successfully complete all screening and diagnostic testing and receive a prescription for treatment, they still may not be treated if their payer policy includes coverage restrictions, such as prior authorization (PA).10

Given that only 16% of chronically infected patients are eventually prescribed treatment,11 it is important to identify steps in the SLTC process where patient retention is lowest and improve retention at those points. To examine this issue, we developed a model that simulates the HCV SLTC process from antibody testing through treatment initiation. The minimum number of visits required prior to a treatment decision varied from 2 to 4, and the resulting costs, yield, and patients lost to follow-up were estimated depending on patients’ insurance provider (Medicaid, Medicare, or commercial).


Baseline Model Framework

A discrete-time Markov model was developed to simulate the HCV SLTC process and was stratified by insurance type: Medicaid, Medicare, and commercial. The model follows 10,000 patients from antibody screening through treatment initiation until 1 of 5 conditions is met: (1) they are found not to have chronic HCV, (2) a “no treatment recommended” decision is made, (3) PA is denied, (4) treatment is initiated, or (5) they drop out before meeting any of the prior conditions and are lost to follow-up (henceforth, “lost”).

The state transition model (Figure 1) was adapted from CDC guidance.4 Patients enter the model and receive an antibody test. Those who are antibody-negative are not infected with HCV, require no additional testing, and have completed the SLTC process. Patients who are antibody-positive (Ab+) continue to confirmatory RNA testing or are lost.

RNA testing assesses the presence of chronic infection. Patients who are RNA-negative have no active infection and have completed the screening process. Patients who test RNA-positive and have been infected longer than 6 months are chronically infected and continue to a specialist for further testing or are lost.

Patients with chronic HCV receive genotype testing and noninvasive liver fibrosis staging at a specialist visit; results determine their treatment regimen and duration. Biopsies account for fewer than 10% of fibrosis staging tests12 and were excluded from our model. At this stage, patients either receive a “no treatment recommended” decision, are prescribed treatment, or are lost. Patients who receive a decision of no treatment recommended have completed the screening process.

If treatment is recommended, patients transition through additional stages before receiving therapy. We do not explicitly model additional tests that may follow the treatment recommendation, such as NS5A resistance testing or renal function testing; however, such tests could be conducted during the specialist visit, which avoids additional visits during the SLTC process. At least 29 states require some duration of sobriety for Medicaid patients.13 Therefore, Medicaid enrollees in our model must meet sobriety requirements and obtain PA before initiating treatment. Commercial and Medicare enrollees must obtain PA.

Model parameters were drawn from the published literature and stratified by insurance type where possible. Key parameters for the model include transition probabilities, average timing values, and per visit costs. HCV prevalence plays a key role in model outcomes because it determines the number of Ab+ patients who progress beyond the initial state. Of our 3 insurance strata, Medicaid has the highest prevalence of Ab+ patients (16%), followed by Medicare (9%) and commercial (5%).14,15 The proportion of Ab+ patients with chronic HCV is the same for all strata (79.7%).16 The eAppendix (available at provides a full description of the model assumptions and parameters.

Model Scenarios

The baseline scenario previously described requires at least 4 visits before chronically infected patients receive a treatment recommendation (Figure 1). We also estimated reflex and consolidated scenarios in which chronically infected patients require a minimum of 3 or 2 visits, respectively, before treatment recommendation. All stages of the SLTC process in baseline are included in reflex and consolidated, but they are condensed to varying degrees. Beyond the treatment decision step, the 3 scenarios are identical.

Baseline includes the SLTC steps recommended in current HCV guidelines, with each step in the process requiring a separate visit and only specialists providing genotype testing, fibrosis staging, and treatment decisions. Baseline thus requires 4 visits for a chronically infected patient to receive a treatment recommendation and is the least efficient scenario.

Reflex also includes the SLTC steps recommended in current HCV guidelines, but reflexes antibody and RNA testing so that 2 blood samples are collected from a single draw at the first visit and if the first is Ab+, the second is automatically tested for HCV RNA.17,18 Thus, the process can be completed in 3 visits rather than 4.

Reflex also eliminates the specialist visit for less clinically complex patients who can be effectively managed by primary care physicians (PCPs). To be conservative, we assumed that only patients with fibrosis scores below F2 can be managed by PCPs, which resulted in 60% of patients requiring a specialist visit.19

Consolidated represents a hypothetical best-case scenario in which all tests are reflexed and a specialist visit is not required for patients with fibrosis scores below F2. This scenario requires at least 2 visits for chronically infected patients to receive a treatment decision and provides the fewest opportunities for patients to be lost.

Although pangenotypic treatment is now available, guidelines still recommend genotype testing, and all 3 scenarios include it. Genotype testing can also be used to guide treatment for patients who do not receive pangenotypic treatment. To reflex noninvasive fibrosis staging, diagnostic tests using a blood draw (eg, AST [aspartate aminotransferase] to Platelet Ratio Index or FibroTest) would be required.20 Although these may be uncommon as the primary means for fibrosis staging in current practice, they are feasible. Current HCV treatment guidelines recommend combined blood- and image-based fibrosis testing, so our consolidated scenario should be considered exploratory, as an estimate of potential benefits from an SLTC process that minimizes patient visits.3

Model Outcomes

We estimated several outcomes for each scenario and insurance type: number of patients lost at each stage, yield (percentage of patients entering the model who complete the process and initiate treatment), conditional yield (percentage of patients with chronic HCV who complete the process and initiate treatment), and several cost outcomes. Total screening costs include the cost of antibody testing, RNA testing, genotype testing, fibrosis staging, and, when relevant, specialist and sobriety costs. Total costs include screening costs plus the cost of treatment. We assumed that treatment cost equals the wholesale acquisition cost of sofosbuvir 400 mg/velpatasvir 100 mg (Epclusa) discounted by 46%.3 To estimate the cost to identify and link 1 additional patient to care, we calculated the number of patients screened to yield 1 patient in the genotype/fibrosis step of the model and calculated the cost of antibody and RNA testing for those patients. This outcome provides an additional measure of efficiency of the SLTC process prior to receiving a treatment recommendation and is independent of the number of patients treated. Other outcomes are presented in the eAppendix, including cost per person screened, timing-related results, and yield conditional on the number of Ab+ patients.


Lost to Follow-Up and Yield

SLTC results for each strata and scenario are presented in Figures 2, 3, and 4. The figures show the number of patients lost after each step; fewer patients lost indicates a more efficient process. In baseline, 12% of Medicaid, 6% of Medicare, and 4% of commercial patients are lost before treatment. Across all insurance types, most patients are lost after RNA testing. For reflex and consolidated, 4.0% and 0.7%, respectively, are lost before treatment for Medicaid, 2.0% and 0.5% for Medicare, and 1.0% and 0.3% for commercial patients.

Yield and total lost results are presented in the Table. Yield incorporates both HCV prevalence in the screened population and the likelihood of loss. High yields therefore result from high prevalence, screening process efficiency, or both. Baseline yields are 0.5%, 0.7%, and 0.2% for Medicaid, Medicare, and commercial patients, respectively. The higher efficiency of the reflex and consolidated models translates into higher yields; reflex yields are 3.5%, 3.1%, and 0.9%, respectively, and 4.9%, 4.4%, and 1.2% in consolidated.

Conditional yield describes the efficiency of the screening process for chronically infected patients. Baseline conditional yields are 4%, 9%, and 5% for Medicaid, Medicare, and commercial, respectively, increasing to 28%, 44%, and 22% in reflex and 31%, 49%, and 24% in consolidated.


The Table also presents total costs, total screening costs, screening costs per patient treated, and the cost to identify 1 additional patient and link them to care. We used undiscounted costs in our model; therefore, our cost results represent an upper bound.

Total costs are driven by the total treated, and in baseline are highest for Medicare ($3.1 million) and lowest for commercial ($1.0 million); total costs increase substantially for reflex and consolidated, ranging from $3.8 million to $15.1 million and $5.3 million to $21.0 million, respectively. Although commercial has the lowest total screening cost, it also treats the fewest patients, leading to higher per person costs. In baseline, the cost per person treated is $7843 for Medicaid, $4833 for Medicare, and $14,176 for commercial.

For baseline, the cost to identify 1 additional chronically infected patient and link them to care is highest in the commercial population ($2546) and lowest in the Medicare population ($1539), but Medicaid sees the largest reductions in that cost when the process is collapsed from baseline to reflex or consolidated.

Alternative Analyses

Three alternative analyses were conducted: (1) “fixed prevalence,” which assumed the same HCV Ab+ prevalence across insurance types; (2) “no genotype,” which removed genotype testing; and (3) “no sobriety requirements,” which removed sobriety requirements. Results for alternative analyses are presented in the eAppendix.

“Fixed prevalence” allows us to compare the efficiency of the SLTC process across insurance types while holding constant population disease prevalence. Commercial has the highest per person costs in the main analysis, but Medicaid and commercial costs are similar in the fixed prevalence analysis, suggesting that lower per person costs in Medicaid are driven primarily by higher prevalence.

Although genotype testing is still recommended in HCV treatment guidelines, “no genotype” explores the impact of removing genotype testing, which may be possible with pangenotypic therapies. Because genotype testing occurs during the same visit as fibrosis staging, removing it reduces per person costs by $351 but does not reduce visits.

“No sobriety requirements” removes a barrier to treatment initiation that occurs late in the SLTC process and affects only the Medicaid population. Removing sobriety testing reduces screening costs by $48 per drug test.


Comparing baseline, reflex, and consolidated results shows the value of streamlining the SLTC process. Collapsing baseline to reflex reduces the number of patients lost prior to receiving a treatment recommendation by 62% to 66%. Further streamlining to consolidated reduces the number lost by 92% to 95%. Because the Medicaid population has the most inefficient SLTC process to begin with, it experiences the largest improvements from streamlining the process.

Although our analysis focuses on the number of visits as a measure of SLTC process efficiency, the underlying prevalence in a given population also plays an important role in the process. For example, although the Medicaid subpopulation loses the most patients, it also has the highest Ab+ prevalence (16%), resulting in higher yield versus the commercial population. Moreover, in consolidated, only 20 additional Medicaid patients need to be screened on average to get 1 additional chronically infected patient into treatment compared with 100 additional patients screened to achieve the same result in the commercial population.

Our findings are consistent with those of a recently published study of the care continuum for patients with HCV diagnosed in 2 urban emergency departments.21 In the study, the Medicaid process was less efficient, with only 8.5% of RNA-positive patients initiating treatment compared with 12% of Medicare patients. Additionally, 82% of Ab+ patients completed viral load testing, similar to our finding of approximately 80% of patients progressing to RNA testing. Generally, the literature presents conditional yield rather than yield; conditional yield estimates range from 3.9% to 24%.11,14,22-24 Our baseline conditional yields (3.7%-9.5%) are on the lower end compared with recent studies. Our baseline Ab+ conditional yields (eAppendix) range from 2.7% to 7.5%, which is consistent with findings from 2 recent studies (3.3% and 4.0%).25,26

Not surprisingly, total costs increase dramatically from baseline to reflex and consolidated, because more patients receive treatment. This paper is not intended as a cost-benefit exercise, nor do we model other medical expenditures for patients with HCV. However, the increased treatment costs are arguably of high value because identifying and treating more patients will provide benefits associated with reduced transmission rates, long-term cost savings on medical expenditures related to untreated HCV, and a reduction in liver transplants.27-29

Reducing the number of patients lost decreases screening costs per person treated because the total system costs are spread among more patients. This aligns with prior literature showing that expanded HCV screening provides the most value when coupled with expanded treatment.30 Additionally, there are costs associated with the SLTC process that are difficult to measure (eg, patient navigation, social work) and are not included in our analysis. It is likely that Medicaid patients would benefit most from these services and incur additional costs, but this population also experiences the greatest gains from reflex and consolidated.

For all insurance types, a majority of patients are lost prior to visiting a specialist, which suggests that having insurance does not eliminate inefficiencies associated with multiple visits required in the SLTC process. Although our 3 scenarios focus on streamlining the SLTC process prior to treatment recommendation, barriers to treatment exist in later stages of the process. Specifically, PA poses a significant barrier for patients who are prescribed treatment, particularly in the Medicaid and commercial populations. Of patients who seek PA, 46% and 55% of Medicaid and commercial patients, respectively, are denied, whereas only 13% of Medicare patients are denied.

A patient who is denied PA is comparable with one who is lost, and for patients who are eventually denied, streamlining the process simply delays the point at which they are lost. This delay increases overall costs from screening and time spent in the process but does not change the disease outcome because treatment is not received. Consequently, to maximize the number of patients treated, barriers to treatment must be reduced.


We note several limitations. Our parameter values come from the literature and were not available for all insurance types in many cases. Some of our parameters may not be generalizable because they are derived from small samples or high-risk subpopulations. In cases where required parameter values were unavailable, we relied on assumptions, detailed in the eAppendix, to populate the model.

Although the consolidated scenario demonstrates the value of streamlining the SLTC process, it represents a hypothetical process. Novel real-world screening models, such as Project ECHO, attempt to achieve similar efficiency gains through telemedicine, but they have not been broadly adopted.31 Additionally, the decision to initiate treatment is a dynamic one (ie, patients who are not initially recommended for treatment may receive a treatment recommendation later). Because we model “no treatment recommended” as an absorbing state, we do not capture the dynamics of the treatment recommendation decision and therefore underestimate the number of patients who ultimately initiate treatment, as well as the costs.

Although our model captures key screening steps and barriers related to obtaining treatment, it relies, like all models, on simplifications and abstractions that may not generalize. For example, we do not consider variability within insurers; our classification of a single broad “commercial” stratification does not allow for the effect of plan-specific features, such as narrow networks, on the SLTC process. We also do not consider the site where patients are screened or the composition of patients receiving screening, both of which may impact screening outcomes. Our assumption that fibrosis staging can be reflexed could result in some patients’ fibrosis scores being misclassified because blood tests are not sensitive enough to rule out substantial fibrosis.20,32,33

Finally, we do not explicitly model capacity constraints, but we model wait times between stages. Explicitly including capacity constraints would further affect patient wait times between stages, particularly in consolidated, which assumes that the entire SLTC process occurs at a single site.

Future research should focus on identifying opportunities to improve the STLC process for patients across screening sites and insurance providers, as well as collecting more granular real-world data for the SLTC process. Other real-world features should be considered, such as the decision to enter screening, dynamic treatment recommendations, and capacity constraints. Finally, it will be useful to understand the relative importance of other mechanisms for improving SLTC process efficiency, such as patient navigation, decreased wait times between appointments, and conducting all HCV screening and additional care at 1 location.


Substantial advances in treatment have improved the outlook for patients with HCV, but continuing efforts are needed to increase the number of patients who complete the SLTC process. Appropriate care can increase the number of patients screened, evaluated, and treated for, and cured of, HCV. Initiatives to address the efficiency of the SLTC process should be tailored to reflect nuances in different insurance populations and access to resources. Our findings highlight the importance of removing inefficiencies in the early SLTC stages (eg, antibody and RNA testing). However, consolidating the early part of the SLTC process is not sufficient because patients also encounter barriers later, usually at the PA stage. Reducing the number of visits required to obtain treatment, as well as removing other barriers, will increase the number of patients who obtain treatment. 


The authors would like to thank Alisher Sanetullaev, Alison Silverstein, and Emma van Eijndhoven for their valuable research support.Author Affiliations: Sol Price School of Public Policy and Schaeffer Center for Health Policy and Economics at the University of Southern California (KM); Precision Health Economics (JS, LY, JC), Los Angeles, CA; Value of Life Sciences Innovation Project, Schaeffer Center for Health Policy and Economics at the University of Southern California (KVN), Los Angeles, CA.

Source of Funding: Funding for this study was provided by Gilead Sciences, Inc, to Precision Health Economics.

Author Disclosures: At the time this research was conducted, Dr Mulligan, Mr Sullivan, Ms Yoon, and Ms Chou were employed by Precision Health Economics (PHE), which consults for pharmaceutical clients. Mr Sullivan owns options to purchase stock in PHE. Dr Van Nuys is a health economics consultant with clients in the pharmaceutical industry; she worked on this project as a consultant to PHE.

Authorship Information: Concept and design (KM, JS, LY, KVN); acquisition of data (KM, LY); analysis and interpretation of data (KM, JS, KVN); drafting of the manuscript (KM, LY, JC, KVN); critical revision of the manuscript for important intellectual content (KM, JS, JC, KVN); statistical analysis (JS); administrative, technical, or logistic support (JC); and supervision (KVN).

Address Correspondence to: Karen Mulligan, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. Email:

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. 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.

3. American Association for the Study of Liver Diseases; Infectious Diseases Society of America. HCV guidance: recommendations for testing, managing, and treating hepatitis C. HCV Guidelines website. Accessed February 2017.

4. CDC. Testing for HCV infection: an update of guidance for clinicians and laboratorians. MMWR Morb Mortal Wkly Rep. 2013;62(18):362-365.

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. Klevens RM, Miller J, Vonderwahl C, et al. Population-based surveillance for hepatitis C virus, United States, 2006-2007. Emerg Infect Dis. 2009;15(9):1499-1502. doi: 10.3201/eid1509.081050.

7. McGibbon E, Bornschlegel K, Balter S. Half a diagnosis: gap in confirming infection among hepatitis C antibody-positive patients. Am J Med. 2013;126(8):718-722. doi: 10.1016/j.amjmed.2013.01.031.

8. Hézode C. Pan-genotypic treatment regimens for hepatitis C virus: advantages and disadvantages in high- and low-income regions. J Viral Hepat. 2017;24(2):92-101. doi: 10.1111/jvh.12635.

9. Arora S, Thornton K, Murata G, et al. Outcomes of treatment for hepatitis C virus infection by primary care providers. N Engl J Med. 2011;364(23):2199-2207. doi: 10.1056/NEJMoa1009370.

10. Do A, Mittal Y, Liapakis A, et al. Drug authorization for sofosbuvir/ledipasvir (Harvoni) for chronic HCV infection in a real-world cohort: a new barrier in the HCV care cascade. PLoS One. 2015;10(8):e0135645. doi: 10.1371/journal.pone.0135645.

11. 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.

12. Masadeh M, Shen H, Hasan Y, Johannes A, Sanchez AJ. Hepatitis C treatment with direct acting antiviral therapy: report of a global survey. Hepatology. 2016;64(suppl 1):472A. Abstract 938. doi: 10.1002/hep.28798.

13. National Viral Hepatitis Roundtable; Harvard Law School Center for Health Law and Policy Innovation. Hepatitis C: the state of Medicaid access: preliminary findings: national summary report. Center for Health Law and Policy Innovation website. Published November 14, 2016. Accessed January 20, 2017.

14. Coyle C, Viner K, Hughes E, et al; CDC. Identification and linkage to care of HCV-infected persons in five health centers - Philadelphia, Pennsylvania, 2012-2014. MMWR Morb Mortal Wkly Rep. 2015;64(17):459-463.

15. Geboy A, Cha H, Perez I, et al. Low HCV testing uptake of the current birth cohort guidelines. Presented at: 22nd Conference on Retroviruses and Opportunistic Infections; February 23-26, 2015; Seattle, WA. Accessed June 10, 2016.

16. Armstrong GL, Wasley A, Simard EP, McQuillan GM, Kuhnert WL, Alter MJ. The prevalence of hepatitis C virus infection in the United States, 1999 through 2002. Ann Intern Med. 2006;144(10):705-714. doi: 10.7326/0003-4819-144-10-200605160-00004.

17. Viral hepatitis: laboratory tests and hepatitis C. US Department of Veterans Affairs website. Accessed January 20, 2017.

18. Hep C RNA reflex tests are now available from commercial labs. Hep Free NYC website. Published 2014. Accessed December 10, 2017.

19. 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.

20. Chou R, Wasson N. Blood tests to diagnose fibrosis or cirrhosis in patients with chronic hepatitis C virus infection: a systematic review [erratum in Ann Intern Med. 2013;159(4):308. doi: 10.7326/0003-4819-159-4-201308200-00022]. Ann Intern Med. 2013;158(11):807-820. doi: 10.7326/0003-4819-158-11-201306040-00005.

21. Anderson ES, Galbraith JW, Deering LJ, et al. Continuum of care for hepatitis C virus among patients diagnosed in the emergency department setting. Clin Infect Dis. 2017;64(11):1540-1546. doi: 10.1093/cid/cix163.

22. Yawn BP, Wollan P, Gazzuola L, Kim WR. Diagnosis and 10-year follow-up of a community-based hepatitis C cohort. J Fam Pract. 2002;51(2):135-140.

23. Falade-Nwulia O, Mehta SH, Lasola J, et al. Public health clinic-based hepatitis C testing and linkage to care in Baltimore. J Viral Hepat. 2016;23(5):366-374. doi: 10.1111/jvh.12507.

24. Fishbein DA, Lo Y, Reinus JF, Gourevitch MN, Klein RS. Factors associated with successful referral for clinical care of drug users with chronic hepatitis C who have or are at risk for HIV infection. J Acquir Immune Defic Syndr. 2004;37(3):1367-1375. doi: 10.1097/01.qai.0000131932.21612.49.

25. Viner K, Kuncio D, Newbern EC, Johnson CC. The continuum of hepatitis C testing and care. Hepatology. 2015;61(3):783-789. doi: 10.1002/hep.27584.

26. Norton BL, Southern WN, Steinman M, et al. No differences in achieving hepatitis C virus care milestones between patients identified by birth cohort or risk-based screening. Clin Gastroenterol Hepatol. 2016;14(9):1356-1360. doi: 10.1016/j.cgh.2016.04.017.

27. Jena AB, Stevens W, Gonzalez YS, et al. The wider public health value of HCV treatment accrued by liver transplant recipients. Am J Manag Care. 2016;22(spec no 6):SP212-SP219.

28. 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.

29. Wedemeyer H, Duberg AS, Buti M, et al. Strategies to manage hepatitis C virus (HCV) disease burden. J Viral Hepat. 2014;21(suppl 1):60-89. doi: 10.1111/jvh.12249.

30. Linthicum MT, Gonzalez YS, Mulligan K, et al. Value of expanding HCV screening and treatment policies in the United States. Am J Manag Care. 2016;22(6 spec no):SP227-SP235.

31. Arora S, Kalishman S, Thornton K, et al. Expanding access to hepatitis C virus treatment—Extension for Community Healthcare Outcomes (ECHO) project: disruptive innovation in specialty care. Hepatology. 2010;52(3):1124-1133. doi: 10.1002/hep.23802.

32. Castéra L, Sebastiani G, Le Bail B, de Lédinghen V, Couzigou P, Alberti A. Prospective comparison of two algorithms combining non-invasive methods for staging liver fibrosis in chronic hepatitis C. J Hepatol. 2010;52(2):191-198. doi: 10.1016/j.jhep.2009.11.008.

33. Sebastiani G, Halfon P, Castera L, et al. SAFE biopsy: a validated method for large-scale staging of liver fibrosis in chronic hepatitis C. Hepatology. 2009;49(6):1821-1827. doi: 10.1002/hep.22859.

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