Karen Mulligan, PhD

 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.

 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.

 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.

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

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.

 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.

 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.

 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.

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.

 The need for specialty care may disproportionately impact patients with less access to care, such as those who use community health centers.

 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).

Given that only 16% of chronically infected patients are eventually prescribed treatment,

 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).

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”).

 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 tests

 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.

 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%).

 The proportion of Ab+ patients with chronic HCV is the same for all strata (79.7%).

) provides a full description of the model assumptions and parameters.

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.

 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.

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.

 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.

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%.

 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.

SLTC results for each strata and scenario are presented in 

. 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 

. 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.

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.

 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%.

 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%).

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.

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.

 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.

Articles

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.

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

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.

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).

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.

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

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.

 Through recent pharmaceutical breakthroughs, short courses of treatment with direct-acting antivirals (DAAs) provide a high likelihood of cure.

 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 society

; 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,

 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.

The misalignment between short-term costs and long-term benefits is of particular concern for Medicare, which insures individuals 65 or older.

 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.

 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.

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.

 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”

; a detailed description of the model is available in the 

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%.

 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.

The model assumes Other Adults is a closed cohort in which infected individuals do not transmit HCV to uninfected individuals.

 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.

 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.

 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.

 Due to the small sample of NHANES respondents in the MSM-HIV exposure group, we used prevalence estimates from the published literature.

 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.

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.

 All cost parameters were inflated to 2015 US dollars, and future costs and QALYs were discounted at an annual 3% rate.

 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 competition

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.

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.

 The alternative scenarios assumed that private insurers expand treatment coverage to either fibrosis stages F2-F4 or F0-F4.

Baseline Distribution of Insurance Status

 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.

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.

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.

 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

 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. 

 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.

 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 

). 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.

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&mdash;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.

 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.

 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.

 Alternatively, an explicit credit could be granted to each payer that treats a patient.

 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).

 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.

 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.

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.

 Finally, NHANES also does not capture homeless and incarcerated populations, both of which have high HCV prevalence and limited treatment access.

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. 

 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.

 Support for this research was provided by AbbVie, Inc.

 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.

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.

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

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

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.

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.

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

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,

 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.

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.

 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.

 Novel HCV regimens, including direct-acting antivirals (DAAs), have increased cure rates dramatically, which may affect the value of expanded screening.

 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.

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,

 and even fewer patients completed the treatment regimen and achieved SVR.

 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.

 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.

 Unfortunately, more than 40% of physicians are unaware of current guidelines,

 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 costs

; yet recent research suggests the value of their long-term health benefits is likely to be even higher.

 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.

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.

 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.

Once infected, individuals progress through disease states according to transition probabilities drawn from the literature (see 

). 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.

 Outside of the PWID and MSM-HIV groups, the risk of HCV transmission is low.

 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.

 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.

 Screening costs included the cost of an HCV antibody test (enzyme-linked immunoassay) and a level-1 outpatient visit.

 Medical expenditures for diagnosed patients were computed by disease state and diagnosis status.

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.

 For details on model parameters, dynamics, and assumptions, see the eAppendix.

 The model explores 3 scenarios for the frequency and inclusiveness of screening in clinical practice (see 

). We used AASLD/IDSA screening guidelines to define screening practice,

 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.

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.

 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 imperfect

; 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.

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 practice

 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).

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 $150,000

; 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.

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.

 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.

Costs and QALY gains used to calculate cumulative net social value over the 20-year time horizon are presented in 

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 

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

Karen Mulligan, PhD

Articles

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

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

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