Organs for transplantation are scarce, but new medical therapies can prevent organ failure and the need for transplants. We sought to describe the unique value created by treatments that spare organs from failure and thus conserve donated organs for transplant into others, using hepatitis C virus (HCV) as a case study. 

Using data on trends in chronic liver disease, liver disease progression, and liver transplant allocation models, as well as the effectiveness of new HCV treatments, we estimate the potential effects of systematic HCV screening and treatment on the demand for liver transplants in the United States. We estimate the spillover benefits to patients with all-cause liver disease in terms of increased availability of transplants and life-years gained.

We estimated that systematic HCV screening and treatment could spare 10,490 liver transplants to HCV-infected patients from 2015 to 2035. An estimated 7321 transplants would accrue to patients with end-stage liver disease without HCV and 3169 transplants to those with uncured HCV, providing approximately 52,700 and 22,800 additional life-years, respectively. 

Treatment advances for HCV have the potential to generate considerable spillover benefits to patients awaiting transplants for non—HCV-mediated liver failure. For other diseases in which organ transplants are in short supply, our study provides a novel pathway by which positive spillovers may accrue from treatments that prevent end-stage organ disease.

Am J Manag Care. 2016;22(5 Spec Issue No. 6):SP212-SP219

		Organs for transplantation are scarce, but innovative hepatitis C virus (HCV) therapies can prevent liver failure and the need for transplants.

		Systematic HCV screening and treatment would spare 10,490 livers for transplant over 20 years, with almost 70% of these livers benefitting patients with non—HCV-mediated liver failure.

		The increased availability of donor livers will lead to 52,700 additional life-years for patients without HCV infection and 22,800 life-years for patients with HCV, providing economic values of $7.9 billion and $3.5 billion, respectively.

		Such spillover benefits to untreated populations underscore the value of innovative therapies in preventing organ failure.

Medical treatments can offer value to society that extends beyond the patients who are directly treated. For example, vaccinations not only reduce the likelihood of infection in those who are vaccinated, they also reduce the spread of infection. Such positive spillovers generate considerable value.

 These spillovers may also result from the treatment of diseases that would otherwise lead to organ damage and transplantation. For example, improvements in nephropathy treatment within diabetes reduce the incidence of end-stage renal disease, thus sparing kidneys for use in patients with end-stage renal disease who do not have diabetes.

The availability of organs for transplantation is scarce. In the United States alone, more than 7000 individuals die awaiting organ transplantation each year.

 In this study, we explored this idea by applying it to recently introduced therapies for hepatitis C virus (HCV) infection. An estimated 3 million individuals in the United States are affected by chronic HCV, a condition associated with long-term injury to the liver and with complications including cirrhosis, hepatocellular carcinoma, and, ultimately, liver failure (see 

 The most common reason for liver transplantation in the United States is end-stage liver disease (ESLD), and currently, nearly 50% (14,000/29,000) of ESLD cases among transplant recipients are due to HCV.

Until recently, treatments for HCV were neither particularly effective nor well tolerated.

 However, newer HCV therapies suppress the virus in more than 90% of patients, making an effective cure of HCV highly likely for the majority of those affected.

 Curing patients of HCV obviates their need for future liver transplantation due to HCV, thus creating opportunities for transplantation into patients with other forms of ESLD.

Currently, only one-third of Americans who need liver transplants receive them,

 and shortages are expected to rise as the transplant waiting list continues to grow while the supply of organs remains flat.

 Obesity and the aging of the population are reducing the quality of deceased donor livers, while obesity-related liver disease is also increasing the demand for them.

We simulated the effect of a systematic HCV screening and treatment program in the United States on the number of livers spared from transplantation into patients with HCV-mediated ESLD. We estimated the number of these spared livers that could be transplanted into patients with other forms of ESLD, as well as the resulting benefits to both groups. Our analysis takes a broader perspective than do existing models of HCV burden

 because it recognizes that treatment of patients with HCV creates positive spillovers for non-HCV patients with ESLD.

Our study relied on 2 main data sources: first, the National Health and Nutrition Examination Survey (NHANES)

a biennial survey administered to a nationally representative sample of the US population. Along with a survey of health and healthcare utilization, participants also give blood samples and undergo other diagnostic tests. This data set was used to generate trends over time in liver disease and in key risk factors. Our second data source was the United Network for Organ Sharing (UNOS) database, which holds information on every patient on the waiting list for organ transplantation in the United States since 1987, including patient characteristics, primary disease, source of organ, and time spent on waiting list.

We developed an epidemiologic-economic model to estimate how a systematic HCV screening and treatment program would: a) reduce the number of livers transplanted into patients with HCV with ESLD and b) increase the number of spared livers that could be transplanted into patients with other forms of ESLD. We converted livers spared into life-years by using prior literature on life expectancy gained from transplants. Finally, we transformed these longevity benefits into economic values using conventional estimates of the value of a statistical life-year.

We compared the effects of 2 types of HCV screening and treatment interventions (“real-world” and “comprehensive”) with a baseline in which screening and treatment did not change from the status quo. First, we simulated the baseline annual demands for liver transplants from 2015 to 2035, for patients with ESLD, according to underlying disease (ie, HCV, alcohol-related, and nonalcoholic fatty liver disease [NAFLD]). Second, using historical data on national liver transplantation rates, we projected the supply of livers available for transplantation from 2015 to 2035. Third, we performed a simulation of how 2 different interventions in HCV screening and treatment would reduce the demand for liver transplantation among patients with HCV-mediated ESLD during 2015 to 2035. Together, these steps estimated, year-by-year, the impact of systematic HCV screening and treatment on the number of livers newly spared for transplantation.

Projected Annual Baseline Demand for HCV-Mediated Liver Transplants

We obtained HCV prevalence data from 2001 to 2012 from NHANES and annual HCV incidence data from 2001 to 2010 from the CDC.

 The incidence of HCV was estimated to be 17,000 cases in 2010 (see eAppendix).

 In our baseline scenario, we assumed constant incidence of 18,000 cases per year from 2011 onward.

To estimate the baseline demand for transplants among patients with HCV-mediated ESLD, we adapted previous HCV modeling studies

 to develop a Monte Carlo Markov simulation model that transitioned patients through each stage of liver disease, from chronic infection without liver fibrosis to decompensated cirrhosis or hepatocellular carcinoma. The latter 2 states comprise candidacy for liver transplantation (

 describes the Markov model). The conceptual flow diagram highlights the inputs to the model, steps taken to validate key model assumptions, and the model outputs: a) livers spared from transplantation into patients with HCV as a result of a systematic HCV screening and treatment intervention, and b) the allocation of these spared livers to patients without HCV and patients with uncured HCV on the transplant list.

Projected Annual Baseline Demand for Non—HCV-Mediated Liver Transplants

To project the baseline demand for liver transplants among individuals with non—HCV-mediated ESLD, we used data from UNOS. We counted the number of individuals on the liver transplant waiting list with a primary diagnosis of NAFLD or liver disease due to alcohol, iron deposition, or copper deposition. As illustrated in 

, the incidence of NAFLD on the transplant waitlist increased significantly in the last decade. From historical data, we linearly extrapolated the annual incidence of NAFLD from 2015 to 2035, and we assumed that rates of liver disease due to alcohol or iron/copper deposition increased linearly until 2025. Based on prior studies,

 we allowed for a “leveling off” between 2025 and 2035 by reducing the linearly extrapolated rates by 1.5% per year.

Projected Annual Number of Patients Receiving Transplant

Using our incidence forecasts, we projected the annual number of patients with HCV-mediated and non—HCV-mediated ESLD receiving liver transplants. Not all patients on the waiting list receive transplants; the likelihood of transplant varies by etiology of liver disease and clinical severity. UNOS data record whether and when a patient receives a liver transplant, as well as time spent on the waitlist. Using individual-level data from UNOS, we estimated the likelihood of liver transplantation as a function of time spent on the waitlist, adjusting for patient age, sex, and body mass index (BMI). Empirically, the probability of transplantation varied by sex and BMI among patients with HCV-mediated liver disease, but not among those without it. We applied sex- and BMI-specific transplant probabilities to HCV-mediated ESLD incidence estimates to project transplants for patients with HCV from 2015 to 2035. For non–HCV-mediated patients with ESLD, we used unadjusted Kaplan-Meier estimates of the probability of transplantation at a given year (45.7%, 52.3%, 54.8%, 56.4%, and 57.3%, after 1, 2, 3, 4, and 5 years, respectively).

Projected Supply of Livers Available for Transplantation

The supply of livers available for transplantation, at approximately 6700 per year (2.25 per 100,000 population), remained the same from 2005 to 2013. We conservatively assumed the liver supply would grow proportionately with the population from 2015 to 2035. We conducted several sensitivity analyses around this assumption, as described below.

Effect of HCV Screening and Treatment on Rates of HCV-Mediated ESLD and Liver Transplants

We simulated the annual impact of implementing two 5-year policies to screen for and treat HCV. We assumed that treatment with novel HCV therapies leads to sustained virologic response (SVR) rates of 90% among patients with liver disease fibrosis in stages F0 to F4 where severity of diseases increases with fibrosis stage.

 Subjects achieving SVR at stage F4 were assumed to either remain in SVR or to transition to either decompensated liver disease (at an annual probability of 0.008) or hepatocellular carcinoma (at an annual probability of 0.005).

In reality, some patients may not accept screening, others may not receive their results, and still others who are aware of their HCV infection may not initiate treatment. Our real-world screening and treatment intervention rate relies on prior studies to assume that the percent of patients accepting HCV screening is 91%

 and the percent of those screened positive who initiate treatment is 80% (regardless of liver fibrosis stage).

We calculated the difference in the transplant waitlist size between the real-world scenario and a baseline scenario in which HCV screening and treatment remained at current rates from 2015 to 2035. In the real-world scenario, screening would take place over 5 years during 2015 to 2019, with 100% of patients with HCV aware of their disease status by 2019. In the baseline scenario, we assumed that 25% of subjects between F0 and F3 would be aware of their HCV status, and 75% of subjects would be aware of it at F4.

 In both scenarios, we used Markov models to project the annual number of patients with and without HCV on the liver transplantation waitlist from 2015 to 2035. Finally, we calculated the number of livers spared and the number of additional patients receiving transplants in the real-world scenario. (Additional details are in the eAppendix.)

Measuring Spillovers in Terms of Number and Value of Life-Years Gained

To estimate the economic value of these gains, we applied a value of $150,000 per life-year.

 We computed life-years gained from newly available liver transplants using prior research, estimating that each new transplant adds 7.2 years of life.

 To calculate social value as life-years gained multiplied by $150,000, we utilized prior research estimating the value of a quality-adjusted life-year.

We conducted 3 additional sensitivity analyses. First, we simulated the impact of a comprehensive HCV screening and treatment scenario, with 100% take-up of screening and treatment and 90% effectiveness for those treated. This exercise assessed the potential maximum impact of a systematic screening and treatment program. Second, we assumed a rising, rather than constant, incidence of liver disease due to alcohol, iron deposition, or copper deposition. Third, we simulated the impact of an opt-out organ donation policy, which allows organs from deceased donors to be used unless the deceased has officially requested otherwise.

 Under current law, individuals must opt in and officially request to make their organs available for potential transplants in the event of death. Based on recent meta-analyses, we estimated that an opt-out policy in the United States would increase the supply of livers by 45%.

The impact of a systematic HCV screening and treatment program on the total number of livers spared from 2015 to 2035 is shown in 

. The projected numbers of patients with HCV with each stage of liver fibrosis were lower under a systematic HCV screening and treatment program (our real-world scenario) relative to baseline. For example, the projected number of patients with HCV and decompensated liver failure was 30% lower under a systematic HCV screening and treatment program, and deaths from liver disease fell by 22%. The projected total number of new patients with HCV to be added to the liver transplant list between 2015 and 2035 was 28% lower under a systematic HCV screening and treatment program.

Our model predicted that 45,541 livers would be transplanted into patients with HCV during the 2015-to-2035 period under the baseline scenario compared with 35,052 under the real-world HCV screening and treatment program. The 10,490 spared livers were projected to be primarily allocated to individuals on the waitlist with non—HCV-mediated ESLD (7321 liver transplants), with a lower proportion allocated to those with uncured HCV-mediated ESLD (3169 transplants). 

 shows these spared livers broken down by subsets of patient characteristics.

The total life-years gained from additional transplants in each group were projected to be 52,711 years (7321 

 7.2 years), respectively. This amounts to $7.9 billion in economic value accrued to patients with non—HCV-mediated liver disease over this time period, with an additional $3.5 billion accruing to patients with HCV. Of note, the total number of transplants and life-years gained from newly allocated transplants to patients with non–HCV-mediated ESLD were approximately twice as large as those gained by patients with uncured HCV ESLD requiring transplantation. This is due to the larger projected prevalence of patients who do not have HCV on the transplant waiting list after implementation of the HCV screening and treatment program.

 shows the projected year-by-year cumulative number of liver transplants to patients without HCV and those with uncured HCV on the liver transplant list. At each year, the cumulative number of spared livers transplanted into patients with non—HCV-mediated ESLD is roughly twice that among those with uncured HCV on the transplant list. From 2015 to 2035, our model projected 80,451 liver transplantations to patients without HCV, of which the 7321 newly allocated spared livers would account for approximately 9% of the total.

 shows how our projections were affected by not only various sensitivity analyses regarding future trends in NAFLD and other liver disease, but by a comprehensive HCV screening and treatment scenario, in which both screening uptake and treatment rates were 100%, and by an opt-out organ donation policy in the United States that was estimated to increase the supply of transplantable livers by 45%.

We found that increasing trends in NAFLD and liver disease due to alcohol or iron/copper deposition would increase the total number of spared livers allocated to these patients from 7321 to 7653 between 2015 and 2035. The use of an opt-out policy for organ donation would further increase the number of livers spared from 7653 to 10,953, and a comprehensive HCV screening and treatment intervention would further raise the number of spared livers from 10,953 to 16,557.

Articles

POLICY

Advances in treatment for hepatitis C virus (HCV) have the potential to generate considerable spillover benefits to patients awaiting transplants, especially among those with non—HCV-mediated liver failure.

The Wider Public Health Value of HCV Treatment Accrued by Liver Transplant Recipients

To study rates and predictors of hepatitis C virus (HCV) cure among human immunodeficiency virus (HIV)/HCV-coinfected patients, and then to evaluate the effect of attendance at clinic visits on HCV cure.

Retrospective cohort study of adult HIV/HCV-coinfected patients who initiated and completed treatment for HCV with direct-acting antivirals (DAAs) between January 1, 2014, and June 30, 2015.

Eighty-four participants reported completing treatment. The median age was 58 years (interquartile ratio, 50-66); 88% were male and 50% were black. One-third were cirrhotic and half were HCV-treatment—experienced. The most commonly used regimen was sofosbuvir/ledipasvir (40%) followed by simeprevir/sofosbuvir (30%). Cure was achieved in 83.3%, 11.9% relapsed, and 2.3% experienced virological breakthrough. Two patients (2.3%) did not complete treatment based on pill counts and follow-up visit documentation. In multivariable analysis, cure was associated with attendance at follow-up clinic visits (odds ratio [OR], 9.0; 95% CI, 2.91-163) and with use of an integrase-based HIV regimen versus other non-integrase regimens, such as non-nucleoside analogues or protease inhibitors (OR, 6.22; 95% CI 1.81-141). Age, race, genotype, presence of cirrhosis, prior HCV treatment, HCV regimen, and pre-treatment CD4 counts were not associated with cure.

Real-world HCV cure rates with DAAs in HCV/HIV coinfection are lower than those seen in clinical trials. Cure is associated with attendance at follow-up clinic visits and with use of an integrase-based HIV regimen. Future studies should evaluate best antiretroviral regimens, predictors of attendance at follow-up visits, impact of different monitoring protocols on medication adherence, and interventions to ensure adequate models of HIV/HCV care.

Am J Manag Care. 2016;22(5 Spec Issue No. 6):SP198-SP204

Clinical trials have shown excellent cure rates for hepatitis C virus (HCV) among the HIV/HCV population. We examined data for real-world cure rates of HCV and predictors of cure in this population.

Real-world HCV cure rates when using direct-acting antivirals (DAAs) in HIV/HCV coinfection are lower than those seen in clinical trials. 

Cure is associated with attendance at follow-up clinic visits and with the use of  integrase-based antiretroviral regimens, compared to non—integrase-based HIV regimens, such as non-nucleoside analogues and protease inhibitors. 

Future studies should evaluate best antiretroviral regimens, predictors of attendance to follow-up visits, impact of different monitoring protocols on medication adherence, and interventions to ensure adequate models of HIV/HCV care.

Globally, an estimated 30% of the 33 million people living with HIV infection also have hepatitis C virus (HCV) coinfection.

 The natural history of HCV infection is altered in the HIV-infected host: patients with coinfection are half as likely to spontaneously clear HCV viremia, tend to have higher HCV ribonucleic acid (RNA) levels, and have accelerated progression to hepatic fibrosis and decompensated liver disease.

 Despite the use of antiretroviral therapy, the proportion of deaths caused by HCV-related end-stage liver disease and hepatocellular carcinoma have increased in the HIV-infected population.

Historically, patients coinfected with HIV/HCV who are treated with interferon and ribavirin (IFN/RBV) for HCV had lower rates of sustained virological response (SVR) than those without HIV infection.

 In contrast to the IFN/RBV treatment, the direct-acting antivirals (DAAs) have demonstrated similar rates of HCV cure in monoinfected and coinfected patients in clinical trials where strict follow-up is mandated.

 However, data on rates of HCV cure with the use of DAAs in patients coinfected with HIV/HCV in real-world settings are lacking.

Treatment of HIV/HCV coinfection poses specific challenges, as it is preferred that patients be on stable antiretroviral therapy (ART) prior to initiating HCV treatment, and that there be careful consideration of drug-drug interactions. In addition, due to the high cost of DAAs, insurance companies may have specific criteria to approve HCV treatment in the HIV-infected population. Access to care, frequent monitoring of HIV treatment, and adherence to antiretroviral therapy have been associated with faster time to HIV viral suppression and increased survival in the HIV-infected population.

 In real-world settings, these and other factors may be less optimal than in randomized clinical trials; therefore, we aimed to describe predictors of HCV cure among patients with HIV/HCV coinfection in 3 large urban outpatient settings. We hypothesized that real-world rates of HCV cure in the HIV-infected population are lower than those that have been described in clinical trials, and that attendance at clinical monitoring visits is associated with higher cure rates.

Understanding real-world data is key to inform interventions and treatment protocols and to potentially increase cure rates in the HIV/HCV-coinfected population.

This was a retrospective cohort study of adult patients coinfected with HIV/HCV receiving medical care at 3 large outpatient settings in south Florida: 1) the Miami Veterans Affairs (VA) Healthcare System serves approximately 153,000 veterans in 3 counties by operating 372 hospital beds, a community living center, and 7 satellite clinics; 2) the University of Miami Health system (UHealth) is a private academic institution operating 3 hospitals and 30 outpatient facilities serving 4 counties; and 3) the Jackson Health System (JHS), a public academic institution with 6 hospitals, 12 specialty care centers, and 2 long-term care centers, serves as a tertiary referral center for the state of Florida.

We reviewed medical charts of individuals with HIV/HCV coinfection who initiated and completed treatment for HCV with DAAs in any of these 3 institutions between January 1, 2014, and June 30, 2015.

All adults with HIV/HCV coinfection who initiated and completed HCV therapy with DAAs were included. HCV treatment was provided to patients 18 years or older with no evidence of active drug or alcohol abuse and who were on ART for HIV.

Our primary outcome was HCV cure. Cure was defined as an undetectable HCV RNA at 12 weeks after therapy completion (SVR at week 12 [SVR12] post treatment). A relapse was defined as recurrence of HCV RNA in a patient who had an end-of-treatment response (undetectable HCV RNA at the end of treatment). A virologic breakthrough refers to the reappearance of HCV RNA while still on therapy in a patient who had suppressed their viral level earlier in the course of therapy.

 We defined completion of treatment as self-reported completion of DAA therapy for the duration recommended by the provider. The selection and duration of HCV therapy strictly followed the guidelines of the American Association for the Study of Liver Diseases, and these choices were based on HCV genotype, degree of hepatic fibrosis, risk of drug interactions, etc.

Our main exposure was attendance at follow-up clinic visits. Attendance at follow-up clinic visits was defined as attendance at clinic visits at week 4; once during weeks 6, 7, or 8; and week 12 of HCV treatment, independent of attendance at laboratory visits. The 3 study settings have different monitoring protocols for patients coinfected with HIV/HCV; however, all sites required clinic visits on weeks 4, 6 to 8, and 12. We describe below the monitoring protocols at each site:

The treatment is initiated and monitored by the hepatology clinic. The treatment protocol requires clinic and laboratory visits every 2 weeks for the first 2 months, and then monthly, thereafter. A key characteristic is that the VA pharmacy dispenses DAAs during follow-up visits only if the medication for the previous period has been completed.

 The HIV provider or the hepatologist initiates treatment. The treatment protocol recommends follow-up clinic and laboratory visits every 4 weeks. During the initial visit, the patient receives prescriptions with refills to complete HCV treatment. The pharmacy assists in obtaining approval for medications, but medications are provided by community pharmacies based on initial prescription orders and are independent of follow-up visits or laboratory results.

The HIV provider initiates treatment for HCV. The treatment protocol recommends follow-up clinic and laboratory visits every 4 weeks; however, during the initial visit, the patient receives prescriptions with refills to complete HCV treatment. The JHS pharmacy assists in obtaining approval for medications and informs the ordering physician when medications are approved. Medications are provided by community pharmacies based on initial prescription orders and are independent of follow-up visits or lab results.

Other exposure variables included the following: sociodemographic characteristics (age, gender, race, location of treatment); clinical assessments (baseline HIV viral load and median CD4 count, ART regimen during HCV treatment, HCV genotype, HCV viral load, presence of cirrhosis, prior history of HCV treatment and agents used for treatment, presence of hepatitis B coinfection, HCV treatment regimen, concurrent use of ribavirin with DAAs for HCV treatment); attendance at treatment laboratory visits (defined as lab tests done on week 4, once during weeks 6 to 8, on week 12 and at the end of treatment); and posttreatment lab visits.

We reviewed all medical charts and provider notes, and we collected exposure, outcome, and covariate data. We also collected adverse events documented in the medical chart on each follow-up visit. We reviewed information on all follow-up visits as documented in the medical chart.

We used REDCap version 6.5.2 electronic data capture tools, hosted at University of Miami, to collect and manage study data. We used descriptive statistics to report the baseline characteristics of the population. We conducted univariable analysis with calculation of odds ratio, 95% CI, and χ

 value of .05 was considered to indicate statistical significance). In order to identify predictors, we used logistic regression analysis. Our multivariable analysis included the variables that were significant in the univariate analysis, and those that have been described to be associated with cure in the literature (ie, absence of cirrhosis, HCV genotype, aspartate aminotransferase platelet ratio index score, duration of treatment, race, and prior history of treatment). SPSS version 22 statistical software (IBM Corp, Armonk, New York) was used for analysis.

Institutional review board approvals from the University of Miami, JHS’s Jackson Memorial Hospital, and the VA Hospital administration were obtained prior to performing any study-related procedures. Due to the retrospective nature of this study, a waiver of informed consent was granted.

Eighty-four participants reported completing treatment and were evaluated 12 weeks post treatment. The median age was 58 years (interquartile ratio [IQR], 50-66), 88% were men, and 50% were black (

). Approximately 90% of the patients had undetectable HIV viral load at baseline and had a median CD4 count of 604 cells/mm

 (IQR, 812-374). Use of ART was as follows: over half of the patients were on an integrase-based HIV regimen and 20% were on a non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimen. More than two-thirds of our patients were infected with genotype 1a with a median HCV viral load of 14.9 log10IU/mL (IQR, 12.4-17.4). A third of the population had cirrhosis, and half had prior history of HCV treatment. Of the prior HCV treatment regimens used, about 40% were interferon-based. Hepatitis B coinfection was found in 16% of the patients.

The most commonly used regimen was sofosbuvir and ledipasvir, which was prescribed to approximately 40% of the patients. The second most common regimen was simeprevir with sofosbuvir (without ribavirin) prescribed to about 30% of the patients, followed by simeprevir with sofosbuvir (with ribavirin) prescribed to about 15% of the patients.

The regimens appeared to be well tolerated with two-thirds of the patients reporting no adverse effects. Patients on ribavirin were noted to have more fatigue and anemia (n = 9 and 8) versus those not on ribavirin (n = 5 and 0), respectively.

Among the 84 participants included in the study, 83.3% attained SVR12 (n = 70), which, in this study, is defined as having achieved cure; 11.9% relapsed (n = 10), 2.3% experienced virological breakthrough (n = 2), and 2 patients (2.3%) were deemed not to have completed treatment based on pill counts and follow-up visit documentation (

Univariate and multivariate analyses of demographics and clinical factors, and their associations with cure, were conducted and are illustrated in 

. Factors associated with cure in univariate analysis were: attendance at follow-up visits (odds ratio [OR], 7.89; 95% CI, 2.26-27.4); receiving care at the Miami VA site (OR, 3.98; 95% CI, 1.13-13.9); attending treatment laboratory visits (OR, 6.6; 95% CI, 1.92-22.6). Age, race, genotype, presence of cirrhosis, prior HCV treatment, HCV regimen, hepatitis B virus coinfection, week 4 SVR, CD4 counts, and pre-treatment HCV RNA were not associated with cure.

In multivariable analysis, factors associated with cure were attendance at follow-up clinic visits (OR, 9.0; 95% CI, 2.91-163) and use of an integrase-based HIV regimen versus a non—integrase-based regimen such as NNRTI or protease inhibitors (OR, 6.22; 95% CI, 1.81-141). We excluded attendance at laboratory visits and treatment at the VA from the model due to collinearity with attendance at follow-up clinic visits.

Of the 12 patients who failed treatment, 8 were black, 4 were cirrhotic, 10 were infected with genotype 1a, 2 were infected with genotype 1b, 5 were treatment-experienced, and 7 were treated with simeprevir plus sofosbuvir. Three of the 7 patients treated with simeprevir plus sofosbuvir were cirrhotic. About two-thirds of the patients who failed therapy did not have attendance at follow-up clinic visits. However, race, presence of cirrhosis, HCV genotype, and drugs used for treatment of HCV were not associated with cure. (For a detailed description of the patients who failed treatment, and adverse effects experienced, please see the 

HCV cure rates among patients coinfected with HIV/HCV seeking care in real-world settings are lower than those reported in clinical trials.

 Achieving cure (defined by SVR12 in this study) was closely associated with attendance at follow-up clinic visits and to the use of an integrase-based HIV regimen compared with non—integrase-based regimens such as NNRTI or protease inhibitors. Our study did not show an association of black race or cirrhosis with decreased cure rates, although others have previously found this association.

 Nevertheless, our study may lack the sufficient statistical power to detect this association.

The main contribution of our study to real-world clinical practice is the relationship between attendance at follow-up clinic visits and cure rates. Treatment of HCV in the HIV-infected population is challenging due to accelerated liver disease, concomitant comorbidities, and difficulties managing HCV-HIV drug-drug interactions. The importance of adequate adherence to clinical care has previously been described in populations infected only with HIV, in which greater frequency of visits and early linkage to care were associated with faster time to HIV viral suppression.

 The reason for this relationship has not been fully described in HIV, although these authors have hypothesized medication adherence as a possible mediator.

Frequency of clinic visits has been associated with higher medication adherence in populations with other chronic diseases.

 In our study, we found that attendance at visits was more common among the veterans cared for at the Miami VA. This group was very similar to the other 2—those being treated at UHealth and JHS&mdash;with the exception of a higher prevalence of black race and HCV treatment naïve patients (Table 1). However, the Miami VA, being a closed system, used key strategies to incentivize attendance, such as scheduling more frequent visits and only refilling medications at follow-up visits, once the pharmacist has ensured that all medication bottles were empty. This contrasts with the 2 fee-for-service sites that had less-frequent scheduled visits and provided the entire regimen at the beginning of the treatment. Even though this latter protocol may have been designed to improve access to the medications, our findings suggest that providing refills during follow-up visits may be more effective. We could not evaluate the mechanism through which the VA treatment protocol may influence cure; however, we can hypothesize that a likely mechanism is an improvement in medication adherence. Frequent clinic visits may address known barriers to adherence, such as misconceptions on the efficacy of the medication, fear of side effects, low self-efficacy, mistrust, depression, and other psychological factors, to name a few.

Future studies should include measures of adherence to DAAs to better elucidate the mechanisms through which different treatment protocols can achieve HCV cure. Nevertheless, our findings suggest that fee-for-service health systems should work with payers and other stakeholders to develop feasible and successful models of care for HCV treatment in the HIV-infected population. If medical visits facilitate cure through medication adherence, models of care could include ancillary services such as in-person or phone-based behavioral interventions led by counselors, which have been shown to increase medication adherence among patients with HIV.

 Such interventions could be introduced as a model of care that does not use clinic infrastructure (already at maximum use) and could be a more feasible strategy to provide cost-efficient, high-quality care. Interventions to increase adequate follow-up for HCV treatment in patients coinfected with HIV have the potential to increase cure rates and reduce treatment costs by ensuring that treatment is provided as prescribed.

In this study, we also found that patients receiving integrase inhibitors for the treatment of HIV had higher rates of cure when treated for HCV. This may be related to the fact that integrase inhibitors have fewer drug-drug interactions with HCV medications (hence lower chances of sub therapeutic drug levels for HCV) than do NNRTIs or protease inhibitors.

 Future studies should evaluate the hypothesis that better tolerability of the integrase-based regimen may improve adherence to the HCV regimen as well.

This study has several limitations related to the retrospective nature of its design and the small sample size. First, we could not compare medication adherence among the 3 groups (which may mediate the effect of follow-up visits) because all clinics used different strategies to address adherence. We also did not collect data on socioeconomic status, health literacy, and depression, all of which have been linked to medication adherence and outcomes, raising the possibility of residual confounding. Additionally, although this study involves 3 large hospital sites, the small sample size precludes generalization of the study findings and increases the chances of a type 2 error, such as the ability to detect associations among cirrhosis, black race, and cure. It should be noted that 3 cirrhotic patients who failed therapy were prescribed only 12 weeks of sofosbuvir/simeprevir treatment (instead of 24 weeks), which would have resulted in inadequate duration of therapy and, therefore, spuriously low cure rates. Finally, we could not adjust the model by provider or specialty, as several providers could deliver follow-up care to the same patient.

Real-world HCV cure rates when using DAA in HIV/HCV coinfection are lower than those seen in clinical trials. Cure is associated with attendance at follow-up clinic visits and the use of integrase-based HIV regimens as opposed to NNRTIs or protease inhibitors. Future studies should evaluate predictors of adequate attendance at follow-up visits, the role of medication adherence, and the effect of integrase-based regimen on HCV cure to inform best models of HCV/HIV care.

The authors gratefully acknowledge use of the services of the Miami Center for AIDS Research at the University of Miami (P30AI073961).

 Jackson Memorial Hospital (SL, AMP, MS), Miami, FL; University of Miami Hospital (SL, MA, MS, DJ, MDPH), Miami, FL; University of Miami Leonard M. Miller School of Medicine (ALA, GG-W, AP, KP), Miami, FL; Veterans Affairs Hospital (MDPH), Miami, FL.

 Dr Jayaweera is a consultant for Bristol-Myers Squibb (BMS) and Gilead; he has also previously received grants from Gilead, BMS, ViiV, and Janssen. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SL, MA, MDPH, DJ, KP, MS, GG-W); acquisition of data (SL, ALA, MDPH, DJ, AP, KP, MS, GG-W); analysis and interpretation of data (SL, MA, ALA, MDPH, DJ, AMP, AP, KP, GG-W); drafting of the manuscript (SL, MA, MDPH, AMP, AP); critical revision of the manuscript for important intellectual content (MA, ALA, MDPH, DJ, AMP, MS); statistical analysis (SL, MA); provision of patients or study materials (MDPH, MS); administrative, technical, or logistic support (MDPH); and supervision (MA, MDPH).

CLINICAL

The authors examine real-world hepatitis C virus cure rates with direct-acting antivirals among patients coinfected with HIV.

Improving HCV Cure Rates in HIV-Coinfected Patients - A Real-World Perspective

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.

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.

Special Issue: HCV

The Wider Public Health Value of HCV Treatment Accrued by Liver Transplant Recipients

Improving HCV Cure Rates in HIV-Coinfected Patients - A Real-World Perspective

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

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

A Way Out of the Dismal Arithmetic of Hepatitis C Treatment

Sofosbuvir Initial Therapy Abandonment and Manufacturer Coupons in a Commercially Insured Population

Real-World Outcomes of Ledipasvir/Sofosbuvir in Treatment-Naïve Patients With Hepatitis C

Coverage for Hepatitis C Drugs in Medicare Part D

Does Patient Cost Sharing for HCV Drugs Make Sense?