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Effect of Interferon-Free Regimens on Disparities in Hepatitis C Treatment of US Veterans

Open ArchivePublished:March 07, 2018DOI:https://doi.org/10.1016/j.jval.2017.12.025

      Abstract

      Objectives

      To determine whether implementation of interferon-free treatment for hepatitis C virus (HCV) reached groups less likely to benefit from earlier therapies, including patients with genotype 1 virus or contraindications to interferon treatment, and groups that faced treatment disparities: African Americans, patients with HIV co-infection, and those with drug use disorder.

      Methods

      Electronic medical records of the US Veterans Health Administration (VHA) were used to characterize patients with chronic HCV infection and the treatments they received. Initiation of treatment in 206,544 patients with chronic HCV characterized by viral genotype, demographic characteristics, and comorbid medical and mental illness was studied using a competing events Cox regression over 6 years.

      Results

      With the advent of interferon-free regimens, the proportion treated increased from 2.4% in 2010 to 18.1% in 2015, an absolute increase of 15.7%. Patients with genotype 1 virus, poor response to previous treatment, and liver disease had the greatest increase. Large absolute increases in the proportion treated were observed in patients with HIV co-infection (18.6%), alcohol use disorder (11.9%), and drug use disorder (12.6%) and in African American (13.7%) and Hispanic (13.5%) patients, groups that were less likely to receive interferon-containing treatment. The VHA spent $962 million on interferon-free treatments in 2015, 1.5% of its operating budget.

      Conclusions

      The proportion of patients with HCV treated in VHA increased sevenfold. The VHA was successful in implementing interferon treatment in previously undertreated populations, and this may become the community standard of care.

      Keywords

      Introduction

      Interferon-free direct-acting antiviral regimens have become the primary treatment for chronic hepatitis C virus (HCV) infection [
      • Conjeevaram H.
      Continued progress against hepatitis C infection.
      ,
      • Liang T.J.
      • Ghany M.G.
      Therapy of hepatitis C—back to the future.
      ,
      • Ward J.W.
      • Mermin J.H.
      Simple, effective, but out of reach? Public health implications of HCV drugs.
      ]. Sustained virologic response (SVR) is achieved by 90% or more of those treated [
      • Hull M.W.
      • Yoshida E.M.
      • Montaner J.S.
      Update on current evidence for hepatitis C therapeutic options in HCV mono-infected patients.
      ,
      • McConachie S.M.
      • Wilhelm S.M.
      • Kale-Pradhan P.B.
      New direct-acting antivirals in hepatitis C therapy: a review of sofosbuvir, ledipasvir, daclatasvir, simeprevir, paritaprevir, ombitasvir and dasabuvir.
      ,
      • Gutierrez J.A.
      • Lawitz E.J.
      • Poordad F.
      Interferon-free, direct-acting antiviral therapy for chronic hepatitis C.
      ]. The regimens have replaced the combination of pegylated interferon α and ribavirin, which is only 70% effective for viral genotypes 2 and 3 and less than 50% effective for genotype 1 [
      • Desai A.P.
      • Reau N.
      Naives, nonresponders, relapsers: Who is there left to treat?.
      ]. The direct-acting antiviral medications boceprevir and telaprevir improved the effectiveness of interferon therapy, but exacerbated side effects [
      • Liang T.J.
      • Ghany M.G.
      Current and future therapies for hepatitis C virus infection.
      ]. The new regimens are easier to administer, have a shorter treatment duration, and have fewer side effects than interferon-containing treatments [
      • Deuffic-Burban S.
      • Yazdanpanah Y.
      Fair prices for new direct-acting antiviral agents (DAAs) to make treatment for all affordable.
      ].
      The high cost of interferon-free treatment has limited its use throughout the world [
      • Deuffic-Burban S.
      • Yazdanpanah Y.
      Fair prices for new direct-acting antiviral agents (DAAs) to make treatment for all affordable.
      ,
      • Colombo M.
      Interferon-free therapy for hepatitis C: the hurdles amid a golden era.
      ,
      • Urrutia J.
      • Porteny T.
      • Daniels N.
      What does it mean to put new hepatitis C drugs on a list of essential medicines?.
      ]. In the United States, private insurance [
      • Trooskin S.B.
      • Reynolds H.
      • Kostman J.R.
      Access to costly new hepatitis C drugs: medicine, money, and advocacy.
      ,
      • Leston J.
      • Finkbonner J.
      The need to expand access to hepatitis C virus drugs in the Indian Health Service.
      ], Medicaid programs [
      • Barua S.
      • Greenwald R.
      • Grebely J.
      • et al.
      Restrictions for Medicaid reimbursement of sofosbuvir for the treatment of hepatitis C virus infection in the United States.
      ,
      • Canary L.A.
      • Klevens R.M.
      • Holmberg S.D.
      Limited access to new hepatitis C virus treatment under state Medicaid programs.
      ,
      • Simon T.G.
      • Chung R.T.
      The new hepatitis C virus bottleneck: Can delaying therapy be justified?.
      ], and the US Indian Health Service [
      • Leston J.
      • Finkbonner J.
      The need to expand access to hepatitis C virus drugs in the Indian Health Service.
      ] restricted interferon-free treatment to reduce its budgetary impact. Treatment was also limited by the lack of insurance coverage associated with the most important risk factors for HCV infection, including mental illness, substance use disorders, and homelessness [
      • Trooskin S.B.
      • Reynolds H.
      • Kostman J.R.
      Access to costly new hepatitis C drugs: medicine, money, and advocacy.
      ,
      • Stepanova M.
      • Younossi Z.M.
      Interferon-free regimens for chronic hepatitis C: barriers due to treatment candidacy and insurance coverage.
      ].
      The total budgetary impact is large because of the high price of medication (>$80,000/patient) and the high prevalence of chronic HCV infection. Treatment of 2.3 million treatment-eligible patients in the United States over 5 years represents an annual cost of $136 billion (in 2014 US dollars) [
      • Chhatwal J.
      • Kanwal F.
      • Roberts M.S.
      • Dunn M.A.
      Cost-effectiveness and budget impact of hepatitis C virus treatment with sofosbuvir and ledipasvir in the United States.
      ]. Total cost may approach $250 billion (also in 2014 US dollars), which is the annual US expenditure on all medications [
      • Trooskin S.B.
      • Reynolds H.
      • Kostman J.R.
      Access to costly new hepatitis C drugs: medicine, money, and advocacy.
      ]. Although competition from newer treatments, negotiated discounts, and manufacturer rebates to health plans have lowered cost [
      • Ward J.W.
      • Mermin J.H.
      Simple, effective, but out of reach? Public health implications of HCV drugs.
      ,
      • Trooskin S.B.
      • Reynolds H.
      • Kostman J.R.
      Access to costly new hepatitis C drugs: medicine, money, and advocacy.
      ], concerns about affordability still limit treatment.
      These new treatments are reaching patients with genotype 1 infection [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ] and those with HCV-HIV co-infection [

      Beguelin C, Suter A, Bernasconi E, et al. Trends in HCV treatment uptake, efficacy and impact on liver fibrosis in the Swiss HIV Cohort Study. Liver Int [published online ahead of print July 25, 2017]. 〈doi:10.1111/liv.13528〉.

      ,
      • Pradat P.
      • Pugliese P.
      • Poizot-Martin I.
      • et al.
      Direct-acting antiviral treatment against hepatitis C virus infection in HIV-infected patients—“En route for eradication”?.
      ], but they have not eliminated the historical treatment disparity for African Americans [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ,
      • Jung J.
      • Feldman R.
      Racial-ethnic disparities in uptake of new hepatitis C drugs in Medicare.
      ,

      Spradling PR, Xing J, Rupp LB, et al. Uptake of and factors associated with direct-acting antiviral therapy among patients in the Chronic Hepatitis Cohort Study, 2014 to 2015. J Clin Gastroenterol [published online ahead of print June 5, 2017]. 〈doi:10.1097/MCG.0000000000000857〉.

      ,
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ] and patients with substance use disorder [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ]. Initial reports were about orders and approvals for interferon-free therapies [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ,
      • Saab S.
      • Jimenez M.
      • Fong T.
      • et al.
      Accessibility to oral antiviral therapy for patients with chronic hepatitis C in the United States.
      ] or did not separate them from direct-acting antiviral regimens that included interferon [

      Spradling PR, Xing J, Rupp LB, et al. Uptake of and factors associated with direct-acting antiviral therapy among patients in the Chronic Hepatitis Cohort Study, 2014 to 2015. J Clin Gastroenterol [published online ahead of print June 5, 2017]. 〈doi:10.1097/MCG.0000000000000857〉.

      ]. More information is needed about medications dispensed to patients, especially to those in disadvantaged groups.
      We examined the adoption of interferon-free treatment for HCV in the US Veterans Health Administration (VHA) to learn who received this therapy and whether the limitations of interferon-containing treatments have been overcome, including their lack of efficacy against genotype 1 virus, and their low rates of use in VHA patients who were African American [
      • Butt A.A.
      • Justice A.C.
      • Skanderson M.
      • et al.
      Rate and predictors of treatment prescription for hepatitis C.
      ,
      • Kanwal F.
      • Hoang T.
      • Spiegel B.M.
      • et al.
      Predictors of treatment in patients with chronic hepatitis C infection—role of patient versus nonpatient factors.
      ,
      • Kramer J.R.
      • Kanwal F.
      • Richardson P.
      • et al.
      Importance of patient, provider, and facility predictors of hepatitis C virus treatment in veterans: a national study.
      ,
      • Rousseau C.M.
      • Ioannou G.N.
      • Todd-Stenberg J.A.
      • et al.
      Racial differences in the evaluation and treatment of hepatitis C among veterans: a retrospective cohort study.
      ] or Hispanic [
      • Butt A.A.
      • Justice A.C.
      • Skanderson M.
      • et al.
      Rate and predictors of treatment prescription for hepatitis C.
      ,
      • Cheung R.C.
      • Currie S.
      • Shen H.
      • et al.
      Chronic hepatitis C in Latinos: natural history, treatment eligibility, acceptance, and outcomes.
      ] and in patients with HCV-HIV co-infection [
      • Butt A.A.
      • Justice A.C.
      • Skanderson M.
      • et al.
      Rates and predictors of hepatitis C virus treatment in HCV-HIV-coinfected subjects.
      ], mental illness [
      • Butt A.A.
      • Justice A.C.
      • Skanderson M.
      • et al.
      Rate and predictors of treatment prescription for hepatitis C.
      ,
      • Kramer J.R.
      • Kanwal F.
      • Richardson P.
      • et al.
      Importance of patient, provider, and facility predictors of hepatitis C virus treatment in veterans: a national study.
      ], or substance use disorder [
      • Butt A.A.
      • Justice A.C.
      • Skanderson M.
      • et al.
      Rate and predictors of treatment prescription for hepatitis C.
      ,
      • Kramer J.R.
      • Kanwal F.
      • Richardson P.
      • et al.
      Importance of patient, provider, and facility predictors of hepatitis C virus treatment in veterans: a national study.
      ].
      We hypothesized that interferon-free treatment was provided to persons for whom interferon treatment was especially ineffective, those with genotype 1 virus, and to persons in which interferon-containing treatment was contraindicated, including those with alcohol use disorder and depression. We also hypothesized that the expansion of treatment helped remove past disparities in the treatment of African American and Hispanic patients and in the treatment of patients with a diagnosis of HIV or drug use disorder.

      Methods

      Treatment initiation in patients with chronic HCV infection was studied in VHA over 6 years from 2010 to 2015 (unless otherwise noted, year refers to the federal fiscal year, which ends on September 30).

      Cohort

      All VHA patients with a positive test result for HCV RNA between 2000 and 2014 were considered for inclusion. We excluded those who died before 2010, those who were successfully treated before 2010, and those who did not use VHA services between 2010 and 2014. Those with a positive test result before 2010 entered the cohort on the first day of the study. Those with tests done after 2010 entered the cohort on the date of their first positive result.
      Cohort members were characterized by a series of observations that ended with a change in treatment status, the end of each fiscal year, death, or the end of the study. Patients left the cohort at treatment initiation, but rejoined if they failed to achieve SVR or there was re-infection or relapse. Mortality risk continued regardless of treatment status. Race, ethnicity, sex, and HCV genotype were assumed to be time-invariant. All other covariates were specific to the time interval and included an indicator for year. Covariates were selected a priori to address study hypotheses or because they were previously associated with treatment or death.

      Data Sources, Variables, and Data Set Structure

      Laboratory test results, pharmacy records, inpatient and outpatient utilization, and patient demographic characteristics were obtained from the VHA Corporate Data Warehouse, supplemented by vital status data from Medicare and information on race from the Department of Defense.
      HCV viral genotype was represented as an indicator variable for genotype 1, the genotype less effectively treated with interferon-containing regimens. SVR was defined with the definition used in clinical trials and for regulatory approval: no detectable serum HCV RNA in a test that was at least 12 weeks after conclusion of HCV treatment [
      • Chen J.
      • Florian J.
      • Carter W.
      • et al.
      Earlier sustained virologic response end points for regulatory approval and dose selection of hepatitis C therapies.
      ,
      • Martinot-Peignoux M.
      • Stern C.
      • Maylin S.
      • et al.
      Twelve weeks posttreatment follow-up is as relevant as 24 weeks to determine the sustained virologic response in patients with hepatitis C virus receiving pegylated interferon and ribavirin.
      ]. Treatment without SVR was regarded as a partial response if there was a 2 log 10 IU/ml decrease in serum HCV RNA relative to pretreatment levels, or as a null response if this improvement was not achieved.
      Liver disease was characterized by Fibrosis-4 (FIB-4), an index based on three laboratory test results and age. We used standard categories of low, moderate, and high risk of cirrhosis (FIB-4 < 1.45, between 1.45 and 3.25, and >3.25, respectively) [
      • Sterling R.K.
      • Lissen E.
      • Clumeck N.
      • et al.
      Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection.
      ,
      • Vallet-Pichard A.
      • Mallet V.
      • Nalpas B.
      • et al.
      FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection: comparison with liver biopsy and fibrotest.
      ]; liver enzyme and platelet results outside the range of plausible values were excluded [
      • Bambha K.
      • Pierce C.
      • Cox C.
      • et al.
      Assessing mortality in women with hepatitis C virus and HIV using indirect markers of fibrosis.
      ]. Patients were considered HIV-positive from the date of a positive Western blot or a viral load test with detectable virus.
      HCV treatments dispensed since 2000 were extracted from VHA pharmacy data. We defined HCV treatment episodes as starting when the first prescription was dispensed and as ending when the supply of the last prescription should have been exhausted. Gaps of 100 days defined a new treatment episode. The direct cost of HCV medications was obtained from the VHA Managerial Cost Accounting system and was adjusted to 2015 US dollars using the consumer price index for all goods.
      Comorbidities were defined using diagnosis codes assigned in the preceding year. We used previously developed lists of International Classification of Diseases, Ninth Revision, diagnosis codes to define cirrhosis [
      • Davila J.A.
      • Henderson L.
      • Kramer J.R.
      • et al.
      Utilization of surveillance for hepatocellular carcinoma among hepatitis C virus-infected veterans in the United States.
      ], decompensated cirrhosis [
      • Kanwal F.
      • Hoang T.
      • Spiegel B.M.
      • et al.
      Predictors of treatment in patients with chronic hepatitis C infection—role of patient versus nonpatient factors.
      ], medical comorbidities [
      • Quan H.
      • Sundararajan V.
      • Halfon P.
      • et al.
      Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.
      ], mental illness [
      • Blow F.C.
      • McCarthy J.F.
      • Valenstein M.
      • et al.
      Care for VHA Users with Psychosis in the Veterans Health Administration FY11: 13th Annual National Psychosis Registry Report.
      ,
      • Bowersox N.W.
      • Valenstein M.
      • Austin K.
      • et al.
      Patients with Depression Diagnoses and Mental Health Encounters in the Veterans Health Administration FY12: National Registry for Depression Report.
      ], substance use disorder [
      • Kim H.M.
      • Smith E.G.
      • Stano C.M.
      • et al.
      Validation of key behaviourally based mental health diagnoses in administrative data: suicide attempt, alcohol abuse, illicit drug abuse and tobacco use.
      ], and medical contraindications to treatment with the combination of interferon and ribavirin [
      • Talal A.H.
      • LaFleur J.
      • Hoop R.
      • et al.
      Absolute and relative contraindications to pegylated-interferon or ribavirin in the US general patient population with chronic hepatitis C: results from a US database of over 45000 HCV-infected, evaluated patients.
      ,
      • Bini E.J.
      • Brau N.
      • Currie S.
      • et al.
      Prospective multicenter study of eligibility for antiviral therapy among 4,084 U.S. veterans with chronic hepatitis C virus infection.
      ].
      The distances to the nearest VHA primary care clinic and the nearest VHA tertiary care facility were actual road miles calculated by the VHA Planning Systems Support Group on the basis of the residential address at the time of risk. Study procedures, waiver of consent, and waiver of authorization specified by the Health Insurance Portability and Accountability Act were approved by the Institutional Review Board of Stanford University.

      Statistical Analysis

      Cox regression was used to model the time to event, the start of HCV treatment. Although the event represents a benefit, not a harm, this article uses the standard nomenclature and refers to this as the “hazard” of starting treatment. The Cox technique estimates this hazard up until the time the observation is censored by death or the end of the study. Cohort members faced the hazard of starting two different treatments and the risk of dying before treatment could be initiated. Competing risks may bias parameters if the assumption of independent censoring is violated (e.g., if the reason for censoring is related to interferon-free treatment initiation) [
      • Fine J.P.
      • Gray A.M.
      A proportional hazards model for the subdistribution of competing risk.
      ].
      A stratified Cox model [
      • Putter H.
      • Fiocco M.
      • Geskus R.B.
      Tutorial in biostatistics: competing risks and multi-state models.
      ] estimated baseline hazard and parameter estimates for three competing events: initiation of interferon-containing treatment, initiation of interferon-free treatment, and death. The hazard at time t, for event k, for subject i, depended on covariates (Xik) and can be expressed as follows:
      γik(t)=γ0k(t)eXikβk.


      The data set had one observation per time period per strata, with covariates specific to the strata [
      • Putter H.
      • Fiocco M.
      • Geskus R.B.
      Tutorial in biostatistics: competing risks and multi-state models.
      ]. There were up to three observations for each time period, depending on the events that were at risk. Baseline hazard (γ0k) and parameters (βk) were specific to each of the three events. Robust standard errors were estimated to account for correlation of observations over time from the same individual. We estimated 98 parameters for risk of treatment initiation. Using a threshold individual parameter of P less than 0.001, the studywide probability of a type 1 error is a P value of 0.09 (i.e., 1 − [1 − 0.001]98).
      Postestimation tests compared parameters for interferon-free and interferon-containing therapy. The absolute risk of each type of treatment was estimated from the parameters of the Cox model. The probability of treatment after 364 days was determined for each cohort member as if they had the characteristic of interest. The risk difference was estimated as the difference in the mean of these marginal probabilities [
      • Austin P.C.
      Absolute risk reductions and numbers needed to treat can be obtained from adjusted survival models for time-to-event outcomes.
      ]. For groups defined by baseline characteristics, we estimated the hazard of interferon-free treatment relative to interferon-containing treatment. This was the hazard ratio for interferon-free treatment divided by the hazard ratio for interferon-containing treatment. Groups with a relative hazard of more than 1 had greater than average improvement in treatment hazard from the advent of interferon-free treatment; a value less than 1 represented less than average improvement.
      Some data elements were missing. For time-invariant variables, age and sex data were complete; race data were missing for 5.9% of cohort members, and HCV viral genotype data were missing for 25.8%. The fraction of observations with missing values for time-varying covariates included 12.7% for comorbidities, 2.5% for residential address, and 33.3% for FIB-4 scores. Most cohort members had at least one observation with these time-varying variables, including 99.6% who had at least one observation with comorbidities, 99.6% with a residential address, and 94.2% with at least one FIB-4 score.
      Missing data items were multiply imputed (10 times) using the Markov chain Monte-Carlo method, which used all independent variables considered in the model, the previous year’s values of independent variables, and the Nelson-Aalen estimator for the hazard of each event. Cox regressions were estimated for each imputation and combined using standard methods to adjust for variation between imputations [
      • Little R.J.A.
      • Rubin D.B.
      ]. The proportional hazards assumption was tested by a specification that included terms for the interaction of covariates with time. All analyses were conducted using SAS 9.2 (SAS Institute, Cary, NC).

      Results

      There were 274,146 VHA patients positive for HCV RNA between 2000 and 2014. After excluding veterans who died before 2010, those who did not use VHA health services from 2010 to 2014, and those who were successfully treated before 2010, there were 206,544 patients left for inclusion in the cohort (see Fig. 1).
      Fig. 1
      Fig. 1Number of patients included in study cohort. HCV, hepatitis C virus; VHA, Veterans Health Administration.
      The characteristics of these individuals at the time they entered the study are presented in Table 1. At study entry, patients were an average of 56.8 years of age, with 56.9% of patients aged 50 to 59 years. Female patients made up 2.8% of the cohort, African Americans accounted for 36.7%, and 5.6% were Hispanic. Among the 74.2% of patients who had viral genotype testing, a large number (81.5%) were infected with HCV genotype 1. Many patients had signs of liver disease; 7.4% had been assigned a diagnosis of cirrhosis. Among 58.0% of patients with an FIB-4 result in the baseline period, 23.4% had an FIB-4 score of more than 3.25, the level associated with a high probability of cirrhosis. Patients who had a previous unsuccessful treatment accounted for 9.9% of the cohort at entry, including 5.1% who had previous null response, 4.3% who had partial response, and 0.4% who had achieved SVR but later had relapse or re-infection. Many cohort members had a diagnosis of alcohol use disorder (24.1%), drug use disorder (26.6%), or a psychiatric illness, including depression (33.3%), post-traumatic stress disorder (PTSD) (17.8%), psychosis (8.7%), bipolar disorder (6.3%), schizophrenia or schizoaffective disorder (5.8%), and previous suicide attempt (0.7%). HIV co-infection was present in 4.5%. Most members (78.2%) entered the cohort in the first year of the study, with 5% to 7% entering the study in each of the following 4 years. The longitudinal observations of 3.9% of cohort members were interrupted by an unsuccessful treatment attempt.
      Table 1Characteristics of cohort at study entry (N = 206,544)
      CharacteristicPercent
      Age (y), mean ± SD56.8 ± 7.61
       <5010.6%
       50–5956.9%
       60–6928.5%
       70–792.9%
       80+1.1%
      Sex, female2.8%
      Race
       White59.1%
       African American36.7%
       Other4.2%
      Ethnicity, Hispanic5.6%
      Distance to VA primary care
       <5 miles29.6%
       5–15 miles42.8%
       >15 miles27.6%
      Distance to VA tertiary care
       <40 miles40.8%
       40–100 miles25.9%
       >100 miles33.3%
      HCV genotype 181.5%
      FIB-4 scoreMedian 1.88 IQR 1.29–3.01
       <1.4544.0%
       1.45–3.2532.6%
       >3.2523.4%
      Cirrhosis7.4%
      Decompensated cirrhosis0.9%
      Hepatocellular carcinoma1.0%
      History of liver transplant0.8%
      Recent liver transplant0.03%
      Treatment history
       Treatment-naive (never received treatment)90.1%
       Previous treatment with null response5.1%
       Previous treatment with partial response4.3%
       Re-infection/relapse following treatment with SVR0.4%
      Alcohol use disorder24.1%
      Anemia (low hemoglobin level)9.0%
      Bipolar disorder6.3%
      Cancer (other than hepatocellular carcinoma)6.5%
      Myocardial infarction or congestive heart failure4.9%
      Chronic obstructive pulmonary disease16.4%
      Depression33.3%
      Dementia0.4%
      Diabetes without chronic complication22.8%
      Diabetes with chronic complication5.5%
      Drug use disorder26.6%
      Hemoglobinopathy0.1%
      Hepatitis B virus infection0.5%
      HIV-positive4.5%
      History of kidney transplant0.3%
      Neutropenia0.4%
      Pregnancy0.01%
      Psychosis8.7%
      PTSD17.8%
      Peripheral vascular disease4.6%
      Renal failure5.4%
      Retinopathy0.4%
      Schizophrenia5.8%
      Seizure0.1%
      Paralysis1.1%
      Stroke4.2%
      History of suicide attempt0.7%
      Thrombocytopenia3.3%
      Year entered cohort
       201078.2%
       20116.8%
       20125.7%
       20134.8%
       20144.6%
      FIB-4, Fibrosis-4; HCV, hepatitis C virus; IQR, interquartile range; PTSD, post-traumatic stress disorder; SVR, sustained virologic response; VA, Veterans Affairs.
      Annual data on cohort membership, deaths, treatments, and treatment costs during the study are presented in Table 2. In 2010, VHA used interferon-containing regimens to treat 2.4% of patients with chronic HCV infection. In 2015, VHA used interferon-free regimens to treat 18.1% of those with chronic HCV infection. This was an absolute increase of 15.7%. Interferon-free treatment provided in 2015 accounted for 57% of HCV treatment initiated during the 6 years of the study.
      Table 2Risk periods, events, and treatment costs incurred by study cohort, by year
      Risk periods, event rates, and costs by type of HCV treatmentFederal fiscal year
      201020112012201320142015
      Cohort members alive during year161,422169,272174,177176,749178,037169,484
      Deaths per 100 person-years4.094.284.434.885.045.22
      Number with chronic HCV infection161,422167,672171,564171,701171,748161,837
      Life-years of chronic HCV infection147,904155,559159,727160,302160,498145,396
      Treatment starts per 100 person-years of chronic HCV infection
       Interferon-containing treatment2.401.553.151.901.240.16
       Interferon-free treatment2.3018.12
       Total2.401.553.151.913.5418.28
      Treatment cost (’000s, US $2015)
       Interferon dual therapy8,05512,2505779367322394563
       Direct-acting antiviral with interferon therapies40,72550,81379,53127,915
       Interferon-free therapies179,525961,990
       Total8,05512,25046,50454,486261,295994,468
      HCV, hepatitis C virus.
      Over the 6 years of the study, VHA medication costs were $37 million for interferon-ribavirin treatments, $199 million for interferon-containing direct-acting antiviral treatments, and $1.14 billion for interferon-free treatments. In 2010, VHA spent 0.02% of its budget on interferon-containing regimens. In 2015, it spent 1.5% of its budget on interferon-free regimens. Interferon-free regimens accounted for 70% of VHA expenditures on medications to treat HCV during the 6 years of the study.
      The number of patients initiating a new treatment episode during each month of the study is plotted in Figure 2. Interferon-containing direct-acting antiviral regimens included boceprevir in 2012 to 2013 and sofosbuvir in 2014. Use of interferon-free therapies began in 2014, rapidly increased in 2015, and was interrupted by a temporary suspension in May through July because of budgetary concerns. Their use increased at an even higher rate after supplemental funding was released.
      Fig. 2
      Fig. 2Number of patients initiating an HCV treatment episode, by month, from 2010 to 2015. HCV, hepatitis C virus.
      Table 3 presents the adjusted hazard ratios (AHRs), the immediate risk of initiating either interferon-free or interferon-containing treatment relative to the corresponding reference group while controlling for other covariates, and the competing risks of the alternate treatment and death. The table also presents the adjusted absolute probability of interferon-free treatment in 2015, of interferon-containing treatment in 2010, and their difference (adjusted probabilities are not exactly comparable with the unadjusted treatment rates).
      Table 3Absolute probability and hazard ratio of HCV treatment initiation, by type of treatment
      CharacteristicInterferon-free treatmentInterferon-containing treatmentInterferon-free vs. interferon-containing treatment
      Absolute probabilityHazard ratioAbsolute probabilityHazard ratioDifference in absolute probabilityRelative hazard
      Age (y)
       <5014.980.88 (0.82–0.94)
      Significantly different hazard at P < 0.001.
      3.501.47 (1.40–1.56)
      Significantly different hazard at P < 0.001.
      11.470.60 (0.55–0.65)
      Significantly different hazard at P < 0.001.
       50–5916.80Reference2.40Reference14.40Reference
       60–6917.571.05 (1.03–1.08)
      Significantly different hazard at P < 0.001.
      1.820.76 (0.73–0.78)
      Significantly different hazard at P < 0.001.
      15.751.39 (1.33–1.45)
      Significantly different hazard at P < 0.001.
       70+10.380.59 (0.55–0.62)
      Significantly different hazard at P < 0.001.
      0.370.15 (0.13–0.18)*10.013.81 (3.14–4.64)
      Significantly different hazard at P < 0.001.
      Sex
       Male16.74Reference2.04Reference14.70Reference
       Female17.261.04 (0.97–1.11)2.361.16 (1.07–1.26)
      Significantly different hazard at P < 0.001.
      14.900.89 (0.80–0.99)
      Significantly different hazard at P < 0.05.
      Race
       White17.51Reference2.18Reference15.33Reference
       African American15.540.87 (0.85–0.90)
      Significantly different hazard at P < 0.001.
      1.790.82 (0.78–0.85)
      Significantly different hazard at P < 0.001.
      13.751.07 (1.02–1.12)
      Significantly different hazard at P < 0.05.
       Other16.780.95 (0.90–1.01)2.040.94 (0.87–1.01)14.741.02 (0.92–1.12)
      Ethnicity
       Hispanic15.510.91 (0.87–0.96)
      Significantly different hazard at P < 0.001.
      2.020.99 (0.92–1.05)13.480.92 (0.85–1.00)
       Non-Hispanic16.84Reference2.05Reference14.79Reference
      Distance to VA primary care
       <5 miles16.12Reference1.84Reference14.29Reference
       5–15 miles16.191.00 (0.98–1.03)2.071.13 (1.09–1.17)
      Significantly different hazard at P < 0.001.
      14.120.89 (0.85–0.93)
      Significantly different hazard at P < 0.001.
       >15 miles16.941.06 (1.03–1.09)
      Significantly different hazard at P < 0.001.
      2.181.19 (1.14–1.24)
      Significantly different hazard at P < 0.001.
      14.770.89 (0.85–0.94)
      Significantly different hazard at P < 0.001.
      Distance to VA tertiary care
       <40 miles17.60Reference1.70Reference15.90Reference
       40–100 miles15.400.86 (0.84–0.89)
      Significantly different hazard at P < 0.001.
      2.081.23 (1.18–1.28)
      Significantly different hazard at P < 0.001.
      13.320.70 (0.67–0.74)
      Significantly different hazard at P < 0.001.
       >100 miles15.770.88 (0.86–0.91)
      Significantly different hazard at P < 0.001.
      2.391.41 (1.36–1.47)
      Significantly different hazard at P < 0.001.
      13.380.63 (0.60–0.66)
      Significantly different hazard at P < 0.001.
      HCV genotype
       Type 116.971.09 (1.06–1.12)
      Significantly different hazard at P < 0.001.
      1.970.82 (0.79–0.86)
      Significantly different hazard at P < 0.001.
      15.001.32 (1.26–1.38)
      Significantly different hazard at P < 0.001.
       Other15.77Reference2.38Reference13.39Reference
      FIB-4 score
       <1.4516.44Reference2.11Reference14.33Reference
       1.45–3.2512.200.72 (0.69–0.75)
      Significantly different hazard at P < 0.001.
      1.680.79 (0.76–0.83)
      Significantly different hazard at P < 0.001.
      10.520.91 (0.85–0.96)
      Significantly different hazard at P < 0.05.
       >3.2521.621.37 (1.33–1.41)
      Significantly different hazard at P < 0.001.
      2.361.12 (1.07–1.17)
      Significantly different hazard at P < 0.001.
      19.261.22 (1.16–1.29)
      Significantly different hazard at P < 0.001.
      Cirrhosis26.111.87 (1.80–1.93)
      Significantly different hazard at P < 0.001.
      3.331.81 (1.72–1.90)
      Significantly different hazard at P < 0.001.
      22.771.03 (0.97–1.10)
      No cirrhosis15.23Reference1.86Reference13.37Reference
      Decompensated cirrhosis15.910.94 (0.87–1.02)0.810.39 (0.32–0.46)
      Significantly different hazard at P < 0.001.
      15.102.44 (1.99–3.00)
      Significantly different hazard at P < 0.001.
      No decompensated cirrhosis16.77Reference2.08Reference14.70Reference
      Hepatocellular carcinoma11.960.68 (0.63–0.73)
      Significantly different hazard at P < 0.001.
      1.530.74 (0.64–0.85)
      Significantly different hazard at P < 0.001.
      10.430.92 (0.78–1.08)
      No hepatocellular carcinoma16.91Reference2.06Reference14.85Reference
      History of liver transplant27.401.82 (1.67–1.97)
      Significantly different hazard at P < 0.001.
      2.351.15 (0.99–1.33)25.061.58 (1.33–1.87)
      Significantly different hazard at P < 0.001.
      No history of liver transplant16.62Reference2.05Reference14.58Reference
      Recent liver transplant22.901.44 (0.96–2.18)5.472.74 (1.60–4.72)
      Significantly different hazard at P < 0.001.
      17.420.53 (0.27–1.04)
      No recent liver transplant16.75Reference2.05Reference14.70Reference
      Treatment history
       Treatment-naive8.37Reference0.62Reference7.75Reference
       Previous treatment with null response15.982.10 (2.02–2.18)
      Significantly different hazard at P < 0.001.
      1.161.88 (1.78–1.97)
      Significantly different hazard at P < 0.001.
      14.821.12 (1.05–1.19)
      Significantly different hazard at P < 0.001.
       Previous treatment with partial response14.241.83 (1.77–1.89)
      Significantly different hazard at P < 0.001.
      1.492.42 (2.31–2.53)
      Significantly different hazard at P < 0.001.
      12.750.76 (0.71–0.80)
      Significantly different hazard at P < 0.001.
       Re-infection/relapse after SVR5.110.59 (0.47–0.74)
      Significantly different hazard at P < 0.001.
      0.520.83 (0.64–1.09)4.590.70 (0.50–0.99)
      Significantly different hazard at P < 0.05.
      Alcohol use disorder13.750.75 (0.73–0.78)
      Significantly different hazard at P < 0.001.
      1.800.85 (0.81–0.89)
      Significantly different hazard at P < 0.001.
      11.940.89 (0.84–0.94)
      Significantly different hazard at P < 0.001.
      No alcohol problem17.63Reference2.12Reference15.51Reference
      Anemia (low hemoglobin level)15.400.90 (0.86–0.94)
      Significantly different hazard at P < 0.001.
      1.900.92 (0.86–0.97)
      Significantly different hazard at P < 0.05.
      13.500.98 (0.91–1.06)
      No anemia16.93Reference2.07Reference14.86Reference
      Bipolar disorder18.631.14 (1.03–1.25)
      Significantly different hazard at P < 0.05.
      2.341.16 (1.01–1.32)
      Significantly different hazard at P < 0.05.
      16.290.98 (0.84–1.16)
      No bipolar disorder16.66Reference2.03Reference14.62Reference
      Cancer (other than hepatocellular carcinoma)16.240.96 (0.92–1.00)1.920.93 (0.87–1.00)14.321.03 (0.95–1.12)
      No nonliver cancer16.80Reference2.06Reference14.74Reference
      MI or CHF13.200.75 (0.71–0.80)
      Significantly different hazard at P < 0.001.
      1.450.69 (0.63–0.76)
      Significantly different hazard at P < 0.001.
      11.751.09 (0.97–1.22)
      No MI or CHF16.95Reference2.08Reference14.88Reference
      COPD16.040.94 (0.91–0.97)
      Significantly different hazard at P < 0.001.
      1.870.89 (0.85–0.93)
      Significantly different hazard at P < 0.001.
      14.171.05 (1.00–1.11)
      No COPD16.91Reference2.09Reference14.83Reference
      Depression17.881.12 (1.09–1.15)
      Significantly different hazard at P < 0.001.
      2.231.14 (1.10–1.18)
      Significantly different hazard at P < 0.001.
      15.660.98 (0.94–1.03)
      No depression16.19Reference1.96Reference14.23Reference
      Dementia8.710.48 (0.38–0.62)
      Significantly different hazard at P < 0.001.
      0.860.41 (0.25–0.68)
      Significantly different hazard at P < 0.001.
      7.851.17 (0.67–2.04)
      No dementia16.79Reference2.05Reference14.73Reference
      Diabetes without chronic complication17.481.07 (1.04–1.10)
      Significantly different hazard at P < 0.001.
      1.960.94 (0.90–0.98)
      Significantly different hazard at P < 0.05.
      15.521.14 (1.08–1.20)
      Significantly different hazard at P < 0.001.
      No diabetes without complication16.51Reference2.08Reference14.43Reference
      Diabetes with chronic complication16.350.97 (0.92–1.02)1.870.91 (0.83–0.99)
      Significantly different hazard at P < 0.05.
      14.481.07 (0.97–1.18)
      No diabetes with complication16.78Reference2.06Reference14.72Reference
      Drug use disorder14.430.80 (0.78–0.83)
      Significantly different hazard at P < 0.001.
      1.850.87 (0.84–0.91)
      Significantly different hazard at P < 0.001.
      12.570.92 (0.87–0.97)
      Significantly different hazard at P < 0.05.
      No drug dependence or abuse17.51Reference2.12Reference15.39Reference
       Hemoglobinopathy23.821.52 (1.15–2.00)
      Significantly different hazard at P < 0.05.
      2.040.99 (0.60–1.63)21.791.53 (0.87–2.69)
      No hemoglobinopathy16.75Reference2.05Reference14.70Reference
      Hepatitis B virus infection19.271.18 (1.02–1.36)
      Significantly different hazard at P < 0.05.
      2.441.20 (0.95–1.50)16.830.98 (0.75–1.29)
      No hepatitis B virus infection16.74Reference2.05Reference14.69Reference
      HIV-positive20.571.29 (1.24–1.35)
      Significantly different hazard at P < 0.001.
      1.940.94 (0.88–1.01)18.631.37 (1.26–1.49)
      Significantly different hazard at P < 0.001.
      Not HIV-positive16.51Reference2.06Reference14.46Reference
      History of kidney transplant18.001.09 (0.89–1.32)1.060.51 (0.32–0.81)
      Significantly different hazard at P < 0.05.
      16.942.12 (1.29–3.48)
      Significantly different hazard at P < 0.05.
      No history of kidney transplant16.75Reference2.05Reference14.70Reference
      Neutropenia20.981.30 (1.15–1.47)
      Significantly different hazard at P < 0.001.
      2.661.30 (1.09–1.57)
      Significantly different hazard at P < 0.05.
      18.321.00 (0.80–1.25)
      No neutropenia16.73Reference2.05Reference14.68Reference
      Psychosis15.110.88 (0.81–0.96)
      Significantly different hazard at P < 0.05.
      1.740.83 (0.74–0.94)
      Significantly different hazard at P < 0.05.
      13.371.06 (0.91–1.22)
      No psychosis16.90Reference2.08Reference14.82Reference
      PTSD18.201.13 (1.09–1.16)
      Significantly different hazard at P < 0.001.
      2.091.03 (0.98–1.07)16.111.10 (1.04–1.16)
      Significantly different hazard at P < 0.001.
      No PTSD16.43Reference2.04Reference14.39Reference
      PVD16.811.00 (0.95–1.06)1.800.87 (0.80–0.95)
      Significantly different hazard at P < 0.05.
      15.011.15 (1.04–1.28)
      Significantly different hazard at P < 0.05.
      No PVD16.75Reference2.06Reference14.69Reference
      Renal failure13.130.75 (0.71–0.79)
      Significantly different hazard at P < 0.001.
      1.520.73 (0.66–0.80)
      Significantly different hazard at P < 0.001.
      11.611.02 (0.91–1.14)
      No renal failure17.02Reference2.08Reference14.94Reference
      Retinopathy14.930.88 (0.74–1.04)1.970.96 (0.72–1.28)12.960.91 (0.65–1.27)
      No retinopathy16.76Reference2.05Reference14.71Reference
      Schizophrenia13.630.78 (0.74–0.83)
      Significantly different hazard at P < 0.001.
      1.270.61 (0.55–0.66)
      Significantly different hazard at P < 0.001.
      12.361.29 (1.16–1.44)
      Significantly different hazard at P < 0.001.
      No schizophrenia16.91Reference2.09Reference14.82Reference
      Seizure15.940.94 (0.57–1.57)1.370.66 (0.30–1.48)14.561.42 (0.55–3.69)
      No seizure16.76Reference2.05Reference14.71Reference
      Paralysis13.410.78 (0.69–0.87)
      Significantly different hazard at P < 0.001.
      1.100.53 (0.42–0.66)
      Significantly different hazard at P < 0.001.
      12.311.47 (1.14–1.89)
      Significantly different hazard at P < 0.05.
      No paralysis16.79Reference2.06Reference14.73Reference
      Stroke14.840.87 (0.82–0.92)
      Significantly different hazard at P < 0.001.
      1.660.80 (0.73–0.88)
      Significantly different hazard at P < 0.001.
      13.181.08 (0.96–1.21)
      No stroke16.85Reference2.07Reference14.78Reference
      History of suicide attempt13.760.80 (0.66–0.97)
      Significantly different hazard at P < 0.05.
      1.380.67 (0.53–0.84)
      Significantly different hazard at P < 0.001.
      12.381.20 (0.89–1.61)
      No history of suicide attempt16.77Reference2.05Reference14.72Reference
       Thrombocytopenia18.961.17 (1.11–1.22)
      Significantly different hazard at P < 0.001.
      1.940.94 (0.87–1.02)17.021.24 (1.12–1.36)
      Significantly different hazard at P < 0.001.
      No thrombocytopenia16.60Reference2.06Reference14.55Reference
      CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; HCV, hepatitis C virus; MI, myocardial infarction; PTSD, post-traumatic stress disorder; PVD, peripheral vascular disease; SVR, sustained virologic response; VA, Veterans Affairs.
      low asterisk Significantly different hazard at P < 0.001.
      Significantly different hazard at P < 0.05.
      Viral genotype, previous treatment history, and liver disease were all significant predictors of receiving interferon-free treatment. The adjusted probability of initiating interferon-free treatment in 2015 was 17.0% in those with genotype 1 virus (compared with 15.8% with virus of other genotypes; AHR 1.09; 95% confidence interval [CI] 1.06–1.12; P < 0.001). This probability was 16.0% in those with a history of treatment with null response (compared with 8.4% in the treatment-naive; AHR 2.10; 95% CI 2.02–2.18; P < 0.001) and 14.2% in those previously treated with partial response (compared with treatment-naive; AHR 1.83; 95% CI 1.77–1.89; P < 0.001). The probability of initiating interferon treatment in 2015 was 26.1% in those with a diagnosis of cirrhosis (compared with 15.2% in those without a cirrhosis diagnosis; AHR 1.87; 95% CI 1.80–1.93; P < 0.001) and 21.6% in those with an FIB-4 score of more than 3.25 (compared with 16.4% in those with an FIB-4 score of <1.45; AHR 1.37; 95% CI 1.33–1.41; P < 0.001).
      The probability of interferon-free treatment was also significantly greater in patients co-infected with HIV (AHR 1.29; 95% CI 1.24–1.35; P < 0.001) and hepatitis B virus (AHR 1.18; 95% CI 1.02–1.36; P = 0.028) relative to patients without these co-infections. Treatment was more likely in patients with depression (AHR 1.12; 95% CI 1.09–1.15; P < 0.001), PTSD (AHR 1.13; 95% CI 1.09–1.16; P < 0.001), or bipolar disorder (AHR 1.14; 95% CI 1.03–1.25; P = 0.01) relative to patients without these conditions.
      Interferon-free treatment was less likely to be initiated by African American patients relative to white patients (AHR 0.87; 95% CI 0.85–0.90; P < 0.001), by Hispanic relative to non-Hispanic patients (AHR 0.91; 95% CI 0.87–0.96; P < 0.001), by patients with a diagnosis of alcohol use disorder (AHR 0.75; 95% CI 0.73–0.78; P < 0.001), or by patients with a diagnosis of drug use disorder (AHR 0.80; 95% CI 0.78–0.83; P < 0.001). Although the absolute increase for these groups was less than the absolute increase of 15.7% observed for all patients, these groups sustained large improvement, with an absolute increase of 13.8% in African Americans, 13.5% in Hispanics, 11.9% in patients diagnosed with alcohol use disorder, and 12.6% in patients diagnosed with drug use disorder.
      Interferon-free therapy was also less likely to be received by patients with heart disease, stroke, psychosis, schizophrenia, or a history of suicide attempt. Patients who lived more than 15 miles from a VHA primary care clinic were slightly more likely to initiate interferon-free treatment as were patients who lived less than 40 miles from a VHA tertiary care center.
      Table 3 also presents interferon-containing treatment rates. Treatment probability was greater in patients with a diagnosis of cirrhosis (AHR 1.81; 95% CI 1.72–1.90; P < 0.001) and in those with an FIB-4 score of more than 3.25 (AHR 1.12; 95% CI 1.07–1.17; P < 0.001), and lesser in patients with genotype 1 infection (AHR 0.82; 95% CI 0.79–0.86; P < 0.001). Contraindications to treatment with the combination of pegylated interferon α and ribavirin (including hemoglobinopathy, hepatitis B virus infection, neutropenia, retinopathy, seizure disorders, or thrombocytopenia) were not associated with lower probability of interferon-containing treatment, with the exception of anemia (AHR 0.92; 95% CI 0.86–0.97; P < 0.005).
      With the advent of interferon-free treatment, all patient subgroups had large absolute increases in the probability of being treated for HCV. Not all groups benefited equally, and this was evaluated by comparing the parameters for interferon-free treatment with those for interferon-containing treatment.
      The rightmost column of Table 3 presents the hazard of interferon-free treatment relative to interferon-containing treatments, a measure of whether this group had more (or less) than the overall increase in hazard of initiating interferon-free treatment relative to the hazard of initiating interferon-containing treatment.
      Groups that had a relative hazard of more than 1, that is, a greater than average improvement in treatment hazard from the advent of interferon-free treatment, included patients with genotype 1 virus; those with an FIB-4 score of more than 3.25; previous null responders; those with HIV co-infection, PTSD, schizophrenia, and thrombocytopenia; and African American patients. Patients who were female or who had alcohol or substance use disorders had significantly lower than average increases in initiation of interferon-free treatment (relative hazard < 1).
      Female patients were no less likely to initiate interferon-free therapy than male patients (AHR 1.04; 95% CI 0.97–1.11; P = 0.30). This differed from their significant advantage when interferon-containing therapy was used (AHR 1.16; 95% CI 1.07–1.26; P < 0.001) and these hazards were significantly different (relative hazard 0.89; 95% CI 0.80–0.99; P = 0.03). Parameters for pregnancy and interactions between sex and age were estimated but they were not significant and these variables were excluded from the final model.
      Supplemental analyses (not shown in table) tested the proportional hazards assumption. The hazard of interferon treatment decreased over time for those with cirrhosis, a history of liver transplantation, or previous treatment with null response. The hazard of both types of treatment decreased over time for patients with previous partial response. The hazard increased with time for patients with genotype 1 virus, HIV, alcohol use disorder, or schizophrenia. These tests of proportional hazards were statistically significant (P < 0.001).

      Conclusions

      Interferon-free direct-acting antiviral regimens have made HCV treatment more effective, convenient, and tolerable [
      • Gutierrez J.A.
      • Lawitz E.J.
      • Poordad F.
      Interferon-free, direct-acting antiviral therapy for chronic hepatitis C.
      ]. We found that VHA spent $962 million on the direct costs of interferon-free medications in 2015, or 1.5% of the total VHA budget for the year. It treated 18.1% of patients with chronic HCV in the VHA health care system that year, an absolute increase of 15.7% over the 2.7% treated with interferon-containing regimens in 2010.
      We confirmed the hypothesis that interferon-free treatment benefited those less able to benefit from interferon-containing therapies. Patients infected with genotype 1 virus were significantly more likely to initiate interferon-free treatment than those without this genotype (AHR 1.09). Patients who previously failed treatment were more likely to initiate interferon-free treatment than the treatment-naive. We found a large increase in initiation of HCV therapy in patients with cirrhosis or a high FIB-4 score, a finding that is consistent with findings of other recent studies of interferon-free treatment [
      • Jung J.
      • Feldman R.
      Racial-ethnic disparities in uptake of new hepatitis C drugs in Medicare.
      ,

      Spradling PR, Xing J, Rupp LB, et al. Uptake of and factors associated with direct-acting antiviral therapy among patients in the Chronic Hepatitis Cohort Study, 2014 to 2015. J Clin Gastroenterol [published online ahead of print June 5, 2017]. 〈doi:10.1097/MCG.0000000000000857〉.

      ,
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ,
      • Saab S.
      • Jimenez M.
      • Fong T.
      • et al.
      Accessibility to oral antiviral therapy for patients with chronic hepatitis C in the United States.
      ].
      We confirmed that patients with contraindications to interferon-containing treatment benefited from interferon-free treatment. Patients with mental illnesses exacerbated by interferon, depression, PTSD, and bipolar disorder had absolute increases in treatment that were larger than the overall increase.
      We confirmed the hypothesis that the new treatment reduced past disparities faced by patients with alcohol and drug use disorders. Comparing the proportion treated in 2015 with that in 2010, the absolute increase in the proportion of patients treated was 11.9% in patients with alcohol use disorder and 12.6% in patients with drug use disorders. Yet these increases were less than the absolute increase of 15.7% in the proportion of all patients who were treated. A recent Veterans Affairs (VA) study found lower rates of interferon-free treatment in patients with alcohol and drug use disorder and depression [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ]. A study of Medicaid patients also found lower rates in patients with substance use disorder [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ], but higher treatment rates in patients with mental illness [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ].
      We also confirmed the hypothesis that adoption of the new treatment reduced past disparities in the treatment of African American and Hispanic patients and of patients with HIV. We found a large absolute increase in the treatment of African American patients in VHA, narrowing but not eliminating the disparity in their treatment. A disparity in interferon-free treatment of African Americans has been observed in patients covered by Medicaid [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ], Medicare [
      • Jung J.
      • Feldman R.
      Racial-ethnic disparities in uptake of new hepatitis C drugs in Medicare.
      ], and VHA [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ]. The historical disparity for interferon-containing treatment of African American patients has been attributed to a higher prevalence of hard-to-treat host [
      • Ge D.
      • Fellay J.
      • Thompson A.J.
      • et al.
      Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance.
      ] and viral genotypes [
      • Srivastava S.
      • Bertagnolli M.
      • Lewis J.H.
      Sustained virological response rate to pegylated interferon plus ribavirin for chronic hepatitis C in African Americans: results in treatment-naive patients in a university liver clinic.
      ] and a higher prevalence of medical contraindications to interferon [
      • Melia M.T.
      • Muir A.J.
      • McCone J.
      • et al.
      Racial differences in hepatitis C treatment eligibility.
      ]. These factors do not apply to interferon-free treatment, and thus cannot explain the small but continuing disparity.
      We found a large absolute increase in treatment of Hispanic VHA patients, but a disparity relative to non-Hispanic patients. This disparity was not observed in a previous VA study of new treatments [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ].
      VHA eliminated the treatment disparity for patients with HIV co-infection. Co-infected patients now have a greater probability of being treated in VHA than mono-infected patients. This is consistent with other recent studies of infected patients in the United States [

      Spradling PR, Xing J, Rupp LB, et al. Uptake of and factors associated with direct-acting antiviral therapy among patients in the Chronic Hepatitis Cohort Study, 2014 to 2015. J Clin Gastroenterol [published online ahead of print June 5, 2017]. 〈doi:10.1097/MCG.0000000000000857〉.

      ] and Europe [

      Beguelin C, Suter A, Bernasconi E, et al. Trends in HCV treatment uptake, efficacy and impact on liver fibrosis in the Swiss HIV Cohort Study. Liver Int [published online ahead of print July 25, 2017]. 〈doi:10.1111/liv.13528〉.

      ,
      • Pradat P.
      • Pugliese P.
      • Poizot-Martin I.
      • et al.
      Direct-acting antiviral treatment against hepatitis C virus infection in HIV-infected patients—“En route for eradication”?.
      ], but other reports found equal probability of treatment for dual-infected patients sponsored by Medicaid [
      • Clements K.M.
      • Clark R.E.
      • Lavitas P.
      • et al.
      Access to new medications for hepatitis C for Medicaid members: a retrospective cohort study.
      ] and Medicare [
      • Jung J.
      • Feldman R.
      Racial-ethnic disparities in uptake of new hepatitis C drugs in Medicare.
      ] and lower uptake in patients with co-occurring dual infection and drug use disorder in Europe [
      • van Santen D.K.
      • van der Helm J.J.
      • Lindenburg K.
      • et al.
      HIV and hepatitis C treatment uptake among people who use drugs participating in the Amsterdam Cohort Studies, 1985–2015.
      ].
      We found no difference between sexes in initiation of interferon-free therapy, and no significant difference in sex by age. This contradicts a recent VA study that found lower rates of interferon-free treatment initiation in younger women [
      • Kanwal F.
      • Kramer J.R.
      • El-Serag H.B.
      • et al.
      Race and gender differences in the use of direct acting antiviral agents for hepatitis C virus.
      ].
      We found lower rates of interferon-free treatment initiation in those who lived further from tertiary care centers, which conflicts with the findings of an earlier study [
      • Rongey C.
      • Shen H.
      • Hamilton N.
      • et al.
      Impact of rural residence and health system structure on quality of liver care.
      ].
      This study has the following limitations. We did not control for changes in veteran eligibility category, which can affect co-payments. We did not have information from other health plans that may have treated cohort members, but we excluded patients who did not use VHA services between 2009 and 2014. Although cohort members could have obtained other coverage, we may have observed most treatments because VHA appears to be more generous than other plans in providing HCV treatment. Laboratory testing was done for clinical purposes, not according to a research protocol, resulting in missing information on HCV genotype or FIB-4 score. Missing values were multiply imputed. This method relies on the assumption that data are missing at random, that is, that missing values are related to observed covariates only. Given our epidemiological knowledge of HCV and relevant characteristics, we believe this to be a reasonable assumption. If data are not missing at random, the parameters most likely to be biased are those for viral genotype and FIB-4, the variables with the greatest number of missing values, but other parameters could also be biased.
      In 2015, VHA provided interferon-free treatment to 18.1% of its health system users with chronic HCV. This was made possible by a special $1 billion federal appropriation and negotiated discounts for medication [
      • Graham J.
      VA extends new hepatitis C drugs to all veterans in its health system.
      ]. Use of interferon-free direct-acting antiviral regimens closed previous treatment gaps for HIV-positive patients. Modest treatment gaps remain for African American and Hispanic patients and for those with substance use disorder.
      Lacking special appropriation or the same negotiated discounts, other health plans are limiting treatment to patients with demonstrated liver disease [
      • Leston J.
      • Finkbonner J.
      The need to expand access to hepatitis C virus drugs in the Indian Health Service.
      ,
      • Simon T.G.
      • Chung R.T.
      The new hepatitis C virus bottleneck: Can delaying therapy be justified?.
      ,
      • Brennan T.
      • Shrank W.
      New expensive treatments for hepatitis C infection.
      ] who are abstinent from drugs [
      • McCance-Katz E.F.
      • Valdiserri R.O.
      Hepatitis C virus treatment and injection drug users: it is time to separate fact from fiction.
      ]. These restrictions raise concerns about equity and efficiency in the use of scarce treatment resources. Limiting treatment to patients with demonstrated liver disease is not cost-effective because this strategy reduces an important benefit of treatment, the prevention of liver damage [
      • Linas B.P.
      • Morgan J.R.
      • Pho M.T.
      • et al.
      Cost effectiveness and cost containment in the era of interferon-free therapies to treat hepatitis C virus genotype 1.
      ]. Excluding people who inject drugs from treatment is also not cost-effective because this strategy inhibits the benefits of preventing spread of infection to other people who inject drugs [
      • Bennett H.
      • Gordon J.
      • Jones B.
      • et al.
      Hepatitis C disease transmission and treatment uptake: impact on the cost-effectiveness of new direct-acting antiviral therapies.
      ].
      Current US guidelines recommend treatment of all patients with HCV infection [
      American Association for the Study of Liver Diseases, Infectious Diseases Society of America
      ]. The VHA appears to be successful in reaching previously undertreated populations, an approach that may ultimately become the community standard of care.
      Source of financial support: This research was supported by the VA Health Services Research and Development Service study (IIR 12-059) and by a grant from the National Institutes of Health (grant no. R01 DA15612-016). D. K. Owens, P. G. Barnett, M. Holodniy, and S. M. Asch are supported by the Department of Veterans Affairs. J. D. Goldhaber-Fiebert is supported in part by a Career Development Award from the National Institute on Aging (grant no. K01 AG037593-01A1).

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