A Novel Strategy for Increasing Access to Treatment for Hepatitis C Virus Infection for Medicaid Beneficiaries

Closing the hepatitis C treatment gap: United States strategies to improve retention in care

The hepatitis C virus (HCV) treatment landscape is shifting given the advent of direct-acting antivirals and a global call to action by the World Health Organization. Eliminating HCV is now an issue of healthcare delivery. Treatment is limited by the complexity of the HCV care continuum, expensive therapy and competing health burdens experienced by an underserved HCV population. The objective of this literature review was to assess strategies to improve retention in HCV care, with particular focus on those implemented in the United States. We identified barriers in HCV care retention and propose solutions to increase HCV treatment delivery. The following recommendations are herein described: improving the cohesion of health services through localized care and integrated case management, expanding the supply of non-specialist HCV treatment providers, leveraging patient navigators and care coordinators, improving adherence through directly observed therapy and reducing cost barriers through value-based payment and pharmaceutical subscription models.

The Payer License Agreement, or "Netflix model," for hepatitis C virus therapies enables universal treatment access, lowers costs and incentivizes innovation and competition

BACKGROUND AND AIMS

High unit prices of treatments limit access. For epidemics like that of hepatitis C virus (HCV), reduced treatment access increases prevalence and incidence, making the infectious disease increasingly difficult to manage. The objective of the current study was to construct and test an alternative pricing model, the Payer License Agreement (PLA), and determine whether it could improve outcomes, cut costs and incentivize innovation versus the current unit-based pricing model.

METHODS

We built and used computational models of hepatitis C disease progression, treatment, and pricing in historical and future scenarios and quantitatively analyzed their economic and epidemiological impact in three high-income countries.

RESULTS

This study had three key results regarding HCV treatment. First, if the PLA model had been implemented when interferon-free direct-acting antiviral (DAA) combinations launched, the number of patients treated and cured would have more than doubled in the first three years, while the liver-related deaths (LRDs) would have decreased by around 40%. Second, if the PLA model had been implemented beginning in 2018, the year that several Netflix-like payment models were under implementation, the number of treated and cured patients would nearly double, and the LRDs would decline by more than 55%. Third, implementing the PLA model would result in a decline in total payer costs of more than 25%, with an increase to pharmaceutical manufacturer revenues of 10%. These results were true across the three healthcare landscapes studied, the USA, the UK and Italy, and were robust against variations to critical model parameters through sensitivity analysis.

CONCLUSIONS AND RELEVANCE

These results suggest that implementation of the PLA model in high-income countries across a variety of health system contexts would improve patient outcomes at lower payer cost with more stable revenue for pharmaceutical manufacturers. Health policy-makers in high-income countries should consider the PLA model for application to more cost-effective management of HCV, and explore its application for other infectious diseases with curative therapies available now or soon.

The Potential Benefits of Deferred Payment for a Hypothetical Gene Therapy for Congestive Heart Failure: A Cost-Consequence Analysis

BACKGROUND

The emergence of potentially curative pharmacologic treatments that deliver long-term clinical benefits with a limited number of doses may create short-term budget challenges for payers as their unit price can be high.

OBJECTIVE

This paper tests the clinical and financial properties of a deferred payment model (DPM) in hypothetical therapy for congestive heart failure (CHF) from the perspective of payers, manufacturers, and patients.

METHODS

We present an empirical analysis of longitudinal data for cardiovascular admissions and mortality using a Markov transition model for patient progression under different payment scenarios. The model calculates life-years gained and avoided cardiovascular admissions under the status quo and deferred payment and a hypothetical budget constraint. We tracked over 91,000 Medicare fee-for-service beneficiaries over a period of 5 years (2009-2014) using MedPAR 5% data files.

RESULTS

We find that a DPM is associated with earlier treatment and a consequent improvement in clinical outcomes. A 25% down-payment is associated with the highest relative improvement and reduces hospital admissions by 0.52% (by 2611 vs. 2071 cases) and mortality by 0.29% (by 799 vs. 648 cases), both relative to the status quo payment. Deferred payment results in limited financial gains for payers or manufacturers, primarily because of the small share of expected cost savings on the total cost of therapy. Our results are robust to changes in relative risk for cardiovascular admissions and a change in the cost of therapy.

CONCLUSIONS

A DPM may result in faster access to CHF gene therapy and may thus reduce hospital admissions and mortality in contrast to a status quo payment with the same budget constraint. Although the financial benefits of a DPM in CHF gene therapy are limited, it is possible that deferred payments will show greater promise for treatments with higher cost offsets.

The Ethics of Precision Rationing: Human Genetics and the Need for Debate on Stratifying Access to Medication

Rising prices for new, transformative therapies are challenging health systems around the world, leading many payers and providers to begin rationing access to treatments, even in the countries that have been most resistant to doing so. This is the case for direct-acting antivirals (DAAs) for the treatment of hepatitis C virus (HCV). However, little attention has been paid to the increasing role that human genetics might play in rationing decisions. Researchers have already proposed that genetic markers associated with spontaneous HCV clearance could be used to restrict DAA access for some patients, although treatment would be medically beneficial for those patients. Would such forms of rationing present a form of genetic discrimination? And what of the public health implications of these approaches? Here we present an ethical analysis of such proposals for "precision rationing" and raise 4 key areas of concern. We argue that ethical issues arising in this area are not substantively different from the pressing ethical issues regarding rationing and discrimination more broadly, but provide important impetus for motivating broad public debate to find ethically sound ways of managing genomics and new expensive medications.

Estimating the price at which hepatitis C treatment with direct-acting antivirals would be cost-saving in Japan

In Japan, 1.5-2 million people are chronically infected with hepatitis C virus (HCV) infection. New direct-acting antiviral agents (DAA) offer an unprecedented opportunity to cure HCV. While the price of HCV treatment decreased recently in most countries, it remains one of the highest in Japan. Our objective was to evaluate the cost-effectiveness of HCV treatment in patients of different age groups and to estimate the price at which DAAs become cost-saving in Japan. A previously developed microsimulation model was adapted to the Japanese population and updated with Japan-specific health utilities and costs. Our model showed that compared with no treatment, the incremental cost-effectiveness ratio (ICER) of DAAs at a price USD 41,046 per treatment was USD 9,080 per quality-adjusted life year (QALY) gained in 60-year-old patients. HCV treatment became cost-effective after 9 years of starting treatment. However, if the price of DAAs is reduced by 55-85% (USD 6,730 to 17,720), HCV treatment would be cost-saving within a 5 to 20-year time horizon, which should serve to increase the uptake of DAA-based HCV treatment. The payers of health care in Japan could examine ways to procure DAAs at a price where they would be cost-saving.

The 'Netflix plus model': can subscription financing improve access to medicines in low- and middle-income countries?

At present, pay for prescription models are insufficient at containing costs and improving access to medicines. Subscription financing through tenders, licensing fees and unrestricted or fixed volumes can benefit stakeholders across the supply chain. Pharmaceutical manufacturers can reduce the need for marketing expenses and gain certainty in revenue. This will decrease costs, improve predictability in budget expenditure for payers and remove price as a barrier of access from patients. Inherently, low- and middle-income countries lack the purchasing power to leverage price discounts through typical price arrangements. These markets can realise substantial savings for branded and generic medicines through subscription financing. Procuring of on-patent and off-patent drugs requires separate analysis for competition effects, the length of contract and encouraging innovation in the medicine pipeline. Prices of competitive on-patent medicines and orphan drugs can be reduced through increased competition and volume. Furthermore, pooling expertise and resources through joint procurement has the potential for greater savings. Incentivising research and development within the pharmaceutical industry is essential for sustaining a competitive market, preventing monopolies and improving access to expensive treatments. However, technical capacity, forecasting demand and the quality of generic medicines present limitations which necessitate government support and international partnerships. Ultimately, improving access requires progressive financing mechanisms with patients and cost containment in mind.

The impact of direct-acting anti-virals on the hepatitis C care cascade: identifying progress and gaps towards hepatitis C elimination in the United States

BACKGROUND

The hepatitis C virus (HCV) care cascade has changed dramatically following the introduction of direct-acting anti-virals (DAAs). Up-to-date estimates of the cascade are needed to monitor progress, identify key gaps and inform policy.

AIM

To estimate the current and future HCV care cascade in the United States, nationally and in select subpopulations of interest.

METHODS

We used a previously validated mathematical model to simulate the landscape of HCV in the United States from 2011 onwards, accounting for HCV screening policy updates, newer HCV treatments and rising HCV incidence.

RESULTS

By the end of 2018, of 4.29 million HCV persons alive, 2.71 million (63%) were actively viremic, 2.24 million (52%) aware and 1.58 million (37%) cured. By 2030, under the status quo, of 3.65 million HCV persons alive, 1.88 million (51%) would be viremic, 2.25 million (62%) aware and 1.77 million (49%) cured. The HCV care cascade in 2018 differed substantially by subpopulation: of 1.34 million incarcerated HCV persons, 96% were viremic, 36% aware and 4% cured; of 0.87 million HCV persons in Medicare, 31% were viremic, 72% aware and 69% cured; and of 0.37 million HCV persons in Medicaid, 49% were viremic, 54% aware and 51% cured. Implementing universal screening, providing unrestricted treatment and controlling HCV incidence were factors found to have the largest effect on improving the HCV care cascade.

CONCLUSIONS

Since the launch of DAAs, the HCV care cascade has shifted towards higher awareness and treatment rates; however, additional interventions are needed to move towards HCV elimination.

Machine learning models to predict disease progression among veterans with hepatitis C virus

Background

Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.

Methods and findings

We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000–2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (APRIs) > 2 after time zero given the infrequency of liver biopsy in clinical practice and that APRI is a validated non-invasive biomarker of fibrosis in CHC. We excluded those with initial APRI > 2 or pre-existing diagnosis of cirrhosis, hepatocellular carcinoma or hepatic decompensation. Enrollment was defined as the date of the first APRI. Time zero was defined as 2 years after enrollment. Cross-sectional (CS) models used predictors at or closest before time zero as a comparison. Longitudinal models used CS predictors plus longitudinal summary variables (maximum, minimum, maximum of slope, minimum of slope and total variation) between enrollment and time zero. Covariates included demographics, labs, and body mass index. Model performance was evaluated using concordance and area under the receiver operating curve (AuROC). A total of 72,683 individuals with CHC were analyzed with the cohort having a mean age of 52.8, 96.8% male and 53% white. There are 11,616 individuals (16%) who met the primary outcome over a mean follow-up of 7 years. We found superior predictive performance for the longitudinal Cox model compared to the CS Cox model (concordance 0.764 vs 0.746), and for the longitudinal boosted-survival-tree model compared to the linear Cox model (concordance 0.774 vs 0.764). The accuracy of the longitudinal models at 1,3,5 years after time zero also showed superior performance compared to the CS model, based on AuROC.

Conclusions

Boosted-survival-tree based models using longitudinal information are statistically superior to cross-sectional or linear models for predicting development of cirrhosis in CHC, though all four models were highly accurate. Similar statistical methods could be applied to predict outcomes in other non-linear chronic disease states.