Assessing uncertainty in the burden of hepatitis C virus: Comparison of estimated disease burden and treatment costs in the UK.

F Gubay,P J White,C Metzig,I Abubakar,R Staunton

doi:10.1111/jvh.12847

F Gubay, P J White + Show 3 more

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https://doi.org/10.1111/jvh.12847

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Abstract

SummaryHepatitis C virus (HCV) is a major and growing public health concern. We need to know the expected health burden and treatment cost, and understand uncertainty in those estimates, to inform policymaking and future research. Two models that have been important in informing treatment guidelines and assessments of HCV burden were compared by simulating cohorts of individuals with chronic HCV infection initially aged 20, 35 and 50 years. One model predicts that health losses (measured in quality‐adjusted life‐years [QALYs]) and treatment costs decrease with increasing initial age of the patients, whilst the other model predicts that below 40 years, costs increase and QALY losses change little with age, and above 40 years, they decline with increasing age. Average per‐patient costs differ between the models by up to 38%, depending on the patients' initial age. One model predicts double the total number, and triple the peak annual incidence, of liver transplants compared to the other model. One model predicts 55%‐314% more deaths than the other, depending on the patients' initial age. The main sources of difference between the models are estimated progression rates between disease states and rates of health service utilization associated with different disease states and, in particular, the age dependency of these parameters. We conclude that decision‐makers need to be aware that uncertainties in the health burden and economic cost of HCV disease have important consequences for predictions of future need for care and cost‐effectiveness of interventions to avert HCV transmission, and further quantification is required to inform decisions.

Highlights

The detriment to health and cost to health services caused by liver damage arising from chronic hepatitis C virus (HCV) infection are Abbreviations: DAAs, direct acting antivirals; HCC, hepatocellular carcinoma; NHS, National Health Service; People who inject drugs (PWID), people who inject drugs; QALYs, quality-adjusted life-years; SVR, sustained virologic response.important determinants of the cost-effectiveness of interventions to avert HCV transmission, and of treatment strategies targeted at different stages of disease
The detriment to health and cost to health services caused by liver damage arising from chronic hepatitis C virus (HCV) infection are Abbreviations: DAAs, direct acting antivirals; HCC, hepatocellular carcinoma; NHS, National Health Service; PWID, people who inject drugs; QALYs, quality-adjusted life-years; SVR, sustained virologic response
Individuals with mild chronic HCV may progress to moderate disease, cirrhosis, decompensated cirrhosis and hepatocellular carcinoma (HCC), whereas model A uses age-invariant annual probabilities of progression estimated by Shepherd et al,[9] and model B has age-dependent probabilities (Table S2)

Summary

Introduction

The detriment to health and cost to health services caused by liver damage arising from chronic hepatitis C virus (HCV) infection are Abbreviations: DAAs, direct acting antivirals; HCC, hepatocellular carcinoma; NHS, National Health Service; PWID, people who inject drugs; QALYs, quality-adjusted life-years; SVR, sustained virologic response.important determinants of the cost-effectiveness of interventions to avert HCV transmission, and of treatment strategies targeted at different stages of disease. There is uncertainty in disease progression rates, and health detriment and costs of treatment associated with the different disease stages.[1] Here, we explore the uncertainty arising from these factors by comparing the models used in two key papers modelling progression of chronic HCV infection. |2 informed UK guidance,[3] European recommendations[4] and an estimate of UK HCV burden,[5] and Harris et al.’s model[6] has informed an important review[7] and an analysis of treatment prioritization.[8] Here, we compare the two models in terms of estimated health detriment (quality-adjusted life-year [QALY] loss) and costs to the health service, using a model structure that can represent both models (Figure 1)

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