ObjectivesMathematical models are increasingly important in planning for the upcoming chronic hepatitis C (CHC) elimination efforts. Such models require reliable natural history inputs to make accurate predictions on health and economic outcomes. Yet, hepatitis C virus disease progression is known to vary widely in the literature and published inputs are currently outdated. The objectives of this study were to obtain updated estimates of fibrosis progression rates (FPR) in treatment-naïve patients with CHC and to explore sources of heterogeneity.DesignA systematic review was conducted using Ovid-MEDLINE, Ovid-EMBASE and PubMed databases (January 1990 to January 2018) to identify observational studies of hepatic fibrosis in treatment-naïve patients with CHC.OutcomesStage-constant FPRs were estimated for each study given the reported fibrosis scores and duration of infection. Stage-specific FPRs (ie, F0→F1; F1→F2; F2→F3; F3→F4) were estimated using Markov maximum likelihood estimation. Estimates were pooled using random-effects meta-analysis and heterogeneity was evaluated by stratification and random-effects meta-regression.ResultsThe review identified 111 studies involving 131 groups of patients (n=42 693). The pooled stage-constant FPR was 0.094 (95% CI 0.088 to 0.100); stage-specific FPRs were F0→F1: 0.107 (95% CI 0.097 to 0.118); F1→F2: 0.082 (95% CI 0.074 to 0.091); F2→F3: 0.117 (95% CI 0.107 to 0.129); F3→F4: 0.116 (95% CI 0.104 to 0.131). Stratified analysis revealed substantial variation in progression by study population. Meta-regression indicated associations between progression and infection age, duration, source, viral genotype and study population. Findings indicate that FPRs display substantial heterogeneity across study populations and pooled values from more homogenous subpopulations should be considered when estimating prognosis.ConclusionsThis large meta-analysis presents updated prognostic estimates for CHC derived from newer studies using better diagnostic methods and improves estimates for important patient populations in terms of clinical policy (eg, injection drug users, non-clinical populations, liver clinic patients) and should be a valuable resource for patients, clinicians and clinical policymakers.

Patients identified as having chronic hepatitis C (CHC) infection can be effectively and rapidly treated using direct-acting antiviral agents. However, there remains a substantial burden of subclinical undetected infection. This study estimates the prevalence and undiagnosed proportion of CHC in British Columbia (BC) and Ontario, Canada, using a model-based approach, informed by provincial populationlevel health administrative data. A two-step approach was used: Step 1) Two population-based retrospective analyses of administrative health data for a cohort of British Columbians and a cohort of Ontarians with CHC were conducted to generate population-level statistics of CHC-related health events; Step 2) using a validated natural history model of hepatitis C virus (HCV) infection, the historical prevalence of CHC was back-calculated from the data collected in Step 1. Our retrospective study found that, in BC and Ontario, the number of newly diagnosed CHC cases is declining yearly while the complications of the disease are increasing yearly. BC had a 2014 CHC prevalence of 1.04% (95% CI: 0.84%-1.44%), with 33.3% (95% CI: 25.5%-42.0%) of CHC cases undiagnosed. Ontario had a 2014 CHC prevalence of 0.91% (95% CI: 0.83%-1.02%) with 36.0% (95% CI: 31.2%-38.9%) of CHC cases undiagnosed. Our study offers robust estimates based on the integration of a validated natural history model with population-level health administrative data on HCV-related events, which can provide vital evidence for policymakers to develop appropriate policies to achieve elimination targets. Our approach can also be applied to produce robust region-specific estimates in other countries.