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Between 10 April and 27 April, published rolling-average case rates for COVID-19 were incorrect due to an error in population estimates used to calculate these metrics. As of 4pm on 28 April, these have been corrected. More information.

BackEntry title:

Hepatitis C estimated cases by year (lower bound)

Last updated on Thursday, 10 April 2025 at 03:30pm

Summary

Topic
Hepatitis-C
Category
cases
API name
hepatitis-c_cases_prevalenceByYearLowerBound

Rationale

The prevalence of hepatitis C means the total number of people estimated to be living with the virus at a single point in time. It describes the total disease burden. It is important to monitor prevalence for tracking access to prevention and curative treatment and identifying groups of people who are disproportionately affected.

Definition

This metric shows the lower bound of the 95% credible interval for the HCV prevalence estimate. This is the total number of people estimated to be living with hepatitis C per year between 2012 and 2023. This includes both individuals living with diagnosed and undiagnosed hepatitis C.

Methodology

A modelling approach is used to estimate the prevalence of hepatitis C virus, including both diagnosed and undiagnosed infections.

Multiple sources of routine surveillance data are included to track progress over time. This includes detection of the virus in the blood (seroprevalence data – the number of persons in a population who test positive for hepatitis C virus) and cases of hepatitis C virus-related liver disease. It also includes information on the number of people who inject drugs and other risk groups.

The hepatitis C virus burden model is used to estimate chronic prevalence (continued presence of hepatitis C virus for 6 months or more after acquiring infection). It includes the following data sources:

  • prevalence of hepatitis C in people who inject drugs over time, which informs the number of new cases in a given timeframe (incidence) via a “force of infection” model using 20 years of cross-sectional (a snapshot in time of the study population)Unlinked Anonymous Monitoring (UAM) Survey data
  • rates of disease progression from the Trent cohort (annual, age-specific probabilities of progression through mild, moderate, cirrhosis, hepatitis C virus-related hepatocellular carcinoma (HCC) and/or End-Stage Liver Disease (ESLD) states)
  • disease endpoint data (age-specific hepatitis C virus-related ESLD and HCC from Hospital Episode Statistics (HES), 2011 onwards)
  • rates of injecting cessation
  • mortality (drug-related mortality for people currently injecting, plus background mortality)
  • recent estimates of the number of people who inject drugs
  • background rates of infection in never-injecting populations
  • treatment data to model, and predict, the impact of treatment scale up and those clearing chronic infection through sustained virological response (SVR) (IMS sales data, the NHS England’s hepatitis C virus Patient Registry and Treatment Outcome System)

The model reconstructs the epidemic of injecting drug use and associated hepatitis C virus infections.

The advantage of the model is that surveillance data alone provides information only on infections in people who are currently injecting. Data on disease progression and endpoints (using ‘back-calculation’ methods, whereby the current levels of the health impacts as a result of infection are used to work backwards to build a historic picture) provides information on longer-term infections, but prevalence in people who have acquired hepatitis C virus infection more recently (that is, currently injecting) is highly uncertain.

The disadvantage of the model is the reliance on knowledge of the disease progression process. Also, the model allows for the inflow of people who recently started injecting drugs but does not account for changes due to migration. Migration in non-injecting groups is also not accounted for. Future work will aim to address these limitations and explore the use of other sources of surveillance data in prevalence modelling.

The model shows the total number of chronic infections over time, and the current and future burden in terms of hepatitis C virus-related cirrhosis, ESLD and HCC. The model also estimates underlying rates of incident chronic infection (new and reinfections). However, these models do not provide good estimates at specific points in time. Other work has been carried out to generate incidence estimates in people who inject drugs for monitoring purposes.

The model calculates the proportion of people living with chronic hepatitis C for each year out of all those who have ever had hepatitis C (that is people who are hepatitis C virus antibody positive who may have a current or past infection) and who are still alive in that year. Individuals with past infection may have cleared their infection through achieving a sustained virological response (SVR) post treatment, or through the body clearing the infection on its own.

Caveats

The model calculates the proportion of people living with chronic hepatitis C out of all those alive who have ever had hepatitis C (that is people who are hepatitis C virus antibody positive). The latter may have cleared their infection through achieving a sustained virological response (SVR) post treatment, or through the body clearing the infection on its own.

The model assumes that the proportion of people who spontaneously cleared hepatitis C without treatment is a fixed quantity with no uncertainty (24% of infections spontaneously clear without treatment). Therefore prior to direct-acting antiviral (DAA) treatment, the credible interval (CrI) for the proportion of chronic infections is very narrow.

The model estimates the percentage of the adult (aged 16 years and over) population with chronic hepatitis C infection. Virtually all infections are in those aged 16 years and over, so a more accurate picture is given by excluding children from the denominator.

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