Estimating the epidemiology of chronic Hepatitis B Virus (HBV) infection in the UK: what do we know and what are we missing?
Campbell C., Wang T., Burrow R., Mandal S., Hippisley-Cox J., Barnes E., Matthews PC.
Background: HBV is the leading global cause of cirrhosis and primary liver cancer. However, the UK HBV population has not been well characterised, and estimates of UK HBV prevalence and/or incidence vary widely between sources. We aimed to i) extract and summarise existing national HBV prevalence estimates, ii) add a new estimate based on primary care data, and; iii) critique data sources from which estimates were derived. Methods: We undertook a narrative review, searching for national estimates of CHB case numbers in the UK (incorporating incidence, prevalence and/or test positivity data) across a range of overlapping sources, including governmental body reports, publications from independent bodies (including medical charities and non-governmental organisations) and articles in peer-reviewed scientific journals. An alternative proxy for population prevalence was obtained via the UK antenatal screening programme which achieves over 95% coverage of pregnant women. We also searched for diagnoses of HBV in the QResearch primary care database based on laboratory tests and standardised coding. Results: We identified six CHB case number estimates, of which three reported information concerning population subgroups, including number of infected individuals across age, sex and ethnicity categories. Estimates among sources reporting prevalence varied from 0.27% to 0.73%, congruent with an estimated antenatal CHB prevalence of <0.5%. Our estimate, based on QResearch data, suggests a population prevalence of ~0.05%, reflecting a substantial underestimation based on primary care records. Discussion: Estimates varied by sources of error, bias and missingness, data linkage, and “blind spots” in HBV diagnoses testing/registration. The UK HBV burden is likely to be concentrated in vulnerable populations who may not be well represented in existing datasets including those experiencing socioeconomic deprivation and/or homelessness, ethnic minorities and people born in high-prevalence countries. This could lead to under- or over-estimation of population prevalence estimation. Multi-agency collaboration is required to fill evidence gaps.