1. Estimating the number of livebirths to Hepatitis C seropositive women in England in 2013 and 2018 using Bayesian modelling.
- Author
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Dema, Emily, Stander, Julian, Cortina-Borja, Mario, Thorne, Claire, and Bailey, Heather
- Subjects
HEPATITIS C virus ,LOGISTIC regression analysis ,MATERNAL age ,PREGNANT women ,HEPATITIS ,MEDICAL screening - Abstract
Background: The UK National Screening Committee currently recommends against antenatal screening for Hepatitis C virus (HCV) infection in England due to lack of HCV prevalence data and treatment licensed for use in pregnancy. We aimed to produce regional and national estimates of the number and proportion of livebirths to HCV seropositive women in England in 2013 and 2018. Methods: A logistic regression model fitted in the Bayesian framework estimated probabilities of HCV seropositivity among 24,599 mothers delivering in the North Thames area of England in 2012 adjusted by maternal age and region of birth. These probabilities were applied to the underlying population structures of women delivering livebirths in England in 2013 and 2018 to estimate the number of livebirths to HCV seropositive women in these years nationally and by region. The Bayesian approach allowed the uncertainty associated with all estimates to be properly quantified. Results: Nationally, the estimated number of livebirths to women seropositive for HCV for England was 464 (95% credible interval [CI] 300–692) in 2013 and 481 (95%CI 310–716) in 2018, or 70.0 (95%CI 45.0–104.1) per 100,000 and 76.9 (95%CI 49.5–114.4) per 100,000 in these years respectively. Regions with the highest estimated number of livebirths to HCV seropositive women in 2013 and 2018 included London with 118.5 and 124.4 and the South East with 67.0 and 74.0 per 100,000 livebirths. Conclusion: Few previous studies have investigated HCV among pregnant women in England. These findings complement and supplement existing research by providing national and regional estimates for the number of livebirths to HCV seropositive women in England. Bayesian modelling allows future national and regional estimates to be produced and the associated uncertainty to be properly quantified. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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