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Epidemiological modelling to address burden of hepatitis B in resource poor settings: Impact & cost-effectiveness of intervention strategies

Amanda Shevanthi Nayagam

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Medical Research Council (MRC)
BACKGROUND: Despite childhood vaccination programmes, Hepatitis B virus (HBV) is still widespread in resource-poor settings (eg. in The Gambia 15-20% of adults are estimated to be chronically infected with HBV). Evidence in high-income countries shows that HBV treatment is effective in reducing HCC, as well as, cost effective (CE). However, there has been minimal action or research into developing similar intervention programmes in sub-Saharan Africa. Disease modelling & CE analyses, demonstrating the likely significant public health impact, may help change this lack of commitment. AIMS & OBJECTIVES: Generation of epidemiological models to address the burden of hepatitis B and hepatocellular carcinoma in resource poor settings: Impact and cost-effectiveness of screening & treatment programmes. METHODOLOGY: The main data source for Africa specific epidemiological and cost data will be the PROLIFICA programme, a 5 year, EU funded, community screening and treatment platform & HCC case control platform in West Africa. After systematic literature review into the natural history of HBV, the development of HBV markov state transmission model, using these transmission parameters, will be developed. Computational programming will use the force of infection, chronic carriage and age dependent models to model HBV related morbidity and mortality and the impact of screening and treatment. Inputting parameters generated from PROLIFICA, will allow outcome predictions specific to West Africa, with potential future applicability, once validated, to other sub-Saharan countries. CE of this hepatitis B screening and treatment programmes from a provider, as well as a societal perspective, will be evaluated by collecting both health and cost data, from the PROLIFICA programme, health facilities in the Gambia and household questionnaires. Embedding these results into the disease burden model will output cost per episode and DALYs averted by screening and treatment.

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