The transmission dynamics of hepatitis B in the UK: A mathematical model for evaluating costs and effectiveness of immunization programmes

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Abstract

Complex hepatitis B (HBV) epidemiology makes it difficult to evaluate and compare effectiveness of different immunization policies. A method for doing so is presented using a mathematical model of HBV transmission dynamics which can represent universal infant and adolescent vaccination strategies and those targeted at genito-urinary (GU) clinic attenders and infants born to infectious mothers. Model structure, epidemiological underpinning, and parameterization, are described. Data from the UK National Survey of Sexual Attitudes and Lifestyles is used to define patterns of sexual activity and GU clinic attendance; data deficiencies are discussed, in particular that of UK seroprevalence of HBV markers stratified by age, sex, and risk factors. General model predictions of endemic HBV marker prevalence in homosexual and heterosexual populations seem consistent with published UK data. The simulations exhibit non-linearities in the impact of different vaccination strategies. Estimated number of carriers prevented per vaccine dose for each strategy provides a measure of costs and benefits, varying temporally over the course of a programme, and with level of vaccine coverage. Screening before vaccination markedly increases payback per dose in homosexuals but not in heterosexuals; mass infant vaccination gives the poorest effectiveness ratio and vaccination of infants after antenatal screening the best; in general, increasing vaccine coverage yields lower pay-back per dose. The model provides a useful framework for evaluating costs and benefits of immunization programmes, but for precise quantitative comparison more UK epidemiological data is urgently needed.

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APA

Williams, J. R., Nokes, D. J., Medley, G. F., & Anderson, R. M. (1996). The transmission dynamics of hepatitis B in the UK: A mathematical model for evaluating costs and effectiveness of immunization programmes. Epidemiology and Infection, 116(1), 71–89. https://doi.org/10.1017/S0950268800058970

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