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Assessing the Epidemiology of Hepatitis C to Inform Public Health Strategies towards Hepatitis C Elimination

Aisyah, Dewi Nur; (2018) Assessing the Epidemiology of Hepatitis C to Inform Public Health Strategies towards Hepatitis C Elimination. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Background: Major advances in hepatitis C virus (HCV) treatment suggest that HCV might be eliminated in the future. In this thesis, I have undertaken a series of studies (systematic review, secondary analysis of existing cohort study, modelling) to investigate factors that are likely to impact on the feasibility of achieving HCV elimination. // Overarching Aim: To improve our understanding of the epidemiology and natural history of hepatitis C in order to inform public health strategies working towards HCV elimination. // Methods: Systematic review and meta-analysis assessing HCV spontaneous clearance rate and its predictors (Chapters 2&3). Prevalence surveys to assess the burden of and risk factors for HCV in Guernsey (Chapter 4) and in vulnerable populations in London (Chapter 5). Development of a mathematical model to estimate the required scale-up of DAA treatment that would be required to eliminate HCV in PWID in London by 2030 (Chapter 6). // Findings: • HCV prevalence was high in people screened in homeless centres, a prison and drug treatment centres. Increased case finding is needed in these settings. • 35% of patients spontaneously clear HCV by 12 months - it may be appropriate to have a year observation before instigating treatment for recently infected patients in low and middle-income countries with low healthcare budgets. • A wide range of risk factors predict spontaneous clearance. Notably drug users and those with HIV are less likely to spontaneously clear than other groups. Thus, early treatment for high risk groups is recommended for those who are less likely to achieve clearance and pose a higher risk of onward transmission. • IL28B rs8103142, IL28B rs12979860, and IL28B rs8099917 are important host genetics predictors of clearance. • Treatment prioritisation with “watch and wait” approach is probably more appropriate to be implemented for developing and less developed countries, where large number of HCV patients were infected by iatrogenic transmission and usually those settings have limited DAAs drugs supply. However, for developed countries such as UK, treatment prioritisation is probably less relevant as the majority of HCV infection came from PWID and DAA’s treatment are available. • The modelling suggests that elimination of HCV in PWID in London by 2030 would require 46% annual treatment coverage of those infected – this represents a major scale up from current activity. Retreatment of treatment failures lowers the coverage needed to 29.5%. The model is highly sensitive to: SVR (Sustained Virologic Response - suggesting need to support adherence and prevent resistance) and injecting duration (suggesting the need for drug treatment services). // Conclusion: Hepatitis C elimination would require substantial additional investment to raise treatment coverage and prevent transmission through injecting drug use. My work has identified a number of approaches would could support efforts to achieve this goal.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Assessing the Epidemiology of Hepatitis C to Inform Public Health Strategies towards Hepatitis C Elimination
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Third party copyright material has been removed from this e-thesis.
Keywords: hepatitis C, epidemiology, elimination, public health
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
URI: https://discovery.ucl.ac.uk/id/eprint/10058645
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