Modelling the impact on Hepatitis C transmission of reducing syringe sharing: London case study.

Vickerman, P; Hickman, M; Judd, A; (2007) Modelling the impact on Hepatitis C transmission of reducing syringe sharing: London case study. International journal of epidemiology, 36 (2). pp. 396-405. ISSN 0300-5771 DOI:

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BACKGROUND: Hepatitis C virus (HCV) prevalence and incidence among injecting drug users (IDUs) has increased in London and rest of UK. To inform public health action, mathematical modelling is used to explore the possible impact of strategies to decrease syringe sharing. METHODS: A mathematical model was developed to simulate HCV transmission amongst IDUs in London. Because of parameter uncertainty, numerical search algorithms were used to obtain different model fits to HCV seroprevalence data from London for 2002-03. These simulations were used to explore the likely impact of HCV prevention activities that reduce syringe sharing amongst all IDUs, IDUs that have injected for greater than one year, or IDUs with lower or higher frequencies of syringe sharing. RESULTS: Key differences between model fits centred on how they simulated the high HCV incidence amongst new injectors, either through assuming increased HCV infectivity during acute infection, a large sub-group of high frequency syringe sharers, or increased sharing among new IDUs. Despite parameter uncertainty, the model projections suggest that modest reductions in syringe sharing frequency (<25%) will reduce the HCV seroprevalence in newly initiated IDUs (injecting less than four years) but much larger and sustained reductions (>50%) are required to reduce the HCV seroprevalence in long-term IDUs (injecting more than 8 years). Critically the model also suggested that large reductions in HCV seroprevalence will be achieved only if interventions target all IDUs and reach IDUs within 12 months of injecting. Discussion Public health interventions must reduce syringe sharing amongst all IDUs, including newly initiated IDUs, and be sustained for many years to reduce HCV infection. More accurate data on key behavioural (sharing frequency) and biological (percentage of infected IDUs that clear infection) parameters is required to improve model projections.

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Global Health and Development
Research Centre: Social and Mathematical Epidemiology (SaME)
PubMed ID: 17218325
Web of Science ID: 248084200030


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