Abstract
BackgroundHepatitis C virus (HCV) elimination is being seriously considered globally. Current elimination models require a combination of highly effective HCV treatment and harm reduction, but high treatment costs make such strategies prohibitively expensive. Vaccines should play a key role in elimination but their best use alongside treatments is unclear. For three vaccines with different efficacies we used a mathematical model to estimate the additional reduction in HCV prevalence when vaccinating after treatment; and to identify in which settings vaccines could most effectively reduce the number of treatments required to achieve fixed reductions in HCV prevalence among people who inject drugs (PWID).MethodsA deterministic model of HCV transmission among PWID was calibrated for settings with 25, 50 and 75 % chronic HCV prevalence among PWID, stratified by high-risk or low-risk PWID. For vaccines with 30, 60 or 90 % efficacies, different rates of treatment and vaccination were introduced. We compared prevalence reductions achieved by vaccinating after treatment to prevent reinfection and vaccinating independently of treatment history in the community; and by allocating treatments and vaccinations to specific risk groups and proportionally across risk groups.ResultsVaccinating after treatment was minimally different to vaccinating independently of treatment history, and allocating treatments and vaccinations to specific risk groups was minimally different to allocating them proportionally across risk groups. Vaccines with 30 or 60 % efficacy provided greater additional prevalence reduction per vaccination in a setting with 75 % chronic HCV prevalence among PWID than a 90 % efficacious vaccine in settings with 25 or 50 % chronic HCV prevalence among PWID.ConclusionsVaccinating after treatment is an effective and practical method of administration. In settings with high chronic HCV prevalence among PWID, even modest coverage with a low-efficacy vaccine could provide significant additional prevalence reduction beyond treatment alone, and would likely reduce the cost of achieving prevalence reduction targets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0440-2) contains supplementary material, which is available to authorized users.
Highlights
Hepatitis C virus (HCV) elimination is being seriously considered globally
[6, 7] and is estimated to be greater than 50 % in many countries. For this reason elimination models have focused on this group, using a combination of treatment as prevention and harm reduction, including opioid substitution therapy (OST) and needle and syringe programmes (NSPs) [8, 9]
Model description We used a modified version of the open deterministic compartment model of HCV transmission among people who inject drugs (PWID) from Martin et al [25] shown in Fig. 1, assuming a prophylactic vaccine with efficacy ε was available that offered a duration of immunity greater than the length of injecting career and perfect protection for a proportion ε of PWID vaccinated, and no protection for the remaining (1−ε) who ‘fail vaccination’
Summary
Current elimination models require a combination of highly effective HCV treatment and harm reduction, but high treatment costs make such strategies prohibitively expensive. For three vaccines with different efficacies we used a mathematical model to estimate the additional reduction in HCV prevalence when vaccinating after treatment; and to identify in which settings vaccines could most effectively reduce the number of treatments required to achieve fixed reductions in HCV prevalence among people who inject drugs (PWID). [6, 7] and is estimated to be greater than 50 % in many countries For this reason elimination models have focused on this group, using a combination of treatment as prevention and harm reduction, including opioid substitution therapy (OST) and needle and syringe programmes (NSPs) [8, 9]. A number of fixed-dose combinations such as sofosbuvir and ledipasvir [10,11,12,13], and paritaprevir/ritonavir/ombitasvir and
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