In 2015, the WHO set coverage targets to eliminate hepatitis C (HCV) as a public health threat, including a 90% reduction in new chronic infections and a 65% reduction in mortality from chronic infections by 2030. HCV-HIV coinfection affects 25% of the 70,000 HIV-infected persons in Canada. Coinfection increases progression to cirrhosis, end-stage liver disease, and liver cancer. Recent treatments, oral direct acting antivirals (DAAs), have dramatically increased cure rates in coinfected patients previously difficult to treat. Curing HCV in all coinfected persons, and thus reducing disease burden and eliminating new infections, is now possible.
Yet to do so requires that DAAs be scaled-up to all those affected. In particular, there is a growing epidemic of HCV-HIV coinfection in indigenous people who inject drugs - people who often have not accessed care and treatment. Simultaneously, HCV incidence is rising in HIV-positive men who have sex with men. Further, both these populations have high reinfection rates. To reach the WHO targets, group-specific tailored interventions need to be designed and evaluated before being implemented at scale. My project aims to estimate the potential population-level impact of promising interventions to eliminate HCV in key HIV coinfected populations in Canada. To this end, I will use mathematical models (computer simulations of epidemics). When combined with strong data, they allow us to make predictions about the course of an epidemic in response to different interventions and provide valuable evidence to more efficiently design studies to test interventions in real-life settings. Mathematical models can also be updated with results of real-life interventions as evidence accrues.
My overall objective is to assess the potential of specific interventions to reduce HCV incidence and prevalence among priority populations. These results will provide the evidence needed to test and scale-up elimination strategies tailored to vulnerable populations.