1. Please give a summary of your research.
Chronic hepatitis C virus (HCV) infection leads to insidiously progressive liver disease potentially resulting in cirrhosis, liver failure, and hepatocellular carcinoma. With approximately 150 million people infected, HCV is considered a significant global health problem. The recent introduction of direct-acting antiviral (DAA) treatments brings us closer to HCV elimination by providing shortened treatment duration and high cure rates/sustained virological response (SVR) for chronic HCV infection. Australia is one of the leaders in achieving HCV elimination by providing subsidised DAA treatment to residents chronically infected with HCV, without restrictions. However, as a vaccine is yet to exist, reinfection remains to be an obstacle in achieving this goal. Also, there remains inaccessible marginalised populations susceptible to HCV infection.
My research aims to inform public health decisions in DAA treatment delivery, and other HCV prevention programs to keep Australia "on track" for HCV elimination. With a current focus in reaching people who inject drugs (PWID) in Australian prisons, I have developed an individual-based mathematical model that represents the Australian prison setting. The model includes differing prison security classifications, as well as the dynamic movements between prisons, and to and from the community.
The model was calibrated predominantly against epidemiological data collected in the prisons in Australia's most populous state - New South Wales (NSW). This model is currently being utilised to perform in silico testing of DAA delivery, upscale of opioid substitution therapy, and provision of needle and syringe exchange in the prison setting to assess the impact of these strategies in the incidence and prevalence of HCV in this setting. A dynamic community-based component that reflects the impact of current community-based intervention strategies is also being explored and will reveal the overall impact of scaling up HCV intervention programs in the prison setting.
2. Please include any additional details you would like to share
I have developed a stochastic individual-based model to describe the prison setting including the three major prison security classifications (minimum, medium, and maximum security prison), demographic characteristics, risk behaviour characteristics of each prisoner in the prison population, and fibrosis stage (F0-F4) for those with chronic HCV. The total population consisting of the number of prisoners in each prison security classification, was simulated based on the flux of prisoners newly incarcerated from the community, and those released back to the community, as well as transfers between prison security classifications. Each prisoner is represented as an individual agent described by age, Indigenous status, risk group, security location, liver disease stage, time of infection, re-infection status, enrollment into OST, enrollment into NSP, and enrollment into DAA treatment. Throughout the simulations, the model stores, updates, and monitors these characteristics for each individual. Specific injecting risk behaviours considered in the model included: in-prison IDU, injecting frequency, opioid use, and sharing of injecting equipment. For individuals infected with HCV, current liver disease stage, time of infection, reinfection status, as well as participation in specific HCV intervention programs are also monitored. Currently, we are evaluating the scale up of DAA treatment, OST, and the introduction of NSP, as well as the combination of these intervention programs in prison. Preliminary projections reveal the potential of DAA scale up, OST scale up, and NSP in mitigating HCV transmission in this setting.