Matthew Hickman is Professor of Public Health and Epidemiology at University of Bristol, National Drug and Alcohol Research Centre, University of New South Wales and Glasgow Caledonian University and Honorary Public Health Consultant at Bristol City Council and UK Health Security Agency, and Honorary Senior Principal Fellow at Burnet Institute (Melbourne). He is co-PI of EPTioPE and Director of NIHR Health Protection Research Unit on Evaluation and Behavioural Science and co-PI on NIH NIDA TRANSFORM (Transforming the evidence base on estimating prevalence of opioid use disorder and expanded access to interventions to prevent drug related deaths: an international data linkage study); and NIHR PHR EPEHSUS (Evaluating the Public Health Impact of Interventions for the Prevention of Drug-related Deaths in the Population: in Scotland).

Abstract

Dynamic and economic modelling of hepatitis C treatment as prevention and progress towards World Health Organization elimination targets

Direct measures of hepatitis C virus (HCV) incidence (from serological markers or ideally follow-up studies) are uncertain and difficult to measure over time. Instead insights and evidence can be generated from dynamic deterministic models which used public health surveillance and other data to simulate the population of People Who Inject Drugs (PWID) and trends in HCV treatment and HCV prevalence and incidence. These show that trends in chronic HCV (more easily measured) track HCV incidence estimates; and can generate critical threshold values for regions to consider whether they are on track or need to increase HCV testing. Current provisional models suggest that Tayside met WHO elimination targets by 2020 and Bristol and Scotland by 2024 and England on track for 2030. Economic models show that scaling up HCV treatment was highly cost-effective and probably cost-saving over 50 years at HCV treatment discounted price of £4K. The main benefits of HCV treatment as prevention accruing from averting secondary infections and costs of future liver disease (principally costs of advanced cirrhosis). We have developed theoretical models that show the potential impact of Opioid Agonist Treatment (OAT) on mortality which can be extended (in a similar way to HCV models) and calibrated to current trends and project the impact of OAT and other interventions on preventing drug-related deaths.