The prospective PhD student will be based within Population Health Sciences (PHS) in Bristol Medical School at the University of Bristol and linked to the Department of Mathematical Sciences at the University of Bath. There is a wide range of collaborative, multi-disciplinary research taking place in PHS; with clinicians, epidemiologists, health economists, mathematical modellers, public health physicians and statisticians working in the department. This wide range of expertise makes an excellent environment in which to conduct a PhD.
People who inject drugs have increased risk of hepatitis C virus (HCV) and drug related death. In the UK, 85% of HCV is transmitted through injecting and drug related deaths have increased 3-fold in 10 years. This PhD will develop mathematical models to determine the effect of interventions on HCV and drug related death syndemics in multiple settings (UK and Kentucky, USA) and find which combination has most impact.
Infectious disease mathematical modelling is a growing research area. Infectious disease transmission models can help understand the progression of epidemics, including what the main drivers of infection are, and how a disease may have spread in a population. Modelling can also project the likely future epidemic trajectory if everything stays the same or if interventions to reduce incidence of disease are implemented, thereby providing a means to guide policy makers on optimal intervention strategies.
Globally, around 71 million people are chronically infected with hepatitis C virus (HCV). HCV causes significant mortality; however new highly efficacious treatments have made HCV easily curable. In many settings HCV disproportionately affects high-risk populations including people who inject drugs (PWID). PWID are also at risk of drug-related death with overdose the leading cause of avoidable death among PWID. The World Health Organisation have set HCV elimination targets for 2030, while in 2012 the Commission on Narcotic Drugs recommended national drug policies include effective measures to prevent and treat drug overdoses. However, in many settings the number of drug related deaths are still increasing; in Scotland (UK) the number of drug related deaths increased by 27% in 2018 and is now more than double the number from 10 years ago, while in Kentucky (USA) the number of drug-related deaths increased by a third from 2012-2017.
Previous modelling to capture HCV epidemics and determine the number of PWID needing treatment to achieve the World Health Organisation elimination targets has not explicitly accounted for overdose related death, and potential interventions to reduce this. These interventions include increased opioid substitution treatment and monitoring, particularly during periods known to be higher risk, supervised drug consumption rooms and provision of naloxone (overdose-reversal drug) kits. Therefore, the aims of this PhD will be:
1. Use mathematical and statistical infectious disease modelling to develop site-specific models that examine HCV and fatal overdose syndemics.
2. Use the models to understand trends in overdose death
3. Evaluate the effect of different strategies to decrease fatal overdose and its effect on (a) the HCV epidemic and (b) the treatment rate required for HCV elimination.
Through existing collaborations, Scotland, Bristol/South West and Kentucky will be modelled, with the student travelling to these sites. Each model will account for differences in population dynamics (e.g. high-risk behaviours such as increased risk of overdose following incarceration) and will incorporate site-specific data on the HCV epidemic and overdose deaths. Outputs will be used to help understand trends in overdose death, and how addressing fatal overdose affects targets required to achieve HCV elimination.
Prospective students will be highly numerate; the project will include training in modelling, epidemiological analysis and survival analysis. The PhD will result in high impact journal articles, and results being presented at national/international meetings and conferences.
Applications are welcome from highly numerate individuals across a wide range of disciplines who have, or are expected to achieve, at least a 2:1 or higher degree (or equivalent) and/or with a research Masters degree in a relevant discipline
How to apply: You can apply for the studentship through the MRC GW4 BioMed DTP web site (https://www.gw4biomed.ac.uk/doctoral-students/
) until 5pm on Monday 25th November 2019.
Contact: Professor Matthew Hickman ([email protected]
) or Dr Hannah Fraser ([email protected]