Economic analyses of decision support tools for the early detection of cancer

Introduction

Upper gastro-intestinal (UGI) cancer (oesophagus, gastric, pancreatic, gallbladder and biliary tract) prognosis remain very poor worldwide.(1) In the years prior to UGI cancer diagnosis, many patients present at primary care with symptoms and receive treatments that may be suggestive of UGI cancer risk.(2, 3) Innovative tools which combine existing and novel cancer predictors and their changes over time are being developed to better identify patents at increased risk of undiagnosed UGI cancer. This studentship is an integral part of a larger programme of work to develop and evaluate a UGI Multi-Cancer Early Detection (MCED) Platform for use in primary care. The work will inform considerations for the value of the platform for health systems. (4) The student will develop knowledge and expertise in the area of cancer epidemiology, health economics and health technology assessment.(5) S/he will work on the development of the methodology for the assessment of the care pathway, health benefits, healthcare resource needs and cost-effectiveness of UGI Multi-Cancer Early Detection tools in the UK National Health Service (NHS). This program offers an exciting opportunity for quantitatively-minded students to develop new analytical skills and expertise to work with electronic health databases and other digital resources, which are integral to today’s health care research.

This studentship will form an integral part of a large multi-institution research programme (CanDetect) which aims to develop and evaluate a UGI Multi-Cancer Early Detection (MCED) Platform for use in primary care.

Research aims and objectives

The overall aim of this doctoral research is to develop a body of work in the area of economics of UGI cancer diagnosis and management. This will involve using large UK healthcare databases (e.g. comprehensive health care, cancer registries and death registration databases) to assess disease risks, care pathways, health outcomes and healthcare resources and their variation across the UK population. Developing a decision analytic health policy model of UGI cancer diagnosis and treatment to inform assessments of the longterm health outcomes and costs related to new UGI cancer diagnosis strategies is a further project aim.

Specific aims:

1)     Review of health economic frameworks to evaluate strategies for UGI cancer diagnosis and treatment, including disease risks, care pathways, and quality of life evidence informing such frameworks.

2)     Investigation of healthcare pathways for suspected UGI cancer, health outcomes and healthcare costs using large linked UK health databases.

3)     Development of cost-effectiveness decision analytic model of UGI cancer diagnosis and treatment in UK

4)     Assessment of the cost-effectiveness of new strategies for UGI cancer diagnosis in UK primary care

Support and training

The doctoral student will be supported by a team with expertise in health economics, decision-analytic modelling, general practice, cancer research and statistics. Training and courses will be tailored to meet the particular needs of the applicant. The student will join a lively community of postgraduate students across the Institute and Faculty and will have opportunity for networking, training, dissemination at meetings and conferences to build their research network and learn new skills.

Funding Availability: This 4-year studentship is funded through a Cancer Research UK Programme Grant (CanDetect) and comes with a tax-free stipend of £23,000. Funding for this project is available to UK citizens.

To apply please submit an application on Queen Mary’s application site here, including:

1)     A covering letter (less than 2 pages A4) outlining why you are interested in this project and your suitability for the role

2)     A CV (less than 2 pages A4)

3) Two academic referees/ references

4)   An example of your written academic work in English (e.g. dissertation, project, paper)

Prospective applicants are welcome to contact Prof. Borislava Mihaylova ([email protected]), Dr Runguo Wu ([email protected]) or Dr Garth Funston ([email protected]) for informal discussion before applying.