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PhD Studentships

PhD studentships in the Faculty of Health and Medicine

North West Cancer Fully Funded PhD Studentship: A Mixed Methods Study of Inequalities Experienced by People with Cancer and Palliative Care Needs

An introduction to the studentship by Dr Amy Gadoud

Aim of PhD

A fully funded NWCR PhD which explores and determines the factors associated with inequalities experienced by people with cancer, with particular focus on delays in recognising palliative care needs. This is a novel PhD which will develop expertise in high quality mixed research methods and in health inequalities.


Background

Early palliative care in cancer consistently leads to better patient and caregiver outcomes but often the need for palliative care is not recognised. Socioeconomic position and other factors are known to have an impact on accessing palliative care services and place of death, but these factors have mostly been investigated regarding access to specialist palliative care services or data using hospital records. The global pandemic and the recent report from the Chief Medical Officer, Professor Whitty have highlighted the urgent need to tackle health inequalities in coastal communities. This project will use a unique large dataset of primary care and linked secondary care data and qualitative interviews, to explore inequalities in recognising palliative care need among people with cancer in a coastal area of North West England.

What the student will do?

The student will conduct a mixed method study with three concurrent phases and a final consensus and prioritisation of recommendations phase. There is some flexibility in the approach, depending on the student’s interests.

Phase 1 is a systematic literature review to identify factors associated with inequalities in recognising a need for palliative care among people with cancer.

Phase 2 is a cross sectional analysis of contemporaneously collected GP and linked hospital records of cancer decedents (2018-2020) in a North West area to determine the impact of patient characteristics (e.g. age, gender, social deprivation) on outcomes relevant to palliative care (such as recognition of palliative care, death in hospital and hospital admissions in the last year of life).

Phase 3 uses indepth interviews with General Practitioners (GPs) in the North West to explore perceived factors associated with inequalities in recognising when a palliative care approach is needed.

Phase 4 utilises the results from phases 1-3 with a panel of key stakeholders (clinicians, policy makers, commissioners, academics and research partners) to identify and develop recommendations about how the factors associated with inequalities for people with cancer can be overcome.

Training available

As well as the fortnightly supervision, training is provided across all stages of the programme, with emphasis placed on the elements of particular relevance to this PhD (including research ethics, systematic literature reviews, secondary data analysis, qualitative data collection and analysis etc). Alongside the training offered as part of the Division of Health Research PhD programme, additional quantitative data analysis training and support can be accessed through Lancaster Medical School’s postrgraduate courses and from the Maths and Stats Hub (MASH) at Lancaster University.

PhD students within the Faculty of Health and Medicine are encouraged to access a range of interdisciplinary research training from across the university, and to engage with the Lancaster University Doctoral Academy (www.lancaster.ac.uk/research/doctoral-academy).

Salary and additional benefits

A three year fully funded PhD studentship, which includes UK home tuition fees, stipend of £19,000 per year and all research costs including salary for data analysist and Public Patient Involvement (PPI) coordinator, remuneration for GP participants, equipment and dissemination costs. Total studenship award £99,189.

Eligibility

The award is available to applicants with a home fee status only.

Start date 1st October 2022.

For informal enquires please contact Dr. Amy Gadoud a.gadoud@lancaster.ac.uk.

Selection process

Send CV and supporting letter detailing how your interests and experience relate to the project to Dr Amy Gadoud a.gadoud@lancaster.ac.uk by Thursday 4th August. Interviews will be held via Teams on 11th August. Shortlisted candidates will be asked to give a five minute presentation on: How do social inequalities affect health in the UK?

Scaling up Quality Improvement in the National Health Service: Identifying critical success factors

A PhD scholarship funded by Lancashire Teaching Hospitals NHS Trust is available to study how we improve the quality of health services at scale. This is an exciting and timely project given that the NHS Long Term Plan identified the role of quality improvement in building a sustainable NHS for the future, highlighting the need for local health systems to have the capability to implement change effectively.

Starting date for the studentship will be spring 2022.

Research about how organisations have successfully adopted quality improvement approaches and what the critical factors for success are, is limited. There is a growing body of evidence which explores how individual organisations have adopted robust quality improvement methods, but there is a gap in the current evidence for translating improvement at an organisational level to a system level – such as at the level of an Integrated Care System. The aim of this project is to therefore investigate the adoption of quality improvement at a system level and to identify the critical factors for success.

This will be a mixed methods study, addressing the following objectives:

  • To determine the influence of QI on cultural and leadership behaviours to positively impact patient and staff experience and outcomes.
  • To explore the experiences of the board and senior leadership team and staff in adopting a QI approach as business as usual.
  • To explore the fidelity of the quality improvement methods when used at a system level.
  • To investigate the impact of using data for improvement to engage staff to lead improvements at a system level.

The project proposal and approach will be shaped by the student and supervisors in collaboration with an advisory group, including patients and the public.

Applicant Requirements

Skills and disciplines: We are open to applicants from all disciplinary backgrounds, but preference will be given to candidates with experience in health and/or social care, and in using qualitative approaches.

You will need excellent communication skills, and a willingness to work as part of a team.

Applicants should hold a 1st or 2.1 honours degree in a relevant discipline to the PhD area.

The Application Process

Applications should be made directly to Prof Jo Rycroft-Malone at j.rycroft-malone1@lancaster.ac.uk and should include:

1. CV (max 2 A4 sides), including details of two academic references

2. A cover letter outlining your qualifications and interest in the studentship (max 2 A4 sides)

Closing date for applications - 29th November 2021

We encourage you to contact either Prof Jo Rycroft-Malone at j.rycroft-malone1@lancaster.ac.uk and/or Dr Ailsa Brotherton Ailsa.Brotherton@lthtr.nhs.uk to discuss your application.

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ESRC PhD Studentship: Improving respiratory care via statistical modelling of multiple sources of data

Data science and statistics are rapidly transforming the face of health care and are leading to efficiency increases in prevention, diagnosis and treatment for a multitude of diseases.

This PhD project will address respiratory disease, using as a case study data from the Morecambe Bay area in the Northwest of England, and will aim to use multiple sources of data to identify factors that may be useful to improve the care of people suffering from respiratory disease in deprived communities.

Respiratory disease remains a leading cause of morbidity and mortality in the UK and has strong links with poor housing, deprivation and health inequalities; this is particularly the case in the Morecambe Bay Area, which contains some of the most deprived communities in the country. Nationally, there is currently considerable focus on the importance of robust recognition and diagnosis of respiratory disease in the primary care setting. This is important not only for correct and timely treatment of individual patients but also to reduce the burden on local health services caused by non-elective admissions and lengthy hospital stays. In addition, the national policy requires accurate diagnostic coding to inform strategic health planning for respiratory disease. Tasked with addressing these issues, the Morecambe Bay Respiratory Network (MBRN) is the emerging integrated respiratory service in Bay and Health Care Partners. The three main objectives of the PhD are:

  • Develop statistical models for the spatio-temporal epidemiology of respiratory disease in the MBRN patch. Identify the relationships between incidence of respiratory disease, deprivation and housing in our locality; determine the factors affecting space-time changes in patterns of respiratory disease.
  • Develop machine learning classification algorithms to understand the features of diagnostic quality for the four main chronic respiratory diseases (COPD, Asthma, Bronchiectasis and ILD). Identify areas of good practice and areas that may require improved training in respiratory care and support from population health strategies.
  • Develop statistical models for predicting how well patients control their symptoms and evaluate outcomes 1 year from initial diagnoses. Compare expected to actual outcomes in patients on the MRBN pathway vs control groups in and out of Morecambe Bay, thus evaluating the clinical benefits of MBRN.

The PhD project will be based in the Royal Lancaster Infirmary Business Intelligence/Data Science Unit and at the Data Science Institute at Lancaster University and will form part of the portfolio of research associated with our forthcoming Health Innovation Campus and the NIHR Applied Research Collaboration North West Coast. The supervisory team includes both academic and key clinical partners to ensure the research goals are both intellectually-innovative and of practical relevance and utility to the NHS in our local area and more widely at the national level. The student will receive advanced training at the machine learning / statistics / epidemiology interface.

Funding Notes

The is an ESRC studentship which will pay UK/EU tuition fees and a starting stipend of approx £18,000 per annum.

How to Apply

The successful candidate will be highly motivated, capable of independent work, with a first-class Bachelor's degree, or distinction at Master statistics, or a related discipline with substantial statistical content. Applicants must have an interest in Health Data Science, together with good interpersonal and communications skills.

Interested and appropriately qualified applicants should contact Professor Jo Knight (jo.knight@lancaster.ac.uk) or Dr Frank Dondelinger (f.dondelinger@lancaster.ac.uk) for further information.

Please include an up-to-date CV. The project will start in October 2020. The first round interviews will be held in late February with second-round interviews in late March if required.

The Lancaster University campus is situated in a beautiful 360 acre parkland site at Bailrigg, just 3 miles from Lancaster City Centre. Lancaster University is one of Britain’s top universities, with over 12,000 students and 2,500 employees within the Bailrigg campus that is now almost a small town in its own right. For those applicants who enjoy the outdoors, living in Lancaster offers easy access to the Lake District and Yorkshire Dales.