Increasing numbers of adults are living with multiple health conditions (multi-morbidity)* that span their physical and mental health. People with a significant number of conditions, particularly over 75 years old are said to be frail**. Individual conditions are subject to improvement and deterioration over time, and may influence (and be influenced by) other co-occurring conditions. For example, deterioration in a mental health condition may lead to deterioration in a musculoskeletal condition due to a reduction in physical exercise which ultimately leads to an increase in frailty.
Understanding how physical and mental health conditions progress over time and influence one another is an important factor in determining effective and efficient patient care. The most common mental health problem is low mood or depression. At present, guidelines for depression management in the context of long-term physical conditions use a stepped care approach. This involves providing the least intrusive, most effective intervention first and if the person does not show benefit, moving on to providing more intensive interventions in a graded manner, until benefit is achieved. Importantly, initial treatment choice and subsequent escalation are currently based solely on the person’s presenting symptoms, rather than other features of their illness, or other aspects of their mental and physical health. This lack of treatment tailoring means that some patients who might spontaneously recover from their depression are offered treatment they do not require, patients who will develop chronic and severe problems are offered low-key treatments that are unlikely to be effective, and those in the middle range are at risk of being missed altogether.
Routinely-collected NHS data have the potential to drive understanding and improve decisions about treatment by facilitating analysis of a potentially wide range of conditions, for large and diverse patient populations, over significant time periods. With an appropriate legal and ethical basis, routinely-collected NHS data can be used to study populations, interventions and outcomes where conventional methods of research data collection may not be practicable or would introduce significant biases.
These two scholarships provide an opportunity to develop and apply novel methods for the analysis of multi-morbidity spanning physical and mental health using routinely-collected NHS data. You will work as part of a multi-disciplinary team including clinicians, statisticians and computer scientists to ensure that clinical, statistical and technical dimensions are jointly considered in the development and application of their analytical methods.
You will be based in the Leeds Institute of Health Sciences, School of Medicine, University of Leeds. The projects will build on existing work within the Institute of Health Sciences which is exploring the interactions between musculoskeletal conditions, frailty, and depression. Training will be provided in relevant areas.
You should hold a first degree equivalent to at least a UK upper second class honours degree in a relevant subject. This project would suit someone with a background in a health or medical science, computer science or statistics
The Faculty minimum requirements for candidates whose first language is not English are:
• British Council IELTS – score of 6.5 overall, with no element less than 6.0
• TOEFL iBT – overall score of 92 with the listening and reading element no less than 21, writing element no less than 22 and the speaking element no less than 23.
How to apply:
To apply for this project applicants should complete a Faculty Scholarship Application form using the link below https://medicinehealth.leeds.ac.uk/downloads/download/78/fmh_scholarship_application_form_2018_2019
and send this alongside a a full academic CV, degree certificates and transcripts (or marks so far if still studying) to the Faculty Graduate School at [email protected]
We also require 2 academic references to support your application. Please ask your referees to send these references on your behalf, directly to [email protected]
by no later than Friday 13 September 2019.
Any queries regarding the application process should be directed to [email protected]