Deadline: 19th June 2020 Interview: 1st July and 7th July 2020 Duration: 3 years, commencing October 2020 Stipend: £17,803
NIHR ARC North Thames and the NIHR SPHR invite applications for its jointly-funded 3-year PhD studentship to begin September 2020. Supervisors are drawn from across both the NIHR ARC North Thames and NIHR SPHR. This collaborative initiative allows unparalleled access to leading applied and public health experts, supervisors who are leaders in their field, channels for dissemination of research, participation in bespoke training, and a strong network and community of graduate students and researchers.
NIHR ARC North Thames
NIHR ARC North Thames is a research partnership committed to identifying the health and care problems that most concern everyone in our region, designing innovative research in response and then quickly putting findings into practice. Led by Professor Rosalind Raine (UCL), the ARC is a collaboration of 50+ partners including universities, NHS trusts, local authorities, clinical commissioning groups, UCLPartners, patient/public organisations and industry.
NIHR School for Public Health Research (SPHR)
The NIHR School for Public Health Research (SPHR) (https://sphr.nihr.ac.uk/) is a unique collaboration between leading academic centres in England. Established in 2012, NIHR SPHR aims to conduct high quality research to build the evidence base for effective public health practice. Our research looks at what works practically to improve population health and reduce health inequalities, can be applied across the country, and better meets the needs of policymakers, practitioners and the public.
An increasing number of children are living with multi-morbidity, defined as living with two or more long-term conditions. These children have higher mortality and are more likely to require emergency hospital admission and integrated health, education support and social care services than other children. Multi-morbidity in children often clusters with other indicators of social disadvantage, including low socio-economic status, non-majority ethnic group or parental disability. There is great interest from families and policy makers in monitoring health outcomes for children with multi-morbidity, in order to reduce the inequalities faced by affected children and their families.
The aim of this project is to use machine learning to characterise groups of children with distinct longitudinal trajectories of medical complexity/multi-morbidity in NHS Databases (Hospital Episode Statistics and the Clinical Practice Research Datalink). The student will then use this classification to examine the impact of socio-economic inequalities on health outcomes for affected children. The specific objectives are to: 1) Develop and validate an unsupervised machine learning algorithm to characterise groups of children with multi-morbidity using hospital admission and/or primary care data 2) Use the childhood multi-morbidity classification to predict key health outcomes including mortality, emergency hospital admissions, or polypharmacy, according to socio-economic status, ethnic group and parental disability 3) Explore risk factors for childhood multi-morbidity
This is an excellent opportunity for an individual with strong quantitative skills to gain experience in applying machine learning methods to large NHS datasets, and carry out research to support children with complex conditions and their families.
• Candidates should hold a Master’s in a relevant discipline (or complete their Master’s by September 2020) and have a minimum of a 2:1 or equivalent in their first degree. • All applicants require excellent written and verbal communication skills and should be willing to work collaboratively in multi-disciplinary and multi-professional teams. • Due to funding restrictions, applicants must be UK/EU nationals. Please see UK Council for International Student Affairs (UKCISA - https://tinyurl.com/s9vjh86) for criteria. • Applicants should preferably have knowledge of the UK health and care system.
How to apply
Your application should consist of: • A CV (qualifications, work experience, publications, presentations and prizes) & contact details of two academic referees. • A personal statement (300 words) describing your suitability for the proposed project including how your research experience, skills and interests relate to the topic. • A 1-page proposal of how you would develop the PhD project that you are applying for.
For applications and enquiries, please email [Email Address Removed]