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PhD studentship in epidemiology and statistics: Using data science to guide health and educational interventions for children with neurodevelopmental conditions


   UCL Great Ormond Street Institute of Child Health

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  Prof Ruth Gilbert, Prof Bianca De Stavola  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

A 3-year PhD Studentship in epidemiology and statistics funded by the Down’s Syndrome Association is available within the Department of Population Policy and Practice at the Great Ormond Street UCL Institute of Child Health, within the Child Health Informatics Group. The studentship will commence from early 2023 onwards, under the supervision of Prof. Ruth Gilbert and Prof. Bianca De Stavola. Clinical supervision will be provided by Dr Jill Ellis (consultant community paediatrician, Newham Local Authority). The post is funded by the Down Syndrome Association, who will support engagement with children and families affected by Down Syndrome throughout the research study.

Background:

Evidence to guide policy and services depends increasingly on applying data science methods to large administrative datasets. Skills in data science are critical for health researchers of the future. This PhD studentship offers training in health data science using the ECHILD database – a newly linked administrative database that links health, education and social care data for all children in England. The student will learn data science, epidemiology and statistical methods through using the ECHILD database and produce research articles relevant to policy and practice

Hypothesis/Aims:

The studentship will seek to understand and describe the inter-relationships between the social and educational support received by children over time and later health outcomes using methods for longitudinal analyses. One group of children of particular interest for this PhD will be those affected by certain neurodevelopment conditions, to be identified within the ECHILD data. The PhD could also develop into considering the application of causal inference methods to assess the impact of specific educational interventions on health and/or education outcomes of these children. For example, the student could study whether the type or timing of special educational needs support makes a difference to their participation in schooling or use of hospital services.

Research and Policy Outputs:

The research from this PhD will provide important evidence for children and young people affected by neurodevelopmental conditions and their families. The findings will also be relevant to policy makers and services. Approximately 6-7% of the child population have a neurodevelopmental condition recorded in hospital data. These children and young people often need repeated interventions from specialist healthcare, and support from special educational needs and social care services. There is a need for evidence on whether earlier or more proactive interventions improve outcomes for children with neurodevelopmental conditions. For example, early intensive special educational needs support might enhance participation in school (thereby reducing absence and increasing learning) and reduce deterioration in mental or physical health that might need hospital care. Evidence from this study will be relevant to health and education systems and policy affecting better integration of services for children with neurodevelopmental conditions.

Environment:

The student will work within the Child Health Informatics Group, a group of over 20 researchers using administrative data, who share methods, code and support each other. The supervisory team includes Profs Gilbert (epidemiologist) and De Stavola (statistician), Dr Maria Peppa (epidemiologist) and a clinical specialist, Dr Jill Ellis, who supports children with neurodevelopmental conditions in the community.

Learning outcomes:

The student will learn about all aspects of data science from permissions and governance, through to analyses and reporting. The student will also engage with young people with neurodevelopmental conditions, their parents, and some practitioners who support them, in order to develop the study and get feedback on the potential implications of the findings for families and policy. The student will learn about:

  1. Data stewardship, including data governance, ethics and confidentiality, and principles of responsible data science.
  2. Defining the research objective and analysis plan to inform data extraction, and derivation and cleaning of cohorts for analyses. The ECHILD database contains records on 15 million children and young people in England followed from birth onwards.
  3. Derivation of variables: The student will learn to derive relevant exposure and outcome variables. This may involve searching the literature for validated code lists to phenotype certain exposures or outcomes. The study could use machine learning, latent class methods, and/or rules based on expert knowledge.
  4. Analyses: the student will undertake longitudinal analyses to describe the distribution of exposures and outcomes and may address prognostic or causal questions.
  5. The student could use causal inference methods in the thesis to understand the impact of particular interventions, such as special educational needs provision, on health or educational outcomes.
  6. Understanding context, triangulating findings, and determining implications for policy and practice.

Personal Specification:

Applicants should have, or expect to receive an upper second-class Bachelor’s degree and a Master’s degree (or equivalent work experience) in a relevant discipline or an overseas qualification of an equivalent standard.

Eligibility:

This studentship covers the cost of tuition fees based on the UK (Home) rate. Non-UK students can apply but will have to personally fund the difference between the UK (Home) rate and the overseas rate where they are not eligible for UK fee status. NB: You will be asked about your likely fee status at the interview so we would advise you to contact the UCL Graduate Admissions Office for advice should you be unsure whether or not you meet the eligibility criteria for Home fee status. Further information on Brexit and the definitions for fee status assessment can be found on the UCL website and also the UKCISA website (Higher Education: Definitions for fee status assessment).

Application:

To apply, please send a current CV including the contact details of two professional referees as well as a cover letter to [Email Address Removed]. Enquiries regarding the post can be made to Prof. Ruth Gilbert ([Email Address Removed]).

Deadline for receipt of applications: 28th November 2022

Interview date: TBC


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