UCL Great Ormond Street Institute of Child Health


Great Ormond Street Institute of Child Health


UCL PhD studentship in Epidemiology and Statistics

Using data science to guide health adn educational interventions for children with neurodevelopmental conditions.

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.


Project description


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


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.


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.

Plan for PhD:
The ECHILD database is already linked and available to access from the start of the project. The student will develop the plan for their thesis, with support from the supervisors.

0-6 months

The first 6 months will focus on establishing the core aim of the thesis. The student will review the literature to develop the overall aim of the thesis and specific objectives. To develop specific research questions, comparator groups, outcomes, and analyses, they will consult a paediatrician working with children affected by neurodevelopmental conditions and parents/children in affected families. The student will become familiar with the ECHILD database and will be able to derive a suitable cohort for their analyses and assess data quality.

6-11 month:
1. The student will work on the first publication arising from their thesis and prepare a report to upgrade from MPhil to PhD at 12 months. The focus of the first publication will depend on the thesis plan developed by the student, supported by the supervisory team. The student will derive cohorts of children with neurodevelopmental conditions and appropriate comparison groups. The student may choose to focus on a specific clinical subgroup or more broadly on neurodevelopmental conditions.

As data for all three services (hospital, education and social care) are reasonably complete from 2012 onwards, the student will be encouraged to examine inter-relationships between health, and education and/or social interventions care up to early adolescence (age 11-12). They will be encouraged to assess risk factors for service provision, such as individual characteristics and area level factors such as policy changes over time and across the 152 local government areas in England.

12-29 months:
2. The student will develop analyses to assess how services relate to each other. For example, the student could use causal inference methods to assess the influence of level of special educational needs provision on education outcomes (e.g.: measures of participation in school, such as absence), or on health outcomes (for example unplanned hospital visits). The student will be expected to explore different methodological approaches and triangulate results. For example, the student may examine the effect of increased special needs provision at local government area level and at individual level.

30-35 months

3. The student will write up their thesis in the final six months along with papers for peer reviewed publication.

The student will receive a starting stipend of £19,668 per annum (including London weighting) as well as the cost of tuition fees based on UK fee status.  Students with advanced quantitative, postgraduate training (i.e., MSc in statistics/quantitative longitudinal methods) will be eligible for a supplementary stipend of £3000 per annum.

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.


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).


To apply, please send a current CV including the contact details of two professional referees as well as a cover letter to m.lilliman@ucl.ac.uk. Enquiries regarding the post can be made to Prof. Ruth Gilbert (r.gilbert@ucl.ac.uk).

Deadline for receipt of applications: 28th November 2022

Interview date: TBC