Health data science to understand pregnancy intention and reduce maternity disparities (code T1.1_UCL)

The National Institute for Health and Care Research (NIHR) Inequalities Challenge: Maternity Disparities Consortium (MDC) is proud to announce its PhD Studentship competition. We are seeking the next generation of researchers to help us tackle persistent inequalities in maternity. 

The MDC brings together a UK wide collaboration of universities, NHS trusts, councils, charities, and industry partners. You can find information about our consortium members here. Our goal is to improve outcomes for women and babies before, during, and after pregnancy through inclusive, evidence-based research. 

Capacity Development

The MDC capacity development programme aims to build the next generation of maternity disparities research leaders, strengthen the workforce to deliver and sustain impact across the UK and deliver on the government priorities including the Women’s Health Strategy for England and 10 Year Health Plan for England.  

The Consortium focuses on growing long-term research capacity and expertise, especially within underrepresented disciplines, professions and underserved communities; inspiring and supporting the next generation of researchers with the right skills mix to work across clinical, practice and research sectors.  

The Opportunity

We are offering up to 13 funded PhD studentships across a range of potential projects at universities across our Consortium. We are specifically looking to grow research capacity in methodological areas, including but not limited to, health economics, statistics, trial design, digital research, data science, where expertise is currently limited. 

Start: January 2027 

Duration: Up to 3 years (Full-Time) or 4 years at 0.8WTE (Part-Time) 

Funding:  

• Full home tuition fees covered (at 2026/27 UK rates). 
• Tax-free annual stipend at UKRI rates (£21,805/£23,805 outside London/ inside London from 1^st October 2026). 
• £5,000 (per award) contribution towards research, travel, training and consumables. 
• International students are welcome to apply but must secure alternative funding for the difference between UK and Overseas tuition fees.

The Project

Background. Unplanned pregnancy is associated with adverse outcomes (e.g. increased
risk of preterm birth, and low birth weight), yet most evidence is cross-sectional,
retrospective and rarely uses validated measures of pregnancy intention, limiting causal
insight and actionable mitigation strategies. This PhD will use the validated London
Measure of Unplanned Pregnancy (LMUP) captured in routine care to model causal
pathways from preconception health and pregnancy intention to outcomes of
national and patient priority, including preterm birth and gestational diabetes,
thereby informing personalised prevention and support.

Data & setting. We will leverage a unique, multi-site routine dataset (UCLH, Homerton,
St Thomas’s), currently exceeding 35,000 pregnancies, with linked outcomes, and existing
NHS ethics and CAG approvals ensuring there will be no delay to the start of the PhD.
There are no health and safety considerations.

Methods. We will apply modern causal inference methods and explore the integration of
artificial intelligence (AI). This includes exploratory machine-learning models that
complement theory-driven Directed Acyclic Graphs (DAGs) to identify complex relationships
of pregnancy intention, socio-demographic factors, healthcare utilisation and outcomes.
Where free-text booking or clinical notes are available, natural language processing will
extract social/contextual risk factors (e.g., housing insecurity, support networks) not reliably
coded in structured fields, allowing richer adjustment and sensitivity analyses. All models
will incorporate fairness checks and pre-specification, with internal validation and
transparent reporting, and will be interpreted with Theme 1 public contributors and
community partners. These methods will significantly strengthen analytic power and support
future clinical decision-support applications. We will estimate the costs associated with
clinical risk decision pathways using data on secondary care utilisation to update estimates
on the costs of unplanned pregnancy, which are currently 16 years out of date.

Expected impact. This project will clarify how pregnancy intention and preconception
health contribute to adverse outcomes, moving from correlational evidence to
decision-relevant causal pathways that identify modifiable points for intervention and inform
personalisation of care. Outputs will be used to iterate the our intervention providing
targeted support in community/primary care (e.g., tailored preconception advice,
continuity of care for those at higher risk). Updated estimates of the costs of
unplanned pregnancy are vital for our economic evaluation. The harmonised dataset
can be used for additional analyses where causal machine learning can estimate how a risk
might change with intervention for future decision-support.

Contact Supervisor- Jenny Hall: [email protected]

Who is a MDC PhD studentship for? 

We welcome applications from individuals passionate about maternal health equity with particular methodology knowledge and experience. We are committed to building a supportive, inclusive, and caring research community. We particularly encourage applications from: 

• People from underrepresented ethnic backgrounds. 
• People with disabilities, neurodivergent conditions, and long-term health requirements 
• Candidates of all genders, ages, and beliefs. 
• We encourage applicants with experience of a wide range of methodologies to apply if they believe they can bring valuable skills to research on maternity inequalities. If it is not immediately obvious, please use your cover letter to explain how your disciplinary background will allow you to carry out your research. 

Eligibility Criteria

Essential

• First/Upper Second-Class Honours degree (2:1 or above) in a relevant subject. 
• A Master’s degree or equivalent in a relevant field. 
• Understanding of and commitment to tackling maternity inequalities. 
• Relevant previous research experience – this experience may be relevant to either the research or methodological area. 
• Excellent written and verbal communication skills. 
• Highly motivated. 
• Able to work both independently and as part of a team. 
• Able to plan and manage own work. 
• Strong quantitative research skills and experience analysing complex datasets, ideally including routinely collected data.
• International applicants are welcome to apply for studentships, but applicants who are selected for interview must be able to provide proof of funding for international fees and immigration costs prior to their interview.   
• English language requirement which can be found on individual university postgraduate admission web pages.

Desirable

• Experience using statistical software such as R, Stata or Python.
• Awareness of causal inference methods.
• AI approaches for health research.
• Analysis of free text clinical data.

*All students will be expected to be based at their host university and meet the individual university PhD studentship regulations. Please check individual university postgraduate admissions.

Accessibility and Support

We want you to perform at your best. You may contact individual supervisors when asking for more details about the projects and your suitability for the project. General enquiries about the recruitment process and MDC, including if you require any reasonable adjustments for the application process or the interview (such as alternative formats, extra time, or specific technological needs), please contact:  [email protected].  

NIHR and DHSC have a duty as a public body to promote equality of opportunity.  We will contact all applicants throughout the application period with an Equality Monitoring Form. Monitoring ensures that all applications to NIHR MDC Programmes are treated equally in terms of gender, ethnicity and/or disability. Information will be anonymised and stored separately from your application, only be used for the purpose of monitoring equal opportunities and be kept securely and in confidence.