2026-27 Project (Morris & Bowen & Bhaskaran)
Assessing the safety of antibiotics for urinary tract infections in pregnancy using routinely collected data
SUPERVISORY TEAM
Supervisor
Professor Joan Morris at City St George’s
School of Health & Medical Sciences, Department of Global, Public and Population Health and Policy
Email: jmorris@sgul.ac.uk
Co-Supervisor
Dr Liza Bowen at City St George’s
School of Health & Medical Sciences, Department of Global, Public and Population Health and Policy
Email: lbowen@sgul.ac.uk
Co-Supervisor
Professor Krishnan Bhaskaran at LSHTM
Faculty of Epidemiology & Population Health, Department of Non-communicable Disease Epidemiology
Email: krishnan.bhaskaran@lshtm.ac.uk
PROJECT SUMMARY
Project Summary
Urinary tract infections are the most common infections during pregnancy, yet data on the safety for mother and fetus of some of the antibiotics used is incomplete. This is because pregnant women are often excluded from drug trials. Using data from electronic health records is becoming an accepted method of evaluating the safety of medications used in pregnancy. This pharmacoepidemiology PhD will assess the safety of antibiotics for UTI used in pregnancy using NHS data records from primary care (Clinical Practice Research Datalink), hospital admissions (Hospital Episode Statistics (HES), including maternity data) and mortality data from the Office for National Statistics. The student will work on defining and validating outcomes and then perform statistical analyses to identify risks of adverse outcomes. Potential biases and confounding by indication will be quantified. Candidates will develop their methodological expertise in Health Data Science while answering a clinically important question for the mother and infant.
Project Key Words
HER, epidemiology, statistics, pregnancy, infection, antibiotics
MRC LID Themes
- Health Data Science
Skills
MRC Core Skills
- Quantitative skills
Skills we expect a student to develop/acquire whilst pursuing this project:
- Designing epidemiological studies
- Management of large electronic health datasets
- Statistical analysis of longitudinal data
- Coding in STATA and/or R
- Identifying methods to deal with confounding and bias in electronic healthcare record data Writing papers for publications
- Presenting work at scientific conferences
- Public engagement for dissemination of research findings
Routes
Which route/s are available with this project?
- 1+4 = No
- +4 = Yes
Possible Master’s programme options identified by supervisory team for 1+4 applicants:
- Not applicable
Full-time/Part-time Study
Is this project available for full-time study? Yes
Is this project available for part-time study? No
Location & Travel
Students funded through MRC LID are expected to work on site at their primary institution. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training for research degree study and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.
Day-to-day work (primary location) for the duration of this research degree project will be at: City St George’s – Tooting campus, London
Travel requirements for this project: A previous MRC LID PhD student has had a very successful 3-month placement with the department of pharmacy at the University of Oslo, working with Scandinavian registry data. We have ongoing collaborations with this department and would investigate doing a similar placement if it was of interest to the candidate.
Eligibility/Requirements
Particular prior educational requirements for a student undertaking this project
- Minimum standard institutional eligibility criteria for doctoral study at City St George’s
- Masters-level training in epidemiology or statistics
Other useful information
- Potential Industrial CASE (iCASE) conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
Project objectives:
Urinary tract infections are the most common infections during pregnancy, and if untreated pose risks to both maternal and fetal health. Adequate safety data on the use of antibiotics in pregnancy is important for clinical decision making and to reduce anxieties around medication use for pregnant women. However, there is limited evidence on safety in pregnancy for some antibiotics used to treat UTI because pregnant women are often excluded from drug trials. A potential solution is the use of electronic health record data to detect potential harms from medications taken in pregnancy. Signal detection analyses using EUROmediCAT (European research consortium on medication safety in pregnancy) data have identified some antibiotics used to treat UTI in pregnancy that require assessment for potential teratogenicity. The aim of this project is to use linked electronic health records with data on medications prescribed in pregnancy for UTIs and the subsequent pregnancy, fetal and infant outcomes to investigate the safety of these medications in more detail.
Techniques to be used:
To assess the safety of antibiotics for UTI used in pregnancy, this pharmacoepidemiology project will use linked data from databases of routinely collected data in the UK. This includes records from primary care (Clinical Practice Research Datalink), hospital admissions (Hospital Episode Statistics (HES), including maternity data) and mortality data from the Office for National Statistics. Outcomes will include fetal outcomes (e.g. miscarriage, stillbirth, premature delivery), infant outcomes (e.g. small for gestational age at delivery, neonatal death), and maternal outcomes (e.g. maternal medical conditions arising in pregnancy, maternal delivery complications). The student will identify existing algorithms for identifying the outcomes in health care data, will code them in STATA or R and will validate them in these data. Cohort studies examining the risk of adverse outcomes on specific medication exposures in pregnancy will then be performed using R or STATA statistical packages. Methodological considerations will include disentangling associations with medications from associations with infections (confounding by indication), dealing with uncertain pregnancy outcomes, and assessing potential biases.
Confirmed availability of any required databases or specialist materials:
The lead supervisor is the PI of an NIHR funded study which covers access to the CPRD primary care data, pregnancy registry and linked hospital data. The student will be added to the project protocol subject to approval from CPRD’s Research Data Governance Process.
Potential risks to the project and plans for their mitigation:
We have grant funding to cover access to the data required and have budgeted for an extension of this access to cover the period of the PhD project. The project will be part of a wider body of work on medication use in pregnancy, which has approval from CPRD’s Research Data Governance Process. We would need to add the student to the approved protocol and in theory there is a risk of this addition not being granted, but the supervisory team has experience in adding collaborators to approved CPRD studies and consider the risk of this to be very low.
Further reading
Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)
Other pre-application materials: None
Additional information from the supervisory team
The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.
Morris & Bowen & Bhaskaran Recording
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