Postgraduate research 

Public Health PhD/iPhD/MD

crowded city street

Our aim is to be the world-leading centre for public research and education, working to improve health and wellbeing through understanding how disease occurs across populations and evaluating the effectiveness of population health interventions.

  • PhD: 3-4 years full-time; 5 years part-time;
  • MD (Doctor of Medicine): 2 years full-time; 4 years part-time;
  • IPhD: 5 years full-time;

Research projects

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Adverse health, neurodevelopmental and educational outcomes in offspring following in utero exposure to maternal medication

Supervisors: Dr Michael Fleming, Professor Daniel MackayProfessor Jill Pell

Project outline: Pregnancy is a vulnerable period when the foetus undergoes rapid development; therefore, exposure to adverse risk factors can have lifelong implications. Use of medicines during pregnancy is avoided where possible but is sometimes unavoidable. Whilst acute adverse effects following foetal exposure in utero have been assessed for several medicines, possible longer-term effects, specifically offspring neurodevelopmental delay and educational outcomes, are not well understood. Scotland and Wales are both world leading in having comprehensive countrywide health and education data which can be linked at an individual level enabling novel research to answer such questions.

Hypothesis: We hypothesise that some medications taken during pregnancy will be associated with poor child health, child neurodevelopmental and child educational outcomes.

Main aim: The overall research aim is to study pregnancy and long-term adverse outcomes in the offspring associated with antenatal exposure to medication. We aim to link pregnant mothers taking medication for specific conditions to a range of datasets to investigate the impact of antenatal drug exposure on obstetric and foetal outcomes, and longer-term health outcomes in the children.

Objectives: We specifically want to investigate the association between specific medications taken during pregnancy.

  1. subsequent pregnancy outcomes
  2. subsequent child health outcomes
  3. subsequent child neurodevelopmental outcomes
  4. subsequent child educational outcomes

Methods: The Scottish maternity database (SMR02) collects data on all births occurring in Scotland and includes maternal, obstetric and child factors and outcomes. Retrospective linkage to national prescribing data (2009-2019) will provide information on all prescribed medications dispensed to women immediately prior to, and during, their pregnancy (2010-2019) including dose and duration; whilst linkage to other health and education datasets will enable us to study a wide range of outcomes.

We are specifically interested in pregnant women receiving medication to treat the following conditions: epilepsy, depression, ADHD, psychosis, asthma, diabetes (Type1, Type2 and gestational), hypertension, musculoskeletal pain, migraine, indigestion/heartburn/reflux, nausea/morning sickness, opiate drug withdrawal, and any infection necessitating prescription of antibiotics.

Medications of interest therefore include: antiepileptics, antidepressants, antipsychotics, psychotropic agents, antihypertensives, antiasthma, methadone and buprenorphine, NSAIDs/paracetamol, antimigraine, H2 antagonists and proton pump inhibitors, anti-emetics, oral hypoglycaemics and insulin, and antibiotics.

We will compare pregnancy, foetal and child outcomes for women receiving medications for specific conditions during the antenatal period compared to those who did not. We will utilise the SAIL Databank to replicate / validate our Scotland-wide research on pregnant women living in Wales using comparable population-wide Welsh data. The Welsh data will enable us to investigate the same outcomes described below and, in addition to national prescribing data, provides the added strength of national primary care data which is not available in Scotland. These data will enable us to additionally identify mother and child conditions recorded through GP records and will enable our comparison group of women not receiving the drugs of interest to be split further into those who have the underlying condition but are not medicated and those who do not have the underlying condition at all. Analysing these groups will enable us to differentiate between associations with the underlying condition and associations with the drugs used to treat them.

Expected outcomes: Demonstration of adverse associations or failure to demonstrate adverse associations would both be informative to clinicians and pregnant women in terms of caution or reassurance regarding use of the specific drug during pregnancy.

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Developing a risk stratification tool to detect ADHD in children and adolescents

Supervisors: Dr Michael FlemingProfessor Daniel MackayProfessor Jill Pell

Background: ADHD is associated with adverse impacts on health, education, and employment however there are currently delays, of sometimes years, before it is diagnosed and therefore managed, causing preventable distress to the child, family, and teachers as well as lasting psychological, educational, and social disadvantage. We hypothesize that development of a risk stratification tool will enable ADHD to be detected and managed earlier; thereby reducing the adverse impact on affected children and their families.

Aims: We will undertake individual-level record linkage of several Scotland-wide education and health databases. Education records (including exam results, absenteeism, exclusion, additional support needs and leaver destination) for all pupils attending school in Scotland between 2009 and 2020 will be linked to prescribing data, maternity records, neonatal admissions, child health records, acute and psychiatric hospitalisations, and deaths. We will ascertain cases of ADHD using prescribing data to identify children dispensed one or more medications approved solely for the treatment of ADHD: methylphenidate, dexamphetamine, atomoxetine, or lisdexamphetamine.

The Scottish pupil census holds information on all children attending local authority maintained primary, secondary, and special schools in Scotland which covers 95% of the school aged (4-19 years) population. Accessing data between 2009 and 2020 will yield linked records pertaining to over 1 million schoolchildren. Our previous work investigating educational and health outcomes of schoolchildren treated for ADHD uncovered an ADHD prevalence of 1.0%; therefore, using the same methodology, we expect to identify more than 10,000 children with ADHD. After initial data cleaning, merging and recoding, we will firstly determine the risk factors associated with ADHD including maternal medication, maternal antecedents (smoking, age, parity, previous abortions), pregnancy outcomes (birthweight and intrauterine growth restriction, Apgar score, mode of delivery, gestational age), early life hospitalisations (neonatal, acute, psychiatric), early life growth trajectories and development (pre-school cognitive measures), early life injury/trauma (hospitalisations), childhood medication for other chronic conditions (depression, anxiety, asthma), sociodemographic factors, and school progress(absenteeism, exclusion, special educational need, attainment, and unemployment on leaving school).

To explore the development of a risk stratification tool we will randomly split the data into training, validation, and test datasets. After appropriate transformation and scaling of data, we will train and fine-tune several classifiers (e.g. logistic regression, linear discriminant analysis, support vector machines (SVM) and random forests) to predict the outcome of ADHD, using K-fold cross validation to reduce the risk of overfitting. Each classifier will be evaluated using the confusion matrix to derive estimates of precision (true positives divided by the sum of true and false positives) and recall (true positives divided by the sum of true positives and false negatives).

This metric is preferred to receiver operating characteristic curves when the class that is being predicted is rare. We will select the appropriate threshold for classification based on inspection of precision-recall versus threshold plots and precision versus recall curves. Should the individual classifiers prove a mediocre fit, we will explore the possibility of further development and evaluation using ensemble methods, which often produce better predictions than one preferred classifier. The preferred model will be useful to clinicians to help identify children who require further investigation to enable earlier diagnoses of ADHD. Analyses will most likely be performed using R and Anaconda Python.

Training outcomes: The student will undergo training (via courses and self-learning) in the following: Safe researcher training, R programming, statistical methods, data linkage methods, analysing ‘big’ data, machine learning techniques, additional statistical programming packages (if needed) such as SPSS, Stata, SAS, and python.

References: 

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Developing a risk stratification tool to detect neurodevelopmental multimorbidity in children and adolescents

Supervisors: Dr Michael FlemingProfessor Daniel MackayProfessor Jill Pell

Background: Research including our own show that children with single and multiple neurodevelopmental disorders exhibit poorer health, education, and employment outcomes 1 however current delays, of sometimes years, before neurodevelopmental conditions are diagnosed and managed cause preventable distress to the child, family, and teachers and lasting educational and social disadvantage. We hypothesise that developing a risk stratification tool will enable neurodevelopmental multimorbidity to be detected and managed earlier; thereby reducing adversity on affected children and their families.

Aims: To link Scotland-wide health and education data together and develop and validate a tool to predict neurodevelopmental multimorbidity in children and adolescents that can be used clinically to support earlier detection, and hence earlier interventions and support. We will undertake individual-level record linkage of several Scotland-wide education and health databases. Education records (exam results, absenteeism, exclusion, additional support needs and leaver destination) for all pupils attending Scottish schools between 2009 and 2020 will be linked to prescribing data, maternity records, neonatal admissions, child health records, acute and psychiatric hospitalisations, health behaviour in school aged children survey data, and deaths. The Scottish pupil census holds information on all children attending local authority maintained primary, secondary, and special schools in Scotland covering 95% of school aged (4-19 years) children. Data between 2009 and 2020 will comprise records pertaining to over 1 million schoolchildren. We will focus on attention deficit hyperactivity disorder (ADHD), epilepsy, and severe depression (ascertained from prescribing data) and autism, intellectual disabilities, dyslexia, dyscalculia, and language and speech disorders ascertained from school records of special educational need. Children having two or more of those conditions will be classified as having neurodevelopmental multimorbidity.

After data cleaning, merging and recoding, we will determine risk factors associated with neurodevelopmental multimorbidity including maternal medication, maternal antecedents (smoking, age, parity, previous abortions), pregnancy outcomes (birthweight and intrauterine growth restriction, Apgar score, mode of delivery, gestational age), early life hospitalisations (neonatal, acute, psychiatric), early life growth trajectories and development (pre-school cognitive measures), early life injury/trauma (hospitalisations), childhood medication for other chronic conditions, sociodemographic factors and health behaviours, and school progress (absenteeism, exclusion, special educational need, attainment, and unemployment on leaving school).

To develop a risk stratification tool we will randomly split the data into training, validation, and test datasets. After appropriate transformation and scaling of data, we will train and fine-tune several classifiers (e.g. logistic regression, linear discriminant analysis, support vector machines (SVM) and random forests) to predict the outcome of neurodevelopmental multimorbidity, using K-fold cross validation to reduce the risk of overfitting. Each classifier will be evaluated using the confusion matrix to derive estimates of precision (true positives divided by the sum of true and false positives) and recall (true positives divided by the sum of true positives and false negatives). This metric is preferred to receiver operating characteristic curves when the class being predicted is rare. We will select the appropriate threshold for classification based on inspection of precision-recall versus threshold plots and precision versus recall curves. Should the individual classifiers prove a mediocre fit, we will explore further development and evaluation using ensemble methods, which often produce better predictions than one preferred classifier. The preferred model will be useful to clinicians to help identify children who require further investigation to enable earlier diagnoses of neurodevelopmental multimorbidity. Analyses will most likely be performed using R and Anaconda Python.

Training outcomes: The student will undergo training (via courses and self-learning) in the following: Safe researcher training, R programming, statistical methods, data linkage methods, analysing ‘big’ data, machine learning techniques, additional statistical programming packages (if needed) such as SPSS, Stata, SAS, and python.

References: 

Fleming M, et al. Neurodevelopmental multimorbidity and educational outcomes of Scottish schoolchildren: A population-based record linkage cohort study. PLoS Medicine. 2020; 17(10): e1003290 

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Overview

Public Health research plays a vital role in understanding the impact of biological, social, behavioural, economic, cultural and environmental factors on our health. Our interests span medical, environmental and social sciences and offer students an opportunity to train in a unique interdisciplinary culture and environment.

The advent of large scale data sets from health services, the environment, public services and the private sector is heralding something of a revolution in approaches to public health. For the first time, we are potentially able to see both how people’s health is created, maintained or damaged over time, but also the impact of interventions and policies aimed at improving and protecting health.

PGR students in public health can access the researcher training programmes in the Colleges of Medical, Veterinary and Life Sciences (MVLS) and the College of Social Science (CoSS).  This ensures that PGR students graduate with robust, transferable skills that are relevant to future employment in academia and public, private and third sector organisations. 

Our research objectives are to:

  • understand the natural course and impact of cardiovascular and other chronic diseases
  • contribute to service developments to improve cancer survival
  • contribute to a healthier population and environment through policy-related research
  • reduce health inequalities of the working age population through cutting-edge, policy informing research

Individual research projects are tailored around the expertise of principal investigators within public health and the Institute of Health and Wellbeing. Our supervisors use a variety of approaches to understand complex problems including complex statistical analysis, data linkage, longitudinal epidemiological and advanced meta-analysis, but also in depth qualitative techniques and the analysis of new media and policy documentation. We have excellent engagement with the government, the NHS and local authorities, other statutory public organisations and third sector organisations.

Specific areas of interest include:

  • the potential for different aspects of environment to positively influence population health and reduce health inequalities
  • the differences between chronological and biological ageing and its influence on coronary arterial disease
  • evaluating the effects of legislation on population health, such as smoke free legislation
  • understanding the interaction of genetic and non-genetic risk factors on population health
  • the evaluation of complex public health interventions
  • the impact on health of supporting disadvantaged groups into employment

Studying for a PhD in a vibrant, interdisciplinary environment will equip you with transferable research skills that are relevant to a range of career options in the public, private and charitable sectors. Many students find employment in the University sector after completing their studies or choose to pursue careers in health services, government or NGOs with a focus on global health improvement.

Many of our project supervisors have strong academic connections with international collaborators in universities and research institutes across the world. Funds are available through the college of Medical, Veterinary and Life Sciences to allow international visits to teams and data centres where part of your project can be carried out, if you and your supervisor decide this would enhance your research and training. This provides an excellent opportunity for networking and increasing your scientific knowledge and skill set.

Study options

PhD

  • Duration: 3/4 years full-time; 5 years part-time

Individual research projects are tailored around the expertise of principal investigators.

Integrated PhD programmes (5 years)

All applicants must have full funding before starting their iPhD programme.

Our Integrated PhD allows you to combine masters level teaching with your chosen research direction in a 1+3+1 format. 

International students with MSc and PhD scholarships/funding do not have to apply for 2 visas or exit and re-enter the country between programmes. International and UK/EU students may apply.

Year 1

Taught masters level modules are taken alongside students on our masters programmes. Our research-led teaching supports you to fine tune your research ideas and discuss these with potential PhD supervisors. You will gain a valuable introduction to academic topics, research methods, laboratory skills and the critical evaluation of research data. Your grades must meet our requirements in order to gain entry on to a PhD research programme. If not, you will receive the masters degree only.

Years 2, 3 and 4

PhD programme with research/lab work, completing an examinable piece of independent research in year 4.

Year 5

Thesis write up.

MD (Doctor of Medicine)

  • Duration: 2 years full-time; 4 years part-time (for medically-qualified graduates only)

Entry requirements

A 2.1 Honours degree or equivalent.

English language requirements

Subject to confirmation for 2022 entry

For applicants whose first language is not English, the University sets a minimum English Language proficiency level.

International English Language Testing System (IELTS) Academic module (not General Training)

  • 6.5 with no sub-test under 6.0. 
  • Tests must have been taken within 4 years 5 months of start date. Combined scores from two tests taken within 6 months of each other can be considered.

Common equivalent English language qualifications

All stated English tests are acceptable for admission to this programme:

TOEFL (ib, my best or athome)

  • 90 with minimum R 20, L 19, S 19, W 23. 
  • Tests must have been taken within 4 years 5 months of start date. Combined scores from two tests taken within 6 months of each other can be considered.

PTE (Academic)

  • 60 with minimum 59 in all sub-tests.
  • Tests must have been taken within 4 years 5 months of start date. Combined scores from two tests taken within 6 months of each other can be considered.

Glasgow International College English Language (and other foundation providers)

  • 65%.
  • Tests are accepted for academic year following sitting.

University of Glasgow Pre-sessional courses

  • Tests are accepted for academic year following sitting.

Alternatives to English Language qualification

  • Undergraduate degree from English speaking country (including Canada if taught in English)
  • Undergraduate 2+2 degree from English speaking country
  • Undergraduate 2+2 TNE degree taught in English in non-English speaking country
  • Masters degree from English speaking country
  • Masters degree (equivalent on NARIC to UK masters degree) taught in English in non-English speaking country.

For international students, the Home Office has confirmed that the University can choose to use these tests to make its own assessment of English language ability for visa applications to degree level programmes. The University is also able to accept an IELTS test (Academic module) from any of the 1000 IELTS test centres from around the world and we do not require a specific UKVI IELTS test for degree level programmes. We therefore still accept any of the English tests listed for admission to this programme.

Pre-sessional courses

The University of Glasgow accepts evidence of the required language level from the English for Academic Study Unit Pre-sessional courses. We also consider other BALEAP accredited pre-sessional courses:

Fees and funding

Fees

2022/23

  • UK: £4596
  • International & EU: £23,950

Prices are based on the annual fee for full-time study. Fees for part-time study are half the full-time fee.

Alumni discount

We offer a 20% discount to our alumni on all Postgraduate Research and full Postgraduate Taught Masters programmes. This includes University of Glasgow graduates and those who have completed Junior Year Abroad, Exchange programme or International Summer School with us. The discount is applied at registration for students who are not in receipt of another discount or scholarship funded by the University. No additional application is required.

Possible additional fees

  • Re-submission by a research student £540
  • Submission for a higher degree by published work £1,355
  • Submission of thesis after deadline lapsed £350
  • Submission by staff in receipt of staff scholarship £790

Depending on the nature of the research project, some students will be expected to pay a bench fee (also known as research support costs) to cover additional costs. The exact amount will be provided in the offer letter.

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2021/22 fees

  • UK: £4,500
  • International & EU: £23,000

Additional fees for all students:

  • Re-submission by a research student £540
  • Submission for a higher degree by published work £1,355
  • Submission of thesis after deadline lapsed £350
  • Submission by staff in receipt of staff scholarship £790

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Funding

The iPhD  is not supported by University of Glasgow Scholarship/Funding

Support

The College of Medical, Veterinary and Life Sciences Graduate School provides a vibrant, supportive and stimulating environment for all our postgraduate students. We aim to provide excellent support for our postgraduates through dedicated postgraduate convenors, highly trained supervisors and pastoral support for each student.
 
Our overarching aim is to provide a research training environment that includes:

  • provision of excellent facilities and cutting edge techniques
  • training in essential research and generic skills
  • excellence in supervision and mentoring
  • interactive discussion groups and seminars
  • an atmosphere that fosters critical cultural policy and research analysis
  • synergy between research groups and areas
  • extensive multidisciplinary and collaborative research
  • extensive external collaborations both within and beyond the UK 
  • a robust generic skills programme including opportunities in social and commercial training

Resources

Public health at our School of Health and Wellbeing cuts across the College of Medicine, Veterinary and Life Sciences and the College of Social Science. This offers students an opportunity to train in a unique interdisciplinary culture and environment and to access the researcher training programmes in both colleges.

PhD students working with our supervisors are exposed to cutting edge methodologies relevant to public health research. There is a culture of supporting innovative research ideas and our track record of interdisciplinary working supports students interested in reducing the global burden of disease.

We work with data from world renowned datasets and longitudinal cohorts including MIDSPANUK BiobankScottish Coronary Revascularisation register, Heartstart

 There is an opportunity to work with colleagues from our internationally recognised research centres:

We strive to achieve a global impact in terms of both health improvement and reductions in social inequalities of health. In order to realise this goal, we share knowledge through collaborations with academics and other partners in 73 countries across the world.

How to apply

Identify potential supervisors

All Postgraduate Research Students are allocated a supervisor* who will act as the main source of academic support and research mentoring. You may want to identify a potential supervisor and contact them to discuss your research proposal before you apply. Please note, even if you have spoken to an academic staff member about your proposal you still need to submit an online application form.

You can find relevant academic staff members with our staff research interests search.

*iPhD applicants do not need to contact a supervisor, as you will start your programme by choosing a masters from our Taught degree programmes A-Z [do not apply directly to a masters].

Gather your documents

Before applying please make sure you gather the following supporting documentation:

  1. Final or current degree transcripts including grades (and an official translation, if needed) – scanned copy in colour of the original document.
  2. Degree certificates (and an official translation, if needed): scanned copy in colour of the original document
  3. Two references on headed paper and signed by the referee. One must be academic, the other can be academic or professional [except iPhD applicants, where only one academic or professional reference is required]. References may be uploaded as part of the application form or you may enter your referees contact details on the application form. We will then email your referee and notify you when we receive the reference.  We can also accept confidential references direct to rio-researchadmissions@glasgow.ac.uk, from the referee’s university or business email account.
  4. Research proposal, CV, samples of written work as per requirements for each subject area. iPhD applicants do not need to submit any of these as you will start your programme by choosing a masters.

Notes for iPhD applicants

  • add 'I wish to study the MSc in (chosen subject) as the masters taught component of the iPhD' in the research proposal box
  • write 'n/a' for the supervisor name
Apply now

I've applied. What next?

If you have any other trouble accessing Applicant Self-Service, please see Application Troubleshooting/FAQs. 

Contact us

Before you apply

PhD/MSc/MD: email mvls-gradschool@glasgow.ac.uk

iPhD: email mvls-iphd@glasgow.ac.uk

After you have submitted your application

PhD/MSc/MD/iPhD: contact our Admissions team

Any references may be submitted by email to: rio-researchadmissions@glasgow.ac.uk