2026-27 Project (Suffel & Strongman & Warren-Gash)
Do hypnotic drugs increase the risk of dementia?
SUPERVISORY TEAM
Supervisor
Dr Anne Suffel at LSHTM
Faculty of Epidemiology & Population Health, Department of Non-communicable Disease Epidemiology
Email: Anne.Suffel@lshtm.ac.uk
Co-Supervisor
Dr Helen Strongman at LSHTM
Faculty of Epidemiology & Population Health, Department of Non-communicable Disease Epidemiology
Email: Helen.Strongman@lshtm.ac.uk
Co-Supervisor
Professor Charlotte Warren-Gash at LSHTM
Faculty of Epidemiology & Population Health, Department of Non-communicable Disease Epidemiology
Email: Charlotte.Warren-Gash1@lshtm.ac.uk
PROJECT SUMMARY
Project Summary
Hypnotic drugs such as benzodiazepines and z-drugs have been linked to elevated risk of dementia. However, it remains unclear whether this is caused by the drugs themselves, the sleep disturbances or other commonly prescribed medication in people with sleeping issues. The project will use very large, routinely collected, electronic health record datasets (from primary and secondary care) to investigate the relationship between hypnotic drug use and the risk of dementia. It will explore common patterns of prescriptions for hypnotic drugs, potential interaction with other medications, and consider dose-response-relationship with respect to risk of dementia. Addressing these questions will improve safe prescribing practices with the potential to reduce the risk of dementia in those with sleep disturbances.
Project Key Words
dementia; hypnotics; sleep; pharmacodepidemiology; machine learning
MRC LID Themes
- Health Data Science
Skills
MRC Core Skills
- Quantitative skills
Skills we expect a student to develop/acquire whilst pursuing this project:
- Data management and analyses of large linked electronic health record datasets
- Conducting pharmacoepidemiolocical cohort studies using multivariable regression techniques, machine learning methods and causal inference skills
- Designing and interpreting studies, taking into account the strengths and limitations of using electronic health record data for epidemiological research
- Quantitative bias analysis to explore systematic errors that may influence measures of associations
- Understanding of research ethics and data security
- Advanced skills in statistical packages (e.g., R, STATA)
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? Yes
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: LSHTM – Bloomsbury, London
Travel requirements for this project: None
Eligibility/Requirements
Particular prior educational requirements for a student undertaking this project
- Minimum standard institutional eligibility criteria for doctoral study at LSHTM
- Applicants must hold or expect to obtain before the start of the PhD, a relevant MSc (Epidemiology, Health Data Science, Medical Statistics, or equivalent) awarded with good grades, or have a combination of relevant qualifications and experience demonstrating equivalent ability and attainment.
Other useful information
- Potential Industrial CASE (iCASE) conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
Both lack of sleep and the use of hypnotic drugs such as benzodiazepines and z-drugs have been repeatedly associated with increased dementia risk. However, previous studies have suffered from confounding by indication, short follow-up periods, and a lack of clear dose-response evidence. Studies on dementia risk in users of z-drugs have shown mixed results. Further, it remains unclear how hypnotics interact with other drugs prescribed for similar indications, such as antidepressants or antipsychotics, which are also linked to dementia via anticholinergic effects. Project objectives The overall aim of this project is to investigate a causal association between hypnotic drugs and dementia.
Specific objectives:
- To describe prescription patterns of hypnotics with respect to demographic factors and underlying health conditions.
- To analyse clusters of co-prescribing between hypnotics and other anticholinergic drugs.
- To examine the association between hypnotics and dementia risk, accounting for confounding by indication, cumulative drug exposure, and co-prescription with other potentially high-risk drugs. The student will use UK primary care electronic health records from the Clinical Practice Research Datalink (CPRD) Aurum, linked to hospital admission data (Hospital Episodes Statistics) and mortality records from the Office for National Statistics (ONS). If primary care linkage is more widely available in UK Biobank by the time of the study, Objective 3 will be repeated using Biobank data, which includes self-reported sleep behaviour and quality.
1. The student will first conduct a descriptive study, examining trends in hypnotic prescribing by drug type (benzodiazepines, z-drugs, melatonin), demographic characteristics (age, gender, ethnicity, region, deprivation), and underlying conditions (e.g., insomnia, anxiety, mental health disorders).
2. Machine learning methods will be used to identify common clusters of co-prescribing involving hypnotics and other drugs linked to elevated dementia risk. This will help generate suitable exposure definitions for the next stage.
3. A matched cohort study will assess dementia risk by:
- comparing benzodiazepine users by indication (anxiety vs sleep disturbances),
- comparing individuals by cumulative exposure to different hypnotics, and
- comparing use of hypnotics in combination with other co-prescribed drugs hypothesised to increase dementia risk.
To reduce confounding, the student will use methods such as propensity score matching and directed acyclic graphs to identify causal pathways.
Confirmed availability of required databases or specialist materials
LSHTM holds a multi-study license for linked CPRD data, renewed annually. The supervisory team has extensive experience in accessing and analysing these datasets. The Electronic Health Records Group provides additional support and resources. UK Biobank is accessible via LSHTM for an additional cost, if expanded primary care linkage becomes available.
Potential risks and mitigation plans
Delays in accessing linked datasets will be mitigated by providing the student with a synthetic CPRD dataset to develop statistical code. UK Biobank sleep data can only be used if wider linkage to prescription and primary care records becomes available, which currently applies to only a subset of the cohort. However, the project is not dependent on Biobank data.
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.
Suffel & Strongman & Warren-Gash Recording
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