2026-27 Project (Slingsby & Bidulka & Clark)
Predicting and mapping damp and mould in English housing to estimate impacts on hospital admissions for pneumonia and severe asthma
SUPERVISORY TEAM
Supervisor
Dr Aidan Slingsby at City St George’s
School of Science & Technology, Department of Computer Science
Email: a.slingsby@city.ac.uk
Co-Supervisor
Dr Patrick Bidulka at LSHTM
Faculty of Public Health & Policy, Department of Health Services Research and Policy
Email: patrick.bidulka1@lshtm.ac.uk
Co-Supervisor
Dr Sierra Clark at City St George’s
School of Health & Medical Sciences, Department of Global, Public and Population Health and Policy
Email: siclark@sgul.ac.uk
PROJECT SUMMARY
Project Summary
Serious problems of damp and mould impacts between 3-6% of households in England, placing around 2 million people at risk of respiratory illness. Children, older adults, and those with chronic conditions are particularly vulnerable, and the NHS spends nearly £895 million annually treating conditions related to damp housing. Yet there is no national dataset on damp and mould distribution to guide proactive interventions. This PhD will create and map the first nationally standardised, geographically granular dataset of damp and mould risks in England and link it with administrative health data to investigate hospital admissions for asthma and pneumonia in children. The project will use Energy Performance Certificate data (~23 million records) and Hospital Episode Statistics, combining modelling, epidemiological analysis, and data visualisation. Outputs will inform housing and health policy and provide much needed evidence on the true burden of disease from damp and mouldy housing in England.
Project Key Words
Mould, Damp, Visualisation, HES, Pneumonia, Asthma
MRC LID Themes
- Health Data Science
- Infectious Disease
Skills
MRC Core Skills
- Quantitative skills
- Interdisciplinary skills
Skills we expect a student to develop/acquire whilst pursuing this project:
- Topical expertise in indoor air quality / damp and mould issues
- Data science and visualisation
- Environmental Epidemiology
- Statistical Methods
- Geographical Information Systems (GIS) and mapping
- Smart and routine BIGdata
- Administrative health data
- Public Health and Environmental Health Policy
Routes
Which route/s are available with this project?
- 1+4 = Yes
- +4 = Yes
Possible Master’s programme options identified by supervisory team for 1+4 applicants:
- City St Georges – Master of Public Health (MPH)
- City St George’s – MSc Data Science
- City St Georges – MSc Health Policy
- LSHTM – MSc Climate Change & Planetary Health
- LSHTM – MSc Demography & Health
- LSHTM – MSc Epidemiology
- LSHTM – MSc Health Data Science
- LSHTM – MSc Public Health
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: City St George’s – Clerkenwell campus, 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 City St George’s
- We invite motivated students with an undergraduate degree (1st or 2.1) in Geography; Environmental Science; Biomedical Science; Sociology; Data Science or allied subjects with a quantitative component to apply.
- Candidates should preferably have a Master’s degree in discipline with a significant quantitative component (Epidemiology, Social or Medical Statistics, Public Health, Population Health, Data Science, Economics, Quantitative Social Science, Geography or related). However, a 1+4 route is possible for those without a Masters.
Other useful information
- Potential Industrial CASE (iCASE) conversion? = No
PROJECT IN MORE DETAIL
Scientific description of this research project
The UK has an ageing housing stock, often poorly insulated and energy inefficient, leading to damp and mould. These conditions foster allergens, respiratory irritants, and infectious agents, and mould releases spores, fragments, and microbial VOCs into indoor air. In England and Wales, 3-6% of households are significantly affected, putting around 2 million people at risk of respiratory illness. Children, the elderly, and those with chronic conditions are especially vulnerable, and severe, prolonged exposure can be fatal.
Despite the scale of the issue, there is no national, standardised dataset on damp and mould distribution. This gap makes it difficult to identify high-risk housing and target interventions, policies, and public health resources. Current estimates suggest the NHS spends £895 million annually treating conditions linked to damp homes, with wider societal costs of £15.4 billion. However, epidemiological evidence directly linking damp and mould exposure to hospital admissions for asthma and pneumonia remains limited, leaving the true health burden unclear.
To address this, the proposed PhD will create the first nationally standardised, geographically granular dataset of damp and mould risks across England. This dataset will be linked to administrative health records to quantify the impact of damp and mouldy housing on hospital admissions for pneumonia and severe asthma exacerbations amongst children.
The PhD will:
- Apply a recently developed damp and mould model to the Energy Performance Certificate (EPC) register, generating nationwide risk estimates.
- Develop an interactive mapping and data visualisation platform to show spatial and temporal trends and inequalities in damp and mould exposures.
- Link household-level damp and mould estimates with administrative health data to conduct a novel epidemiological analysis exploring associations with hospitalisations for pneumonia and severe asthma exacerbations in children.
The student will work with a range of population-based and geographic datasets. Primary datasets include:
- Energy Performance Certificate (EPC) data, publicly available, with over 23 million household records for housing in England between 2008-2025.
- Hospital Episode Statistics (HES) administrative data on hospital admissions. HES will be linked to damp and mould estimates via Unique Property Reference Numbers (UPRNs), enabled by new initiatives such as the Healthy Household Project (https://www.adruk.org/our-work/browse-all-projects/healthy-households/). Following application to access data, the dataset will be housed and the analysis undertaken within City St. George’s Secure Trusted Research Environment.
This project will harness smart and routine datasets to model damp and mould risks across millions of households and evaluate associations with hospitalisations for related respiratory diseases. Outputs will include new dataset infrastructure, modelling code, and a visualisation and mapping dashboard. The work will provide actionable insights, supporting local authorities in targeting inspections and remediation efforts towards high-risk areas. It will also make information accessible to communities, raising awareness of damp and mould as a major housing and health issue. This project will unlock the potential for large-scale, longitudinal research on the health impacts of damp and mould.
The PhD will benefit from a multidisciplinary supervisory team and link into a new EPSRC-funded network on indoor air quality (Airhub, SC co-I), expanding the student’s network and training opportunities.
Further reading
Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)
- 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.
Slingsby & Bidulka & Clark Recording
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