Background

Acute respiratory infections are common, particularly in young children and older adults. Examples of acute respiratory infections include COVID-19, influenza (flu), pneumonia, respiratory syncytial virus (RSV) and Streptococcus pneumoniae. The NHS was under unprecedented pressure as a result of the compound effects of the ongoing COVID-19 pandemic, NHS staff absences and vacancies, and the cost-of-living crisis. There were increases in the incidence and severity of respiratory syncytial virus (RSV) in parts of the United States and Europe and other respiratory illnesses such as Streptococcus A and these impacted in the UK too. RSV in adults alone is estimated to result in approximately 487,000 GP episodes, 18,000 hospitalisations and nearly 8,500 deaths per season.[1] Annually respiratory illness cost the UK at least £11 billion.[2] There is considerable policy interest in understanding who might be most at risk of poor health or hospitalisation in winter to predict and manage demand on health and care services. Better understanding of these risks is also essential for targeted preventive actions (such as vaccination, antiviral/antibiotics treatment, monoclonal antibody treatment, optimising care for individual with pre-existing conditions).

Aims

Our primary aim is to derive and validate a risk prediction model for adults with winter respiratory disease who experience health outcomes necessitating hospital admission, by using Scotland-wide national surveillance dataset.

Specifically, our objectives are to:

1. Identify adults with winter respiratory disease from linked electronic health records in Scotland at different severity levels (i.e., attending unscheduled care vs. hospital admission).

2. Describe the demographic, socio-economic and clinical characteristics of adults with winter respiratory disease, their needs and service use patterns, focusing on the modifiable risk factors and predictors.

3. Investigate how these risks and needs vary by socioeconomic status, ethnicity, multimorbidity or vaccination status.

4. Derive and internally validate prediction models for winter respiratory disease in Scotland.

Training Outcomes

Completion of this PhD will provide the student with an in-depth understanding of population health methodologies and the ability to communicate and collaborate with scientists across disciplines (epidemiology, biostatistics, mathematics and potentially social science). Core technical areas of learning will include respiratory epidemiology, predictive modelling, mathematical modelling, and scientific programming and databases. The student will develop or extend their programming expertise in languages, such as R or Python. Emphasis will be placed on developing and sharing code for the wider scientific community through platforms such as GitHub. The student will have the opportunity to attend training courses in statistical methods for risk prediction and prognostic models e.g., at Keele University. Soft skills in scientific communication and collaboration will be fostered via the interdisciplinary supervisory team and participation in different forums and conferences and through the opportunity to submit journal articles.

Q&A Session

If you have any questions regarding this project, you are invited to attend a Q&A session hosted by the Supervisor(s) on Tuesday 12th December at 11.30am GMT via Microsoft Teams. Click here to join the session: https://teams.microsoft.com/l/meetup-join/19%253ameeting_MjhlMzA1YjEtMzJkNC00OGY3LTg0OGQtMThjYzExZGVmYmRj%2540thread.v2/0?context=%257b%2522Tid%2522%253a%25222e9f06b0-1669-4589-8789-10a06934dc61%2522%252c%2522Oid%2522%253a%252226ee1f9f-be22-4675-9b79-1c02f5f3ee3c%2522%257d