EAVE II Long COVID in Scotland
Research type
Research Study
Full title
Developing and validating a risk prediction model for Long COVID-19 in Scotland
IRAS ID
303054
Contact name
Aziz Sheikh
Contact email
Sponsor organisation
University of Edinburgh
Clinicaltrials.gov Identifier
N/A, N/A
Duration of Study in the UK
1 years, 11 months, 28 days
Research summary
Research Summary
In December 2019, an outbreak of a novel coronavirus was reported in Wuhan, China. The World Health Organization (WHO) declared the outbreak a global pandemic named coronavirus disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome 2 (SARS-CoV-2) coronavirus. Most patients with SARS-CoV-2 coronavirus recover within a few weeks. Some people, however, continue to have symptoms that last for weeks or months. These long-term symptoms are commonly referred to as “Long COVID” and can involve different body systems, including the cardiovascular, respiratory, musculoskeletal, and nervous systems and they may also result in mental health problems. The prevalence and who are at highest risk of Long COVID is poorly understood.
We aim to develop and validate a risk prediction model to identify who is at greatest risk of developing Long COVID. We will do this using the Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 (EAVE II), a COVID-19 platform consisting of all Scottish residents registered with a General Practice (GP) (~99% of the population), with linkages to routine electronic health records in primary care, secondary care, virological/serological testing, vaccination and mortality data in Scotland. We will use a derived operational definition for Long COVID and will analyse routine healthcare data to develop and validate the risk prediction model.Summary of Results
We used health care data from all adults in Scotland. We linked datasets capturing different information including GP and hospital visits, lab tests, COVID variant and prescribing.
Four ways of identifying long COVID in health records were used: i) clinical codes for long COVID ii) long COVID in free text iii) long COVID sick notes iv) our ‘operational definition’.
Using the four measures allowed us to identify how many people in Scotland had long COVID, and when and where the cases occurred.
We made a prediction model to identify risk factors for long COVID. The model can calculate the probability that someone will develop long COVID after COVID-19 infection.
Patient and Public Involvement (PPI) members contributed to grant application, study design, recommending predictors for the prediction model, interpreting of results, feedback on all outputs, media interviews, linking to patient groups and attending steering group meetings.REC name
West of Scotland REC 5
REC reference
22/WS/0071
Date of REC Opinion
24 May 2022
REC opinion
Favourable Opinion