Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care

Lead Research Organisation: University College London


We will address the following patient defined questions: What is long-COVID and how is it diagnosed? Why have I got long-COVID? What effects will long-COVID have on my health, ability to work and family? What are my chances of recovery? How will this research ensure I am getting the right treatment and support for long-COVID?
Physical and mental health consequences of C-19 infection, termed long-COVID, occur frequently. Our understanding of long-COVID, including how best to diagnose, risk factors, health and economic consequences, is poor, limiting efforts to help people. We will use a combination of national anonymised linked primary care electronic health records, and longitudinal studies of people of all ages across the country. We have asked participants about C-19 infections, long-COVID symptoms, and have collected health and socioeconomic information for many years before the pandemic. From these studies, we will ask people reporting long-COVID, and comparator groups, to wear a wrist band measuring exercise ability, breathing, and heart rate, and complete online questionnaires on mental health and cognitive function. They will also be invited to clinic for non-invasive imaging to look at potential damage to vital organs, such as the brain, lungs and heart. Patients, members of the public overseeing electronic health record research, and study members have been involved in shaping the research questions, and will be consulted for the duration of the project. In addition, people with long-COVID and their families, from the studies, will be involved in shaping the diagnostic tools for long-COVID, and aiding our understanding of determinants of recovery, and responses to therapy. We will share findings with bodies involved in guidelines (NICE, who are also part of this project), with government (via the publications).

Technical Summary

This COVID-19 Long COVID award is jointly funded (50:50) between UKRI/Medical Research Council and the National Institute for Health Research. The figure displayed is the UKRI/MRC amount only, each partner is contributing equally towards the project so the Total Fund Amount is £9,593,946.

Long-term health consequences of C-19 (long-COVID) occur frequently. Most infections are not hospitalised; population studies are the place to understand individual and societal challenges of long-COVID. We will address the following questions: 1. How do we define and diagnose the sub-phenotypes of long-COVID? 2. What are the predictors of long-COVID, and what are the mechanisms of the sub-phenotypes? 3. What are the long-term health (physical and mental), and socioeconomic consequences? What factors enhance recovery? 4. What is the level of GP adherence to NICE diagnosis and management guidelines? Can a pop-up tool in medical records enhance adherence? We have an established consortium of experts and platforms uniting linked national primary care registries and population cohorts. The national coverage of primary care registries captures all individuals presenting to their GP, with linked prescribing, consulting, referral and outcome data. Many with long-COVID do not seek care. Population cohorts, with repeat C-19 related questionnaires, overcome this limitation. Further, the standardised pre-pandemic health data enables dissection of the effects of infection versus progression of co-morbidity. Questionnaires will identify long-COVID cases across cohorts. A subgroup of 200 cases will be matched to three sets of controls (C-19 +, long-COVID-), (C-19-, long-COVID+), and (C-19-, long-COVID-). They will wear a device capturing exercise capacity, heart rate and respiration, and complete regular online questionnaires on mental health and cognition. They will attend clinic for imaging to assess target organ damage. Qualitative work with people with long-COVID will inform diagnostic criteria and understanding of the lived experience. Parallel analysis of cohorts and registries will address each question. With NICE, we will quantify adherence to diagnostic and management guidelines in GP records, and pilot a pop-up intervention to enhance adherence. Our findings will enhance diagnostic criteria, identify pathways for bespoke sub-phenotype intervention, and inform plans for health service delivery.


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