Characterisation, determinants, mechanisms and consequences of the long-term effects of COVID-19: providing the evidence base for health care
- Funded by UK Research and Innovation (UKRI)
- Total publications:96 publications
Grant number: MC_PC_20051
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Key facts
Disease
COVID-19Start & end year
20212024Known Financial Commitments (USD)
$6,459,124.15Funder
UK Research and Innovation (UKRI)Principal Investigator
Professor Nishi ChaturvediResearch Location
United KingdomLead Research Institution
University College LondonResearch Priority Alignment
N/A
Research Category
Clinical characterisation and management
Research Subcategory
Disease pathogenesis
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Unspecified
Vulnerable Population
Unspecified
Occupations of Interest
Unspecified
Abstract
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 enhancerecovery? 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 effectsof 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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