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BACKGROUND: Following COVID-19, up to 40% of people have ongoing health problems, referred to as "post-acute COVID-19" or 'long covid'. Long covid varies from a single persisting symptom to a complex multi-system disease. Research has flagged that this condition is under recorded in primary care record; and seeks to better define its clinical characteristics and management. Phenotypes provide a standard method for case definition and identification from routine data and are usually machine processable. A long covid phenotype can underpin research into this condition. OBJECTIVE: To develop a phenotype for long covid to inform the epidemiology and future research into this condition. We will compare clinical symptoms in people with long covid before and after their index infection, recorded between 1st-March-2020 and 1st-April-2021. We will also compare people recoded as having acute infection with those with long covid who have been hospitalised with those who are not. METHODS: We used data from the Primary Care Sentinel Cohort of the Oxford-Royal College of General Practitioners Research and Surveillance Centre database. This network is recruited to be nationally representative of the English population. We developed a long covid phenotype using our established three-step ontological method: (1)Ontological step: Defining the reasoning process underpinning the phenotype; (2)Coding step: Exploring what clinical terms are available; (3)Logical extract model: testing performance. We created a version of this phenotype using Protégé in the ontology web language for Bioportal and using Phenoflow. We used the phenotype to compare people with long covid with: (1) Their symptoms in the year prior to acquiring COVID-19; and (2) People with acute COVID-19. We also compared hospitalised people with long covid with those not hospitalised. We compared socio-demographic details, comorbidities and Office of National Statistics defined long covid symptoms between groups. We used descriptive statistics and logistic regression. RESULTS: The long covid phenotype differentiates people hospitalised with long covid from people who are not, and where no index infection is identified. The PCSC (N=7.4 million) includes 428,479 patients with acute COVID-19 diagnosis confirmed by a laboratory test and 10,772 patients with clinically diagnosed COVID-19. A total of 7,471 (1.74%;95CI=1.70-1.78) people were coded as having long covid; 1,009 (13.5%;95CI=12.7-14.3) had a hospital admission related to acute COVID-19; 6,462 (86.5%;95CI=85.7-87.3) were not hospitalised, of whom 2,728 had no COVID-19 index date recorded. 15.6% (95CI=14.7-16.5) of people with long covid were hospitalised compared to 4.9% (95CI=4.8-5.0, P

Original publication




Journal article


JMIR Public Health Surveill

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