- Published
- 17 May 2024
- Journal article
Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis
- Authors
-
- Source
- eClinicalMedicine
Abstract
Background: Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods: With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5–179) and 100.5 in men (99.5–102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation: In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID.
Cite as
Henderson, A., Butler-Cole, B., Tazare, J., Tomlinson, L., Marks, M., Jit, M., Briggs, A., Lin, L., Carlile, O., Bates, C., Parry, J., CJ Bacon, S., Dillingham, I., Dennison, W., Costello, R., Wei, Y., Walker, A., Hulme, W., Goldacre, B., Mehrkar, A., Mackenna, B., Walker, A., Green, A., Mehrkar, A., Schaffer, A., Brown, A., Goldacre, B., FC Butler-Cole, B., Mackenna, B., Morton, C., Walters, C., Stables, C., Cunningham, C., Wood, C., Andrews, C., Evans, D., Hickman, G., Curtis, H., Drysdale, H., Dillingham, I., Morley, J., Massey, J., Nab, L., Hopcroft, L., Fisher, L., Bridges, L., Wiedemann, M., DeVito, N., Macdonald, O., Inglesby, P., Smith, R., Croker, R., Park, R., Higgins, R., Bacon, S., Davy, S., Maude, S., O'Dwyer, T., Ward, T., Speed, V., Hulme, W., Hart, L., Stokes, P., Bhaskaran, K., Costello, R., Cowling, T., Douglas, I., Eggo, R., Evans, S., Forbes, H., Grieve, R., Grint, D., Herrett, E., Langan, S., Mahalingasivam, V., Mansfield, K., Mathur, R., McDonald, H., Parker, E., Rentsch, C., Schultze, A., Smeeth, L., Tazare, J., Tomlinson, L., Walker, J., Williamson, E., Wing, K., Wong, A., Zheng, B., Bates, C., Cockburn, J., Parry, J., Hester, F., Harper, S., O'Hanlon, S., Eavis, A., Jarvis, R., Avramov, D., Griffiths, P., Fowles, A., Parkes, N., Perera, R., Harrison, D., Khunti, K., Sterne, J., Quint, J., Herrett, E. & Eggo, R. 2024, 'Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis', eClinicalMedicine, 72, article no: 102638. http://dx.doi.org/10.1016/j.eclinm.2024.102638
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- Repository URI
- https://eprints.gla.ac.uk/334106/