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. 2024 May 17:72:102638.
doi: 10.1016/j.eclinm.2024.102638. eCollection 2024 Jun.

Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis

Alasdair D Henderson  1 , Ben Fc Butler-Cole  2 , John Tazare  1 , Laurie A Tomlinson  1 , Michael Marks  1 , Mark Jit  1 , Andrew Briggs  1 , Liang-Yu Lin  1 , Oliver Carlile  1 , Chris Bates  3 , John Parry  3 , Sebastian Cj Bacon  2 , Iain Dillingham  2 , William A Dennison  4 , Ruth E Costello  1 , Yinghui Wei  5 , Alex J Walker  2 , William Hulme  2 , Ben Goldacre  2 , Amir Mehrkar  2 , Brian MacKenna  2 ; OpenSAFELY Collaborative; Emily Herrett  1 , Rosalind M Eggo  1
Collaborators, Affiliations

Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis

Alasdair D Henderson et al. 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.

Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

Keywords: Descriptive cohort; Long COVID; Vaccination.

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Conflict of interest statement

All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare the following: LAT reports grants from MRC, Wellcome, NIHR in the past 3 years. MJ reports funding from BMGF, Gavi, RCUK, BMGF, WHO, Gavi, Wellcome Trust, European Commission, InnoHK, TFGH, CDC to their institution over the past 3 years. AB has received consultancy fees within the past 3 years from AstraZeneca, Takeda, Daiichi-Sankyo, Eisai, Roche, Novartis, Idorsia & Rythmn. CB & JP are employees of TPP (Leeds) Ltd who provide SystmOne and process data for OpenSafely under the instruction of NHS England. REC holds personal shares in AstraZeneca unrelated to this work. BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he has previously been a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. AM has represented the RCGP in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research (GDPPR); the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital. AM has consulted for health care vendors, the last time in 2022; the companies consulted in the last 3 years have no relationship to OpenSAFELY. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Dynamics of long COVID recording in EHRs. A: Weekly count of long COVID codes (any long COVID code, red; of which were diagnosis codes, blue). Records were searched for a diagnosis code first (Any long COVID diagnosis code), if no code existed then we searched for a referral code (Any long COVID code). If neither code type existed then the individual was classified as not having long COVID. B: Weekly proportion of the 5 most common long COVID codes amongst all new long COVID codes recorded that week. C: Weekly count of all long COVID codes stratified by the number of vaccine doses received ≥14 weeks prior to the long COVID code.
Fig. 2
Fig. 2
Primary care coding of long COVID codes over time. A: Daily counts of any long COVID code (red) and long COVID diagnoses only (blue). Records were searched for a diagnosis code first (Any long COVID diagnosis code), if no code existed then we searched for a referral code (Any long COVID code). If neither code type existed then the individual was classified as not having long COVID. B: Weekly counts of the three most common long COVID codes in primary care, and the remaining codes grouped as “other”. Counts less than 10 are suppressed.
Fig. 3
Fig. 3
Rates of recorded long COVID in primary care records per 100,000 person-years. Rate of any long COVID code (red) and long COVID diagnoses only (blue). IMD: index of multiple deprivation. Records were searched for a diagnosis code first (Any long COVID diagnosis code), if no code existed then we searched for a referral code (Any long COVID code). If neither code type existed then the individual was classified as not having long COVID.
Fig. 4
Fig. 4
Sankey diagram of the transition from the presence of a SARS-COV-2 test to a COVID-19 hospitalisation to a first long COVID record in primary care for 55,465 participants with a long COVID record.
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