Evidence map›Paper›PMID 40500151›Full record

ArticleBJGP open2025

Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records.

Yinghui Wei, Elsie Mf Horne, Rochelle Knight, Genevieve Cezard, Alex J Walker, Louis Fisher, Rachel Denholm, Kurt Taylor, Venexia Walker, Stephanie Riley and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BJGP open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

26 authors.

Yinghui WeiCentre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK y.wei3@exeter.ac.uk.ORCID https://orcid.org/0000-0002-7873-0009
Elsie Mf HornePopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-5545-7628
Rochelle KnightPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-4128-6821
Genevieve CezardDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0002-3011-7416
Alex J WalkerThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0003-4932-6135
Louis FisherThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-0295-3812
Rachel DenholmPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0002-8067-5440
Kurt TaylorPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0003-4690-063X
Venexia WalkerPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0001-5064-446X
Stephanie RileyCentre for Mathematical Sciences, University of Plymouth, Plymouth, UK.ORCID https://orcid.org/0000-0002-7429-6655
Dylan M WilliamsMRC Unit for Lifelong Health and Ageing, University College London (UCL), London, UK.ORCID https://orcid.org/0000-0002-3825-2487
Robert WillansNational Institute for Health and Care Excellence, London, UK.ORCID https://orcid.org/0000-0002-3461-9691
Simon DavyThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0001-9890-3619
Sebastian BaconThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-6354-3454
Ben GoldacreThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-5127-4728
Amir MehrkarThe Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-2098-1278
Spiros DenaxasInstitute of Health Informatics, UCL, London, UK.ORCID https://orcid.org/0000-0001-9612-7791
Felix GreavesNational Institute for Health and Care Excellence, London, UK.ORCID https://orcid.org/0000-0001-9393-3122
Richard J SilverwoodCentre for Longitudinal Studies, UCL Social Research Institute, UCL, London, UK.ORCID https://orcid.org/0000-0002-2744-1194
Aziz SheikhUsher Institute, The University of Edinburgh, Edinburgh, UK.ORCID https://orcid.org/0000-0001-7022-3056
Nish ChaturvediMRC Unit for Lifelong Health and Ageing, University College London (UCL), London, UK.ORCID https://orcid.org/0000-0002-6211-2775
Angela M WoodDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, UK.ORCID https://orcid.org/0000-0002-7937-304X
John MacleodPopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0001-8202-1144
Claire StevesDepartment of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.ORCID https://orcid.org/0000-0002-4910-0489
Jonathan SternePopulation Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.ORCID https://orcid.org/0000-0001-8496-6053
UK COVID-19 Longitudinal Health and Wellbeing National Core Study and CONVALESCENCE study

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinically coded long COVID cases in electronic health records (EHRs) are incomplete, despite reports of rising cases of long COVID.

aimTo determine patient characteristics associated with clinically coded long COVID. DESIGN &

settingWith the approval of NHS England, we conducted a cohort study using EHRs within the OpenSAFELY-TPP platform in England, to study patient characteristics associated with clinically coded long COVID from 29 January 2020 to 31 March 2022.

methodWe summarised the distribution of characteristics for people with clinically coded long COVID. We estimated age-sex adjusted hazard ratios (aHRs) and fully aHRs for coded long COVID. Patient characteristics included demographic factors, and health behavioural and clinical factors.

resultsAmong 17 986 419 adults, 36 886 (0.21%) were clinically coded with long COVID. Patient characteristics associated with coded long COVID included female sex, younger age (aged <60 years), obesity, living in less deprived areas, ever smoking, greater consultation frequency, and history of diagnosed asthma, mental health conditions, pre-pandemic post-viral fatigue, or psoriasis. These associations were attenuated following two doses of COVID-19 vaccines compared with before vaccination. Differences in the predictors of coded long COVID between the pre-vaccination and post-vaccination cohorts may reflect the different patient characteristics in these two cohorts rather than the vaccination status. Incidence of coded long COVID was higher in those with hospitalised COVID-19 than with those with non-hospitalised COVID-19.

conclusionWe identified variation in coded long COVID by patient characteristic. Results should be interpreted with caution as long COVID was likely under-recorded in EHRs.

Indexed as

COVID-19Long COVIDpost-acute COVID-19 syndromeSARS-CoV-2

Identifiers

PMID40500151
PMCPMC12820521

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.