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Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health
Yinghui Wei1,2,3, Elsie Mf Horne2,4, Rochelle Knight2,4,5,6
1Centre for Mathematical Sciences, School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK y.wei3@exeter.ac.uk.
Long COVID coding in electronic health records is incomplete. Key patient factors like female sex, younger age, obesity, and certain health conditions are linked to coded long COVID, but results need cautious interpretation.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Electronic health records (EHRs) show incomplete clinical coding for long COVID.
- Despite rising reports, accurate long COVID case identification remains a challenge.
Purpose of the Study:
- To identify patient characteristics associated with clinically coded long COVID.
- To analyze demographic, behavioral, and clinical factors linked to long COVID diagnoses in EHRs.
Main Methods:
- A large cohort study utilizing England's OpenSAFELY-TPP electronic health records (2020-2022).
- Analysis included demographic, health behavior, and clinical factors.
- Estimated hazard ratios for coded long COVID, comparing pre- and post-vaccination periods.
Main Results:
- 0.21% of 17.9 million adults had coded long COVID.
- Associated factors included female sex, younger age, obesity, lower deprivation, smoking, frequent consultations, asthma, mental health conditions, post-viral fatigue, and psoriasis.
- Associations were attenuated post-vaccination; incidence higher in hospitalized COVID-19 cases.
Conclusions:
- Significant variation exists in patient characteristics associated with coded long COVID.
- Under-recording of long COVID in EHRs necessitates cautious interpretation of findings.
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