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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.
Background:
Clinically coded long COVID cases in electronic health records (EHRs) are incomplete, despite reports of rising cases of long COVID.
Aim:
To determine patient characteristics associated with clinically coded long COVID.
Design & Setting:
With 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.
Method:
We 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.
Results:
Among 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.
Conclusion:
We identified variation in coded long COVID by patient characteristic. Results should be interpreted with caution as long COVID was likely under-recorded in EHRs.
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