{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:32:32Z","timestamp":1772253152006,"version":"3.50.1"},"reference-count":36,"publisher":"Royal College of General Practitioners","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BJGP Open"],"accepted":{"date-parts":[[2025,4,30]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Clinically coded long COVID cases in electronic health records (EHRs) are incomplete, despite reports of rising cases of long COVID.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To determine patient characteristics associated with clinically coded long COVID.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design &amp; setting<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>We summarised the distribution of characteristics for people with clinically coded long COVID. We estimated age\u2013sex adjusted hazard ratios (aHRs) and fully aHRs for coded long COVID. Patient characteristics included demographic factors, and health behavioural and clinical factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 &lt;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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3399\/bjgpo.2024.0140","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T10:35:12Z","timestamp":1749638112000},"page":"BJGPO.2024.0140","source":"Crossref","is-referenced-by-count":0,"title":["Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records"],"prefix":"10.3399","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7873-0009","authenticated-orcid":false,"given":"Yinghui","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5545-7628","authenticated-orcid":false,"given":"Elsie 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