Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis
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 i...
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| Veröffentlicht in: | EClinicalMedicine 2024-06, Vol.72, p.102638-102638, Article 102638 |
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| creator | Henderson, Alasdair D. Butler-Cole, Ben FC Tazare, John Tomlinson, Laurie A. Marks, Michael Briggs, Andrew Lin, Liang-Yu Carlile, Oliver Bates, Chris Parry, John Bacon, Sebastian CJ Dillingham, Iain Dennison, William A. Costello, Ruth E. Wei, Yinghui Goldacre, Ben Mehrkar, Amir MacKenna, Brian Green, Amelia Mehrkar, Amir Schaffer, Andrea Brown, Andrew Goldacre, Ben Butler-Cole, Ben MacKenna, Brian Morton, Caroline Walters, Caroline Stables, Catherine Cunningham, Christine Wood, Christopher Andrews, Colm Evans, David Hickman, George Curtis, Helen Drysdale, Henry Dillingham, Iain Morley, Jessica Massey, Jon Nab, Linda Hopcroft, Lisa Fisher, Louis Bridges, Lucy Wiedemann, Milan DeVito, Nicholas Macdonald, Orla Inglesby, Peter Smith, Rebecca Park, Robin Higgins, Rose Bacon, Sebastian Davy, Simon Maude, Steven Ward, Tom Speed, Victoria Hulme, William Hart, Liam Stokes, Pete Bhaskaran, Krishnan Costello, Ruth Cowling, Thomas Douglas, Ian Eggo, Rosalind Evans, Stephen Forbes, Harriet Grieve, Richard Grint, Daniel Langan, Sinead Mahalingasivam, Viyaasan Mansfield, Kathryn Mathur, Rohini McDonald, Helen Parker, Edward Rentsch, Christopher Schultze, Anna Smeeth, Liam Tazare, John Tomlinson, Laurie Walker, Jemma Williamson, Elizabeth Wing, Kevin Wong, Angel Zheng, Bang Bates, Christopher Parry, John Hester, Frank Harper, Sam O'Hanlon, Shaun Eavis, Alex Jarvis, Richard Avramov, Dima Griffiths, Paul Fowles, Aaron Parkes, Nasreen Perera, Rafael Harrison, David Khunti, Kamlesh Sterne, Jonathan Quint, Jennifer Herrett, Emily Eggo, Rosalind M. |
| description | 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.
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.
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.
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.
This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073). |
| doi_str_mv | 10.1016/j.eclinm.2024.102638 |
| format | Article |
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Butler-Cole, Ben FC ; Tazare, John ; Tomlinson, Laurie A. ; Marks, Michael ; Briggs, Andrew ; Lin, Liang-Yu ; Carlile, Oliver ; Bates, Chris ; Parry, John ; Bacon, Sebastian CJ ; Dillingham, Iain ; Dennison, William A. ; Costello, Ruth E. ; Wei, Yinghui ; Goldacre, Ben ; Mehrkar, Amir ; MacKenna, Brian ; Green, Amelia ; Mehrkar, Amir ; Schaffer, Andrea ; Brown, Andrew ; Goldacre, Ben ; Butler-Cole, Ben ; MacKenna, Brian ; Morton, Caroline ; Walters, Caroline ; Stables, Catherine ; Cunningham, Christine ; Wood, Christopher ; Andrews, Colm ; Evans, David ; Hickman, George ; Curtis, Helen ; Drysdale, Henry ; Dillingham, Iain ; Morley, Jessica ; Massey, Jon ; Nab, Linda ; Hopcroft, Lisa ; Fisher, Louis ; Bridges, Lucy ; Wiedemann, Milan ; DeVito, Nicholas ; Macdonald, Orla ; Inglesby, Peter ; Smith, Rebecca ; Park, Robin ; Higgins, Rose ; Bacon, Sebastian ; Davy, Simon ; Maude, Steven ; Ward, Tom ; Speed, Victoria ; Hulme, William ; Hart, Liam ; Stokes, Pete ; Bhaskaran, Krishnan ; Costello, Ruth ; Cowling, Thomas ; Douglas, Ian ; Eggo, Rosalind ; Evans, Stephen ; Forbes, Harriet ; Grieve, Richard ; Grint, Daniel ; Langan, Sinead ; Mahalingasivam, Viyaasan ; Mansfield, Kathryn ; Mathur, Rohini ; McDonald, Helen ; Parker, Edward ; Rentsch, Christopher ; Schultze, Anna ; Smeeth, Liam ; Tazare, John ; Tomlinson, Laurie ; Walker, Jemma ; Williamson, Elizabeth ; Wing, Kevin ; Wong, Angel ; Zheng, Bang ; Bates, Christopher ; Parry, John ; Hester, Frank ; Harper, Sam ; O'Hanlon, Shaun ; Eavis, Alex ; Jarvis, Richard ; Avramov, Dima ; Griffiths, Paul ; Fowles, Aaron ; Parkes, Nasreen ; Perera, Rafael ; Harrison, David ; Khunti, Kamlesh ; Sterne, Jonathan ; Quint, Jennifer ; Herrett, Emily ; Eggo, Rosalind M.</creator><creatorcontrib>Henderson, Alasdair D. ; Butler-Cole, Ben FC ; Tazare, John ; Tomlinson, Laurie A. ; Marks, Michael ; Briggs, Andrew ; Lin, Liang-Yu ; Carlile, Oliver ; Bates, Chris ; Parry, John ; Bacon, Sebastian CJ ; Dillingham, Iain ; Dennison, William A. ; Costello, Ruth E. ; Wei, Yinghui ; Goldacre, Ben ; Mehrkar, Amir ; MacKenna, Brian ; Green, Amelia ; Mehrkar, Amir ; Schaffer, Andrea ; Brown, Andrew ; Goldacre, Ben ; Butler-Cole, Ben ; MacKenna, Brian ; Morton, Caroline ; Walters, Caroline ; Stables, Catherine ; Cunningham, Christine ; Wood, Christopher ; Andrews, Colm ; Evans, David ; Hickman, George ; Curtis, Helen ; Drysdale, Henry ; Dillingham, Iain ; Morley, Jessica ; Massey, Jon ; Nab, Linda ; Hopcroft, Lisa ; Fisher, Louis ; Bridges, Lucy ; Wiedemann, Milan ; DeVito, Nicholas ; Macdonald, Orla ; Inglesby, Peter ; Smith, Rebecca ; Park, Robin ; Higgins, Rose ; Bacon, Sebastian ; Davy, Simon ; Maude, Steven ; Ward, Tom ; Speed, Victoria ; Hulme, William ; Hart, Liam ; Stokes, Pete ; Bhaskaran, Krishnan ; Costello, Ruth ; Cowling, Thomas ; Douglas, Ian ; Eggo, Rosalind ; Evans, Stephen ; Forbes, Harriet ; Grieve, Richard ; Grint, Daniel ; Langan, Sinead ; Mahalingasivam, Viyaasan ; Mansfield, Kathryn ; Mathur, Rohini ; McDonald, Helen ; Parker, Edward ; Rentsch, Christopher ; Schultze, Anna ; Smeeth, Liam ; Tazare, John ; Tomlinson, Laurie ; Walker, Jemma ; Williamson, Elizabeth ; Wing, Kevin ; Wong, Angel ; Zheng, Bang ; Bates, Christopher ; Parry, John ; Hester, Frank ; Harper, Sam ; O'Hanlon, Shaun ; Eavis, Alex ; Jarvis, Richard ; Avramov, Dima ; Griffiths, Paul ; Fowles, Aaron ; Parkes, Nasreen ; Perera, Rafael ; Harrison, David ; Khunti, Kamlesh ; Sterne, Jonathan ; Quint, Jennifer ; Herrett, Emily ; Eggo, Rosalind M. ; The OpenSAFELY Collaborative ; OpenSAFELY Collaborative</creatorcontrib><description>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.
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.
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.
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.
This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).</description><identifier>ISSN: 2589-5370</identifier><identifier>EISSN: 2589-5370</identifier><identifier>DOI: 10.1016/j.eclinm.2024.102638</identifier><identifier>PMID: 38800803</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Descriptive cohort ; Long COVID ; Vaccination</subject><ispartof>EClinicalMedicine, 2024-06, Vol.72, p.102638-102638, Article 102638</ispartof><rights>2024 The Authors</rights><rights>2024 The Authors.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c226t-8906debb145b84cb41e10a8bb5a48bb1fd27852b2639ae2c8cec2e903f0809ef3</cites><orcidid>0000-0002-8903-4906 ; 0000-0002-0362-6717 ; 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Kamlesh</creatorcontrib><creatorcontrib>Sterne, Jonathan</creatorcontrib><creatorcontrib>Quint, Jennifer</creatorcontrib><creatorcontrib>Herrett, Emily</creatorcontrib><creatorcontrib>Eggo, Rosalind M.</creatorcontrib><creatorcontrib>The OpenSAFELY Collaborative</creatorcontrib><creatorcontrib>OpenSAFELY Collaborative</creatorcontrib><title>Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis</title><title>EClinicalMedicine</title><addtitle>EClinicalMedicine</addtitle><description>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.
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.
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.
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.
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Oliver ; Bates, Chris ; Parry, John ; Bacon, Sebastian CJ ; Dillingham, Iain ; Dennison, William A. ; Costello, Ruth E. ; Wei, Yinghui ; Goldacre, Ben ; Mehrkar, Amir ; MacKenna, Brian ; Green, Amelia ; Mehrkar, Amir ; Schaffer, Andrea ; Brown, Andrew ; Goldacre, Ben ; Butler-Cole, Ben ; MacKenna, Brian ; Morton, Caroline ; Walters, Caroline ; Stables, Catherine ; Cunningham, Christine ; Wood, Christopher ; Andrews, Colm ; Evans, David ; Hickman, George ; Curtis, Helen ; Drysdale, Henry ; Dillingham, Iain ; Morley, Jessica ; Massey, Jon ; Nab, Linda ; Hopcroft, Lisa ; Fisher, Louis ; Bridges, Lucy ; Wiedemann, Milan ; DeVito, Nicholas ; Macdonald, Orla ; Inglesby, Peter ; Smith, Rebecca ; Park, Robin ; Higgins, Rose ; Bacon, Sebastian ; Davy, Simon ; Maude, Steven ; Ward, Tom ; Speed, Victoria ; Hulme, William ; Hart, Liam ; Stokes, Pete ; Bhaskaran, Krishnan ; Costello, Ruth ; Cowling, Thomas ; Douglas, Ian ; Eggo, Rosalind ; Evans, Stephen ; Forbes, Harriet ; Grieve, Richard ; Grint, Daniel ; Langan, Sinead ; Mahalingasivam, Viyaasan ; Mansfield, Kathryn ; Mathur, Rohini ; McDonald, Helen ; Parker, Edward ; Rentsch, Christopher ; Schultze, Anna ; Smeeth, Liam ; Tazare, John ; Tomlinson, Laurie ; Walker, Jemma ; Williamson, Elizabeth ; Wing, Kevin ; Wong, Angel ; Zheng, Bang ; Bates, Christopher ; Parry, John ; Hester, Frank ; Harper, Sam ; O'Hanlon, Shaun ; Eavis, Alex ; Jarvis, Richard ; Avramov, Dima ; Griffiths, Paul ; Fowles, Aaron ; Parkes, Nasreen ; Perera, Rafael ; Harrison, David ; Khunti, Kamlesh ; Sterne, Jonathan ; Quint, Jennifer ; Herrett, Emily ; Eggo, Rosalind M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c226t-8906debb145b84cb41e10a8bb5a48bb1fd27852b2639ae2c8cec2e903f0809ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Descriptive cohort</topic><topic>Long COVID</topic><topic>Vaccination</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Henderson, Alasdair D.</creatorcontrib><creatorcontrib>Butler-Cole, Ben FC</creatorcontrib><creatorcontrib>Tazare, John</creatorcontrib><creatorcontrib>Tomlinson, Laurie A.</creatorcontrib><creatorcontrib>Marks, Michael</creatorcontrib><creatorcontrib>Briggs, Andrew</creatorcontrib><creatorcontrib>Lin, Liang-Yu</creatorcontrib><creatorcontrib>Carlile, Oliver</creatorcontrib><creatorcontrib>Bates, Chris</creatorcontrib><creatorcontrib>Parry, John</creatorcontrib><creatorcontrib>Bacon, Sebastian CJ</creatorcontrib><creatorcontrib>Dillingham, Iain</creatorcontrib><creatorcontrib>Dennison, William A.</creatorcontrib><creatorcontrib>Costello, Ruth E.</creatorcontrib><creatorcontrib>Wei, Yinghui</creatorcontrib><creatorcontrib>Goldacre, Ben</creatorcontrib><creatorcontrib>Mehrkar, Amir</creatorcontrib><creatorcontrib>MacKenna, Brian</creatorcontrib><creatorcontrib>Green, Amelia</creatorcontrib><creatorcontrib>Mehrkar, Amir</creatorcontrib><creatorcontrib>Schaffer, Andrea</creatorcontrib><creatorcontrib>Brown, Andrew</creatorcontrib><creatorcontrib>Goldacre, Ben</creatorcontrib><creatorcontrib>Butler-Cole, Ben</creatorcontrib><creatorcontrib>MacKenna, Brian</creatorcontrib><creatorcontrib>Morton, Caroline</creatorcontrib><creatorcontrib>Walters, Caroline</creatorcontrib><creatorcontrib>Stables, Catherine</creatorcontrib><creatorcontrib>Cunningham, Christine</creatorcontrib><creatorcontrib>Wood, Christopher</creatorcontrib><creatorcontrib>Andrews, Colm</creatorcontrib><creatorcontrib>Evans, David</creatorcontrib><creatorcontrib>Hickman, George</creatorcontrib><creatorcontrib>Curtis, Helen</creatorcontrib><creatorcontrib>Drysdale, Henry</creatorcontrib><creatorcontrib>Dillingham, Iain</creatorcontrib><creatorcontrib>Morley, Jessica</creatorcontrib><creatorcontrib>Massey, Jon</creatorcontrib><creatorcontrib>Nab, 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Thomas</creatorcontrib><creatorcontrib>Douglas, Ian</creatorcontrib><creatorcontrib>Eggo, Rosalind</creatorcontrib><creatorcontrib>Evans, Stephen</creatorcontrib><creatorcontrib>Forbes, Harriet</creatorcontrib><creatorcontrib>Grieve, Richard</creatorcontrib><creatorcontrib>Grint, Daniel</creatorcontrib><creatorcontrib>Langan, Sinead</creatorcontrib><creatorcontrib>Mahalingasivam, Viyaasan</creatorcontrib><creatorcontrib>Mansfield, Kathryn</creatorcontrib><creatorcontrib>Mathur, Rohini</creatorcontrib><creatorcontrib>McDonald, Helen</creatorcontrib><creatorcontrib>Parker, Edward</creatorcontrib><creatorcontrib>Rentsch, Christopher</creatorcontrib><creatorcontrib>Schultze, Anna</creatorcontrib><creatorcontrib>Smeeth, Liam</creatorcontrib><creatorcontrib>Tazare, John</creatorcontrib><creatorcontrib>Tomlinson, Laurie</creatorcontrib><creatorcontrib>Walker, Jemma</creatorcontrib><creatorcontrib>Williamson, Elizabeth</creatorcontrib><creatorcontrib>Wing, Kevin</creatorcontrib><creatorcontrib>Wong, Angel</creatorcontrib><creatorcontrib>Zheng, Bang</creatorcontrib><creatorcontrib>Bates, Christopher</creatorcontrib><creatorcontrib>Parry, John</creatorcontrib><creatorcontrib>Hester, Frank</creatorcontrib><creatorcontrib>Harper, Sam</creatorcontrib><creatorcontrib>O'Hanlon, Shaun</creatorcontrib><creatorcontrib>Eavis, Alex</creatorcontrib><creatorcontrib>Jarvis, Richard</creatorcontrib><creatorcontrib>Avramov, Dima</creatorcontrib><creatorcontrib>Griffiths, Paul</creatorcontrib><creatorcontrib>Fowles, Aaron</creatorcontrib><creatorcontrib>Parkes, Nasreen</creatorcontrib><creatorcontrib>Perera, Rafael</creatorcontrib><creatorcontrib>Harrison, David</creatorcontrib><creatorcontrib>Khunti, Kamlesh</creatorcontrib><creatorcontrib>Sterne, Jonathan</creatorcontrib><creatorcontrib>Quint, Jennifer</creatorcontrib><creatorcontrib>Herrett, Emily</creatorcontrib><creatorcontrib>Eggo, Rosalind M.</creatorcontrib><creatorcontrib>The OpenSAFELY Collaborative</creatorcontrib><creatorcontrib>OpenSAFELY Collaborative</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>EClinicalMedicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Henderson, Alasdair D.</au><au>Butler-Cole, Ben FC</au><au>Tazare, John</au><au>Tomlinson, Laurie A.</au><au>Marks, Michael</au><au>Briggs, Andrew</au><au>Lin, Liang-Yu</au><au>Carlile, Oliver</au><au>Bates, Chris</au><au>Parry, John</au><au>Bacon, Sebastian CJ</au><au>Dillingham, Iain</au><au>Dennison, William A.</au><au>Costello, Ruth E.</au><au>Wei, Yinghui</au><au>Goldacre, Ben</au><au>Mehrkar, Amir</au><au>MacKenna, Brian</au><au>Green, Amelia</au><au>Mehrkar, Amir</au><au>Schaffer, Andrea</au><au>Brown, Andrew</au><au>Goldacre, Ben</au><au>Butler-Cole, Ben</au><au>MacKenna, Brian</au><au>Morton, Caroline</au><au>Walters, Caroline</au><au>Stables, Catherine</au><au>Cunningham, Christine</au><au>Wood, Christopher</au><au>Andrews, Colm</au><au>Evans, David</au><au>Hickman, George</au><au>Curtis, Helen</au><au>Drysdale, Henry</au><au>Dillingham, Iain</au><au>Morley, Jessica</au><au>Massey, Jon</au><au>Nab, Linda</au><au>Hopcroft, Lisa</au><au>Fisher, Louis</au><au>Bridges, Lucy</au><au>Wiedemann, Milan</au><au>DeVito, Nicholas</au><au>Macdonald, Orla</au><au>Inglesby, Peter</au><au>Smith, Rebecca</au><au>Park, Robin</au><au>Higgins, Rose</au><au>Bacon, Sebastian</au><au>Davy, Simon</au><au>Maude, Steven</au><au>Ward, Tom</au><au>Speed, Victoria</au><au>Hulme, William</au><au>Hart, Liam</au><au>Stokes, Pete</au><au>Bhaskaran, Krishnan</au><au>Costello, Ruth</au><au>Cowling, Thomas</au><au>Douglas, Ian</au><au>Eggo, Rosalind</au><au>Evans, Stephen</au><au>Forbes, Harriet</au><au>Grieve, Richard</au><au>Grint, Daniel</au><au>Langan, Sinead</au><au>Mahalingasivam, Viyaasan</au><au>Mansfield, Kathryn</au><au>Mathur, Rohini</au><au>McDonald, Helen</au><au>Parker, Edward</au><au>Rentsch, Christopher</au><au>Schultze, Anna</au><au>Smeeth, Liam</au><au>Tazare, John</au><au>Tomlinson, Laurie</au><au>Walker, Jemma</au><au>Williamson, Elizabeth</au><au>Wing, Kevin</au><au>Wong, Angel</au><au>Zheng, Bang</au><au>Bates, Christopher</au><au>Parry, John</au><au>Hester, Frank</au><au>Harper, Sam</au><au>O'Hanlon, Shaun</au><au>Eavis, Alex</au><au>Jarvis, Richard</au><au>Avramov, Dima</au><au>Griffiths, Paul</au><au>Fowles, Aaron</au><au>Parkes, Nasreen</au><au>Perera, Rafael</au><au>Harrison, David</au><au>Khunti, Kamlesh</au><au>Sterne, Jonathan</au><au>Quint, Jennifer</au><au>Herrett, Emily</au><au>Eggo, Rosalind M.</au><aucorp>The OpenSAFELY Collaborative</aucorp><aucorp>OpenSAFELY Collaborative</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis</atitle><jtitle>EClinicalMedicine</jtitle><addtitle>EClinicalMedicine</addtitle><date>2024-06</date><risdate>2024</risdate><volume>72</volume><spage>102638</spage><epage>102638</epage><pages>102638-102638</pages><artnum>102638</artnum><issn>2589-5370</issn><eissn>2589-5370</eissn><abstract>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.
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.
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.
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.
This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38800803</pmid><doi>10.1016/j.eclinm.2024.102638</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-8903-4906</orcidid><orcidid>https://orcid.org/0000-0002-0362-6717</orcidid><orcidid>https://orcid.org/0000-0002-7585-4743</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2589-5370 |
| ispartof | EClinicalMedicine, 2024-06, Vol.72, p.102638-102638, Article 102638 |
| issn | 2589-5370 2589-5370 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3060747207 |
| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Alma/SFX Local Collection |
| subjects | Descriptive cohort Long COVID Vaccination |
| title | Clinical coding of long COVID in primary care 2020–2023 in a cohort of 19 million adults: an OpenSAFELY analysis |
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