Understanding the Clinical Characteristics and Timeliness of Diagnosis for Patients Diagnosed With Long Covid: A Retrospective Observational Cohort Study From North West London Article Swipe
Denys Prociuk
,
Jonathan Clarke
,
Darren Smith
,
Ruairidh Milne
,
Cassie Lee
,
Simon de Lusignan
,
Ghazala Mir
,
Johannes H. De Kock
,
Erik Mayer
,
Brendan Delaney
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/hex.70429
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/hex.70429
Background Long Covid is a multisystem condition first identified in the Covid‐19 pandemic, characterised by a wide range of symptoms including fatigue, breathlessness and cognitive impairment. Considerable disagreement exists in who is most at risk of developing long Covid, driven in part by incomplete coding of a long Covid diagnosis in medical records. Objective To describe the incidence and impact of long Covid. Design A retrospective observational cohort study. Setting and Participants An integrated primary and secondary care dataset from North West London, covering over 2.7 million patients. Patients with long Covid were identified through clinical terms in their primary care records. Main Variables Studied Multivariate logistic regression was used to identify factors associated with having a long Covid diagnosis, while multivariate quantile regression was used to identify factors predicting the time a long Covid diagnosis was recorded. Results A total of 6078 patients were identified with a long Covid clinical term in their primary care record, 0.33% of the total registered adult population. Women, those aged 41–70 years or of Asian or mixed ethnicity, were more likely to have a recorded long Covid diagnosis, alongside those with pre‐existing anxiety, asthma, depressive disorder or eczema and those living outside of the least or most socio‐economically deprived areas. Men, those aged 41–70 years, or of black ethnicity, were diagnosed earlier in the pandemic, while those with depressive disorder were diagnosed later. Discussion Long Covid is poorly coded in primary care records, and significant differences exist between patient groups in the likelihood of receiving a long Covid diagnosis. A recorded long Covid diagnosis is more likely in women, some ethnic minority patients and those with pre‐existing long‐term conditions. Conclusion The experience of patients with long Covid provides a crucial insight into inequities in access to timely care for complex multisystem conditions and the importance of effective health informatics practices to provide robust, timely analytical support for front line clinical services. Patient and Public Contribution This study was co‐designed, conducted and written in conjunction with people with long Covid.
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Metadata
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- Language
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- Landing Page
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- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/hex.70429
- OA Status
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- References
- 21
- OpenAlex ID
- https://openalex.org/W4414508322
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4414508322Canonical identifier for this work in OpenAlex
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https://doi.org/10.1111/hex.70429Digital Object Identifier
- Title
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Understanding the Clinical Characteristics and Timeliness of Diagnosis for Patients Diagnosed With Long Covid: A Retrospective Observational Cohort Study From North West LondonWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-25Full publication date if available
- Authors
-
Denys Prociuk, Jonathan Clarke, Darren Smith, Ruairidh Milne, Cassie Lee, Simon de Lusignan, Ghazala Mir, Johannes H. De Kock, Erik Mayer, Brendan DelaneyList of authors in order
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https://doi.org/10.1111/hex.70429Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/hex.70429Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/hex.70429Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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21Number of works referenced by this work
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| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |