Understanding the clinical characteristics and timeliness of diagnosis for patients diagnosed with Long COVID: A retrospective observational cohort study from North West London
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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.
Methods
This was a retrospective observational cohort study using 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 record. Multivariate logistic regression was used to identify factors associated with having Long COVID diagnosis, while multivariate quantile regression was used to identify factors predicting the time a Long COVID diagnosis was recorded.
Findings
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 to 70 years or of Asian 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 most socioeconomically deprived areas. Men, those aged 41 to 70 years, or of black ethnicity were diagnosed earlier in the pandemic, while those with depressive disorder were diagnosed later.
Interpretation
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. Long COVID is more likely in those with pre-existing long-term conditions and is also associated with the frequent incidence of new long-term conditions. 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.
Funding
National Institute for Health and Care Research (NIHR) Ref: COV-LT2-0016
