People in different age groups show different symptoms of early COVID-19 infection, new research has found, with distinctions between men and women also reported.
The team led by researchers from King’s College London looked at data from April to October last year and found that the most notable differences were between the younger age groups – 16 to 59 years – compared to older age groups – 60 to 80 years and above.
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They examined 18 symptoms, which had different relevance for early detection in different groups. Overall, the most significant symptoms for early detection included loss of smell, chest pain, persistent cough, abdominal pain, blisters on the feet, eye soreness and unusual muscle pain.
However, for people aged 60 and over, the significance of loss of smell reduced, and was not relevant for those over the age of 80. In these older age groups, there were other key early symptoms, including diarrhoea, while one known symptom of disease, fever, was not an early feature in any age group.
The differences in early symptoms between men and women was notable – it was found that women were more likely to report loss of smell, chest pain and a persistent cough, while men were more likely to report shortness of breath, fatigue, chills and shivers.
Lead author, Dr Claire Steves, Reader at King’s College London, said: “It’s important people know the earliest symptoms are wide-ranging and may look different for each member of a family or household. Testing guidance could be updated to enable cases to be picked up earlier, especially in the face of new variants which are highly transmissible. This could include using widely available lateral flow tests for people with any of these non-core symptoms.”
Researchers analysed data from the ZOE COVID Symptom Study app. Those contributing to the app are encouraged to get tested as soon as they report any new symptoms, as part of a joint initiative with the Department of Health and Social Care.
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After modelling the early symptoms of COVID-19, researchers were successful in detecting 80% of cases when using three days of symptoms self-reported by users.
Dr Liane dos Santos Canas, first author from King’s College London, said: “Currently, in the UK, only a few symptoms are used to recommend self-isolation and further testing. Using a larger number of symptoms and only after a few days of being unwell, using AI, we can better detect COVID-19 positive cases. We hope such a method is used to encourage more people to get tested as early as possible to minimise the risk of spread.”
Dr Marc Modat, Senior Lecturer at King’s College London, added: “As part of our study, we have been able to identify that the profile of symptoms due to COVID-19 differs from one group to another. This suggests that the criteria to encourage people to get tested should be personalised using individuals’ information such as age. Alternatively, a larger set of symptoms could be considered, so the different manifestations of the disease across different groups are taken into account.”
The paper has been published in The Lancet Digital Health.