Classifying and Differentiating Individuals with Respiratory Syncytial Virus, Influenza, and COVID-19 Cases in OpenSAFELY Between 2016 and 2024
Prestige, E.; Warren-Gash, C.; Quint, J. K.; Evans, D.; Costello, R. E.; Mehrkar, A.; Bacon, S.; Goldacre, B.; Barley-McMullen, S.; Yameen, F.; Shah, P.; Natt, M.; Alder, Y.; Hulme, W. J.; Parker, E. P. K.; Eggo, R. M.
Show abstract
Electronic health records (EHRs) are a rich source of data which can be used to analyse health outcomes using computable phenotypes. With the approval of NHS England we used the OpenSAFELY secure analytics platform to design and assess phenotypes to classify three key respiratory viruses - respiratory syncytial virus (RSV), influenza, and COVID-19 - in English coded health data between September 2016 and August 2024. We compared specific and sensitive phenotypes to one another and to publicly available surveillance data. Cases from both phenotypes showed similar seasonal patterns to surveillance data. Sensitive phenotypes led to increased risk of misclassification than specific phenotypes for mild cases. For severe cases the risk of misclassification was higher in infants than for older adults, irrespective of the phenotype used. The phenotypes presented here offer a solution to classifying respiratory viruses from coded health records in the absence of testing information.
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