%0 Journal Article
%T Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England.
%A Wang HI
%A Doran T
%A Crooks MG
%A Khunti K
%A Heightman M
%A Gonzalez-Izquierdo A
%A Qummer Ul Arfeen M
%A Loveless A
%A Banerjee A
%A Van Der Feltz-Cornelis C
%J J Infect
%V 89
%N 4
%D 2024 Aug 7
%M 39121972
%F 38.637
%R 10.1016/j.jinf.2024.106235
%X <B>OBJECTIVE: </B>This study examines clinically confirmed long-COVID symptoms and diagnosis among individuals with COVID in England, aiming to understand prevalence and associated risk factors using electronic health records. To further understand long COVID, the study also explored differences in risks and symptom profiles in three subgroups: hospitalised, non-hospitalised, and untreated COVID cases.<BR><B>METHODS: </B>A population-based longitudinal cohort study was conducted using data from 1,554,040 individuals with confirmed SARS-CoV-2 infection via Clinical Practice Research Datalink. Descriptive statistics explored the prevalence of long COVID symptoms 12 weeks post-infection, and Cox regression models analysed the associated risk factors. Sensitivity analysis was conducted to test the impact of right-censoring data.<BR><B>RESULTS: </B>During an average 400-day follow-up, 7.4% of individuals with COVID had at least one long-COVID symptom after acute phase, yet only 0.5% had long-COVID diagnostic codes. The most common long-COVID symptoms included cough (17.7%), back pain (15.2%), stomach-ache (11.2%), headache (11.1%), and sore throat (10.0%). The same trend was observed in all three subgroups. Risk factors associated with long-COVID symptoms were female sex, non-white ethnicity, obesity, and pre-existing medical conditions like anxiety, depression, type II diabetes, and somatic symptom disorders.<BR><B>CONCLUSIONS: </B>This study is the first to investigate the prevalence and risk factors of clinically confirmed long-COVID in the general population. The findings could help clinicians identify higher risk individuals for timely intervention and allow decision-makers to more efficiently allocate resources for managing long-COVID.