COVID-19 in Great Britain: epidemiological and clinical characteristics of the first few hundred (FF100) cases: a descriptive case series and case control analysis
Nicola L Boddington,
Jemma L Walker,
Helen I McDonald,
Tatiana Garcia Vilaplana,
Daniel J Grint,
Kathryn E Mansfield,
Christopher T Rentsch,
Ian J Douglas,
Mary E Ramsay,
Jamie Lopez Bernal
Posted 22 May 2020
medRxiv DOI: 10.1101/2020.05.18.20086157
Posted 22 May 2020
Objectives: Following detection of the first virologically-confirmed cases of COVID-19 in Great Britain, an enhanced surveillance study was initiated by Public Health England to describe the clinical presentation, course of disease and identify risk factors for infection of the first few hundred cases. Methods: Information was collected on the first COVID-19 cases according to the First Few X WHO protocol. Case-control analyses of the sensitivity, specificity and predictive value of symptoms and risk factors for infection were conducted. Point prevalences of underlying health conditions among the UK general population were presented. Findings: The majority of FF100 cases were imported (51.4%), of which the majority had recent travel to Italy (71.4%). 24.7% were secondary cases acquired mainly through household contact (40.4%). Children had lower odds of COVID-19 infection compared with the general population. The clinical presentation of cases was dominated by cough, fever and fatigue. Non-linear relationships with age were observed for fever, and sensitivity and specificity of symptoms varied by age. Conditions associated with higher odds of COVID-19 infection (after adjusting for age and sex) were chronic heart disease, immunosuppression and multimorbidity. Conclusion: This study presents the first epidemiological and clinical summary of COVID-19 cases in Great Britain. The FFX study design enabled systematic data collection. The study was able to characterize the risk factors for infection with population prevalence estimates setting these relative risks into a public health context. It also provides important evidence for generating case definitions to support public health risk assessment, clinical triage and diagnostic algorithms.
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