Risk factors and symptom clusters for Long Covid: analysis of United Kingdom symptom tracker app data

Abstract

AbstractBackgroundLong Covid, characterised by symptoms after Covid-19 infection which persist for longer than 12 weeks, is becoming an important societal and economic problem. As Long Covid was novel in 2020, there has been debate regarding its aetiology and whether it is one, or multiple, syndromes. This study assessed risk factors associated with Long Covid and examined symptom clusters that might indicate sub-types.Methods4,040 participants reporting for >4 months in the Covid Symptom Study App were included. Multivariate logistic regression was undertaken to identify risk factors associated with Long Covid. Cluster analysis (K-modes and hierarchical agglomerative clustering) and factor analysis were undertaken to investigate symptom clusters.ResultsLong Covid affected 13.6% of participants. Significant risk factors included being female (P< 0.01), pre-existing poor health (P< 0.01), and worse symptoms in the initial illness. A model incorporating sociodemographics, comorbidities, and health status predicted Long Covid with an accuracy (AUROC) of 76%. The three clustering approaches gave rise to different sets of clusters with no consistent pattern across methods.ConclusionsOur model of risk factors may help clinicians predict patients at higher risk of Long Covid, so these patients can rest more, receive treatments, or enter clinical trials; reducing the burden of this long-term and debilitating condition. No consistent subtypes were identified.