Long COVID is not a uniform syndrome: Evidence from person-level symptom clusters using latent class analysis

Background: The current study aims to enhance insight into the heterogeneity of long COVID by identifying symptom clusters and associated socio-demographic and health determinants.
Methods: A total of 458 participants (Mage 36.0 +/- 11.9; 46.5% male) with persistent symptoms after COVID- 19 completed an online self-report questionnaire including a 114-item symptom list. First, a k-means clustering analysis was performed to investigate overall clustering patterns and identify symptoms that provided meaningful distinctions between clusters. Next, a step-three latent class analysis (LCA) was per- formed based on these distinctive symptoms to analyze person-centered clusters. Finally, multinominal logistic models were used to identify determinants associated with the symptom clusters.
Results: From a 5-cluster solution obtained from k-means clustering, 30 distinctive symptoms were se- lected. Using LCA, six symptom classes were identified: moderate (20.7%) and high (20.7%) inflammatory symptoms, moderate malaise-neurocognitive symptoms (18.3%), high malaise-neurocognitive-psychosocial symptoms (17.0%), low-overall symptoms (13.3%) and high overall symptoms (9.8%). Sex, age, employment, COVID-19 suspicion, COVID-19 severity, number of acute COVID-19 symptoms, long COVID symptom duration, long COVID diagnosis, and impact of long COVID were associated with the different symptom clusters.
Conclusions: The current study's findings characterize the heterogeneity in long COVID symptoms and underscore the importance of identifying determinants of different symptom clusters. (c) 2023 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/
4.0/).