Clusters of post-acute COVID-19 symptoms: a latent class analysis across 9 databases and 7 countries

Journal article

Objective: Prior evidence has suggested the multisystem symptomatic manifestations of post-acute COVID-19 condition (PCC). Here we conducted a network cluster analysis of 24 World Health Organization–proposed symptoms to identify potential latent subclasses of PCC.

Study Design and Setting: Individuals with a positive test of or diagnosed with SARS-CoV-2 after September 2020 and with at least 1 symptom within ≥90 to 365 days following infection were included. Subanalyses were conducted among people with ≥3 different symptoms. Summary characteristics were provided for each cluster. All analyses were conducted separately in 9 databases from 7 countries, including data from primary care, hospitals, national health claims and national health registries, allowing to compare clusters across the different healthcare settings.

Results: This study included 787,078 persons with PCC. Single-symptom clusters were common across all databases, particularly for joint pain, anxiety, depression and allergy. Complex clusters included anxiety-depression and abdominal-gastrointestinal symptoms.

Conclusion: Substantial heterogeneity within and between PCC clusters was seen across health-care settings. Current definitions of PCC should be critically reviewed to reflect this variety in clinical presentation.

Authors: López-Güell, K; Català, M; Dedman, D; Duarte-Salles, T; Kolde, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Journal of Clinical Epidemiology
• Volume: 185
• Article number: 111867
• Publication date: 2025-06-13
• Acceptance date: 2025-06-10
• DOI: 10.1016/j.jclinepi.2025.111867
• EISSN: 1878-5921
• ISSN: 0895-4356
• Language: English
• Keywords: post-acute COVID-19 condition; Long COVID; real-world data; clustering; latent class analysis