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

Abstract

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.

Keywords: Clustering; Latent class analysis; Long COVID; Post-acute COVID-19 condition; Real-world data.