Unveiling long COVID symptomatology, co-occurrence trends, and symptom distress post SARS-CoV-2 infection

Abstract

Background

Long COVID, an emerging public health issue, is characterized by persistent symptoms following SARS-CoV-2 infection. This study aims to explore the relationship between post-COVID-19 symptomatology and patient distress employing Latent Class Analysis to uncover symptom co-occurrence patterns and their association with distress.

Methods

A cross-sectional study was conducted using an online survey among 240 participants from a university and affiliated hospital of southern Taiwan. The survey quantified distress due to persistent symptoms and assessed the prevalence of Long COVID, symptom co-occurrence, and latent symptom classes. Latent Class Analysis (LCA) identified distinct symptom patterns, and multiple regression models evaluated associations between symptom patterns, distress, and demographic factors.

Results

The study found that 80 % of participants experienced Long COVID, with symptoms persisting for over three months. Individuals with multiple COVID-19 infections showed a significant increase in general (β = 1.79), cardiovascular (β = 0.61), and neuropsychological symptoms (β = 2.18), and higher total distress scores (β = 6.35). Three distinct symptomatology classes were identified: "Diverse", "Mild", and "Severe" symptomatology. The "Mild Symptomatology" class was associated with lower distress (−10.61), while the "Severe Symptomatology" class showed a significantly higher distress due to symptoms (13.32).

Conclusion

The study highlights the significant impact of Long COVID on individuals, with distinct patterns of symptomatology and associated distress. It emphasizes the cumulative effect of multiple COVID-19 infections on symptom severity and the importance of tailored care strategies.