Objectives: This study aimed to explore the 1-year temporal change in prevalence, variety, and potential risk factors of long COVID symptoms and to further predict the prognostic trends of long COVID.
Methods: We searched electronic databases for related studies published from January 2020 to February 2022 and conducted 1-group meta-analysis and locally weighted regression to explore the monthly temporal change in the prevalence of each long COVID symptom in 1-year follow-up period.
Results: A total of 137 studies were included in meta-analysis, including 134 093 participants. The temporal change of any long COVID symptom showed a steep decrease initially (from 92% at acute phase to 55% at 1-month follow-up), followed by stabilization at approximately 50% during 1-year follow-up. Six months or more after the acute phase, the odds ratio of population characteristic-related factors increased, such as female (from 1.62 to 1.82), whereas the odds ratio value of acute phase-related factors (severe or critical cases and hospitalization) decreased. As for specific symptoms, approximately two-thirds of the symptoms did not significantly reduce during the 1-year follow-up, and the neuropsychiatric symptoms showed a higher long-term prevalence (approximately 25%) and longer persistence than physical symptoms.
Conclusions: The temporal changes in the prevalence and characteristics speculate that long COVID may persist longer than expected. In particular, we should pay more attention to neuropsychiatric symptoms and other symptoms for which there is no significant downward trend in prevalence. The influence of acute phase-related factors for long COVID gradually decreases over time, whereas the influence of population characteristic-related factors gradually increases.
Keywords: COVID-19; long COVID symptoms; meta-analysis; systematic review; temporal change.
Copyright © 2023. Published by Elsevier Inc.