Symptoms and signs of long COVID: A rapid review and meta-analysis

Quin Healey*, Aziz Sheikh, Luke Daines, Eleftheria Vasileiou

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract / Description of output

Background: Long COVID is defined as symptoms and signs related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are present at least four weeks following acute infection. These symptoms and signs are poorly characterised but may be associated with significant morbidity. We sought to synthesise the evidence on their incidence to guide future research, policy and practice.

Methods: We searched Medline and Embase for longitudinal cohort studies from January 2020 to July 2021 that investigated adults with long COVID at least four weeks after acute infection. Risk of bias was assessed using the Joanna Briggs Institute checklist for cohort studies. Random-effects meta-analyses were performed with subgroup analysis by follow-up time (4-12 vs more than 12 weeks).

Results: 19 studies were included, 13 of which included patients hospitalised with COVID-19. The total sample size was 10 643 and the follow-up time ranged from 30 to 340 days. Risk of bias was assessed as high in one study, moderate in two studies and low in the remaining 16 studies. The most common symptoms and signs seen at any time point in long COVID were fatigue (37%; 95% confidence interval (CI) = 23-55), dyspnoea (21%; 95% CI = 14-30), olfactory dysfunction (17%; 95% CI = 9-29), myalgia (12%; 95% CI = 5-25), cough (11%; 95% CI = 6-20) and gustatory dysfunction (10%; 95% CI = 7-17). High heterogeneity was seen for all meta-analyses and the presence of some funnel plot asymmetry may indicate reporting bias. No effect of follow-up time was found for any symptom or sign included in the subgroup analysis.

Conclusions: We have summarised evidence from longitudinal cohort studies on the most common symptoms and signs associated with long COVID. High heterogeneity seen in the meta-analysis means pooled incidence estimates should be interpreted with caution. This heterogeneity may be attributable to studies including patients from different health care settings and countries.

Original languageEnglish
Pages (from-to)05014
JournalJournal of Global Health
Publication statusPublished - 21 May 2022

Keywords / Materials (for Non-textual outputs)

  • Adult
  • COVID-19/complications
  • Cohort Studies
  • Humans
  • Longitudinal Studies
  • SARS-CoV-2


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