Long COVID Prevalence and Risk Factors: A Systematic Review and Meta-Analysis of Prospective Cohort Studies
Bibliographic record
Abstract
Background: Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), affects millions globally, with persistent symptoms impacting quality of life. This meta-analysis synthesizes prospective cohort studies to estimate the prevalence of Long COVID symptoms and identify risk factors. Methods: We systematically searched PubMed for prospective cohort studies (2020–2025) on Long COVID, focusing on prevalence and risk factors. Studies with ≥100 participants and follow-up ≥3 months were included. Data were extracted on symptom prevalence (e.g., fatigue, dyspnoea) and risk factors (e.g., sex, hospitalization). Random-effects models were used to pool prevalence and odds ratios (OR). Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS). Results: Fourteen prospective studies (n = 168,679) were included. Pooled prevalence of Long COVID was 18.0% (95% CI: 12.5–23.5%, I2 = 9.8%) among survivors followed for ≥6 months. Fatigue (41.0%, 95% CI: 33.2–49.4%) and dyspnoea (22.5%, 95% CI: 15.6–29.8%) were the most common symptoms. Female sex (OR = 1.52, 95% CI: 1.25–1.92) and prior hospitalization (OR = 2.35, 95% CI: 1.98–2.90) were significant risk factors. High heterogeneity (I2 > 90%) was noted. Conclusions: Long COVID affects over one-fifth of SARS-CoV-2 survivors, with fatigue and dyspnoea persisting in many. Female sex and severe acute infection increase risk. Standardized definitions and longer follow-up are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".