Health-related quality of life in COVID-19 patients: a systematic review and meta-analysis of EQ-5D studies
Bibliographic record
Abstract
Abstract Background COVID-19 has affected millions globally, with a significant proportion experiencing long-COVID and impaired health-related quality of life (HRQoL). This systematic review and meta-analysis aimed to synthesize the existing literature on HRQoL in COVID-19 patients. Methods We conducted a systematic search of PubMed, Embase, Web of Science, Scopus, and the Cochrane Library for studies published between December 2019 and March 2025. Eligible studies were peer-reviewed and assessed HRQoL in COVID-19 patients using the EQ-5D instrument. Study quality and risk of bias were evaluated using the Newcastle-Ottawa Scale. Pooled health utility values were estimated using a random-effects model, and heterogeneity was assessed via I2 statistics. Predictors of poor HRQoL were qualitatively narrated. Results Out of 3539 references, 187 studies with 116,525 participants were analyzed. The majority (80.2%) used the EQ-5D-5 L version. The pooled mean EQ-5D utility score was 0.76 (95% CI 0.74–0.79, I2 = 99.9%) while the mean EQ-5D Visual Analogue Scale (VAS) score was 70.76 (95% CI 68.48–73.04; I2 = 99.7%). Pain/discomfort and anxiety/depression were the most affected domains, reported by 51% and 46% of patients, respectively. Subgroup analysis showed significant differences in HRQoL based on national income status (p = 0.038) and geographic region (p < 0.001). Common predictors of lower HRQoL included older age, female gender, disease severity, comorbidities, and post-COVID-19 symptoms. Conclusion This systematic review demonstrates a substantial reduction in HRQoL among COVID-19 patients compared to the general population. The pooled utility values of COVID-19 contribute to understanding patients’ HRQoL and can assist in calculating Quality-Adjusted Life Years. This provides essential data for future economic evaluations and informs health policy decisions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.090 | 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".