The impact of COVID-19 on health-related quality of life: a systematic review and evidence-based recommendations
Bibliographic record
Abstract
Abstract Objective This systematic review examines the impact of COVID-19 on Health-Related Quality of Life (HRQoL) across different populations, focusing on demographic, socio-economic, and COVID-19-related factors. Methods A comprehensive search of PubMed from 2020 to 2022 was conducted, identifying 37 studies that met the inclusion criteria. Studies were assessed using the Appraisal Tool for Cross-Sectional Studies, Newcastle–Ottawa Scale, and Consolidated Health Economic Evaluation Reporting Standards tools. Data extraction included study characteristics, HRQoL measures, and health state utility values. Results Thirty-seven studies were conducted with a total of 46,709 individuals and 274 HSUVs ranging from 0.224 to 1. Research included Europe (n = 20), North America (n = 4), Asia (n = 11), South America (n = 1), and Africa (n = 1). Utility was measured using 15D (n = 3), EQ-5D-5L (n = 24), EQ-5D-3L (n = 8), VAS (n = 1), and TTO (n = 1). The review found significant decreases in HRQoL among COVID-19 survivors, particularly those with severe symptoms, due to persistent fatigue, breathlessness, and psychological distress. Quarantine and isolation measures also negatively impacted HRQoL, with increased anxiety and depression. Vaccination status influenced HRQoL, with vaccinated individuals reporting better outcomes. Socio-demographic factors such as age, gender, education, employment, marital status, and income significantly affected HRQoL, with older adults, females, and unemployed individuals experiencing lower HRQoL. Conclusions COVID-19 has profoundly affected HRQoL, highlighting the need for comprehensive post-recovery rehabilitation programs and targeted public health interventions. Addressing socio-demographic disparities is crucial to mitigate the pandemic’s impact on HRQoL. Policymakers and healthcare providers should implement strategies to support affected populations, emphasizing mental health support, social support systems, and vaccination programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".