Overweight and obesity as risk factors for COVID-19-associated hospitalisations and death: systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To quantify the current weight of evidence of the association between overweight and obesity as risk factors for COVID-19-related hospitalisations (including hospital admission, intensive care unit admission, invasive mechanical ventilation) and death, and to assess the magnitude of the association and the potential dose-response relationships. Design: PubMed, Embase, Cochrane, Web of Sciences, WHO COVID-19 database and Google Scholar were used to identify articles published up to 20 July 2021. Peer-reviewed studies reporting adjusted estimates of the association between overweight or obesity and COVID-19 outcomes were included. Three authors reviewed the articles and agreed. The quality of eligible studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. Random-effects meta-analysis was used to estimate the combined effects. Results: A total of 208 studies with 3 550 997 participants from over 32 countries were included in this meta-analysis. Being overweight was associated with an increased risk of COVID-19-related hospitalisations (OR 1.19, 95% CI 1.12 to 1.28, n=21 studies), but not death (OR 1.02, 95% CI 0.92 to 1.13, n=21). However, patients with obesity were at increased risk of both COVID-19-related hospitalisations (OR 1.72, 95% CI 1.62 to 1.84, n=58) and death (OR 1.25, 95% CI 1.19 to 1.32, n=77). Similarly, patients with extreme obesity were at increased risk of COVID-19-related hospitalisations (OR 2.53, 95% CI 1.67 to 3.84, n=12) and death (OR 2.06, 95% CI 1.76 to 3.00, n=19). There was a linear dose-response relationship between these obesity categories and COVID-19 outcomes, but the strength of the association has decreased over time. Conclusion: Being overweight increases the risk of COVID-19-related hospitalisations but not death, while obesity and extreme obesity increase the risk of both COVID-19-related hospitalisations and death. These findings suggest that prompt access to COVID-19 care, prioritisation for COVID-19 vaccination and other preventive measures are warranted for this vulnerable group.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".