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 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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".