Prevalence of Obesity and its Effects in Patients With COVID-19: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) is an emerging infectious disease worldwide. Obesity has been proven to increase the susceptibility of an individual to infections, but the relationship between obesity and COVID-19 is still unclear. This study aimed to conduct a systematic review and meta-analysis of the prevalence of obesity and its effects in patients with COVID-19. Methods: Web of Science, PubMed and Embase were searched for English language studies up to May 22, 2020. We used a random or fixed-effects model to calculate pooled prevalence rates and odds ratio (OR) with 95% confidence intervals (CI). Results: Twelve studies with a total of 14 364 patients met the inclusion criteria. The pooled prevalence of obesity in patients with COVID-19 was 32.0% (95% CI, 26%-38%, P < .001). The prevalence of obesity in ICU COVID-19 patients were 37.0% (95% CI, 29%-46%, P < .001). Comparing between obese and non-obese patients, the meta-analysis showed that obesity was an important risk factor associated with COVID-19 patients needed for ICU care (OR: 1.36, 95% CI 1.22-1.52, P < .001). Conclusion: Obesity was highly prevalent (32.0%) in patients with COVID-19, especially in ICU patients (37.0%), and was an important risk factor for COVID-19 patients needed for ICU care.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".