Are Patients with Coronavirus Disease 2019 and Obesity at a Higher Risk of Hospital and Intensive Care Unit Admissions? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obesity, common condition among patients with COVID-19, contributes to illness severity during hospitalization. To date, knowledge on the prevalence, risk of hospital and intensive care units (ICU) admissions and mortality is limited. Therefore, systematic review and meta-analysis were conducted using a PRISMA guideline. PURPOSE: The study aimed to address the prevalence, risk of hospital and ICU admissions and mortality among patients with COVID-19 and obesity. METHODS: The Newcastle–Ottawa scale was used to assess the quality of a study. Primary outcomes were the prevalence and risk of hospitalization, and secondary outcomes were the risk of ICU admissions and mortality risk. Mantel–Haenszel with random effects was applied, and the effect measure was odds ratio (OR) with 95% confidence interval (CI). RESULTS: Nine studies were included in the systematic review, and only four studies for meta-analysis. Among 29,776 patients with COVID-19, obesity was identified as the second-highest comorbidity. The prevalence rates of obesity and severe obesity among patients with COVID-19 were 26.1% and 15.5%, respectively. Obesity resulted in significantly increased risk of hospital admission (OR = 1.99, 95% CI = 1.12–3.53, p = 0.02) and ICU admission (OR = 1.77, 95% = CI 1.52–2.06, p < 0.00001). Severe obesity had a significantly increased risk of ICU admission (OR = 1.79, 95% CI = 1.42–2.25, p < 0.00001). The mortality rate of patients with COVID-19 and obesity was about 30.5% (438/1,434), and 19.7% (2,777/14,095) of them recovered from COVID-19. CONCLUSION: Obesity poses as nearly twice the risk of hospital and ICU admissions, and severe obesity contributes to almost twice the risk of ICU admissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.008 | 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".