COVID-19 and comorbidities: a systematic review and meta-analysis
Bibliographic record
Abstract
SARS-CoV-2 has caused a worldwide pandemic that began with an outbreak of pneumonia cases in the Hubei province of China. Knowledge of those most at risk is integral for treatment, guideline implementation, and resource allocation. We conducted a systematic review and meta-analysis to evaluate comorbidities associated with severe and fatal cases of COVID-19. A search was conducted on PubMed and EmBase on 20 April 2020. Pooled estimates were collected using a random-effects model. Thirty-three studies were included in the systematic review and twenty-two in the meta-analysis. Of the total cases 40.80% (95%CI: 35.49%, 46.11%) had comorbidities, while fatal cases had 74.37% (95%CI: 55.78%, 86.97%). Hypertension was more prevalent in severe [47.65% (95%CI: 35.04%, 60.26%)] and fatal [47.90% (95%CI: 40.33%, 55.48%)] cases compared to total cases [14.34% (95%CI: 6.60%, 28.42%)]. Diabetes was more prevalent among fatal cases [24.89% (95%CI: 18.80%, 32.16%)] compared to total cases [9.65% (95%CI: 6.83%, 13.48%)]. Respiratory diseases had a higher prevalence in fatal cases [10.89% (95%CI: 7.57%, 15.43%)] in comparison to total cases [3.65% (95%CI: 2.16%, 6.1%)]. Studies assessing the mechanisms accounting for the associations between severe cases and hypertension, diabetes, and respiratory diseases are crucial in understanding this new disease, managing patients at risk, and developing policies and guidelines that will reduce future risk of severe COVID-19 disease.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.037 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".