Prevalence of Comorbidities in COVID-19 Patients: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
BACKGROUND: In this study, we aimed to assess the prevalence of comorbidities in the confirmed COVID-19 patients. This might help showing which comorbidity might pose the patients at risk of more severe symptoms. METHODS: ). We reviewed 33 papers' full text out of 1053 papers. There were 32 papers from China and 1 from Taiwan. There was no language or study level limit. Prevalence of comorbidities including hypertension, diabetes mellitus, cardiovascular disease, chronic lung disease, chronic kidney disease, malignancies, cerebrovascular diseases, chronic liver disease and smoking were extracted to measure the pooled estimates. We used OpenMeta and used random-effect model to do a single arm meta-analysis. RESULTS: The mean age of the diagnosed patients was 51 years. The male to female ratio was 55 to 45. The most prevalent finding in the confirmed COVID-19 patients was hypertension, which was found in 1/5 of the patients (21%). Other most prevalent finding was diabetes mellitus (DM) in 11%, cerebrovascular disease in 2.4%, cardiovascular disease in 5.8%, chronic kidney disease in 3.6%, chronic liver disease in 2.9%, chronic pulmonary disease in 2.0%, malignancy in 2.7%, and smoking in 8.7% of the patients. CONCLUSION: COVID-19 infection seems to be affecting every race, sex, age, irrespective of health status. The risk of symptomatic and severe disease might be higher due to the higher age which is usually accompanied with comorbidities. However, comorbidities do not seem to be the prerequisite for symptomatic and severe COVID-19 infection, except hypertension.
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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 | 0.003 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".