Comparative profile for COVID-19 cases from China and North America: Clinical symptoms, comorbidities and disease biomarkers
Bibliographic record
Abstract
BACKGROUND: Large inter-individual and inter-population differences in the susceptibility to and outcome of severe acute respiratory syndrome coronavirus 2 or coronavirus disease 2019 (COVID-19) have been noted. Understanding these differences and how they influence vulnerability to infection and disease severity is critical to public health intervention. AIM: To analyze and compare the profile of COVID-19 cases between China and North America as two regions that differ in many environmental, host and healthcare factors related to disease risk. METHODS: We conducted a meta-analysis to examine and compare demographic information, clinical symptoms, comorbidities, disease severity and levels of disease biomarkers of COVID-19 cases from clinical studies and data from China (105 studies) and North America (19 studies). RESULTS: COVID-19 patients from North America were older than their Chinese counterparts and with higher male: Female ratio. Fever, cough, fatigue and dyspnea were the most common clinical symptoms in both study regions (present in about 30% to 75% of the cases in both regions). Meta-analysis for the prevalence of comorbidities (such as obesity, hypertension, diabetes, cardiovascular diseases, chronic obstructive pulmonary disease, cancer, and chronic kidney diseases) in COVID-19 patients were all significantly more prevalent in North America compared to China. Comorbidities were positively correlated with age but at a significantly younger age range in China compared to North American. The most prevalent infection outcome was acute respiratory distress syndrome which was 2-fold more frequent in North America than in China. Levels of C-reactive protein were 4.5-fold higher in the North American cases than in cases from China. CONCLUSION: The differences in the profile of COVID-19 cases from China and North America may relate to differences in environmental-, host- and healthcare-related factors between the two regions. Such inter-population differences-together with intra-population variability-underline the need to characterize the effect of health inequities and inequalities on public health response to COVID-19 and can assist in preparing for the re-emergence of the epidemic.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".