Comparative profile for COVID-19 cases from China and North America: Clinical symptoms, comorbidities and disease biomarkers
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Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,304 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle