Epidemiological Pattern and Case Fatality Rate among COVID-19 Patients during First and Second Wave of Pandemic in Madhesh Province, Nepal
Notice bibliographique
Résumé
Background & Objectives: Evaluating multi-wave patterns of COVID-19 infection, hospitalization, and mortality across spatial scales can inform public health strategies for future pandemics. However, there is limited understanding of these patterns in developing countries, including Nepal. This study aimed to analyze epidemiological patterns, fatality rates, and factors associated with severe outcomes during the first and second waves of COVID-19 in Madhesh Province, Nepal. Materials and Methods: This retrospective cross-sectional study used provincial health records from Madhesh Province, covering April 9, 2020, to December 15, 2021, to analyze 37,551 positive COVID-19 cases and 1,037 deaths across two waves. The study examined changes in COVID-19-related deaths, with data on demographics, residence, isolation sites, treatment hospitals, care levels, and testing laboratories. The frequency and percentage of the variables were presented. The case fatality rate (CFR) for different categories were calculated. Additionally, the case fatality rate ratio (CFRR) for the first wave against second wave was obtained. Finally, case fatality risk ratios with 95% confidence intervals were presented. A p-value of <0.05 was set as statistically significant. Results: The case fatality rate (CFR) for COVID-19 was significantly higher in the second wave, especially among the elderly (≥47 years), and in institutional isolation (7.82%). Tertiary level care and private hospitals consistently showed higher CFRs. Furthermore, the multivariable analysis of risk ratios (RR) for COVID-19 case fatality in revealed that the 25–34 year age group had the highest RR (2.39). Similarly, males had a higher RR (1.42) than females, and institutional isolation had a substantially higher RR (93.33) compared to home isolation. Primary level care RR (28.11) and government hospitals RR (4.32) showed a higher risk. Place of residence also impacted the RR, with Sarlahi (7.68) having higher. Conclusion: A significant increase in COVID-19 case fatality rates during the second wave, particularly among the elderly and those in institutional isolation. Higher mortality was observed in tertiary hospitals and among those who were tested in private laboratories, with substantial variations in risk ratios based on age, isolation type, and place of residence.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,020 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».