The association between non-communicable diseases and COVID-19 severity and mortality among infected hospitalized healthcare workers in 29 countries: a cohort study
Notice bibliographique
Résumé
Background: Due to occupational exposure, healthcare workers (HCWs) have a higher risk of Coronavirus Disease 2019(COVID-19) infection than the general population. Non-communicable diseases (NCDs) may increase the risk of COVID-19-related morbidity and mortality among HCWs, potentially reducing the available health workforce. We examined the association between NCDs and COVID-19 disease severity and mortality among infected HCWs. Methods: This cohort study used data from the International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC) database. HCWs hospitalized between January 2020 and January 2023 due to clinically suspected or laboratory-confirmed COVID-19 were eligible for inclusion. Variables collected included demographic data, comorbidities, and hospitalization outcomes. Descriptive statistics were reported using mean/standard deviation (SD), median/interquartile range (IQR), or frequencies and proportions. For each NCD, the relative risk of death, adjusted for age and sex, was calculated using log-binomial regression as well as the population-attributable fraction. Results: There were 17,502 HCWs, 95.7% of whom had a confirmed COVID-19 diagnosis. The majority were female (66.5%) and the mean age (SD) was 49.8 (14.3) years. Roughly, half (51.42%) of HCWs had no comorbidities, 29.28% had one comorbidity, 14.68% had 2 comorbidities and <5% had ≥3 comorbidities. The most common comorbidities were diabetes mellitus (49.40%) and cardiovascular disease (36.90%). Approximately one-fifth of the HCWs had severe COVID-19 (16.95%) and 10.68% of the HCWs with COVID-19 died. Being ≥45 years old, male gender, smoking, obesity, and certain NCDs increased the risk of COVID-19 severity and mortality. Obesity and diabetes mellitus were the leading risk factors in terms of the population-attributable risk for COVID-19 severity (6.89%) and mortality (36.00%) respectively. Conclusions: Many HCWs with COVID-19 had one or more NCDs. Obesity and diabetes mellitus increased COVID-19 severity and mortality risk. Reducing the prevalence of obesity and diabetes mellitus would yield the biggest reduction in COVID-19-related morbidity and mortality among HCWs.
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 | 0,013 | 0,117 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,004 |
| 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 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 ».