Infection and mortality of healthcare workers worldwide from COVID-19: a scoping review
Notice bibliographique
Résumé
Abstract Objectives To estimate COVID-19 infections and deaths in healthcare workers (HCWs) from a global perspective. Design Scoping review. Methods Two parallel searches of academic bibliographic databases and grey literature were undertaken. Governments were also contacted for further information where possible. Due to the time-sensitive nature of the review and the need to report the most up-to-date information for an ever-evolving situation, there were no restrictions on language, information sources utilised, publication status, and types of sources of evidence. The AACODS checklist was used to appraise each source of evidence. Outcome measures Publication characteristics, country-specific data points, COVID-19 specific data, demographics of affected HCWs, and public health measures employed Results A total of 152,888 infections and 1413 deaths were reported. Infections were mainly in women (71.6%) and nurses (38.6%), but deaths were mainly in men (70.8%) and doctors (51.4%). Limited data suggested that general practitioners and mental health nurses were the highest risk specialities for deaths. There were 37.17 deaths reported per 100 infections for healthcare workers aged over 70. Europe had the highest absolute numbers of reported infections (119628) and deaths (712), but the Eastern Mediterranean region had the highest number of reported deaths per 100 infections (5.7). Conclusions HCW COVID-19 infections and deaths follow that of the general world population. The reasons for gender and speciality differences require further exploration, as do the low rates reported from Africa and India. Although physicians working in certain specialities may be considered high-risk due to exposure to oronasal secretions, the risk to other specialities must not be underestimated. Elderly HCWs may require assigning to less risky settings such as telemedicine, or administrative positions. Our pragmatic approach provides general trends, and highlights the need for universal guidelines for testing and reporting of infections in HCWs. Summary Box What is already known on this topic In China, studies documented over 3,300 confirmed cases of infected healthcare workers in early March. In the United States, as high as 19% of patients had been identified as healthcare workers. There are no studies that perform a global examination of COVID-19 infections and deaths in the health workforce. What this study adds To our knowledge, this is the first study assessing the number of healthcare workers who have been infected with or died from COVID-19 globally. The data from our study suggest that although infections were mainly in women and nurses, COVID-19 related deaths were mainly in men and doctors; in addition, our study found that Europe had the highest numbers of infection and death, but the lowest case-fatality-rate, while the Eastern Mediterranean had the highest case-fatality-rate.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,020 | 0,017 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».