The Impact of COVID-19 on Healthcare-Associated Infections: A Survey of Acute Care Hospitals
Notice bibliographique
Résumé
Background: The COVID-19 pandemic has placed an enormous strain on the healthcare system, including infection prevention and control. The response to the COVID-19 pandemic required extraordinary resources, which were often diverted from routine infection prevention and control activities and may have contributed to increased rates of HAI in the acute care setting. However, the impact of the COVID-19 pandemic on infection prevention and control departments, including staffing and resources, and on routine infection prevention and control activities is not well-described. The objective of this study was to describe the impact of the COVID-19 pandemic on IPC departments and department response to the pandemic. Methods: Between August and December of 2023, we conducted an electronic survey of all acute care facilities participating in the National Healthcare Safety Network. Survey data were analyzed using descriptive statistics. Results: Over 594 infection control departments participated in the survey, representing 1,400 NHSN facilities (20% response rate based on number of eligible NHSN facilities). Half of the respondents reported that their hospital experienced increases in the following HAI rates during the first two years of the pandemic: central-line associated bloodstream infections (54%), catheter-associated urinary tract infections (46%) and ventilator associated pneumonia (45%). When asked to identify the top three contributors to increased HAI rates in their facility, respondents cited the following factors: staffing shortages (70%), patient acuity (69%), use of travel nurses (48%), increased device utilization (37%), and reduced bedside acuity (31%). Respondents reported that their department utilized the following actions to decrease these HAI rates: increased rounding and monitoring of IPC procedures (81%), reeducation of frontline staff on IPC policies and procedures (77%), environmental care rounds (69%), monitoring of isolation compliance (66%), HAI Task Force/Committee (57%), nurse-driven catheter removal protocols (53%), and insertion prevention protocols (53%). When asked if the department experienced applied pressure or attempts to influence HAI reporting due to the increase in HAI rates in the facility experienced in the wake of the pandemic, 19% of respondents reported increased pressure from management/C-suite and 7% reported increased pressure from providers. Conclusion: The COVID-19 pandemic had a substantial impact on IPC departments in acute care hospitals and had a profound effect on IPC staffing, resources and routine IPC activities. Future work needs to identify best practices and lessons learned from the pandemic to inform future pandemic preparedness.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».