2303. Detection of COVID-19 Outbreaks in Hospitals Using Built Environment Testing for SARS-CoV-2
Notice bibliographique
Résumé
Abstract Background Environmental testing for SARS-CoV-2 including assessment of wastewater and the built environment is a useful tool for population-level surveillance for COVID-19. Detection of SARS-CoV-2 on the floor of healthcare facilities has been strongly associated with cases of COVID-19 (1,2). By introducing routine floor-swabbing in hospitals, we may be able to predict outbreaks earlier allowing for additional control measures. Methods We implemented floor swabbing surveillance for SARS-CoV-2 to aid the identification of COVID-19 cases and outbreaks in hospitals. Swabs were taken weekly at eight hospital in-patient wards in healthcare worker-only (HCW) areas at two hospitals in Ontario, Canada, for a 39-week period (July 2022 to March 2023). HCW cases and outbreaks were managed as per usual processes at the facilities. A logistic regression model with ward-level random intercepts was developed using weekly viral copies (VC) to predict a contemporaneous outbreak in the same ward in the same week. Grouped 5-fold cross-validation was used to evaluate model outbreak discrimination. Results SARS-CoV-2 RNA was detected on 537 of 760 collected swabs (71%). Hospital A had more frequent detection and higher levels of SARS-CoV-2 (swab positivity = 90% [95% CI: 85%-93%], mean VC = 23, [19-29]) than Hospital B (swab positivity = 60% [55%-64%], mean VC = 7.9 [6.5-9.7]) (Figure 1). There were seven outbreaks at Hospital A and four at Hospital B. Outbreaks at both hospitals consisted of mostly patient cases (Hospital A: 95%, Hospital B: 82%). The odds ratio of outbreak for every unit increase in viral copies (log-transformed) was 21.0 [5.6-79]. The cross-validated area under the receiver operating curve for SARS-CoV-2 viral copies for predicting a contemporaneous outbreak (Figure 2) was 0.86 [95%CI 0.82 – 0.9]. Figure 1. Distribution of SARS-CoV-2 copies (plus one) values from PCR testing of floor swabs, stratified by hospital and outbreak status at time of sampling. Jittered points show the copies plus one values for each individual swab; boxplots show the median, IQR, and range of these values by site (indicated by colors), in outbreak and non-outbreak periods (indicated on y-axis). Figure 2. Cross-validation receiver operating characteristic (ROC) curves (with mean ROC in blue) for predicting contemporaneous outbreaks from SARS-CoV-2 viral copies. Conclusion Detection of SARS-CoV-2 on floors in HCW-only areas is associated with COVID-19 outbreaks in those hospital wards. Despite swabbing exclusively in HCW-only areas, outbreaks were driven by patient cases at both hospitals. These results support the potential role for built environment sampling to support hospital COVID-19 outbreak identification and may fill gaps in traditional clinical surveillance methods. Disclosures Evgueni Doukhanine, MSc, DNA Genotek: DNA Genotek provided sampling swabs in-kind for this study in an unrestricted fashion. Michael Fralick, MD, ProofDx: Advisor/Consultant
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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».