Connectivity between long-term care homes and subsequent SARS-CoV-2 outbreaks
Notice bibliographique
Résumé
Abstract Objectives To describe the relationship between individual workers employed at more than one LTCH (inter-LTCH connectivity) across long-term care homes (LTCH) and SARS-CoV-2 outbreaks. Design A retrospective cohort study using long-term care home surveillance and mobile geolocation data. Setting Using data observed between February 26 th , 2020, and August 31 st , 2020, from Ontario, the province where close to one-third of the Canada’s SARS-CoV-2 cases among long-term care homes residents were reported. Participants We included all 179 LTCH in the Greater Toronto Area (population 6.7 million, where close to 50% of Ontario population resides). Exposures The main exposure of interest was the inter-LTCH connectivity, generated from geographic position system location data procured across apps on different platforms. Main outcomes and measures Three outcomes were examined: 1) at least one SARS-CoV-2 diagnosis among residents, 2) cumulative cases among residents in each facility, and 3) time to first outbreak. Results The median degree of connectivity for LTCH that experienced an outbreak (59%; 106/179) was 1.2 times the degree of those without an outbreak (6 compared to 5). LTCH with higher inter-LTCH connectivity also had larger numbers of residents and beds, and were more likely to have for-profit ownership. After adjusting for facility-level and neighbourhood-level factors, every additional connection to another LTCH increased the odds of an outbreak in the respective LTCH by 8% (adjusted odds ratio=1.08, 90% credible interval [CrI]: 1.02-1.09). Inter-LTCH connectivity was also associated with higher risk of earlier occurrence of a first SARS-CoV-2 case (adjusted hazard ratio=1.05, 90%CrI: 1.02-1.09), but not with outbreak size. Conclusions and Relevance Staff cohorting was associated with reduced importation risk of SARS-CoV-2 cases into LTCH. However, findings suggest that once importation has occurred, other facility-level factors including facility infrastructure and staff benefits are more important in shaping outbreak size. Implementing these structural strategies to meet the LTCH workers and residents’ needs are pivotal to prevent and manage future respiratory virus outbreaks. Key points Question Were movement of long-term care homes (LTCH) workers between facilities (staff connectivity) associated with the risk, size, and timing of SARS-CoV-2 outbreaks in these facilities during the first wave of the COVID-19 pandemic. Finding After adjusting for facility-level and neighbourhood-level factors, a higher degree of staff connectivity between LTCH was associated with a greater risk of outbreaks (2.2-fold the risk of a LTCH connected with 10 more other LTCHs) and a higher risk of experiencing an earlier outbreak (1.7-fold the hazard with 10 more staff connections with other LTCH). However, we did not observe an association between connectivity and the size of outbreaks. Meaning “One-site” strategy to cohort staff by facility and minimizing movement may reduce risk of pathogen importation. However, structural strategies (e.g. improve facility design and infrastructure) to reduce nosocomial transmission within these facilities remain pivotal to prevent and manage future respiratory virus outbreaks.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».