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Enregistrement W4205845460 · doi:10.47326/ocsat.2021.02.07.1.0

COVID-19 and Ontario’s Long-Term Care Homes

2021· report· en· W4205845460 sur OpenAlexaboutno aff
Nathan M. Stall, Kevin A. Brown, Antonina Maltsev, Aaron Jones, Andrew P. Costa, Vanessa Allen, Adalsteinn Brown, Gerald A. Evans, David N. Fisman, Jennie Johnstone, Peter Jüni, Kamil Malikov, Allison McGeer, Paula A. Rochon, Beate Sander, Brian Schwartz, Samir K. Sinha, Kevin Smith, Ashleigh R. Tuite, Michael Hillmer

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineHealth Professions
ThématiqueGeriatric Care and Nursing Homes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLong-term carePandemicOutbreakStaffingCoronavirus disease 2019 (COVID-19)Psychological interventionMedicineEnvironmental healthPublic healthGeographyGerontologySocioeconomicsBusinessNursingVirologyInfectious disease (medical specialty)SociologyDisease

Résumé

récupéré en direct d'OpenAlex

Key Message Ontario long-term care (LTC) home residents have experienced disproportionately high morbidity and mortality, both from COVID-19 and from the conditions associated with the COVID-19 pandemic. There are several measures that could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes, if implemented. First, temporary staffing could be minimized by improving staff working conditions. Second, homes could be further decrowded by a continued disallowance of three- and four-resident rooms and additional temporary housing for the most crowded homes. Third, the risk of SARS-CoV-2 infection in staff could be minimized by approaches that reduce the risk of transmission in communities with a high burden of COVID-19. Summary Background The Province of Ontario has 626 licensed LTC homes and 77,257 long-stay beds; 58% of homes are privately owned, 24% are non-profit/charitable, 16% are municipal. LTC homes were strongly affected during Ontario’s first and second waves of the COVID-19 pandemic. Questions What do we know about the first and second waves of COVID-19 in Ontario LTC homes? Which risk factors are associated with COVID-19 outbreaks in Ontario LTC homes and the extent and death rates associated with outbreaks? What has been the impact of the COVID-19 pandemic on the general health and wellbeing of LTC residents? How has the existing Ontario evidence on COVID-19 in LTC settings been used to support public health interventions and policy changes in these settings? What are the further measures that could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes? Findings As of January 14, 2021, a total of 3,211 Ontario LTC home residents have died of COVID-19, totaling 60.7% of all 5,289 COVID-19 deaths in Ontario to date. There have now been more cumulative LTC home outbreaks during the second wave as compared with the first wave. The infection and death rates among LTC residents have been lower during the second wave, as compared with the first wave, and a greater number of LTC outbreaks have involved only staff infections. The growth rate of SARS-CoV-2 infections among LTC residents was slower during the first two months of the second wave in September and October 2020, as compared with the first wave. However, the growth rate after the two-month mark is comparatively faster during the second wave. The majority of second wave infections and deaths in LTC homes have occurred between December 1, 2020, and January 14, 2021 (most recent date of data extraction prior to publication). This highlights the recent intensification of the COVID-19 pandemic in LTC homes that has mirrored the recent increase in community transmission of SARS-CoV-2 across Ontario. Evidence from Ontario demonstrates that the risk factors for SARS-CoV-2 outbreaks and subsequent deaths in LTC are distinct from the risk factors for outbreaks and deaths in the community (Figure 1). The most important risk factors for whether a LTC home will experience an outbreak is the daily incidence of SARS-CoV-2 infections in the communities surrounding the home and the occurrence of staff infections. The most important risk factors for the magnitude of an outbreak and the number of resulting resident deaths are older design, chain ownership, and crowding. Figure 1. Anatomy of Outbreaks and Spread of COVID-19 in LTC Homes and Among Residents Figure from Peter Hamilton, personal communication. Many Ontario LTC home residents have experienced severe and potentially irreversible physical, cognitive, psychological, and functional declines as a result of precautionary public health interventions imposed on homes, such as limiting access to general visitors and essential caregivers, resident absences, and group activities. There has also been an increase in the prescribing of psychoactive drugs to Ontario LTC residents. The accumulating evidence on COVID-19 in Ontario’s LTC homes has been leveraged in several ways to support public health interventions and policy during the pandemic. Ontario evidence showed that SARS-CoV-2 infections among LTC staff was associated with subsequent COVID-19 deaths among LTC residents, which motivated a public order to restrict LTC staff from working in more than one LTC home in the first wave. Emerging Ontario evidence on risk factors for LTC home outbreaks and deaths has been incorporated into provincial pandemic surveillance tools. Public health directives now attempt to limit crowding in LTC homes by restricting occupancy to two residents per room. The LTC visitor policy was also revised to designate a maximum of two essential caregivers who can visit residents without time limits, including when a home is experiencing an outbreak. Several further measures could be effective in preventing COVID-19 outbreaks, hospitalizations, and deaths in Ontario’s LTC homes. First, temporary staffing could be minimized by improving staff working conditions. Second, the risk of SARS-CoV-2 infection in staff could be minimized by measures that reduce the risk of transmission in communities with a high burden of COVID-19. Third, LTC homes could be further decrowded by a continued disallowance of three- and four-resident rooms and additional temporary housing for the most crowded homes. Other important issues include improved prevention and detection of SARS-CoV-2 infection in LTC staff, enhanced infection prevention and control (IPAC) capacity within the LTC homes, a more balanced and nuanced approach to public health measures and IPAC strategies in LTC homes, strategies to promote vaccine acceptance amongst residents and staff, and further improving data collection on LTC homes, residents, staff, visitors and essential caregivers for the duration of the COVID-19 pandemic. Interpretation Comparisons of the first and second waves of the COVID-19 pandemic in the LTC setting reveal improvement in some but not all epidemiological indicators. Despite this, the second wave is now intensifying within LTC homes and without action we will likely experience a substantial additional loss of life before the widespread administration and time-dependent maximal effectiveness of COVID-19 vaccines. The predictors of outbreaks, the spread of infection, and deaths in Ontario’s LTC homes are well documented and have remained unchanged between the first and the second wave. Some of the evidence on COVID-19 in Ontario’s LTC homes has been effectively leveraged to support public health interventions and policies. Several further measures, if implemented, have the potential to prevent additional LTC home COVID-19 outbreaks and deaths.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,069
Score d'incertitude au seuil0,500

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0030,002
Communication savante0,0030,002
Science ouverte0,0020,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0170,001

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.

Tête enseignante Opus0,094
Tête enseignante GPT0,452
Écart entre enseignants0,358 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations25
Publié2021
Routes d'admission1
Résumé présentoui

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