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Enregistrement W3170676118 · doi:10.1016/s2666-7568(21)00119-7

COVID-19 susceptibility in long-term care facilities

2021· letter· en· W3170676118 sur OpenAlexafffundabout
Melissa K. Andrew, Lisa Barrett

Notice bibliographique

RevueThe Lancet Healthy Longevity · 2021
Typeletter
Langueen
DomaineHealth Professions
ThématiqueGeriatric Care and Nursing Homes
Établissements canadiensDalhousie University
Organismes subventionnairesCanadian Institutes of Health ResearchPublic Health Agency of CanadaGilead SciencesAbbVieSanofiMerckGlaxoSmithKlinePfizer
Mots-clésCoronavirus disease 2019 (COVID-19)Term (time)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineEnvironmental scienceVirologyIntensive care medicineBusinessOutbreakInternal medicinePhysicsInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Disease outbreaks in long-term care facilities (LTCFs) have been a large driver of morbidity and mortality during the COVID-19 pandemic. This susceptibility to outbreaks in LTCFs is likely to be multifactorial, including frailty of residents, structural and environmental characteristics of buildings (eg, shared spaces, ventilation, and outdoor access), staffing policies and models, and the value society places on older people and LTCFs.1Andrew M Searle SD McElhaney JE et al.COVID-19, frailty and long-term care: Implications for policy and practice.J Infect Dev Ctries. 2020; 14: 428-432Crossref PubMed Scopus (34) Google Scholar Understanding of the true burden of COVID-19 in LTCFs has been limited by gaps in measurement and reporting. The impact of COVID-19 has evolved during subsequent pandemic waves, but estimates suggest that internationally more than 40% of deaths have occurred among residents of LTCFs,2International Long-Term Care Policy Networkhttps://ltccovid.org/international-reports-on-covid-19-and-long-term-care/Date accessed: April 27, 2021Google Scholar with many national and local estimates indicating a much higher death toll. Additionally, LTCF staff have been greatly affected as frontline workers.3White EM Wetle TF Reddy A Baier RR Front-line nursing home staff experiences during the COVID-19 Pandemic.J Am Med Dir Assoc. 2021; 22: 199-203Summary Full Text Full Text PDF PubMed Scopus (193) Google Scholar, 4Sarabia-Cobo C Pérez V de Lorena P et al.Experiences of geriatric nurses in nursing home settings across four countries in the face of the COVID-19 pandemic.J Adv Nurs. 2021; 77: 869-878Crossref PubMed Scopus (60) Google Scholar Patterns of vulnerability to SARS-CoV-2 infection and transmission within LTCF environments are thus crucial to addressing the pandemic at both clinical and policy levels. In The Lancet Healthy Longevity, Maria Krutikov and colleagues5Krutikov M Palmer T Tut G et al.Incidence of SARS-CoV-2 infection according to baseline antibody status in staff and residents of 100 long-term care facilities (VIVALDI): a prospective cohort study.Lancet Healthy Longev. 2021; 2: e362-e370Summary Full Text Full Text PDF PubMed Scopus (37) Google Scholar studied the incidence of SARS-CoV-2 reinfection according to baseline antibody status in staff and residents in 100 LTCFs in England. Their findings make an important contribution to ongoing efforts to understand SARS-CoV-2 immunity and correlates of protection. The authors enrolled 682 LCTF residents from 86 LCTFs and 1429 staff from 97 LCTFs, and did three rounds of nucleocapsid and spike protein IgG antibody testing in June, August, and October, 2020. From Oct 1, 2020, participants were monitored for reinfection using regular PCR tests done on a routine basis in these populations (weekly for staff and monthly for residents). Data were linked to results from the UK national testing programme (using National Health Service [NHS] number) and a Care Quality Commission location identifier for each LTCF. No individual-level health data were reported other than antibody titres, testing data, and symptoms in case of illness. Analyses were adjusted for age and sex, and stratified by LCTF and region. At baseline, IgG antibodies to nucleocapsid were identified in 226 (33%) of 682 residents and 408 (29%) of 1429 staff members; 39 (17%) of the 226 residents and 102 (25%) of 408 staff members who were antibody-positive at baseline tested antibody-negative at a later round of testing. The risk of reinfection was substantially lower for residents who were antibody-positive at baseline than residents who were antibody-negative at baseline (adjusted hazard ratio [aHR] 0·15 [95% CI 0·05–0·44]), with the same pattern observed in staff members (aHR 0·39 [0·19–0·82]). Ten members of staff and four residents were re-infected; nearly all were symptomatic, although the profile of symptoms seemed to differ between residents and staff members, whereby more residents reported fever and more staff reported coughs. The authors acknowledged that the difference in testing frequency between staff and residents could have led to underascertainment of asymptomatic infections, particularly for residents and also any staff who were not tested weekly. The authors concluded that the risk of infection was substantially lower in residents and staff who had baseline antibody positivity; it seems that this protective immunity conferred by previous infection reduced the risk of reinfection by approximately 85% for residents and 61% for staff. This study by Krutikov and colleagues has many strengths. The study included a large sample across 100 facilities in a context of high levels of community transmission of SARS-CoV-2. SARS-CoV-2 testing protocols and measures of humoral immunity were robust. Linkage of records was possible for many study participants, although some participants (who were mainly staff members) could not be linked, which introduced some potential for bias. Viral genome sequencing was not reported, thus the impact of variants of concern on reinfection remains unclear. Other study limitations include absence of other measures of individual health status (such as comorbidities and frailty) and reliance on measures of antibody titres versus cell-mediated immunity. Correlates of immunity for SARS-CoV-2 are yet to be defined, and are particularly unclear for older adults who often have dysregulated patterns of humoral and cell-mediated immunity compared with more frequently described younger populations. In this study, antibody levels were the only measure of immune response. Considering the increasingly recognised importance of cell-mediated immunity in responses to viral infections, including SARS-CoV-2, in older adults,6Pawelec G Bronikowski A Cunnane SC et al.The conundrum of human immune system “senescence”.Mech Ageing Dev. 2020; 192111357Crossref PubMed Scopus (41) Google Scholar future studies will ideally include assays of cell-mediated immunity to investigate whether patterns of protection vary between humoral and cell-mediated immunity. In order to understand immune responses and protection from severe outcomes and reinfection, the immune responses (humoral and cell-mediated immunity) and other host features of vulnerability to disease or resilience must be understood.7McElhaney JE Verschoor CP Andrew MK Haynes L Kuchel GA Pawelec G The immune response to influenza in older humans: beyond immune senescence.Immun Ageing. 2020; 17: 10Crossref PubMed Scopus (74) Google Scholar, 8Fulop T McElhaney J Pawelec G et al.Frailty, Inflammation and immunosenescence.Interdiscip Top Gerontol Geriatr. 2015; 41: 26-40Crossref PubMed Scopus (71) Google Scholar The influence of frailty and comorbidity on, and in relation to, immune responses remains to be understood and will be important foci of further study. The study by Krutikov and colleagues also provides the opportunity to consider the importance of facility-level and policy factors for SARS-CoV-2 outbreaks in LTCFs.9Kruse FM, Mah JC, Metsemakers S, Andrew MK, Sinha SK, Jeurissen PPT. Relationship between the ownership status of nursing homes and their outcomes during the COVID-19 pandemic: a rapid literature review. J Long-Term Care (in press).Google Scholar All of the facilities included in the reporting of the study to date were privately owned by a single health-care group. As data emerge on the differences in transmission rates and outcomes of COVID-19 in LTCFs with different physical characteristics and organisational, funding, and profit models, it is clear that facility-level factors will need to be assessed to inform efforts to improve care in LTCFs. Addressing the susceptibility of LTCF environments, residents, and staff members to COVID-19 outbreaks will require a concerted effort to understand the many factors underlying immune responses and health status, including host vulnerability to disease, and the structural and policy considerations at facility and jurisdictional levels.10Rochon PA Finding solutions for an age-old problem.Lancet. 2021; 397: 1534-1535Summary Full Text Full Text PDF Google Scholar The findings of Krutikov and colleagues contribute important knowledge to this effort. Crucial next steps also include further understanding of the association between frailty and immune regulation and the assessment of immune responses to vaccines in LTCF populations. Ongoing efforts are needed to better protect the health and wellbeing of LCTF residents and staff members. MKA reports grants from the Canadian Frailty Network, Canadian Institutes of Health Research, Public Health Agency of Canada, Sanofi, Pfizer, and GlaxoSmithKline, and personal fees from Pfizer, Sanofi, and Seqirus, outside the submitted work. LB reports grants from CIHR, the Canadian COVID Immunity Task Force, Gilead, ViiV, Abbvie, Merck, and honoraria for advisory activities from Gilead, ViiV, Abbvie, and Merck outside the submitted work. Incidence of SARS-CoV-2 infection according to baseline antibody status in staff and residents of 100 long-term care facilities (VIVALDI): a prospective cohort studyThe presence of IgG antibodies to nucleocapsid protein was associated with substantially reduced risk of reinfection in staff and residents for up to 10 months after primary infection. Full-Text PDF Open Access

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,052
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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.

Tête enseignante Opus0,123
Tête enseignante GPT0,431
Écart entre enseignants0,308 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations6
Publié2021
Routes d'admission3
Résumé présentoui

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