RESUMO
CONTEXTE: L'épidémiologie de l'infection au SRAS-CoV-2 dans les résidences pour aînés (offrant une aide à la vie autonome), est pour une bonne part inconnue. Nous avons étudié le lien entre les caractéristiques des résidences et des communautés avoisinantes et le risque d'éclosion de SRAS-CoV-2 dans les résidences pour aînés depuis le début de la première vague de la pandémie de COVID-19. MÉTHODES: Nous avons procédé à une étude de cohorte rétrospective dans la population des résidences pour aînés certifiées en Ontario, au Canada, entre le 1er mars et le 18 décembre 2020. Notre paramètre principal était toute éclosion de SRAS-CoV-2 (≥ 1 cas confirmé parmi les résidents ou le personnel au moyen d'un test d'amplification des acides nucléiques). Nous avons utilisé la méthode des risques proportionnels avec prédicteurs chronologiques pour modéliser les liens entre les caractéristiques des résidences et des communautés avoisinantes et les éclosions de SRAS-CoV-2. RÉSULTATS: Notre cohorte a inclus l'ensemble des 770 résidences privées pour aînés (RPA) certifiées en Ontario qui hébergeaient 56 491 résidents. On a dénombré 273 (35,5 %) résidences pour aînés qui ont connu 1 éclosion de SRAS-CoV-2 ou plus; 1944 résidents (3,5 %) et 1101 employés (3,0 %) ont contracté l'infection. Ces cas étaient inégalement distribués entre les résidences. En effet, 2487 cas parmi les résidents et le personnel (81,7 %) sont survenus dans 77 résidences (10 %). Le rapport de risque ajusté d'une éclosion de SRAS-CoV-2 dans une résidence a été clairement associé aux établissements qui avaient une grande capacité d'accueil, qui comportaient des unités de soins de longue durée, qui appartenaient à de plus grandes bannières et offraient plusieurs services sur place, qui se trouvaient dans des régions marquées par une hausse de l'incidence régionale de SRAS-CoV-2 et où la concentration ethnique à l'échelle de la communauté était supérieure. INTERPRÉTATION: Certaines caractéristiques facilement identifiables des résidences pour aînés sont associées de manière indépendante aux éclosions de SRAS-CoV-2 et peuvent faciliter l'évaluation des risques et orienter la priorisation de la vaccination.
RESUMO
BACKGROUND: The epidemiology of SARS-CoV-2 infection in retirement homes (also known as assisted living facilities) is largely unknown. We examined the association between home-and community-level characteristics and the risk of outbreaks of SARS-CoV-2 infection in retirement homes since the beginning of the first wave of the COVID-19 pandemic. METHODS: We conducted a population-based, retrospective cohort study of licensed retirement homes in Ontario, Canada, from Mar. 1 to Dec. 18, 2020. Our primary outcome was an outbreak of SARS-CoV-2 infection (≥ 1 resident or staff case confirmed by validated nucleic acid amplification assay). We used time-dependent proportional hazards methods to model the associations between retirement home- and community-level characteristics and outbreaks of SARS-CoV-2 infection. RESULTS: Our cohort included all 770 licensed retirement homes in Ontario, which housed 56 491 residents. There were 273 (35.5%) retirement homes with 1 or more outbreaks of SARS-CoV-2 infection, involving 1944 (3.5%) residents and 1101 staff (3.0%). Cases of SARS-CoV-2 infection were distributed unevenly across retirement homes, with 2487 (81.7%) resident and staff cases occurring in 77 (10%) homes. The adjusted hazard of an outbreak of SARS-CoV-2 infection in a retirement home was positively associated with homes that had a large resident capacity, were co-located with a long-term care facility, were part of larger chains, offered many services onsite, saw increases in regional incidence of SARS-CoV-2 infection, and were located in a region with a higher community-level ethnic concentration. INTERPRETATION: Readily identifiable characteristics of retirement homes are independently associated with outbreaks of SARS-CoV-2 infection and can support risk identification and priority for vaccination.
Assuntos
COVID-19/epidemiologia , Instituição de Longa Permanência para Idosos , Casas de Saúde , Pandemias , Idoso , Idoso Fragilizado , Humanos , Incidência , Ontário/epidemiologia , Aposentadoria , Estudos Retrospectivos , SARS-CoV-2RESUMO
Literature and original analysis of healthcare costs have shown that a small proportion of patients consume the majority of healthcare resources. A proactive approach is to target interventions towards those patients who are at risk of becoming high-cost users (HCUs). This approach requires identifying high-risk patients accurately before substantial avoidable costs have been incurred and health status has deteriorated further. We developed a predictive model to identify patients at risk of becoming HCUs in Ontario. HCUs were defined as the top 5% of patients incurring the highest costs. Information was collected on various demographic and utilization characteristics. The modelling technique used was logistic regression. If the top 5% of patients at risk of becoming HCUs are followed, the sensitivity is 42.2% and specificity is 97%. Alternatives for implementation of the model include collaboration between different levels of healthcare services for personalized healthcare interventions and interventions addressing needs of patient cohorts with high-cost conditions.
Assuntos
Custos de Cuidados de Saúde , Serviços de Saúde/estatística & dados numéricos , Modelos Estatísticos , Efeitos Psicossociais da Doença , Feminino , Previsões/métodos , Custos de Cuidados de Saúde/tendências , Serviços de Saúde/economia , Humanos , Masculino , Razão de Chances , OntárioRESUMO
Approximately 1.5% of ontario's population, represented by the top 5% highest cost-incurring users of ontario's hospital and home care services, account for 61% of hospital and home care costs. Similar studies from other jurisdictions also show that a relatively small number of people use a high proportion of health system resources. Understanding these high-cost users (hcus) can inform local healthcare planners in their efforts to improve the quality of care and reduce burden on patients and the healthcare system. To facilitate this understanding, we created a profile of hcus using demographic and clinical characteristics. The profile provides detailed information on hcus by care type, geography, age, sex and top clinical conditions.