Evaluating methods for risk prediction of Covid-19 mortality in nursing home residents before and after vaccine availability: a retrospective cohort study.

BACKGROUND: SARS-CoV-2 vaccines are effective in reducing hospitalization, COVID-19 symptoms, and COVID-19 mortality for nursing home (NH) residents. We sought to compare the accuracy of various machine learning models, examine changes to model performance, and identify resident characteristics that have the strongest associations with 30-day COVID-19 mortality, before and after vaccine availability. METHODS: We conducted a population-based retrospective cohort study analyzing data from all NH facilities across Ontario, Canada. We included all residents diagnosed with SARS-CoV-2 and living in NHs between March 2020 and July 2021. We employed five machine learning algorithms to predict COVID-19 mortality, including logistic regression, LASSO regression, classification and regression trees (CART), random forests, and gradient boosted trees. The discriminative performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) for each model using 10-fold cross-validation. Model calibration was determined through evaluation of calibration slopes. Variable importance was calculated by repeatedly and randomly permutating the values of each predictor in the dataset and re-evaluating the model's performance. RESULTS: A total of 14,977 NH residents and 20 resident characteristics were included in the model. The cross-validated AUCs were similar across algorithms and ranged from 0.64 to 0.67. Gradient boosted trees and logistic regression had an AUC of 0.67 pre- and post-vaccine availability. CART had the lowest discrimination ability with an AUC of 0.64 pre-vaccine availability, and 0.65 post-vaccine availability. The most influential resident characteristics, irrespective of vaccine availability, included advanced age (≥ 75 years), health instability, functional and cognitive status, sex (male), and polypharmacy. CONCLUSIONS: The predictive accuracy and discrimination exhibited by all five examined machine learning algorithms were similar. Both logistic regression and gradient boosted trees exhibit comparable performance and display slight superiority over other machine learning algorithms. We observed consistent model performance both before and after vaccine availability. The influence of resident characteristics on COVID-19 mortality remained consistent across time periods, suggesting that changes to pre-vaccination screening practices for high-risk individuals are effective in the post-vaccination era.

Short-term mortality and palliative care use after delayed hospital discharge: a population-based retrospective cohort study.

OBJECTIVES: In Canada, patients whose acute medical issues have been resolved but are awaiting discharge from hospital are designated as alternate level of care (ALC). We investigated short-term mortality and palliative care use following ALC designation in Ontario, Canada. METHODS: We conducted a population-based retrospective cohort study of adult, acute care hospital admissions in Ontario with an ALC designation between January and December 2021. Our follow-up window was until 90 days post-ALC designation or death. Setting of discharge and death was determined using admission and discharge dates from multiple databases. We measured palliative care using physician billings, inpatient palliative care records and palliative home care records. We compared the characteristics of ALC patients by 90-day survival status and compared palliative care use across settings of discharge and death. RESULTS: We included 54 839 ALC patients with a median age of 80 years. Nearly one-fifth (18.4%) of patients died within 90 days. Patients who died were older, had more comorbid conditions and were more likely to be male. Among those who died, 35.1% were never discharged from hospital and 20.3% were discharged but ultimately died in the hospital. The majority of people who died received palliative care following their ALC designation (68.1%). CONCLUSIONS: A significant proportion of patients experiencing delayed discharge die within 3 months, with the majority dying in hospitals despite being identified as ready to be discharged. Future research should examine the adequacy of palliative care provision for this population.

Impact of a vaccine passport on first-dose SARS-CoV-2 vaccine coverage by age and area-level social determinants of health in the Canadian provinces of Quebec and Ontario: an interrupted time series analysis.

BACKGROUND: In Canada, all provinces implemented vaccine passports in 2021 to reduce SARS-CoV-2 transmission in non-essential indoor spaces and increase vaccine uptake (policies active September 2021-March 2022 in Quebec and Ontario). We sought to evaluate the impact of vaccine passport policies on first-dose SARS-CoV-2 vaccination coverage by age, and area-level income and proportion of racialized residents. METHODS: We performed interrupted time series analyses using data from Quebec's and Ontario's vaccine registries linked to census information (population of 20.5 million people aged ≥ 12 yr; unit of analysis: dissemination area). We fit negative binomial regressions to first-dose vaccinations, using natural splines adjusting for baseline vaccination coverage (start: July 2021; end: October 2021 for Quebec, November 2021 for Ontario). We obtained counterfactual vaccination rates and coverage, and estimated the absolute and relative impacts of vaccine passports. RESULTS: In both provinces, first-dose vaccination coverage before the announcement of vaccine passports was 82% (age ≥ 12 yr). The announcement resulted in estimated increases in coverage of 0.9 percentage points (95% confidence interval [CI] 0.4-1.2) in Quebec and 0.7 percentage points (95% CI 0.5-0.8) in Ontario. This corresponds to 23% (95% CI 10%-36%) and 19% (95% CI 15%-22%) more vaccinations over 11 weeks. The impact was larger among people aged 12-39 years. Despite lower coverage in lower-income and more-racialized areas, there was little variability in the absolute impact by area-level income or proportion racialized in either province. INTERPRETATION: In the context of high vaccine coverage across 2 provinces, the announcement of vaccine passports had a small impact on first-dose coverage, with little impact on reducing economic and racial inequities in vaccine coverage. Findings suggest that other policies are needed to improve vaccination coverage among lower-income and racialized neighbourhoods and communities.

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes.

BACKGROUND: There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations. OBJECTIVES: To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care. SEARCH METHODS: We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section. SELECTION CRITERIA: We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy. DATA COLLECTION AND ANALYSIS: We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available. MAIN RESULTS: We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other). AUTHORS' CONCLUSIONS: There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

The association between frailty, long-term care home characteristics and COVID-19 mortality before and after SARS-CoV-2 vaccination: a retrospective cohort study.

BACKGROUND: The relative contributions of long-term care (LTC) resident frailty and home-level characteristics on COVID-19 mortality has not been well studied. We examined the association between resident frailty and home-level characteristics with 30-day COVID-19 mortality before and after the availability of SARS-CoV-2 vaccination in LTC. METHODS: We conducted a population-based retrospective cohort study of LTC residents with confirmed SARS-CoV-2 infection in Ontario, Canada. We used multi-level multivariable logistic regression to examine associations between 30-day COVID-19 mortality, the Hubbard Frailty Index (FI), and resident and home-level characteristics. We compared explanatory models before and after vaccine availability. RESULTS: There were 11,179 and 3,655 COVID-19 cases in the pre- and post-vaccine period, respectively. The 30-day COVID-19 mortality was 25.9 and 20.0% during the same periods. The median odds ratios for 30-day COVID-19 mortality between LTC homes were 1.50 (95% credible interval [CrI]: 1.41-1.65) and 1.62 (95% CrI: 1.46-1.96), respectively. In the pre-vaccine period, 30-day COVID-19 mortality was higher for males and those of greater age. For every 0.1 increase in the Hubbard FI, the odds of death were 1.49 (95% CI: 1.42-1.56) times higher. The association between frailty and mortality remained consistent in the post-vaccine period, but sex and age were partly attenuated. Despite the substantial home-level variation, no home-level characteristic examined was significantly associated with 30-day COVID-19 mortality during either period. INTERPRETATION: Frailty is consistently associated with COVID-19 mortality before and after the availability of SARS-CoV-2 vaccination. Home-level characteristics previously attributed to COVID-19 outcomes do not explain significant home-to-home variation in COVID-19 mortality.

Inappropriate use of clinical practices in Canada: a systematic review.

BACKGROUND: Inappropriate health care leads to negative patient experiences, poor health outcomes and inefficient use of resources. We aimed to conduct a systematic review of inappropriately used clinical practices in Canada. METHODS: We searched multiple bibliometric databases and grey literature to identify inappropriately used clinical practices in Canada between 2007 and 2021. Two team members independently screened citations, extracted data and assessed methodological quality. Findings were synthesized in 2 categories: diagnostics and therapeutics. We reported ranges of proportions of inappropriate use for all practices. Medians and interquartile ranges (IQRs), based on the percentage of patients not receiving recommended practices (underuse) or receiving practices not recommended (overuse), were calculated. All statistics are at the study summary level. RESULTS: We included 174 studies, representing 228 clinical practices and 28 900 762 patients. The median proportion of inappropriate care, as assessed in the studies, was 30.0% (IQR 12.0%-56.6%). Underuse (median 43.9%, IQR 23.8%-66.3%) was more frequent than overuse (median 13.6%, IQR 3.2%-30.7%). The most frequently investigated diagnostics were glycated hemoglobin (underused, range 18.0%-85.7%, n = 9) and thyroid-stimulating hormone (overused, range 3.0%-35.1%, n = 5). The most frequently investigated therapeutics were statin medications (underused, range 18.5%-71.0%, n = 6) and potentially inappropriate medications (overused, range 13.5%-97.3%, n = 9). INTERPRETATION: We have provided a summary of inappropriately used clinical practices in Canadian health care systems. Our findings can be used to support health care professionals and quality agencies to improve patient care and safety in Canada.

Identifying Unexpected Deaths in Long-Term Care Homes.

OBJECTIVES: Predicting unexpected deaths among long-term care (LTC) residents can provide valuable information to clinicians and policy makers. We study multiple methods to predict unexpected death, adjusting for individual and home-level factors, and to use as a step to compare mortality differences at the facility level in the future work. DESIGN: We conducted a retrospective cohort study using Resident Assessment Instrument Minimum Data Set assessment data for all LTC residents in Ontario, Canada, from April 2017 to March 2018. SETTING AND PARTICIPANTS: All residents in Ontario long-term homes. We used data routinely collected as part of administrative reporting by health care providers to the funder: Ontario Ministry of Health and Long-Term Care. This project is a component of routine policy development to ensure safety of the LTC system residents. METHODS: Logistic regression (LR), mixed-effect LR (mixLR), and a machine learning algorithm (XGBoost) were used to predict individual mortality over 5 to 95 days after the last available RAI assessment. RESULTS: We identified 22,419 deaths in the cohort of 106,366 cases (mean age: 83.1 years; female: 67.7%; dementia: 68.8%; functional decline: 16.6%). XGBoost had superior calibration and discrimination (C-statistic 0.837) over both mixLR (0.819) and LR (0.813). The models had high correlation in predicting death (LR-mixLR: 0.979, LR-XGBoost: 0.885, mixLR-XGBoost: 0.882). The inter-rater reliability between the models LR-mixLR and LR-XGBoost was 0.56 and 0.84, respectively. Using results in which all 3 models predicted probability of actual death of a resident at <5% yielded 210 unexpected deaths or 0.9% of the observed deaths. CONCLUSIONS AND IMPLICATIONS: XGBoost outperformed other models, but the combination of 3 models provides a method to detect facilities with potentially higher rates of unexpected deaths while minimizing the possibility of false positives and could be useful for ongoing surveillance and quality assurance at the facility, regional, and national levels.

Stakeholder Perspectives of Attributes and Features of Context Relevant to Knowledge Translation in Health Settings: A Multi-Country Analysis.

BACKGROUND: Context is recognized as important to successful knowledge translation (KT) in health settings. What is meant by context, however, is poorly understood. The purpose of the current study was to elicit tacit knowledge about what is perceived to constitute context by conducting interviews with a variety of health system stakeholders internationally so as to compile a comprehensive list of contextual attributes and their features relevant to KT in healthcare. METHODS: A descriptive qualitative study design was used. Semi-structured interviews were conducted with health system stakeholders (change agents/KT specialists and KT researchers) in four countries: Australia, Canada, the United Kingdom, and the United States. Interview transcripts were analyzed using inductive thematic content analysis in four steps: (1) selection of utterances describing context, (2) coding of features of context, (3) categorizing of features into attributes of context, (4) comparison of attributes and features by: country, KT experience, and role. RESULTS: A total of 39 interviews were conducted. We identified 66 unique features of context, categorized into 16 attributes. One attribute, Facility Characteristics, was not represented in previously published KT frameworks. We found instances of all 16 attributes in the interviews irrespective of country, level of experience with KT, and primary role (change agent/KT specialist vs. KT researcher), revealing robustness and transferability of the attributes identified. We also identified 30 new context features (across 13 of the 16 attributes). CONCLUSION: The findings from this study represent an important advancement in the KT field; we provide much needed conceptual clarity in context, which is essential to the development of common assessment tools to measure context to determine which context attributes and features are more or less important in different contexts for improving KT success.

COVID-19 Case Age Distribution: Correction for Differential Testing by Age.

BACKGROUND: Despite expected initial universal susceptibility to a novel pandemic pathogen like SARS-CoV-2, the pandemic has been characterized by higher observed incidence in older persons and lower incidence in children and adolescents. OBJECTIVE: To determine whether differential testing by age group explains observed variation in incidence. DESIGN: Population-based cohort study. SETTING: Ontario, Canada. PARTICIPANTS: Persons diagnosed with SARS-CoV-2 and those tested for SARS-CoV-2. MEASUREMENTS: Test volumes from the Ontario Laboratories Information System, number of laboratory-confirmed SARS-CoV-2 cases from the Integrated Public Health Information System, and population figures from Statistics Canada. Demographic and temporal patterns in incidence, testing rates, and test positivity were explored using negative binomial regression models and standardization. Sources of variation in standardized ratios were identified and test-adjusted standardized infection ratios (SIRs) were estimated by metaregression. RESULTS: Observed disease incidence and testing rates were highest in the oldest age group and markedly lower in those younger than 20 years; no differences in incidence were seen by sex. After adjustment for testing frequency, SIRs were lowest in children and in adults aged 70 years or older and markedly higher in adolescents and in males aged 20 to 49 years compared with the overall population. Test-adjusted SIRs were highly correlated with standardized positivity ratios (Pearson correlation coefficient, 0.87 [95% CI, 0.68 to 0.95]; P < 0.001) and provided a case identification fraction similar to that estimated with serologic testing (26.7% vs. 17.2%). LIMITATIONS: The novel methodology requires external validation. Case and testing data were not linkable at the individual level. CONCLUSION: Adjustment for testing frequency provides a different picture of SARS-CoV-2 infection risk by age, suggesting that younger males are an underrecognized group at high risk for SARS-CoV-2 infection. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.

Ontario's COVID-19 Modelling Consensus Table: mobilizing scientific expertise to support pandemic response.

SETTING: COVID-19 has highlighted the need for credible epidemiological models to inform pandemic policy. Traditional mechanisms of commissioning research are ill-suited to guide policy during a rapidly evolving pandemic. At the same time, contracting with a single centre of expertise has been criticized for failing to reflect challenges inherent in specific modelling approaches. INTERVENTION: This report describes an alternative approach to mobilizing scientific expertise. Ontario's COVID-19 Modelling Consensus Table (MCT) was created in March 2020 to enable rapid communication of credible estimates of the impact of COVID-19 and to accelerate learning on how the disease is spreading and what could slow its transmission. The MCT is a partnership between the province and academic modellers and consists of multiple groups of experts, health system leaders, and senior decision-makers. Armed with Ministry of Health data, the MCT meets once per week to share results from modelling exercises, generate consensus judgements of the likely future impact of COVID-19, and discuss decision-makers' priorities. OUTCOMES: The MCT has enabled swift access to data for participants, a structure for developing consensus estimates and communicating these to decision-makers, credible models to inform health system planning, and increased transparency in public reporting of COVID-19 data. It has also facilitated the rapid publication of research findings and its incorporation into government policy. IMPLICATIONS: The MCT approach is one way to quickly draw on scientific advice outside of government and public health agencies. Beyond speed, this approach allows for nimbleness as experts from different organizations can be added as needed. It also shows how universities and research institutes have a role to play in crisis situations, and how this expertise can be marshalled to inform policy while respecting academic freedom and confidentiality.

RéSUMé: LIEU: La COVID-19 a mis en évidence le besoin de modèles épidémiologiques crédibles pour éclairer la politique pandémique. Les mécanismes habituels pour commander des travaux de recherche sont peu propices à orienter les politiques lors d'une pandémie qui évolue rapidement. En même temps, la passation de contrats avec un seul centre d'expertise est critiquée, car elle ne tient pas compte des difficultés inhérentes de certaines approches de modélisation. INTERVENTION: Le présent rapport décrit une approche de rechange pour mobiliser le savoir scientifique. L'Ontario a créé en mars 2020 une Table de concertation sur la modélisation (TCM) qui permet de communiquer de façon rapide et fiable les estimations des effets de la COVID-19 et d'apprendre plus vite comment la maladie se propage et ce qui pourrait en ralentir la transmission. La TCM, un partenariat entre les modélisateurs de la province et des milieux universitaires, est composée de plusieurs groupes d'experts, de dirigeants du système de santé et de décideurs de haut niveau. Armée des données du ministère de la Santé, la TCM se réunit une fois par semaine pour partager les résultats d'exercices de modélisation, générer des jugements consensuels sur les futurs effets probables de la COVID-19 et discuter des priorités des décideurs. RéSULTATS: La TCM rend possible un accès rapide aux données pour les participants, une structure pour élaborer des estimations consensuelles et les communiquer aux décideurs, des modèles fiables pour éclairer la planification du système de santé, ainsi qu'une transparence accrue dans la communication des données sur la COVID-19 au public. Elle facilite aussi la publication rapide des résultats de recherche et leur intégration dans la politique gouvernementale. CONSéQUENCES: L'approche de la TCM est un moyen d'obtenir rapidement des conseils scientifiques à l'extérieur du gouvernement et des organismes de santé publique. Au-delà de sa rapidité, cette approche offre une grande souplesse, car des experts de différents organismes peuvent être ajoutés au besoin. Elle montre aussi que les universités et les établissements de recherche ont un rôle à jouer dans les situations de crise, et qu'il est possible de mobiliser leurs compétences pour éclairer les politiques tout en respectant la liberté et la confidentialité des milieux de la recherche et de l'enseignement.

Facteurs de risque d'éclosion de SRAS-CoV-2 dans les résidences pour aînés en Ontario, au Canada: étude de cohorte à l'échelle de la population.

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.

Risk factors for outbreaks of SARS-CoV-2 infection at retirement homes in Ontario, Canada: a population-level cohort study.

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.

Derivation and Validation of Clinical Prediction Rules for COVID-19 Mortality in Ontario, Canada.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing a high-mortality global pandemic. The clinical spectrum of disease caused by this virus is broad, ranging from asymptomatic infection to organ failure and death. Risk stratification of individuals with coronavirus disease 2019 (COVID-19) is desirable for management, and prioritization for trial enrollment. We developed a prediction rule for COVID-19 mortality in a population-based cohort in Ontario, Canada. METHODS: Data from Ontario's provincial iPHIS system were extracted for the period from January 23 to May 15, 2020. Logistic regression-based prediction rules and a rule derived using a Cox proportional hazards model were developed and validated using split-halves validation. Sensitivity analyses were performed, with varying approaches to missing data. RESULTS: Of 21 922 COVID-19 cases, 1734 with complete data were included in the derivation set; 1796 were included in the validation set. Age and comorbidities (notably diabetes, renal disease, and immune compromise) were strong predictors of mortality. Four point-based prediction rules were derived (base case, smoking excluded, long-term care excluded, and Cox model-based). All displayed excellent discrimination (area under the curve for all rules > 0.92) and calibration (P > .50 by Hosmer-Lemeshow test) in the derivation set. All performed well in the validation set and were robust to varying approaches to replacement of missing variables. CONCLUSIONS: We used a public health case management data system to build and validate 4 accurate, well-calibrated, robust clinical prediction rules for COVID-19 mortality in Ontario, Canada. While these rules need external validation, they may be useful tools for management, risk stratification, and clinical trials.

Identifying and selecting implementation theories, models and frameworks: a qualitative study to inform the development of a decision support tool.

BACKGROUND: Implementation theories, models and frameworks offer guidance when implementing and sustaining healthcare evidence-based interventions. However, selection can be challenging given the myriad of potential options. We propose to inform a decision support tool to facilitate the appropriate selection of an implementation theory, model or framework in practice. To inform tool development, this study aimed to explore barriers and facilitators to identifying and selecting implementation theories, models and frameworks in research and practice, as well as end-user preferences for features and functions of the proposed tool. METHODS: We used an interpretive descriptive approach to conduct semi-structured interviews with implementation researchers and practitioners in Canada, the United States and Australia. Audio recordings were transcribed verbatim. Data were inductively coded by a single investigator with a subset of 20% coded independently by a second investigator and analyzed using thematic analysis. RESULTS: Twenty-four individuals participated in the study. Categories of barriers/facilitators, to inform tool development, included characteristics of the individual or team conducting implementation and characteristics of the implementation theory, model or framework. Major barriers to selection included inconsistent terminology, poor fit with the implementation context and limited knowledge about and training in existing theories, models and frameworks. Major facilitators to selection included the importance of clear and concise language and evidence that the theory, model or framework was applied in a relevant health setting or context. Participants were enthusiastic about the development of a decision support tool that is user-friendly, accessible and practical. Preferences for tool features included key questions about the implementation intervention or project (e.g., purpose, stage of implementation, intended target for change) and a comprehensive list of relevant theories, models and frameworks to choose from along with a glossary of terms and the contexts in which they were applied. CONCLUSIONS: An easy to use decision support tool that addresses key barriers to selecting an implementation theory, model or framework in practice may be beneficial to individuals who facilitate implementation practice activities. Findings on end-user preferences for tool features and functions will inform tool development and design through a user-centered approach.

Understanding context: A concept analysis.

AIMS: To conduct a concept analysis of clinical practice contexts (work environments) in health care. BACKGROUND: Context is increasingly recognized as important to the development, delivery, and understanding of implementation strategies; however, conceptual clarity about what comprises context is lacking. DESIGN: Modified Walker and Avant concept analysis comprised of five steps: (1) concept selection; (2) determination of aims; (3) identification of uses of context; (4) determination of its defining attributes; and (5) definition of its empirical referents. METHODS: A wide range of databases were systematically searched from inception to August 2014. Empirical articles were included if a definition and/or attributes of context were reported. Theoretical articles were included if they reported a model, theory, or framework of context or where context was a component. Double independent screening and data extraction were conducted. Analysis was iterative, involving organizing and reorganizing until a framework of domains, attributes. and features of context emerged. RESULT: We identified 15,972 references, of which 70 satisfied our inclusion criteria. In total, 201 unique features of context were identified, of these 89 were shared (reported in two or more studies). The 89 shared features were grouped into 21 attributes of context which were further categorized into six domains of context. CONCLUSION: This study resulted in a framework of domains, attributes and features of context. These attributes and features, if assessed and used to tailor implementation activities, hold promise for improved research implementation in clinical practice.