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Background: Disability-related considerations have largely been absent from the COVID-19 response, despite evidence that people with disabilities are at elevated risk for acquiring COVID-19. We evaluated clinical outcomes in- patients who were admitted to hospital with COVID-19 with a disability compared with patients without a disability. Methods: We conducted a retrospective cohort study that included adults with COVID-19 who were admitted to hospital and discharged between Jan. 1, 2020, and Nov. 30, 2020, at 7 hospitals in Ontario, Canada. We compared in-hospital death, admission to the intensive care unit (ICU), hospital length of stay and unplanned 30-day readmission among patients with and without a physical disability, hearing or vision impairment, traumatic brain injury, or intellectual or developmental disability, overall and stratified by age ([less than or equal to] 64 and [greater than or equal to] 65 yr) using multivariable regression, controlling for sex, residence in a long-term care facility and comorbidity. Results: Among 1279 admissions to hospital for COVID-19, 22.3% had a disability. We found that patients with a disability were more likely to die than those without a disability (28.1% v. 17.6%), had longer hospital stays (median 13.9 v. 7.8 d) and more readmissions (17.6% v. 7.9%), but had lower ICU admission rates (22.5% v. 28.3%). After adjustment, there were no statistically significant differences between those with and without disabilities for in-hospital death or admission to ICU. After adjustment, patients with a disability had longer hospital stays (rate ratio 1.36, 95% confidence interval [CI] 1.19-1.56) and greater risk of readmission (relative risk 1.77, 95% CI 1.14-2.75). In age-stratified analyses, we observed longer hospital stays among patients with a disability than in those without, in both younger and older subgroups; readmission risk was driven by younger patients with a disability. Interpretation: Patients with a disability who were admitted to hospital with COVID-19 had longer stays and elevated readmission risk than those without disabilities. Disability-related needs should be addressed to support these patients in hospital and after discharge., A successful public health response to the COVID-19 pandemic requires accurate and timely identification of, and support for, high-risk groups. There is increasing recognition that marginalized groups, including congregate care [...]

CONTEXTE: Les caracteristiques des patients, les soins cliniques, l'utilisation des ressources et les issues cliniques des personnes atteintes de la maladie a coronavirus 2019 (COVID-19) hospitalisees au Canada ne sont pas bien connus. METHODES: Nous avons recueilli des donnees sur tous les adultes hospitalises atteints de la COVID-19 ou de l'influenza ayant obtenu leur conge d'unites medicales ou d'unites de soins intensifs medicaux et chirurgicaux entre le 1er novembre 2019 et le 30 juin 2020 dans 7 centres hospitaliers de Toronto et de Mississauga (Ontario). Nous avons compare les issues cliniques des patients a l'aide de modeles de regression multivariee, en tenant compte des facteurs sociodemographiques et de l'intensite des comorbidites. Nous avons valide le degre d'exactitude de 7 scores de risque mis au point a l'externe pour determiner leur capacite a predire le risque de deces chez les patients atteints de la COVID-19. RESULTATS: Parmi les hospitalisations retenues, 1027 patients etaient atteints de la COVID-19 (age median de 65 ans, 59,1% d'hommes) et 783 etaient atteints de l'influenza (age median de 68 ans, 50,8% d'hommes). Les patients ages de moins de 50 ans comptaient pour 21,2% de toutes les hospitalisations dues a la COVID-19 et 24,0% des sejours aux soins intensifs. Com pa rativement aux patients atteints de l'influenza, les patients atteints de la COVID-19 presentaient un taux de mortalite perhospitaliere (mortalite non ajustee 19,9% c. 6,1%; risque relatif [RR] ajuste 3,46%, intervalle de confiance [IC] a 95% 2,56-4,68) et un taux d'utilisation des ressources des unites de soins intensifs (taux non ajuste 26,4% c. 18,0%; RR ajuste 1,50, IC a 95% 1,25-1,80) significativemen t plus eleves, ainsi qu'une duree d'hospitalisation (duree mediane non ajustee 8,7 jours c. 4,8 jours; rapport des taux d'incidence ajuste 1,45; IC a 95% 1,25-1,69) significativement plus longue. Le taux de rehospitalisation dans les 30 jours n'etait pas significativement different (taux non ajuste 9,3% c. 9,6%; RR ajuste 0,98%, IC a 95% 0,70-1,39). Trois scores de risque utilisant un pointage pour predire la mortalite perhospitaliere ont montre une bonne discrimination (aire sous la courbe [ASC] de la fonction d'efficacite du recepteur [ROC] 0,72-0,81) et une bonne calibration. INTERPRETATION: Durant la premiere vague de la pandemie, l'hospitalisation des patients atteints de la COVID-19 etait associee a des taux de mortalite et d'utilisation des ressources des unites de soins intensifs et a une duree d'hospitalisation significativement plus importants que les hospitalisations des patients atteints de l'influenza. De simples scores de risque peuvent predire avec une bonne exactitude le risque de mortalite perhospitaliere des patients atteints de la COVID-19., Des etudes internationales ont rapporte que les patients hospitalises en raison de la maladie a coronavirus 2019 (COVID-19) presentaient des taux eleves de maladie grave et de mortalite (1-5). Deux [...]

BACKGROUND: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19. RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration. INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy., International studies report that patients admitted to hospital with coronavirus disease 2019 (COVID-19) have high rates of critical illness and mortality. (1-5) Two small Canadian case series have described care [...]

Sudden cardiac arrest is a significant public health concern. Successful treatment of cardiac arrest is extremely time sensitive, and use of an automated external defibrillator (AED) where possible significantly increases the probability of survival. Placement of AEDs in public locations can improve survival by enabling bystanders to treat victims of cardiac arrest prior to the arrival of emergency medical responders, thus shortening the time between collapse and treatment. However, since the exact locations of future cardiac arrests cannot be known a priori, AEDs must be placed strategically in public locations to ensure their accessibility in the event of an out-of-hospital cardiac arrest emergency. In this paper, we propose a data-driven optimization model for deploying AEDs in public spaces while accounting for uncertainty in future cardiac arrest locations. Our approach involves discretizing a continuous service area into a large set of scenarios, where the probability of cardiac arrest at each location is itself uncertain. We model uncertainty in the spatial risk of cardiac arrest using a polyhedral uncertainty set that we calibrate using historical cardiac arrest data. We propose a solution technique based on row-and-column generation that exploits the structure of the uncertainty set, allowing the algorithm to scale gracefully with the total number of scenarios. Using real cardiac arrest data from the City of Toronto, we conduct an extensive numerical study on AED deployment public locations. We find that hedging against cardiac arrest location uncertainty can produce AED deployments that outperform an intuitive sample average approximation by 9%-15% and cuts the performance gap with respect to an ex post model by half. Our findings suggest that accounting for cardiac arrest location uncertainty can lead to improved accessibility of AEDs during cardiac arrest emergencies and the potential for improved survival outcomes. Funding: This research was partially supported by the National Science Foundation [CMMI 1265671], the National Natural Science Foundation of China [Grants 71210002 and 71332005], the Natural Sciences and Engineering Research Council of Canada, and the Ontario Ministry of Training, Colleges and Universities. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/opre.2017.1660. Keywords: robust optimization * row-and-column generation * healthcare operations * facility location, 1. Introduction Sudden cardiac arrest is a leading cause of death in North America and is responsible for more than 400,000 deaths each year (Heart and Stroke Foundation 2013, Mozaffarian [...]

We generalize the standard method of solving inverse optimization problems to allow for the solution of inverse problems that would otherwise be ill posed or infeasible. In multiobjective linear optimization, [...]

Radiation therapy is subject to uncertainties that need to be accounted for when determining a suitable treatment plan for a cancer patient. For lung and liver tumors, the presence of [...]