Publications by authors named "C Webber"

Objective: The COVID-19 pandemic highlighted and exacerbated health inequities worldwide. While several studies have examined the impact of individual social factors on COVID infection, our objective was to examine how interactions of social factors were associated with the risk of testing positive for SARS-CoV-2 during the first two years of the pandemic.

Study Design And Setting: We conducted an observational cohort study using linked health administrative data for Ontarians tested for SARS-CoV-2 between January 1st, 2020, and December 31st, 2021.

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Objectives: To examine transitions to a nursing home among residents of assisted living relative to community-dwelling home care recipients.

Design: Population-based retrospective cohort study emulating a target trial.

Setting And Participants: Linked, individual-level health system data were obtained from older adults (≥65 years of age) who made an incident application for a bed in a nursing home in Ontario, Canada, between April 1, 2014, and March 31, 2019, and were followed until December 31, 2019.

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Introduction: Older adults are at increased risk of severe illness and mortality from Coronavirus disease of 2019 (COVID-19) infection. However, public health strategies aimed at reducing spread of COVID-19 may have resulted in increased mental health symptoms, particularly among older adults. Currently, little is known about whether older Veterans were more likely to experience persistent mental health symptoms during the COVID-19 pandemic than non-Veterans.

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Article Synopsis
  • A survival prognostication model was developed for pancreatic ductal adenocarcinoma (PDAC) patients using clinical data, patient-reported symptoms, and treatment specifics to aid in their treatment decisions.
  • The study analyzed data from 17,450 PDAC patients in Ontario over a 13-year period, finding significant predictors of survival, including tumor location, disease stage, hospitalizations, heart failure, and various pain and performance scores.
  • The model demonstrated good predictive accuracy with a C index of 0.76, suggesting it could enhance shared decision-making between patients and healthcare providers regarding treatment options.
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