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Background: The effects of the COVID-19 pandemic have been more pronounced for socially disadvantaged populations. We sought to determine how access to SARS-CoV-2 testing and the likelihood of testing positive for COVID-19 were associated with demographic factors, socioeconomic status (SES) and social determinants of health (SDH) in three Canadian provinces. Methods: An observational population-based cross-sectional study was conducted for the provinces of Ontario, Manitoba and New Brunswick between March 1, 2020 and April 27, 2021, using provincial health administrative data. After excluding residents of long-term care homes, those without current provincial health insurance and those who were tested for COVID-19 out of province, records from provincial healthcare administrative databases were reviewed for 16,900,661 healthcare users. Data was modelled separately for each province in accordance to a prespecified protocol and follow-up consultations among provincial statisticians and collaborators. We employed univariate and multivariate regression models to examine determinants of testing and test results. Results: After adjustment for other variables, female sex and urban residency were positively associated with testing, while female sex was negatively associated with test positivity. In New Brunswick and Ontario, individuals living in higher income areas were more likely to be tested, whereas in Manitoba higher income was negatively associated with both testing and positivity. High ethnocultural composition was associated with lower testing rates. Both high ethnocultural composition and high situational vulnerability increased the odds of testing positive for SARS-CoV-2. Discussion: We observed that multiple demographic, income and SDH factors were associated with SARS-CoV-2 testing and test positivity. Barriers to healthcare access identified in this study specifically relate to COVID-19 testing but may reflect broader inequities for certain at-risk groups. [ABSTRACT FROM AUTHOR]

Background: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. Methods: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARSCoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. Results: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. Interpretation: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Objectives: Existing tools to predict the risk of complications among people with type 2 diabetes poorly discriminate high- from low-risk patients. Our aim in this study was to develop risk prediction scores for major type 2 diabetes complications using real-world clinical care data, and to externally validate these risk scores in a different jurisdiction., Methods: Using health-care administrative data and electronic medical records data, risk scores were derived using data from 25,088 people with type 2 diabetes from the Canadian province of Ontario, followed between 2002 and 2017. Scores were developed for major clinically important microvascular events (treatment for retinopathy, foot ulcer, incident end-stage renal disease), cardiovascular disease events (acute myocardial infarction, heart failure, stroke, amputation), and mortality (cardiovascular, noncardiovascular, all-cause). They were then externally validated using the independent data of 11,416 people with type 2 diabetes from the province of Manitoba., Results: The 10 derived risk scores had moderate to excellent discrimination in the independent validation cohort, ranging from 0.705 to 0.977. Their calibration to predict 5-year risk was excellent across most levels of predicted risk, albeit with some displaying underestimation at the highest levels of predicted risk., Conclusions: The DIabeteS COmplications (DISCO) risk scores for major type 2 diabetes complications were derived and externally validated using contemporary real-world clinical data. As a result, they may be more accurate than other risk prediction scores derived using randomized trial data. The use of more accurate risk scores in clinical practice will help improve personalization of clinical care for patients with type 2 diabetes., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)