Disproportionate Rates of COVID-19 Among Black Canadian Communities: Lessons from a Cross-Sectional Study in the First Year of the Pandemic
Bibliographic record
Abstract
BACKGROUND: Racialized communities, including Black Canadians, have disproportionately higher COVID-19 cases. We examined the extent to which SARS-CoV-2 infection has affected the Black Canadian community and the factors associated with the infection. METHODS: We conducted a cross-sectional survey in an area of Ontario (northwest Toronto/Peel Region) with a high proportion of Black residents along with 2 areas that have lower proportions of Black residents (Oakville and London, Ontario). SARS-CoV-2 IgG antibodies were determined using the EUROIMMUN assay. The study was conducted between August 15, 2020, and December 15, 2020. RESULTS: Among 387 evaluable subjects, the majority, 273 (70.5%), were enrolled from northwest Toronto and adjoining suburban areas of Peel, Ontario. The seropositivity values for Oakville and London were comparable (3.3% (2/60; 95% CI 0.4-11.5) and 3.9% (2/51; 95% CI 0.5-13.5), respectively). Relative to these areas, the seropositivity was higher for the northwest Toronto/Peel area at 12.1% (33/273), relative risk (RR) 3.35 (1.22-9.25). Persons 19 years of age or less had the highest seropositivity (10/50; 20.0%, 95% CI 10.3-33.7%), RR 2.27 (1.23-3.59). There was a trend for an interaction effect between race and location of residence as this relates to the relative risk of seropositivity. INTERPRETATION: During the early phases of the pandemic, the seropositivity within a COVID-19 high-prevalence zone was threefold greater than lower prevalence areas of Ontario. Black individuals were among those with the highest seroprevalence of SARS-CoV-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".