1823. Disproportionate Rates of COVID-19 Among Black Canadian Communities: Lessons from the First Year of the Pandemic
Bibliographic record
Abstract
Abstract Background Black North American communities have been disproportionately affected by COVID-19. These data have been largely based on case counts, hospitalizations and mortality data. Serologic testing enables a more complete determination of infection burden by documenting infection in persons with symptomatic as well as asymptomatic infection. We used serologic testing to determine the extent to which SARS-CoV-2 had penetrated into the Black community. We examined risk factors associated with seropositivity, including the presence of medical comorbidities and the social determinants of health. Methods We conducted a cross-sectional survey in a COVID-19 high-prevalence zone in Ontario along with 2 areas that have lower rates of COVID-19 cases. SARS-CoV-2 IgG antibodies were determined using the EUROIMMUN assay. The study samples were collected between August 15, 2020, and December 15, 2020 prior to the deployment of COVID-19 vaccines. Proportions were compared using Fishers Exact test or chi-square; potential risk factors were examined using a multiple logistic regression approach. Results Among 387 evaluable subjects, the majority, 274 (70.8%) were enrolled from northwest Greater Toronto Area (GTA) and adjoining suburban areas of Peel, Ontario with a high proportion of Black residents. The seropositivity rates for the lower prevalence areas (Oakville and London, Ontario) were comparable (3.3% (2/60; 95% CI 0.4-11.5) and 3.9% (2/51; 95% CI 0.5-13.5), respectively). The seropositivity rate for the northwest GTA was 12.6% (26/206); RR 3.5, 95% CI 1.3-9.8). Persons under the age of 19 years had the highest seropositivity rate (10/50; 20.0%, 95% CI 10.3-33.7%). Front-line workers were greater than 3 times more likely to be seropositive compared with non-frontline workers (13.0 vs 3.2%; p=.01; RR 3.3 (95% CI 1.3 – 8.3). There was an interaction effect between race and location of residence as this relates to the relative risk of seropositivity. Conclusion During the pre-vaccine phase of the COVID-19 pandemic, the seropositivity rate for SARS-CoV-2 within a COVID-19 high-prevalence area was 3-fold greater than lower prevalence areas of Ontario, Canada. The data help to define the burden of COVID-19 within a community with a high proportion of Black residents compared with other communities. Disclosures All Authors: No reported disclosures
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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) | 0.006 | 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".