SARS‐CoV‐2 seroprevalence among blood donors after the first COVID‐19 wave in Canada
Bibliographic record
Abstract
BACKGROUND: Case detection underestimates the burden of the COVID-19 pandemic. Following the first COVID-19 wave, we estimated the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among blood donors across Canada. STUDY DESIGN AND METHODS: This serial cross-sectional study was conducted between May 9 and July 21, 2020 from blood donors donating at all Canadian Blood Services locations. We used the Abbott Architect assay to detect SARS-CoV-2 IgG antibodies from retention plasma. Seroprevalence was standardized to population-level demographics and assay characteristics were adjusted using the Rogan-Gladen equation. Results were stratified by region, age, ethnicity, ABO groups, and quantiles of material and social deprivation indices. Temporal trends were evaluated at 2-week intervals. Univariate and multivariate logistic regression compared SARS-CoV-2 reactive to non-reactive donors by sociodemographic variables. RESULTS: Overall 552/74642 donors, had detectable antibodies, adjusted seroprevalence was 7.0/1000 donors (95% CI; 6.3, 7.6). Prevalence was differential by geography, Ontario had the highest rate, 8.8/1000 donors (7.8, 9.8), compared to the Atlantic region 4.5/1000 donors (2.6, 6.4); adjusted odds ratio (aOR) 2.2 (1.5, 3.3). Donors that self-identified as an ethnic minority were more likely than white donors to be sero-reactive aOR 1.5 (1.2, 1.9). No temporal trends were observed. DISCUSSION: Worldwide, blood services have leveraged their operational capacity to inform public health. While >99% of Canadians did not show humoral evidence of past infection, we found regional variability and disparities by ethnicity. Seroprevalence studies will continue to play a pivotal role in evaluating public health policies by identifying trends and monitor disparities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".