SARS‐CoV‐2 seroprevalence among blood donors after the first COVID‐19 wave in Canada
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it