SARS-CoV-2 seroprevalence in Nova Scotia blood donors
Bibliographic record
Abstract
Background: SARS-CoV-2 seroprevalence monitors cumulative infection rates irrespective of case testing protocols. We aimed to describe Nova Scotia blood donor seroprevalence in relation to public health policy and reported data over the course of the COVID-19 pandemic (May 2020 to August 2022). Methods: Monthly random Nova Scotia blood donation samples (24,258 in total) were tested for SARS-CoV-2 infection antibodies (anti-nucleocapsid) from May 2020 to August 2022, and vaccination antibodies (anti-spike) from January 2021 to August 2022. Multivariable logistic regression for infection antibodies and vaccination antibodies separately with month, age, sex, and racialization identified independent predictors. The provincial nucleic acid amplification test (NAAT)-positive case rate over the pandemic was calculated from publicly available data. Results: Anti-N seroprevalence was 3.8% in January 2022, increasing to 50.8% in August 2022. The general population COVID-19 case rate was 3.5% in January 2022, increasing to 12.5% in August 2022. The percentage of NAAT-positive samples in public health laboratories increased from 1% in November 2021 to a peak of 30.7% in April 2022 with decreasing numbers of tests performed. Higher proportions of younger donors as well as Black, Indigenous, and racialized blood donors were more likely to have infection antibodies ( p < 0.01). Vaccination antibodies increased to 100% over 2021, initially in older donors (60+ years), and followed by progressively younger age groups. Conclusions: SARS-CoV-2 infection rates were relatively low in Nova Scotia until the more contagious Omicron variant dominated, after which about half of Nova Scotia donors had been infected despite most adults being vaccinated (although severity was much lower in vaccinated individuals). Most COVID-19 cases were detected by NAAT until Omicron arrived. When NAAT testing priorities focused on high-risk individuals, infection rates were better reflected by seroprevalence.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".