Anti-SARS-CoV-2 antibody levels in a cohort of health care workers before and after the Omicron wave in Canada
Bibliographic record
Abstract
Introduction: We measured anti-S immunoglobulin G (IgG) levels in a cohort of health care workers (HCWs) to explore factors affecting the levels of vaccine-induced IgG antibodies and their relationship with risk of incident SARS-CoV-2 infection throughout the first seven epidemic waves. Methods: A convenience sample of HCWs from one acute care hospital and four long-term care homes had anti-S SARS-CoV-2 IgG antibody levels at the beginning of the pandemic (T1) and during Omicron waves 5-7 (T2). Poisson analysis was conducted to assess predicted levels of antibodies by covariates (health and social conditions), number, timing and type of vaccines, as well as history of previous SARS-CoV-2 infection. Antibody levels assessed between October 2021 and August 2022 were also analyzed in relation to incident cases of Omicron infections. Results: Of the HCWs who provided one (n = 128) or two blood samples (n = 146), 53% were vaccine naïve at T1 and 1.4% were so at T2. The mean SARS-CoV-2 IgG concentration was 648 binding antibody units/mL at T1 and 1,913 binding antibody units/mL at T2. Income insufficiency and the presence of more than one chronic condition were associated with lower antibody levels at T2. Antibody levels were higher in HCWs with prior SARS-CoV-2 infection and increased with more vaccine doses received. Hybrid immunity elicited higher levels of antibodies in HCWs at T1 and T2. Waning of antibody levels over time was seen after vaccination with a third dose at T2. A correlation between antibody levels and subsequent risk of Omicron infection was not found. Conclusions: Our results suggest that timing and prioritization of anti-SARS-CoV-2 vaccination needs to consider the health and socioeconomic factors of HCWs, and the waning effects of vaccines.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".