Predictors of SARS-CoV-2 anti-Spike IgG antibody levels following two COVID-19 vaccine doses among children and adults in the Canadian CHILD Cohort
Bibliographic record
Abstract
Abstract Background Vaccination helps prevent SARS-CoV-2 infection and severe COVID-19. However, vaccine-induced humoral immune responses vary among individuals and wane over time. We aimed to describe the SARS-CoV-2 anti-spike IgG antibody response to vaccination and identify health and demographic factors associated with this response among children and adults. Methods We studied a subset of double-vaccinated children (n= 151; mean age: 12 ±1.5 years, 46% female) and adults (n= 995; 44 ±6.0 years, 60% female) from the Canadian CHILD Cohort. Dried blood spots were collected over two time periods (March 2021 to September 2021; October 2021 to January 2022). Antibody levels were quantified using automated chemiluminescent ELISAs. Demographic, vaccination, and health data were collected via online questionnaires. Associations were determined using multivariable regression. Results Our cohort had SARS-CoV-2 anti-spike seropositivity rate of 97% following two COVID-19 vaccine doses. In both children and adults, the highest antibody levels were observed around three months post-vaccination and did not differ by biological sex. Higher antibody levels were associated with: prior SARS-CoV-2 infection (β=0.15 scaled luminescence units, 95%CI, 0.06-0.24), age <18 years (β=0.15, 95%CI 0.05-0.26) and receiving the Moderna mRNA (β=0.23, 95%CI 0.11-0.34) or Pfizer-BioNTech mRNA vaccines (β= 0.10, 95%CI, 0.02-0.18) vs. a combination of mRNA and Oxford-AstraZeneca viral vector vaccines. There were no differences in antibody levels when comparing a 3-8 vs. 9-16-week interval between vaccine doses. Interpretation We identified key factors associated with post-vaccination antibody responses in children and adults, which could help improve future vaccine development and deployment among different population subgroups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".