Hemagglutination-Inhibition Antibodies and Protection against Influenza Elicited by Inactivated and Live Attenuated Vaccines in Children
Bibliographic record
Abstract
BACKGROUND: Hemagglutinin (HA)-inhibiting antibodies contribute to the immune defense against influenza infection. However, there are insufficient data on the extent of correlation between vaccine-elicited HA antibodies and protection in children against different influenza strains, particularly when comparing live attenuated influenza vaccines (LAIV) versus inactivated influenza vaccines (IIV). METHODS: We measured postvaccination hemagglutination-inhibition (HAI) titers in 3-15-year-old participants of a cluster-randomized controlled trial of trivalent LAIV(3) versus IIV(3) in Canadian Hutterite colonies. We assessed HAI titers as predictors of symptomatic, reverse transcription polymerase chain reaction (RT-PCR)-confirmed influenza over 3 influenza seasons using Cox proportional hazards regression models with vaccine type as a covariate. RESULTS: For each log2 unit increase in postvaccination HAI against A/H1N1 in 2013-2014, A/H3N2 2014-2015, and B/Yamagata in 2013-2014 (each the predominant circulating strain for the respective influenza season), the reduction in the risk of confirmed influenza was equal to 29.6% (95% confidence interval [CI], 17.1%-39.5%), 34.8% (95% CI, 17.2%-47.9%), and 31.8% (95% CI, 23.8%-38.5%), respectively. No reduction in the risk of influenza was observed with B/Yamagata-specific HAI titers in 2012-2013, which was dominated by a mixture of Yamagata and Victoria strains. Despite the overall lower HAI titers in the LAIV3 group, both H1N1 and H3N2 HAI titers were associated with protection against subtype matched influenza. CONCLUSIONS: Both LAIV3- and IIV3-elicited HA antibodies are associated with protection against influenza infection in seasons when the vaccine strains match the circulating influenza strain subtypes, supporting the use of HAI as a correlate of protection for both vaccine types in children.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".