Exploring concerns associated with the vaccination of children and adolescents with cystic fibrosis with the live attenuated intranasal influenza vaccine
Bibliographic record
Abstract
The live-attenuated intranasal influenza virus vaccine (LAIV) is one of two vaccines recommended for seasonal use in the pediatric population with cystic fibrosis (CF) in Canada. The acknowledged benefits of LAIV must be balanced against concerns stemming from the inoculation of CF patients, that have pre-existing respiratory and inflammatory problems, with live influenza viruses. The overall aim of my doctoral thesis was thus to explore the effects of vaccination with LAIV in patients with CF, 2-19 years of age, using previously collected prospective data.Limited research exists evaluating the type, timing, frequency and duration of adverse events following LAIV in children and adolescents with CF; I aimed to fill these knowledge gaps in the first manuscript. Specifically, using Bayesian change point analysis methodology, I determined that the risk of reporting at least one respiratory, systemic, localized and/or gastrointestinal symptom was highest during days 0-6 following vaccination. I then estimated the relative incidence of adverse events following LAIV in days 0-6 compared to days 7-55 following vaccination using adjusted, hierarchical, zero-inflated Poisson regression models. While I found an increased risk of reporting all solicited symptoms in the first week compared to subsequent weeks following LAIV, these symptom episodes were acute, often lasting 1-2 days. I did not observe an increased risk of hospitalization or antibiotic prescription for respiratory problems in the risk period compared to the control period. Another concern associated with the administration of LAIV is the detection, shedding, and thus the potential transmission, of LAIV viruses from recent vaccinees to other individuals. In the second manuscript, I explored the detection profile of influenza viruses following LAIV in pediatric vaccinees with CF. Overall, the detection of influenza viruses occurred up to at least 7 days following administration of LAIV in patients with CF and their healthy siblings. Using a Bayesian hierarchical logistic regression model, I determined that increasing age (in years) was associated with decreasing odds of influenza detection following LAIV. Results further demonstrated that patients with CF had higher odds of influenza detection on both the first and second days following LAIV administration compared to healthy participants. Lastly, it remains unclear whether the detection of influenza viruses differs in LAIV vaccinees with pre-existing respiratory virus infections – which may be attributable to a phenomenon known as viral interference. Thus, in the third manuscript, I explored the potential impact of a non-influenza respiratory virus (NIRV) in individuals recently vaccinated with LAIV on the detection of influenza RNA in the week following vaccination. I determined that the observed proportion of subjects in whom influenza RNA was detected and the duration of detection differed slightly between NIRV-positive and -negative subjects. However, wide credible intervals preclude definitive conclusions. I also determined that the imperfect diagnostic sensitivity and specificity of the tests used to identify NIRVs and influenza did not greatly impact results.Knowledge gained from my doctoral thesis contributes to the body of evidence used in the determination of LAIV safety in eligible vaccinees 2-19 years old with CF, helps to better understand the epidemiology of the vaccine virus and serves as the springboard for multiple future research projects.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".