Using Laboratory-Confirmed Outcomes to Study Pediatric Influenza and Influenza Vaccine Epidemiology in Ontario
Bibliographic record
Abstract
Annual epidemics of seasonal influenza continue to cause substantial morbidity in young children. Influenza is among several respiratory viruses that cause illness in children, in addition to a large burden on the healthcare system and society, but is the only one for which a vaccine is available. In this dissertation, I present three studies regarding the epidemiology of influenza in children under the age of five years using a novel approach of linking laboratory and health administrative data in Ontario. In a systematic review and meta-analysis of children presenting to healthcare who are tested for influenza, I found that 20% (95%CI 15%-25%) have laboratory-confirmed influenza, with variation across subgroups. I found that influenza represents a large overall burden of disease and a substantial proportion of healthcare encounters for respiratory illnesses. In a retrospective cohort study, I compared the characteristics, outcomes, and relative severity of illness of children who were hospitalized and tested for influenza A, influenza B, and respiratory syncytial virus (RSV) and were positive for only a single virus. I found that in-hospital outcomes and post-discharge healthcare utilization in children with no identified comorbidities were similar by virus type, but post-discharge healthcare utilization was higher for those with influenza in those with underlying comorbidities. I observed similar severity of illness, based on in-hospital outcomes, and cost of hospitalization between viruses. In a test-negative study, I estimated vaccine effectiveness (VE) against laboratory-confirmed influenza hospitalizations in children aged 6-59 months for the 2010-11 to 2013-14 seasons. I found that overall, VE was 51% (95%CI 38%-61%) for any vaccination, with variation by vaccination status (full vs. partial), season, age group, and subtype. These results indicate that large numbers of pediatric hospitalizations resulting from influenza infection could be prevented by promoting seasonal influenza vaccination each year. Overall, these results contribute to our understanding of pediatric respiratory viruses in Ontario, with each individual chapter adding to our knowledge regarding burden, severity, and prevention of influenza infection. These results can inform families, clinicians, public health practitioners, and policy makers in order to help reduce the impact of annual influenza infection in young 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".