1188. The Effect Of The COVID-19 Pandemic On Influenza-Related Hospitalization, Intensive Care Admission And Mortality In Canadian Children
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic resulted in unprecedented implementation of wide-ranging public health measures globally. During the pandemic, dramatic decreases in seasonal influenza virus detection have been reported worldwide. Information on pediatric influenza-related hospitalizations is limited. We describe influenza-related hospitalization in Canadian children during the 2020/2021 influenza season compared to ten previous seasons. Methods Data on influenza-related hospitalizations, intensive care unit (ICU) admissions and in-hospital deaths in children across Canada were obtained from the Canadian Immunization Monitoring Program, ACTive (IMPACT). This national surveillance initiative comprises 90% of all tertiary care pediatric beds in Canada. The total study period included eleven influenza seasons from September 2010 to April 2021 inclusive. Time series modelling was used to compare trends in influenza-related hospitalizations during the 2020/2021 season (September 2020 to April 2021 inclusive) with the ten previous seasons. Results During the 2020/2021 influenza season there were no pediatric influenza infection-related hospitalizations. This was a significant decrease compared to the predicted total influenza-related hospitalizations for this period (p< 0.001). No pediatric ICU admission or deaths were reported for the 2020/2021 influenza season. Conclusion We show complete absence of influenza infection-related hospitalization in children in Canada during the 2020/2021 season. This significant decrease is likely related in large part to non-pharmacological public health interventions implemented during the COVID-19 pandemic, although the potential role of viral interference is unknown. Our findings suggest measures such as use of facemasks, hand-washing, distancing and school closures may be beneficial for influenza control and mitigation of future influenza epidemics. Disclosures Helen E. Groves, PhD, MBBCh BAO, Abbvie (Other Financial or Material Support, Dr. Groves reports personal fees from Honoraria received from Abbvie for education meeting presentation, not relevant to the submitted work.) Jesse Papenburg, MD, AbbVie (Grant/Research Support, Other Financial or Material Support, Personal fees)Medimmune (Grant/Research Support)Sanofi Pasteur (Grant/Research Support)Seegene (Grant/Research Support, Other Financial or Material Support, Personal fees) Manish Sadarangani, BM BCh, DPhil, GlaxoSmithKline (Grant/Research Support)Merck (Grant/Research Support)Pfizer (Grant/Research Support)Sanofi Pasteur (Grant/Research Support)Seqirus (Grant/Research Support)Symvivo (Grant/Research Support)VBI Vaccines (Research Grant or Support) Shaun Morris, MD, MPH, DTM&H, FRCPC, FAAP, GSK (Speaker’s Bureau)Pfizer (Advisor or Review Panel member)Pfizer (Grant/Research Support)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 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".