Burden of influenza, respiratory syncytial virus, and other respiratory viruses and the completeness of respiratory viral identification among respiratory inpatients, Canada, 2003‐2014
Bibliographic record
Abstract
BACKGROUND: A regression-based study design has commonly been used to estimate the influenza burden; however, these estimates are not timely and many countries lack sufficient virological data. Alternative approaches that would permit a timelier assessment of the burden, including a sentinel surveillance approach recommended by the World Health Organization (WHO), have been proposed. We aimed to estimate the hospitalization burden attributable to influenza, respiratory syncytial virus (RSV), and other respiratory viruses (ORV) and to assess both the completeness of viral identification among respiratory inpatients in Canada and the implications of adopting other approaches. METHODS: Respiratory inpatient records were extracted from the Canadian Discharge Abstract Database from 2003 to 2014. A regression model was used to estimate excess respiratory hospitalizations attributable to influenza, RSV, and ORV by age group and diagnostic category and compare these estimates with the number with a respiratory viral identification. RESULTS: An estimated 33 (95% CI: 29, 38), 27 (95% CI: 22, 33), and 27 (95% CI: 18, 36) hospitalizations per 100 000 population per year were attributed to influenza, RSV, and ORV, respectively. An influenza virus was identified in an estimated 78% (95% CI: 75, 81) and 17% (95% CI: 15, 21) of respiratory hospitalizations attributed to influenza for children and adults, respectively, and 75% of influenza-attributed hospitalizations had an ARI diagnosis. CONCLUSIONS: Hospitalization rates with respiratory viral identification still underestimate the burden. Approaches based on acute respiratory case definitions will likely underestimate the burden as well, although each proposed method should be compared with regression-based estimates of influenza-attributed burden as a way of assessing their validity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".