Influenza Hospitalization Burden by Subtype, Age, Comorbidity, and Vaccination Status: 2012–2013 to 2018–2019 Seasons, Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Influenza immunization programs aim to reduce the risk and burden of severe outcomes. To inform optimal program strategies, we monitored influenza hospitalizations over 7 seasons, stratified by age, comorbidity, and vaccination status. METHODS: We assembled data from 4 hospitals involved in an active surveillance network with systematic collection of nasal samples and polymerase chain reaction testing for influenza virus in all patients admitted through the emergency department with acute respiratory infection during the 2012-2013 to 2018-2019 influenza seasons in Quebec, Canada. We estimated seasonal, population-based incidence of influenza-associated hospitalizations by subtype predominance, age, comorbidity, and vaccine status, and derived the number needed to vaccinate to prevent 1 hospitalization per stratum. RESULTS: The average seasonal incidence of influenza-associated hospitalization was 89/100 000 (95% confidence interval, 86-93), lower during A(H1N1) (49-82/100 000) than A(H3N2) seasons (73-143/100 000). Overall risk followed a J-shaped age pattern, highest among infants 0-5 months and adults ≥75 years old. Hospitalization risks were highest for children <5 years old during A(H1N1) but for highest adults aged ≥75 years during A(H3N2) seasons. Age-adjusted hospitalization risks were 7-fold higher among individuals with versus without comorbid conditions (214 vs 30/100 000, respectively). The number needed to vaccinate to prevent hospitalization was 82-fold lower for ≥75-years-olds with comorbid conditions (n = 1995), who comprised 39% of all hospitalizations, than for healthy 18-64-year-olds (n = 163 488), who comprised just 6% of all hospitalizations. CONCLUSIONS: In the context of broad-based influenza immunization programs (targeted or universal), severe outcome risks should be simultaneously examined by subtype, age, comorbidity, and vaccine status. Policymakers require such detail to prioritize promotional efforts and expenditures toward the greatest and most efficient program impact.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".