Influenza hospitalization burden by subtype, age, comorbidity and vaccination status: 2012/13 to 2018/19 seasons, Quebec, Canada
Bibliographic record
Abstract
ABSTRACT Background The primary objective of influenza immunization programs is to reduce the risk and burden of severe outcomes. To inform optimal program strategies, we monitored influenza hospitalizations over several seasons of varying subtype predominance, stratified by age, comorbidity and vaccination status. Methods We assembled data from an active hospital-based surveillance network involving systematic swabbing and PCR-confirmation of influenza virus infection by type/subtype during peak-weeks of seven influenza seasons (2012/13 to 2018/19) in Quebec, Canada. We estimated seasonal, population-based incidence of influenza-associated hospitalizations (interpreted as risk) by subtype, age, comorbidity and vaccine status, and derived the number-needed-to-vaccinate to prevent one hospitalization per stratum. Results The average seasonal incidence of influenza-associated hospitalization was 89/100,000 (95%CI: 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. Hospitalization risks were highest for children <5 years during A(H1N1) but for adults ≥75 years during A(H3N2) subtype- predominant seasons. Age-adjusted hospitalization risks were 7-fold higher among individuals with versus without comorbidities (214 versus 30/100,000). The number-needed-to-vaccinate to prevent hospitalization was 82-fold lower for ≥75-years-olds with comorbidity (n=1,995), 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 further promotional efforts and expenditures toward the greatest and most efficient program impact. 40-word summary This hospital-based study involving systematic PCR testing over seven seasons revealed important differences in influenza hospitalization risk by subtype, age, comorbidity, and vaccination status. The findings highlight the need for data-driven decision-making to optimize vaccination strategies and minimize healthcare burden.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".