Comparison of influenza and COVID-19 hospitalizations in British Columbia, Canada: a population-based study
Bibliographic record
Abstract
Abstract Objective To compare the population rate of COVID-19 and influenza hospitalizations by age, COVID-19 vaccine status and pandemic phase. Design Observational retrospective study Setting Residents of British Columbia (population 5.3 million), Canada Participants Hospitalized patients due to COVID-19 or historical influenza Main outcome measures This population based study in a setting with universal healthcare coverage, used COVID-19 case and hospital data for COVID-19 and influenza. Admissions were selected from March 2020 to February 2021 for the annual cohort and the first 8 weeks of 2022 for the peak cohort of COVID-19 (Omicron era). Influenza annual and peak cohorts were from three years with varying severity: 2009/10, 2015/16, and 2016/17. We estimated hospitalization rates per 100,000 population by age group. Results Similar to COVID-19 with median age 66 (Q1-Q3 44-80), influenza 2016/17 mostly affected older adults, with median age 78 (64-87). COVID-19 and influenza 2016/17 hospitalization rate by age group were “J” shaped. The rates for mostly unvaccinated COVID-19 patients in 2020/21 in the context of public health restrictions were significantly higher than influenza among individuals 30 to 69 years of age, and comparable to a severe influenza year (2016/17) among 70+. In early 2022 (Omicron peak), rates primarily due to COVID-19 among vaccinated adults were comparable with influenza 2016/17 in all age groups while rates among unvaccinated COVID-19 patients were still higher than influenza among 18+. In the pediatric population, COVID-19 hospitalization rates were similar to or lower than influenza. Conclusions Our paper highlighted the greater population-level impact of COVID-19 compared with influenza in terms of adult hospitalizations, especially among those unvaccinated. However, influenza had greater impact than COVID-19 among <18 regardless of vaccine status or the circulating variant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| 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) | 0.002 | 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".