Comparison of influenza and COVID-19 hospitalisations in British Columbia, Canada: a population-based study
Bibliographic record
Abstract
INTRODUCTION: We compared the population rate of COVID-19 and influenza hospitalisations by age, COVID-19 vaccine status and pandemic phase, which was lacking in other studies. METHOD: We conducted a population-based study using hospital data from the province of British Columbia (population 5.3 million) in Canada with universal healthcare coverage. We created two cohorts of COVID-19 hospitalisations based on date of admission: annual cohort (March 2020 to February 2021) and peak cohort (Omicron era; first 10 weeks of 2022). For comparison, we created influenza annual and peak cohorts using three historical periods years to capture varying severity and circulating strains: 2009/2010, 2015/2016 and 2016/2017. We estimated hospitalisation rates per 100 000 population. RESULTS: COVID-19 and influenza hospitalisation rates by age group were 'J' shaped. The population rate of COVID-19 hospital admissions in the annual cohort (mostly unvaccinated; public health restrictions in place) was significantly higher than influenza among individuals aged 30-69 years, and comparable to the severe influenza year (2016/2017) among 70+. In the peak COVID-19 cohort (mostly vaccinated; few restrictions in place), the hospitalisation rate was comparable with influenza 2016/2017 in all age groups, although rates among the unvaccinated population were still higher than influenza among 18+. Among people aged 5-17 years, COVID-19 hospitalisation rates were lower than/comparable to influenza years in both cohorts. The COVID-19 hospitalisation rate among 0-4 years old, during Omicron, was higher than influenza 2015/2016 and 2016/2017 and lower than 2009/2010 pandemic. CONCLUSIONS: During first Omicron wave, COVID-19 hospitalisation rates were significantly higher than historical influenza hospitalisation rates for unvaccinated adults but were comparable to influenza for vaccinated adults. For children, in the context of high infection levels, hospitalisation rates for COVID-19 were lower than 2009/2010 H1N1 influenza and comparable (higher for 0-4) to non-pandemic years, regardless of the vaccine status.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".