Population Attributable Mortality Associated with Respiratory Viruses in Ontario
Bibliographic record
Abstract
Abstract Background Respiratory viruses are major contributors to population mortality, but cause-of-death coding undercounts their impact. Ecological regression models linking viral circulation to mortality fluctuations can address this limitation. Aim To estimate the population attributable fraction (PAF) of mortality associated with influenza A and B, respiratory syncytial virus (RSV), and SARS-CoV-2 in Ontario, Canada (1993–2025), and to characterize temporal changes in virus-attributable mortality across the pandemic transition. Methods We analysed monthly all-cause mortality data with laboratory surveillance indicators for influenza A, B, RSV, and SARS-CoV-2. Negative binomial models with secular trends, Fourier seasonal terms, and population offsets were fit for pre-pandemic (January 1993–February 2020) and combined pandemic (March 2020–February 2025) periods. PAFs were derived from counterfactual predictions setting viral coefficients to zero. Sensitivity analyses examined temporal stratification of the pandemic period (Public Health Emergency of International Concern [PHEIC] period: March 2020–April 2023; post-PHEIC: May 2023–February 2025), exclusion of the early pandemic period (March–June 2020), and models without Fourier seasonal adjustment. Wald tests compared coefficients across specifications. Results Pre-pandemic, influenza A accounted for 1.8% (95% CI 1.4–2.3%) of mortality; influenza B showed no detectable impact. RSV demonstrated inverse associations in seasonally adjusted models but positive associations (PAF 1.9%, 95% CI 1.3–2.4%) without seasonal adjustment. Over the combined pandemic period (March 2020–February 2025; n=60 months), amid elevated baseline mortality (IRR 1.050, P=0.027), SARS-CoV-2 accounted for 6.1% (95% CI 4.2–8.0%) of deaths, approximately 4-fold the pre-pandemic influenza A burden, despite widespread vaccination and antiviral availability. Model-estimated SARS-CoV-2-attributable deaths closely matched reported COVID-19 deaths from Public Health Ontario over the same period. Temporal stratification identified a significant increase in SARS-CoV-2-attributable mortality in the post-PHEIC period (PAF 9.8%, 95% CI 1.1–17.7%; p=0.027), while post-PHEIC influenza A and B attributable fractions did not differ significantly from pre-pandemic baselines. Excluding March-June 2020 yielded a conservative SARS-CoV-2 PAF of 5.7% (95% CI 3.3–8.1%), confirming robustness of primary estimates. Meta-analyses showed substantial heterogeneity for influenza A (I²=92.8%) and RSV (I²=89.1%) across modeling approaches, but minimal heterogeneity for SARS-CoV-2 (I²=5.5%). Conclusion SARS-CoV-2 was associated with a 3–4-fold higher population mortality burden than seasonal influenza A despite available countermeasures. Post-PHEIC data suggest that the burden of respiratory virus mortality, including for influenza, has not returned to pre-pandemic levels, highlighting the continued importance of respiratory virus prevention strategies. Estimates for influenza A and RSV were sensitive to seasonal adjustment, highlighting the importance of modelling choices when quantifying virus-attributable mortality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".