Population Attributable Mortality Associated with Respiratory Viruses in Ontario

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 to 2024). 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 stratified into pre-pandemic (1993 to February 2020) and pandemic (March 2020 to March 2024) periods. PAFs were derived from counterfactual predictions setting viral coefficients to zero. Sensitivity analyses excluded seasonal terms; Wald tests compared coefficients across model specifications. Results: Pre pandemic, influenza A accounted for 1.8% (95% CI 1.4 to 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 to 2.4%) without seasonal adjustment. During 2020 to 2024, amid elevated baseline mortality (IRR 1.050, P=0.027), SARS CoV 2 dominated, accounting for 6.5% (95% CI 4.5 to 8.4%) of deaths, 3.6 times the pre-pandemic influenza A burden, despite widespread vaccination and antiviral availability. Model-estimated SARS CoV 2 deaths (18,052) matched reported COVID 19 deaths (18,603). Meta analyses showed substantial heterogeneity for influenza A (I squared=93.7%) and RSV (I squared=88.5%) across periods and modeling approaches, but minimal heterogeneity for SARS CoV 2 (I-squared=2.5%). Conclusion: SARS-CoV-2 demonstrated 3 to 4-fold higher mortality burden than seasonal influenza A despite available countermeasures. 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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