BACKGROUND: Influenza pandemics emerge at irregular and unpredictable intervals to cause substantial health, economic and social burdens. Optimizing health-system response is vital to mitigating the consequences of future pandemics. METHODS: We developed a mathematical model to assess the preparedness of Canadian health systems to accommodate pandemic-related increases in patient demand. We identify vulnerable areas, assess the potential of inter-wave vaccination to mitigate impacts and evaluate the association between demographic and health-system characteristics in order to identify predictors of pandemic consequences. RESULTS: Modelled average attack rates were 23.7-37.2% with no intervention and 2.5-6.4% with pre-vaccination. Peak acute-care demand was 7.5-19.5% of capacity with no intervention and 0.6-2.6% with pre-vaccination. The peak ICU demand was 39.3-101.8% with no intervention and 2.9-13.3% with pre-vaccination. Total mortality was 2258-7944 with no intervention and 88-472 with pre-vaccination. Regions of Southern Ontario were identified as most vulnerable to surges in patient demand. The strongest predictors of peak acute-care demand and ICU demand were acute-care bed capacity (RÂ =Â -0.8697; r(2)Â =Â 0.7564) and ICU bed capacity (RÂ =Â -0.8151; r(2)Â =Â 0.6644), respectively. Demographic characteristics had mild associations with predicted pandemic consequences. CONCLUSION: Inter-wave vaccination provided adequate acute-care resource protection under all scenarios; ICU resource adequacy was protected under mild disease assumptions, but moderate and severe diseases caused demand to exceed expected availability in 21% and 49% of study areas, respectively. Our study informs priority vaccine distribution strategies for pandemic planning, emphasizing the need for targeted early vaccine distribution to high-risk individuals and areas.
National assessment of Canadian pandemic preparedness: Employing InFluNet to identify high-risk areas for inter-wave vaccine distribution.
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作者:Saunders-Hastings Patrick, Hayes Bryson Quinn, Smith Robert, Krewski Daniel
| 期刊: | Infectious Disease Modelling | 影响因子: | 2.500 |
| 时间: | 2017 | 起止号: | 2017 Jul 5; 2(3):341-352 |
| doi: | 10.1016/j.idm.2017.06.005 | ||
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