Saunders-Hastings Patrick, Hayes Bryson Quinn, Smith Robert, Krewski Daniel
University of Ottawa, McLaughlin Centre for Population Health Risk Assessment, 850 Peter Morand Crescent, Ottawa, Ontario, K1G 5Z3, Canada.
University of Ottawa, School of Epidemiology, Public Health, and Preventive Medicine, Faculty of Medicine, 451 Smyth Road, Ottawa, ON, K1H 8M5, Canada.
Infect Dis Model. 2017 Jul 5;2(3):341-352. doi: 10.1016/j.idm.2017.06.005. eCollection 2017 Aug.
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.
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.
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 (R = -0.8697; r = 0.7564) and ICU bed capacity (R = -0.8151; r = 0.6644), respectively. Demographic characteristics had mild associations with predicted pandemic consequences.
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.
流感大流行不定期且不可预测地出现,造成巨大的健康、经济和社会负担。优化卫生系统应对措施对于减轻未来大流行的后果至关重要。
我们开发了一个数学模型,以评估加拿大卫生系统应对大流行相关患者需求增加的准备情况。我们确定脆弱领域,评估波间疫苗接种减轻影响的潜力,并评估人口和卫生系统特征之间的关联,以确定大流行后果的预测因素。
在无干预情况下,模拟的平均感染率为23.7%-37.2%,接种疫苗前为2.5%-6.4%。无干预时,急性护理需求峰值为容量的7.5%-19.5%,接种疫苗前为0.6%-2.6%。无干预时,重症监护病房需求峰值为39.3%-101.8%,接种疫苗前为2.9%-13.3%。无干预时总死亡人数为2258-7944人,接种疫苗前为88-472人。安大略省南部地区被确定为最易受患者需求激增影响的地区。急性护理需求峰值和重症监护病房需求的最强预测因素分别是急性护理床位容量(R = -0.8697;r = 0.7564)和重症监护病房床位容量(R = -0.8151;r = 0.6644)。人口特征与预测的大流行后果有轻微关联。
波间疫苗接种在所有情况下都提供了足够的急性护理资源保护;在轻度疾病假设下,重症监护病房资源充足得到保护,但在中度和重度疾病情况下,分别有21%和49%的研究区域需求超过预期可利用量。我们的研究为大流行规划的优先疫苗分配策略提供了信息,强调需要有针对性地尽早向高危个人和地区分配疫苗。