Zhejiang Lab, Kechuang Avenue, Hangzhou, 311121, China.
Academy of Pharmacy, Xi'an Jiaotong-Liverpool University, Ren'ai Road 111, Suzhou, 215123, China.
BMC Public Health. 2024 May 18;24(1):1344. doi: 10.1186/s12889-024-18785-3.
Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.
气候变化通过气温升高、强降水和最严重的空气污染增加了疾病风险。本文研究了 2000-2020 年期间加拿大极端温度的频率和强度增加与每月超额死亡率之间的关系。根据加拿大和美国精算协会开发的严重天气监测措施的两个最具代表性的月度 T90 和 T10 数据集,将极端关联在加拿大的五个分块的四个年龄组之间进行了比较。我们利用组合季节性自回归综合移动平均 (ARIMA) 和双变量过阈值 (POT) 方法,通过极值尾指数和 Pickands 依赖函数图研究极值关联。结果表明,如果出现异常低温且强度极端(所有地区,除了东北大西洋(NEA)、北部平原(NPL)和西北太平洋(NWP)地区的 0-44 岁年龄组),则极有可能发生超额死亡(超过 10%),而极端高温的频繁出现似乎对健康没有显著影响(除了 NWP 地区外)。由于 NWP 和中北极地区(CAR)的人口健康与极端频繁的高低温高度相关(所有地区,所有年龄组),因此应特别关注这两个地区。所揭示的极端依赖性有望帮助利益相关者避免因极端天气事件的意外后果而采取有针对性的健康保护策略,从而避免重大影响。该新的极值依赖方法有望在进一步研究极端气象暴露、社会经济因素与健康结果之间的相互作用中得到应用。