Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, Guangdong, China; Department of Health Resource, Guangzhou Center of Health Information, Guangzhou, Guangdong, China.
Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Environ Res. 2019 Sep;176:108510. doi: 10.1016/j.envres.2019.05.041. Epub 2019 May 31.
Prior studies that examined the association between temperature and mortality relied on mean temperature, maximum temperature, minimum temperature, humidex, and daily temperature variability, not accounting for variations in hourly temperature throughout the day. We proposed an indicator, excess degree-hours, to examine the association between temperature and mortality.
A distributed lag non-linear model (DLNM) was used to determine the hot (27.8 °C) and cold (24.3 °C) threshold. Hourly temperature in Guangzhou, China were summarized with extreme heat expressed as sum of degree-hours >27.8 °C and extreme cold as sum of degree-hours <24.3 °C within one day from January 1, 2012 to December 31, 2015. We then estimated the associations of daily mortality with hot and cold degree-hours in both hot and cold season. We also calculated the mortality burden of excess degree-hours.
An interquartile range (IQR) increase of hot degree-hours was associated with 2.11% (95% confidence interval [95% CI]: 1.25%, 2.98%), 3.74% (95% CI: 0.71%, 6.86%), and 2.63% (95% CI: 0.70%, 4.59%) increments in non-injury related death, respiratory mortality, and cardiovascular mortality, respectively. While the corresponding excess risk for an IQR increase of cold degree-hours was 2.42% (95% CI: 1.97%, 2.88%), 3.16% (95% CI: 2.57%, 3.76%), and 2.93% (95% CI: 1.98%, 3.88%). The estimated mortality burdens for hot and cold degree-hours were 1366,2465, respectively.
The excess degree-hours reduced to a single indication in duration and intensity is an approach and shows a different perspective and significant extreme weather effects on human health.
先前研究温度与死亡率之间的关联时，依赖于平均温度、最高温度、最低温度、湿热指数和日温度变异性，而没有考虑到一天中每小时温度的变化。我们提出了一个指标，即多余度小时数，以研究温度与死亡率之间的关联。
采用分布式滞后非线性模型（DLNM）确定热（27.8°C）和冷（24.3°C）阈值。在中国广州，将 1 天内的极端高温表示为>27.8°C 的度小时数之和，极端低温表示为<24.3°C 的度小时数之和，汇总 2012 年 1 月 1 日至 2015 年 12 月 31 日的每小时温度。然后，我们估计了热季和冷季每日死亡率与热和冷多余度小时数的关联。我们还计算了多余度小时数的死亡负担。
与热多余度小时数的一个四分位距（IQR）增加相关的是，非伤害相关死亡、呼吸死亡率和心血管死亡率分别增加了 2.11%（95%置信区间[95%CI]：1.25%，2.98%）、3.74%（95%CI：0.71%，6.86%）和 2.63%（95%CI：0.70%，4.59%）。而相应的 IQR 增加引起的冷多余度小时数的超额风险为 2.42%（95%CI：1.97%，2.88%）、3.16%（95%CI：2.57%，3.76%）和 2.93%（95%CI：1.98%，3.88%）。热和冷多余度小时数的估计死亡负担分别为 1366 人、2465 人。
多余度小时数减少为一个持续时间和强度的单一指标是一种方法，它展示了一个不同的视角和极端天气对人类健康的显著影响。
