Li Y H, Luo S Q, Lan L, Jin M G, Yang C, He J Y, Li H B, Li C C, Cheng Y B, Jin Y L
Division of Policy, Regulation and Standard, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China.
Public Health and Safety Monitoring Department, Chongqing Municipal Center for Disease Control and Prevention, Chongqing 404000, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2017 Mar 10;38(3):303-308. doi: 10.3760/cma.j.issn.0254-6450.2017.03.006.
To understand the associations between extremely low and high air temperature and the years of life lost (YLL) due to diabetes deaths in Chongqing and Harbin with different climatic characteristics in China. A double threshold B-spline distributed lag non-linear model (DLNM) was used to investigate the lag and cumulative effects of extremely low and high air temperature on YLL due to diabetes for lag 0-30 days by using the urban meteorological and diabetes mortality data of Chongqing (2011-2013) and Harbin (2008-2010). The effects were expressed as relative risk (). In Chongqing, the cold effects on YLL due to diabetes were delayed by four days and lasted for three days (lag4-6) with the highest of 1.304 (95:1.033-1.647) at lag5. The hot effects were delayed by one day (lag1) with of 1.321 (95:1.061-1.646). In Harbin, the extreme cold effects on YLL were delayed by four days and lasted for seven days (lag4-10) with the highest of 1.309 (95: 1.088-1.575) at lag6. The hot effects were delayed by one day and lasted for four days (lag1-4) with the highest of 1.460 (95:1.114-1.915) at lag2. The unit risk for cold and hot effects was 43.7% (=0.005 5) and 18.0% (=0.000 2) in Chongqing and 15.0% (=0.000 8) and 29.5% (=0.001 2) in Harbin, respectively. Both extremely low air temperature and extremely high air temperature might increase the years of life lost due to diabetes in cities with different climate characteristics. Health education about diabetes prevention should provide information about the effects of extreme weather events.
为了解中国气候特征不同的重庆和哈尔滨极低和极高气温与糖尿病死亡所致寿命损失年数(YLL)之间的关联。采用双阈值B样条分布滞后非线性模型(DLNM),利用重庆(2011 - 2013年)和哈尔滨(2008 - 2010年)的城市气象数据和糖尿病死亡率数据,研究极低和极高气温对糖尿病所致YLL在0 - 30天滞后的滞后和累积效应。效应以相对风险()表示。在重庆,糖尿病所致YLL的寒冷效应延迟4天,持续3天(滞后4 - 6天),滞后5天时最高相对风险为1.304(95%置信区间:1.033 - 1.647)。炎热效应延迟1天(滞后1天),相对风险为1.321(95%置信区间:1.061 - 1.646)。在哈尔滨,极低气温对YLL的效应延迟4天,持续7天(滞后4 - 10天),滞后6天时最高相对风险为1.309(95%置信区间:1.088 - 1.575)。炎热效应延迟1天,持续4天(滞后1 - 4天),滞后2天时最高相对风险为1.460(95%置信区间:1.114 - 1.915)。重庆寒冷和炎热效应的单位风险分别为43.7%(=0.005 5)和18.0%(=0.000 2),哈尔滨分别为15.0%(=0.000 8)和29.5%(=0.001 2)。极低气温和极高气温都可能增加不同气候特征城市因糖尿病所致的寿命损失年数。糖尿病预防的健康教育应提供极端天气事件影响的相关信息。