EN中文

地球信息科学学报 >

2017 , Vol. 19 >Issue 3: 336 - 345

DOI: https://doi.org/10.3724/SP.J.1047.2017.00336

地理空间分析综合应用

低温对中国居民健康影响的空间差异性分析

  • 王琛智 , 1, 2 ,
  • 张朝 , 1, 2, 3, * ,
  • 周脉耕 4 ,
  • 殷鹏 4 ,
  • 陶福禄 5 ,
  • 金月雄 6
展开
  • 1. 北京师范大学地表过程与资源生态国家重点实验室,北京 100875
  • 2. 北京师范大学减灾与应急管理研究院,北京 100875
  • 3. 北京师范大学环境演变与自然灾害教育部重点实验室,北京 100875
  • 4. 中国疾病预防控制中心慢性非传染性疾病预防控制中心,北京 100050
  • 5. 中国科学院地理科学与资源研究所,北京 100101
  • 6. 中国人民财产保险股份有限公司湖州分公司,湖州 313000
*通讯作者：张 朝（1971-）,女,湖南湘潭人,副教授,研究方向为自然灾害风险评价,地理化学与人类健康。E-mail:

作者简介：王琛智（1992-）,男,江苏徐州人,硕士生,主要从事公共健康风险分析,灾害风险分析等方面的研究。E-mail:

收稿日期: 2016-06-01

  要求修回日期: 2016-07-11

  网络出版日期: 2017-03-20

基金资助

国家自然科学基金项目（41571493、41571088）

教育部-国家外国专家局高等学校创新引智计划（B08008）

收起

Analyzing the Spatial Differences of the Relationships Between Low Temperature and Health Risk in China

  • WANG Chenzhi , 1, 2 ,
  • ZHANG Zhao , 1, 2, 3, * ,
  • ZHOU Maigeng 4 ,
  • YIN Peng 4 ,
  • TAO Fulu 5 ,
  • JIN Yuexiong 6
Expand
  • 1. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing 100875, China
  • 2. Academy of Disaster Reduction and Emergency Management, MOE & MCA, Beijing Normal University, Beijing 100875, China
  • 3. Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China
  • 4. Surveillance Branch, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 10050,China
  • 5. Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 6. Peoples Insurance Company of China Huzhou branch, Huzhou 313000, China
*Corresponding author: ZHANG Chao, E-mail:

Received date: 2016-06-01

  Request revised date: 2016-07-11

  Online published: 2017-03-20

Copyright

《地球信息科学学报》编辑部 所有
Fold

摘要

全球变暖导致气象灾害频发,尤其是极端天气事件。极端温度对公共健康的影响已成为当今研究的热点问题之一。相比于发达国家,中国在该领域研究起步较晚。虽然已有出色的成果,但在以下3个方面还略显不足：① 大多数研究基于一个城市或几个城市,缺乏基于大量数据的区域尺度的研究;② 已有研究往往按地理因素或行政单位来划分区域,而忽视区域内部温度的异质性;③ 相比高温热浪,鲜少有研究关注低温冷害的影响。针对上述问题,本文收集了中国疾病预防控制中心2007-2012年全国127个站点的数据,利用分布式滞后非线性模型,探究了中国5个温度带温度与居民非意外死亡之间的暴露-反应曲线。在此基础上,定义当地温度分布1%处的温度为极端低温,根据温度-死亡风险曲线,计算了冷害造成的死亡风险。结果表明,不同温度带的温度-死亡关系曲线呈现U型或J型。极端低温对北亚热带影响最小,其相对风险为1.27（95%CI: 0.94-1.72）;对中亚热带影响最大,其相对风险为1.93（95%CI: 1.08-3.60）。随着温度带温度的升高,低温冷效应的影响呈现“M”型,这一特征与不同温度带经济发展有关。因此,不同地区的政府除了应着力提高地区经济发展外,还应根据地区特征,采取更积极有效的措施来应对低温冷害可能给当地公共健康造成的威胁。

关键词： 温度与公共健康; 低温; DLNM模型; 气候变化; 空间分异

本文引用格式

王琛智 , 张朝 , 周脉耕 , 殷鹏 , 陶福禄 , 金月雄 . 低温对中国居民健康影响的空间差异性分析[J]. 地球信息科学学报, 2017 , 19(3) : 336 -345 . DOI: 10.3724/SP.J.1047.2017.00336

Abstract

Global warming has increased the frequency of meteorological disasters, especially extreme temperature events. Many previous studies have reported that human health risk is very sensitive to temperature and climate change was considered to be the most severe global health threat in the 21st century. Nowadays, the research on the impact of extreme temperature on public health has been a hotspot. Compared to those in developed countries, the related studies have started late in China. Moreover, there are three limitations in these studies. (1) Most of such studies focused only on one city or a few cities and the studies on the whole country are few.(2) The previous studies have not quantitatively identified the influence of temperature on health because the spatial scales were based on administrative regions, not on temperature zones. (3) Comparing with many studies on hot wave, relatively fewer are concerned with the influence of extreme low temperature. To overcome aforementioned problems, we collected the mortality dataset and meteorological variables of 127 communities in China during 2007 to 2012 from China Center for Disease Control and Prevention and pooled the community-specific cold risk in various latitude-effected temperature zones with the meta-analysis method. Then, we utilized the Distributed lag non-linear model (DLNM) at community level to investigate temperature-mortality relationship in different temperature zones and calculated the relative risk (RR) of extreme low temperature on mortality. The results showed that although temperature-mortality curves at the community level appeared huge differences, the pooled curves were generally U- or J- shaped in these five zones. Temperature-mortality curves in three zones (the sub-temperate region, warm temperate region and north subtropical region) were all U-shaped, indicating both low and high temperatures could increase significantly mortality risk. Moreover, the curves appeared J-shaped in other two zones (the middle subtropical region and south subtropical region). The most significant cold effect was observed in middle subtropical, with a RR of 1.93 (95% CI: 1.08-3.60); while the cold effect in north subtropical was not so obvious, with a RR of 1.27 (95% CI: 0.94-1.72). Based on this, an M-shaped curve of the cold risk was found across Chinese mainland. This means the risks of cold-related mortality are high in warm temperature and middle subtropical zone, moderate in sub-temperate and south subtropical zone, and low in north subtropical zone. Low temperature does show significant impact on temperature-mortality risk, but considering the M-shaped risk curve, we believe social-economic factors should also be taken into consideration. To explain this phenomenon, we collected the social-economic data including population and GDP and found that the highest per capita GDP matched with the lowest cold-related risk, while the related lower per capita GDP matched with the highest cold-related risk. Based on these findings, different characteristics of mortality of cold stress highlighted that not only ambient temperature but also social-economic condition can be a main factor controlling health risk. Our findings also suggest that more adaptive and effective measures especially increasing investment on public health are necessary, especially for the middle subtropical zone, to reduce health risks in China.

Key words： temperature and public health; low temperature; DLNM model; climate change; spatial difference

1 引言

现有大量研究表明,地球正经历一次以全球变暖为主要特征的气候变化。气候变化和人类的生产生活息息相关。世界卫生组织（WHO）认为,全球气候变化是21世纪人类健康面临的最大挑战[1]。据WHO估计,自20世纪70年代以来,气候变化直接或间接造成的居民死亡已达150 000人[2]。因此,理解环境温度与人类健康的关系,评估极端事件（热浪或寒潮）对居民健康的影响是应对气候变化对公共健康影响的重要举措。
在全球气候变化的大背景下,越来越多的研究人员开始关注气温-死亡关系及其特征。欧洲、美国等发达国家就环境温度对居民死亡的影响做了大量的研究,特别是对高温热浪影响的评估。在欧洲地区,西班牙地区的温度与非意外死亡响应曲线整体呈现“V”型,但各地存在着差异[3];而基于欧洲地区15个城市的研究表明社会经济因素对温度-死亡风险的关系有修饰作用[4];由于欧洲地区高温热浪影响较为显著,有大量的研究分别探索了德国、法国[5-6]等国家高温热浪和居民健康的关系。北美地区,Anderson等利用美国107个城市的人口死亡数据和温度数据,发现气温-死亡风险曲线存在明显的空间差异性,同时高温热浪对死亡的影响时间短且剧烈[7];Curriero等基于美国东部11个城市的研究表面,不同地区的最适环境温度有差异,跟当地环境温度的上四分位数相近[8];近年来,中国学者也开始关注气候变化背景下环境温度与居民健康关系的问题。相关研究的不仅讨论了不同空间尺度的温度-死亡风险关系[9],还涉及气温与不同疾病死亡关系的讨论[10]。此外,还有一些研究关注高温热浪对中国居民的影响[11]。
大量研究表明,温度-死亡关系是一个复杂的地理现象,不仅受环境的影响,还受经济发展水平、基础医疗条件等因素的制约[4,7,12]。中国幅员辽阔,地区之间自然因素和社会发展水平有明显差异。然而,目前中国关于环境温度与居民健康方面的研究多为基于单个城市或几个城市的研究,并且这些城市大多为上海、天津、南京、武汉、兰州等大城 市[13-17]。IPCC指出气候变暖对中国影响尤为突 出[18-19]。因此,关注气候变化对中国公共健康的影响十分必要。但现有研究仍存在3个方面问题：① 缺乏区域尺度的研究。已有研究大多还是集中在沿海沿江大城市,但仅靠一个或几个城市的结果难以表征区域尺度温度对公共健康的影响;② 已有研究区域的常常按行政区域划分,这种方式忽略了一个区域可能包含两个或多个温度带的情况,使结果不能充分体现区域内温度与死亡的关系;③ 较多的研究着眼于高温热浪对健康影响的评估,对于冷效应造成的健康风险鲜少有关注。气候变化会使寒潮加剧,而已有研究表明极端低温会导致居民死亡风险的增加[20-21]。
针对上述问题,本文基于大量站点级数据,利用分布式滞后非线性模型,定量描述不同地区居民的气温-死亡关系,评估不同区域低温冷害的风险,分析空间差异性,进而比较不同区域低温效应对居民健康的影响。研究结果可以作为相关部门应对气候变化对公共卫生所造成影响进行评估时的理论依据与支撑。

2 研究区地理背景和数据

为进一步探究低温与中国居民死亡风险的关系,本研究以温度带来划分研究区域。依据郑景云等[22-23]对中国气候的区划,将中国从北到南依次划分为寒温带、中温带、暖温带、北亚热带、中亚热带和南亚热带以及高原气候区。根据站点范围,研究区选取中温带、暖温带、北亚热带、中亚热带和南亚热带,共计5个温度带。研究区站点分布如图1所示。
Fig. 1 The distribution map of 127 DSPs covered in this study

图1 本研究所用死亡监测站点空间分布示意图

研究所用数据主要包括人口死亡数据、气象数据和经济数据。死亡数据是从中国疾病预防控制中心（Chinese Center for Disease Control and Prevention, CDC）的死因监测点系统（Death Surveillance Points System, DSP）收集获取的。所使用的数据集包含2007-2012年127个监测站点逐日的居民死亡数据。死因监测系统所包含的站点是利用分层聚类随机抽样法所选取的具有代表性的区县,监测结果准确全面[24-25]。死亡数据资料包括死者个人信息、国际疾病伤害及死因分类标准（ICD-10）的编码以及死亡日期等信息。为探究低温对中国居民健康的影响,选取数据不应针对一种或几种死因,因此,本研究选取了非意外死亡数据。研究所用的气象数据集是基于中国气象科学数据共享服务网的600个站点的所制作气象变量插值数据。该数据集的精度和适用性较好[26],得到广泛认可。所选气象指标包括日均气温、日最高气温、日最低气温、日相对湿度共4项。其中,利用日均气温和日相对湿度构造模型,日最高最低气温用来描述各站点在不同温度带的气象状况。此外,为进一步探究极端低温对居民健康的影响与社会经济因素的关系,本研究根据统计年鉴资料收集整理了各站点2007-2012年人均GDP和人口数据,最终选择各个站点6年人均GDP均值作为衡量社会经济发展水平的指标。

3 温度对中国居民健康的影响分析

3.1 模型构建

高温对人体影响较快,高温日后3天内就会增大人体死亡风险;而低温影响较为缓慢持续时间较长,一般滞后时间可达2-3周[27-28]。因此,为充分地表征滞后效应对居民死亡风险增加的影响,本文选取了分布式滞后非线性模型（DLNM）来描述气温-死亡风险关系[29]。
模型构建分为2个阶段：① 利用死亡数据、气象数据逐站点构建气温-死亡风险关系模型;② 为探究不同温度带的气温-死亡人口差异性,利用多元Meta分析,将同一温度带内不同站点的气温-死亡风险进行综合,获得不同温度带的气温-死亡风险关系,最终分析其差异性[30]。
在第①阶段,首先利用DLNM模型来建立各个监测站点的气温-死亡风险模型。以日非意外死亡人数为因变量,利用日平均气温和滞后时间建立交叉基函数为自变量,同时控制星期几和节假日等混杂因素的干扰,分析温度、湿度与人口死亡数量间的关系。具体模型公式如式（1）所示。
LogE ( Y t ) = α + cb ( T mean , lag ) + ns ( R h t , df = 3 ) + ns ( Tim e t , df = 9 ) + βDo w t + γHoilda y t （1）
式中：Yt为t日的死亡人数;cb是日均温度和最大滞后天数建立的交叉矩阵;ns是自然立方样条函数;Rht是相对湿度;Timet为时间序列变量;Dowt是哑变量,用来描述时间t为星期几;Holidayt是描述t是否为节假日的二元变量,用以判断医院的影响;研究中参考历年政府放假安排进行编码,γ为其系数;Dow和Holidaytt都是依照模型原理并结合已有研究进行选取[31-32];df为自由度,本文自由度选取依据相关文献[9,11,29]并结合赤池信息准则实现的;α为模型截距。根据已有相关研究结合对最大滞后天数敏感性的分析,本研究的最大滞后天数lag设置为21。利用R语言（R 3.1.2）及DLNM函数包构建模型,计算各站点不同温度的相对风险（RR）。
在第②阶段,根据各个站点的气温-死亡风险关系,利用多元Meta分析的方法来综合同一温度带内站点尺度结果,获得二者在区域尺度上的关系。多元Meta分析专门用来综合小尺度的,受多变量影响的非线性关系,从而获得区域尺度的2个变量之间的关系及特征。有大量的研究采用该方法来计算大尺度的[30,33]。研究中采用了Gasparrini等开发的适用于R语言的多元Meta分析的函数包（“mvmeta”）。
为探究低温对中国居民健康的影响,根据已有大量的研究,将每个地区1%处温度的死亡风险作为表征该地区低温冷效应影响的指标[9]。基于上述两个阶段构建的模型和所选指标,结合不同温度带气温特征,进而分析低温对中国居民死亡风险的空间差异性。此外,由于模型对滞后天数较为敏感,需分析不同滞后天数对结果的影响。
为进一步验证分析结果的有效性,选择了基于空间分异理论的地理探测器模型[34-35]来对比Meta分析结果。该模型最早就应用于健康风险评估领域,包括：风险探测器、因子探测器、生态探测器和交互探测器。在环境健康、区域发展、考古等[36-38]研究领域广泛应用。本文利用该模型来确定不同温度带的低温风险是否和Meta分析结果一致。

3.2 结果分析

表1汇总了研究区的基本情况。具体包括了2007到2012年间5个温度带共计127个死亡监测站点的人口死亡信息,气象因素,人均GDP和区域人口信息。本研究共包含了约180万个死亡案例。从表1可进一步看出,每日平均非意外死亡人数的范围为3-9。日均气温随着温度带纬度降低而逐渐升高,大致从6°到21°。研究区相对湿度的范围为57.64%到74.87%。大量环境健康方面的研究表明,社会经济因素对于气温-死亡关系具有修饰作用。因此,除环境要素外,本文还考虑了不同地区人口状况和社会经济状况。
Tab. 1 Summary statistics for five temperature zones from 2007 to 2012

表1 各区域2007-2012年气象因素、居民死亡情况和经济因素基本情况

中温带 暖温带 北亚热带 中亚热带 南亚热带
死亡监测站数目 (城市,农村) 26 (12, 14) 35 (14,21) 28 (12, 16) 19 (6, 13) 14 (5, 9)
日均非意外死亡数目 6(0, 65) 9(0,78) 8(0, 142) 7(0, 67) 9(0, 58)
气温均值/°C (最小值,最大值) 6.22 (-0.50, 33.08) 12.38 (-8.36, 32.80) 16.96 (-5.76, 33.89) 18.18 (-5.41, 34.15) 21.29 (0.21, 32.25)
相对湿度均值/% 58.69 62.47 72.92 74.73 74.87
人均GDP/元 24235.61 45266.47 56338.99 26131.88 45960.38
人口/万人 1521.48 1995.26 1488.86 996.54 875.07
3.2.1 模型参数敏感性分析
利用DLNM模型分析气温-死亡关系,滞后时间的选取会直接影响死亡相对风险的结果。因此,本文尝试给出能够充分表征冷效应影响结果的最大滞后天数。选取的最大滞后时间分别为3、7、15、21 d,结果如图2所示。由图2可看出,低温冷效应的风险随着最大滞后时间的增加而增强,不同最大滞后天数计算的低温冷效应的影响差异较为显著的。换言之,低温冷效应造成的相对风险对最大滞后天数较为敏感。通过对模型敏感性的分析,结合已有研究,最终选取21 d为最大滞后天数。
Fig. 2 Relationship between different lag days and model results

图2 不同滞后时间与模型结果的关系

3.2.2 站点尺度低温与居民非意外死亡的暴露-反应关系分析
本文利用DLNM模型绘制了127个站点的气温与居民非意外死亡的暴露-反应曲线,进而计算了低温冷效应对居民的影响。由于数据量较大,文中仅列出不同温度带部分站点的暴露-反应曲线,如表2所示。相对风险超过1说明存在风险,相对风险越大表明温度对死亡造成的影响越显著。相对风险值最低点的横坐标我们称之为MMT（Min-Mortality Temperature）,也可理解为最适环境温度。从表2可看出,不同区域的气温-死亡关系曲线大致呈现U型、V型和J型,这与大量已有的结论一致,即低温和高温都会造成死亡风险的增加。此外,本文还计算了127个站点高温和低温的相对风险的均值。其中,高温热效应造成的相对风险为1.17,低温冷效应的相对风险为1.63（高温定义为该站点日均温度分布的99%处的温度,低温定义为1%处的温度）。
Tab. 2 List of temperature-mortality curves at community level in different temperature zones

表2 不同温度带站点尺度温度-死亡曲线列举

3.2.3 不同温度带低温与居民死亡关系分析
图3是利用多元Meta分析函数包对站点结果进行综合后,分离出低温效应致死风险得到的结果。从图3（a）可看出,不同温度带低温冷效应影响有明显区别。在北亚热带冷效应致死风险最低,为1.27（CI 95%：0.94, 1.72）;在中亚热带冷效应影响最为显著为1.93（CI 95%: 1.08, 3.60）。图3（b）可看出,随着温度带温度的升高,冷效应的风险并不是单纯的递增或递减,而是呈现“M”型。
Fig. 3 Relationships between low temperature and mortality risk across different temperature zones of China

图3 中国不同温度带低温与死亡风险关系

3.2.4 低温致死风险的空间差异性分析
为验证Meta分析得到不同温度带低温影响死亡风险的可靠性,利用地理探测器中的风险探测器来搜索风险较高和较低区域,同时比较不同区域之间风险是否有显著性差异。
表3是利用地理探测器得到的不同温度带低温风险的平均结果与Meta分析结果的对比。由表3可以看出,与Meta分析结果基本一致,说明Meta分析得到的低温风险的空间分布特征是可靠的。表4反映了风险探测器表征的不同温度带低温风险是否存在显著性差异。表4中的“Y”代表2个结果在95%的置信度上被认为存在着显著性差异,而”N”代表不存在着差异。由表4可以看出,北亚热带和其余4个带存在着显著性差异,这与中国大陆不同温度带低温风险呈现“M”形的特点相一致。
Tab. 3 A comparison of the risk calculated by Geodetector and Meta analysis

表3 地理探测器计算低温风险与Meta分析结果对比

分层变量 中温带 暖温带 北亚热带 中亚热带 南亚热带
地理探测器分析结果 1.69 1.81 1.20 1.92 1.64
Meta分析风险结果 1.61 1.78 1.27 1.93 1.56
Tab. 4 Significant difference of cold effect among different temperature zones

表4 不同温度带冷效应风险显著性差异

Sig. t test:0.05 中温带 暖温带 北亚热带 中亚热带 南亚热带
中温带
暖温带 N
北亚热带 Y Y
中亚热带 N N Y
南亚热带 N N Y N
为进一步探究低温冷效应呈现“M”型的原因,判断非环境因素是否对其有修饰作用,本文引入了区域人均GDP这一指标来反映社会经济因素。根据已有各个站点6年平均人均GDP,计算不同温度带人均GDP均值作为区域社会经济因素指标。从图4可看出,北亚热带整体人均GDP最高,而中亚热带整体人均GDP最低。这说明社会经济因素对于低温致死风险具有一定的影响,对低温-死亡风险有修饰作用。
Fig. 4 Cold-effect risk in different temperature zones and their situation of GDP per capita

图4 不同温度带冷效应风险和人均GDP情况

4 讨论

本文利用分布式非线性滞后模型对127个死亡监测站的气温-死亡关系进行了建模,基于各站点结果的基础上,利用多元Meta分析得到不同温度带内居民的气温与死亡风险之间的关系,进而得到低温冷效应对居民健康的影响。就已有研究来看,本文是目前使用死亡监测站数据最多,涵盖中国范围最广的研究,能够充分说明低温冷效应对中国居民健康的影响情况。
结合上述分析结果,需要进一步对2个方面进行阐述：① 分析低温冷害的空间特征;② 分析和讨论影响气温-死亡关系的因素。
已有关于中国大尺度的研究都是从行政区域的角度来综合站点尺度的气温-死亡风险关系[9]。虽然气温-死亡关系受到多种因素的影响,但大量国内外研究表明,温度是影响居民死亡风险最为重要的因素[39]。以行政区域来综合站点尺度结果的局限性在于没有充分考虑区域温度内在的差异性。因此,本文尝试以温度带作为分区指标,尝试更直观地反应不同温度条件下居民的致死风险。结合表2和图3可发现,在中亚热带和南亚热带区域,冷效应对死亡的影响远大于热效应。这说明人体对于环境温度具有适应性,处于较低纬度温度带的人群,由于长期处于温度较高的环境中,生理和行为对低温适应性较差。因此,对于长期居住于中亚热带和南亚热带的居民,政府部门应积极采取措施,降低低温冷害对这些地区公共健康的不利影响。
气温-死亡风险受到包括社会经济水平、医疗卫生状况、区域内的人口结构特征在内的多种因素的影响。目前,考虑社会经济因素对气温-死亡关系修饰作用的研究较为多样：Healy等发现冬季严寒对较为爱尔兰等地居民死亡风险的影响较为显著,而对温度差异不大的斯堪的纳维亚地区和丹麦的影响并不显著,他认为是经济差异导致的结果[40]。对于高温热效应,Zanobetti和Schwartz发现中央空调有利于削弱热浪的影响[41]。所以,针对图4中不同温度带的低温冷效应致死风险呈现出M型,社会经济因素可能对其有修饰作用。为量化这种差异,对5个温度带内的冷效应致死风险和人均GDP进行相关性分析,二者在显著性为95%的水平上相关性为-0.62,说明人均GDP和冷效应致死风险呈现出一种负相关关系。因此,对“M”型给出以下解释：中温带平均温度较低,人们对环境具有适应性;在南亚热带常年温度较高,低温冷害发生频率低。所以这2个温度带低温冷害的风险水平不高。而暖温带、北亚热带和中亚热带,这3个温度带四季分明,社会经济因素对低温的影响较为显著。① 低温冷害致死风险在北亚热带最低是因为该区域经济最为发达,包括了长江三角洲地区以及武汉、合肥等长江中游大城市,基础设施较好,医疗完善;② 低温致死风险在中亚热带最高的原因,一方面是该气温带整体温度高、对低温的适应性差,另一方面是因为该温度带范围内主要是四川、湖南、贵州等西南经济落后地区,医疗设施不够完善使得低温冷害致死风险最大;③ 对于暖温带冷效应致死风险较高的是由于在这个区域死亡监测点在农村较多人口多集中于农村。虽然经济水平整体较好,但是该区域包括了北京、天津、西安等大城市,城乡之间医疗条件与基础设施仍有差异,因而区域的低温冷害的影响略高于中温带。基于上述讨论可以认为,提高区域经济发展,完善农村地区医疗设施对于中国大部分地区提高低温冷害对公共健康的抵抗性有积极作用。
本研究还存在一些局限性：① 利用插值数据获取的县级尺度的气温存在一定的不确定性,进一步的研究中应尽可能多的收集县级尺度的气象数据;② 已有研究表明[42-44],空气污染对气温-死亡关系有修饰作用,但本研究由于缺乏空气污染指标的数据而未考虑这种影响,进一步的研究尝试将空气中污染物加入到DLNM模型中,从而完善研究结果; ③ 研究主要关注的是低温冷效应的空间分布特征,对于死亡人口的年龄结构没有详细考虑,而不同年龄阶层死亡风险必然不同,进一步的研究将在小尺度上研究不同年龄阶层死亡风险和环境温度的关系。

5 结论

基于大量死亡数据,本文利用DLNM模型分析了低温冷害对中国居民健康的影响,得到如下结论：
（1）在站点尺度上,温度-死亡风险曲线虽然存在明显的差异,但大致仍呈现U型、V型和J型,表明极端低温会对居民健康产生不利影响,且存在空间差异。
（2）为削弱其余环境因素对温度-死亡关系的修饰作用,尽可能突出低温对死亡风险的影响,本研究将站点按温度带进行综合,发现不同温度带的低温对居民的影响有明显空间差异性。随着温度带气温的增高,冷效应对非意外死亡风险的影响呈现出“M”型,即在中温带和南亚热带冷效应影响较低,在暖温带和中亚热带冷效应影响较高,在北亚热带影响最低。
（3）社会经济因素对低温冷害的致死风险有修饰作用。结合不同温度带的人均GDP数据进行比较,发现低温冷效应与人均GDP水平呈现明显负相关,即社会经济因素会对气温-死亡关系起到修饰作用。

The authors have declared that no competing interests exist.

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
WHO. The World Health Report 2008 - primary Health Care(Now More Than Ever)[Z]. 2010.

[2]
Lopez A D.Global burden of disease and risk factors[M]. Oxford University Press, 2006.

[3]
Díaz J, Carmona R, Mirón I J, et al.Geographical variation in relative risks associated with heat: Update of Spain's Heat Wave Prevention Plan[J]. Environment International, 2015,85:273-283.The updating of prevention plans is regarded as crucial for optimising the operation of these plans in terms of reducing the effect of high temperatures on population health.

DOI PMID

[4]
Analitis A, Katsouyanni K, Biggeri A, et al.Effects of cold weather on mortality: Results from 15 european cities within the PHEWE project[J]. American Journal Of Epidemiology, 2008,168:1397-1408.Weather-related health effects have attracted renewed interest because of the observed and predicted climate change. The authors studied the short-term effects of cold weather on mortality in 15 European cities. The effects of minimum apparent temperature on cause- and age-specific daily mortality were assessed for the cold season (October-March) by using data from 1990-2000. For city-specific analysis, the authors used Poisson regression and distributed lag models, controlling for potential confounders. Meta-regression models summarized the results and explored heterogeneity. A 1 degrees C decrease in temperature was associated with a 1.35% (95% confidence interval (CI): 1.16, 1.53) increase in the daily number of total natural deaths and a 1.72% (95% CI: 1.44, 2.01), 3.30% (95% CI: 2.61, 3.99), and 1.25% (95% CI: 0.77, 1.73) increase in cardiovascular, respiratory, and cerebrovascular deaths, respectively. The increase was greater for the older age groups. The cold effect was found to be greater in warmer (southern) cities and persisted up to 23 days, with no evidence of mortality displacement. Cold-related mortality is an important public health problem across Europe. It should not be underestimated by public health authorities because of the recent focus on heat-wave episodes.

DOI PMID

[5]
Schuster C, Burkart K, Lakes T.Heat mortality in Berlin - Spatial variability at the neighborhood scale[J]. Urban Climate. 2014,10(Part 1):134-147.Heat stress increasingly affects urban populations in higher geographical latitudes. Related adverse health effects are expected to increase due to urbanization, population aging, and global warming. While many studies have examined the relationship between heat and mortality, only a few have examined the intra-urban spatial variability between them. This missing research is particularly evident for northern mid-latitude cities, where populations are not prepared for heat stress. The aim of this study is therefore to investigate heat-related excess mortality in its spatial variability at the neighborhood scale (397 planning areas) for Berlin, the capital of Germany. We analyzed age-standardized mortality rates by calculating the relative heat mortality risk ratio for months with and without severe heat waves. Local indicators of spatial association were used to locate spatial clusters. The results highlighted the intra-urban variability of heat-related excess mortality, and demonstrated clustering for the planning areas of Berlin. Temporal aggregation of mortality data enabled a neighborhood-scale analysis. Resulting heat-related excess mortality maps allow urban decision makers to identify hot spots for emergency and adaptation planning, and serve as a basis for further investigations of heat stress risk on an individual level.

DOI

[6]
Laaidi M, Laaidi K, Besancenot J P.Temperature-related mortality in France, a comparison between regions with different climates from the perspective of global warming[J]. International Journal Of Biometeorology, 2006,51:145-153.lt;a name="Abs1"></a>This paper aims to explain the results of an observational population study that was carried out between 1991 and 1995 in six regions (departments) in France. The study was to assess the relationship between temperature and mortality in a few areas of France that offer widely varying climatic conditions and lifestyles, to determine their thermal optimum, defined as a 3°C temperature band with the lowest mortality rate in each area, and then to compare the mortality rates from this baseline band with temperatures above and below the baseline. The study period was selected because it did not include extreme cold or hot events such as a heatwave. Data on daily deaths from each department were first used to examine the entire population and then to examine men, women, various age groups and various causes of death (respiratory disease, stroke, isch?mic heart disease, other disease of the circulatory system, and all other causes excluding violent deaths). Mean temperatures were provided by the National Weather Service. The results depicted an asymmetrical V- or U-shaped relationship between mortality and temperature, with a thermal optimum lower for the elderly, and generally lower for women than for men except in Paris. The relationship was also different depending on the cause of death. In all cases, more evidence was collected showing that cold weather was more deadly than hot weather, and it would now be interesting to enlarge the study to include years with cold spells and heatwaves. Furthermore, the results obtained could be of great use in estimating weather-related mortality as a consequence of future climate-change scenarios.

DOI PMID

[7]
Anderson B G, Bell M L.Weather-related mortality how heat, cold, and heat waves affect mortality in the United States[J]. Epidemiology, 2009,20:205-213.Many studies have linked weather to mortality; however, role of such critical factors as regional variation, susceptible populations, and acclimatization remain unresolved. We applied time-series models to 107 US communities allowing a nonlinear relationship between temperature and mortality by using a 14-year dataset. Second-stage analysis was used to relate cold, heat, and heat wave effect estimates to community-specific variables. We considered exposure timeframe, susceptibility, age, cause of death, and confounding from pollutants. Heat waves were modeled with varying intensity and duration. Heat-related mortality was most associated with a shorter lag (average of same day and previous day), with an overall increase of 3.0% (95% posterior interval: 2.4%-3.6%) in mortality risk comparing the 99th and 90th percentile temperatures for the community. Cold-related mortality was most associated with a longer lag (average of current day up to 25 days previous), with a 4.2% (3.2%-5.3%) increase in risk comparing the first and 10th percentile temperatures for the community. Mortality risk increased with the intensity or duration of heat waves. Spatial heterogeneity in effects indicates that weather-mortality relationships from 1 community may not be applicable in another. Larger spatial heterogeneity for absolute temperature estimates (comparing risk at specific temperatures) than for relative temperature estimates (comparing risk at community-specific temperature percentiles) provides evidence for acclimatization. We identified susceptibility based on age, socioeconomic conditions, urbanicity, and central air conditioning. Acclimatization, individual susceptibility, and community characteristics all affect heat-related effects on mortality.

DOI PMID

[8]
Curriero F C, Heiner K S, Samet J M, et al.Temperature and mortality in 11 cities of the eastern United States[J]. American Journal Of Epidemiology, 2002,155:80-87.

[9]
Ma W J, Wang L J, Lin H L, et al.The temperature-mortality relationship in China: An analysis from 66 Chinese communities[J]. Environmental Research, 2015,137:72-77.BACKGROUND: Previous studies examining temperature-mortality associations in China focused on a single city or a small number of cities. A multi-city study covering different climatic zones is necessary to better understand regional differences in temperature risk on mortality in China. METHODS: Sixty-six communities from 7 regions across China were included in this study. We first used a Distributed Lag Non-linear Model (DLNM) to estimate community-specific effects of temperature on non-accidental mortality during 2006-2011. A multivariate meta-analysis was then applied to pool the estimates of community-specific effects. RESULTS: A U-shaped curve was observed between temperature and mortality at the national level in China, indicating both low and high temperatures were associated with increased mortality risk. The overall threshold was at about the 75th percentile of the pooled temperature distribution. The relative risk was 1.61 (95% CI: 1.48-1.74) for extremely cold temperature (1st percentile of temperature), and 1.21 (95% CI: 1.10-1.34) for extreme hot temperature (99th percentile of temperature) at lag0-21 days. The temperature-mortality relationship is different for different regions. Compared with north China, south China had a higher minimum mortality temperature (MMT), and there was a larger cold effect in the more southern parts of China and a more pronounced hot effect in more northern parts. CONCLUSIONS: Both cold and hot temperatures increase mortality risk in China, and the relationship varies geographically. Our findings suggest that public health policies for climate change adaptation should be tailored to the local climate conditions. Copyright 漏 2014 Elsevier Inc. All rights reserved.

DOI PMID

[10]
Chen R J, Wang C C, Meng X, et al.Both low and high temperature may increase the risk of stroke mortality[J]. Neurology, 2013,81:1064-1070.Objective: To examine temperature in relation to stroke mortality in a multicity time series study in China.<br/>Methods: We obtained data on daily temperature and mortality from 8 large cities in China. We used quasi-Poisson generalized additive models and distributed lag nonlinear models to estimate the accumulative effects of temperature on stroke mortality across multiple days, adjusting for long-term and seasonal trends, day of the week, air pollution, and relative humidity. We applied the Bayesian hierarchical model to pool city-specific effect estimates.<br/>Results: Both cold and hot temperatures were associated with increased risk of stroke mortality. The potential effect of cold temperature might last more than 2 weeks. The pooled relative risks of extreme cold (first percentile of temperature) and cold (10th percentile of temperature) temperatures over lags 0-14 days were 1.39 (95% posterior intervals [PI] 1.18-1.64) and 1.11 (95% PI 1.06-1.17), compared with the 25th percentile of temperature. In contrast, the effect of hot temperature was more immediate. The relative risks of stroke mortality over lags 0-3 days were 1.06 (95% PI 1.02-1.10) for extreme hot temperature (99th percentile of temperature) and 1.14 (95% PI 1.05-1.24) for hot temperature (90th percentile of temperature), compared with the 75th percentile of temperature.<br/>Conclusions: This study showed that both cold and hot temperatures were associated with increased risk of stroke mortality in China. Our findings may have important implications for stroke prevention in China.

[11]
Ma W J, Zeng W L, Zhou M G, et al.The short-term effect of heat waves on mortality and its modifiers in China: An analysis from 66 communities[J]. Environment International, 2015,75:103-109.BACKGROUND: Many studies have reported increased mortality risk associated with heat waves. However, few have assessed the health impacts at a nation scale in a developing country. This study examines the mortality effects of heat waves in China and explores whether the effects are modified by individual-level and community-level characteristics. METHODS: Daily mortality and meteorological variables from 66 Chinese communities were collected for the period 2006-2011. Heat waves were defined as 鈮2 consecutive days with mean temperature 鈮95th percentile of the year-round community-specific distribution. The community-specific mortality effects of heat waves were first estimated using a Distributed Lag Non-linear Model (DLNM), adjusting for potential confounders. To investigate effect modification by individual characteristics (age, gender, cause of death, education level or place of death), separate DLNM models were further fitted. Potential effect modification by community characteristics was examined using a meta-regression analysis. RESULTS: A total of 5.0% (95% confidence intervals (CI): 2.9%-7.2%) excess deaths were associated with heat waves in 66 Chinese communities, with the highest excess deaths in north China (6.0%, 95% CI: 1%-11.3%), followed by east China (5.2%, 95% CI: 0.4%-10.2%) and south China (4.5%, 95% CI: 1.4%-7.6%). Our results indicate that individual characteristics significantly modified heat waves effects in China, with greater effects on cardiovascular mortality, cerebrovascular mortality, respiratory mortality, the elderly, females, the population dying outside of a hospital and those with a higher education attainment. Heat wave mortality effects were also more pronounced for those living in urban cities or densely populated communities. CONCLUSION: Heat waves significantly increased mortality risk in China with apparent spatial heterogeneity, which was modified by some individual-level and community-level factors. Our findings suggest adaptation plans that target vulnerable populations in susceptible communities during heat wave events should be developed to reduce health risks. Copyright 漏 2014 Elsevier Ltd. All rights reserved.

DOI PMID

[12]
Son J Y, Lee J T, Anderson G B, et al.Vulnerability to temperature-related mortality in Seoul, Korea[J]. Environmental Research Letters. 2011,6:034027.Studies indicate that the mortality effects of temperature may vary by population and region, although little is known about the vulnerability of subgroups to these risks in Korea. This study examined the relationship between temperature and cause-specific mortality for Seoul, Korea, for the period 2000-7, including whether some subgroups are particularly vulnerable with respect to sex, age, education and place of death. The authors applied time-series models allowing nonlinear relationships for heat- and cold-related mortality, and generated exposure-response curves. Both high and low ambient temperatures were associated with increased risk for daily mortality. Mortality risk was 10.2% (95% confidence interval 7.43, 13.0%) higher at the 90th percentile of daily mean temperatures (25 掳C) compared to the 50th percentile (15 掳C). Mortality risk was 12.2% (3.69, 21.3%) comparing the 10th (-1 掳C) and 50th percentiles of temperature. Cardiovascular deaths showed a higher risk to cold, whereas respiratory deaths showed a higher risk to heat effect, although the differences were not statistically significant. Susceptible populations were identified such as females, the elderly, those with no education, and deaths occurring outside of a hospital for heat- and cold-related total mortality. Our findings provide supportive evidence of a temperature-mortality relationship in Korea and indicate that some subpopulations are particularly vulnerable.

DOI PMID

[13]
Kan H D, London S J, Chen H L, et al.Diurnal temperature range and daily mortality in Shanghai, China[J]. Environmental Research, 2007,103:424-431.Although the relationship between temperature level and mortality outcomes has been well established, it is still unknown whether within-day variation in temperature, e.g. diurnal temperature range (DTR), is a risk factor for death independent of the corresponding temperature. Moreover, DTR is a meteorological indicator associated with global climate change which may be related to a variety of health outcomes. We hypothesized that large diurnal temperature change might be a source of additional environmental stress and therefore a risk factor for death. We used daily weather and mortality data from Shanghai, China to test this hypothesis. We conducted a time-series study to examine the association between DTR and mortality outcomes from 2001 to 2004. A semi-parametric generalized additive model (GAM) was used to assess the acute effect of DTR on mortality after controlling for covariates including time trend, day of the week (DOW), temperature, humidity, and outdoor air pollution. We found a strong association between DTR and daily mortality after adjustment for those potential confounders. A 1 degrees C increment of the 3-day moving average of DTR corresponded to a 1.37% (95% CI 1.08-1.65%) increase in total non-accidental mortality, a 1.86% (95% CI 1.40-2.32%) increase in cardiovascular mortality, and a 1.29% (95% CI 0.49-2.09%) increase in respiratory mortality. The effects of DTR on total non-accidental and cardiovascular mortality were significant on both "cold" (below 23 degrees C) and "warm" (at least 23 degrees C) days, although respiratory mortality was only significantly associated with DTR on "cold" days. This study suggests within-day variation in temperature may be a novel risk factor for death.

DOI PMID

[14]
Guo Y M, Barnett A G, Pan X C, et al.The impact of temperature on mortality in Tianjin, China: A case-crossover design with a distributed lag nonlinear model[J]. Environmental Health Perspectives, 2011,119:1719-1725.BACKGROUND: Although interest in assessing the impacts of temperature on mortality has increased, few studies have used a case-crossover design to examine nonlinear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature mortality relationship in China or on what temperature measure is the best predictor of mortality.<br/>OBJECTIVES: Our objectives were to use a distributed lag nonlinear model (DLNM) as a part of case-crossover design to examine the nonlinear and distributed lag effects of temperature on mortality in Tianjin, China and to explore which temperature measure is the best predictor of mortality.<br/>METHODS: We applied the DLNM to a case-crossover design to assess the nonlinear and delayed effects of temperatures (maximum, mean, and minimum) on deaths (nonaccidental, cardiopulmonary, cardiovascular, and respiratory).<br/>RESULTS: A U-shaped relationship was found consistently between temperature and mortality. Cold effects (i.e., significantly increased mortality associated with low temperatures) were delayed by 3 days and persisted for 10 days. Hot effects (i.e., significantly increased mortality associated with high temperatures) were acute and lasted for 3 days and were followed by mortality displacement for nonaccidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature.<br/>CONCLUSIONS: In Tianjin, extreme cold and hot temperatures increased the risk of mortality. The effects of cold last longer than the effects of heat. Combining the DLNM and the case-crossover design allows the case-crossover design to flexibly estimate the nonlinear and delayed effects of temperature (or air pollution) while controlling for season.

DOI

[15]
陈美池,牛静萍,阮烨,等.兰州市日均气温与心血管疾病日入院人次的时间序列研究[J].环境与健康杂志,2014,31(5):391-394.目的研究日均气温对心血管疾病日入院人次的影响。方法收集兰州市城区4家综合性医院2004年1月1日至2007年12月31日心血管疾病患者入院资料和该地区同期气象因素及大气污染物（PM10、SO2、NO2）的时间序列资料。选用时间序列分析的广义相加模型，在控制星期几效应及其他混杂因素的基础上，探讨日均气温对兰州市居民心血管疾病日入院人次的影响。结果当兰州市日均气温为10℃时，气温对居民心血管疾病日入院人次的影响最小。日均气温低于10℃时，每降低1℃滞后3d时因心血管疾病而发生入院治疗的超额危险度最大，为2．55％（R尺95％CI：0．9552-0．9938）；日均气温高于10℃时，每升高1℃时当天因心血管疾病而发生入院治疗的超额危险度最大，为1．33％（RR95％CI：1．0070—1．0196）。结论兰州市日均气温低于10℃时，居民心血管疾病日入院人次随着气温的降低而增加；当日均气温高于10℃时，日入院人次随着气温的升高而增加。

[ Chen M C, Niu J P, Ruan Y, et al.Relationship between daily mean temperature and number of daily hospitalization of cardiovascular diseases in Lanzhou: a time-series study[J]. Journal of Environ Health, 2014,31(5):391-394. ]

[16]
吴凡. 南京市高温热浪对天气敏感性疾病的影响研究[D].南京:南京信息工程大学,2013.

[ Wu F.Research of the effect of high temperature and heat wave on weather sensitive diseases in Nanjing Area[D]. Nanjing: Nanjing University of Information Science & Technology, 2013. ]

[17]
刘学恩,李群娜,赵宗群.气温及冷空气对武汉市心脑血管疾病死亡率的影响[J].中国公共卫生,2002,8(8):56-58.目的探讨气温及冷空气对武汉市心脑血管疾病死亡人数和死亡率的影响,并为 制订防治对策提供依据.方法对武汉市1991～1998年(除去1995年)共1 220例因心脑血管疾病死亡的病例进行分析.描述并分析心脑血管疾病死亡人数和死亡率与月平均气温和冷空气的关系.结果心脑血管疾病(CVD)死亡率在冬 天有一个主峰,7月份还有一个次峰.相关分析显示:夏季气温与CVD死亡率为正相关,中年组(G1:年龄45～65岁)相关系数无显著意义,老年组 (G2:年龄≥65岁)有显著性意义(P＜0.05);其余三季气温和CVD死亡率呈负相关,中老年组均有显著性(P<0.05).回归分析显示: 夏季气温与CVD死亡率的回归方程老年组(G2)为,Y=0.86T-7.154(P＜0.05).其余三季气温与CVD死亡率的回归方程中年组(G1) 为:Y＝6.175-0.125T(P＜0.05);老年组(G2)为:Y＝24.58-0.415T(P＜0.05).从冷空气与CVD死亡人数的月分 布曲线可知武汉的冷空气对死亡率影响不大,冷空气与CVD死亡率的相关系数无显著性(P＞0.05).结论气温与CVD死亡率在夏季呈正相关,在其余三季 呈负相关.

DOI

[ Liu X E, Li N Q, Zhao Z Q.Influence of temperature and cold air on mortality of cardio -and cerebral vascular diseases[J]. China Public Health, 2002,8(8):56-58. ]

[18]
丁一汇,任国玉,石广玉,等.气候变化国家评估报告(I):中国气候变化的历史和未来趋势[J].气候变化研究进展,2006,2(1):3-8.中国的气候变化与全球变化有相当的一致性，但也存在明显差别。在全球变暖背景下，近100 a来中国年平均地表气温明显增加，升温幅度比同期全球平均值略高。近100 a和近50 a的降水量变化趋势不明显，但1956年以来出现了微弱增加的趋势。近50 a来中国主要极端天气气候事件的频率和强度也出现了明显的变化。研究表明，中国的CO2年排放量呈不断增加趋势，温室气体正辐射强迫的总和是造成气候变暖的主要原因。对21世纪气候变化趋势做出的预测表明：未来20～100 a，中国地表气温增加明显，降水量也呈增加趋势。

[ Ding Y H, Ren G Y, Shi G Y, et al.National assessment report of climate change (I): Climate change in China and its future trend[J]. Advances in Climate Change Research, 2006,2(1):3-8. ]

[19]
秦大河,罗勇,陈振林,等.气候变化科学的最新进展:IPCC第四次评估综合报告解析[J].气候变化研究进展,2007,3(6):311-314.政府间气候变化专门委员会（IPCC）第四次评估报告综合报告于2007年11月17日在西班牙正式发布。综合报告将温室气体排放、大气温室气体浓度与地球表面温度直接联系起来，综合评估了气候变化科学、气候变化的影响和应对措施的最新研究进展。综合报告指出：控制温室气体排放量的行动刻不容缓；能否减小全球变暖所带来的负面影响，将在很大程度上取决于人类在今后二三十年中在削减温室气体排放方面所作的努力和投资。这对国际社会和各国政府制定经济社会发展政策，适应和减缓气候变化有一定的指导和促进作用。

[ Qin D H, Luo Y, Chen Z L, et al.Latest advances in climate change sciences: Interpretation of the synthesis report of the IPCC fourth assessment report[J]. Advances in Climate Change Research, 2006,3(6):311-314. ]

[20]
Gómez-Acebo I, Llorca J, Dierssen T.Cold-related mortality due to cardiovascular diseases, respiratory diseases and cancer: a case-crossover study[J]. Public Health. 2013,127(3):252-258.There is a striking association between the extreme cold temperatures and mortality from cancer, not previously reported, which is more remarkable in the elderly. These results could be explained by a harvesting effect in which the cold acts as a trigger of death in terminally ill patients at high risk of dying a few days or weeks later.

DOI PMID

[21]
钟堃,刘玲,张金良.北京市寒潮天气对居民心脑血管疾病死亡影响的病例交叉研究[J].环境与健康杂志,2010,27(2):100-105.目的研究寒潮天气对北京市居民心脑血管疾病Et死亡人数的影响。方法北京市居民死亡资料来自北京市疾病预防控制中心，气象资料来自北京城市气象研究所。运用病例交叉的设计思想，采用单向回顾性1：1对照和双向对称性1：2对照设计，分析北京市1998年1月1日-2000年6月30日期间6次寒潮天气与北京市城8区居民每日心血管疾病、急性心肌梗死和脑血管疾病死亡的关系。病例选取为冬季所有心脑血管疾病死亡病例，选取死亡发生前第7天和发生后第7天作为对照（或者死亡前、后第14天作为对照）。结果研究时间段内共出现6次寒潮，日最低气温下降明显且伴随有湿度下降和气压上升的第3次寒潮期间3种死因的双向对称性对照设计的滞后期均为0d，居民每日心血管疾病、急性心肌梗死、脑血管疾病死亡的OR值分别为1．500（95％CI：1．032～2．181），1．913（95％CI：1．066—3．432）、1．679（95％CI：1．139～2．474）。其他5次寒潮对心脑血管病人的死亡未见明显影响。结论研究期间北京地区的绝大多数寒潮天气未对居民心脑血管疾病的死亡产生明显影响；但是温度降幅大且伴随高气压的寒潮可能会造成心脑血管疾病死亡风险升高，值得关注。

[ Zhong K, Liu L, Zhang J L.Impact of cold wave on mortality of cerebra-cardiovascular diseases in Beijing: A case-crossover study[J]. Journal of Environ Health, 2010,27(2):100-105. ]

[22]
郑景云,卞娟娟,葛全胜,等.1981-2010年中国气候区划[J].科学通报,2013(30):3088-3099.

[ Zheng J Y, Bian J J, Ge Q S, et al.The climate regionalization in China for 1981-2010[J]. Chin Sci Bull (Chin Ver), 2013,30:3088-3099. ]

[23]
郑景云,尹云鹤,李炳元.中国气候区划新方案[J].地理学报,2010,65(1):3-12.根据全国609个气象站1971-2000年的日气象观测资料,遵循地带性与非地带性相结合、发生同一性与区域气候特征相对一致性相结合、综合性和主导因素相结合、自下而上和自上而下相结合、空间分布连续性与取大去小等5个基本原则,在充分吸纳已有气候区划基本理论与区划方法的基础上,参照中国科学院《中国自然地理》编辑委员会制定的气候区划三级指标体系,对我国气候进行重新区划 结果将我国划分为12个温度带、24个干湿区、56个气候区。与先前区划方案相比发现：20世纪70年代以来,中国气候带、区的总体格局并未发生明显变化,但一些重要的气候分界线却出现了一定程度的移动。其中亚热带北界与暖温带北界均出现了北移,北方地区的半湿润与半干旱分界线也出现了不同程度的东移与南扩,同时中温带、暖温带、北亚热带和中亚热带的三级气候区也出现了一定程度的变动 这种变化可能主要是因为20世纪80年代以后我国大多数地区出现不同程度的增暖及北方一些区域出现干旱化而引起的 且与本区划所采用的资料站点和部分区划原则有一定更新有关。

DOI

[ Zheng J Y, Yin Y H, Li B Y.A new scheme for climate regionalization in China[J]. Acta Geographica Sinica, 2010,65(1):3-12. ]

[24]
Yang G, Hu J, Rao K Q, et al.Mortality registration and surveillance in China: History, current situation and challenges[J]. Population Health Metrics,2005,3(1):1-9.The Chinese vital registration system currently covers 41 urban and 85 rural centres, accounting for roughly 8 % of the national population. Quality of registration is better in urban than in rural areas, and eastern than in western regions, resulting in significant biases in the overall statistics. The Ministry of Health introduced the Disease Surveillance Point System in 1980, to generate cause specific mortality statistics from a nationally representative sample of sites. Currently, the sample consists of 145 urban and rural sites, covering populations from 30,000 鈥 70,000, and a total of about 1 % of the national population. Causes of death are derived through a mix of medical certification and 'verbal autopsy' procedures, applied according to standard guidelines in all sites. Periodic evaluations for completeness of registration are conducted, with subsequent corrections for under reporting of deaths.Results from the DSP have been used to inform health policy at national, regional and global levels. There remains a need to critically validate the information on causes of death, and a detailed validation exercise on these aspects is currently underway. In general, such sample based mortality registration systems hold much promise as models for rapidly improving knowledge about levels and causes of mortality in other low-income populations.Data on the causes, levels and patterns of mortality are critical to support the development of evidence-based health policy. Cause of death statistics represent the longest historical series of data on the health of populations, in some cases extending back well over 150 years [1]. Yet, complete vital registration systems, which have traditionally generated these data, are often difficult and expensive to establish and maintain in developing countries. China is no exception. With 1.3 billion people, complete registration and medical certification of deaths is logistically and financially unattainable at present. However, morta

DOI PMID

[25]
周脉耕,姜勇,黄正京,等.全国疾病监测点系统的调整与代表性评价[J].疾病监测,2010,25(3):239-244.目的在原全国疾病监测点系统（disease surveillance points system,DSPs）基础上进行系统调整并评价其代表性。方法利用2000年人口普查资料和全国市县调查数据,对1989年调整后的全国疾病监测点系 统的代表性进行评价,评价方法为分别对DSPs所有县（区）、城市部分、农村部分及东、中、西部城市农村部分的人均国内生产总值、非农业人口比重、15岁 以上人口文盲率、0~14岁人口占整个人口的比例、≥65岁人口占整个人口的比例、死亡率、出生率等指标使用u检验与全国相应地区进行比较,并在评价结果 的基础上进行系统调整。具体步骤为按照东、中、西部的经济指标和县（区）人口数,将全国所有县（区）分成54层,对照全国各层中县（区）的实际数,确定全 国疾病监测点系统中相应各层的县（区）理论数,然后对目前监测系统各层中监测点的数量和分布进行调整。进而对调整之后的系统进行代表性评价。结果全国、全 国农村、全国城市,东、中、西部的城市,以及西部农村,与原全国疾病监测点系统中的相应地区之间均有一个或多个指标差异有统计学意义。调整后的全国疾病监 测点系统共161个县（区）,其中包含63个区和98个县（县级市）。对其进行代表性评价结果显示,DSPs中除城乡合计外,农村、城市,东、中、西部的 城市、农村,与全国相应地区之间各类指标差异均无统计学意义。结论调整后的全国疾病监测点系统对全国城市和全国农村均具有良好的代表性,对全国合计的估计 需要校正城市和农村的比例方可代表全国水平。

DOI

[ Zhou M G, Jiang Y, Huang Z J, et al.Adjustment and representativeness evaluation of national disease surveillance points system[J]. Disease Surveillance, 2010,25(3):239-244. ]

[26]
Yuan W, Xu B, Chen Z, et al.Validation of China-wide interpolated daily climate variables from 1960 to 2011[J]. Theoretical and Applied Climatology. 2015,119(3-4):689-700.Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961鈥2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2 ) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2 , and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66鈥95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

DOI

[27]
Gasparrini A.Modeling exposure-lag-response associations with distributed lag non-linear models[J]. Statistics In Medicine, 2014,33:881-899.In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure-lag-response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others.

DOI PMID

[28]
Wu W, Xiao Y Z, Li G C, et al.Temperature-mortality relationship in four subtropical Chinese cities: A time-series study using a distributed lag non-linear model[J]. Science of the Total Environment, 2013,449:355-362.Background Numerous studies have reported the association between ambient temperature and mortality. However, few multicity studies have been conducted in subtropical regions in developing countries. The present study assessed the health effects of temperature on mortality in four subtropical cities of China.Methods We used “double threshold-natural cubic spline” distributed lag non-linear model (DLNM) to investigate the cold and hot effects on mortality at different lags in four subtropical cities. Then we conducted a meta-analysis to estimate the overall cold and hot effects on mortality at different lag days.ResultsA U-shaped relationship between temperature and mortality was found in the four cities. Cold effect was delayed and persisted for about 27 days, whereas hot effect was acute and lasted for 3 days. In Changsha, Kunming, Guangzhou and Zhuhai, a 1 °C decrease of temperature under the low threshold was associated with a lag0–27 cumulative relative risk (RR) of 1.061 (95% confidence interval (CI): 1.023–1.099), 1.044 (95% CI: 1.033–1.056), 1.096 (95% CI: 1.075–1.117) and 1.111 (95% CI: 1.078–1.145) for total mortality, respectively. And RR for 1 °C increase of temperature above the hot threshold at the lag0 was 1.020 (95% CI: 1.003–1.037), 1.017 (95% CI: 1.004–1.030), 1.029 (95% CI: 1.020–1.039) and 1.023 (95% CI: 1.004–1.042), respectively. The cold and hot effects were greater among the elderly in Changsha, Guangzhou and Zhuhai. Meta analysis showed that the hot effect decreased gradually with lag days, with the greatest effect at current day (RR = 1.023, 95% CI: 1.015–1.031); while the cumulative cold effect increased gradually with lag days, with the highest effect at lag0–27 (RR = 1.076, 95% CI: 1.046–1.107).Conclusion Both low and high temperatures were associated with increased mortality in the four subtropical Chinese cities, and cold effect was more durable and pronounced than hot effect.

DOI PMID

[29]
Gasparrini A, Armstrong B, Kenward M.Distributed lag non-linear models[J]. Stat Med, 2010,29:2224-2234.Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure-response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure-response dependencies and delayed effects. This methodology is based on the definition of a 'cross-basis', a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987-2000.

DOI PMID

[30]
Gasparrini A, Armstrong B.Multivariate meta-analysis: A method to summarize non-linear associations[J]. Statistics In Medicine. 2011,30:2504-2506.Multivariate meta-analysis represents a promising statistical tool in several research areas. Here we provide a brief overview of the application of this methodology to combining complex multi-parameterized relationships, such as non-linear or delayed associations, in multi-site studies. The discussion focuses on the advantages over simpler univariate methods, estimation and computational issues and directions for further research.

DOI PMID

[31]
Gasparrini A.Modeling exposure-lag-response associations with distributed lag non-linear models[J]. Stat Med. 2014,33(5) 881-899.In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure-lag-response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others.

DOI PMID

[32]
Dominici F, Peng R D, Bell M L, et al.Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases[J]. JAMA, 2006,295(10):1127-1134.Evidence on the health risks associated with short-term exposure to fine particles (particulate matter 200,000) with 11.5 million Medicare enrollees (aged >65 years) living an average of 5.9 miles from a PM2.5 monitor. Daily counts of county-wide hospital admissions for primary diagnosis of cerebrovascular, peripheral, and ischemic heart diseases, heart rhythm, heart failure, chronic obstructive pulmonary disease, and respiratory infection, and injuries as a control outcome. There was a short-term increase in hospital admission rates associated with PM2.5 for all of the health outcomes except injuries. The largest association was for heart failure, which had a 1.28% (95% confidence interval, 0.78%-1.78%) increase in risk per 10-microg/m3 increase in same-day PM2.5. Cardiovascular risks tended to be higher in counties located in the Eastern region of the United States, which included the Northeast, the Southeast, the Midwest, and the South. Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases.

DOI PMID

[33]
Riley R D, Price M J, Jackson D, et al.Multivariate meta-analysis using individual participant data[J]. RESEARCH SYNTHESIS METHODS. 2015,6(2):157-174.When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted

DOI PMID

[34]
Wang J, Zhang T, Fu B.A measure of spatial stratified heterogeneity[J]. Ecological Indicators. 2016,67:250-256.Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose a q-statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. The q value is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. The q-statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.

DOI

[35]
Wang J F, Li X H, Christakos G, et al.Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China[J]. International Journal of Geographical Information Science, 2010,24(1):107-127.Physical environment, man‐made pollution, nutrition and their mutual interactions can be major causes of human diseases. These disease determinants have distinct spatial distributions across geographical units, so that their adequate study involves the investigation of the associated geographical strata. We propose four geographical detectors based on spatial variation analysis of the geographical strata to assess the environmental risks of health: the risk detector indicates where the risk areas are; the factor detector identifies factors that are responsible for the risk; the ecological detector discloses relative importance between the factors; and the interaction detector reveals whether the risk factors interact or lead to disease independently. In a real‐world study, the primary physical environment (watershed, lithozone and soil) was found to strongly control the neural tube defects (NTD) occurrences in the Heshun region (China). Basic nutrition (food) was found to be more important than man‐made pollution (chemical fertilizer) in the control of the spatial NTD pattern. Ancient materials released from geological faults and subsequently spread along slopes dramatically increase the NTD risk. These findings constitute valuable input to disease intervention strategies in the region of interest.

DOI

[36]
杨忍,刘彦随,龙花楼,等.中国村庄空间分布特征及空间优化重组解析[J].地理科学, 2016,36(2):170-179.以中国电子地图数据和分县经济社会数据为基础,利用最邻近距离R指数模型分析中国村庄分布模式格局,结合地理探测器的研究方法对影响因素进行探测识别,同时解析乡村空间优化重组背景和模式。研究得出以下主要结论：①中国村庄空间分布呈现出聚集、随机、离散均匀分布的并存空间分布模式,村庄空间分布模式区域差异特征显著。东南半壁的村庄分布密度远大于西北半壁,不同地域类型区的村庄空间分布模式表现出各异的特征。平原地区的村庄空间分布密集,空间分布模式以随机、分散为主,村庄之间邻近距离较近。高寒山区、沙漠边缘地带,村庄空间分布密度极低,村庄之间邻近距离偏大,村庄空间分布相对聚集。丘陵、山地交汇过渡地带,村庄空间分布密度较大,空间分布模式偏向随机分布。②村庄分布受到传统因素和经济发展双重因子的影响,传统影响因素依然在发挥作用,但经济发展的影响愈加明显。不同区域地形、水资源条件对村庄分布影响显著。交通条件、产业非农化、经济发展、农业现代化发展对乡村生活、生产空间的空间形态和分布模式产生剧烈影响。③伴随乡村各种生产要素非农化流失,村庄空间亟待优化重组,优化以镇区为依托的中心村-基层村体系空间组织结构应为乡村物质空间优化重组有效选择。④在不同地域类型区域,村镇格局的空间优化重组形态可以采用放射均衡、放射非均衡、多核心均衡、走廊式布局模式及混合模式。

[ Yang R, Liu Y S, Long H L, et al.Spatial distribution characteristics and optimized reconstructing analysis of rural settlement in China.[J] Scientia Geographica Sinica, 2016,36(2):170-179. ]

[37]
通拉嘎,徐新良,付颖,等.地理环境因子对螺情影响的探测分析[J].地理科学进展,2014,33(5):625-635.近年来，由于自然环境、经济社会等因素影响，中国血吸虫病疫情呈回升态势，表现为急性感染人数和血吸虫病患病人数增多，局部地区钉螺扩散明显，感染性钉螺分布范围逐渐扩大等。钉螺是血吸虫的唯一中间宿主，控制钉螺对血吸虫病防治具有重要意义。本文根据钉螺的生态学特征，综合高程、水文、土地利用、土壤、植被等因子，基于地理探测器模型分析了地理环境因子对2009年湖北省钉螺分布的影响。结果表明①在垸内型流行区，整个流行季（3-10月）、特别是7-9月期间的植被覆盖与钉螺分布范围有关，密螺地带的特征为土壤质地粉砂含量适中、黄红壤和淹育水稻土，第一季度有较高的植被覆盖度；②在垸外型流行区，湖泊滩地、高覆盖度草地是其主要分布环境，而第一季度较高的植被覆盖，尤其是荻、芦苇等植被类型是高密度地区的环境特征；③在山地丘陵，河流附近的林地和耕地，潴育或淹育水稻土是钉螺密集分布的环境。筛选出的地理环境指示因子可用于遥感技术监测钉螺孳生地，从而为采取有效的控螺措施提供科学依据。

DOI

[ Tong L G, Xu X L, Fu Y, et al.Impact of environmental factors on snail distribution using geographical detector model[J] Progress in Geography, 2014,33(5):625-635. ]

[38]
毕硕本,计晗,陈昌春,等.地理探测器在史前聚落人地关系研究中的应用与分析[J].地理科学进展,2015,34(1):118-127.遗址—河流距离是史前聚落遗址人地关系研究的重要内容。本文以河南省卢氏县为例,引入地理探测器模型,采用基于PD,H值计算的连续性地理数据最优离散法,获取遗址—河流相关性定量数值,讨论和总结模型中等间距(EI)、百分位(QV)、自然断点(NB)、几何间隔(GI)4种分类方法,分别在裴李岗时期、仰韶前期、仰韶后期、龙山时期所表现的性能及适用情况;并在此基础上揭示聚落的结构、发展规律、分布和范围规律。研究结果表明:14个文化时期分别采用NB、QV、NB、GI及其分类数分别为8,8,8,6时,为离水距离因子的最优离散。该因子对遗址密度分布的决定力分别为39.5%、70.8%、73.0%和59.8%;2洪水切割作用导致河岸两旁的阶地逐渐崩塌,阶地的面积越来越小,古人类为了赢得更多的生存空间,将遗址沿河散开呈条带装分布。当阶地面积小到一定程度,聚落沿河流长度扩展的成本过高,便开始向远离河流部分扩张,因此离河距离因子呈先升后降;3从Q值提升强度看,NB/EI>GI>QV;从提升效率看,EI/GI>NB>QV;从决定力大小来看,GI>QV/NB>EI;4聚落结构由裴李岗时期的简单、稀疏和松散不断发展,分别在仰韶前、后期和龙山时期出现两极化、三段式,聚落发展影响因素由人口数量增长变为社会内部结构变化,聚落的分布和古人类活动范围距河流约正常人步行1~2.5 h的距离,且不断扩大。

DOI

[ Bi S B, Ji H, Chen C C, et al.Application of geographical detector in human-environment relationship study of prehistoric settlements[J]. Progress in Geography, 2015,34(1):118-127. ]

[39]
Basu R.High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008[J]. Environmental Health. 2009, 8.Background This review examines recent evidence on mortality from elevated ambient temperature for studies published from January 2001 to December 2008. Methods PubMed was used to search for the following keywords: temperature, apparent temperature, heat, heat index, and mortality. The search was limited to the English language and epidemiologic studies. Studies that reported mortality counts or excess deaths following heat waves were excluded so that the focus remained on general ambient temperature and mortality in a variety of locations. Studies focusing on cold temperature effects were also excluded. Results Thirty-six total studies were presented in three tables: 1) elevated ambient temperature and mortality; 2) air pollutants as confounders and/or effect modifiers of the elevated ambient temperature and mortality association; and 3) vulnerable subgroups of the elevated ambient temperature-mortality association. The evidence suggests that particulate matter with less than 10 um in aerodynamic diameter and ozone may confound the association, while ozone was an effect modifier in the warmer months in some locations. Nonetheless, the independent effect of temperature and mortality was withheld. Elevated temperature was associated with increased risk for those dying from cardiovascular, respiratory, cerebrovascular, and some specific cardiovascular diseases, such as ischemic heart disease, congestive heart failure, and myocardial infarction. Vulnerable subgroups also included: Black racial/ethnic group, women, those with lower socioeconomic status, and several age groups, particularly the elderly over 65 years of age as well as infants and young children. Conclusion Many of these outcomes and vulnerable subgroups have only been identified in recent studies and varied by location and study population. Thus, region-specific policies, especially in urban areas, are vital to the mitigation of heat-related deaths.

DOI PMID

[40]
Healy J D.Excess winter mortality in Europe: a cross country analysis identifying key risk factors[J]. Journal of Epidemiology and Community Health. 2003,57:784-789.Much debate remains regarding why certain countries experience dramatically higher winter mortality. Potential causative factors other than cold exposure have rarely been analysed. Comparatively less research exists on excess winter deaths in southern Europe. Multiple time series data on a variety of risk factors are analysed against seasonal-mortality patterns in 14 European countries to identify key relations Subjects and setting: Excess winter deaths (all causes), 1988-97, EU-14.Coefficients of seasonal variation in mortality are calculated for EU-14 using monthly mortality data. Comparable, longitudinal datasets on risk factors pertaining to climate, macroeconomy, health care, lifestyle, socioeconomics, and housing were also obtained. Poisson regression identifies seasonality relations over time.Portugal suffers from the highest rates of excess winter mortality (28%, CI=25% to 31%) followed jointly by Spain (21%, CI=19% to 23%), and Ireland (21%, CI=18% to 24%). Cross country variations in mean winter environmental temperature (regression coefficient (beta)=0.27), mean winter relative humidity (beta=0.54), parity adjusted per capita national income (beta=1.08), per capita health expenditure (beta=-1.19), rates of income poverty (beta=-0.47), inequality (beta=0.97), deprivation (beta=0.11), and fuel poverty (beta=0.44), and several indicators of residential thermal standards are found to be significantly related to variations in relative excess winter mortality at the 5% level. The strong, positive relation with environmental temperature and strong negative relation with thermal efficiency indicate that housing standards in southern and western Europe play strong parts in such seasonality.High seasonal mortality in southern and western Europe could be reduced through improved protection from the cold indoors, increased public spending on health care, and improved socioeconomic circumstances resulting in more equitable income distribution.

DOI PMID

[41]
O'Neill M S, Zanobetti A, Schwartz J. Modifiers of the temperature and mortality association in seven US cities[J]. American Journal Of Epidemiology. 2003,157:1074-1082.This paper examines effect modification of heat- and cold-related mortality in seven US cities in 1986-1993. City-specific Poisson regression analyses of daily noninjury mortality were fit with predictors of mean daily apparent temperature (a construct reflecting physiologic effects of temperature and humidity), time, barometric pressure, day of the week, and particulate matter less than 10 micro m in aerodynamic diameter. Percentage change in mortality was calculated at 29 degrees C apparent temperature (lag 0) and at -5 degrees C (mean of lags 1, 2, and 3) relative to 15 degrees C. Separate models were fit to death counts stratified by age, race, gender, education, and place of death. Effect estimates were combined across cities, treating city as a random effect. Deaths among Blacks compared with Whites, deaths among the less educated, and deaths outside a hospital were more strongly associated with hot and cold temperatures, but gender made no difference. Stronger cold associations were found for those less than age 65 years, but heat effects did not vary by age. The strongest effect modifier was place of death for heat, with out-of-hospital effects more than five times greater than in-hospital deaths, supporting the biologic plausibility of the associations. Place of death, race, and educational attainment indicate vulnerability to temperature-related mortality, reflecting inequities in health impacts related to climate change.

PMID

[42]
Katsouyanni K, Pantazopoulou A, Touloumi G, et al.Evidence for interaction between air-pollution and high-temperature in the gausation of excess mortality[J]. Archives Of Environmental Health. 1993,48:235-242.Studies have demonstrated repeatedly that air pollution in Athens is associated with a small but statistically significant increase in mortality. Extremely high air temperatures can also cause excess mortality. This study investigated whether air pollution and air temperature have synergistic effects on excess mortality in Athens. Data concerning the increased number of deaths in July 1987 (when a major "heat wave" hit Greece) were compared to the deaths in July of the 6 previous years. This comparison revealed a greater increase in the number of deaths in Athens (97%), compared to all other urban areas (33%) and to all non-urban areas (27%). Data on the daily levels of smoke, sulfur dioxide, and ozone; the number of deaths that occurred daily; and meteorological variables were collected for a 5-y period. The daily value of Thom's discomfort index was calculated. Multiple linear regression models were used to investigate main and interactive effects of air temperature and Thom's discomfort index and air pollution indices. The daily number of deaths increased by more than 40 when the mean 24-h air temperature exceeded 30 degrees C. The main effects of an air pollution index are not statistically significant, but the interaction between high levels of air pollution and high temperature (> or = 30 degrees C) are statistically significant (p < .05) for sulfur dioxide and are suggestive (p < .20) for ozone and smoke. Similar results were obtained when the discomfort index was used, instead of temperature in the models.

DOI PMID

[43]
Qian Z M, He Q C, Lin H M, et al.High temperatures enhanced acute mortality effects of ambient particle pollution in the "Oven" city of Wuhan, China[J]. Environmental Health Perspectives, 2008,116:1172-1178.We investigated whether the effect of air pollution on daily mortality is enhanced by high temperatures in Wuhan, China, using data from 2001 to 2004. Wuhan has been called an "oven" city because of its hot summers. Approximately 4.5 million permanent residents live in the 201-km(2) core area of the city.We used a generalized additive model to analyze pollution, mortality, and covariate data. The estimates of the interaction between high temperature and air pollution were obtained from the main effects and pollutant-temperature interaction models.We observed effects of consistently and statistically significant interactions between particulate matter < or = 10 microm (PM(10)) and temperature on daily nonaccidental (p = 0.014), cardiovascular (p = 0.007), and cardiopulmonary (p = 0.014) mortality. The PM(10) effects were strongest on extremely high-temperature days (daily average temperature, 33.1 degrees C), less strong on extremely low-temperature days (2.2 degrees C), and weakest on normal-temperature days (18.0 degrees C). The estimates of the mean percentage of change in daily mortality per 10-mug/m(3) increase in PM(10) concentrations at the average of lags 0 and 1 day during hot temperature were 2.20% (95% confidence interval), 0.74-3.68) for nonaccidental, 3.28% (1.24-5.37) for cardiovascular, 2.35% (-0.03 to 4.78) for stroke, 3.31% (-0.22 to 6.97) for cardiac, 1.15% (-3.54% to 6.07) for respiratory, and 3.02% (1.03-5.04) for cardiopulmonary mortality.We found synergistic effects of PM(10) and high temperatures on daily nonaccidental, cardiovascular, and cardiopulmonary mortality in Wuhan.

DOI PMID

[44]
Ren C, Williams G, Morawska L, et al.Ozone modifies associations between temperature and cardiovascular mortality - The analysis using the NMMAPS data[J]. Epidemiology. 2007,18:S69-S70.Both ambient ozone and temperature are associated with human health. However, few data are available on whether ozone modifies temperature effects. This study aims to explore whether ozone modified associations between maximum temperature and cardiovascular mortality in the USA.The authors obtained data from the US National Morbidity, Mortality, and Air Pollution Study (NMMAPS) website. They used two time-series Poisson regression models (a response surface model and a stratification model) to examine whether ozone modified associations between maximum temperature and cardiovascular mortality (CVM) in 95 large US communities during 1987-2000 in summer (June to September). Bayesian meta-analysis was used to pool estimates in each community.The response surface model was used to examine the joint effects of temperature and ozone on CVM in summer. Results indicate that ozone positively modified the temperature-CVM associations across the different regions. The stratification model quantified the temperature-CVM associations across different levels of ozone. Results show that in general the higher the ozone concentration, the stronger the temperature-CVM associations across the communities. A 10 degrees C increase in temperature on the same day was associated with an increase in CVM by 1.17% and 8.31% for the lowest and highest level of ozone concentrations in all communities, respectively.Ozone modified temperature effects in different regions in the USA. It is important to evaluate the modifying role of ozone when estimating temperature-related health impacts and to further investigate the reasons behind the regional variability and mechanism for the interaction between temperature and ozone.

DOI

Options
文章导航

/

〈 〉