Relationship between heat index and mortality of 6 major cities in Taiwan

百分位 泊松回归 分布滞后 人口学 极热 公共卫生 热指数 地理 环境科学 医学 环境卫生 统计 气象学 气候变化 人口 数学 生物 生态学 护理部 社会学 相对湿度
作者
Tzu-Ching Sung,Pei-Chih Wu,Shih‐Chun Candice Lung,Chuan‐Yao Lin,Mu-Jean Chen,Huey‐Jen Su
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:442: 275-281 被引量:56
标识
DOI:10.1016/j.scitotenv.2012.09.068
摘要

Increased mortality, linked to events of extreme high temperatures, is recognized as one critical challenge to the public health sector. Therefore, this ecological study was conducted to assess whether this association is also significant in Taiwan and the characteristics of the relationship. Daily mean heat indices, from 1994 through 2008, were used as the predictor for the risk of increased mortality in populations from 6 major Taiwanese cities. Daily mortality data from 1994 through 2008 were retrieved from the Taiwan Death Registry, Department of Health, Taiwan, and meteorological data were acquired from the Central Weather Bureau. Poisson regression analyses using generalized linear models were applied to estimate the temperature-mortality relationship. Daily mean heat indices were calculated and used as the temperature metric. Overall, increased risk ratios in mortality were associated with increased daily mean heat indices. Significantly increased risk ratios of daily mortality were evident when daily mean heat indices were at and above the 95th percentile, when compared to the lowest percentile, in all cities. These risks tended to increase similarly among those aged 65 years and older; a phenomenon seen in the cities of Keelung, Taipei, Taichung, Tainan, and Kaohsiung, but not Chiayi. Being more vulnerable to heat stress is likely restricted to a short-term effect, as suggested by lag models which showed that there was dominantly an association during the period of 0 to 3 days. In Taiwan, predicting city-specific daily mean heat indices may provide a useful early warning system for increased mortality risk, especially for the elderly. Regional differences in health vulnerabilities should be further examined in relation to the differential social-ecological systems that affect them.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
小叶子发布了新的文献求助10
1秒前
AI_S完成签到,获得积分10
1秒前
cc完成签到,获得积分10
1秒前
许初完成签到,获得积分10
2秒前
默默诗筠完成签到,获得积分10
3秒前
3秒前
研友_VZG7GZ应助乐陶采纳,获得10
3秒前
义气的钥匙完成签到,获得积分10
3秒前
莫泊桑完成签到,获得积分10
3秒前
白betty完成签到,获得积分10
3秒前
Aria完成签到,获得积分10
4秒前
4秒前
鸡爪完成签到,获得积分10
5秒前
5秒前
深情安青应助情殇采纳,获得10
5秒前
HBY发布了新的文献求助10
6秒前
6秒前
haha发布了新的文献求助30
6秒前
zrx完成签到,获得积分10
6秒前
adam发布了新的文献求助10
6秒前
MN1发布了新的文献求助10
6秒前
管锦发布了新的文献求助10
7秒前
7秒前
国服懒羊羊完成签到,获得积分10
7秒前
上官若男应助幸运采纳,获得10
7秒前
sunshine完成签到 ,获得积分20
8秒前
zgq完成签到,获得积分10
8秒前
zrx发布了新的文献求助10
9秒前
科研通AI5应助Eason小川采纳,获得10
9秒前
酷波er应助明亮的翠风采纳,获得10
10秒前
11秒前
11秒前
7799发布了新的文献求助10
11秒前
Jasper应助lql采纳,获得10
12秒前
dddsss完成签到,获得积分10
13秒前
我讨厌文献综述完成签到 ,获得积分10
13秒前
bzdjsmw完成签到 ,获得积分10
13秒前
14秒前
华仔应助八九采纳,获得10
14秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
On translated images, stereotypes and disciplines 200
New Syntheses with Carbon Monoxide 200
Faber on mechanics of patent claim drafting 200
Quanterion Automated Databook NPRD-2023 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3834344
求助须知:如何正确求助?哪些是违规求助? 3376864
关于积分的说明 10495644
捐赠科研通 3096375
什么是DOI,文献DOI怎么找? 1704930
邀请新用户注册赠送积分活动 820309
科研通“疑难数据库(出版商)”最低求助积分说明 771966