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Estimated Effect of Temperature on Years of Life Lost : A Retrospective Time-Series Study of Low-, Middle-, and High-Income Regions

机译:温度对丧失生命年的估计影响:低,中,高收入地区的回顾性时间序列研究

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摘要

BACKGROUND: Numerous studies have reported a strong association between temperature and mortality. Additional insights can be gained from investigating the effects of temperature on years of life lost (YLL), considering the life expectancy at the time of death. OBJECTIVES: The goal of this work was to assess the association between temperature and YLL at seven low-, middle-, and high-income sites. METHODS: We obtained meteorological and population data for at least nine years from four Health and Demographic Surveillance Sites in Kenya (western Kenya, Nairobi), Burkina Faso (Nouna), and India (Vadu), as well as data from cities in the United States (Philadelphia, Phoenix) and Sweden (Stockholm). A distributed lag nonlinear model was used to estimate the association of daily maximum temperature and daily YLL, lagged 0-14 d. The reference value was set for each site at the temperature with the lowest YLL. RESULTS: Generally, YLL increased with higher temperature, starting day 0. In Nouna, the hottest location, with a minimum YLL temperature at the first percentile, YLL increased consistently with higher temperatures. In Vadu, YLL increased in association with heat, whereas in Nairobi, YLL increased in association with both low and high temperatures. Associations with cold and heat were evident for Phoenix (stronger for heat), Stockholm, and Philadelphia (both stronger for cold). Patterns of associations with mortality were generally similar to those with YLL. CONCLUSIONS: Both high and low temperatures are associated with YLL in high-, middle-, and low-income countries. Policy guidance and health adaptation measures might be improved with more comprehensive indicators of the health burden of high and low temperatures such as YLL.
机译:背景:大量研究报告了温度与死亡率之间的强烈关联。考虑到死亡时的预期寿命,通过研究温度对失去生命的年限(YLL)的影响,可以获得更多的见解。目的:这项工作的目的是评估七个低,中,高收入地点的温度与YLL之间的关系。方法:我们从肯尼亚(肯尼亚西部,内罗毕),布基纳法索(努纳)和印度(瓦杜)的四个卫生和人口监测点获得了至少九年的气象和人口数据,以及来自美国城市的数据州(费城,凤凰城)和瑞典(斯德哥尔摩)。分布式滞后非线性模型用于估计每日最高温度和每日YLL的关联,滞后0-14 d。为每个站点在最低YLL的温度下设置参考值。结果:通常,从0天开始,YLL随温度升高而升高。在Nouna这个最热的地区,YLL温度在第一个百分位处处于最低,YLL随着温度的升高而持续增加。在Vadu,YLL随着热量增加而增加,而在内罗毕,YLL随着低温和高温增加而增加。凤凰城(热度更高),斯德哥尔摩和费城(冷度都更强)明显与冷热关联。死亡率的关联模式通常与YLL相似。结论在高,中,低收入国家,高温和低温都与YLL有关。可以通过更全面的高温和低温健康负担指标,例如黄杨LLL,来改善政策指导和健康适应措施。

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