Lee Mihye, Shi Liuhua, Zanobetti Antonella, Schwartz Joel D
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA.
Environ Res. 2016 Nov;151:610-617. doi: 10.1016/j.envres.2016.08.029. Epub 2016 Sep 7.
There are many studies that have posited an association between extreme temperature and increased mortality. However, most studies use temperature at a single station per city as the reference point to analyze deaths. This leads to exposure misclassification and usually the exclusion of exurban, small town, and rural populations. In addition, few studies control for confounding by PM, which is expected to induce upward bias. The high-resolution temperature and PM data at a resolution of 1km were derived from satellite images and other land use sources. To capture the nonlinear association of temperature with mortality we fit a piecewise linear spline function for temperature, with a change in slope at -1°C and 28°C, the temperature threshold at which mortality in Georgia, North Carolina, and South Carolina increases due to cold and heat, respectively. We conducted stratified analyses by age group, sex, race, education, and urban vs nonurban, as well as sensitivity analyses of different temperature threshold and covariate sets. We found a 0.19% (95% CI=-0.98, 1.34%) increase in mortality for each 1°C decrease in temperature below -1°C and a 2.05% (95% CI=0.87, 3.24%) increase in mortality for each 1°C increase in temperature above 28°C, a 79.8% larger effect size for heat compared to the station-based metric. The effect estimates relying on the monitoring stations were 0.09% (95% CI=-0.79, 0.95%) and 1.14% (95% CI=0.08, 1.57%) for the equivalent temperature changes. The estimates were not confounded by PM. Children under 15 years of age had the largest percentage increase per 1°C increase in temperature (8.19%, 95% CI=-0.38 to 17.49%) followed by Blacks (4.35%, 95% CI=2.22 to 6.53%). Higher education was a protective factor for the effect of extreme temperature on mortality. There was a suggestion that people in less urban areas were more susceptible to extreme temperature. The relationship between temperature and mortality was stronger when using exposure data with more spatial variability than using exposure data based on existing monitors alone.
有许多研究假定极端温度与死亡率上升之间存在关联。然而,大多数研究将每个城市单个站点的温度作为分析死亡情况的参考点。这会导致暴露误分类,并且通常会将城市远郊、小镇和农村人口排除在外。此外,很少有研究控制颗粒物(PM)造成的混杂因素,而这预计会导致偏差向上。分辨率为1千米的高分辨率温度和PM数据来自卫星图像及其他土地利用数据源。为了捕捉温度与死亡率之间的非线性关联,我们针对温度拟合了一个分段线性样条函数,在-1°C和28°C处斜率发生变化,这两个温度阈值分别是佐治亚州、北卡罗来纳州和南卡罗来纳州因寒冷和炎热导致死亡率上升的温度阈值。我们按年龄组、性别、种族、教育程度以及城市与非城市进行了分层分析,还对不同温度阈值和协变量集进行了敏感性分析。我们发现,温度在-1°C以下每降低1°C,死亡率增加0.19%(95%置信区间=-0.98,1.34%);温度在28°C以上每升高1°C,死亡率增加2.05%(95%置信区间=0.87,3.24%),与基于站点的指标相比,炎热导致的效应量要大79.8%。对于同等的温度变化,依赖监测站的效应估计值分别为0.09%(95%置信区间=-0.79,0.95%)和1.14%(95%置信区间=0.08,1.57%)。这些估计值未受颗粒物的混杂影响。15岁以下儿童温度每升高1°C,死亡率增加的百分比最大(8.19%,95%置信区间=-0.38至17.49%),其次是黑人(4.35%,95%置信区间=2.22至6.53%)。高等教育是极端温度对死亡率影响的一个保护因素。有迹象表明,城市程度较低地区的人更容易受到极端温度的影响。与仅使用基于现有监测器的暴露数据相比,使用具有更多空间变异性的暴露数据时,温度与死亡率之间的关系更强。