Anderson Brooke G, Bell Michelle L
Environmental Engineering Program, Yale University, New Haven, CT, USA.
Epidemiology. 2009 Mar;20(2):205-13. doi: 10.1097/EDE.0b013e318190ee08.
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.
许多研究已将天气与死亡率联系起来;然而,区域差异、易感人群和适应等关键因素的作用仍未得到解决。
我们将时间序列模型应用于美国的107个社区,通过使用14年的数据集来分析温度与死亡率之间的非线性关系。采用第二阶段分析将寒冷、炎热和热浪效应估计值与社区特定变量联系起来。我们考虑了暴露时间范围、易感性、年龄、死因以及污染物的混杂因素。对不同强度和持续时间的热浪进行了建模。
与热相关的死亡率与较短的滞后时间(同一天和前一天的平均值)最为相关,将社区第99百分位数和第90百分位数的温度进行比较,死亡率风险总体增加3.0%(95%后验区间:2.4%-3.6%)。与冷相关的死亡率与较长的滞后时间(当前天到前25天的平均值)最为相关,将社区第1百分位数和第10百分位数的温度进行比较,风险增加4.2%(3.2%-5.3%)。死亡率风险随着热浪的强度或持续时间增加。效应的空间异质性表明,一个社区的天气-死亡率关系可能不适用于另一个社区。绝对温度估计值(比较特定温度下的风险)的空间异质性大于相对温度估计值(比较社区特定温度百分位数下的风险),这为适应提供了证据。我们根据年龄、社会经济状况、城市化程度和中央空调确定了易感性。
适应、个体易感性和社区特征都会影响与热相关的死亡率效应。