School of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
Sci Total Environ. 2011 Aug 15;409(18):3431-7. doi: 10.1016/j.scitotenv.2011.05.027. Epub 2011 Jun 12.
The relationship between temperature and mortality has been explored for decades and many temperature indicators have been applied separately. However, few data are available to show how the effects of different temperature indicators on different mortality categories, particularly in a typical subtropical climate.
To assess the associations between various temperature indicators and different mortality categories in Brisbane, Australia during 1996-2004.
We applied two methods to assess the threshold and temperature indicator for each age and death groups: mean temperature and the threshold assessed from all cause mortality was used for all mortality categories; the specific temperature indicator and the threshold for each mortality category were identified separately according to the minimisation of AIC. We conducted polynomial distributed lag non-linear model to identify effect estimates in mortality with one degree of temperature increase (or decrease) above (or below) the threshold on current days and lagged effects using both methods.
Akaike's Information Criterion was minimized when mean temperature was used for all non-external deaths and deaths from 75 to 84 years; when minimum temperature was used for deaths from 0 to 64 years, 65-74 years, ≥ 85 years, and from the respiratory diseases; when maximum temperature was used for deaths from cardiovascular diseases. The effect estimates using certain temperature indicators were similar as mean temperature both for current day and lag effects.
Different age groups and death categories were sensitive to different temperature indicators. However, the effect estimates from certain temperature indicators did not significantly differ from those of mean temperature.
人们已经研究了温度与死亡率之间的关系几十年，并分别应用了许多温度指标。然而，很少有数据表明不同的温度指标如何对不同的死亡类别产生影响，尤其是在典型的亚热带气候中。
评估 1996 年至 2004 年澳大利亚布里斯班不同温度指标与不同死亡类别的关系。
我们应用了两种方法来评估每个年龄组和死亡组的温度指标和阈值：所有死因死亡率的平均温度和阈值用于所有死亡率类别；根据 AIC 的最小化，分别为每个死亡率类别确定特定的温度指标和阈值。我们使用多项式分布滞后非线性模型来识别死亡率中每增加（或减少）一度温度的效应估计值（当前日和滞后日），这两种方法都使用了。
当平均温度用于所有非外部死亡和 75 至 84 岁的死亡时，赤池信息量准则最小；当最低温度用于 0 至 64 岁、65 至 74 岁、≥ 85 岁和因呼吸系统疾病而死亡时；当最高温度用于心血管疾病死亡时。使用特定温度指标的效应估计值与平均温度的当前日和滞后日效应相似。
不同年龄组和死亡类别对不同的温度指标敏感。然而，某些温度指标的效应估计值与平均温度的差异并不显著。
