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[背景]人们不断地发现温度和死亡率之间(主要集中在大城市)呈现出U/J形关系,在低纬度地区具有较高的最低死亡率温度(MMT),人们将其解释为人类对气候适应的一种指标。[目的]用高分辨率网格来划分空间,以评估一个气候多样的地区在经过了一段明显的气候变暖期后的温度死亡率关系。[方法]对法国大陆地区(1968—2009年)自然原因死亡的65岁以上的16 487 668份死亡证明书进行分析。用一个30 km×30 km的网格覆盖法国地图。采用广义相加回归模型评估对应每个方格的温度死亡率关系,并提取MMT与RM25和RM25/18(分别为25℃/MMT和25℃/18℃的死亡率比)。分析的3个时期包括:1968—1981年(P1)、1982—1995年(P2)和1996—2009年(P3)。[结果]所计算的42年间,所有温度死亡率曲线都呈U/J形。MMT和夏季平均气温均呈强相关性。MMT平均值从P1的17.5℃上升到P2的17.8℃和P3的18.2℃,在P1和P3之间同时观察到夏季气温平行升高。MMT的时间增加趋势低于根据地理分析得到的预期值。随着气候变暖,25℃时的RM25/18死亡率比较18℃时显著下降(P=5×10-5):P1时为18%,P2时为16%,P3时为15%。[结论]本文的时空分析结果表明,一部分人适应了气候变化,甚至在农村地区也是如此。
[Background] The U / J relationship between temperature and mortality (mainly concentrated in large cities) has been continuously found, with a high minimum temperature of death (MMT) in low latitudes, which is interpreted as human An indicator of climate adaptation. [Objective] To classify the space using a high-resolution grid to assess the temperature mortality after a period of significant warming in a climatically diverse area. [Method] An analysis of 16 487 668 death certificates over the age of 65 in the mainland of France (1968-2009) due to natural causes was conducted. Cover the French map with a 30 km × 30 km grid. The generalized additive regression model was used to assess the temperature-mortality relationship for each box and the MMT was extracted with RM25 and RM25 / 18 (25 / MMT and 25/18 ° C mortality, respectively). The three analyzes included: 1968-1981 (P1), 1982-1995 (P2) and 1996-2009 (P3). [Results] All the temperature mortality curves were U / J in the 42 years calculated. MMT and summer mean temperature are strongly correlated. The mean MMT increased from 17.5 ° C at P1 to 17.8 ° C at P2 and 18.2 ° C at P3, with concurrent increases in summer temperatures between P1 and P3. The time trend of MMT is lower than expected from the geographical analysis. With the warming of climate, the mortality of RM25 / 18 at 25 ℃ decreased significantly at 18 ℃ (P = 5 × 10-5): 18% at P1, 16% at P2 and 15% at P3. [Conclusion] The results of space-time analysis in this paper show that some people have adapted to climate change, even in rural areas.