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Attributable risk of mortality associated with heat and heat waves: A time-series study in Kerman, Iran during 2005-2017

机译:归因于热和热浪波有关的死亡风险:2005 - 2017年伊朗克尔曼的时间序列研究

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The association between heat or heat waves and mortality should often be reported in a way that makes it sensible by health policymakers. In this study we aimed to assess the effect of heat and heat waves on mortality using attributable risks during 2005-2017. Nine heat waves were defined using a combination of severity and duration of mean daily temperature. Heat wave effects were assessed using added and main effects. Added effects were assessed as a binary variable and main effects were assessed by comparing the median temperature (in heat wave days) to Minimum Mortality Temperature (MMT). The effects of heat, mild heat and extreme heat on mortality were also assessed. Distributed Lag Non-linear Models were used to assess the relations in a bi-dimensional perspective in which the quadratic b-spline was chosen as the basis function for the dimension of the exposure and the natural cubic b-spline was chosen for lag dimension. The backward perspective was used to estimate the attributable risks. The total mortality attributed to non-optimal temperatures for all days was 1.91% (CI 95%: -6.36, 8.47). The attributable risks (AR) were 2.23%, 2.02% and 0.25% for heat, mild heat and extreme heat days, respectively. AR was more for females and the above 65 years old groups than other groups in heat, mild heat and extreme heat days. While the stronger heat waves defined based on temperature above the 95 and 98th percentile had a significant attributable risk for total mortality in the added effects; the weaker heat waves (defined based on temperature above of the 90th percentile (HW1, HW2, HW3) had higher attributable risks, significant for HW1 and HW2, in the main effects. Apparently weaker heat waves show more immediate effects, while stronger heat waves increase mortality over several days.
机译:通常应该以卫生政策制定者使其明智的方式报告热量或热波和死亡之间的关联。在这项研究中,我们旨在在2005 - 2017年期间使用可归因于可归属风险评估热波对死亡率的影响。使用平均每日温度的严重程度和持续时间的组合来定义九波。使用添加和主要效果评估热波效应。评估额外的效果作为二元变量,通过将中值温度(在热波天)与最小死亡率(MMT)进行比较来评估主要效果。还评估了热量,轻度和极端热量对死亡率的影响。分布式滞后非线性模型用于评估双维透视中的关系,其中选择二次B样条曲线作为曝光尺寸的基函数,并且选择天然立方B样条曲线用于滞后尺寸。向后透视用于估计可归因的风险。归因于所有天的非最佳温度的总死亡率为1.91%(CI 95%:-6.36,8.47)。可归因的风险(AR)分别为热量,温度和极端热量为2.23%,2.02%和0.25%。 ar更适合女性,以上65岁的群体比其他热量,温和的热量和极端热量的群体。虽然基于95和98百分位高于95和98百分位的温度定义的更强的热波具有显着的可归因于增加效果的死亡率;较弱的热波(基于90百分位数的温度(HW1,HW2,HW3)具有更高的可归因风险,在主要效果中具有较高的可归属风险,显着较弱的热波显示出更多立即效果,而热量较强增加了几天死亡率。

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