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Spatiotemporal or temporal index to assess the association between temperature variability and mortality in China?

机译：时空或时空指数来评估中国温度变化与死亡率之间的关系？

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摘要

Epidemiological studies increasingly provide evidence about the adverse health effects of temperature variability (TV), which reflects short-term intra- and inter-day temperature change. However, calculation of TV only considers the temporal variability and lacks spatial variability. This study intends to investigate whether the lack of spatial variability in TV calculations has biased the health effect estimates. We collected daily data from the fine-gridded hourly temperatures and more than 2 million all-cause mortality counts in Zhejiang province in China from 2009 to 2015. A spatiotemporal TV index was developed by calculating the standard deviation of the hourly temperatures based on records from multiple sites. This new index could be compared to the two typical temporal TV indices that are calculated based on the hourly temperatures from one-site and area-average records. The three types of TV are compared using a three-stage analytical approach: district-specific time series Poisson regression, meta-analysis, and calculation of attributable mortality fraction. We observe that both spatiotemporal and temporal TVs produce very similar TV-mortality associations, attributable mortality fractions, and model fits at the district level. For example, the mortality increase associated for every increase of 1 degrees C during 0-7 exposure days is 1.53% (95% CI: 1.31, 1.73) in spatiotemporal TV, whereas it is 1.48% (95% CI: 1.27, 1.68) and 1.45% (95% CI: 1.24, 1.67) in the one-site and area-average temporal TV, respectively. Thus, time series models using temporal TV index are equally good at estimating the associations between TV and mortality as spatiotemporal TV at the district level in population-based epidemiological studies in China. Epidemiological studies using temperature from one site or the averages of multiple sites in TV calculation will not bias the effect estimates of TV. Our study could provide an important guidance method for future TV-related research in China and even in other countries.
机译：流行病学研究越来越多地提供有关温度变化（TV）对健康的不利影响的证据，该变化反映了短期内日间和日间温度变化。但是，电视的计算仅考虑时间可变性，而缺乏空间可变性。这项研究旨在调查电视计算中缺乏空间变异性是否对健康影响估计值产生了偏差。我们收集了2009年至2015年浙江省小时细小气温和200万以上全因死亡率的每日数据。通过计算时空电视的标准差，得出了时空电视指数。多个站点。可以将这一新指标与两个典型的时态电视指标进行比较，后者是根据一站式和区域平均记录的每小时温度计算得出的。使用三阶段分析方法对三种类型的电视进行比较：地区特定时间序列泊松回归，荟萃分析和归因死亡率分数的计算。我们观察到时空电视和时空电视都产生非常相似的电视死亡率关联，可归因的死亡率分数以及模型在地区一级的拟合度。例如，在时空电视中，在0-7天内，每升高1摄氏度，死亡率增加为1.53％（95％CI：1.31，1.73），而死亡率为1.48％（95％CI：1.27，1.68）一站式和区域平均时态电视分别为1.45％（95％CI：1.24、1.67）。因此，在中国基于人群的流行病学研究中，使用时态电视指数的时间序列模型与地区时空电视一样，在评估电视与死亡率之间的关联方面同样出色。在电视计算中使用一个站点的温度或多个站点的平均值进行的流行病学研究不会使电视的效果估计值产生偏差。我们的研究可以为今后中国乃至其他国家与电视相关的研究提供重要的指导方法。

著录项

  • 来源
    《Environmental research》 |2019年第3期|344-350|共7页
  • 作者

    Hu Kejia; Li Shanshan; Zhong Jieming; Yang Xuchao; Fei Fangrong; Chen Feng; Zhao Qi; Zhang Yunquan; Chen Gongbo; Chen Qian; Ye Tingting; Guo Yuming; Qi Jiaguo;

  • 作者单位

    Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China|Monash Univ, Dept Epidemiol & Prevent Med, Sch Publ Hlth & Prevent Med, Melbourne, Vic 3004, Australia;

    Monash Univ, Dept Epidemiol & Prevent Med, Sch Publ Hlth & Prevent Med, Melbourne, Vic 3004, Australia;

    Zhejiang Prov Ctr Dis Control & Prevent, 3399 Binsheng Rd, Hangzhou 310051, Zhejiang, Peoples R China;

    Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China|Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48823 USA;

    Zhejiang Prov Ctr Dis Control & Prevent, 3399 Binsheng Rd, Hangzhou 310051, Zhejiang, Peoples R China;

    Zhejiang Inst Meteorol Sci, Hangzhou 310008, Zhejiang, Peoples R China;

    Monash Univ, Dept Epidemiol & Prevent Med, Sch Publ Hlth & Prevent Med, Melbourne, Vic 3004, Australia;

    Wuhan Univ, Dept Epidemiol & Biostat, Sch Hlth Sci, Wuhan 430071, Hubei, Peoples R China|Wuhan Univ Sci & Technol, Hubei Prov Key Lab Occupat Hazard Identificat & C, Sch Publ Hlth, Wuhan 430065, Hubei, Peoples R China;

    Monash Univ, Dept Epidemiol & Prevent Med, Sch Publ Hlth & Prevent Med, Melbourne, Vic 3004, Australia;

    Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China;

    Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China;

    Monash Univ, Dept Epidemiol & Prevent Med, Sch Publ Hlth & Prevent Med, Melbourne, Vic 3004, Australia;

    Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48823 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Temperature variability; Temperature change; Mortality; Risk; China;

    机译：温度变化;温度变化;死亡率;风险;中国;

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