赵琦, 李珊珊, 郭玉明. 随时间变化的分布滞后非线性模型应用介绍:以气温与死亡关系为例[J]. 环境与职业医学, 2020, 37(1): 9-14. doi: 10.13213/j.cnki.jeom.2020.19485
引用本文: 赵琦, 李珊珊, 郭玉明. 随时间变化的分布滞后非线性模型应用介绍:以气温与死亡关系为例[J]. 环境与职业医学, 2020, 37(1): 9-14. doi: 10.13213/j.cnki.jeom.2020.19485
ZHAO Qi, LI Shan-shan, GUO Yu-ming. Time-varying distributed lag non-linear model: Using temperature-mortality association as an example[J]. Journal of Environmental and Occupational Medicine, 2020, 37(1): 9-14. doi: 10.13213/j.cnki.jeom.2020.19485
Citation: ZHAO Qi, LI Shan-shan, GUO Yu-ming. Time-varying distributed lag non-linear model: Using temperature-mortality association as an example[J]. Journal of Environmental and Occupational Medicine, 2020, 37(1): 9-14. doi: 10.13213/j.cnki.jeom.2020.19485

随时间变化的分布滞后非线性模型应用介绍:以气温与死亡关系为例

Time-varying distributed lag non-linear model: Using temperature-mortality association as an example

  • 摘要: 背景

    多项研究发现室外气温与健康之间的关系会随着时间的推移而发生变化。但是目前大部分研究方法只是简单地把时间序列数据分为不同时间段进行分析,由于模型稳定性等原因,特别是对于小样本数据集,该方法并不能很好地用于揭示两者关系的时间变化趋势。为此,推荐使用不破坏时间序列数据的方法进行研究。

    目的

    介绍随时间变化的分布滞后非线性模型(DLNM),并以芝加哥市1987-1997年死亡数据为例,比较该模型与普通DLNM在研究室外气温对人群死亡影响随时间变化特征中的表现,以体现随时间变化DLNM的优势。

    方法

    介绍随时间变化DLNM的基本数学结构,并比较其与普通DLNM的异同。下载R软件"dlnm"程序包中内置的芝加哥市1987-1997年每日死亡和环境暴露数据(日均气温、相对湿度和可吸入颗粒物)。采用随时间变化DLNM(基于类泊松回归),估计1987年和1997年的气温-死亡累积效应。采用普通DLNM(基于类泊松回归),估计1987-1997年、1987-1989年、1995-1997年、1987年和1997年的气温-死亡累积效应,并与随时间变化DLNM的结果相比较。

    结果

    芝加哥市1987-1997年累积0~30 d的日均气温-死亡关系近似V型,19.2℃时的死亡风险最低。随时间变化DLNM分析结果显示,与死亡相对风险最低的气温值(MMT)相比,极端冷气温(日均气温第1百分位,-15.8℃)的相对危险度(RR),即冷温效应,由1987年的1.59(95% CI:1.25~2.01)降低至1997年的1.50(95% CI:1.13~1.98),但差异无统计学意义(P=0.756)。极端热气温(日均气温第99百分位,28.9℃)的RR,即热温效应,由1987年的1.04(95% CI:0.85~1.28)升高至1997年的1.75(95% CI:1.39~2.21),差异有统计学意义(P=0.001)。采用普通DLNM拟合的1987-1989年和1995-1997年的冷温和热温效应,与随时间变化DLNM拟合的1987-1997年的冷温和热温效应有相似变化趋势。采用普通DLNM拟合的单一年份(1987年和1997年)与随时间变化DLNM拟合的气温-死亡累积效应相比,普通DLNM拟合结果更不稳定。

    结论

    随时间变化DLNM能很好地应用于时间序列分析,估计气温等环境暴露因素的健康效应的长期变化趋势。

     

    Abstract: Background

    Numerous studies have reported that the association between ambient temperature and human health may vary over time. However, most studies simply divide the time-series dataset into subsets of different time periods, which may not properly capture the temporal change of the temperature-health association due to modelling instability, particularly for small-size datasets. It is recommended to apply statistical strategies that do not break the structure of original time-series dataset.

    Objective

    This study introduces time-varying distributed lag non-linear model (DLNM) and compare its performance with ordinary DLNM in exploring the temporal change in the association between ambient temperature and daily mortality using data from Chicago between 1987 and 1997 for presenting the advantages of time-varying DLNM.

    Methods

    The mathematic structures of ordinary and time-varying DLNMs were introduced and compared. Daily data on all-cause mortality and environmental exposures including ambient temperature, relative humidity, and inhalable particulate matter were collected from Chicago between 1987 and 1997 from the dlnm package in R software. Quasi-Poisson regression with time-varying DLNM was applied to examine the temporal change in the temperature-mortality association in 1987 and 1997, respectively. Quasi-Poisson regression with ordinary DLNM was applied to examine the temperature-mortality association in 1987-1997, 1987-1989, 1995-1997, 1987, and 1997, respectively, and the results were compared with those using time-varying DLNM.

    Results

    The association between daily mean temperature and mortality accumulated across lag 0-30 days was V-shaped in Chicago between 1987 and 1997, with the minimum morality temperature (MMT) being 19.2℃. The results of time-varying DLNM indicated that compared with MMT the relative risk (RR) of extremely cold temperature (the 1st percentile of average temperature, -15.8℃) i.e. cold effect, decreased insignificantly from 1.59 (95% CI:1.25-2.01) in 1987 to 1.50 (95% CI:1.13-1.98) in 1997 (P=0.756). By contrast, the RR of extremely high temperature (the 99th percentile of average temperature, 28.9℃), i.e. heat effect, increased significantly from 1.04 (95% CI:0.85-1.28) to 1.75 (95% CI:1.39-2.21) during the same time period (P=0.001). Similar temporal variations were also observed in the 1987-1989 dataset and the 1995-1997 dataset analyzed using ordinary DLNM. The results of ordinary DLNM on single-year datasets (i.e. in 1987 and 1997, respectively) were less stable, compared with the results of time-varying DLNM.

    Conclusion

    Time-varying DLNM can be used in the time-series analysis to examine the temporal variation in the association between environmental exposures (such as ambient temperature) and health outcomes.

     

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