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The short-term effect of air pollution on cardiovascular mortality in Tianjin, China : comparison of time series and case–crossover analyses

机译:空气污染对天津市心血管疾病死亡率的短期影响:时间序列和病例交叉分析的比较

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摘要

Background: Many studies have illustrated that ambient air pollution negatively impacts on health. However,udlittle evidence is available for the effects of air pollution on cardiovascular mortality (CVM) in Tianjin, China. Also, no study has examined which strata length for the time-stratified case–crossover analysis givesudestimates that most closely match the estimates from time series analysis. Objectives: The purpose of this study was to estimate the effects of air pollutants on CVM in Tianjin, China, and compare time-stratified case–crossover and time series analyses. Method: A time-stratified case–crossover and generalized additive model (time series) were applied to examine the impact of air pollution on CVM from 2005 to 2007. Four time-stratified case–crossover analysesudwere used by varying the stratum length (Calendar month, 28, 21 or 14 days). Jackknifing was used to compare the methods. Residual analysis was used to check whether the models fitted well. Results: Both case–crossover and time series analyses show that air pollutants (PM10, SO2 and NO2) were positively associated with CVM. The estimates from the time-stratified case–crossover varied greatly withudchanging strata length. The estimates from the time series analyses varied slightly with changing degrees ofudfreedom per year for time. The residuals from the time series analyses had less autocorrelation than thoseudfrom the case–crossover analyses indicating a better fit. Conclusion: Air pollution was associated with an increased risk of CVM in Tianjin, China. Time series analysesudperformed better than the time-stratified case–crossover analyses in terms of residual checking.
机译:背景:许多研究表明,环境空气污染对健康有负面影响。但是,在中国天津,空气污染对心血管死亡率(CVM)的影响的证据很少。另外,没有研究检查时间分层案例交叉分析的哪个层次长度给出估计最匹配时间序列分析的估计。目的:本研究的目的是评估中国天津市空气污染物对CVM的影响,并比较时间分层的病例交叉法和时间序列分析法。方法:采用时间分层的案例交叉法和广义加性模型(时间序列)来研究2005年至2007年空气污染对CVM的影响。通过改变层长,使用了四个时间分层的案例交叉法(日历月(28、21或14天)。使用Jackknifing来比较这些方法。残差分析用于检查模型是否拟合良好。结果:案例分析和时间序列分析均表明,空气污染物(PM10,SO2和NO2)与CVM正相关。时间分层案例交叉的估计值随着长度的变化而变化很大。时间序列分析的估计值随每年 udfreedom程度的变化而略有不同。时间序列分析中的残差与案例交叉分析中的残差相比,自相关性较低,表明拟合度更高。结论:空气污染与中国天津CVM风险增加有关。在残差检查方面,时间序列分析的表现优于时间分层的案例交叉分析。

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