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    潘阳, 曲洋明, 刘建伟, 何英华. 长春市PM2.5污染对老年人群心脑血管疾病死亡影响的时间序列分析[J]. 环境卫生学杂志, 2019, 9(1): 8-13. DOI: 10.13421/j.cnki.hjwsxzz.2019.01.002
    引用本文: 潘阳, 曲洋明, 刘建伟, 何英华. 长春市PM2.5污染对老年人群心脑血管疾病死亡影响的时间序列分析[J]. 环境卫生学杂志, 2019, 9(1): 8-13. DOI: 10.13421/j.cnki.hjwsxzz.2019.01.002
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    PAN Yang, QU Yangming, LIU Jianwei, HE Yinghua. Time Series Analysis on PM2.5 Pollution Effect on Mortality of Cardiovascular and Cerebrovascular Diseases in Elderly People in Changchun[J]. Journal of Environmental Hygiene, 2019, 9(1): 8-13. DOI: 10.13421/j.cnki.hjwsxzz.2019.01.002
    Citation: PAN Yang, QU Yangming, LIU Jianwei, HE Yinghua. Time Series Analysis on PM2.5 Pollution Effect on Mortality of Cardiovascular and Cerebrovascular Diseases in Elderly People in Changchun[J]. Journal of Environmental Hygiene, 2019, 9(1): 8-13. DOI: 10.13421/j.cnki.hjwsxzz.2019.01.002
    shu

    长春市PM2.5污染对老年人群心脑血管疾病死亡影响的时间序列分析

    Time Series Analysis on PM2.5 Pollution Effect on Mortality of Cardiovascular and Cerebrovascular Diseases in Elderly People in Changchun

      摘要
    • 摘要:
      目的 探索PM2.5对老年敏感人群因心脑血管疾病死亡的影响，为制定有针对性的环境健康防控措施提供科学依据。
      方法 收集2014年1月1日-2017年12月31日长春市每日人群死亡资料、环保监测资料（包括PM2.5、PM10、SO2、NO2、O3和CO）、气象资料（包括平均温度和平均相对湿度）。采用时间序列分析的方法评估空气污染物对老年人群心脑血管疾病死亡的影响，采用超额危险度（ER）评价空气污染物每升高10 μg/m3（CO每升高1 mg/m3）人群心脑血管疾病死亡风险的增加量。采用SPSS 13.0进行描述性分析，相关性检验采用Spearman相关分析，采用R3.5.0软件进行时间序列分析。
      结果 2014-2017年，长春市老年人因心脑血管疾病死亡共26 498人，平均每日死亡18人；PM2.5、PM10、SO2、NO2、O3、CO平均质量浓度分别为55.9、93.3、30.7、40.8、89.1和1.0 mg/m3，颗粒物超标情况较重，其他污染物超标情况较少。单污染物模型拟合结果，PM2.5在滞后1 d时对老年人因心脑血管疾病死亡的影响存在统计学意义（P < 0.05），超额死亡风险（ER）为0.378%；在累积滞后第1、第2和第3天均呈现出显著性（P < 0.05），并在累计滞后第3天达到最大，超额死亡风险（ER）为0.442%。NO2在滞后1和3天以及累积滞后3 d时，O3在当天以及累积滞后（1~3）d时，CO在滞后1 d以及累计滞后（1~3）d时，均可增加老年人心脑血管疾病死亡风险。多污染物模型拟合结果，在分别调整PM10、SO2、NO2、O3、CO以及全污染物后，PM2.5对死亡的影响效应消失（P>0.05）。
      结论 长春市PM2.5污染与老年人群因心脑血管疾病死亡风险增加有关，但原因可能是PM2.5与其他各种空气污染物综合作用的结果。

       

      Abstract:
      Objectives To explore the influence of PM2.5 on the mortality of the elderly sensitive population with cardiovascular and cerebrovascular diseases(CCVd), and provide scientific basis for formulating targeted environmental health prevention and control measures.
      Methods Daily death data, environmental monitoring data (including PM2.5, PM10, SO2, NO2, O3 and CO) and meteorological data (including average temperature and average relative humidity) of Changchun in 2014-2017 were collected. A time series analysis method was used to evaluate the effect of air pollutants on the death from CCVd in the elderly. Excess risk (ER) was used to evaluate the increased risk of CCVd in the population with each increase of 10 μg/m3 (CO=1 mg/m3) air pollutants. SPSS 13.0 was used for descriptive analysis. Spearman correlation analysis was used for correlation test, and R3.5.0 software was used for time series analysis.
      Results A total of 26 498 elderly people died from CCVd in Changchun in 2014-2017, with an average of 18 deaths per day. The mean mass concentrations of PM2.5, PM10, SO2, NO2, O3 and CO were 55.9, 93.3, 30.7, 40.8, 89.1 and 1.0 mg/m3 respectively. The concentration of particulate matter exceeded the limit level seriously, and other pollutants were less in exceeding the limit. According to the fitting result of the single-pollutant model, PM2.5 had a statistically significant impact on the death of the elderly due to CCVd in lag 1 day (P < 0.05), the excess risk (ER) was 0.378%, which showed a significant cumulative delayed effect on lag (1~3) days(P < 0.05), and reached the maximum level on cumulative lag 3 days, and the excess risk (ER) was 0.442%. NO2 (lag 1, lag 3, and cumulative lag 3 days), O3 (the same day and cumulative lag(1~3) days), CO (lag 1, and cumulative lag (1~3) days) could increase the risk of dying from CCVd in the elderly. According to the fitting result of the multi-pollutant model, after adjusting PM10, SO2, NO2, O3, CO and all pollutants, the influence of PM2.5 on the death disappeared (P>0.05).
      Conclusions PM2.5 pollution in Changchun might increase the risk of mortality in elderly people with CCVd, and the risk might be the result of the combination of PM2.5 with other air pollutants.

       

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