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上海市崇明区大气污染物与医院门诊量关系的时间序列研究

Time-series study on relationship between air pollutants and outpatient visits in Chongming District of Shanghai

    摘要
  • 摘要:
    目的 崇明岛作为生态休闲旅游岛，岛内空气质量备受关注。本研究拟分析上海市崇明区大气污染物浓度对该区医院门诊量的影响，为制定生态岛大气污染防控政策提供科学依据。

    方法 收集2014-2017年上海市崇明区大气污染资料、气象资料及上海市第十人民医院崇明分院每日总门诊、呼吸系统疾病门诊、循环系统疾病门诊病例。使用广义线性模型分析大气污染物水平与该医院门诊量的暴露反应关系。计算大气污染物每升高10 μg/m3，日门诊量的相对危险度(RR)和超额危险度(ER)。

    结果 研究期间，PM2.5、O3、PM10、NO2、SO2和CO日均浓度分别为38.6 μg/m3、107.3μg/m3、48.8 μg/m3、17.0 μg/m3、9.2 μg/m3和0.4 mg/m3，PM2.5、O3、PM10、NO2超标率分别为10.17%、9.28%、1.77%、0.28%，SO2和CO未超出限值。日均门诊总量为1 393人次，呼吸系统疾病日均门诊量为123人次，循环系统疾病日均门诊量为167人次。在总门诊中，污染物每升高10 μg/m3，PM2.5、PM10、NO2、SO2和O3使门诊量增加(ER)分别为0.17%、0.16%、0.60%、1.39%和0.10%；在呼吸系统疾病门诊中，污染物每升高10 μg/m3，PM2.5、PM10、NO2、SO2和O3使门诊量增加分别为0.38%、0.26%、0.98%、1.61%和0.27%；在循环系统疾病门诊中，污染物每升高10 μg/m3，PM2.5、PM10、NO2、SO2和O3使门诊量增加分别为0.27%、0.19%、0.83%、1.45%和0.15%。除CO外，5种不同大气污染物对总门诊量、呼吸系统疾病门诊量、循环系统疾病门诊量均有滞后效应，且不同污染物滞后日期不同。PM2.5对三类门诊量最强滞后效应日期均为lag1；PM10对三类门诊量最强滞后效应日期分别为lag1、lag1、lag3；NO2对三类门诊量最强滞后效应日期分别为lag1、lag1、lag2；SO2对三类门诊量最强滞后效应日期均为lag3；O3对三类门诊量最强滞后效应日期分别为lag2、lag3、lag2。

    结论 大气污染物(PM2.5、PM10、NO2、SO2和O3)浓度升高会增加该区医院门诊量，且1~3 d均有滞后效应。

     

    Abstract:
    Objectve As an eco-tourism island, Chongming Island has atracted much atenton on its air quality. In this study, we analyze the impact of air pollutant concentratons on outpatent visits in Chongming District of Shanghai, and provide a scientific basis for formulating air pollution preventon and control policies for the ecological island.

    Methods We collected data on air polluton, meteorological factors, and daily outpatent visits registered in Shanghai Tenth People's Hospital Chongming Branch for all purposes, respiratory diseases, and circulatory diseases from 2014 to 2017. A generalized linear model was used to analyze the exposure-response relatonship between concentratons of selected air pollutants and outpatent visit volume. A 10 μg/m3 increase in selected air pollutants was used to compute the relatve risk (RR) and excess risk (ER) of daily outpatent visit volume.

    Results During the study period, the daily average concentrations of PM2.5, O3, PM10, NO2, SO2, and CO were 38.6 μg/m3, 107.3 μg/m3, 48.8 μg/m3, 17.0 μg/m3, 9.2 μg/m3, and 0.4 mg/m3, respectvely; the unqualifed rates of PM2.5, O3, PM10, and NO2 were 10.17%, 9.28%, 1.77%, and 0.28%, respectvely, but SO2 and CO did not exceed the limits. The daily outpatent volumes for all purposes, respiratory diseases, and circulatory diseases were 1 393, 123, and 167 person-tmes, respectvely. For total outpatent visits, each 10 μg/m3 increase of PM2.5, PM10, NO2, SO2, and O3 elevated the outpatent volume (ER) by 0.17%, 0.16%, 0.60%, 1.39%, and 0.10%, respectvely. For respiratory outpatent visits, each 10 μg/m3 increase of PM2.5, PM10, NO2, SO2, and O3 elevated the outpatent volume by 0.38%, 0.26%, 0.98%, 1.61%, and 0.27%, respectvely. For circulatory outpatent visits, each 10 μg/m3 increase of PM2.5, PM10, NO2, SO2, and O3 elevated the outpatent volume by 0.27%, 0.19%, 0.83%, 1.45%, and 0.15%, respectvely. Five selected air pollutants, except CO, had different lag effects on the three categories of outpatent volume. The strongest lag effects of PM2.5 on total, respiratory, and circulatory disease outpatent visits were all on lag1; for PM10, on lag1, lag1, and lag3, respectvely; for NO2, on lag1, lag1, and lag2, respectvely; for SO2, all on lag3; for O3, on lag2, lag3, and lag2, respectvely.

    Conclusion The increases of concentratons of selected air pollutants (PM2.5, PM10, NO2, SO2, and O3) would increase the outpatent visit volume of the hospital, and the related lag effects range from 1 to 3 days.

     

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