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    高衍新, 孙文, 杜英林, 张晓. 气温与PM2.5交互作用对呼吸系统疾病就诊风险的影响研究: 基于山东省四城市分析[J]. 环境卫生学杂志, 2023, 13(1): 30-36. DOI: 10.13421/j.cnki.hjwsxzz.2023.01.004
    引用本文: 高衍新, 孙文, 杜英林, 张晓. 气温与PM2.5交互作用对呼吸系统疾病就诊风险的影响研究: 基于山东省四城市分析[J]. 环境卫生学杂志, 2023, 13(1): 30-36. DOI: 10.13421/j.cnki.hjwsxzz.2023.01.004
    shu
    GAO Yan-xin, SUN Wen, DU Ying-lin, ZHANG Xiao. Interaction effects between air temperature and PM2.5 on the risk of daily hospital visits for respiratory system diseases: based on four cities in Shandong province, China[J]. Journal of Environmental Hygiene, 2023, 13(1): 30-36. DOI: 10.13421/j.cnki.hjwsxzz.2023.01.004
    Citation: GAO Yan-xin, SUN Wen, DU Ying-lin, ZHANG Xiao. Interaction effects between air temperature and PM2.5 on the risk of daily hospital visits for respiratory system diseases: based on four cities in Shandong province, China[J]. Journal of Environmental Hygiene, 2023, 13(1): 30-36. DOI: 10.13421/j.cnki.hjwsxzz.2023.01.004
    shu

    气温与PM2.5交互作用对呼吸系统疾病就诊风险的影响研究: 基于山东省四城市分析

    Interaction effects between air temperature and PM2.5 on the risk of daily hospital visits for respiratory system diseases: based on four cities in Shandong province, China

      摘要
    • 摘要:
      目的 探讨山东省四城市气温与大气细颗粒物(PM2.5)对人群呼吸系统疾病影响的交互作用。
      方法 分别收集四城市部分医院内科2019—2021年逐日呼吸系统疾病门诊量以及气象、大气PM2.5等逐日监测资料, 对气象因素、PM2.5、人群呼吸系统疾病门诊量进行Pearson相关分析, 采用分布式滞后非线性模型估计气温与大气PM2.5对人群呼吸系统疾病就诊的影响, 利用超额相对风险(relative excess risk due to interaction, RERI)评估大气PM2.5与气温之间的潜在交互作用。
      结果 日均气温与PM2.5呈负相关关系, 相关系数为-0.471(P < 0.01);呼吸系统疾病就诊量与日均气温、平均相对湿度呈负相关关系而与PM2.5呈正相关关系, 相关系数分别为-0.059、-0.056、0.10(P < 0.01);济南市日均气温和PM2.5之间存在拮抗交互作用, 其RERI值及95%CI为-0.11(-0.15, -0.08);淄博、滨州、菏泽三城市日均气温和PM2.5之间表现为协同交互作用, 其RERI值及95%CI分别为0.04 (0.04, 0.04)、0.10 (0.10, 0.11)、0.03 (0.02, 0.05), 且具有统计学意义(P＜0.05)。
      结论 日平均气温对PM2.5所致呼吸系统疾病的发生可能存在修饰作用, 且二者的交互效应可能存在地域性差异。

       

      Abstract:
      Objective To explore the interaction effects between air temperature and fine particulate matter (PM2.5) on population's respiratory system diseases in four cities of Shandong province, China.
      Methods The daily data on meteorological factors, PM2.5 mass concentrations and the number of visits for respiratory diseases to departments of internal medicine in some hospitals were collected in the four cities from 2019 to 2021. Pearson correlation analysis was performed to analyze the relationship between meteorological factors, PM2.5 mass concentrations and the number of visits for respiratory diseases. The distributed lag nonlinear model was used to estimate the effects of air temperature on visit for respiratory diseases. The potential interaction between PM2.5 and air temperature was evaluated using the relative excess risk due to interaction (RERI).
      Results The average daily temperature was negatively correlated with PM2.5, mass concentrations with correlation coefficient being -0.471(P < 0.01). The number of visits for respiratory diseases was negatively correlated with the average daily temperature (r =-0.059, P < 0.01) and average relative humidity (r =-0.056, P < 0.01), while positively correlated with PM2.5 mass concentrations (r =0.10, P < 0.01). The average daily temperature and PM2.5 mass concentrations showed antagonistic interaction in Jinan RERI =-0.11, 95%confidence interval (CI): -0.15, -0.08), and synergistic interaction between average daily temperature and PM2.5 mass concentrations in Zibo (RERI =0.04, 95%CI: 0.04, 0.04, Binzhou (RERI =0.10, 95%CI: 0.10 to 0.11) and Heze (RERI =0.03, 95%CI: 0.02, 0.05), all with statistical significance (P < 0.05).
      Conclusion Daily average air temperature might modify the effects of PM2.5 on the number of visits for respiratory diseases, and their interaction effect might have regional differences.

       

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