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    兰州市大气PM2.5暴露对COPD入院的影响

    Impact of atmospheric PM2.5 exposure on hospital admissions for chronic obstructive pulmonary disease in Lanzhou, China

      摘要
    • 摘要:
      目的 分析2016—2019年兰州市每日大气细颗粒物(fine particulate matter, PM2.5)暴露与慢性阻塞性肺疾病(chronic obstructive pulmonary disease, COPD)入院人次数的关系, 评估环境PM2.5暴露对COPD入院的影响。
      方法 收集2016—2019年兰州市大气污染物、气象因素和COPD入院数据, 采用季节性趋势分解模型分析各因素的时间变化趋势, 采用主成分分析对气象和污染物数据进行降维并纳入模型, 采用广义相加模型和分布滞后非线性模型评估PM2.5暴露导致的COPD入院风险。
      结果 2016—2019年兰州市大气PM2.5浓度总体呈下降趋势。PM2.5暴露会增加COPD入院的风险且存在滞后效应, PM2.5浓度每增加10 μg/m3, COPD 14日累积入院风险的RR值为1.071(95% CI: 1.045~1.097);在冷季节PM2.5暴露的单日滞后风险峰值高于暖季节, 在暖季节PM2.5暴露的14日累积滞后风险高于冷季节; 年龄≥ 65岁及女性COPD患者受PM2.5暴露的影响更大; 环境PM2.5暴露对COPD的影响随浓度的增高而增大, 当PM2.5浓度 < 27 μg/m3时风险结果无统计学意义。
      结论 大气PM2.5对COPD的影响与年龄、性别、冷暖季节及PM2.5浓度有关, 应根据不同季节PM2.5污染特征, 因地制宜制定防控策略和措施, 尽可能降低PM2.5暴露的危害。

       

      Abstract:
      Objective To analyze the relationship between daily atmospheric exposure to fine particulate matter (PM2.5) and the number of hospital admissions for chronic obstructive pulmonary disease (COPD) in Lanzhou, China, from 2016 to 2019, and to assess the impact of environmental PM2.5 exposure on hospital admissions of COPD.
      Methods Data on atmospheric pollutants, meteorological factors, and hospital admissions of COPD in Lanzhou from 2016 to 2019 were collected. A seasonal trend decomposition model was used to analyze the temporal trends of each factor. A principal component analysis was employed to reduce the dimensionality of meteorological and pollutant data and incorporate them into the model. The risk of hospital admissions for COPD due to PM2.5 exposure was assessed using a generalized additive model and a distributed lag non-linear model.
      Results The overall atmospheric PM2.5 concentration in Lanzhou showed a declining trend from 2016 to 2019. Atmospheric PM2.5 exposure increased the risk of hospital admissions for COPD and exhibited a lag effect. For every 10 μg/m3 increase in PM2.5 concentration, the relative risk (RR) of cumulative hospital admissions for COPD over 14 days was 1.071 (95% confidence interval: 1.045-1.097). The peak single-day lag risk from PM2.5 exposure was higher in the cold season than in the warm season, while the 14-day cumulative lag risk was higher in the warm season than in the cold season. Patients aged ≥ 65 years and female patients were more affected by PM2.5 exposure. The impact of environmental PM2.5 exposure on COPD increased with the concentration and was not statistically significant when the PM2.5 concentration was < 27 μg/m3.
      Conclusion The impact of atmospheric PM2.5 on COPD is related to age, sex, season (cold or warm), and PM2.5 concentration. Therefore, prevention and control strategies and measures should be formulated according to the characteristics of PM2.5 pollution in different seasons to minimize the harm of PM2.5 exposure.

       

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