Correlation between environmental factors and pediatric respiratory disease visits in a central hospital of Shanghai
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摘要:
目的 探讨环境因素与医院呼吸道疾病就诊的关联及其滞后效应，为儿科呼吸道疾病的预测和医院诊疗工作提供科学依据。 方法 收集2017—2019年上海闵行区中心医院儿科呼吸道疾病门急诊就诊的503 899例病例及同期气象信息，构建分布滞后非线性模型(DLNMS)，探讨儿科呼吸道疾病就诊病例与各环境因素的相关性，定量评估环境因素对呼吸道疾病就诊病例的累积滞后效应。 结果 在各环境因素中，与儿童呼吸道疾病就诊有关联的因素包括温度、细颗粒物(PM2.5)、可吸入颗粒物(PM10)、NO2和SO2。校正温度后，PM2.5和PM10质量体积浓度未对儿童呼吸道疾病就诊显示出显著的即时效应和滞后效应。儿童呼吸道疾病就诊病例相对危险值(RR)随着NO2质量体积浓度升高而升高，NO2质量体积浓度≥55 μg/m3时，即时效应、滞后3，5和7 d影响均有统计学意义(RR值分别为1.05，1.13，1.17，1.21，P值均＜0.05)。RR值和SO2质量体积浓度呈倒“U”型，SO2质量体积浓度≥5 μg/m3时，滞后3，5和7 d影响均有统计学意义(RR值分别为1.03，1.03，1.04，P值均＜0.05)。 结论 高浓度NO2和SO2均会增加儿童呼吸道疾病的风险，并具有一定的滞后性。 Abstract:Objective To explore the correlation and lag effects of environmental factors on pediatric respiratory disease visits at hospital, so as to provide scientific basis for disease prediction and optimizing clinical diagnosis and treatment. Methods Data from 503 889 pediatric respiratory disease outpatient and emergency visits a central hospital in Minhang District of Shanghai between 2017 and 2019, along with concurrent meteorological data were collected. A distributed lag non-linear models (DLNM) was constructed to explore the specific relationship between pediatric respiratory disease consultations and various environmental factors and to quantify the cumulative lag effects of environmental factors on respiratory disease consultations. Results Among the environmental factors, temperature, fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2) were associated with pediatric respiratory disease visits. After adjusting for temperature, PM2.5 and PM10 concentrations did not show significant immediate or lag effects. The relative risk (RR) of pediatric respiratory disease visits increased with rising NO2 concentrations. When NO2 concentration ≥55 μg/m3, significant immediate and lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.05, 1.13, 1.17, and 1.21(P < 0.05). The RR values showed an inverted "U"-shaped relationship with SO2 concentrations. When SO2 concentration ≥5 μg/m3, significant lagged effects (lag 3, 5, and 7 days) were observed. The RR values were 1.03, 1.03, and 1.04 (P < 0.05). Conclusion High concentrations of NO2 and SO2 increase the risk of pediatric respiratory disease visits, with observable lag effects. -
Key words:
- Respiratory tract diseases /
- Ambulatory care /
- Environment /
- Models, statistical /
- Child
1) 利益冲突声明 所有作者声明无利益冲突。2) 周霜霜与蔡逸舟为共同第一作者。 -
图 1 闵行区中心医院2017—2019年儿童呼吸道疾病就诊时间分布
Figure 1. Time distribution of children respiratory disease visits central hospital of Minhang District from 2017 to 2019
图 2 儿童呼吸道疾病就诊病例和NO2质量体积浓度相对风险累积效应
Figure 2. Cumulative effect of relative risk of NO2 mass volume concentration on pediatric respiratory disease cases
图 3 儿童呼吸道就诊病例和SO2质量体积浓度相对风险累积效应
Figure 3. Cumulative effect of relative risk of SO2 mass volume concentration on pediatric respiratory disease cases
表 1 闵行区环境因素与2017—2019年儿童呼吸系统疾病就诊相关性分析(r值，n= 503 899)
Table 1. Correlation analysis of environmental factors and pediatric respiratory disease visits in Minhang District from 2017 to 2019(r, n=503 899)
变量 SO2 NO2 PM2.5 PM10 温度 相对湿度 NO2 0.42* PM2.5 0.72* 0.52* PM10 0.51* 0.65* 0.78* 温度 -0.35* -0.55* -0.31* -0.34* 相对湿度 -0.56* -0.09 -0.51* -0.15* 0.12* 就诊人数 0.17* 0.57 0.23* 0.29* -0.40* -0.09* 注： * P＜0.05。 
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表 2 闵行区环境因素对2017—2019年儿童呼吸道疾病就诊的分布滞后效应分析[RR值(95%CI)，n=503 899]
Table 2. Distribution lag effect analysis of environmental factors on children's respiratory disease visits in Minhang District from 2017 to 2019[RR(95%CI), n=503 899]
环境因素 质量体积浓度/(μg·m-3) 滞后0 d 滞后3 d 滞后5 d 滞后7 d PM2.5 22 1.00(0.99~1.02) 0.97(0.94~1.00) 0.96(0.92~1.00) 0.96(0.92~1.01) 35 1.01(0.98~1.04) 0.95(0.90~1.00) 0.94(0.88~1.00) 0.94(0.86~1.01) 54 1.01(0.97~1.06) 0.92(0.86~1.00) 0.91(0.82~1.00) 0.91(0.81~1.02) PM10 30 0.98(0.96~1.01) 0.97(0.93~1.02) 0.97(0.92~1.03) 0.95(0.89~1.01) 43 0.98(0.94~1.02) 0.95(0.89~1.02) 0.96(0.88~1.04) 0.92(0.83~1.02) 59 0.97(0.92~1.02) 0.94(0.86~1.03) 0.94(0.85~1.05) 0.89(0.79~1.02) NO2 16 1.01(1.00~1.03) 1.02(0.99~1.05) 1.03(0.99~1.06) 1.02(0.99~1.07) 55 1.05(1.00~1.10)* 1.13(1.04~1.22)* 1.17(1.06~1.30)* 1.21(1.08~1.36)* 84 1.08(1.02~1.14)* 1.25(1.14~1.37)* 1.37(1.23~1.53)* 1.50(1.32~1.71)* SO2 5 0.99(0.98~1.00) 1.03(1.01~1.04)* 1.03(1.01~1.05)* 1.04(1.11~1.07)* 10 0.96(0.92~1.03) 1.11(1.03~1.20)* 1.14(1.04~1.24)* 1.18(1.06~1.32)* 15 0.97(0.92~1.03) 1.13(1.03~1.23)* 1.13(1.02~1.26)* 1.17(1.02~1.33)* 注： * P＜0.05。
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