Causal relationship between outdoor atmospheric quality and pediatric asthma visits in hangzhou.
Causal relationship between outdoor atmospheric quality and pediatric asthma visits in hangzhou.
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DOI：
10.1016/j.heliyon.2023.e14271
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发表时间：
2023-03
期刊：
影响因子：
4
通讯作者：
Yang, Xin
中科院分区：
文献类型：
--
作者：
Feng, Yuqing;Wang, Yingshuo;Wu, Lei;Shu, Qiang;Li, Haomin;Yang, Xin
许多空气污染物和气候变量已被证明与儿童哮喘显著相关，并使哮喘症状恶化。然而，其确切的因果影响仍不清楚。我们探讨了空气污染物、气候和儿童哮喘患者每日就诊之间的因果关系，并具有短期滞后效应。基于8年的每日环境数据和每日儿科哮喘患者就诊，使用斯皮尔曼相关性分析来选择与任何时间（滞后1 - 6天）的每日儿科哮喘患者就诊相关的空气污染物和气候变量。我们将这些环境变量视为治疗，并使用Dowhy库（一个Python库，用于通过绘制模型进行因果推理，定量评估因果效应并验证因果假设）构建多治疗和单治疗因果推理模型，以估计这些相关变量与滞后时间内的每日儿科哮喘患者就诊之间的定量因果效应。多治疗因果推断模型是一个包含8种治疗（能见度、降水、PM10、PM2.5、SO2、NO2、AQI和CO）、1种结局（每日儿科哮喘患者访视）和5种混杂因素（湿度、温度、海平面气压、风速和未观察到的混杂因素"U"）的模型。单治疗因果推断模型为8个模型，每个模型有1个治疗，1个结局和12个混杂因素。斯皮尔曼相关分析表明，降水量、风速、能见度、空气质量指数、PM2. 5、PM10、SO2、NO2和CO在所有时间点均为显著相关变量（p <0. 05）。多处理模型显示，合并处理对短期滞后具有显著的因果关系（lag1-lag6; p <0.05）。因果关系主要由SO2引起。在单一处理模型中，能见度、SO2、NO2和CO在任何一个时间都表现出显著的因果效应（p <0.05）。SO2和CO表现出较强的正因果效应。在lag5时，SO2的因果效应达到最大值（因果效应= 11.41，p <0.05）。CO的最大因果效应出现在lag3（因果效应= 10.67，p <0.05）。在这八年期间，杭州的SO2、CO和NO2的改善估计每年分别减少哮喘就诊8478.03、3131.08和1341.39次。SO2、NO2、CO和能见度对儿童哮喘患者的日常就诊有因果关系; SO2是最关键的致病变量，因果关系相对较高，其次是CO。杭州地区大气质量的改善有效地降低了哮喘的发病率。
Many air pollutants and climate variables have proven to be significantly associated with pediatric asthma and have worsened asthma symptoms. However, their exact causal effects remain unclear. We explored the causality between air pollutants, climate, and daily pediatric asthma patient visits with a short-term lag effect. Based on eight years of daily environmental data and daily pediatric asthma patient visits, Spearman correlation analysis was used to select the air pollutants and climate variables that correlated with daily pediatric asthma patient visits at any time (with a lag of 1–6 days). We regarded these environmental variables as treatments and built multiple- and single-treatment causal inference models using the Dowhy library (a Python library for causal inference by graphing the model, quantitatively evaluating causal effects, and validating the causal assumptions) to estimate the quantitative causal effect between these correlated variables and daily pediatric asthma patient visits in lag time. The multiple-treatment causal inference model was a model with 8 treatments (Visibility, Precipitation, PM10, PM2.5, SO2, NO2, AQI and CO), 1 outcome (daily pediatric asthma patients visits), and 5 confounders (Humidity, Temperature, Sea level pressure, wind speed and unobserved confounders “U”). Single-treatment causal inference models were 8 models, and each model has 1 treatment, 1 outcome and 12 confounders. Spearman correlation analysis showed that precipitation, wind speed, visibility, air quality index, PM2.5, PM10, SO2, NO2, and CO were significantly associated variables at all times (p < 0.05). The multiple-treatment model showed that pooled treatments had significant causality for the short-term lag (lag1–lag6; p < 0.05). Causality was mainly due to SO2. In the single-treatment models, visibility, SO2, NO2, and CO exhibited significant causal effects at any one time (p < 0.05). SO2 and CO exhibited stronger positive causal effects. The causal effect of SO2 reached its maxima (causal effect = 11.41, p < 0.05) at lag5. The greatest causal effect of CO appeared at lag3 (causal effect = 10.67, p < 0.05). During the eight year-period, the improvements in SO2, CO, and NO2 in Hangzhou were estimated to reduce asthma visits by 8478.03, 3131.08, and 1341.39 per year, respectively. SO2, NO2, CO, and visibility exhibited causal effects on daily pediatric asthma patient visits; SO2 was the most crucial causative variable with a relatively higher causal effect, followed by CO. Improvements in atmospheric quality in the Hangzhou area have effectively reduced the incidence of asthma.
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影响因子：
3.7
作者：
Zhou, Xiaojian;Hong, Jianguo
通讯作者：
Hong, Jianguo
DOI：
10.5588/ijtld.14.0170
发表时间：
2014-11-01
影响因子：
4
作者：
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通讯作者：
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168.9
作者：
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通讯作者：
Balmes, John R.
影响因子：
2.7
作者：
Reno AL;Brooks EG;Ameredes BT
通讯作者：
Ameredes BT