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Fluctuations in air pollution give risk warning signals of asthma hospitalization

机译：空气污染的波动给哮喘住院的危险警告信号

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Recent studies have implicated that air pollution has been associated with asthma exacerbations. However, the key link between specific air pollutant and the consequent impact on asthma has not been shown. The purpose of this study was to quantify the fluctuations in air pollution time-series dynamics to correlate the relationships between statistical indicators and age-specific asthma hospital admissions. An indicators-based regression model was developed to predict the time-trend of asthma hospital admissions in Taiwan in the period 1998-2010. Five major pollutants such as particulate matters with aerodynamic diameter less than 10 μm (PM_(10)), ozone (O_3), nitrogen dioxide (NO_2), sulfur dioxide (SO_2), and carbon monoxide (CO) were included. We used Spearman's rank correlation to detect the relationships between time-series based statistical indicators of standard deviation, coefficient of variation, skewness, and kurtosis and monthly asthma hospitalization. We further used the indicators-guided Poisson regression model to test and predict the impact of target air pollutants on asthma incidence. Here we showed that standard deviation of PM_(10) data was the most correlated indicators for asthma hospitalization for all age groups, particularly for elderly. The skewness of O_3 data gives the highest correlation to adult asthmatics. The proposed regression model shows a better predictability in annual asthma hospitalization trends for pediatrics. Our results suggest that a set of statistical indicators inferred from time-series information of major air pollutants can provide advance risk warning signals in complex air pollution-asthma systems and aid in asthma management that depends heavily on monitoring the dynamics of asthma incidence and environmental stimuli.
机译：最近的研究表明，空气污染与哮喘发作有关。但是，尚未显示出特定的空气污染物与对哮喘的影响之间的关键联系。这项研究的目的是量化空气污染时间序列动态的波动，以关联统计指标和特定年龄的哮喘住院患者之间的关系。建立了基于指标的回归模型，以预测台湾在1998年至2010年期间哮喘住院患者的时间趋势。包括五种主要污染物，例如空气动力学直径小于10μm的颗粒物（PM_（10）），臭氧（O_3），二氧化氮（NO_2），二氧化硫（SO_2）和一氧化碳（CO）。我们使用Spearman等级相关性来检测基于时间序列的标准偏差，变异系数，偏度和峰度的统计指标与哮喘住院患者之间的关系。我们进一步使用了指标指导的泊松回归模型来测试和预测目标空气污染物对哮喘发病率的影响。在这里，我们显示PM_（10）数据的标准差是所有年龄组（尤其是老年人）哮喘住院治疗的最相关指标。 O_3数据的偏度与成人哮喘患者的相关性最高。所提出的回归模型显示了小儿每年哮喘住院治疗趋势的更好的可预测性。我们的结果表明，从主要空气污染物的时间序列信息推断出的一组统计指标可以在复杂的空气污染-哮喘系统中提供预先的风险预警信号，并在很大程度上依赖于监测哮喘发病率和环境刺激因素的哮喘治疗中提供帮助。

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