首页> 外文期刊>International Journal of Environmental Research and Public Health >Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States
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Using Structural Equation Modeling to Assess the Links between Tobacco Smoke Exposure, Volatile Organic Compounds, and Respiratory Function for Adolescents Aged 6 to 18 in the United States

机译:使用结构方程模型评估美国6至18岁青少年的烟草烟雾暴露,挥发性有机化合物与呼吸功能之间的联系

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Asthma is an inflammatory airway disease that affects 22 million Americans in the United States. Research has found associations between impaired respiratory function, including asthma and increased symptoms among asthmatics, and common indoor air pollutants, including tobacco smoke exposure and volatile organic compounds (VOCs). However, findings linking VOC exposure and asthma are inconsistent and studies are of mixed quality due to design limitations, challenges measuring VOC exposure, small sample sizes, and suboptimal statistical methodologies. Because of the correlation between tobacco smoke exposure and VOCs, and associations between both tobacco smoke and VOCs with respiratory function, it is crucial that statistical methodology employed to assess links between respiratory function and individual air pollutants control for these complex relationships. This research uses Structural Equation Modeling (SEM) to assess the relationships between respiratory function, tobacco smoke exposure, and VOC exposure among a nationally-representative sample of adolescents. SEM allows for multiple outcome variables, the inclusion of both observed and latent variables, and controls the effects of confounding and correlated variables, which is critically important and is lacking in earlier studies when estimating the effects of correlated air pollutants on respiratory function. We find evidence of associations between respiratory function and some types of VOCs, even when controlling for the effects of tobacco smoke exposure and additional covariates. Furthermore, we find that poverty has an indirect effect on respiratory function through its relationships with tobacco smoke exposure and some types of VOCs. This analysis demonstrates how SEM is a robust analytic tool for assessing associations between respiratory function and multiple exposures to pollutants.
机译:哮喘是一种炎症性呼吸道疾病,在美国影响2200万美国人。研究发现,包括哮喘在内的呼吸功能受损和哮喘患者中症状增加与常见的室内空气污染物(包括暴露于烟草烟雾和挥发性有机化合物(VOC))之间存在关联。然而,由于设计局限性,测量VOC暴露的挑战,样本量小以及统计方法不够理想,将VOC暴露与哮喘相关的发现并不一致,并且研究质量参差不齐。由于烟草烟雾暴露与VOC之间的相关性,以及烟草烟雾和VOC与呼吸功能之间的关联,对于这些复杂的关系,采用统计学方法评估呼吸功能与单个空气污染物之间的联系至关重要。这项研究使用结构方程模型(SEM)评估了代表国家的青少年样本中呼吸功能,烟草烟雾暴露和VOC暴露之间的关系。 SEM允许多个结果变量,包括观察到的变量和潜在变量,并控制混杂变量和相关变量的影响,这在评估相关空气污染物对呼吸功能的影响时至关重要,而且在早期研究中还没有。我们发现,即使控制烟草烟雾暴露和其他协变量的影响,呼吸功能与某些VOC类型之间也存在关联。此外,我们发现贫困通过与烟草烟雾暴露和某些VOC的关系而间接影响呼吸功能。这项分析表明SEM如何成为评估呼吸功能与污染物多次接触之间关联的强大分析工具。

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