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

Abstract

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.

Indoor Air Pollution (Carbon Dioxide and Total Volatile Organic Compound) and Pulmonary Disorders in Junior High School Students in Depok, West Java

“…The total concentration of VOC in Junior High School B is lower due to the low source of VOC in the classroom and the students are having the exam during the research. VOC sources in the classroom come from household products use including solvent, adhesive, paint, cleaning product, furniture and air freshener (Shook-Sa, Chen, & Zhou, 2017). This is similar to research conducted in Texas that VOC concentrations came from cleaning agents, furniture polish, materials used in arts and crafts activities, use of hot water, and deodorizing cakes used in urinal pots were the main sources for indoor concentration high (Greenwald et al, 2013;Raysoni et al, 2017).…”

“…VOCs are compounds that have a high impact on respiratory disorders, one of which can cause allergies and asthma (Mögel et al, 2011). The relationship of VOC exposures include xylene, ethylbenzene and toluene are related to asthma and asthma symptom, while the pulmonary disorders are related to the exposure of environmental endotoxin and indoor air exposure including tobacco smoke and volatile organic compounds (Shook-Sa et al, 2017).…”

Past mercury exposure and current symptoms of nervous system dysfunction in adults of a First Nation community (Canada)

“…SEM allows for data from multiple exposures and multiple outcomes to be considered simultaneously [45]. It is based on an interrelated system of linear regression equations that extend the possibility of relations between observed and latent variables, as well as direct and indirect (mediation) relations and allows for flexibility in modeling covariance structures [19,39,46,47]. Finally, SEM provides path diagrams that are useful to understand and interpret the contribution of the various variables.…”

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

“…SEM is useful for estimating and understanding the network of relationships between variables (latent, observed, and error variables) and it is also employed to estimate the degree of model fit and allows for the presence of measurement errors in independent and dependent variables ( 102 ). As researchers turn to modeling multi-pollutant mixtures, SEM is increasingly used to estimate the impact of multi-pollutant mixtures on health ( 103 , 104 ). For instance, SEM assessed the relationship between respiratory function, tobacco smoke exposure, and volatile organic compound exposure in a nationally representative sample of adolescents, revealing associations between respiratory function and certain types of volatile organic compounds ( 104 ).…”