# The Combined Effect of Indoor Air Quality and Socioeconomic Factors on Health in Northeast China
## 1 Introduction  
China's rapid economic growth, urbanization, and industrialization over the past four decades has increased concern about air pollution. It was estimated that air pollution in China could lead to approximately 1.6 million deaths in 2015 [1], and cost 1.4 trillion dollar in 2010 [2]. Moreover, millions of Chinese people in modern society spend about 83.3% of their time indoors (such as residences, office buildings, schools, daycare centers and public spaces) [3]. Approximately 67% of the time spent indoors is spent in their residences [4], and the figure is even higher during the outbreak of novel coronavirus pneumonia epidemic currently. The indoor air quality in residential buildings has far-reaching implications for occupants' health and wellbeing. Considerable scientific progress has been made in the understanding of health effects related to the indoor air quality over the past decades. These efforts tend to address indoor air pollution sources and exposures and associated health concerns, such as sulfur dioxide [5], nitrogen oxides [6], ozone [7], particulate matter [8], formaldehyde [9], radon [10], tobacco smoke [11], VOC [12], pesticides [13] and SVOC (Semi-Volatile Organic Compounds) [14]. More recently, research has increasingly shown the effect of socioeconomic factors (e.g., income, education, and job) on the relationship between indoor air quality and occupants' health. On the one hand, socioeconomic factors can influence indoor air pollution exposures directly. For example, low-income families were likely to live in dilapidated houses with deteriorating building structures and poor inferiors building materials, which led to highindoor air pollution levels [15]. Crowding was highly associated with low household economic status, which can increase high indoor air pollution level [16]. The education level may affect residential indoor air exposures, as it provided access to information on building maintenance, household hygiene, and the proper use of indoor consumer products [17]. On the other hand, socioeconomic factors, such as income, education, and occupation, have been recognized as the determinants of health [18]. For instance, a large body of research has suggested that people with higher education and income level tend to have better health [19]. One of the reasons was that these socioeconomic factors influenced the access to health care and nutrition [20]. Complex relationships exist between residential indoor air quality, health and socioeconomic factors. Few studies have identified the combined effects of residential indoor air quality and socioeconomic factors on occupants' health. There are multiple indoor air pollution exposures in the residential housing simultaneously, leading to multiple adverse health problems. Moreover, socioeconomic factors cannot be concretely defined like weight or length, because of involving several aspects, e.g., income, education, and job status. However, the standard regression-based models have limited ability to interpret the complexity. For example, [PERSON] et al. applied multivariate regression to examine the associations between socioeconomic factors and indoor pollutant levels in French dwellings [17]. [PERSON] et al. proposed a multivariable Poisson regression model to establish the relationship between socioeconomic status, indoor air pollution and respiratory symptoms in South India [21]. [PERSON] et al. developed a quantile regression model to analyze the link between education level of the occupants and indoor volatile organic compounds concentrations in Germany [22]. [PERSON] et al. used the general linear model to identify the effects of socioeconomic factors on indoor PM\({}_{10}\) concentrations in Korea [23]. Such models only allow expressing the relations between one dependent variable and multiple independent variables. Moreover, there are often the high correlations among socioeconomic or indoor air pollution indicators (i.e., multi-collinearity), resulting in models being incorrectly estimated.