Housing characteristics, home environmental factors, and pulmonary function deficit in Chinese children: Results from the Seven Northeast Cities (SNEC) Study
Abstract
To assess the effects of housing characteristics and home environmental factors on lung function of Chinese children, 6740 children (aged 6–16 years) were recruited from seven cities in Northeast China in 2012. Performance of lung function was determined by comparison of forced vital capacity (FVC), forced expiratory volume (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (MMEF). Multivariate regression models were used to evaluate the associations with lung function deficit. The results showed that housing conditions were associated with lung function deficit in children. The adjusted odds ratios were 0.47 (95% CI: 0.26–0.83) for FVC for “ping-fang” housing compared with “dan-yuan-lou-fang” housing and 2.90 (95% CI: 2.43–3.47) for FEV1 with home renovations completed within two years compared with counterparts. The linear regression models consistently showed a significant association of housing conditions and home environmental factors with lung function measurements across subjects. A residence taller than seven stories was negatively associated with FEV1 (β = −55; 95% CI: −97 to −13). In conclusion, housing conditions and home environmental factors are particularly important to the development of lung function and respiratory health in children. These factors are concerning and action should be taken to improve them.
Introduction
The World Health Organization (WHO) estimates that there are more than 3.8 million annual deaths attributable to indoor air pollution, with a great burden of disease in developing and developed countries (Lim et al. 2012; Thomas et al. 2015; Asikainen et al. 2016). Associations between exposure to various forms of indoor air pollution and lung function outcomes in developed countries have been previously explored (Mendell et al. 2011; Lane et al. 2013; Khreis et al. 2017; Park et al. 2017). For instance, living in a home with active mold exposure has been associated with asthma development and exacerbation as well as diagnoses of asthma, dyspnea, wheeze, cough, respiratory infection, allergic rhinitis, and other upper respiratory tract symptoms (Mendell et al. 2011). Home proximity to roadways has been used as an exposure proxy for pollutants emitted through automobile traffic (Lane et al. 2013; Khreis et al. 2017; Park et al. 2017), which has been associated with reduced lung function growth in school-aged children in the United States and Sweden (Gauderman et al. 2007; Schultz et al. 2012). Additionally, exposure to environmental tobacco smoke (ETS) (Chen et al. 2014; Hu et al. 2017; Milanzi et al. 2017) allergens is known to play a role in respiratory irritation. Potentially, exposure to endotoxins, pets, pests, and certain microorganisms could also play a similar role (Bertelsen et al. 2010).
Efforts have been made to elucidate the contribution of modern building materials and furnishings to respiratory health, although causal links have yet to be established (Hulin et al. 2012). Volatile organic compounds (VOCs) have been associated with various measures of lung dysfunction (Billionnet et al. 2011; Franck et al. 2014; Sukul et al. 2016; van Vliet et al. 2017; Yamada et al. 2017). Phthalates have also been implicated as a possible chemical exposure related to numerous developmental perturbations affecting children (Braun et al. 2013; Huang et al. 2015). Flame-retardants, common in building materials and furnishings, have been associated with poor respiratory health outcomes (Araki et al. 2014). Additionally, persistent wheeze and lung function deficits in nonatopic children have been associated with maternal exposure to household cleaning products during pregnancy (Henderson et al. 2008).
Of course, children possess an increased risk for adverse health effects associated with household air pollution because they have a higher per-minute respiratory rate, underdeveloped immune and respiratory systems, and longer home-stay relative to adults (Bennett et al. 2007; Siezen et al. 2009; McInnes et al. 2011). While numerous epidemiologic studies have assessed associations between exposure to ambient air pollution and lung function decline in children (Gauderman et al. 2002; He et al. 2010; Roy et al. 2012; Zeng et al. 2016), less is known about the adverse respiratory health outcomes associated with exposure to indoor air pollution in children. This is particularly true of those in developing countries.
Few studies have investigated adverse respiratory health effects associated with exposure to household sources of indoor air pollution in Chinese children. The hypothesis of this investigation was that housing characteristics play a role in child lung function. It examined the associations between lung function measures and exposure to various forms of indoor air pollution in school-aged Chinese children using data from the Seven Northeast Cities (SNEC) Study.
Materials and methods
Subject recruitment
A multi-site cross-sectional design was used to examine health outcomes of children in relation to their housing conditions and proximity to certain environmental factors. Participants were selected from 20 million residents of 14 cities in Liaoning Province, China. Seven cities—Shenyang, Dalian, Anshan, Fushun, Benxi, Liaoyang, and Dandong—were selected “to maximize the inter- and intra-city gradients of the pollutants of interest and also to minimize the correlation between district specific ambient pollutants” (Zeng et al. 2016). Participants were randomly selected from one to two classrooms representing each grade in one elementary and one middle school. Both schools were within one mile of a local air monitoring station. The participants included only those children who had continuously resided in their district for two or more years prior to the initiation of this study. The final sample was comprised of 3382 boys and 3358 girls.
This study was conducted according to the World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. Ethical approval was obtained for this project from the Human Studies Committee of Sun Yat-sen University, the site of the study (Ethics Approval Number: L2014016). Written informed consent was obtained from each participant, as well as their parents, prior to data collection.
Questionnaire survey
School administrators facilitated the distribution of questionnaires and forms through trained classroom teachers. Teachers obtained written consent from the parent(s), who answered the questions. Parents were given the option to attend an evening meeting to complete the questionnaire or to complete it at home and return the responses in a provided sealed envelope. A summary description of select variables is available in Table 1. The questionnaire, in Chinese, is included in the Supplementary Material 1.
Table 1.
| Characteristic | Description | Prevalence |
|---|---|---|
| Housing type | ||
| Dan-yuan-lou-fang | An apartment unit in a multi-story, multi-unit building. | 87.2% |
| Ping-fang | A one-story house, typically with a small yard. | 9.8% |
| Other | Dormitory or any other unspecified housing type. | 3.0% |
| Environmental factor | ||
| Proximity to emissions | A pollution source, such as a factory or smokestack, within 100 m of a household. | 37.9% |
| Indoor coal use | The use of coal for cooking or heating inside a household. | 10.0% |
| Ventilation use | The use, within a household, of any of the following ventilation devices: exhaust fan, chimney, or fume hood (typically above a cook stove). | 87.3% |
| Humidifier use | The use of humidifier devices within a household. | 16.2% |
| Pet-keeping | Living with the following pets: dogs, cats, farm animals (including birds, chickens, ducks, or geese), and others. | 21.3% |
| Recent home renovation | Any of the following renovations to a household within the previous 2 years: installation of linoleum floor, new paint, installation of particleboard, new furniture, new wall covering, or new suspended ceiling. | 35.9% |
| Recent mold/water in the home | Signs of flooding, water damage, or mold growth (on any surfaces other than food) in the household in the past 12 months. | 13.3% |
| Recent pests in the home | Exposure to pests, determined by visual confirmation of vermin (separate questions for cockroaches, mice, or rats) in the household in the past 12 months. | 35.9% |
| ETS during the first 24 months of life | There were smokers in regular contact with the child between birth and 2 years of age, including regular visitors (e.g., grandparents or babysitters). | 20.7% |
| ETS during pregnancy | During pregnancy, the mother was exposed to environmental tobacco smoking. | 19.4% |
| Current ETS | Daily smoking by any individual in the household. | 48.7% |
Note: ETS, environmental tobacco smoking.
Lung function measurement
Body weight and body stature were measured using a standardized protocol. Body mass index (BMI) was calculated as weight in kilograms divided by stature in meters squared. Lung function data, including forced vital capacity (FVC), forced expiratory volume (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (MMEF), were collected using Spirolab MIR portable electronic spirometers (Rome, Italy). These spirometers have been independently evaluated in an occupational setting and found to have sensitivity and specificity values of 97.75% and 73.04%, respectively (Mehrparvar et al. 2014). The model used in this study was automatically calibrated using the manufacturer’s process. Lung function data were collected per the manufacturer’s recommendations by two skilled research technicians. The American Thoracic Society and the European Respiratory Society standards were used, and spirometry was performed only after the participant was able to understand and follow the provided instructions. The age range of the study participants was 6–16 years.
The children were asked to stand comfortably for the assessment and a nose clip was applied to obstruct nasal airflow during measurements. Measurements for total lung capacity and residual volume were taken. FVC and FEV1 values were recorded in milliliters and were the peak value(s) from any of three satisfactory curves when at least two of the three curves differed by no more than 5%. Corrections for body temperature and saturated pressure were applied.
EpiData Entry was used for data input and notation. Double entry verification was used to ensure quality data, which were also encrypted. Predicted lung function values were calculated from reference equations including variables of height, weight, and age (Ma et al. 2013). Lung function impairment was classified in terms of measured spirometric performances above or below predicted lung function values as follows: FEV1 < 85% of predicted, FVC < 85% of predicted, PEF < 75% of predicted, and MMEF < 75% of predicted.
Statistical analysis
Data were assessed for normality using the Shapiro–Wilk test and for homogeneity using Bartlett’s test for unequal variances. Relative frequencies and differences between categorical variables were calculated using the χ2 test. Mean ± SD and differences between continuous variables were calculated using a t-test.
In addition to reporting the exposure–response relationship by using health outcomes as continuous variables, this study evaluated whether this relationship reaches a degree of damage on lung function by using health outcomes as binary variables. For categorical lung function measurements, multilevel logistic regression was used to estimate the association between indoor air pollutants and measures of respiratory function. Specifically, the prevalence odds ratios (PORs) of respiratory function with respective 95% confidence intervals (CIs) were estimated for indoor air pollutants. The primary explanatory variables of interest for this study were: housing type, age of the home, structure of the home, floor area of residence, number of rooms, distance from traffic, pollution source near the home, pets in the home, home renovation, mold, pests, indoor coal use, ventilation device or humidifier device use, and ETS exposure. PORs were mutually adjusted for potential confounders, including age, gender, BMI, parental education, breastfeeding status, income, ETS, family history of atopy, parents as responders, and study districts. For continuous lung function measurements, we used a two-stage multiple linear regression model to estimate effects. An a priori p value of <0.05 was used to determine statistical significance for all tests. All analyses were conducted using the GLIMMIX and MIXED procedures in SAS® software (version 9.4 (SAS Institute, Cary, NC, USA)) to account for the hierarchical structure of the data (i.e., participants nested within study districts). A complete description of the analytical methods is included in the study by Dong et al. (2008).
Results
Spirometric means of lung function and standard deviations were calculated for 3382 male and 3358 female participants. A complete list of population characteristics is available in Table 2, with differences noted by gender.
Table 2.
| Male | Female | Total | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characteristic | n | % | Mean | SD | n | % | Mean | SD | n | % | Mean | SD |
| City | ||||||||||||
| Shenyang | 584 | 52.7 | — | — | 525 | 47.3 | — | — | 1109 | 16.5 | — | — |
| Dalian | 247 | 48.4 | — | — | 263 | 51.6 | — | — | 510 | 7.6 | — | — |
| Fushun | 1363 | 50.6 | — | — | 1332 | 49.4 | — | — | 2695 | 40.0 | — | — |
| Anshan | 333 | 51.3 | — | — | 316 | 48.7 | — | — | 649 | 9.6 | — | — |
| Benxi | 346 | 47.8 | — | — | 378 | 52.2 | — | — | 724 | 10.7 | — | — |
| Dandong | 387 | 49.2 | — | — | 399 | 50.8 | — | — | 786 | 11.7 | — | — |
| Liaoyang | 122 | 45.7 | — | — | 145 | 54.3 | — | — | 267 | 4.0 | — | — |
| Child characteristic | ||||||||||||
| Age (years) | — | — | 11.6 | 2.1 | — | — | 11.5 | 2.0 | — | — | 11.6 | 2.1 |
| Height (cm)a | — | — | 155.6 | 14.0 | — | — | 152.3 | 10.8 | — | — | 154 | 12.6 |
| Weight (kg)a | — | — | 51.2 | 17.3 | — | — | 45.7 | 13.2 | — | — | 48.4 | 15.6 |
| Weekly exercise (h)a | — | — | 7.9 | 7.6 | — | — | 7.4 | 7.9 | — | — | 7.6 | 7.7 |
| Child breastfeda | 2312 | 68.4 | — | — | 2439 | 72.6 | — | — | 4751 | 70.5 | — | — |
| Family characteristic | ||||||||||||
| Parents as responders | 3146 | 93.0 | — | — | 3145 | 93.7 | — | — | 6291 | 93.3 | — | — |
| Family history of atopy | 669 | 19.8 | — | — | 721 | 21.5 | — | — | 1390 | 20.6 | — | — |
| Parent education level | ||||||||||||
| <High school | 2101 | 62.1 | — | — | 2110 | 62.8 | — | — | 4211 | 62.5 | — | — |
| Family annual income | ||||||||||||
| ≤5000 RMB | 375 | 11.1 | — | — | 383 | 11.4 | — | — | 758 | 11.2 | — | — |
| 5000–10 000 RMB | 431 | 12.7 | — | — | 445 | 13.2 | — | — | 284 | 13.0 | — | — |
| 10 000–30 000 RMB | 1197 | 35.4 | — | — | 1197 | 35.7 | — | — | 2394 | 35.5 | — | — |
| 30 000–100 000 RMB | 1250 | 37.0 | — | — | 1187 | 35.4 | — | — | 2437 | 36.2 | — | — |
| >100 000 RMB | 129 | 3.8 | — | — | 146 | 4.3 | — | — | 275 | 4.1 | — | — |
| Age of the home | ||||||||||||
| <5 years | 408 | 12.1 | — | — | 441 | 13.1 | — | — | 849 | 12.6 | — | — |
| 5–10 years | 548 | 16.2 | — | — | 577 | 17.2 | — | — | 1125 | 16.7 | — | — |
| 10–20 years | 1629 | 48.2 | — | — | 1562 | 46.5 | — | — | 3191 | 47.3 | — | — |
| ≥20 years | 797 | 23.5 | — | — | 778 | 23.2 | — | — | 1575 | 23.4 | — | — |
| Home structure | ||||||||||||
| <3 stories | 1059 | 31.3 | — | — | 1073 | 32.0 | — | — | 2132 | 31.6 | — | — |
| 3–7 stories | 1942 | 57.4 | — | — | 1928 | 57.4 | — | — | 3870 | 57.4 | — | — |
| >7 stories | 381 | 11.3 | — | — | 357 | 10.6 | — | — | 738 | 11.0 | — | — |
| Floor area of the home | ||||||||||||
| <15 m2 | 608 | 18.0 | — | — | 661 | 19.7 | — | — | 1269 | 18.8 | — | — |
| 15–25 m2 | 1651 | 48.8 | — | — | 1642 | 48.9 | — | — | 3293 | 48.9 | — | — |
| 25–35 m2 | 780 | 23.1 | — | — | 726 | 21.6 | — | — | 1506 | 22.3 | — | — |
| ≥35 m2 | 343 | 10.1 | — | — | 329 | 9.8 | — | — | 672 | 10.0 | — | — |
| Number of rooms in the home | ||||||||||||
| <3 | 1316 | 38.9 | — | — | 1272 | 37.9 | — | — | 2588 | 38.4 | — | — |
| ≥3 | 2066 | 61.1 | — | — | 2086 | 62.1 | — | — | 4152 | 61.6 | — | — |
| Distance from the home to traffic | ||||||||||||
| <20 m | 630 | 18.6 | — | — | 625 | 18.6 | — | — | 1255 | 18.6 | — | — |
| 20–100 m | 1191 | 35.2 | — | — | 1198 | 35.7 | — | — | 2389 | 35.5 | — | — |
| >100 m | 1561 | 46.2 | — | — | 1535 | 45.7 | — | — | 3096 | 45.9 | — | — |
Note: SD, standard deviation; RMB, Chinese Yuan.
a
Significant differences between males and females indicated by the results of t or χ2 tests, p < 0.05.
Calculated odds ratios
Group comparisons of lung function across housing characteristics suggested several statistically significant relationships. Figure 1 displays odds ratios (ORs) for FVC and FEV1. Figure 2 shows results for PEF and MMEF. “Ping-fang” housing (the equivalent of a single-family home) had a prevalence rate (PR) of 10% (Table 1) and was related to increased odds of FEV1 deficit (OR = 1.36, 95% CI: 1.03–1.80) when compared with “dan-yuan-lou-fang” housing (an apartment unit in a multi-story, multi-unit building). However, the “other” housing (PR = 3%) indicated greater FVC volumes (OR = 0.47, 95% CI: 0.26–0.83) compared with dan-yuan-lou-fang housing. Greater housing age was generally associated with better lung function. Residents of homes that were 5–10 years old (PR = 17%) yielded greater FVC volumes (OR = 0.75, 95% CI: 0.57–0.99) and FEV1 volumes (OR = 0.68, 95% CI: 0.51–0.93). Residents of homes that were 10–20 years old (PR = 47%) had greater FVC volumes (OR = 0.75, 95% CI: 0.60–0.94), FEV1 volumes (OR = 0.66, 95% CI: 0.51–0.84), and MMEF rates (OR = 0.78, 95% CI: 0.60–0.99). Finally, residents of homes >20 years old (PR = 23%) had greater FEV1 volumes (OR = 0.74, 95% CI: 0.56–0.97). All of these values reflecting home age were in comparison with houses <5 years old. Participants residing in dwellings within a floor area range of 15–25 m2 had greater PEF rate (OR = 0.73, 95% CI: 0.57–0.93), whereas those within 35 m2 or greater had higher MMEF rate (OR = 0.69, 95% CI: 0.49–0.98) when compared with homes with floor areas <25 m2 (PR = 10%). No group differences were observed for story level or number of rooms.
Fig. 1.
Fig. 2.
Environmental conditions proximate to the dwelling contributed to low performance in spirometric measures. Participants residing in homes that were within 20 m of a major roadway (PR = 19%) were more likely to have increased odds of lung function deficit for all measures: FCV deficit (OR = 1.89; 95% CI: 1.55–2.29), FEV1 deficit (OR = 1.71; 95% CI: 1.37–2.13), PEF deficit (OR = 1.37; 95% CI: 1.06–1.76), and MMEF deficit (OR = 1.28; 95% CI: 1.03–1.59). Residents within a range of 20–100 m of a major roadway (PR = 35%) exhibited increased odds of FVC deficit (OR = 1.32; 95% CI: 1.11–1.57) compared with participants living more than 100 m from a major roadway (PR = 46%). Participants residing in homes within 100 m of a pollution source, such as a factory or smokestack (PR = 38%), were more likely to have increased odds of lung function deficit for all measures: FCV deficit (OR = 1.24; 95% CI: 1.06–1.44), FEV1 deficit (OR = 1.22; 95% CI: 1.03–1.46), PEF deficit (OR = 1.22; 95% CI: 1.03–1.46), and MMEF deficit (OR = 1.40; 95% CI: 1.19–1.66).
Other qualities of the home environment were also shown to have bearing on the odds of reduced lung function. Keeping of pets in the home (PR = 22%) was associated with increased odds of lung function deficit in residents for all measures: FCV deficit (OR = 1.29; 95% CI: 1.08–1.54), FEV1 deficit (OR = 1.46; 95% CI: 1.19–1.787), PEF deficit (OR = 1.38; 95% CI: 1.11–1.73), and MMEF deficit (OR = 1.38; 95% CI: 1.14–1.68). Similarly, home renovation within the previous two years (PR = 36%) was associated with increased odds of lung function deficit in residents for all measures: FCV deficit (OR = 1.86; 95% CI: 1.59–2.17), FEV1 deficit (OR = 2.90; 95% CI: 2.43–3.47), PEF deficit (OR = 1.51; 95% CI: 1.24–1.84), and MMEF deficit (OR = 1.94, 95% CI: 1.64–2.30). A pest sighting in the home during the previous 12 months (PR = 36%) was associated only with increased odds of PEF deficit (OR = 1.25; 95% CI: 1.02–1.53). Indoor coal use was notable only in the odds of reduced FEV1 (OR = 1.44; 95% CI: 1.07–1.93). Humidifier use was only associated with a lower MMEF rate (OR = 1.29; 95% CI: 1.06–1.58). The presence of mold or water damage in the home in the previous 12 months and the use of ventilation had no significant impact on the odds of abnormal spirometry measures.
ETS exposure during pregnancy was associated with increased odds of MMEF deficit (OR = 1.29; 95% CI: 1.06–1.58). ETS exposure in the first two years of life was associated with increased odds of FVC deficit (OR = 1.26; 95% CI: 1.05–1.51). Current ETS exposure was associated with increased odds of lung function deficit in residents for several measures: FCV deficit (OR = 1.51; 95% CI: 1.29–1.77), FEV1 deficit (OR = 1.48; 95% CI: 1.24–1.77), and MMEF deficit (OR = 1.33; 95% CI: 1.12–1.57).
Linear modeling of lung function
Several variables related to housing characteristics were significantly associated with altered lung function (Tables 3 and 4). As shown in Table 3, ping-fang housing was associated with deficits of FVC (β = −112; 95% CI: −159 to −64), FEV1 (β = −85; 95% CI: −127 to −43), and PEF (β = −190; 95% CI: −282 to −97), relative to dan-yuan-lou-fang housing. Residences with a floor area >15 m2 were beneficial for lung function across all spirometry outcomes and all floor area categories, though no trend was noted with increasing floor area. Similarly, homes with more than three rooms were associated with better measures of FVC (β = 40; 95% CI: 11–69), when compared with homes with fewer rooms. The level of the residence being >7 stories was negatively associated with FEV1 (β = −55; 95% CI: −97 to −13). The age of the home exhibited no significant relationship to any of the four lung function indicators.
Table 3.
| FVC (mL) | FEV1 (mL) | PEF (mL/min) | MMEF (mL/min) | |||||
|---|---|---|---|---|---|---|---|---|
| Feature | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI |
| Housing type (Ref: dan-yuan-lou-fang) | ||||||||
| Ping-fang | −111.91 | −159.45 to −64.37 | −85.2 | −127.21 to −43.19 | −189.61 | −281.79 to −97.43 | 8.09 | −60.65 to 76.83 |
| Other | 38.21 | −41.55 to 117.97 | 46.97 | −23.51 to 117.46 | 44.22 | −110.44 to 198.88 | 52.24 | −63.10 to 167.58 |
| Age of the home (Ref: <5 years) | ||||||||
| 5–10 years | −23.97 | −74.16 to 26.21 | 20.06 | −24.29 to 64.40 | −32.01 | −129.31 to 65.29 | 4.83 | −67.66 to 77.30 |
| 10–20 years | −40.65 | −83.39 to 2.09 | 29.75 | −8.01 to 67.52 | −52.98 | −135.84 to 29.87 | 18.45 | −43.27 to 80.17 |
| ≥20 years | −51.02 | −98.24 to −3.81 | 21.86 | −19.86 to 63.58 | −62.81 | −154.35 to 28.73 | 18.31 | −49.88 to 86.50 |
| Home structure (Ref: <3 stories) | ||||||||
| 3–7 stories | 24.51 | −6.02 to 55.05 | 16.17 | −10.81 to 43.13 | 29.71 | −29.50 to 88.92 | −10.66 | −54.77 to 33.45 |
| >7 stories | −33.56 | −81.32 to 14.21 | −54.69 | −96.87 to −12.51 | −43.97 | −136.58 to 48.64 | −45.37 | −114.36 to 23.62 |
| Floor area of the home (Ref: <15 m2) | ||||||||
| 15–25 m2 | 78.8 | 42.10–115.50 | 60.19 | 27.73–92.65 | 122.45 | 51.24–193.66 | 85.28 | 32.21–138.35 |
| 25–35 m2 | 118.48 | 75.10–161.86 | 78.03 | 39.66–116.39 | 172.85 | 88.68–257.01 | 82.36 | 19.63–145.10 |
| ≥35 m2 | 122.9 | 68.37–177.43 | 78.54 | 30.31–126.76 | 183.02 | 77.22–288.82 | 79.99 | 1.14–158.85 |
| Number of rooms in the home (Ref: <3) | ||||||||
| ≥3 | 40.12 | 11.40–68.83 | 24.92 | −0.45 to 50.30 | 51.12 | −4.55 to 106.79 | 11.08 | −30.40 to 52.55 |
| Distance from the home to traffic (Ref: >100 m) | ||||||||
| 20–100 m | −15.22 | −45.30 to 14.87 | −16.48 | −43.08 to 10.12 | −37.16 | −95.53 to 21.21 | −70.18 | −113.64 to −26.72 |
| <20 m | −83.86 | −120.84 to 46.87 | −52.29 | −84.99 to −19.59 | −91.02 | −162.78 to −19.26 | −61.44 | −114.87 to −8.01 |
| Pollution source near the home (Ref: No) | ||||||||
| Yes | −74.56 | −102.33 to −46.79 | −71.33 | −95.85 to −46.81 | −203.28 | −256.99 to −149.58 | −158.05 | −198.04 to −118.07 |
Note: Numbers in bold indicate a p-value <0.05. FVC, forced vital capacity; FEV1, forced expiratory volume; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow; Ref, reference group for comparison.
a
Adjusted for age, gender, body mass index, breast-feeding, parents’ education, family income, home decorations, pet-keeping, current exposure to ETS, family history of atopy, parents as responders, and districts.
Environmental conditions in proximity to the dwelling were also associated with lower relative spirometric outcomes. Homes within <20 m of roadway traffic exhibited lower FEV1 (β = −52; 95% CI: −85 to −20), PEF (β = −91; 95% CI: −163 to −19), and MMEF (β = −61; 95% CI: −115 to −8) values. Participants residing in homes within 100 m of a pollution source had reduced respiratory indicators for all measures.
Other qualities of the home environment of participants also had bearing on lung function measures (Table 4). The most influential variable was having a home renovation within the previous two years of assessment, which was associated with the greatest estimated deficit in lung function for FEV1 (β = −241; 95% CI: −265 to −216), PEF (β = −262; 95% CI: −317 to −207), and MMEF (β = −240; 95% CI: −279 to −198). The presence of pests in the home, indoor coal use, and ETS exposure were also associated with reduced respiratory indicators for all measures. In contrast, ventilation in the home was associated with slightly improved respiratory function measures for FVC (β = 55; 95% CI: 3–95), FEV1 (β = 49; 95% CI: 10–87), and MMEF (β = 77; 95% CI: 12–141).
Table 4.
| FVC (mL) | FEV1 (mL) | PEF (mL/min) | MMEF (mL/min) | |||||
|---|---|---|---|---|---|---|---|---|
| Feature | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI |
| Exposure to pets (Ref: No) | ||||||||
| Yes | −7.99 | −41.65 to 25.66 | −13.41 | −42.40 to 15.57 | −45.42 | −110.35 to 19.51 | −23.04 | −71.29 to 25.21 |
| Home renovation in the past 2 years (Ref: No) | ||||||||
| Yes | −84.01 | −112.39 to −55.63 | −240.73 | −265.17 to −216.28 | −262.07 | −316.84 to −207.31 | −238.28 | −278.97 to −197.58 |
| Mold in the past 12 months (Ref: No) | ||||||||
| Yes | −0.80 | −41.11 to 39.51 | 9.23 | −25.49 to 43.94 | 31.46 | −46.30 to 109.23 | 47.31 | −10.48 to 105.10 |
| Pests in the past 12 months (Ref: No) | ||||||||
| Yes | −55.5 | −84.39 to −26.62 | −50.24 | −75.12 to −25.36 | −122.99 | −178.73 to −67.27 | −63.08 | −104.49 to −21.67 |
| Indoor coal use (Ref: No) | ||||||||
| Yes | −111.34 | −161.7 to −60.99 | −122.27 | −165.63 to −78.91 | −247.09 | −344.21 to −149.96 | −49.99 | −122.17 to 22.18 |
| Ventilation device use (Ref: No) | ||||||||
| Yes | 50.27 | 2.57–94.97 | 48.82 | 10.32–87.33 | 49.72 | −36.53 to 135.96 | 76.51 | 12.42–140.60 |
| Humidifier device use (Ref: No) | ||||||||
| Yes | 1.39 | −35.61 to 38.39 | −7.94 | −39.80 to 23.93 | −10.65 | −82.04 to 60.73 | −33.73 | −86.77 to 19.32 |
| ETS exposure during pregnancy (Ref: No) | ||||||||
| Yes | −12.93 | −47.51 to 21.65 | −31.04 | −60.83 to −1.26 | −44.09 | −110.81 to 22.63 | −104.93 | −157.52 to −55.35 |
| ETS exposure in the first 2 years of life (Ref: No) | ||||||||
| Yes | −27.64 | −61.33 to 6.04 | −36.45 | −65.47 to −7.44 | −37.58 | −102.58 to 27.42 | −72.82 | −121.15 to −24.49 |
| Current ETS exposure (Ref: No) | ||||||||
| Yes | −70.19 | −97.25 to −43.12 | −65.68 | −88.99 to −42.37 | −37.41 | −89.73 to 14.91 | −67.33 | −106.23 to −28.43 |
Note: Numbers in bold indicate a p-value <0.05. FVC, forced vital capacity; FEV1, forced expiratory volume; PEF, peak expiratory flow; MMEF, maximal mid-expiratory flow; ETS, enhanced tobacco smoke; Ref, reference group for comparison.
a
Adjusted for age, gender, body mass index, breast-feeding, parents’ education, family income, home decorations, pet-keeping, current exposure to ETS, family history of atopy, parents as responders, and districts.
ETS exposure during pregnancy was associated with reduced FEV1 volume (β = −31; 95% CI: −61 to −1), and reduced MMEF rate (β = −105; 95% CI: −158 to −55).
Discussion
Few studies have investigated the range of adverse respiratory health effects associated with exposure to household sources of indoor air pollution in Chinese children. The primary strength of this study is the use of objective measures of lung function, building on the work previously performed as part of the SNEC study.
Several residential characteristics and environmental factors that were associated with an increased risk of diminished lung function were identified. Residents of ping-fang housing exhibited FEV1 deficit compared with those living in multi-family structures and dormitories. They also exhibited reduced lung function for FVC, FEV1, and PEF in the linear model. This is contrary to the findings of Zhang et al. (2013) that children in Wuhan, China, residing in apartment buildings had greater odds of being diagnosed with allergic rhinitis (OR = 1.53; 95% CI: 1.16–2.00) than those residing in single-family housing. In the present study, residents living in ping-fang housing are usually poor and the indoor pollution was much heavier than that in the apartments. For example, 73.1% of subjects living in ping-fang housing reported the use of coal for cooking or heating, while only 2.5% of subjects living in apartments reported this use. In addition, the education level and family annual income were lower in the residents of ping-fang housing than those of apartment housing (parental education < high school: 78% vs. 32%; family annual income <10 000 RMB: 37% vs. 22%). Thus, living in ping-fang housing may represent the social economic characteristics of a poverty-stricken population and may hold higher vulnerability for disease risk when exposed to environmental factors.
Previous work by the authors failed to find any significant relationships between housing type and reported respiratory symptoms consistent with asthma (Dong et al. 2008). Langer and Bekö (2013) found higher concentrations of VOCs and formaldehyde in Swedish single-family homes compared with apartment buildings and highlighted the importance of air exchange in residences. This was not considered in this research and could play a role in our observations. Though this study examined the effect of ventilation associated with cooking on respiratory function, it did not address ventilatory air exchange. No significant relationships were observed for ventilation in the logistic model and only small, yet significant, effects were obtained from the linear model.
Taken together, the findings from the age of the residence and recent home renovations bolster previous findings related to respiratory outcomes and new construction in this region of China. In prior work, the authors have speculated that this finding may be relates to self-reported allergic rhinitis in adult women (Dong et al. 2013) and children (Dong et al. 2008). However, the consistent findings of the odds of spirometric deficit and the observed, large effects in the linear model of children in homes recently renovated increases the concern of the impact of indoor air contaminants on children. This suggests that builders and renovators may be choosing materials and construction methods that may be hazardous to the respiratory health of children.
Off-gassing of VOCs is a potential source of respiratory irritants and is dependent on factors related to the manufacturing and composition of building materials, paint and coatings, and furniture. Raw et al. (2004) found increased levels of VOCs and formaldehyde in newer homes and in homes that had undergone recent painting. The work of Jaakkola et al. (2004, 2006) has provided evidence of both ongoing off-gassing of VOCs from paint following renovation and epidemiological evidence to support respiratory effects in adults following renovation. Dong et al. (2014) also found that the students living in newly renovated residences had higher rates of rhinitis, cough, and shortness of breath compared with controls. However, these studies did not evaluate the impact of renovations on lung function and found no significant relationships.
A recent review by Chen et al. (2010) highlighted the inconsistent findings regarding an association between the keeping of dogs and cats and asthma or allergic diseases. The authors concluded that exposure to pets does not have an impact on development of asthma and wheezing (Chen et al. 2010). They also determined that early exposure to cats may cause sensitization to cat allergen, but early exposure to dogs may cause a protective effect (Chen et al. 2010). Our findings of respiratory deficits in children living in homes with pets provide quantitative evidence of the effects of pet-keeping on lung function, though not without some curious inconsistency. The odds of respiratory deficit were significant for all measures of spirometric performance, but the linear modeling of effect indicated no significant deficits associated with pets. That being the case, there are still unresolved mechanisms with respect to respiratory effect and the nature and frequency in which pets interact with people as well as how pets are domiciled (i.e., inside or outside the home). This study was not designed to specifically answer these questions, but serves as a potential avenue for future studies. It should also be noted that this study included poultry in the definition of pet-keeping, which may not be typical in other parts of the world.
The presence of mold or water damage in the home within a year had no statistically significant impact on the odds of respiratory deficit and no significant effects on spirometric performance in the linear model. Some estimates, though not significant, indicated a protective effect with mold or water in the home. However, the exposure to mold was based on parental self-reporting and not all mold and water issues are readily visible. It is known that mold proliferation is dependent on the growth medium and the presence of moisture (Van Loo et al. 2004) and that the presence of mold is linked to respiratory health outcomes (Mendell and Kumagai 2017). However, many questions remain in our understanding of the interaction between fungal organisms, fungal metabolic products, and environments (Nevalainen et al. 2015). Related to this potential source of respiratory irritation is the use of a humidifier, which has previously been found to have an impact on respiratory outcomes. In this study, the use of a humidifier was only associated with reduced MMEF rate.
The presence of pests in the home in the previous year was found to be associated with greater odds of PEF deficit and significant deficits to lung function were observed in all four spirometric measures. The role of pests in asthma and allergy has been noted previously and was recently summarized by Sheehan et al. (2010). Our findings are largely supported by the Sheehan review, but they suggest that future studies should examine the role of pests on objective lung function. They also suggest that intervention studies examining the effect of pest management should consider spirometric performance to assess the effect on pest mitigation in addition to the reporting of asthma symptoms.
Proximity to pollution sources, both from roadway traffic and point sources, was associated with reduced lung function. The Children’s Health Study in Southern California (Gauderman et al. 2004, 2007), the Oslo Birth Cohort (Oftedal et al. 2008), and a recent cohort study by Schultz et al. (2012) represent strong evidence that exposure to pollution from traffic has adverse effects on children’s lung function development, though Schultz et al. (2012) concluded that the effect was greatest in the first year of life. Our findings are consistent with these studies and add further evidence to this relationship. Children who lived within 20 m of traffic, when compared with children who lived more than 100 m from traffic, experienced greater odds of respiratory deficit for all four measures of lung function and significant effects for FEV1, PEF, and MMEF in the linear model.
Several studies have examined the effects of ETS on child respiratory health outcomes at different stages of growth. Some have associated fetal exposure via maternal smoking with respiratory symptoms (Lannerö et al. 2006; Håberg et al. 2007). Others have demonstrated effects due to postnatal exposure to ETS (Öberg et al. 2011; Burke et al. 2012). Our findings indicated that current exposure to ETS exhibited the strongest impact on children who were exposed at the time of the study. The odds of respiratory deficits in FVC, FEV1, and MMEF were significant for current ETS, but only a deficit in MMEF was significant for ETS exposure during pregnancy. Similarly, significant odds of respiratory deficit are noted for FVC, FEV1, and MMEF for current ETS exposure, and detrimental effects are indicated for FEV1 and MMEF in the linear model. Exposure to ETS in the first two years of life did not indicate odds of lung function deficit but exhibited effects in the linear model for FEV1 and MMEF.
Although the data were thoroughly detailed and a large sample (n = 6740) was utilized, our study contained a few limitations. First, the cross-sectional nature of the study cannot establish temporal effects between housing characteristics or environmental exposures and the measured spirometric outcomes. Second, there may have been confounding variables not accounted for in this study that could have played a role in lung function deficit such as body composition (Scott et al. 2012) and dietary factors (Veeranki et al. 2015). Third, although new and renovated homes had higher odds of lung function deficit, no measures of indoor air quality were taken that could implicate particular exposure sources in lung function deficit.
Conclusion
Given the size of the study population and the quantitative measures of lung function, this work provides further evidence regarding household and environmental factors that contribute to lung function deficit. Future work with this population should attempt to better explain the effects of new building practices on residents and resolve the underlying exposure mechanisms—either by removal or mitigation—to reduce the impact of new materials and building practices on child respiratory health. Furthermore, these findings suggest that control of indoor air pollutants and initiation of efforts to protect children from heavy indoor air pollution exposure in China should be a priority.
Acknowledgements
The authors acknowledge the cooperation of the seven cities, school principals, teachers, students, and parents. This research was supported by grants from the National Key Research and Development Program of China (2016YFC0207000), the Major Program of National Natural Science Foundation of China (91543208), the National Natural Science Foundation of China (No. 81472936, No. 81172630), the Guangdong Province Natural Science Foundation (No. 2016A030313342 and No. 2014A030313021), and the China Environmental Protection Foundation (CEPF2008-123-1-5).
References
Araki A, Saito I, Kanazawa A, Morimoto K, Nakayama K, Shibata E, et al. 2014. Phosphorus flame retardants in indoor dust and their relation to asthma and allergies of inhabitants. Indoor Air, 24: 3–15.
Asikainen A, Carrer P, Kephalopoulos S, de Oliveria Fernandes E, Wargocki P, and Hänninen O. 2016. Reducing burden of disease from residential indoor air exposures in Europe (HEALTHVENT project). Environmental Health, 15(Suppl. 1): 35.
Bennett WD, Zeman KL, and Jarabek AM. 2007. Nasal contribution to breathing and fine particle deposition in children versus adults. Journal of Toxicology and Environmental Health, Part A, 71: 227–237.
Bertelsen RJ, Carlsen KC, Carlsen K-H, Granum B, Doekes G, Håland G, et al. 2010. Childhood asthma and early life exposure to indoor allergens, endotoxin and β(1,3)-glucans. Clinical & Experimental Allergy, 40: 307–316.
Billionnet C, Gay E, Kirchner S, Leynaert B, and Annesi-Maesano I. 2011. Quantitative assessments of indoor air pollution and respiratory health in a population-based sample of French dwellings. Environmental Research, 111: 425–434.
Braun JM, Sathyanarayana S, and Hauser R. 2013. Phthalate exposure and children’s health. Current Opinion in Pediatrics, 25: 247–254.
Burke H, Leonardi-Bee J, Hashim A, Pine-Abata H, Chen Y, Cook DG, et al. 2012. Prenatal and passive smoke exposure and incidence of asthma and wheeze: systematic review and meta-analysis. Pediatrics, 129: 735–744.
Chen C-M, Tischer C, Schnappinger M, and Heinrich J. 2010. The role of cats and dogs in asthma and allergy—a systematic review. International Journal of Hygiene and Environmental Health, 213: 1–31.
Chen J-M, Hwang B-F, Chen Y-C, and Lee YL. 2014. Active smoking, environmental tobacco smoke and bronchitic symptoms among adolescents in Taiwan: a prospective cohort study. Preventive Medicine, 65: 116–121.
Dong G-H, Ma Y-N, Ding H-L, Jin J, Cao Y, and Zhao Y-D. 2008. Housing characteristics, home environmental factors and respiratory health in 3945 pre-school children in China. International Journal of Environmental Health Research, 18: 267–282.
Dong G-H, Qian Z, Wang J, Trevathan E, Ma W, Chen W, et al. 2013. Residential characteristics and household risk factors and respiratory diseases in Chinese women: the Seven Northeast Cities (SNEC) study. Science of the Total Environment, 463–464: 389–394.
Dong G-H, Qian Z, Wang J, Trevathan E, Liu M-M, Wang D, et al. 2014. Home renovation, family history of atopy, and respiratory symptoms and asthma among children living in China. American Journal of Public Health, 104: 1920–1927.
Franck U, Weller A, Röder SW, Herberth G, Junge KM, Kohajda T, et al. 2014. Prenatal VOC exposure and redecoration are related to wheezing in early infancy. Environment International, 73: 393–401.
Gauderman WJ, Gilliland GF, Vora H, Avol E, Stram D, McConnell R, et al. 2002. Association between air pollution and lung function growth in southern California children: results from a second cohort. American Journal of Respiratory and Critical Care Medicine, 166: 76–84.
Gauderman WJ, Avol E, Gilliland F, Vora H, Thomas D, Berhane K, et al. 2004. The effect of air pollution on lung development from 10 to 18 years of age. The New England Journal of Medicine, 351: 1057–1067.
Gauderman WJ, Vora H, McConnell R, Berhane K, Gilliland F, Thomas D, et al. 2007. Effect of exposure to traffic on lung development from 10 to 18 years of age: a cohort study. The Lancet, 369: 571–577.
Håberg SE, Stigum H, Nystad W, and Nafstad P. 2007. Effects of pre- and postnatal exposure to parental smoking on early childhood respiratory health. American Journal of Epidemiology, 166: 679–686.
He Q-Q, Wong TW, Du L, Jiang Z-Q, Gao Y, Qiu H, et al. 2010. Effects of ambient air pollution on lung function growth in Chinese schoolchildren. Respiratory Medicine, 104: 1512–1520.
Henderson J, Sherriff A, Farrow A, and Ayres JG. 2008. Household chemicals, persistent wheezing and lung function: effect modification by atopy? European Respiratory Journal, 31: 547–554.
Hu L-W, Yang M, Chen S, Shah K, Hailegiorgis Y, Burgens R, et al. 2017. Effects of in utero and postnatal exposure to secondhand smoke on lung function by gender and asthma status: the Seven Northeastern Cities (SNEC) study. Respiration, 93: 189–197.
Huang H-B, Chen H-Y, Su P-H, Huang P-C, Sun C-W, Wang C-J, et al. 2015. Fetal and childhood exposure to phthalate diesters and cognitive function in children up to 12 years of age: Taiwanese maternal and infant cohort study. PLoS ONE, 10: e0131910.
Hulin M, Simoni M, Viegi G, and Annesi-Maesano I. 2012. Respiratory health and indoor air pollutants based on quantitative exposure assessments. European Respiratory Journal, 40: 1033–1045.
Jaakkola JJK, Parise H, Kislitsin V, Lebedeva NI, and Spengler JD. 2004. Asthma, wheezing, and allergies in Russian schoolchildren in relation to new surface materials in the home. American Journal of Public Health, 94: 560–562.
Jaakkola JJK, Ieromnimon A, and Jaakkola MS. 2006. Interior surface materials and asthma in adults: a population-based incident case-control study. American Journal of Epidemiology, 164: 742–749.
Khreis H, Kelly C, Tate J, Parslow R, Lucas K, and Nieuwenhuijsen M. 2017. Exposure to traffic-related air pollution and risk of development of childhood asthma: a systematic review and meta-analysis. Environment International, 100: 1–31.
Lane KJ, Kangsen Scammell M, Levy JI, Fuller CH, Parambi R, Zamore W, et al. 2013. Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification analysis. Environmental Health, 12: 75.
Langer S, and Bekö G. 2013. Indoor air quality in the Swedish housing stock and its dependence on building characteristics. Building and Environment, 69: 44–54.
Lannerö E, Wickman M, Pershagen G, and Nordvall L. 2006. Maternal smoking during pregnancy increases the risk of recurrent wheezing during the first years of life (BAMSE). Respiratory Research, 7: 3.
Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. 2012. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380: 2224–2260.
Ma Y-N, Wang J, Dong G-H, Liu M-M, Wang D, Liu Y-Q, et al. 2013. Predictive equations using regression analysis of pulmonary function for healthy children in northeast China. PLoS ONE, 8: e63875.
McInnes AD, Sutton RM, Orioles A, Nishisaki A, Niles D, Abella BS, et al. 2011. The first quantitative report of ventilation rate during in-hospital resuscitation of older children and adolescents. Resuscitation, 82: 1025–1029.
Mehrparvar AH, Sakhvidi MJ, Mostaghaci M, Davari MH, Hashemi SH, and Zare Z. 2014. Spirometry values for detecting a restrictive pattern in occupational health settings. Tanaffos, 13: 27–34.
Mendell MJ, and Kumagai K. 2017. Observation-based metrics for residential dampness and mold with dose–response relationships to health: a review. Indoor Air, 27: 506–517.
Mendell MJ, Mirer AG, Cheung K, Tong M, and Douwes J. 2011. Respiratory and allergic health effects of dampness, mold, and dampness-related agents: a review of the epidemiologic evidence. Environmental Health Perspectives, 119: 748–756.
Milanzi EB, Brunekreef B, Koppelman GH, Wijga AH, van Rossem L, Vonk JM, et al. 2017. Lifetime secondhand smoke exposure and childhood and adolescent asthma: findings from the PIAMA cohort. Environmental Health, 16: 14.
Nevalainen A, Täubel M, and Hyvärinen A. 2015. Indoor fungi: companions and contaminants. Indoor Air, 25: 125–156.
Öberg M, Jaakkola MS, Woodward A, Peruga A, and Prüss-Ustün A. 2011. Worldwide burden of disease from exposure to second-hand smoke: a retrospective analysis of data from 192 countries. The Lancet, 377: 139–146.
Oftedal B, Brunekreef B, Nystad W, Madsen C, Walker S-E, and Nafstad P. 2008. Residential outdoor air pollution and lung function in schoolchildren. Epidemiology, 19: 129–137.
Park H-Y, Gilbreath S, and Barakatt E. 2017. Respiratory outcomes of ultrafine particulate matter (UFPM) as a surrogate measure of near-roadway exposures among bicyclists. Environmental Health, 16: 6.
Raw GJ, Coward SKD, Brown VM, and Crump DR. 2004. Exposure to air pollutants in English homes. Journal of Exposure Science & Environmental Epidemiology, 14: S85–S94.
Roy A, Hu W, Wei F, Korn L, Chapman RS, and Zhang J. 2012. Ambient particulate matter and lung function growth in Chinese children. Epidemiology, 23: 464–472.
Schultz ES, Gruzieva O, Bellander T, Bottai M, Hallberg J, Kull I, et al. 2012. Traffic-related air pollution and lung function in children at 8 years of age: a birth cohort study. American Journal of Respiratory and Critical Care Medicine, 186: 1286–1291.
Scott HA, Gibson PG, Garg ML, Pretto JJ, Morgan PJ, Callister R, et al. 2012. Relationship between body composition, inflammation and lung function in overweight and obese asthma. Respiratory Research, 13: 10.
Sheehan WJ, Rangsithienchai PA, Wood RA, Rivard D, Chinratanapisit S, Perzanowski MS, et al. 2010. Pest and allergen exposure and abatement in inner-city asthma: a work group report of the American Academy of Allergy, Asthma & Immunology Indoor Allergy/Air Pollution Committee. The Journal of Allergy and Clinical Immunology, 125: 575–581.
Siezen CLE, Bont L, Hodemaekers HM, Ermers MJ, Doornbos G, van’t Slot R, et al. 2009. Genetic susceptibility to respiratory syncytial virus bronchiolitis in preterm children is associated with airway remodeling genes and innate immune genes. The Pediatric Infectious Disease Journal, 28: 333–335.
Sukul P, Schubert JK, Oertel P, Kamysek S, Taunk K, Trefz P, et al. 2016. FEV manoeuvre induced changes in breath VOC compositions: an unconventional view on lung function tests. Scientific Reports, 6: 28029.
Thomas E, Wickramasinghe K, Mendis S, Roberts N, and Foster C. 2015. Improved stove interventions to reduce household air pollution in low and middle income countries: a descriptive systematic review. BMC Public Health, 15: 650.
van Loo JM, Robbins CA, Swenson L, and Kelman BJ. 2004. Growth of mold on fiberglass insulation building materials—a review of the literature. Journal of Occupational and Environmental Hygiene, 1: 349–354.
van Vliet D, Smolinska A, Jöbsis Q, Rosias P, Muris J, Dallinga J, et al. 2017. Can exhaled volatile organic compounds predict asthma exacerbations in children? Journal of Breath Research, 11: 016016.
Veeranki SP, Gebretsadik T, Mitchel EF, Tylavsky FA, Hartert TV, Cooper WO, et al. 2015. Maternal folic acid supplementation during pregnancy and early childhood asthma. Epidemiology, 26: 934–941.
Yamada Y-I, Yamada G, Otsuka M, Nishikiori H, Ikeda K, Umeda Y, et al. 2017. Volatile organic compounds in exhaled breath of idiopathic pulmonary fibrosis for discrimination from healthy subjects. Lung, 195: 247–254.
Zeng X-W, Vivian E, Mohammed KA, Jakhar S, Vaughn M, Huang J, et al. 2016. Long-term ambient air pollution and lung function impairment in Chinese children from a high air pollution range area: the Seven Northeastern Cities (SNEC) study. Atmospheric Environment, 138: 144–151.
Zhang M, Zhou ES, Ye X, Sun YX, Sundell J, and Yang X. 2013. Indoor environmental quality and the prevalence of childhood asthma and rhinitis in Wuhan area of China. Chinese Science Bulletin, 58: 4223–4229.
Supplementary material
Supplementary Material 1 (PDF / 209 KB)
- Download
- 209.12 KB
Information & Authors
Information
Published In
FACETS
Volume 3 • Number 1 • October 2018
Pages: 242 - 259
Editor: Hugh W. Davies
History
Received: 14 April 2017
Accepted: 20 November 2017
Version of record online: 7 March 2018
Copyright
© 2018 Emo et al. This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Data Availability Statement
All relevant data are within the paper and available from the authors upon request.
Key Words
Sections
Subjects
Authors
Author Contributions
ZQ and G-HD conceived and designed the study.
L-WH, B-YY, and G-HD performed the experiments/collected the data.
BE, L-WH, and B-YY analyzed and interpreted the data.
L-WH, B-YY, KAM, CG, MV, and G-HD contributed resources.
BE, KAM, CG, MV, and ZQ drafted or revised the manuscript.
Competing Interests
The authors have declared that no competing interests exist.
Metrics & Citations
Metrics
Other Metrics
Citations
Cite As
Brett Emo, Li-Wen Hu, Bo-Yi Yang, Kahee A. Mohammed, Christian Geneus, Michael Vaughn, Zhengmin (Min) Qian, and Guang-Hui Dong. 2018. Housing characteristics, home environmental factors, and pulmonary function deficit in Chinese children: Results from the Seven Northeast Cities (SNEC) Study. FACETS.
3(1): 242-259. https://doi.org/10.1139/facets-2017-0036
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
There are no citations for this item