Weifang Zhang1†
Sihui Peng
Jialu Fu
Huihui Wang
Tingzhong Yang- 1The Affiliated Hospital of Stomatology, School of Stomatology, Zhejiang University School of Medicine, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Hangzhou, China
- 2School of Medicine, Jinan University, Guangzhou, China
- 3Epidemiology Department, Emory University, Atlanta, GA, United States
- 4Zhejiang University Educational Foundation, Hangzhou, China
- 5Women' s Hospital/Center for Tobacco Control Research, Zhejiang University School of Medicine, Hangzhou, China
- 6Public Health Program, School of Health and Applied Human Sciences, University of North Carolina, Wilmington, NC, United States
Background: Studies exploring the relationship between air pollution levels and mental stress have rarely been done, and no studies have been done comparing university student mental stress levels based on regional air pollution levels.
Objectives: The objective of this study was to evaluate the association between air pollution and mental stress among university students.
Methods: Participants were 11,942 students, who were identified through a multistage survey sampling process conducted in 50 universities. Regional air pollution levels were retrieved from a national database, and mental stress was measured using a perceived stress scale. Both unadjusted and adjusted methods were utilized in the data analyses.
Results: Mental stress prevalence was 36.9% (95% Confidence Interval: 24.4–49.5%). The final model indicated that regional air pollution levels were positively associated with students' mental stress.
Conclusions: This study provided new and direct evidence of the health hazards of air pollution. The findings underscore the need to develop and implement stringent environmental protection policies, while simultaneously raising public awareness of environmental protection.
Introduction
Air pollution is one of the main environmental problems in many countries, especially developing countries. Short-and long term exposures to air pollution can lead to increased human morbidity and mortality rates (1–4). Studies have found that exposure to air pollution was associated with biological dysregulation, including inflammation (5), and greater risk of cardiovascular disease (6) and premature mortality (7). Many experimental and observational studies also suggested that exposure to air pollution was associated with mental disorders and negative behavioral outcomes. Furthermore, some studies found that there were associations between air pollution and depression (8–10), and psychiatric emergencies (11). Psychological theories offer a window into the possible mechanisms that may link high levels of air pollution with health problems though mental stress. According to the Stimulus, Stress response and Mental health problems (SRM) model, various stimulus (S) induce stress response (R), which in turn leads to mental health problems (M) (12, 13). Some studies found that while air pollution may induce mental stress (14–16), and high perceived mental stress levels have been linked with an increased likelihood of health problems (2, 3, 17). The authors speculated that mental stress was an intermediate variable between air pollution and mental health problems (12, 13). To help confirm this speculation, however, the relationship between air pollution levels and mental stress had to be more fully explored (13). Only a few empirical studies in which air pollution was directly associated with mental stress were found (14–16).
Ecological models have emphasized that mental stress is influenced by both individual and environmental variables. However, many studies about air pollution and mental stress were limited to small, local samples that focused primarily on individual variables (14–16). Mainland China is a vast territory with much cultural diversity and large differences in economic and social development. This creates a situation where different regions have vastly different levels of air pollution. Given China's regional differences in air pollution, it would seem that region of residence might also be related to mental stress, but no studies have directly examined this variable. Region of residence is a widely accepted and stable estimate of people's exposure to air pollution (13, 18). By utilizing a large-scale, national population sample in this study, it will be possible to expand and further clarify the relationship between environmental air pollution and mental stress.
Air pollution has become a major social and public health problem in China. The purpose of this study was to investigate the relationship between regional air pollution levels and non-specific perceived stress. This study may provide supporting information for implementing more stringent environmental protection policies and raising environmental awareness among Chinese citizens.
Methods
Study Area and Participants
This study employed a multistage sampling design. In Stage 1 60 potential universities with medical programs were identified. These universities were part of the Bloomberg Global Initiative Project entitled “Facilitate MOH Endorsement of Tobacco Control Curriculum Implementation through Promoting Tobacco Control Curricula in Medical Schools.” The 60 participating universities represented one-third of the 180 universities that offer medical programs across China, and the universities were differentiated by regional location. Students were recruited at 50 of the 60 universities to complete the survey. Twenty-two were medical universities offering only medical programs, and 28 were comprehensive universities offering medical and non-medical programs. Non-medical students were also randomly recruited from two-thirds of all comprehensive universities.
Stage 2 of the sampling strategy involved the selection of classes within each university. All medical courses were selected in each university, and several non-medical classes (matched for academic level) were selected in each designated comprehensive university.
Data Collection
Once an individual participant was identified and agreed to participate in the study, a structured self-administered questionnaire was administered. The questionnaire was administered during regular lectures and class sessions. It took ~30 min to complete. All responses were anonymous.
A common research protocol was utilized across all 50 universities to assure homogeneity of questionnaire administration and data collection techniques. The study was approved by the Ethics Committee at the Zhejiang University Medical Center, and verbal consent was obtained from all participants prior to data collection.
Measures
Dependent Variable
Mental stress was measured by the Chinese version of the Perceived Stress Scale (CPSS) (19). This scale is comprised of 14 items that address perceptions of stress during the month prior to the survey. Items were rated on a 5-point, Likert type scale, and ranged from 0 (never) to 4 (very often). Item scores were summarized to yield a total stress score, with higher scores indicating higher perceived levels of stress. This scale has been widely used to assess stress in China, and has been shown to be an appropriate indicator of mental health status (20–23). Following previous practice, severe stress was operationalized as a score >25, which was verified by the Receiver Operating Characteristic Curve (ROC), using mental disorders gold standard. This classification has demonstrated acceptable sensitivity and specificity (19). The dependent variable in this study was a categorical variable coded dichotomously as 1 = non severe mental stress and 2 = severe mental stress.
Individual-Level Independent Variables
Sociodemographic questions were utilized to determine such variables as age, gender, ethnicity, father's and mother's occupation, and family income.
University-Level Independent Variables
University type was determined using the China university ranking system (“high level,” “middle level,” and “low level”) as established by the National Ministry of Education (24).
City-Level Independent Variable
Forty-five cities were included in this study. There were several independent variables that reflected potential regional variation in characteristics of the universities. The first aspect was city population, where the universities were located. Cities were categorized as having populations <5 million, from 5 million to <10 million, or 10 million and more. The second independent variable was level of economic development, as measured by per capita Gross Domestic Product (GDP) in Yuan. Both the GDP of the original province from which the students came and of the province where the university was located were measured. Categories were <40,000, from 40,000 to <50,000, 50,000 and more. The above data were obtained from the National Bureau of statistics (25). The next independent variable was the air pollution level, in the cities that the universities were located. The air pollution level refers the amount of chemical pollutants in ambient air, was measured by days of air pollution exceeding the national standard (26). This data was available from the National Bureau of statistics (27).
Data Analysis
All data were entered into a database using Microsoft Excel. The dataset was then imported into SAS (9.3 version) for statistical analyses. Descriptive statistics were calculated for each variable. A logistic model was utilized to assess associations between the dependent and independent variables. Both unadjusted and adjusted methods were considered in the data analyses. SAS survey logistic procedures were applied in the analysis, using the university as the clustering unit, in order to account for a within-clustering correlation, attributable to the complex sample for unadjusted analysis. Associations were confirmed through application of a multilevel logistic regression model using the SAS GlIMMIX procedure (28). Three models for the three-level (individual, universities, and cities) multilevel logistic regression analyses were constructed. The first was the “null” model with random intercepts. It did not include any predictors except a constant, in assessing variation in the likelihood of an individual experiencing severe mental stress. From this model, we entered demographic and regional socioeconomic variables as fixed main effects, with severe mental stress to form the base model. From base model we entered air pollution variables to form the full model to assess the impact of air pollution on severe mental stress. The association between system variables and severe mental stress was expressed in terms of odds ratios and 95% CI < Confidence Interval>were computed. Model fit was assessed by the likelihood of a change in the −2log. We assessed the significance of the random parameter variance estimates using the Wald joint t-test statistic (29).
All analysis were weighted. A weighting factor was applied to each student record to adjust for variation in the probability of selection in each university (18). A non-response weight was not utilized in this study because non-response rates were very low. Since data collection took place in classes, nearly all students present completed the questionnaires.
Results
Valid questionnaires were completed by 97.5% of the potential students, resulting in a sample of 11,942 students from 50 different universities.
Of the sample, 12.8% were <20 years of age, about 24% were either 20 or 21 years old with the remainder of the participants being more than 21 years old. Of the study sample 49.4% were male and 50.6% were female. The majority of participants (60.7%) were freshmen and sophomores and 38.5% of the participants were juniors or seniors. 94.5% of the participants were Han (see Table 1).
Table 1. Demographic characteristics of sample and severe mental stress prevalence.
Severe mental stress prevalence was 36.9% (95% CI: 24.4–49.5%). The unadjusted logistic analysis showed: being junior or senior status, family income, university types, and city population were associated with lower mental stress levels. Being freshmen or sophomores and more air pollution were associated with higher mental stress prevalence.
The basic multilevel logistic regression model found university type, and city population were significantly associated with students' mental stress. The full model showed that type of university, city population, and air pollution were significantly associated with the students' mental stress (see Table 2).
Table 2. Results of multiple level models.
Significant positive correlations were found between city-level air population time and likelihood of severe mental stress, Pearson r were 0.32452 (p < 0.0001). Figure 1 showed that the severe mental stress prevalence increasing with city-level air pollution days at each year increasing.
Figure 1. Scatter plot of the association between city-level air pollution days at each year (MMapi) and severe mental stress prevalence (strp).
Discussion
Based on the results of this study, 36.9% (95% CI: 24.4–49.5%) of university residents were severely stressed. This finding was very similar to stress levels reported in urban residents 36.8% (95% CI: 33.5–40.2%) (19). Over one-third of urban residents and university students are severely stressed. This provides further evidence that mental stress has become a serious social and public issue in China. Numerous studies have also shown an escalation of stress-related health problems (30, 31).
Addressing a gap in the literature, this study found that regional air pollution is associated with students' mental stress. Based on running full analysis models, university students that reside in higher air pollution cities had 6.67 times the likelihood of suffering from severe mental stress, compared to those who reside in lower air pollution cities. This finding is consistent with those from other studies (14–16). This indicates that mental stress is pervasive in Chinese life as is exposure to high levels of pollution. This association can also be explained by SRM model, which offers a window into the possible mechanisms that may link overuse of air pollution and health problems though mental stress (12, 13). A certain amount of stress is generally regarded as motivational, stimulating and life enhancing. Severe and persistent stress, however, may induce psychiatric disorders, other health problems, and adversely influence quality of life (32). The relationship found in this study between pollution levels and mental stress would indicate pollution to be a severe and persistent stressor, which may lead to psychiatric and health disorders. Our study results reinforces findings from prior literature (14–16) that regional air pollution is a stable variable to estimate people's exposure to air pollution.
Air pollution not only causes ill effects to the lungs and respiratory systems of the Chinese people, but it also causes stress and mental disorders, which have their own set of negative health outcomes. Air pollution is not confined to China, but is a problem in many developing countries (1). This study provides direct evidence of potential stress related health hazards due to air pollution. Since 1978, the Chinese economy has grown substantially. With the rapid development of the economy, the environment has been impacted in a negative manner. In recent years, there has been a tension between economic growth and environmental protection. Although policies of environmental protection have been adopted, they have not been strictly enforced (33). The Central government should consider adopting even more stringent environmental protection policies, while simultaneously educating the public about environmental protection and implementing environmental protection technology measures.
Some studies have hypothesized that mental stress is an intermediate variable between exposure to air pollution and health problems (8–10). More studies need to be done to determine the extent to which air pollution may be associated with the risk of developing affective psychiatric disorders. Our study suggests that air pollution is related to mental stress, which provides support for further exploration of this mechanism. While the results from this study are valuable, further studies need to be conducted.
Different from previous studies (34–36), this study that found only a individual-level variable, grade, was significantly associated with mental stress, but more environmental and regional variables were included in the associations. This indicates that environment may be more important to university students' mental stress and health problems than other individual factors. To promote student health, it is important to improve the environment that the student resides in. This study revealed that students enrolled in higher level universities exhibit lower mental stress prevalence than students in lower level universities. It is plausible that higher level universities attract outstanding students, and provide better learning resources and facilities, which results in students exhibiting less mental stress than those in lower level universities. This study also found city population where universities were located was associated with students' stress levels. Those living in larger cities had lower stress levels than those living in smaller cities. Large cities usually have more financial resources and technology, as well as better social services (25). Access to such resources may have a moderating effect on stress.
Conclusion
This study provided new and direct evidence of an association between regional air pollution and stress levels of college students. This may increase the potential for mental disorders and other negative health impacts. This information provides support for the development, implementation, and enforcement of effective environmental protection laws, policies, and interventions. Implementing effective public education campaigns that encourage environmentally conscious actions should also be a high priority.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Author Contributions
WZ and SP drafted the manuscript. TY conceived the study design. JF, KX, HW, and YJ conducted the data collection and survey management. RC polished the language. All authors contributed to the article and approved the submitted version.
Funding
This study was partly funded by the National Nature Science Foundation of China (71490733 and 71473221), Center for Tobacco Control research, and Zhejiang University School of Medicine. We thank local teams from the Facilitate MOH endorsement of tobacco control implementation through promoting tobacco control advocacy capacity in medical schools project (supported by UNION) for organizing the data collection. The project universities, local government, and CDC also partly funded this survey.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1. WHO. Air Quality Guidelines-Global Update 2005. Report on a Working Group Meeting 18–20 October in Bonn. Geneva (2005). Available online at: https://www.euro.who.int/en/health-topics/environment-and-health/Housing-and-health/publications/pre-2009/air-quality-guidelines.-global-update-2005.-particulate-matter,-ozone,-nitrogen-dioxide-and-sulfur-dioxide (accessed May 7, 2021).
2. Arnold SV, Smolderen KG, Buchanan DM, Li Y, Spertus JA. Perceived stress in myocardial infarction: long-term mortality and health status outcomes. J Am Coll Cardiol. (2012) 60:1756–63. doi: 10.1016/j.jacc.2012.06.044
3. Nielsen NR, Kristensen TS, Schnohr P, Gronbaek M. Perceived stress and cause-specific mortality among men and women: results from a prospective cohort study. Am J Epidemiol. (2008) 168:481–91. doi: 10.1093/aje/kwn157
4. Yu RH, Ho SC, Lam CW, Woo JL, Ho SS. Psychological factors and subclinical atherosclerosis in postmenopausal Chinese women in Hong Kong. Maturitas. (2010) 67:186–91. doi: 10.1016/j.maturitas.2010.06.014
5. McDade TW, Hawkley LC, Cacioppo JT. Psychosocial and behavioral predictors of inflammation in middle-aged and older adults: the Chicago health, aging, and social relations study. Psychosomat Med. (2006) 68:376–81. doi: 10.1097/01.psy.0000221371.43607.64
6. Franchini M, Mannucci PM. Air pollution and cardiovascular disease. Thromb Res. (2012) 129:230–4. doi: 10.1016/j.thromres.2011.10.030
7. Lelieveld J, Evans JS, Fnais M, Giannadaki D, Pozzer A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature. (2015) 525:367–71. doi: 10.1038/nature15371
8. Lim YH, Kim H, Kim JH, Bae S, Park HY, Hong YC. Air pollution and symptoms of depression in elderly adults. Environ Health Perspect. (2012) 120:1023–8. doi: 10.1289/ehp.1104100
9. Szyszkowicz M, Rowe BH, Colman I. Air pollution and daily emergency department visits for depression. Int J Occup Med Environ Health. (2009) 22:355–62. doi: 10.2478/v10001-009-0031-6
10. Jacobs SV, Evans GW, Catalano R, Dooley D. Air pollution and depressive symptomatology: exploratory analyses of intervening psychosocial factors. Popul Environ. (1984) 7:260–72. doi: 10.1007/BF01256411
11. Rotton J, Frey J. Psychological costs of air pollution: atmospheric conditions, seasonal trends, and psychiatric emergencies. Popul Environ. (1984) 7:3–16. doi: 10.1007/BF01257469
12. Mehrabian A, Russell JA. An Approach to Environmental Psychology. Cambridge, MA: MIT Press (1974).
13. Yang T. Health Research: Social and Behavioral Theory and Methods. Beijing: People's Medical Publishing House (2018).
14. Rooij S, Costello PM, Veenendaal M, Lillycrop KA, Gluckman PD, Hanson MA, et al. Associations between DNA methylation of a glucocorticoid receptor promoter and acute stress responses in a large healthy adult population are largely explained by lifestyle and educational differences. Psychoneuroendocrinology. (2012) 37:782–8. doi: 10.1016/j.psyneuen.2011.09.010
15. Nuyts V, Nawrot TS, Scheers H, Nemery B, Casas L. Air pollution and self-perceived stress and mood: a one-year panel study of healthy elderly persons. Environ Res. (2019) 177:108644. doi: 10.1016/j.envres.2019.108644
16. Mehta AJ, Kubzansky LD, Coull BA, Kloog I, Koutrakis P, Sparrow D, et al. Associations between air pollution and perceived stress: the Veterans Administration Normative Aging Study. Environ Health. (2015) 14:1–10. doi: 10.1186/1476-069X-14-10
17. Jain S, Mills PJ, von Känel R, Hong SZ, Dimsdale JE. Effects of perceived stress and uplifts on inflammation and coagulability. Psychophysiology. (2007) 44:154–60. doi: 10.1111/j.1469-8986.2006.00480.x
18. Grilli L, Pratesi M. Weighted estimation in multilevel ordinal and binary models in the presence of informative sampling designs. Survey Methodol. (2004) 30:93–103.
19. Yang TZ, Huang HT. An epidemiological study on stress among urban residents in social transition period. Chin J Epidemiol. (2003) 24:760–4.
20. Yang T, Wu D, Zhang W, Cottrell RR, Rockett IRH. Comparative stress levels among residents in three Chinese Provincial Capitals, 2001 and 2008. PLoS ONE. (2012) 7:5225–35. doi: 10.1371/journal.pone.0048971
21. Yang T, Rockett IRH, Lv Q, Cottrell RR. Stress status and related characteristics among urban residents: a six-province capital cities study in China. PLoS ONE. (2012) 7:e30521. doi: 10.1371/journal.pone.0030521
22. Wang J, Xie W, Li L, Zhou W. Correlations of mental stress, coping style and social support among heroin dependence patients. Chin J Drug Depend. (2008) 17:133–7.
23. Li L, Feng L, Wu L, Yang Y. Association between stress and health in radiology physicians. J Environ Occup Med. (2001) 28:156–8. doi: 10.13213/j.cnki.jeom.2011.03.006
24. College Entrance Examination Inquire. (2015). Available online at: http://www.gaokao.com/baokao/lqfsx/ybfsx/. (accessed May 10, 2015).
25. Department of Comprehensive Statistics of National Bureau of Statistics. China Statistical Yearbook for Regional Economy 2013. Beijing: China Statistics Press (2014).
26. National Environmental Protection Ministry /National Quality Supervision and Quarantine General Department. Ambient Air Quality Standards (2012). Available online at: http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/dqhjbh/dqhjzlbz/201203/W020120410330232398521.pdf (accessed May 7, 2021).
27. Department of Urban Social Economic Survey of the National Bureau of Statistics. China City Statistical Yearbook. Beijing: China Statistics Press (2014). p. 342–8.
28. Wang J, Xie H, Jiang B. Multilevel Models: Methods and Application. Beijing: Higher Education Press (2008). p. 127–68.
29. Ene M, Leighton EA, Blue GL, Bell BA. Multilevel Models for Categorical Data Using SAS PROC GLIMMIX: The Basics. Available online at: http://www.analytics.ncsu.edu/sesug/2014/SD-13.pdf.2015,11,2 (accessed October 10, 2015).
30. Phillips MR, Liu HQ, Zhang YP. Suicide and social change in China. Cult Med Psychiatr. (1999) 23:25–50. doi: 10.1023/A:1005462530658
31. Chen WQ, Yu ITS, Wong TW. Impact of occupational stress and other psychosocial factors on musculoskeletal pain among Chinese offshore oil installation workers. Occup Environ Med. (2005) 62:251–6. doi: 10.1136/oem.2004.013680
32. Charney DS, Manji HK. Life stress, genes, and depression: multiple pathways lead to increased risk and new opportunities for intervention. Sci STKE Signal Transduct Know Environ. (2004) 225:re5. doi: 10.1126/stke.2252004re5
33. Wang X, Li F. Econometric analysis of the relationship between economic growth and environmental degradation of Tianjin. Geogr Res. (2005) 24:834–42. doi: 10.3321/j.issn:1000-0585.2005.06.002
34. Dyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression, anxiety, and other indicators of psychological distress among US and Canadian medical students. Acad Med. (2006) 81:354–73. doi: 10.1097/00001888-200604000-00009
35. Ramezankhani A, Gharlipour Z, Babaei Heydarabadi A, Tavassoli E, Motalebi M, Barekati H, et al. Perceived social support, depression, and perceived stress in university students. J Paramed Sci. (2013) 4:31–6.
Keywords: air pollution, mental stress, public health, environmental policy, university students
Citation: Zhang W, Peng S, Fu J, Xu K, Wang H, Jin Y, Yang T and Cottrell RR (2021) Urban Air Pollution and Mental Stress: A Nationwide Study of University Students in China. Front. Public Health 9:685431. doi: 10.3389/fpubh.2021.685431
Received: 25 March 2021; Accepted: 19 May 2021;
Published: 02 July 2021.
Edited by:
Wulf Rössler, Charité – Universitätsmedizin Berlin, GermanyReviewed by:
Xiaozhao Yang, Murray State University, United StatesMiczyslaw Szyszkowicz, Health Canada, Canada
Copyright © 2021 Zhang, Peng, Fu, Xu, Wang, Jin, Yang and Cottrell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tingzhong Yang, tingzhongyang@zju.edu.cn
†These authors have contributed equally to this work and share first authorship